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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] Building an Intelligent Organization AI capabilities are arriving faster than organizations can absorb them. The bottleneck isn't the technology — it's whether your company can describe its own work in a form machines can act on. FOR MID-MARKET AND ENTERPRISE TEAMS SHIPPING PRODUCTION AI SEE THE FRAMEWORK VIEW CASE STUDIES NEW Meet Habitat OS The enterprise runtime behind every production AI build. Your agents run in your cloud, with full auditability and principal-led support. Now with Habitat WorkStreams — a private stream for every person and group, so your best prompts compound across the org instead of dying in one chat history. EXPLORE THE PLATFORM → RESULTS 27% higher conversion Upper Hand 78% faster decisions Rivian 50M+ devices reached Samsung 4 wks to production QB Sync OUR CLIENTS Trusted by industry leaders to deliver AI that performs We've delivered agentic AI solutions across energy, media, consumer products, and enterprise platforms. Every engagement is principal-led and focused on measurable outcomes. UPPER HAND PERPLEXITY SAMSUNG TESLA RIVIAN CON EDISON WEAVEGRID THE INSIGHT The question nobody asks first Can your organization describe its own work? Not the org chart version — the real version. Which rules are documented? Which ones live in someone's head? Which exceptions matter? Until you can answer that, AI tools are just faster ways to do the wrong thing. Most AI initiatives stall at the proof-of-concept stage — not because the pilots fail, but because there's no organizational foundation to build on. The journey from tribal to coherent starts with making your work legible to machines. 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Measurable results.", "y": 266, "body": "Every engagement begins with understanding the problem — not jumping to solutions. See how we've helped organizations move from complexity to clarity, and from concept to production. TALK TO A PRINCIPAL EXPLORE HABITAT OS" }, { "tag": "h2", "title": "Conversational AI", "y": 690, "body": "3 projects" }, { "tag": "h3", "title": "Voice-First AI for 50M+ Smart TVs: Building Perplexity for Samsung", "y": 990, "body": "We built a voice-first AI assistant for Samsung Smart TVs that lets users ask questions naturally and get real-time streaming answers — navigating entirely with a TV remote and speaking through their phone or Bluetooth microphone. ✓ Deployed to 50M+ Samsung Smart TVs globally ✓ Sub-2-second voice recognition latency Perplexity AI • 2025 Read more →" }, { "tag": "h3", "title": "78% Faster Talent Decisions: AI-Powered Celebrity Analytics for Media Networks", "y": 990, "body": "We built a multi-agent AI platform that unifies celebrity data from disparate sources — survey research, social media metrics, and brand affinity studies — letting entertainment executives answer complex casting questions in natural language instead of navigating dozens of spreadsheets. ✓ 78% reduction in time to generate talent reports ✓ 5 specialized AI agents across talent, ratings, and streaming data Upper Hand AI • 2025 Read more →" }, { "tag": "h3", "title": "27% Higher Conversion: Building a Conversational AI Layer for Tesla and Rivian Data", "y": 990, "body": "After years of continued work with Tesla and Rivian data, we built a conversational AI interface that lets EV owners ask natural language questions about their driving data, plan road trips, and get personalized insights — all without the existing mobile team changing a single line of code. ✓ 27% increase in user acquisition and paid conversion ✓ Zero changes required to existing backend infrastructure TezLab • 2025 Read more →" }, { "tag": "h2", "title": "Data Intelligence", "y": 1471, "body": "4 projects" }, { "tag": "h3", "title": "94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service", "y": 1771, "body": "We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy. ✓ 94.5% overall accuracy validated against 88 historical invoices ✓ 97.7% of invoices successfully parsed on first attempt Fountain Creek • 2026 Read more →" }, { "tag": "h3", "title": "From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts", "y": 1771, "body": "We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analyzes content from Twitter/X, YouTube videos, and podcasts. What used to require hours of manual monitoring and note-taking now happens in minutes, with AI-generated summaries that surface the signal from the noise. ✓ 94% reduction in time spent monitoring content sources ✓ AI-generated summaries for 50+ tracked Twitter accounts daily Brooklyn Running • 2025 Read more →" }, { "tag": "h3", "title": "From 15 Minutes to 30 Seconds: AI-Powered Benefits Document Extraction", "y": 1771, "body": "We built a schema-driven AI pipeline that extracts structured data from complex healthcare benefits documents (SBCs), transforming a frustrating 15-minute manual process into a 30-second automated workflow. ✓ 78% reduction in onboarding time ✓ 98% accuracy in data extraction HR Software Provider • 2025 Read more →" }, { "tag": "h3", "title": "Search 10,000 Images in Under 100ms: Vector-Powered Visual Discovery", "y": 2456, "body": "We built Image Browser — a natural language image search system that lets users find images by describing what they're looking for. Instead of manually tagging thousands of photos, CLIP embeddings understand visual content automatically, enabling instant semantic search across entire image libraries. ✓ Sub-100ms search latency across 10,000+ images ✓ Zero manual tagging required — AI understands image content Internal Platform • 2025 Read more →" }, { "tag": "h2", "title": "Platform & Tooling", "y": 2925, "body": "1 project" }, { "tag": "h3", "title": "57,000 Transactions Synced: Automated Construction Project Accounting from QuickBooks to Google Sheets", "y": 2993, "body": "We inherited and stabilized a QuickBooks-to-Google Sheets sync system for a construction company managing multiple active projects. Hourly automated sync pulls labor, materials, and transaction data from QuickBooks Online into per-project reconciliation spreadsheets — giving project managers real-time visibility into costs without leaving the tools they already use. ✓ 57,829 transactions synced from QuickBooks to Google Sheets ✓ Hourly automated sync with zero manual intervention ✓ Per-project reconciliation spreadsheets for multiple active construction sites ✓ Labor date filtering fixed — resolved systematic data gaps for 10+ employees Steering House • 2026 Read more →" }, { "tag": "h2", "title": "Other Projects", "y": 3526, "body": "1 project" }, { "tag": "h3", "title": "6 Commands Replace 10+ Hours/Week: Building an AI-Powered Back Office", "y": 3594, "body": "A complete back office system — project tracking, hiring pipeline, invoicing, client communication, sales CRM — running on Claude Code commands and markdown files. No databases. No SaaS subscriptions. Just AI reading and writing text. ✓ 10+ hours/week of manual operations work eliminated within first week ✓ 50 job candidates researched, evaluated, and contacted in one weekend ✓ 6 active clients with auto-generated weekly status reports ✓ $150K+ in invoices tracked with automated payment follow-ups TheFocus.AI (Internal Deployment) • 2026 Read more →" }, { "tag": "h2", "title": "Ready to achieve similar outcomes?", "y": 4443, "body": "From tribal knowledge to production AI — in weeks, not quarters. Whether you're scoping a first project or evaluating where to start, the conversation begins with understanding the work. TALK TO A PRINCIPAL SEE THE FRAMEWORK" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 4847, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 5297, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 5674, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 5674, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 5674, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 5674, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "EXPLORE HABITAT OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "CONSUMER ELECTRONICS / AI 12 weeks", "href": "https://thefocus.ai/case-study/perplexity-samsung" }, { "text": "Voice-First AI for 50M+ Smart TVs: Building Perplexity for Samsung We built a voice-first AI assistant for Samsung Smart TVs that lets users ask questions naturally and get real-time streaming answers — navigating entirely with a TV remote and speaking through their phone or Bluetooth microphone. ✓ Deployed to 50M+ Samsung Smart TVs globally ✓ Sub-2-second voice recognition latency Perplexity AI • 2025 Read more →", "href": "https://thefocus.ai/case-study/perplexity-samsung" }, { "text": "MEDIA / ENTERTAINMENT 16 weeks (ongoing)", "href": "https://thefocus.ai/case-study/upperhand-ai" }, { "text": "78% Faster Talent Decisions: AI-Powered Celebrity Analytics for Media Networks We built a multi-agent AI platform that unifies celebrity data from disparate sources — survey research, social media metrics, and brand affinity studies — letting entertainment executives answer complex casting questions in natural language instead of navigating dozens of spreadsheets. ✓ 78% reduction in time to generate talent reports ✓ 5 specialized AI agents across talent, ratings, and streaming data Upper Hand AI • 2025 Read more →", "href": "https://thefocus.ai/case-study/upperhand-ai" }, { "text": "TRANSPORTATION / CONSUMER APPS 8 weeks", "href": "https://thefocus.ai/case-study/tezlab-ai" }, { "text": "27% Higher Conversion: Building a Conversational AI Layer for Tesla and Rivian Data After years of continued work with Tesla and Rivian data, we built a conversational AI interface that lets EV owners ask natural language questions about their driving data, plan road trips, and get personalized insights — all without the existing mobile team changing a single line of code. ✓ 27% increase in user acquisition and paid conversion ✓ Zero changes required to existing backend infrastructure TezLab • 2025 Read more →", "href": "https://thefocus.ai/case-study/tezlab-ai" }, { "text": "FOOD SERVICE / RETAIL 4 weeks", "href": "https://thefocus.ai/case-study/fountain-creek" }, { "text": "94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy. ✓ 94.5% overall accuracy validated against 88 historical invoices ✓ 97.7% of invoices successfully parsed on first attempt Fountain Creek • 2026 Read more →", "href": "https://thefocus.ai/case-study/fountain-creek" }, { "text": "MEDIA INTELLIGENCE / CONTENT ANALYTICS 12 weeks", "href": "https://thefocus.ai/case-study/distill" }, { "text": "From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analyzes content from Twitter/X, YouTube videos, and podcasts. What used to require hours of manual monitoring and note-taking now happens in minutes, with AI-generated summaries that surface the signal from the noise. ✓ 94% reduction in time spent monitoring content sources ✓ AI-generated summaries for 50+ tracked Twitter accounts daily Brooklyn Running • 2025 Read more →", "href": "https://thefocus.ai/case-study/distill" }, { "text": "HUMAN RESOURCES / SAAS 6 weeks", "href": "https://thefocus.ai/case-study/onboarding" }, { "text": "From 15 Minutes to 30 Seconds: AI-Powered Benefits Document Extraction We built a schema-driven AI pipeline that extracts structured data from complex healthcare benefits documents (SBCs), transforming a frustrating 15-minute manual process into a 30-second automated workflow. ✓ 78% reduction in onboarding time ✓ 98% accuracy in data extraction HR Software Provider • 2025 Read more →", "href": "https://thefocus.ai/case-study/onboarding" }, { "text": "MEDIA / CONTENT MANAGEMENT 4 weeks", "href": "https://thefocus.ai/case-study/image-browser" }, { "text": "Search 10,000 Images in Under 100ms: Vector-Powered Visual Discovery We built Image Browser — a natural language image search system that lets users find images by describing what they're looking for. Instead of manually tagging thousands of photos, CLIP embeddings understand visual content automatically, enabling instant semantic search across entire image libraries. ✓ Sub-100ms search latency across 10,000+ images ✓ Zero manual tagging required — AI understands image content Internal Platform • 2025 Read more →", "href": "https://thefocus.ai/case-study/image-browser" }, { "text": "CONSTRUCTION / FINANCE 4 weeks (inherited codebase)", "href": "https://thefocus.ai/case-study/qbsync" }, { "text": "57,000 Transactions Synced: Automated Construction Project Accounting from QuickBooks to Google Sheets We inherited and stabilized a QuickBooks-to-Google Sheets sync system for a construction company managing multiple active projects. Hourly automated sync pulls labor, materials, and transaction data from QuickBooks Online into per-project reconciliation spreadsheets — giving project managers real-time visibility into costs without leaving the tools they already use. ✓ 57,829 transactions synced from QuickBooks to Google Sheets ✓ Hourly automated sync with zero manual intervention ✓ Per-project reconciliation spreadsheets for multiple active construction sites ✓ Labor date filtering fixed — resolved systematic data gaps for 10+ employees Steering House • 2026 Read more →", "href": "https://thefocus.ai/case-study/qbsync" }, { "text": "PROFESSIONAL SERVICES 2-hour setup + 4 days integration", "href": "https://thefocus.ai/case-study/ai-back-office" }, { "text": "6 Commands Replace 10+ Hours/Week: Building an AI-Powered Back Office A complete back office system — project tracking, hiring pipeline, invoicing, client communication, sales CRM — running on Claude Code commands and markdown files. No databases. No SaaS subscriptions. Just AI reading and writing text. ✓ 10+ hours/week of manual operations work eliminated within first week ✓ 50 job candidates researched, evaluated, and contacted in one weekend ✓ 6 active clients with auto-generated weekly status reports ✓ $150K+ in invoices tracked with automated payment follow-ups TheFocus.AI (Internal Deployment) • 2026 Read more →", "href": "https://thefocus.ai/case-study/ai-back-office" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Let%27s%20Talk" }, { "text": "SEE THE FRAMEWORK", "href": "https://thefocus.ai/capabilities" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-study/perplexity-samsung", "https://thefocus.ai/case-study/perplexity-samsung", "https://thefocus.ai/case-study/upperhand-ai", "https://thefocus.ai/case-study/upperhand-ai", "https://thefocus.ai/case-study/tezlab-ai", "https://thefocus.ai/case-study/tezlab-ai", "https://thefocus.ai/case-study/fountain-creek", "https://thefocus.ai/case-study/fountain-creek", "https://thefocus.ai/case-study/distill", "https://thefocus.ai/case-study/distill", "https://thefocus.ai/case-study/onboarding", "https://thefocus.ai/case-study/onboarding", "https://thefocus.ai/case-study/image-browser", "https://thefocus.ai/case-study/image-browser", "https://thefocus.ai/case-study/qbsync", "https://thefocus.ai/case-study/qbsync", "https://thefocus.ai/case-study/ai-back-office", "https://thefocus.ai/case-study/ai-back-office", "mailto:hey@thefocus.ai?subject=Let%27s%20Talk", "https://thefocus.ai/capabilities", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] PROVEN IMPACT Real problems. Measurable results. Every engagement begins with understanding the problem — not jumping to solutions. See how we've helped organizations move from complexity to clarity, and from concept to production. TALK TO A PRINCIPAL EXPLORE HABITAT OS Conversational AI 3 projects CONSUMER ELECTRONICS / AI 12 weeks Voice-First AI for 50M+ Smart TVs: Building Perplexity for Samsung We built a voice-first AI assistant for Samsung Smart TVs that lets users ask questions naturally and get real-time streaming answers — navigating entirely with a TV remote and speaking through their phone or Bluetooth microphone. ✓ Deployed to 50M+ Samsung Smart TVs globally ✓ Sub-2-second voice recognition latency Perplexity AI • 2025 Read more → MEDIA / ENTERTAINMENT 16 weeks (ongoing) 78% Faster Talent Decisions: AI-Powered Celebrity Analytics for Media Networks We built a multi-agent AI platform that unifies celebrity data from disparate sources — survey research, social media metrics, and brand affinity studies — letting entertainment executives answer complex casting questions in natural language instead of navigating dozens of spreadsheets. ✓ 78% reduction in time to generate talent reports ✓ 5 specialized AI agents across talent, ratings, and streaming data Upper Hand AI • 2025 Read more → TRANSPORTATION / CONSUMER APPS 8 weeks 27% Higher Conversion: Building a Conversational AI Layer for Tesla and Rivian Data After years of continued work with Tesla and Rivian data, we built a conversational AI interface that lets EV owners ask natural language questions about their driving data, plan road trips, and get personalized insights — all without the existing mobile team changing a single line of code. ✓ 27% increase in user acquisition and paid conversion ✓ Zero changes required to existing backend infrastructure TezLab • 2025 Read more → Data Intelligence 4 projects FOOD SERVICE / RETAIL 4 weeks 94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 h", "height": 6171, "screenshots": { "desktop": "assets/screenshots/desktop/case-studies.png", "mobile": "assets/screenshots/mobile/case-studies.png" } }, { "url": "https://thefocus.ai/capabilities", "path": "/capabilities", "slug": "capabilities", "category": "Core", "status": 200, "title": "AI Maturity Framework & Consulting Services | TheFocus.AI", "metaDescription": "Where is your organization on the AI journey? TheFocus.AI's proven maturity framework (L0–L5) takes mid-market and enterprise teams from tribal knowledge to self-improving AI systems. Production AI in weeks, not quarters.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Where is your organization on the AI journey?", "index": 1 }, { "tag": "h2", "text": "Six levels of AI maturity", "index": 2 }, { "tag": "h3", "text": "Tribal", "index": 3 }, { "tag": "h3", "text": "Experimenting", "index": 4 }, { "tag": "h3", "text": "Legible", "index": 5 }, { "tag": "h3", "text": "Knowledgeable", "index": 6 }, { "tag": "h3", "text": "Adaptive", "index": 7 }, { "tag": "h3", "text": "Self-Improving", "index": 8 }, { "tag": "h3", "text": "Tribal", "index": 9 }, { "tag": "h3", "text": "Experimenting", "index": 10 }, { "tag": "h3", "text": "Legible", "index": 11 }, { "tag": "h3", "text": "Knowledgeable", "index": 12 }, { "tag": "h3", "text": "Adaptive", "index": 13 }, { "tag": "h3", "text": "Self-Improving", "index": 14 }, { "tag": "h2", "text": "Find your starting point", "index": 15 }, { "tag": "h3", "text": "Tribal → Legible", "index": 16 }, { "tag": "h3", "text": "Legible → Knowledgeable", "index": 17 }, { "tag": "h3", "text": "Knowledgeable → Self-Improving", "index": 18 }, { "tag": "h3", "text": "Vibe Check", "index": 19 }, { "tag": "h2", "text": "LEVEL BY LEVEL", "index": 20 }, { "tag": "h3", "text": "Tribal", "index": 21 }, { "tag": "h3", "text": "Experimenting", "index": 22 }, { "tag": "h3", "text": "Legible", "index": 23 }, { "tag": "h3", "text": "Knowledgeable", "index": 24 }, { "tag": "h3", "text": "Adaptive", "index": 25 }, { "tag": "h3", "text": "Self-Improving", "index": 26 }, { "tag": "h2", "text": "How we work with you", "index": 27 }, { "tag": "h3", "text": "What we do", "index": 28 }, { "tag": "h3", "text": "What you get", "index": 29 }, { "tag": "h3", "text": "This is for you if", "index": 30 }, { "tag": "h2", "text": "COMMON QUESTIONS", "index": 31 }, { "tag": "h3", "text": "Do we need clean data first?", "index": 32 }, { "tag": "h3", "text": "What if we don't know exactly what we need?", "index": 33 }, { "tag": "h3", "text": "How is this different from other AI consultancies?", "index": 34 }, { "tag": "h2", "text": "Ready to find your starting point?", "index": 35 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 36 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 37 }, { "tag": "h3", "text": "COMPANY", "index": 38 }, { "tag": "h3", "text": "PRODUCTS", "index": 39 }, { "tag": "h3", "text": "RESOURCES", "index": 40 }, { "tag": "h3", "text": "CONNECT", "index": 41 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Where is your organization on the AI journey?", "y": 266, "body": "Most AI projects fail not because the technology doesn't work — but because the organization can't describe its own work in a form machines can act on. The journey is from tribal to coherent — and each transition has a different blocker. Different departments sit at different points. Find yours below. FIND OUT WHERE YOU ARE SEE THE FRAMEWORK" }, { "tag": "h2", "title": "Six levels of AI maturity", "y": 950, "body": "L0 Tribal Knowledge lives in people's heads L1 Experimenting Individual tools, nothing shared L2 Legible Your organization can describe its own work L3 Knowledgeable Your organization knows what it knows L4 Adaptive The system brings insights to you L5 Self-Improving The system learns from every interaction L0 Tribal Knowledge lives in people's heads L1 Experimenting Individual tools, nothing shared L2 Legible Your organization can describe its own work L3 Knowledgeable Your organization knows what it knows L4 Adaptive The system brings insights to you L5 Self-Improving The system learns from every interaction" }, { "tag": "h3", "title": "Tribal", "y": 1200, "body": "Knowledge lives in people's heads" }, { "tag": "h3", "title": "Experimenting", "y": 1195, "body": "Individual tools, nothing shared" }, { "tag": "h3", "title": "Legible", "y": 1167, "body": "Your organization can describe its own work" }, { "tag": "h3", "title": "Knowledgeable", "y": 1139, "body": "Your organization knows what it knows" }, { "tag": "h3", "title": "Adaptive", "y": 1111, "body": "The system brings insights to you" }, { "tag": "h3", "title": "Self-Improving", "y": 1083, "body": "The system learns from every interaction" }, { "tag": "h3", "title": "Tribal", "y": 0, "body": "Knowledge lives in people's heads" }, { "tag": "h3", "title": "Experimenting", "y": 0, "body": "Individual tools, nothing shared" }, { "tag": "h3", "title": "Legible", "y": 0, "body": "Your organization can describe its own work" }, { "tag": "h3", "title": "Knowledgeable", "y": 0, "body": "Your organization knows what it knows" }, { "tag": "h3", "title": "Adaptive", "y": 0, "body": "The system brings insights to you" }, { "tag": "h3", "title": "Self-Improving", "y": 0, "body": "The system learns from every interaction" }, { "tag": "h2", "title": "Find your starting point", "y": 1470, "body": "Pick the cluster that sounds most like you. Each one maps to a specific engagement. Consulting Track ORGANIZATIONS SCALING PRODUCTION AI L0 — L1 Tribal → Legible ✓ Your team uses ChatGPT individually but there's no organizational strategy ✓ AI feels interesting but you don't know where to begin ✓ The real question isn't which tool to pick — it's whether you can describe your own workflows Assessment & Roadmap 1-2 weeks AI opportunity assessment Process documentation Prioritized roadmap Build vs. buy analysis LET'S MAP YOUR STARTING POINT → L2 — L3 Legible → Knowledgeable ✓ You have AI tools connected to your workflows but they feel generic ✓ Your proprietary data — the documents, surveys, re" }, { "tag": "h3", "title": "Tribal → Legible", "y": 1752, "body": "✓ Your team uses ChatGPT individually but there's no organizational strategy ✓ AI feels interesting but you don't know where to begin ✓ The real question isn't which tool to pick — it's whether you can describe your own workflows Assessment & Roadmap 1-2 weeks AI opportunity assessment Process documentation Prioritized roadmap Build vs. buy analysis LET'S MAP YOUR STARTING POINT →" }, { "tag": "h3", "title": "Legible → Knowledgeable", "y": 1752, "body": "✓ You have AI tools connected to your workflows but they feel generic ✓ Your proprietary data — the documents, surveys, records that make your business unique — is still locked in folders nobody can search ✓ You know it could unlock more, but aren't sure how Build & Ship 2-6 weeks Document ingestion pipeline Semantic knowledge base Conversational query interface Source-attributed answers LET'S UNLOCK YOUR DATA →" }, { "tag": "h3", "title": "Knowledgeable → Self-Improving", "y": 1752, "body": "✓ AI is driving real business outcomes, but you want it to compound ✓ You need systems that learn from themselves, roles that evolve with the technology, and governance that adapts ✓ The question is no longer 'does AI work?' — it's 'how does our organization change?' Ongoing Partnership Monthly retainer Dedicated team access Feedback loop design Continuous optimization Capability expansion Team training LET'S BUILD THE NEXT LEVEL →" }, { "tag": "h3", "title": "Vibe Check", "y": 2697, "body": "Your team built an internal tool with Cursor, Claude, Bolt, or Lovable. It works — but the foundation needs review before IT rollout. We assess the codebase for security holes, architectural risks, and scaling issues, then fix what matters. Two-step: assessment (1-4 days) and cleanup (1-3 weeks). Security audit Architecture review Scalability assessment Prioritized fix list" }, { "tag": "h2", "title": "LEVEL BY LEVEL", "y": 3035, "body": "L0 Tribal Knowledge lives in people's heads Everything is manual. Processes depend on who knows what. A key person leaves and critical workflows break. The common mistake: We need to pick the right AI tool. The reality: This level resolves itself — people start using ChatGPT on their own. The real question is what happens next. L1 Experimenting Individual tools, nothing shared Employees are using ChatGPT or Copilot on their own. Some get real value — but nothing is shared, nothing compounds. When someone leaves, their workflows leave with them. The common mistake: \"We need an AI strategy\" or \"We need better models.\" The reality: The blocker isn't technology — it's that you can't describe you" }, { "tag": "h3", "title": "Tribal", "y": 3116, "body": "Knowledge lives in people's heads" }, { "tag": "h3", "title": "Experimenting", "y": 3430, "body": "Individual tools, nothing shared" }, { "tag": "h3", "title": "Legible", "y": 4060, "body": "Your organization can describe its own work" }, { "tag": "h3", "title": "Knowledgeable", "y": 4642, "body": "Your organization knows what it knows" }, { "tag": "h3", "title": "Adaptive", "y": 5242, "body": "The system brings insights to you" }, { "tag": "h3", "title": "Self-Improving", "y": 5671, "body": "The system learns from every interaction" }, { "tag": "h2", "title": "How we work with you", "y": 6056, "body": "What we do ✓ Walk through the framework with your team ✓ You self-identify where each department sits ✓ We build a roadmap from where you are to where you want to be ✓ Every level up is a clear, scoped engagement What you get ✓ Honest assessment — no upselling to where you don't need to be ✓ Practical roadmap with concrete next steps ✓ Quick wins at your current level while building toward the next ✓ A partner who meets you where you are This is for you if ✓ You're an executive sponsor, IT leader, or line-of-business owner at a mid-market or enterprise organization ✓ You have a product that works — now you want to make it smarter with AI ✓ You don't have an internal AI/ML team (or yours is s" }, { "tag": "h3", "title": "What we do", "y": 6128, "body": "✓ Walk through the framework with your team ✓ You self-identify where each department sits ✓ We build a roadmap from where you are to where you want to be ✓ Every level up is a clear, scoped engagement" }, { "tag": "h3", "title": "What you get", "y": 6128, "body": "✓ Honest assessment — no upselling to where you don't need to be ✓ Practical roadmap with concrete next steps ✓ Quick wins at your current level while building toward the next ✓ A partner who meets you where you are" }, { "tag": "h3", "title": "This is for you if", "y": 6128, "body": "✓ You're an executive sponsor, IT leader, or line-of-business owner at a mid-market or enterprise organization ✓ You have a product that works — now you want to make it smarter with AI ✓ You don't have an internal AI/ML team (or yours is stretched thin) ✓ You need working AI in weeks, not a 6-month \"discovery phase\"" }, { "tag": "h2", "title": "COMMON QUESTIONS", "y": 6764, "body": "Do we need clean data first? No. In fact, discovering that your data isn't clean is part of the value. Most organizations don't realize how much tribal knowledge — naming conventions, exception rules, approval workflows — lives only in people's heads until they try to connect AI to it. We help you through that process. What if we don't know exactly what we need? That's normal — and it's what Assessment engagements are for. Most companies overestimate where they are on the maturity framework. The honest starting point is usually: \"Can we describe our own workflows in a form a machine can follow?\" If the answer is \"not yet,\" that's where we begin. How is this different from other AI consultanc" }, { "tag": "h3", "title": "Do we need clean data first?", "y": 6837, "body": "No. In fact, discovering that your data isn't clean is part of the value. Most organizations don't realize how much tribal knowledge — naming conventions, exception rules, approval workflows — lives only in people's heads until they try to connect AI to it. We help you through that process." }, { "tag": "h3", "title": "What if we don't know exactly what we need?", "y": 6994, "body": "That's normal — and it's what Assessment engagements are for. Most companies overestimate where they are on the maturity framework. The honest starting point is usually: \"Can we describe our own workflows in a form a machine can follow?\" If the answer is \"not yet,\" that's where we begin." }, { "tag": "h3", "title": "How is this different from other AI consultancies?", "y": 7151, "body": "We build. Most AI consultancies deliver decks and recommendations. We deliver production systems — working in weeks, not quarters. And we're honest about organizational readiness: if your processes aren't formalized enough for AI, we'll tell you that and help you fix it, rather than building on a shaky foundation." }, { "tag": "h2", "title": "Ready to find your starting point?", "y": 7388, "body": "We work directly with executive sponsors and technical leaders. The conversation starts with understanding where you are — not selling you where we think you should be. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 7732, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 8182, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 8559, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 8559, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 8559, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 8559, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "FIND OUT WHERE YOU ARE", "href": "https://thefocus.ai/capabilities/#assessment" }, { "text": "SEE THE FRAMEWORK", "href": "https://thefocus.ai/capabilities/#framework" }, { "text": "L0 Tribal Knowledge lives in people's heads", "href": "https://thefocus.ai/capabilities/#level-0" }, { "text": "L1 Experimenting Individual tools, nothing shared", "href": "https://thefocus.ai/capabilities/#level-1" }, { "text": "L2 Legible Your organization can describe its own work", "href": "https://thefocus.ai/capabilities/#level-2" }, { "text": "L3 Knowledgeable Your organization knows what it knows", "href": "https://thefocus.ai/capabilities/#level-3" }, { "text": "L4 Adaptive The system brings insights to you", "href": "https://thefocus.ai/capabilities/#level-4" }, { "text": "L5 Self-Improving The system learns from every interaction", "href": "https://thefocus.ai/capabilities/#level-5" }, { "text": "L0 Tribal Knowledge lives in people's heads", "href": "https://thefocus.ai/capabilities/#level-0" }, { "text": "L1 Experimenting Individual tools, nothing shared", "href": "https://thefocus.ai/capabilities/#level-1" }, { "text": "L2 Legible Your organization can describe its own work", "href": "https://thefocus.ai/capabilities/#level-2" }, { "text": "L3 Knowledgeable Your organization knows what it knows", "href": "https://thefocus.ai/capabilities/#level-3" }, { "text": "L4 Adaptive The system brings insights to you", "href": "https://thefocus.ai/capabilities/#level-4" }, { "text": "L5 Self-Improving The system learns from every interaction", "href": "https://thefocus.ai/capabilities/#level-5" }, { "text": "LET'S MAP YOUR STARTING POINT →", "href": "mailto:hey@thefocus.ai?subject=Assessment%20%26%20Roadmap" }, { "text": "LET'S UNLOCK YOUR DATA →", "href": "mailto:hey@thefocus.ai?subject=Build%20%26%20Ship" }, { "text": "LET'S BUILD THE NEXT LEVEL →", "href": "mailto:hey@thefocus.ai?subject=AI%20Assessment%20-%20Scaling" }, { "text": "BUILT ON HABITAT OS New production AI builds ship on Habitat OS — our enterprise agent runtime. Deployed in your cloud, auditable at the core, operated by named principals. Learn about the platform →", "href": "https://thefocus.ai/habitat-os" }, { "text": "Out of the framework Vibe Check Your team built an internal tool with Cursor, Claude, Bolt, or Lovable. It works — but the foundation needs review before IT rollout. We assess the codebase for security holes, architectural risks, and scaling issues, then fix what matters. Two-step: assessment (1-4 days) and cleanup (1-3 weeks). Security audit Architecture review Scalability assessment Prioritized fix list GET YOUR VIBE CHECK →", "href": "https://thefocus.ai/vibe-check" }, { "text": "→ Vibe Check — audit your team's AI-built tool", "href": "https://thefocus.ai/vibe-check" }, { "text": "→ QB Sync — tribal cost codes discovered & formalized", "href": "https://thefocus.ai/case-study/qbsync" }, { "text": "Monthly Close Stop the 10-day scramble →", "href": "https://thefocus.ai/offerings/monthly-close" }, { "text": "Hiring Pipeline Stop losing candidates to process →", "href": "https://thefocus.ai/offerings/hiring-pipeline" }, { "text": "→ Fountain Creek — invoice parsing, 94.5% validated accuracy", "href": "https://thefocus.ai/case-study/fountain-creek" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities/#assessment", "https://thefocus.ai/capabilities/#framework", "https://thefocus.ai/capabilities/#level-0", "https://thefocus.ai/capabilities/#level-1", "https://thefocus.ai/capabilities/#level-2", "https://thefocus.ai/capabilities/#level-3", "https://thefocus.ai/capabilities/#level-4", "https://thefocus.ai/capabilities/#level-5", "https://thefocus.ai/capabilities/#level-0", "https://thefocus.ai/capabilities/#level-1", "https://thefocus.ai/capabilities/#level-2", "https://thefocus.ai/capabilities/#level-3", "https://thefocus.ai/capabilities/#level-4", "https://thefocus.ai/capabilities/#level-5", "mailto:hey@thefocus.ai?subject=Assessment%20%26%20Roadmap", "mailto:hey@thefocus.ai?subject=Build%20%26%20Ship", "mailto:hey@thefocus.ai?subject=AI%20Assessment%20-%20Scaling", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/vibe-check", "https://thefocus.ai/case-study/qbsync", "https://thefocus.ai/offerings/monthly-close", "https://thefocus.ai/offerings/hiring-pipeline", "https://thefocus.ai/case-study/fountain-creek", "https://thefocus.ai/case-study/tezlab-ai", "https://thefocus.ai/case-study/onboarding", "https://thefocus.ai/offerings/invoice-processing", "https://thefocus.ai/offerings/customer-onboarding", "https://thefocus.ai/case-study/distill", "https://thefocus.ai/case-study/upperhand-ai", "https://thefocus.ai/offerings/executive-reporting", "https://thefocus.ai/case-study/ai-back-office", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] BUILDING AN INTELLIGENT ORGANIZATION Where is your organization on the AI journey? Most AI projects fail not because the technology doesn't work — but because the organization can't describe its own work in a form machines can act on. The journey is from tribal to coherent — and each transition has a different blocker. Different departments sit at different points. Find yours below. FIND OUT WHERE YOU ARE SEE THE FRAMEWORK THE FRAMEWORK Six levels of AI maturity L0 Tribal Knowledge lives in people's heads L1 Experimenting Individual tools, nothing shared L2 Legible Your organization can describe its own work L3 Knowledgeable Your organization knows what it knows L4 Adaptive The system brings insights to you L5 Self-Improving The system learns from every interaction WHERE ARE YOU? Find your starting point Pick the cluster that sounds most like you. Each one maps to a specific engagement. Consulting Track ORGANIZATIONS SCALING PRODUCTION AI L0 — L1 Tribal → Legible ✓ Your team uses ChatGPT individually but there's no organizational strategy ✓ AI feels interesting but you don't know where to begin ✓ The real question isn't which tool to pick — it's whether you can describe your own workflows Assessment & Roadmap 1-2 weeks AI opportunity assessment Process documentation Prioritized roadmap Build vs. buy analysis LET'S MAP YOUR STARTING POINT → L2 — L3 Legible → Knowledgeable ✓ You have AI tools connected to your workflows but they feel generic ✓ Your proprietary data — the documents, surveys, records that make your business unique — is still locked in folders nobody can search ✓ You know it could unlock more, but aren't sure how Build & Ship 2-6 weeks Document ingestion pipeline Semantic knowledge base Conversational query interface Source-attributed answers LET'S UNLOCK YOUR DATA → L4 — L5 Knowledgeable → Self-Improving ✓ AI is driving real business outcomes, but you want it to compound ✓ You need systems that learn from themselves, roles that evolve with the technology, and governance that adapts ✓ The question is no longer 'does AI work?' — it's 'how does our organization change?' Ongoing Partnership Monthly retainer Dedicated team access Feedback loop design Continuous optimization Capability expansion Team training LET'S BUILD THE NEXT LEVEL → BUILT ON HABITAT OS New p", "height": 9056, "screenshots": { "desktop": "assets/screenshots/desktop/capabilities.png", "mobile": "assets/screenshots/mobile/capabilities.png" } }, { "url": "https://thefocus.ai/habitat-os", "path": "/habitat-os", "slug": "habitat-os", "category": "Core", "status": 200, "title": "Habitat OS — Enterprise Agent Platform | TheFocus.AI", "metaDescription": "Habitat OS is TheFocus.AI's enterprise agent runtime for new production AI builds. Runs in your cloud, on your terms, with named principal accountability and an open-source core.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Habitat OS. Production AI, under your control.", "index": 1 }, { "tag": "h2", "text": "What is a Habitat?", "index": 2 }, { "tag": "h2", "text": "Four commitments your security team can verify.", "index": 3 }, { "tag": "h3", "text": "Your cloud, your keys, your rules.", "index": 4 }, { "tag": "h3", "text": "Auditable engine, accountable distribution.", "index": 5 }, { "tag": "h3", "text": "The people who built it run it.", "index": 6 }, { "tag": "h3", "text": "Build one, the next one is easier.", "index": 7 }, { "tag": "h2", "text": "Your best prompts shouldn't die in one person's chat history.", "index": 8 }, { "tag": "h3", "text": "Their stream, their control.", "index": 9 }, { "tag": "h3", "text": "One click lifts an artifact to the team.", "index": 10 }, { "tag": "h3", "text": "Skeptics can watch. Builders get credit.", "index": 11 }, { "tag": "h2", "text": "Productized Habitats, ready to deploy.", "index": 12 }, { "tag": "h3", "text": "Monthly Close", "index": 13 }, { "tag": "h3", "text": "Invoice Processing", "index": 14 }, { "tag": "h3", "text": "Hiring Pipeline", "index": 15 }, { "tag": "h3", "text": "Customer Onboarding", "index": 16 }, { "tag": "h3", "text": "Executive Reporting", "index": 17 }, { "tag": "h3", "text": "QuickBooks Sync", "index": 18 }, { "tag": "h3", "text": "HR Lifecycle", "index": 19 }, { "tag": "h3", "text": "Deck Prep", "index": 20 }, { "tag": "h3", "text": "Executive Planning", "index": 21 }, { "tag": "h3", "text": "Your workflow, your Habitat.", "index": 22 }, { "tag": "h2", "text": "The systems you already run.", "index": 23 }, { "tag": "h2", "text": "From discovery to runtime.", "index": 24 }, { "tag": "h3", "text": "Discovery", "index": 25 }, { "tag": "h3", "text": "Build & Ship", "index": 26 }, { "tag": "h3", "text": "Habitat OS Runtime", "index": 27 }, { "tag": "h2", "text": "What you get. What we're building. What's honest.", "index": 28 }, { "tag": "h3", "text": "Deployment flexibility", "index": 29 }, { "tag": "h3", "text": "Platform-level certifications", "index": 30 }, { "tag": "h3", "text": "Network isolation", "index": 31 }, { "tag": "h3", "text": "Edge protection", "index": 32 }, { "tag": "h3", "text": "Data boundary", "index": 33 }, { "tag": "h3", "text": "Identity & access", "index": 34 }, { "tag": "h3", "text": "Open-source auditability", "index": 35 }, { "tag": "h3", "text": "Principal accountability", "index": 36 }, { "tag": "h3", "text": "TheFocus.AI program compliance", "index": 37 }, { "tag": "h3", "text": "Enterprise controls inherited on day one.", "index": 38 }, { "tag": "h3", "text": "The engine is open-source.", "index": 39 }, { "tag": "h2", "text": "One partner. One invoice. The right model for each job.", "index": 40 }, { "tag": "h3", "text": "You choose. We recommend.", "index": 41 }, { "tag": "h3", "text": "We carry the contracts.", "index": 42 }, { "tag": "h3", "text": "You pay for usage. Itemized.", "index": 43 }, { "tag": "h2", "text": "Questions your team is already asking.", "index": 44 }, { "tag": "h3", "text": "Can we run Habitat OS in our own cloud account?", "index": 45 }, { "tag": "h3", "text": "Who owns the code and the data?", "index": 46 }, { "tag": "h3", "text": "Can we bring our own model providers?", "index": 47 }, { "tag": "h3", "text": "How do you handle model provider outages?", "index": 48 }, { "tag": "h3", "text": "What happens if we stop paying the Habitat OS Runtime?", "index": 49 }, { "tag": "h3", "text": "Do you have SOC 2?", "index": 50 }, { "tag": "h3", "text": "Why Vercel?", "index": 51 }, { "tag": "h3", "text": "How do we pay for the AI models themselves?", "index": 52 }, { "tag": "h3", "text": "How is this different from LangChain, Bedrock Agents, or Copilot Studio?", "index": 53 }, { "tag": "h2", "text": "Ready for production AI your security team can actually approve?", "index": 54 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 55 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 56 }, { "tag": "h3", "text": "COMPANY", "index": 57 }, { "tag": "h3", "text": "PRODUCTS", "index": 58 }, { "tag": "h3", "text": "RESOURCES", "index": 59 }, { "tag": "h3", "text": "CONNECT", "index": 60 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Habitat OS. Production AI, under your control.", "y": 266, "body": "The enterprise runtime for every Habitat we build. Your cloud. Auditable core. Run by the principals who built it. Built for CISOs who have said \"no\" to AI rollouts and are looking for the \"yes\" that doesn't cost them their standards. TALK TO A PRINCIPAL SEE THE ARCHITECTURE" }, { "tag": "h2", "title": "What is a Habitat?", "y": 922, "body": "A Habitat is the living environment for an agent. Not a wrapper around a prompt — a place where an agent exists. Persona, memory, tools, sessions, and sub-agents, all in one governed environment. A HABITAT CONTAINS 01 Persona Who the agent is, what it will and won't do, its authority boundaries. 02 Tools Typed interfaces to your SaaS, your databases, your internal APIs. 03 Memory Persistent institutional knowledge. Facts the agent has learned, decisions it's made, edge cases it's handled. AND PLUGS INTO 04 Sub-agents Specialized workers the Habitat delegates to. Composable, not monolithic. 05 Sessions Every interaction logged, replayable, auditable. Your compliance team gets the paper trail." }, { "tag": "h2", "title": "Four commitments your security team can verify.", "y": 1909, "body": "Banks, private equity firms, healthcare systems, and real estate operators have told us the same thing: they want modern AI, but not if it means giving up sovereignty over their data, their infrastructure, or their accountability chain. Habitat OS is the answer. DEPLOYED WHERE YOU SAY Your cloud, your keys, your rules. Habitat OS runs in your VPC, your dedicated cloud account, or our managed single-tenant environment. Customer-owned data stays customer-side. Model provider keys are yours to bring. On-prem is available case-by-case for regulated deployments. OPEN AT THE CORE Auditable engine, accountable distribution. The Habitat primitives — persona, tools, memory, sub-agents, evals — live i" }, { "tag": "h3", "title": "Your cloud, your keys, your rules.", "y": 2214, "body": "Habitat OS runs in your VPC, your dedicated cloud account, or our managed single-tenant environment. Customer-owned data stays customer-side. Model provider keys are yours to bring. On-prem is available case-by-case for regulated deployments." }, { "tag": "h3", "title": "Auditable engine, accountable distribution.", "y": 2214, "body": "The Habitat primitives — persona, tools, memory, sub-agents, evals — live in our open-source engine, Umwelten. Your security team can read every line. The enterprise distribution adds identity, observability, integrations, and human support." }, { "tag": "h3", "title": "The people who built it run it.", "y": 2572, "body": "Every engagement is led by a named principal. No rotating analysts, no offshore handoffs, no layers between the people who understand your Habitat and the people fixing it when something breaks. Your CISO gets a direct line." }, { "tag": "h3", "title": "Build one, the next one is easier.", "y": 2572, "body": "Your monthly close Habitat, your hiring Habitat, your executive reporting Habitat — all run on the same runtime, share the same identity layer, and benefit from the same operational tooling. Scale across departments without re-architecting each time." }, { "tag": "h2", "title": "Your best prompts shouldn't die in one person's chat history.", "y": 3061, "body": "Every Habitat on Habitat OS has a WorkStream layer — the place where your people actually work with it. Private by default. Promotable on their terms. Discoverable when the artifact is ready for the rest of the org. The fast adopters stop racing away from the rest of the org. The skeptics get to watch before they participate. The work your best people do stops being lost when a tab closes, a laptop retires, or an employee moves on. PRIVATE BY DEFAULT Their stream, their control. Every employee gets a WorkStream. Their prompts, their artifacts, their workflows. Nothing is shared, seen, or indexed until they choose to promote it. PROMOTABLE WHEN READY One click lifts an artifact to the team. W" }, { "tag": "h3", "title": "Their stream, their control.", "y": 3460, "body": "Every employee gets a WorkStream. Their prompts, their artifacts, their workflows. Nothing is shared, seen, or indexed until they choose to promote it." }, { "tag": "h3", "title": "One click lifts an artifact to the team.", "y": 3460, "body": "When a prompt actually works or a workflow is worth keeping, promote it to the team. Promote again to the org. The employee stays in control; the organization stops losing good work to closed tabs." }, { "tag": "h3", "title": "Skeptics can watch. Builders get credit.", "y": 3460, "body": "Teammates can read, fork, and build on promoted artifacts. Power users compound. Skeptics participate at their own pace. No forced migration, no champion burnout." }, { "tag": "h2", "title": "Productized Habitats, ready to deploy.", "y": 5429, "body": "These are the Habitats we've built and shipped. Each one is a starting point — tailored to your processes, running on your infrastructure, extended to your integrations. You don't start from scratch. L1 → L2 → Monthly Close Formalize the close checklist, reconciliation rules, and approval logic into a Habitat that runs every month. L1 → L2 → Invoice Processing Any vendor format in, structured data out. The Habitat learns your exception patterns as it goes. L1 → L2 → Hiring Pipeline Candidate screening, scheduling, and pipeline hygiene. Stops losing candidates to your own process. L1 → L2 → Customer Onboarding New client setup that doesn't depend on who remembers what. The Habitat carries the" }, { "tag": "h3", "title": "Monthly Close", "y": 5682, "body": "Formalize the close checklist, reconciliation rules, and approval logic into a Habitat that runs every month." }, { "tag": "h3", "title": "Invoice Processing", "y": 5682, "body": "Any vendor format in, structured data out. The Habitat learns your exception patterns as it goes." }, { "tag": "h3", "title": "Hiring Pipeline", "y": 5682, "body": "Candidate screening, scheduling, and pipeline hygiene. Stops losing candidates to your own process." }, { "tag": "h3", "title": "Customer Onboarding", "y": 5907, "body": "New client setup that doesn't depend on who remembers what. The Habitat carries the institutional memory." }, { "tag": "h3", "title": "Executive Reporting", "y": 5907, "body": "Real-time answers against your data, not reformatted dashboards. Your executives stop waiting for slides." }, { "tag": "h3", "title": "QuickBooks Sync", "y": 5907, "body": "Project accounting from QuickBooks to your reporting tools, with tribal cost codes formalized into machine-readable rules." }, { "tag": "h3", "title": "HR Lifecycle", "y": 6132, "body": "Onboarding, performance cycles, compensation changes. The workflows currently living in one person's head, formalized." }, { "tag": "h3", "title": "Deck Prep", "y": 6132, "body": "Raw data into board-ready narratives. Your executive voice, applied consistently across every deck." }, { "tag": "h3", "title": "Executive Planning", "y": 6132, "body": "OKR tracking, cross-department alignment, exec briefings that write themselves — grounded in the data your team actually uses." }, { "tag": "h3", "title": "Your workflow, your Habitat.", "y": 6478, "body": "If the thing your team does every week doesn't fit a template, we build it. The Habitat anatomy is the same; the rules are yours." }, { "tag": "h2", "title": "The systems you already run.", "y": 6824, "body": "Habitats plug into the enterprise SaaS your team already uses. Read and write with field-level auth. Respect the access controls you've already configured. Integrations expand per engagement. Snowflake Query your warehouse. Write back reconciled data. Databricks Lakehouse access with governed schemas. Microsoft 365 Teams, Outlook, SharePoint, Excel. Google Workspace Gmail, Drive, Sheets, Calendar. Salesforce CRM read/write with field-level auth. NetSuite Financial systems with role-scoped access. Workday HR and workforce data integration. QuickBooks Accounting for project-based businesses. HRIS + ATS Greenhouse, Lever, Rippling, Gusto. Slack Conversational surface for any Habitat. Don't see " }, { "tag": "h2", "title": "From discovery to runtime.", "y": 7566, "body": "Habitat OS is delivered through a consulting engagement, not a self-serve sign-up. You pay us to understand your workflow, build your first Habitat, and operate the platform it runs on. Each phase maps to a clear contract. 1 1-2 weeks Discovery We map your actual workflow — not the documented version. Every workaround, exception, and judgment call. You receive an honest roadmap with clear build-vs-buy recommendations. ASSESSMENT & ROADMAP ENGAGEMENT → 2 2-6 weeks Build & Ship We formalize your tribal knowledge and deliver your first Habitat running on Habitat OS. Principal-led build, deployed to your environment, with documentation your team can own. BUILD & SHIP ENGAGEMENT → 3 Ongoing Habit" }, { "tag": "h3", "title": "Discovery", "y": 7795, "body": "We map your actual workflow — not the documented version. Every workaround, exception, and judgment call. You receive an honest roadmap with clear build-vs-buy recommendations." }, { "tag": "h3", "title": "Build & Ship", "y": 8029, "body": "We formalize your tribal knowledge and deliver your first Habitat running on Habitat OS. Principal-led build, deployed to your environment, with documentation your team can own." }, { "tag": "h3", "title": "Habitat OS Runtime", "y": 8237, "body": "Your Habitat runs in production. We operate the runtime, maintain integrations, monitor behavior, and close feedback loops. New Habitats extend the same platform." }, { "tag": "h2", "title": "What you get. What we're building. What's honest.", "y": 8612, "body": "Two things make Habitat OS defensible to a CISO: an open-core engine your security team can read, and an enterprise platform underneath that already holds the certifications most AI startups are still chasing. We didn't build that platform — Vercel did. We built on top of it on purpose. Below is the ground truth of where Habitat OS stands today — what's shipped, what's inherited from our platform partner, and what we're still earning at the TheFocus.AI program level. Deployment flexibility Available today Customer VPC, dedicated cloud account, or managed single-tenant on Vercel's enterprise AI Cloud. On-prem deployments supported case-by-case. Your infrastructure team chooses the boundary. P" }, { "tag": "h3", "title": "Deployment flexibility", "y": 8981, "body": "Available today" }, { "tag": "h3", "title": "Platform-level certifications", "y": 8981, "body": "Inherited from Vercel" }, { "tag": "h3", "title": "Network isolation", "y": 9220, "body": "Vercel Secure Compute" }, { "tag": "h3", "title": "Edge protection", "y": 9220, "body": "Vercel Firewall" }, { "tag": "h3", "title": "Data boundary", "y": 9425, "body": "Always customer-controlled" }, { "tag": "h3", "title": "Identity & access", "y": 9425, "body": "Enterprise SSO available" }, { "tag": "h3", "title": "Open-source auditability", "y": 9630, "body": "Public, on GitHub" }, { "tag": "h3", "title": "Principal accountability", "y": 9630, "body": "Always" }, { "tag": "h3", "title": "TheFocus.AI program compliance", "y": 9812, "body": "SOC 2 Type I in progress" }, { "tag": "h3", "title": "Enterprise controls inherited on day one.", "y": 10103, "body": "The managed Habitat OS runtime is deployed on Vercel — our enterprise platform partner. Customers running in our managed single-tenant environment inherit Vercel's platform certifications, network isolation, and edge protection at the infrastructure layer. For customer-VPC deployments, the same runtime is delivered into your cloud boundary. ✓ SOC 2 Type 2 — Security · Confidentiality · Availability ✓ ISO 27001:2022 — Information security management ✓ PCI DSS Level 1 — Payment-grade controls at the edge ✓ HIPAA-ready — BAA available on Enterprise ✓ GDPR + DPA — EU data-handling commitments ✓ Secure Compute — Dedicated VPC + static egress IPs Vercel security posture → Compliance docs →" }, { "tag": "h3", "title": "The engine is open-source.", "y": 10103, "body": "The Habitat primitives — persona, tools, memory, sub-agents, evals — live in our open-source project, Umwelten. Your security team can read every line, fork it, or run it themselves. Habitat OS is the enterprise distribution: hardened, integrated, supported, accountable. ✓ OSI-compatible license — no black boxes at the core ✓ No vendor lock-in — you can run it yourself if we part ways ✓ Public review — your security team can read before signing Umwelten docs → View on GitHub →" }, { "tag": "h2", "title": "One partner. One invoice. The right model for each job.", "y": 10800, "body": "Enterprise AI deals usually force three procurement tracks in parallel: the consultancy, the cloud, and every LLM provider. Habitat OS collapses that into a single commercial relationship. We recommend and fund the right model for each part of your Habitat. You pay for what the Habitat actually uses — billed through us, on one monthly invoice, itemized down to the tool and the model. Model-agnostic by design You choose. We recommend. Claude for reasoning. Gemini for long context. GPT for structured tools. Open-source when it fits. We document the model choice for every tool in your Habitat — and we change it when something better ships, without a procurement cycle. Funded by us We carry the " }, { "tag": "h3", "title": "You choose. We recommend.", "y": 11199, "body": "Claude for reasoning. Gemini for long context. GPT for structured tools. Open-source when it fits. We document the model choice for every tool in your Habitat — and we change it when something better ships, without a procurement cycle." }, { "tag": "h3", "title": "We carry the contracts.", "y": 11199, "body": "Anthropic, OpenAI, Google, Replicate, Vercel AI Gateway — whatever the Habitat needs. Provider MSAs, rate limits, model deprecations, BAAs, and invoice reconciliation are our problem, not your procurement team's." }, { "tag": "h3", "title": "You pay for usage. Itemized.", "y": 11199, "body": "Metered monthly and itemized by Habitat, by tool, and by model. No juggling provider invoices, no reconciling tokens against vendor bills. What you pay is what the Habitat actually consumed." }, { "tag": "h2", "title": "Questions your team is already asking.", "y": 11957, "body": "Can we run Habitat OS in our own cloud account? + Who owns the code and the data? + Can we bring our own model providers? + How do you handle model provider outages? + What happens if we stop paying the Habitat OS Runtime? + Do you have SOC 2? + Why Vercel? + How do we pay for the AI models themselves? + How is this different from LangChain, Bedrock Agents, or Copilot Studio? +" }, { "tag": "h3", "title": "Can we run Habitat OS in our own cloud account?", "y": 12078, "body": "+" }, { "tag": "h3", "title": "Who owns the code and the data?", "y": 12172, "body": "+" }, { "tag": "h3", "title": "Can we bring our own model providers?", "y": 12266, "body": "+" }, { "tag": "h3", "title": "How do you handle model provider outages?", "y": 12360, "body": "+" }, { "tag": "h3", "title": "What happens if we stop paying the Habitat OS Runtime?", "y": 12454, "body": "+" }, { "tag": "h3", "title": "Do you have SOC 2?", "y": 12548, "body": "+" }, { "tag": "h3", "title": "Why Vercel?", "y": 12642, "body": "+" }, { "tag": "h3", "title": "How do we pay for the AI models themselves?", "y": 12736, "body": "+" }, { "tag": "h3", "title": "How is this different from LangChain, Bedrock Agents, or Copilot Studio?", "y": 12830, "body": "+" }, { "tag": "h2", "title": "Ready for production AI your security team can actually approve?", "y": 13086, "body": "The first conversation is about your workflow, your infrastructure, and your constraints — not ours. We'll tell you honestly whether Habitat OS fits. TALK TO A PRINCIPAL READ A CASE STUDY" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 13490, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 13940, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 14317, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 14317, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 14317, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 14317, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Habitat%20OS%20%E2%80%94%20Enterprise%20Inquiry" }, { "text": "SEE THE ARCHITECTURE", "href": "https://thefocus.ai/habitat-os/#architecture" }, { "text": "TALK TO A PRINCIPAL ABOUT WORKSTREAMS", "href": "mailto:hey@thefocus.ai?subject=Habitat%20WorkStreams%20%E2%80%94%20Enterprise%20Inquiry" }, { "text": "L1 → L2 → Monthly Close Formalize the close checklist, reconciliation rules, and approval logic into a Habitat that runs every month.", "href": "https://thefocus.ai/offerings/monthly-close" }, { "text": "L1 → L2 → Invoice Processing Any vendor format in, structured data out. The Habitat learns your exception patterns as it goes.", "href": "https://thefocus.ai/offerings/invoice-processing" }, { "text": "L1 → L2 → Hiring Pipeline Candidate screening, scheduling, and pipeline hygiene. Stops losing candidates to your own process.", "href": "https://thefocus.ai/offerings/hiring-pipeline" }, { "text": "L1 → L2 → Customer Onboarding New client setup that doesn't depend on who remembers what. The Habitat carries the institutional memory.", "href": "https://thefocus.ai/offerings/customer-onboarding" }, { "text": "L2 → L3 → Executive Reporting Real-time answers against your data, not reformatted dashboards. Your executives stop waiting for slides.", "href": "https://thefocus.ai/offerings/executive-reporting" }, { "text": "DISCUSS YOUR WORKFLOW →", "href": "mailto:hey@thefocus.ai?subject=Habitat%20OS%20%E2%80%94%20Enterprise%20Inquiry" }, { "text": "ASSESSMENT & ROADMAP ENGAGEMENT →", "href": "https://thefocus.ai/capabilities#assessment" }, { "text": "BUILD & SHIP ENGAGEMENT →", "href": "https://thefocus.ai/capabilities#assessment" }, { "text": "ONGOING PARTNERSHIP ENGAGEMENT →", "href": "https://thefocus.ai/capabilities#assessment" }, { "text": "Vercel security posture →", "href": "https://vercel.com/security" }, { "text": "Compliance docs →", "href": "https://vercel.com/docs/security/compliance" }, { "text": "Umwelten docs →", "href": "https://umwelten.thefocus.ai/" }, { "text": "View on GitHub →", "href": "https://github.com/The-Focus-AI/umwelten" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Habitat%20OS%20%E2%80%94%20Enterprise%20Inquiry" }, { "text": "READ A CASE STUDY", "href": "https://thefocus.ai/case-studies" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "mailto:hey@thefocus.ai?subject=Habitat%20OS%20%E2%80%94%20Enterprise%20Inquiry", "https://thefocus.ai/habitat-os/#architecture", "mailto:hey@thefocus.ai?subject=Habitat%20WorkStreams%20%E2%80%94%20Enterprise%20Inquiry", "https://thefocus.ai/offerings/monthly-close", "https://thefocus.ai/offerings/invoice-processing", "https://thefocus.ai/offerings/hiring-pipeline", "https://thefocus.ai/offerings/customer-onboarding", "https://thefocus.ai/offerings/executive-reporting", "mailto:hey@thefocus.ai?subject=Habitat%20OS%20%E2%80%94%20Enterprise%20Inquiry", "https://thefocus.ai/capabilities#assessment", "https://thefocus.ai/capabilities#assessment", "https://thefocus.ai/capabilities#assessment", "https://vercel.com/security", "https://vercel.com/docs/security/compliance", "https://umwelten.thefocus.ai/", "https://github.com/The-Focus-AI/umwelten", "mailto:hey@thefocus.ai?subject=Habitat%20OS%20%E2%80%94%20Enterprise%20Inquiry", "https://thefocus.ai/case-studies", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] THE ENTERPRISE RUNTIME FOR PRODUCTION AI Habitat OS. Production AI, under your control. The enterprise runtime for every Habitat we build. Your cloud. Auditable core. Run by the principals who built it. Built for CISOs who have said \"no\" to AI rollouts and are looking for the \"yes\" that doesn't cost them their standards. TALK TO A PRINCIPAL SEE THE ARCHITECTURE THE ANATOMY What is a Habitat? A Habitat is the living environment for an agent. Not a wrapper around a prompt — a place where an agent exists. Persona, memory, tools, sessions, and sub-agents, all in one governed environment. A HABITAT CONTAINS 01 Persona Who the agent is, what it will and won't do, its authority boundaries. 02 Tools Typed interfaces to your SaaS, your databases, your internal APIs. 03 Memory Persistent institutional knowledge. Facts the agent has learned, decisions it's made, edge cases it's handled. AND PLUGS INTO 04 Sub-agents Specialized workers the Habitat delegates to. Composable, not monolithic. 05 Sessions Every interaction logged, replayable, auditable. Your compliance team gets the paper trail. 06 Evals Automated quality checks against known-good behavior. Regressions surface before production does. THE TWO-LEVEL MODEL Habitat OS is the enterprise runtime. A Habitat is the agent environment your team actually uses. Your monthly close Habitat, your hiring Habitat, your reporting Habitat — every one of them runs on Habitat OS. WHY HABITAT OS Four commitments your security team can verify. Banks, private equity firms, healthcare systems, and real estate operators have told us the same thing: they want modern AI, but not if it means giving up sovereignty over their data, their infrastructure, or their accountability chain. Habitat OS is the answer. DEPLOYED WHERE YOU SAY Your cloud, your keys, your rules. Habitat OS runs in your VPC, your dedicated cloud account, or our managed single-tenant environment. Customer-owned data stays customer-side. Model provider keys are yours to bring. On-prem is available case-by-case for regulated deployments. OPEN AT THE CORE Auditable engine, accountable distribution. The Habitat primitives — persona, tools, memory, sub-agents, evals — live in our open-source engine, Umwelten. Your security team can read every line. The enterprise distribution adds ident", "height": 14814, "screenshots": { "desktop": "assets/screenshots/desktop/habitat-os.png", "mobile": "assets/screenshots/mobile/habitat-os.png" } }, { "url": "https://thefocus.ai/about", "path": "/about", "slug": "about", "category": "Core", "status": 200, "title": "About | TheFocus.AI", "metaDescription": "We partner with executive teams and product leadership to deliver production AI in weeks, not quarters. Engineers, designers, and strategists focused on measurable outcomes.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Clarity through partnership", "index": 1 }, { "tag": "h2", "text": "Our Founders", "index": 2 }, { "tag": "h3", "text": "Ben Schippers", "index": 3 }, { "tag": "h3", "text": "Will Schenk", "index": 4 }, { "tag": "h2", "text": "Explore Our Work", "index": 5 }, { "tag": "h2", "text": "Where innovation takes shape", "index": 6 }, { "tag": "h3", "text": "Ready to explore what's possible?", "index": 7 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 8 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 9 }, { "tag": "h3", "text": "COMPANY", "index": 10 }, { "tag": "h3", "text": "PRODUCTS", "index": 11 }, { "tag": "h3", "text": "RESOURCES", "index": 12 }, { "tag": "h3", "text": "CONNECT", "index": 13 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Clarity through partnership", "y": 266, "body": "Principal-led AI consulting for mid-market and enterprise organizations. Every new production build ships on Habitat OS, our enterprise agent runtime. TALK TO A PRINCIPAL SEE THE FRAMEWORK" }, { "tag": "h2", "title": "Our Founders", "y": 1143, "body": "With more than 40 years of combined experience in software engineering and design, Ben and Will have guided organizations through multiple technology cycles. They founded TheFocus.AI with a core belief: the organizations that thrive in the AI era will be those that truly understand what they're building and why. That's why strategic clarity is central to everything we do. Ben Schippers benschippers.com → Will Schenk LinkedIn → We're always looking for exceptional engineers and strategists. If you're passionate about AI and building products that deliver real impact, we'd like to hear from you. See open positions →" }, { "tag": "h3", "title": "Ben Schippers", "y": 1497, "body": "benschippers.com →" }, { "tag": "h3", "title": "Will Schenk", "y": 1497, "body": "LinkedIn →" }, { "tag": "h2", "title": "Explore Our Work", "y": 2227, "body": "Explore applications from TheFocus.AI Labs, including Distill." }, { "tag": "h2", "title": "Where innovation takes shape", "y": 2520, "body": "The Studio is our research lab—where we explore emerging technologies, refine new methodologies, and incubate products. It's designed to move quickly, uncovering opportunities and building solutions that we bring to our clients. Our Studio team is driven by execution and measurable impact. We're continuously experimenting, learning, and pushing the boundaries of what's possible with AI. Want to spend time with us in our studio? APPLY HERE" }, { "tag": "h3", "title": "Ready to explore what's possible?", "y": 3180, "body": "Whether you have a defined initiative or need help finding where AI delivers the most value, the conversation starts with understanding where you are — not selling you where we think you should be. New production AI builds ship on Habitat OS, our enterprise agent runtime. Already have an internal tool an AI-built team shipped? Our Vibe Check gives you an honest assessment and a fix-it roadmap. TALK TO A PRINCIPAL SEE THE FRAMEWORK" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 3561, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 4011, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. 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}, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE THE FRAMEWORK", "href": "https://thefocus.ai/capabilities" }, { "text": "Ben Schippers benschippers.com →", "href": "https://www.benschippers.com/" }, { "text": "Will Schenk LinkedIn →", "href": "https://www.linkedin.com/in/will-schenk-420266/" }, { "text": "See open positions →", "href": "https://thefocus.ai/jobs" }, { "text": "Distill", "href": "https://account.thefocus.ai/" }, { "text": "APPLY HERE", "href": "mailto:studio@thefocus.ai" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE THE FRAMEWORK", "href": 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] OUR STORY Clarity through partnership Principal-led AI consulting for mid-market and enterprise organizations. Every new production build ships on Habitat OS, our enterprise agent runtime. TALK TO A PRINCIPAL SEE THE FRAMEWORK We founded TheFocus.AI because operationalizing AI is genuinely complex—and most organizations don't have the bandwidth to navigate it alone. We're engineers, designers, and strategists who believe clarity comes from deep partnership. We work directly with executive teams and product leadership to deliver production AI in weeks, not quarters. AI is transforming how organizations interact with their data and customers. Interfaces are becoming conversational. Information is becoming actionable. But making that transition requires clarity—understanding what's possible, what's practical, and what delivers real business value. 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NO: RM-2025-INSIGHTS TheFocus.AI Labs • Est. 2024 UNCLASSIFIED RESEARCH REPORTS We write about what we're learning. Research notes, technical deep-dives, and honest observations about AI, software development, and where things are headed. LATEST Neural harness I'm on my second Claude Max plan. Are we going to go for three? Tokens are the bill for running a new native unit of computing. The harness is to neural nets what the compiler is to source code. May 1, 2026 • ESSAY AGENTS HARNESS CODING #002 Laddering up from chatboxes to autonomy Most companies want to go from scattered ChatGPT use directly to agents and autonomy. The middle layers — the ones where deployments either become real or die — get skipped in the planning. April 14, 2026 ESSAY READ → #003 Building an Intelligent Organization From AI pilots to production systems — how we help leadership move up the maturity curve with principal-led engagements. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] THEFOCUS.AI LABS PROJECT: Weekend Warrior Workshop DOC. NO: WW-2025-PROJECTS TheFocus.AI Labs • Est. 2024 23 PROJECTS WEEKEND WARRIOR We learn by building—and we share what we learn. These are hands-on projects you can try yourself, complete with step-by-step instructions and everything you need to get started. FEATURED PROJECT Our top recommendation to get started START HERE Build Your Own Coding Agent A step-by-step tutorial for building an AI coding assistant from scratch. Bootstrap it with an existing agent, then use it to improve itself. START THIS BUILD → MORE PROJECTS Quick builds for a productive afternoon December 5, 2025 The Smallest Coding Agent A complete agentic coding loop in 25 lines of Bun. No frameworks, no dependencies, just bash and a model. VIEW → September 8, 2025 Using repomix to give llm full context How to use repomix to package your entire codebase into a single file for LLM context, with tips for managing token counts and mise integration. VIEW → September 1, 2025 context7-mcp Learn how to integrate Context7 MCP server to get up-to-date code documentation for LLMs and AI code editors, enabling real-time access to the latest library and framework documentation. VIEW → Showing 4 of 23 projects BROWSE ALL 23 PROJECTS → FOR TEAMS SHIPPING PRODUCTION AI Weekend Warrior is where we learn in public. The same team runs principal-led engagements for Samsung, Tesla, Perplexity, and Rivian — production AI in weeks, not quarters. SEE THE FRAMEWORK → THEFOCUS.AI LABS WORKSHOP SERIES NEW PROJECTS ADDED REGULARLY Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. View open positions", "height": 3858, "screenshots": { "desktop": "assets/screenshots/desktop/tools.png", "mobile": "assets/screenshots/mobile/tools.png" } }, { "url": "https://thefocus.ai/reports", "path": "/reports", "slug": "reports", "category": "Lab index", "status": 200, "title": "Research Reports | TheFocus.AI Labs", "metaDescription": "In-depth reports and analysis from TheFocus.AI Labs — model evaluations, tool-use benchmarks, and research on where AI-assisted software is actually headed.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "REPORTS & DEEP DIVES", "index": 1 }, { "tag": "h3", "text": "Local Providers: Which Open Model Should You Actually Run?", "index": 2 }, { "tag": "h3", "text": "Testing 48 LLMs Across 5 Dimensions for $4.63", "index": 3 }, { "tag": "h3", "text": "Can LLMs Use Real-World Tools?", "index": 4 }, { "tag": "h3", "text": "The Car Wash Test: Do LLMs Have Common Sense?", "index": 5 }, { "tag": "h3", "text": "Weekend Coding Agent", "index": 6 }, { "tag": "h3", "text": "AI Engineering Code Summit 2025", "index": 7 }, { "tag": "h3", "text": "June 2025 Coding Agent Report", "index": 8 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 9 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 10 }, { "tag": "h3", "text": "COMPANY", "index": 11 }, { "tag": "h3", "text": "PRODUCTS", "index": 12 }, { "tag": "h3", "text": "RESOURCES", "index": 13 }, { "tag": "h3", "text": "CONNECT", "index": 14 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "REPORTS & DEEP DIVES", "y": 341, "body": "Model evaluations, tool-use benchmarks, and honest field reports on where AI-assisted software is actually headed. Written by the team that ships production AI for Samsung, Tesla, Perplexity, and Rivian." }, { "tag": "h3", "title": "Local Providers: Which Open Model Should You Actually Run?", "y": 778, "body": "We tested 7 model families across 3 local runtimes on a 64 GB Mac — 20 cells, 5 dimensions. gemma-4-26b-a4b hits 95% in 4 minutes. Thinking mode usually hurts except for tool-calling. Interactive charts and full methodology. READ THE REPORT →" }, { "tag": "h3", "title": "Testing 48 LLMs Across 5 Dimensions for $4.63", "y": 1167, "body": "Which model should you actually use? We tested 48 models across reasoning, knowledge, instruction following, coding, and MCP tool use. Claude Sonnet 4.6 leads at 93.8% across all 5 dimensions. Interactive leaderboard with per-model detail panels. READ THE REPORT →" }, { "tag": "h3", "title": "Can LLMs Use Real-World Tools?", "y": 1555, "body": "We gave 39 models access to a Rivian owner's real driving and charging data via MCP tools and asked them to summarize 10 days of activity. Most can call tools. Far fewer call the right tools. READ THE REPORT →" }, { "tag": "h3", "title": "The Car Wash Test: Do LLMs Have Common Sense?", "y": 1882, "body": "We tested 131 AI models with one simple question: \"Should I walk or drive to the car wash?\" Only 24% figured out you need to bring your car. Interactive report with charts and full model comparison. READ THE REPORT →" }, { "tag": "h3", "title": "Weekend Coding Agent", "y": 2244, "body": "Want to understand how AI coding agents actually work? We built one from scratch — and documented every step. This is the tutorial we wish existed when we started. READ THE TUTORIAL →" }, { "tag": "h3", "title": "AI Engineering Code Summit 2025", "y": 2571, "body": "We gathered leading coding agent builders, engineers, and researchers to share what's actually working. Here's what we learned. READ THE REPORT →" }, { "tag": "h3", "title": "June 2025 Coding Agent Report", "y": 2897, "body": "We tested 15 leading AI coding tools and documented the strengths, weaknesses, and surprises. Clear recommendations for different workflows and experience levels. READ THE FULL REPORT (PDF) →" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 3686, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 4136, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] THEFOCUS.AI LABS PROJECT: TheFocus.AI Research Division DOC. NO: RM-REPORTS-INDEX TheFocus.AI Labs • Est. 2024 7 REPORTS REPORTS & DEEP DIVES Model evaluations, tool-use benchmarks, and honest field reports on where AI-assisted software is actually headed. Written by the team that ships production AI for Samsung, Tesla, Perplexity, and Rivian. May 2026 RM-2026-05-LP Local Providers: Which Open Model Should You Actually Run? We tested 7 model families across 3 local runtimes on a 64 GB Mac — 20 cells, 5 dimensions. gemma-4-26b-a4b hits 95% in 4 minutes. Thinking mode usually hurts except for tool-calling. Interactive charts and full methodology. READ THE REPORT → March 2026 RM-2026-03-MS Testing 48 LLMs Across 5 Dimensions for $4.63 Which model should you actually use? We tested 48 models across reasoning, knowledge, instruction following, coding, and MCP tool use. Claude Sonnet 4.6 leads at 93.8% across all 5 dimensions. Interactive leaderboard with per-model detail panels. READ THE REPORT → March 2026 RM-2026-03-MCP Can LLMs Use Real-World Tools? We gave 39 models access to a Rivian owner's real driving and charging data via MCP tools and asked them to summarize 10 days of activity. Most can call tools. Far fewer call the right tools. READ THE REPORT → February 2026 RM-2026-02-CW The Car Wash Test: Do LLMs Have Common Sense? We tested 131 AI models with one simple question: \"Should I walk or drive to the car wash?\" Only 24% figured out you need to bring your car. Interactive report with charts and full model comparison. READ THE REPORT → December 2025 RM-2025-12-WCA Weekend Coding Agent Want to understand how AI coding agents actually work? We built one from scratch — and documented every step. 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If this is your site, make sure that the filename case matches the URL as well as any file permissions. For root URLs (like http://example.com/) you must provide an index.html file. Read the full documentation for more information about using GitHub Pages. GitHub Status — @githubstatus", "height": 1000 }, { "url": "https://thefocus.ai/case-study/qbsync", "path": "/case-study/qbsync", "slug": "case-study--qbsync", "category": "Case study", "status": 200, "title": "57,000 Transactions Synced: Automated Construction Project Accounting from QuickBooks to Google Sheets", "metaDescription": "We inherited and stabilized a QuickBooks-to-Google Sheets sync system for a construction company managing multiple active projects. Hourly automated sync pulls labor, materials, and transaction data from QuickBooks Online into per-project reconciliation spreadsheets — giving project managers real-time visibility into costs without leaving the tools they already use.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "57,000 Transactions Synced: Automated Construction Project Accounting from QuickBooks to Google Sheets", "index": 1 }, { "tag": "h2", "text": "KEY RESULTS", "index": 2 }, { "tag": "h2", "text": "The Challenge", "index": 3 }, { "tag": "h2", "text": "Our Approach", "index": 4 }, { "tag": "h2", "text": "Technical Implementation", "index": 5 }, { "tag": "h3", "text": "Architecture", "index": 6 }, { "tag": "h3", "text": "What We Fixed", "index": 7 }, { "tag": "h3", "text": "Per-Project Reconciliation", "index": 8 }, { "tag": "h2", "text": "Results", "index": 9 }, { "tag": "h2", "text": "What Made This Work", "index": 10 }, { "tag": "h2", "text": "How This Maps to Habitat OS", "index": 11 }, { "tag": "h2", "text": "Facing a similar challenge?", "index": 12 }, { "tag": "h2", "text": "More Case Studies", "index": 13 }, { "tag": "h3", "text": "94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service", "index": 14 }, { "tag": "h3", "text": "From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts", "index": 15 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 16 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 17 }, { "tag": "h3", "text": "COMPANY", "index": 18 }, { "tag": "h3", "text": "PRODUCTS", "index": 19 }, { "tag": "h3", "text": "RESOURCES", "index": 20 }, { "tag": "h3", "text": "CONNECT", "index": 21 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "57,000 Transactions Synced: Automated Construction Project Accounting from QuickBooks to Google Sheets", "y": 269, "body": "We inherited and stabilized a QuickBooks-to-Google Sheets sync system for a construction company managing multiple active projects. Hourly automated sync pulls labor, materials, and transaction data from QuickBooks Online into per-project reconciliation spreadsheets — giving project managers real-time visibility into costs without leaving the tools they already use. Client: Steering House Year: 2026 Services: Development Stack: Python, Flask, Google App Engine, QuickBooks Online API, MongoDB, Google Sheets API, OAuth 2.0 Running on Habitat OS →" }, { "tag": "h2", "title": "KEY RESULTS", "y": 1369, "body": "✓ 57,829 transactions synced from QuickBooks to Google Sheets ✓ Hourly automated sync with zero manual intervention ✓ Per-project reconciliation spreadsheets for multiple active construction sites ✓ Labor date filtering fixed — resolved systematic data gaps for 10+ employees" }, { "tag": "h2", "title": "The Challenge", "y": 1621, "body": "Steering House is a construction company managing multiple active building projects across New York — each with its own budget, labor force, materials spend, and subcontractor commitments. Their financial data lives in QuickBooks Online, but their project managers and accountants work in Google Sheets. The gap between these two systems was a constant source of friction. Someone had to manually pull reports from QuickBooks, reformat the data, and paste it into the right project spreadsheet. Labor hours needed overtime calculations. Material purchases needed to be mapped to the right project. Reconciliation tabs needed to stay current so project managers could compare budgets against actuals. " }, { "tag": "h2", "title": "Our Approach", "y": 2241, "body": "We took over an existing codebase rather than starting from scratch. The first priority was getting the system running again, then diagnosing and fixing the data gaps. Our investigation revealed the problem wasn’t random API flakiness — it was systematic. The sync was querying QuickBooks by LastUpdatedTime (when a record was last modified) rather than TxnDate (when the work actually happened). This meant that approved timesheets falling outside the 14-day fetch window were silently dropped. For newer employees whose time entries were being approved in batch, the data simply never made it through." }, { "tag": "h2", "title": "Technical Implementation", "y": 2578, "body": "" }, { "tag": "h3", "title": "Architecture", "y": 2654, "body": "The system runs as a Flask application on Google App Engine with an hourly cron trigger: QuickBooks Online → OAuth 2.0 → App Engine (Flask) → MongoDB → Google Sheets API Hourly sync cycle: Refresh OAuth tokens Pull recent deletes from QuickBooks (CDC) Fetch updated transactions — labor, purchases, deposits Store raw data in MongoDB Transform and sync to per-project Google Sheets" }, { "tag": "h3", "title": "What We Fixed", "y": 3157, "body": "Query strategy. Switched TimeActivity queries from MetaData.LastUpdatedTime to TxnDate. This aligned data ingestion with pay periods rather than modification timestamps, eliminating the “approved outside the window” failure mode. Date filtering. Implemented the client’s core requirement: filter pre-2026 labor entries from reconciliation sheets while preserving all raw data in MongoDB for troubleshooting. Material, subcontractor, and other cost types sync without date restrictions. Import and deployment. Fixed module import paths for App Engine’s deployment model (absolute imports required) and added proper timeout handling for QuickBooks API calls. Reconciliation integrity. Verified the labo" }, { "tag": "h3", "title": "Per-Project Reconciliation", "y": 3643, "body": "Each active construction project gets its own Google Sheets spreadsheet with: QB Sync tab — raw transaction data from QuickBooks Reconciliation tabs — dated snapshots comparing budgets vs. actuals Labor entries — time tracked by employee, mapped to projects Material entries — purchases and deposits mapped to cost codes Manual overrides — a dedicated sheet for adjustments that don’t come from QuickBooks The system handles multiple concurrent projects (50 Garden Place, 33 Joralemon Street, 35 Joralemon, and others), each with thousands of transaction rows syncing hourly." }, { "tag": "h2", "title": "Results", "y": 4083, "body": "After stabilizing the system: 57,829 total transactions tracked in MongoDB across all projects Hourly sync running reliably on App Engine with zero errors 10 missing employees now showing up correctly in labor reconciliation Per-project sheets updating automatically — project managers check costs without touching QuickBooks Date filtering working as designed — clean 2026 data in reconciliation, full history preserved in database" }, { "tag": "h2", "title": "What Made This Work", "y": 4516, "body": "1. Inheriting vs. rebuilding. The existing codebase had 224 commits of working logic — overtime calculations, project mapping, reconciliation formatting. Rebuilding would have taken months. Instead we diagnosed the specific failures and fixed them in weeks. 2. Systematic diagnosis over guessing. The missing employee data looked like random API issues. The actual cause was a query strategy mismatch — fixable in a few lines once identified. The TimeActivity investigation report gave the client full transparency into what was wrong and why. 3. Google Sheets as the interface. The client’s project managers live in spreadsheets. Rather than building a new dashboard nobody would use, the system pus" }, { "tag": "h2", "title": "How This Maps to Habitat OS", "y": 5022, "body": "This is a financial-sync Habitat — scheduled autonomy over an integration the business depends on every day. The hourly sync cycle, the OAuth token lifecycle, change-data-capture on QuickBooks deletes, and MongoDB-backed raw-data preservation are exactly the primitives Habitat OS standardizes: scheduled agents, durable memory, and observable integrations with full audit trails. What keeps it running seamlessly in the background: the runtime handles the work humans shouldn’t have to think about — expired OAuth tokens refresh automatically, failed API calls retry with exponential backoff, raw data persists even when downstream transforms change, and every sync cycle is traceable from QuickBook" }, { "tag": "h2", "title": "Facing a similar challenge?", "y": 5673, "body": "Let's talk about shipping a production AI system for your team — principal-led, deployed in your cloud on Habitat OS. TALK TO A PRINCIPAL SEE THE FRAMEWORK" }, { "tag": "h2", "title": "More Case Studies", "y": 6010, "body": "FOOD SERVICE / RETAIL 94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy. MEDIA INTELLIGENCE / CONTENT ANALYTICS From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analy" }, { "tag": "h3", "title": "94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service", "y": 6102, "body": "We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy." }, { "tag": "h3", "title": "From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts", "y": 6102, "body": "We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analyzes content from Twitter/X, YouTube videos, and podcasts. What used to require hours of manual monitoring and note-taking now happens in minutes, with AI-generated summaries that surface the signal from the noise." }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 6346, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 6796, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 7173, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 7173, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 7173, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 7173, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← Back to Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Running on Habitat OS →", "href": "https://thefocus.ai/habitat-os" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "monthly-close Habitat", "href": "https://thefocus.ai/offerings/monthly-close" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Re%3A%2057%2C000%20Transactions%20Synced%3A%20Automated%20Construction%20Project%20Accounting%20from%20QuickBooks%20to%20Google%20Sheets" }, { "text": "SEE THE FRAMEWORK", "href": "https://thefocus.ai/capabilities" }, { "text": "FOOD SERVICE / RETAIL 94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy.", "href": "https://thefocus.ai/case-study/fountain-creek" }, { "text": "MEDIA INTELLIGENCE / CONTENT ANALYTICS From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analyzes content from Twitter/X, YouTube videos, and podcasts. What used to require hours of manual monitoring and note-taking now happens in minutes, with AI-generated summaries that surface the signal from the noise.", "href": "https://thefocus.ai/case-study/distill" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/case-studies", "https://thefocus.ai/habitat-os", "https://thefocus.ai/habitat-os", "https://thefocus.ai/offerings/monthly-close", "mailto:hey@thefocus.ai?subject=Re%3A%2057%2C000%20Transactions%20Synced%3A%20Automated%20Construction%20Project%20Accounting%20from%20QuickBooks%20to%20Google%20Sheets", "https://thefocus.ai/capabilities", "https://thefocus.ai/case-study/fountain-creek", "https://thefocus.ai/case-study/distill", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← Back to Case Studies CONSTRUCTION / FINANCE 4 weeks (inherited codebase) 57,000 Transactions Synced: Automated Construction Project Accounting from QuickBooks to Google Sheets We inherited and stabilized a QuickBooks-to-Google Sheets sync system for a construction company managing multiple active projects. Hourly automated sync pulls labor, materials, and transaction data from QuickBooks Online into per-project reconciliation spreadsheets — giving project managers real-time visibility into costs without leaving the tools they already use. Client: Steering House Year: 2026 Services: Development Stack: Python, Flask, Google App Engine, QuickBooks Online API, MongoDB, Google Sheets API, OAuth 2.0 Running on Habitat OS → KEY RESULTS ✓ 57,829 transactions synced from QuickBooks to Google Sheets ✓ Hourly automated sync with zero manual intervention ✓ Per-project reconciliation spreadsheets for multiple active construction sites ✓ Labor date filtering fixed — resolved systematic data gaps for 10+ employees The Challenge Steering House is a construction company managing multiple active building projects across New York — each with its own budget, labor force, materials spend, and subcontractor commitments. Their financial data lives in QuickBooks Online, but their project managers and accountants work in Google Sheets. The gap between these two systems was a constant source of friction. Someone had to manually pull reports from QuickBooks, reformat the data, and paste it into the right project spreadsheet. Labor hours needed overtime calculations. Material purchases needed to be mapped to the right project. Reconciliation tabs needed to stay current so project managers could compare budgets against actuals. A previous developer had built a sync tool — 224 commits over the course of a year — but when Focus.AI was brought in, the system had stopped syncing. The original developer’s infrastructure was unreachable, and the client couldn’t roll out the tool company-wide because of a critical missing feature: the ability to filter historical labor data by date. The specific pain: Every new hire since August 2025 was missing from the synced data. Ten employees’ labor records weren’t showing up in the reconciliation spreadsheets, and nobody knew why. Our Approach We took over an exis", "height": 7670, "screenshots": { "desktop": "assets/screenshots/desktop/case-study--qbsync.png", "mobile": "assets/screenshots/mobile/case-study--qbsync.png" } }, { "url": "https://thefocus.ai/vibe-check", "path": "/vibe-check", "slug": "vibe-check", "category": "Core", "status": 200, "title": "Vibe Check | TheFocus.AI", "metaDescription": "Your team built an internal tool with AI coding tools. Now make it production-ready. Security audits, architecture review, and professional cleanup for AI-generated applications.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Your team built it fast. 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Let's make it enterprise-ready.", "index": 28 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 29 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 30 }, { "tag": "h3", "text": "COMPANY", "index": 31 }, { "tag": "h3", "text": "PRODUCTS", "index": 32 }, { "tag": "h3", "text": "RESOURCES", "index": 33 }, { "tag": "h3", "text": "CONNECT", "index": 34 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Your team built it fast. Now make it last.", "y": 266, "body": "Internal teams are using Cursor, Claude, Bolt, and Lovable to ship tools that actually work. That's the win. But before these apps scale across the organization, they need to be secure, maintainable, and IT-ready. The Vibe Check is how TheFocus.AI applies the same production rigor we bring to enterprise AI systems — scoped to your internal application and delivered in days, not months. GET YOUR VIBE CHECK HOW IT WORKS" }, { "tag": "h2", "title": "The vibe coding wall", "y": 898, "body": "💻 \"It works in demo\" But nobody knows what happens when real traffic hits it. No tests, no monitoring, no confidence before rollout. 😰 \"Nobody wants to touch it\" Every change feels like it might break something. The team has stopped adding features because the risk isn't worth it. 🔓 \"Is this even secure?\" API keys in the code, no auth checks, SQL queries built from user input. Security hasn't reviewed it — and probably shouldn't yet. 🫣 \"IT is going to shut this down\" The team shipped something that works. But before it scales across departments, it needs professional eyes." }, { "tag": "h3", "title": "\"It works in demo\"", "y": 1067, "body": "But nobody knows what happens when real traffic hits it. No tests, no monitoring, no confidence before rollout." }, { "tag": "h3", "title": "\"Nobody wants to touch it\"", "y": 1067, "body": "Every change feels like it might break something. The team has stopped adding features because the risk isn't worth it." }, { "tag": "h3", "title": "\"Is this even secure?\"", "y": 1319, "body": "API keys in the code, no auth checks, SQL queries built from user input. Security hasn't reviewed it — and probably shouldn't yet." }, { "tag": "h3", "title": "\"IT is going to shut this down\"", "y": 1319, "body": "The team shipped something that works. But before it scales across departments, it needs professional eyes." }, { "tag": "h2", "title": "HOW IT WORKS", "y": 2020, "body": "01 The Vibe Check 1-4 days A comprehensive assessment of your codebase. We dig into every layer and give you an honest, prioritized report of what's solid, what's risky, and what needs to change. What you get: Security audit Architecture review Scalability assessment Code quality report Prioritized fix list Effort estimates Start with an assessment → 02 The Cleanup 1-3 weeks We roll up our sleeves and fix what matters. Guided by the assessment, we harden your app for production while keeping everything that works intact. What you get: Security hardening Test coverage CI/CD pipeline Error handling Performance optimization Documentation Clean architecture Let's clean it up →" }, { "tag": "h3", "title": "The Vibe Check", "y": 2101, "body": "1-4 days" }, { "tag": "h3", "title": "The Cleanup", "y": 2443, "body": "1-3 weeks" }, { "tag": "h2", "title": "Every layer of your stack", "y": 2920, "body": "🔒 Security Auth flows, API key exposure, input validation, dependency vulnerabilities, data encryption 🗄️ Data Database design, query performance, backup strategy, migration paths, data integrity 🏗️ Architecture Code organization, separation of concerns, dependency management, API design, state management ⚡ Reliability Error handling, edge cases, logging, monitoring, graceful degradation 📈 Scale Load handling, caching strategy, database indexing, rate limiting, async processing 🔧 DevOps Deployment pipeline, environment config, secrets management, CI/CD, rollback capability" }, { "tag": "h3", "title": "Security", "y": 3076, "body": "Auth flows, API key exposure, input validation, dependency vulnerabilities, data encryption" }, { "tag": "h3", "title": "Data", "y": 3076, "body": "Database design, query performance, backup strategy, migration paths, data integrity" }, { "tag": "h3", "title": "Architecture", "y": 3076, "body": "Code organization, separation of concerns, dependency management, API design, state management" }, { "tag": "h3", "title": "Reliability", "y": 3296, "body": "Error handling, edge cases, logging, monitoring, graceful degradation" }, { "tag": "h3", "title": "Scale", "y": 3296, "body": "Load handling, caching strategy, database indexing, rate limiting, async processing" }, { "tag": "h3", "title": "DevOps", "y": 3296, "body": "Deployment pipeline, environment config, secrets management, CI/CD, rollback capability" }, { "tag": "h2", "title": "Is this for your team?", "y": 3633, "body": "This is for you if: ✓ Your team built an internal tool with AI coding tools (Cursor, Claude, Bolt, Lovable, etc.) ✓ People across the organization are already using it ✓ IT or security needs to review it before wider rollout ✓ You need to hand it off to engineering but don't know its current state ✓ Leadership wants confidence before scaling it across departments This probably isn't for you if: ✗ The tool is still a prototype with no active users ✗ You already have a dedicated engineering team maintaining it ✗ You want the cheapest option (quality assurance isn't a commodity) ✗ The application handles no sensitive data and has no compliance requirements" }, { "tag": "h3", "title": "This is for you if:", "y": 3705, "body": "✓ Your team built an internal tool with AI coding tools (Cursor, Claude, Bolt, Lovable, etc.) ✓ People across the organization are already using it ✓ IT or security needs to review it before wider rollout ✓ You need to hand it off to engineering but don't know its current state ✓ Leadership wants confidence before scaling it across departments" }, { "tag": "h3", "title": "This probably isn't for you if:", "y": 3705, "body": "✗ The tool is still a prototype with no active users ✗ You already have a dedicated engineering team maintaining it ✗ You want the cheapest option (quality assurance isn't a commodity) ✗ The application handles no sensitive data and has no compliance requirements" }, { "tag": "h2", "title": "WHY US", "y": 4189, "body": "We don't just read code — we understand how AI writes it. 40+ years combined experience 50M+ devices reached 3 wks avg cleanup time 0 junior analysts We use AI coding tools ourselves every day — Cursor, Claude Code, and everything in between. We know the patterns these tools produce, the shortcuts they take, and the blind spots they have. That means we can assess your codebase faster, more accurately, and with more nuance than someone who's never worked with AI-generated code." }, { "tag": "h2", "title": "COMMON QUESTIONS", "y": 4602, "body": "How much does this cost? Every codebase is different. Reach out and tell us about your app — we'll give you an honest assessment of what it needs and what it'll take. No surprises. What tech stacks do you work with? If an AI coding tool can generate it, we can assess and fix it. React, Next.js, Python, Node, Rails, mobile apps — we've seen it all. The patterns of AI-generated code are remarkably similar regardless of framework. Will you need to rewrite everything? Almost never. The whole point is to preserve what works and fix what doesn't. Most vibe-coded apps are 70-80% fine — it's the remaining 20-30% that creates all the risk. What if my app is truly unfixable? We'll tell you honestly. I" }, { "tag": "h3", "title": "How much does this cost?", "y": 4675, "body": "Every codebase is different. Reach out and tell us about your app — we'll give you an honest assessment of what it needs and what it'll take. No surprises." }, { "tag": "h3", "title": "What tech stacks do you work with?", "y": 4808, "body": "If an AI coding tool can generate it, we can assess and fix it. React, Next.js, Python, Node, Rails, mobile apps — we've seen it all. The patterns of AI-generated code are remarkably similar regardless of framework." }, { "tag": "h3", "title": "Will you need to rewrite everything?", "y": 4941, "body": "Almost never. The whole point is to preserve what works and fix what doesn't. Most vibe-coded apps are 70-80% fine — it's the remaining 20-30% that creates all the risk." }, { "tag": "h3", "title": "What if my app is truly unfixable?", "y": 5074, "body": "We'll tell you honestly. If a rewrite makes more sense than a cleanup, we'll explain why and give you a roadmap for doing it right. We'd rather give you hard truth than take money for a lost cause." }, { "tag": "h3", "title": "Do I need to give you access to my codebase?", "y": 5207, "body": "Yes — we'll need repo access to do the assessment. We sign NDAs as standard practice and can work within whatever security constraints you have." }, { "tag": "h3", "title": "Can I just get the assessment without the cleanup?", "y": 5340, "body": "Absolutely. Many clients start with The Vibe Check and use the report to guide their own team or hire developers with a clear spec. The assessment stands on its own as a valuable deliverable." }, { "tag": "h2", "title": "Your team's tool works. Let's make it enterprise-ready.", "y": 5553, "body": "Your team did the hard part — they shipped. Now let's make sure the foundation is ready for the rest of the organization. GET YOUR VIBE CHECK SEE HOW IT WORKS" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 5937, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 6387, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 6764, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 6764, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 6764, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 6764, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "TheFocus.AI", "href": "https://thefocus.ai/capabilities" }, { "text": "GET YOUR VIBE CHECK", "href": "mailto:hey@thefocus.ai?subject=Vibe%20Check%20-%20Clean%20Up%20My%20App" }, { "text": "HOW IT WORKS", "href": "https://thefocus.ai/vibe-check/#how-it-works" }, { "text": "Start with an assessment →", "href": "mailto:hey@thefocus.ai?subject=Vibe%20Check%20-%20Clean%20Up%20My%20App" }, { "text": "Let's clean it up →", "href": "mailto:hey@thefocus.ai?subject=Vibe%20Check%20-%20Clean%20Up%20My%20App" }, { "text": "GET YOUR VIBE CHECK", "href": "mailto:hey@thefocus.ai?subject=Vibe%20Check%20-%20Clean%20Up%20My%20App" }, { "text": "SEE HOW IT WORKS", "href": "https://thefocus.ai/vibe-check/#how-it-works" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] VIBE CHECK Your team built it fast. Now make it last. Internal teams are using Cursor, Claude, Bolt, and Lovable to ship tools that actually work. That's the win. But before these apps scale across the organization, they need to be secure, maintainable, and IT-ready. The Vibe Check is how TheFocus.AI applies the same production rigor we bring to enterprise AI systems — scoped to your internal application and delivered in days, not months. GET YOUR VIBE CHECK HOW IT WORKS SOUND FAMILIAR? The vibe coding wall 💻 \"It works in demo\" But nobody knows what happens when real traffic hits it. No tests, no monitoring, no confidence before rollout. 😰 \"Nobody wants to touch it\" Every change feels like it might break something. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] CAREERS Join TheFocus.AI We're an AI-first consultancy looking for people who want to be at the forefront of AI-assisted development. If you're passionate about working with AI to deliver real impact, we'd like to hear from you. Stay in the Loop Get notified about new positions and AI insights. No spam, just opportunities. Subscribe → FILLED Remote (US Timezone) • 15-25 hours/week Junior Full-Stack Developer $35-50/hour Posted January 11, 2026 Position Filled Don't see the right role? We're always interested in meeting talented people. Send us a note at hey@thefocus.ai and tell us what you're working on. Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. View open positions → © 2026 TheFocus.AI. All rights reserved. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← Back to Insights Use Cases Single file swift mini-apps Affordance Use Case Will Schenk August 22, 2025 swift files can be run directly without compiling and without XCode, making it easy to create native UI elements and access all of macOS's APIs. Once you see Swift as a scripting language rather than just an app language, you start wondering what other capabilities are hiding in plain sight. Read more → Don't be passive aggressive with your agents Use Case Essay Will Schenk June 25, 2025 Treat your coding agents as adaptable collaborators—communicate clearly, value efficiency over endurance, match tools to your workflow, skip unnecessary formality, rethink technical debt, and document your development rules for best results. Read more → Schema-Driven AI: Better User Experiences with Structured Output Use Case Process Gemini Will Schenk March 30, 2025 Transforms chatting from simple text generators into powerful data processing engines, enabling extraction of organized information from PDFs, audio files, and more. Here are some practical techniques for building, including audio analysis, pdf data extraction and conversation state management, showcasing how constraint-driven outputs can power rich user experiences. Read more → Image Gen on Apple Silicon Use Case Will Schenk March 21, 2025 We've got the apple silicon, lets download some models and make some pictures Read more → Recipes big and small Use Case Will Schenk March 18, 2025 The hardest thing about living in the future is that we're figuring it out as we go. Here's some notes of things to play with. Read more → Exposing Services with MCP Use Case Will Schenk March 15, 2025 Model Context Protocol bridges the gap between AI models and your applications. Learn how defining simple tools with descriptions and parameters lets Claude intelligently combine services without explicit instructions. Read more → Slicing up a design from figma Use Case Process Compare Will Schenk November 27, 2024 In this hands-on comparison, three coding tools - Cursor, Aider, and v0 - are put to the test as they attempt to replicate a design from Figma into functional HTML and CSS code, revealing their strengths, weaknesses, and quirks. Read more → INTERACTIVE EXPLORATIONS Flux Image Generation Running Flux locally on a MacBook — models, ", "height": 4744, "screenshots": { "desktop": "assets/screenshots/desktop/use-cases.png", "mobile": "assets/screenshots/mobile/use-cases.png" } }, { "url": "https://thefocus.ai/case-study/perplexity-samsung", "path": "/case-study/perplexity-samsung", "slug": "case-study--perplexity-samsung", "category": "Case study", "status": 200, "title": "Voice-First AI for 50M+ Smart TVs: Building Perplexity for Samsung", "metaDescription": "We built a voice-first AI assistant for Samsung Smart TVs that lets users ask questions naturally and get real-time streaming answers — navigating entirely with a TV remote and speaking through their phone or Bluetooth microphone.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Voice-First AI for 50M+ Smart TVs: Building Perplexity for Samsung", "index": 1 }, { "tag": "h2", "text": "KEY RESULTS", "index": 2 }, { "tag": "h3", "text": "The Challenge", "index": 3 }, { "tag": "h3", "text": "Our Approach", "index": 4 }, { "tag": "h3", "text": "Technical Implementation", "index": 5 }, { "tag": "h3", "text": "What Made This Work", "index": 6 }, { "tag": "h3", "text": "Results", "index": 7 }, { "tag": "h3", "text": "Technical Summary", "index": 8 }, { "tag": "h2", "text": "Facing a similar challenge?", "index": 9 }, { "tag": "h2", "text": "More Case Studies", "index": 10 }, { "tag": "h3", "text": "94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service", "index": 11 }, { "tag": "h3", "text": "From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts", "index": 12 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 13 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 14 }, { "tag": "h3", "text": "COMPANY", "index": 15 }, { "tag": "h3", "text": "PRODUCTS", "index": 16 }, { "tag": "h3", "text": "RESOURCES", "index": 17 }, { "tag": "h3", "text": "CONNECT", "index": 18 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Voice-First AI for 50M+ Smart TVs: Building Perplexity for Samsung", "y": 269, "body": "We built a voice-first AI assistant for Samsung Smart TVs that lets users ask questions naturally and get real-time streaming answers — navigating entirely with a TV remote and speaking through their phone or Bluetooth microphone. Client: Perplexity AI Year: 2025 Services: Strategy + Development Stack: SvelteKit, TypeScript, Tailwind CSS, OpenAI Realtime API, Tizen SDK, Vite" }, { "tag": "h2", "title": "KEY RESULTS", "y": 1171, "body": "✓ Deployed to 50M+ Samsung Smart TVs globally ✓ Sub-2-second voice recognition latency ✓ 12-week timeline from kickoff to Samsung QA approval ✓ Multi-language support (English, German, Korean) with automatic dialect detection" }, { "tag": "h3", "title": "The Challenge", "y": 1423, "body": "Perplexity AI wanted to bring their AI-powered answer engine to the living room. Samsung Smart TVs, running on the Tizen platform, reach over 50 million households globally — a massive opportunity to put conversational AI where families gather. But TVs aren’t phones. There’s no keyboard. No touchscreen. No mouse. Users navigate with a remote control’s directional pad, and typing a question using an on-screen keyboard takes forever. The experience had to be fundamentally different. The core constraints: Voice-first interaction — Users should be able to press one button and speak their question naturally Remote navigation — Every element must be accessible via up/down/left/right arrows with cl" }, { "tag": "h3", "title": "Our Approach", "y": 2176, "body": "We began with a week-long discovery phase, studying Tizen’s capabilities and limitations. Samsung TVs support web technologies through a customized Chromium engine, but with important restrictions: no Node.js runtime, limited memory, and strict security policies around microphone access. The key insight: Voice input on a TV comes from external sources — the Samsung SmartThings app on a user’s phone, or a Bluetooth microphone. The Tizen platform provides device APIs to access these inputs, but they require careful permission handling and fallback strategies. We designed the architecture around three principles: Progressive enhancement — Start with keyboard input as the baseline, layer voice o" }, { "tag": "h3", "title": "Technical Implementation", "y": 2758, "body": "Custom Focus Navigation System TV remote navigation is fundamentally different from web accessibility. Users expect: Press “right” → focus moves to the next logical element on the right Press “down” → focus moves to an element below, even if it’s in a different container Press “back” → return to the previous context or exit the modal We built a custom focus management system from scratch: Focus Architecture: ├── Page Registration (usePageFocus hook) │ ├── Default focus element per page │ ├── Back button behavior handlers │ └── Focus restoration on navigation ├── Element Registration (FocusableElement components) │ ├── Directional navigation declarations │ ├── Visual focus ring styling │ └── " }, { "tag": "h3", "title": "What Made This Work", "y": 5278, "body": "1. TV-first architecture. We didn’t adapt a mobile or web app to TV. We built for the 10-foot experience from day one: large touch targets, spatial navigation, voice as the primary input. 2. Rigorous focus testing. We tested every screen flow with only arrow keys and the Enter button before any mouse interaction. Focus bugs that would be minor annoyances on web become app-breaking on TV. 3. Defensive error handling. TVs can’t “just refresh the page.” Every error state has a recovery path, every timeout has user-facing feedback, every microphone failure gracefully falls back to keyboard. 4. Samsung QA preparation. We studied Samsung’s certification requirements early and built compliance into" }, { "tag": "h3", "title": "Results", "y": 5734, "body": "The app passed Samsung’s QA certification and launched globally: 50M+ potential users across Samsung’s Smart TV install base Sub-2-second latency from button press to voice recognition start Multi-language launch with English, German, and Korean support 98%+ crash-free sessions across diverse hardware generations Continuous deployment pipeline with separate release, develop, and test channels The application demonstrates that conversational AI can work beautifully on non-traditional platforms when designed for the medium’s unique constraints." }, { "tag": "h3", "title": "Technical Summary", "y": 6166, "body": "Component Technology Purpose Frontend SvelteKit 2, TypeScript, Tailwind CSS TV-optimized UI with custom focus system Voice Recognition OpenAI Realtime API Real-time speech-to-text with streaming AI Integration Perplexity API, Server-Sent Events Streaming AI responses with citations Platform Tizen SDK, Tizen Web APIs Samsung TV integration, microphone access Build System Vite, Static Adapter Optimized bundle for TV deployment Testing Vitest, JSDOM Component and integration testing Deployment Tizen CLI, Custom Release Pipeline Multi-channel releases (release/develop/test)" }, { "tag": "h2", "title": "Facing a similar challenge?", "y": 6972, "body": "Let's talk about shipping a production AI system for your team — principal-led, deployed in your cloud on Habitat OS. TALK TO A PRINCIPAL SEE THE FRAMEWORK" }, { "tag": "h2", "title": "More Case Studies", "y": 7309, "body": "FOOD SERVICE / RETAIL 94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy. MEDIA INTELLIGENCE / CONTENT ANALYTICS From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analy" }, { "tag": "h3", "title": "94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service", "y": 7401, "body": "We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy." }, { "tag": "h3", "title": "From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts", "y": 7401, "body": "We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analyzes content from Twitter/X, YouTube videos, and podcasts. What used to require hours of manual monitoring and note-taking now happens in minutes, with AI-generated summaries that surface the signal from the noise." }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 7645, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 8095, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 8472, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 8472, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 8472, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 8472, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← Back to Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Re%3A%20Voice-First%20AI%20for%2050M%2B%20Smart%20TVs%3A%20Building%20Perplexity%20for%20Samsung" }, { "text": "SEE THE FRAMEWORK", "href": "https://thefocus.ai/capabilities" }, { "text": "FOOD SERVICE / RETAIL 94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy.", "href": "https://thefocus.ai/case-study/fountain-creek" }, { "text": "MEDIA INTELLIGENCE / CONTENT ANALYTICS From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analyzes content from Twitter/X, YouTube videos, and podcasts. What used to require hours of manual monitoring and note-taking now happens in minutes, with AI-generated summaries that surface the signal from the noise.", "href": "https://thefocus.ai/case-study/distill" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" }, { "text": "Registry", "href": "https://account.thefocus.ai/" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/case-studies", "mailto:hey@thefocus.ai?subject=Re%3A%20Voice-First%20AI%20for%2050M%2B%20Smart%20TVs%3A%20Building%20Perplexity%20for%20Samsung", "https://thefocus.ai/capabilities", "https://thefocus.ai/case-study/fountain-creek", "https://thefocus.ai/case-study/distill", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← Back to Case Studies CONSUMER ELECTRONICS / AI 12 weeks Voice-First AI for 50M+ Smart TVs: Building Perplexity for Samsung We built a voice-first AI assistant for Samsung Smart TVs that lets users ask questions naturally and get real-time streaming answers — navigating entirely with a TV remote and speaking through their phone or Bluetooth microphone. Client: Perplexity AI Year: 2025 Services: Strategy + Development Stack: SvelteKit, TypeScript, Tailwind CSS, OpenAI Realtime API, Tizen SDK, Vite KEY RESULTS ✓ Deployed to 50M+ Samsung Smart TVs globally ✓ Sub-2-second voice recognition latency ✓ 12-week timeline from kickoff to Samsung QA approval ✓ Multi-language support (English, German, Korean) with automatic dialect detection The Challenge Perplexity AI wanted to bring their AI-powered answer engine to the living room. Samsung Smart TVs, running on the Tizen platform, reach over 50 million households globally — a massive opportunity to put conversational AI where families gather. But TVs aren’t phones. There’s no keyboard. No touchscreen. No mouse. Users navigate with a remote control’s directional pad, and typing a question using an on-screen keyboard takes forever. The experience had to be fundamentally different. The core constraints: Voice-first interaction — Users should be able to press one button and speak their question naturally Remote navigation — Every element must be accessible via up/down/left/right arrows with clear visual focus states 10-foot UI — Text and controls must be readable from across a living room Limited hardware — Smart TVs have significantly less processing power than phones or laptops Samsung QA certification — The app had to pass Samsung’s rigorous certification process covering 100+ test cases What Perplexity needed: A team that understood both AI integration and the unique constraints of embedded TV development. Our Approach We began with a week-long discovery phase, studying Tizen’s capabilities and limitations. Samsung TVs support web technologies through a customized Chromium engine, but with important restrictions: no Node.js runtime, limited memory, and strict security policies around microphone access. The key insight: Voice input on a TV comes from external sources — the Samsung SmartThings app on a user’s phone, or a Bluetoot", "height": 8969, "screenshots": { "desktop": "assets/screenshots/desktop/case-study--perplexity-samsung.png", "mobile": "assets/screenshots/mobile/case-study--perplexity-samsung.png" } }, { "url": "https://thefocus.ai/case-study/upperhand-ai", "path": "/case-study/upperhand-ai", "slug": "case-study--upperhand-ai", "category": "Case study", "status": 200, "title": "78% Faster Talent Decisions: AI-Powered Celebrity Analytics for Media Networks", "metaDescription": "We built a multi-agent AI platform that unifies celebrity data from disparate sources — survey research, social media metrics, and brand affinity studies — letting entertainment executives answer complex casting questions in natural language instead of navigating dozens of spreadsheets.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "78% Faster Talent Decisions: AI-Powered Celebrity Analytics for Media Networks", "index": 1 }, { "tag": "h2", "text": "KEY RESULTS", "index": 2 }, { "tag": "h3", "text": "The Challenge", "index": 3 }, { "tag": "h3", "text": "Our Approach", "index": 4 }, { "tag": "h3", "text": "Technical Implementation", "index": 5 }, { "tag": "h3", "text": "What Made This Work", "index": 6 }, { "tag": "h3", "text": "Results", "index": 7 }, { "tag": "h3", "text": "Technical Summary", "index": 8 }, { "tag": "h3", "text": "Approach & Architecture", "index": 9 }, { "tag": "h3", "text": "How This Maps to Habitat OS", "index": 10 }, { "tag": "h2", "text": "Facing a similar challenge?", "index": 11 }, { "tag": "h2", "text": "More Case Studies", "index": 12 }, { "tag": "h3", "text": "94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service", "index": 13 }, { "tag": "h3", "text": "From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts", "index": 14 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 15 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 16 }, { "tag": "h3", "text": "COMPANY", "index": 17 }, { "tag": "h3", "text": "PRODUCTS", "index": 18 }, { "tag": "h3", "text": "RESOURCES", "index": 19 }, { "tag": "h3", "text": "CONNECT", "index": 20 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "78% Faster Talent Decisions: AI-Powered Celebrity Analytics for Media Networks", "y": 269, "body": "We built a multi-agent AI platform that unifies celebrity data from disparate sources — survey research, social media metrics, and brand affinity studies — letting entertainment executives answer complex casting questions in natural language instead of navigating dozens of spreadsheets. Client: Upper Hand AI Year: 2025 Services: Strategy + Development Stack: Next.js 15, TypeScript, Vercel AI SDK, Supabase, PostgreSQL, Anthropic Claude, Radix UI, Recharts Running on Habitat OS →" }, { "tag": "h2", "title": "KEY RESULTS", "y": 1293, "body": "✓ 78% reduction in time to generate talent reports ✓ 5 specialized AI agents across talent, ratings, and streaming data ✓ 15+ query tools with typo-tolerant search and nickname recognition ✓ Zero spreadsheet wrangling — natural language queries replace manual lookups ✓ Cross-demographic analysis that previously required custom analyst work" }, { "tag": "h3", "title": "The Challenge", "y": 1617, "body": "Entertainment networks live and die by casting decisions. When a cable network is developing a new reality show or licensing a movie, they need to answer questions like: Which celebrities have high awareness among women 25-54? Who’s seen as ‘relatable’ versus ‘aspirational’? Which talent over-indexes with our brand affinity compared to competitors? The data to answer these questions existed — scattered across three different research platforms: Survey Platform — Survey-based research with awareness, appeal, and 30+ personality attributes across demographic segments Social Analytics Platform — Social media follower counts across Twitter, Instagram, TikTok, YouTube, and Facebook Affinity Platf" }, { "tag": "h3", "title": "Our Approach", "y": 2482, "body": "We designed a multi-agent architecture where specialized AI systems handle different data domains, unified by a natural language interface that routes questions to the right agent. The key insight: Celebrity analytics isn’t one problem — it’s several. TV ratings require different expertise than social media metrics or survey-based attribute analysis. Rather than building one monolithic AI that does everything poorly, we built focused agents that excel in their domains. This meant solving three interconnected problems: Data unification — Creating a mapping layer that links celebrity identities across systems with different naming conventions Agent specialization — Building domain experts that" }, { "tag": "h3", "title": "Technical Implementation", "y": 3034, "body": "Multi-Source Celebrity Mapping The hardest problem wasn’t AI — it was identity resolution. “Dwayne Johnson” in one system might be “The Rock” in another and “Dwayne ‘The Rock’ Johnson” in a third. We built a fuzzy matching system with confidence scoring: Mapping System: ├── Canonical celebrity ID (internal) ├── Survey platform identifiers (survey-specific) ├── Social platform handles (platform IDs) ├── Affinity platform profiles (Twitter-centric) └── Confidence scores (0.0-1.0) for each link When a user searches for “Pink,” the system disambiguates: Did you mean Pink the singer, or Pink the color brand? The disambiguation uses context from previous queries and explicit confirmation for edge " }, { "tag": "h3", "title": "What Made This Work", "y": 5029, "body": "1. Domain expertise in every tool. Each AI tool was designed with input from researchers who actually use these platforms daily. The tool descriptions include guidance on when to use each metric and how to interpret results — knowledge that took years for analysts to develop. 2. Statistical rigor built-in. The system doesn’t just return numbers — it contextualizes them. “Above average awareness” means something specific (typically 1+ standard deviations above demographic norm), and the AI explains this in its responses. 3. Disambiguation over hallucination. When the AI isn’t sure which celebrity the user means, it asks rather than guessing. This was a non-negotiable requirement from research" }, { "tag": "h3", "title": "Results", "y": 5544, "body": "Within 8 weeks of deployment: 78% faster talent report generation — queries that took half a day now complete in under a minute Democratized access — Product managers and executives can now self-serve insights that previously required analyst time Cross-source analysis — Questions like “Who has both strong social presence AND high network affinity?” are now answerable instantly 141+ GitHub issues resolved during active development, with continuous feedback loops from real users The platform handles thousands of queries monthly, with the Talent Agent (CelebLens) accounting for the majority of usage. The research team shifted from data compilation to strategic interpretation — analyzing the me" }, { "tag": "h3", "title": "Technical Summary", "y": 6089, "body": "Component Technology Purpose Frontend Next.js 15, TypeScript, Radix UI, Tailwind Chat interface, data tables, visualizations AI Framework Vercel AI SDK, Anthropic Claude Multi-agent orchestration, tool calling Database Supabase, PostgreSQL Celebrity mapping, query history, user management Data Sources Survey, Social, Affinity platforms Survey data, social metrics, affinity analysis Charts Recharts Demographic breakdowns, affinity visualizations Auth Supabase Auth, Role-based access Agent-specific permissions per user Deployment Vercel Production hosting with edge functions" }, { "tag": "h3", "title": "Approach & Architecture", "y": 6522, "body": "User Query ↓ Natural Language Router ↓ ┌───────────────────────────────────────────────────┐ │ Agent Selection │ ├──────────┬──────────┬──────────┬─────────────────┤ │ Talent │ Ratings │ AVOD │ Survey │ │ Agent │ Agent │ Agent │ Agent │ ├──────────┴──────────┴──────────┴─────────────────┤ │ Unified Response Layer │ │ (Tables, Charts, CSV Export, Citations) │ └───────────────────────────────────────────────────┘ ↓ PostgreSQL (Celebrity Mapping + Query History) ↓ External APIs (Survey, Social, Affinity platforms) The architecture separates concerns cleanly: the AI handles natural language understanding and tool selection, while structured data pipelines handle the statistical analysis. This me" }, { "tag": "h3", "title": "How This Maps to Habitat OS", "y": 7267, "body": "This is a decision-support Habitat — a multi-agent reasoning layer over fragmented enterprise data sources. The four specialized agents (Talent, Ratings, AVOD, Survey) are exactly the pattern Habitat OS productizes: domain-focused sub-agents coordinated by a natural-language router, with shared memory for celebrity identity resolution and query history, and statistical guardrails (base-size validation, demographic delta calculations) that run on every response. What keeps it running seamlessly in the background: the identity-resolution layer grows as new celebrities and data sources are added; disambiguation logic prevents the hallucinations that have burned research teams with other AI tool" }, { "tag": "h2", "title": "Facing a similar challenge?", "y": 8160, "body": "Let's talk about shipping a production AI system for your team — principal-led, deployed in your cloud on Habitat OS. TALK TO A PRINCIPAL SEE THE FRAMEWORK" }, { "tag": "h2", "title": "More Case Studies", "y": 8497, "body": "FOOD SERVICE / RETAIL 94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy. MEDIA INTELLIGENCE / CONTENT ANALYTICS From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analy" }, { "tag": "h3", "title": "94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service", "y": 8589, "body": "We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy." }, { "tag": "h3", "title": "From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts", "y": 8589, "body": "We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analyzes content from Twitter/X, YouTube videos, and podcasts. What used to require hours of manual monitoring and note-taking now happens in minutes, with AI-generated summaries that surface the signal from the noise." }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 8833, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 9283, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 9660, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 9660, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 9660, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 9660, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← Back to Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Running on Habitat OS →", "href": "https://thefocus.ai/habitat-os" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Re%3A%2078%25%20Faster%20Talent%20Decisions%3A%20AI-Powered%20Celebrity%20Analytics%20for%20Media%20Networks" }, { "text": "SEE THE FRAMEWORK", "href": "https://thefocus.ai/capabilities" }, { "text": "FOOD SERVICE / RETAIL 94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy.", "href": "https://thefocus.ai/case-study/fountain-creek" }, { "text": "MEDIA INTELLIGENCE / CONTENT ANALYTICS From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analyzes content from Twitter/X, YouTube videos, and podcasts. What used to require hours of manual monitoring and note-taking now happens in minutes, with AI-generated summaries that surface the signal from the noise.", "href": "https://thefocus.ai/case-study/distill" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" }, { "text": "Registry", "href": "https://account.thefocus.ai/" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/case-studies", "https://thefocus.ai/habitat-os", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Re%3A%2078%25%20Faster%20Talent%20Decisions%3A%20AI-Powered%20Celebrity%20Analytics%20for%20Media%20Networks", "https://thefocus.ai/capabilities", "https://thefocus.ai/case-study/fountain-creek", "https://thefocus.ai/case-study/distill", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← Back to Case Studies MEDIA / ENTERTAINMENT 16 weeks (ongoing) 78% Faster Talent Decisions: AI-Powered Celebrity Analytics for Media Networks We built a multi-agent AI platform that unifies celebrity data from disparate sources — survey research, social media metrics, and brand affinity studies — letting entertainment executives answer complex casting questions in natural language instead of navigating dozens of spreadsheets. Client: Upper Hand AI Year: 2025 Services: Strategy + Development Stack: Next.js 15, TypeScript, Vercel AI SDK, Supabase, PostgreSQL, Anthropic Claude, Radix UI, Recharts Running on Habitat OS → KEY RESULTS ✓ 78% reduction in time to generate talent reports ✓ 5 specialized AI agents across talent, ratings, and streaming data ✓ 15+ query tools with typo-tolerant search and nickname recognition ✓ Zero spreadsheet wrangling — natural language queries replace manual lookups ✓ Cross-demographic analysis that previously required custom analyst work The Challenge Entertainment networks live and die by casting decisions. When a cable network is developing a new reality show or licensing a movie, they need to answer questions like: Which celebrities have high awareness among women 25-54? Who’s seen as ‘relatable’ versus ‘aspirational’? Which talent over-indexes with our brand affinity compared to competitors? The data to answer these questions existed — scattered across three different research platforms: Survey Platform — Survey-based research with awareness, appeal, and 30+ personality attributes across demographic segments Social Analytics Platform — Social media follower counts across Twitter, Instagram, TikTok, YouTube, and Facebook Affinity Platform — Twitter affinity analysis showing which audiences over-index for following specific celebrities The problem: these datasets didn’t talk to each other. A researcher asking “Who appeals to our network’s viewers and has strong social media presence?” would spend hours cross-referencing spreadsheets, manually matching celebrity names that were spelled differently across systems, and building one-off reports that couldn’t be reused. The scale of the problem: The research team was spending 40+ hours per week on manual data compilation. High-priority talent questions — the ones that came from executives during", "height": 10157, "screenshots": { "desktop": "assets/screenshots/desktop/case-study--upperhand-ai.png", "mobile": "assets/screenshots/mobile/case-study--upperhand-ai.png" } }, { "url": "https://thefocus.ai/case-study/tezlab-ai", "path": "/case-study/tezlab-ai", "slug": "case-study--tezlab-ai", "category": "Case study", "status": 200, "title": "27% Higher Conversion: Building a Conversational AI Layer for Tesla and Rivian Data", "metaDescription": "After years of continued work with Tesla and Rivian data, we built a conversational AI interface that lets EV owners ask natural language questions about their driving data, plan road trips, and get personalized insights — all without the existing mobile team changing a single line of code.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "27% Higher Conversion: Building a Conversational AI Layer for Tesla and Rivian Data", "index": 1 }, { "tag": "h2", "text": "KEY RESULTS", "index": 2 }, { "tag": "h3", "text": "The Challenge", "index": 3 }, { "tag": "h3", "text": "Our Approach", "index": 4 }, { "tag": "h3", "text": "Technical Implementation", "index": 5 }, { "tag": "h3", "text": "What Made This Work", "index": 6 }, { "tag": "h3", "text": "Results", "index": 7 }, { "tag": "h3", "text": "Technical Summary", "index": 8 }, { "tag": "h3", "text": "How This Maps to Habitat OS", "index": 9 }, { "tag": "h2", "text": "Facing a similar challenge?", "index": 10 }, { "tag": "h2", "text": "More Case Studies", "index": 11 }, { "tag": "h3", "text": "94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service", "index": 12 }, { "tag": "h3", "text": "From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts", "index": 13 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 14 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 15 }, { "tag": "h3", "text": "COMPANY", "index": 16 }, { "tag": "h3", "text": "PRODUCTS", "index": 17 }, { "tag": "h3", "text": "RESOURCES", "index": 18 }, { "tag": "h3", "text": "CONNECT", "index": 19 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "27% Higher Conversion: Building a Conversational AI Layer for Tesla and Rivian Data", "y": 269, "body": "After years of continued work with Tesla and Rivian data, we built a conversational AI interface that lets EV owners ask natural language questions about their driving data, plan road trips, and get personalized insights — all without the existing mobile team changing a single line of code. Client: TezLab Year: 2025 Services: Strategy + Development Stack: Next.js 15, TypeScript, MCP SDK, OpenRouter, Mapbox, Recharts, Vercel Running on Habitat OS →" }, { "tag": "h2", "title": "KEY RESULTS", "y": 1293, "body": "✓ 27% increase in user acquisition and paid conversion ✓ Zero changes required to existing backend infrastructure ✓ 8-week timeline from kickoff to production launch ✓ 15+ specialized tools for vehicle data, charging, and trip planning" }, { "tag": "h3", "title": "The Challenge", "y": 1545, "body": "TezLab is the #1-rated companion app for Tesla and Rivian owners. The app tracks everything: battery health, charging efficiency, driving patterns, energy costs, and more. They’ve built a loyal community of EV enthusiasts who obsess over optimizing their vehicle’s performance. But the data was locked in static dashboards. Users who wanted to understand how their efficiency varied with temperature had to manually compare charts. Planning a road trip meant jumping between multiple screens. And the growing demand for “just tell me what I need to know” insights wasn’t being met. The constraint: TezLab’s mobile development team was fully committed to their 2025 roadmap. They couldn’t spare engine" }, { "tag": "h3", "title": "Our Approach", "y": 2058, "body": "We started with a two-day deep dive into TezLab’s existing API architecture. Their backend already exposed rich endpoints for vehicle state, charging history, driving activities, supercharger locations, and trip data. The challenge wasn’t getting the data — it was making it conversational. The key insight: Rather than building a traditional chatbot with hardcoded responses, we designed a system where the LLM could dynamically call TezLab’s APIs as tools. When a user asks “How’s my battery health compared to other Model 3 owners?”, the AI knows to call the car scoring endpoint, interpret the results, and explain them in plain English. This meant building two interconnected systems: An MCP Ser" }, { "tag": "h3", "title": "Technical Implementation", "y": 2593, "body": "The MCP Server Architecture We built a TypeScript MCP server supporting both STDIO (for CLI testing) and SSE (for web clients). Each TezLab API endpoint became a tool with carefully designed schemas: Tools Implemented: ├── Vehicle State (real-time battery, location, climate) ├── Recent Drives (with pagination, time filtering) ├── Recent Charges (home vs. supercharger breakdown) ├── Vehicle Statistics (efficiency, temperature trends) ├── Car Score (battery health comparisons) ├── Supercharger Search (location-based with ratings) ├── Charger Details (availability, pricing, reviews) └── Road Trip Planning (multi-stop routing with charger optimization) The critical design decision: tool descript" }, { "tag": "h3", "title": "What Made This Work", "y": 4240, "body": "1. MCP as an integration pattern. The Model Context Protocol gave us a clean abstraction between the AI layer and TezLab’s APIs. When TezLab adds new endpoints, we add new tools — the LLM automatically learns to use them based on user intent. 2. Rich visual responses. Users don’t want to read paragraphs about their efficiency trends. They want charts. The component-per-tool architecture let us build exactly the right visualization for each data type. 3. Context-aware prompting. The system prompt includes the user’s current vehicle state, location, and preferences. When a user asks “find chargers near me,” the LLM already knows where “me” is. 4. Parallel to existing operations. The entire pro" }, { "tag": "h3", "title": "Results", "y": 4726, "body": "Within 6 weeks of launch: 27% increase in user acquisition and conversion to paid tiers Road trip planning became the most-used feature among Pro subscribers Zero downtime or performance impact on existing mobile apps The AI assistant handles thousands of queries daily with sub-second response times The web application now serves as TezLab’s primary channel for showcasing AI capabilities to potential users, and the MCP server architecture provides a foundation for future AI integrations across their product suite." }, { "tag": "h3", "title": "Technical Summary", "y": 5143, "body": "Component Technology Purpose Frontend Next.js 15, TypeScript, Tailwind, Radix UI Chat interface, rich tool visualizations AI Integration Vercel AI SDK, OpenRouter Streaming responses, model orchestration Tool Server MCP SDK, Express, Zod API-to-tool bridge with schema validation Mapping Mapbox GL, Turf.js Road trip visualization, charger locations Charts Recharts Time-series data visualization Deployment Vercel (frontend), Kubernetes (MCP server) Production infrastructure" }, { "tag": "h3", "title": "How This Maps to Habitat OS", "y": 5531, "body": "This is the archetype of a conversational-data Habitat: a runtime that wraps an existing backend as LLM-callable tools, without asking the underlying product team to change anything. The 15+ MCP tools, context-aware prompting (vehicle state, location, preferences), and custom visualization layer per tool map directly to Habitat OS primitives — tools, memory, and rich response surfaces, orchestrated behind a single natural-language entry point. What keeps it running seamlessly in the background: the MCP architecture self-extends, so when TezLab ships new API endpoints they become new tools the LLM learns to use from the description alone. Token refresh, session management, rate-limit handling" }, { "tag": "h2", "title": "Facing a similar challenge?", "y": 6392, "body": "Let's talk about shipping a production AI system for your team — principal-led, deployed in your cloud on Habitat OS. TALK TO A PRINCIPAL SEE THE FRAMEWORK" }, { "tag": "h2", "title": "More Case Studies", "y": 6729, "body": "FOOD SERVICE / RETAIL 94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy. MEDIA INTELLIGENCE / CONTENT ANALYTICS From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analy" }, { "tag": "h3", "title": "94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service", "y": 6821, "body": "We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy." }, { "tag": "h3", "title": "From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts", "y": 6821, "body": "We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analyzes content from Twitter/X, YouTube videos, and podcasts. What used to require hours of manual monitoring and note-taking now happens in minutes, with AI-generated summaries that surface the signal from the noise." }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 7065, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 7515, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 7892, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 7892, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 7892, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 7892, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← Back to Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Running on Habitat OS →", "href": "https://thefocus.ai/habitat-os" }, { "text": "TezLab", "href": "https://tezlabapp.com/" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Re%3A%2027%25%20Higher%20Conversion%3A%20Building%20a%20Conversational%20AI%20Layer%20for%20Tesla%20and%20Rivian%20Data" }, { "text": "SEE THE FRAMEWORK", "href": "https://thefocus.ai/capabilities" }, { "text": "FOOD SERVICE / RETAIL 94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy.", "href": "https://thefocus.ai/case-study/fountain-creek" }, { "text": "MEDIA INTELLIGENCE / CONTENT ANALYTICS From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analyzes content from Twitter/X, YouTube videos, and podcasts. What used to require hours of manual monitoring and note-taking now happens in minutes, with AI-generated summaries that surface the signal from the noise.", "href": "https://thefocus.ai/case-study/distill" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/case-studies", "https://thefocus.ai/habitat-os", "https://tezlabapp.com/", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Re%3A%2027%25%20Higher%20Conversion%3A%20Building%20a%20Conversational%20AI%20Layer%20for%20Tesla%20and%20Rivian%20Data", "https://thefocus.ai/capabilities", "https://thefocus.ai/case-study/fountain-creek", "https://thefocus.ai/case-study/distill", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← Back to Case Studies TRANSPORTATION / CONSUMER APPS 8 weeks 27% Higher Conversion: Building a Conversational AI Layer for Tesla and Rivian Data After years of continued work with Tesla and Rivian data, we built a conversational AI interface that lets EV owners ask natural language questions about their driving data, plan road trips, and get personalized insights — all without the existing mobile team changing a single line of code. Client: TezLab Year: 2025 Services: Strategy + Development Stack: Next.js 15, TypeScript, MCP SDK, OpenRouter, Mapbox, Recharts, Vercel Running on Habitat OS → KEY RESULTS ✓ 27% increase in user acquisition and paid conversion ✓ Zero changes required to existing backend infrastructure ✓ 8-week timeline from kickoff to production launch ✓ 15+ specialized tools for vehicle data, charging, and trip planning The Challenge TezLab is the #1-rated companion app for Tesla and Rivian owners. The app tracks everything: battery health, charging efficiency, driving patterns, energy costs, and more. They’ve built a loyal community of EV enthusiasts who obsess over optimizing their vehicle’s performance. But the data was locked in static dashboards. Users who wanted to understand how their efficiency varied with temperature had to manually compare charts. Planning a road trip meant jumping between multiple screens. And the growing demand for “just tell me what I need to know” insights wasn’t being met. The constraint: TezLab’s mobile development team was fully committed to their 2025 roadmap. They couldn’t spare engineering cycles for an experimental AI feature, but they didn’t want to miss the opportunity either. What they needed: A partner who could build a complete AI-powered web experience on top of their existing infrastructure — without requiring any backend changes or mobile team involvement. Our Approach We started with a two-day deep dive into TezLab’s existing API architecture. Their backend already exposed rich endpoints for vehicle state, charging history, driving activities, supercharger locations, and trip data. The challenge wasn’t getting the data — it was making it conversational. The key insight: Rather than building a traditional chatbot with hardcoded responses, we designed a system where the LLM could dynamically call TezLab’s APIs a", "height": 8389, "screenshots": { "desktop": "assets/screenshots/desktop/case-study--tezlab-ai.png", "mobile": "assets/screenshots/mobile/case-study--tezlab-ai.png" } }, { "url": "https://thefocus.ai/case-study/fountain-creek", "path": "/case-study/fountain-creek", "slug": "case-study--fountain-creek", "category": "Case study", "status": 200, "title": "94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service", "metaDescription": "We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service", "index": 1 }, { "tag": "h2", "text": "KEY RESULTS", "index": 2 }, { "tag": "h2", "text": "The Challenge", "index": 3 }, { "tag": "h2", "text": "Our Approach", "index": 4 }, { "tag": "h2", "text": "Technical Implementation", "index": 5 }, { "tag": "h3", "text": "The Pipeline", "index": 6 }, { "tag": "h3", "text": "Structured Extraction with Gemini 3", "index": 7 }, { "tag": "h3", "text": "Multi-Page Invoice Handling", "index": 8 }, { "tag": "h3", "text": "Supplier Intelligence", "index": 9 }, { "tag": "h2", "text": "Results", "index": 10 }, { "tag": "h2", "text": "What’s Next", "index": 11 }, { "tag": "h2", "text": "What Made This Work", "index": 12 }, { "tag": "h2", "text": "How This Maps to Habitat OS", "index": 13 }, { "tag": "h2", "text": "Facing a similar challenge?", "index": 14 }, { "tag": "h2", "text": "More Case Studies", "index": 15 }, { "tag": "h3", "text": "From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts", "index": 16 }, { "tag": "h3", "text": "From 15 Minutes to 30 Seconds: AI-Powered Benefits Document Extraction", "index": 17 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 18 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 19 }, { "tag": "h3", "text": "COMPANY", "index": 20 }, { "tag": "h3", "text": "PRODUCTS", "index": 21 }, { "tag": "h3", "text": "RESOURCES", "index": 22 }, { "tag": "h3", "text": "CONNECT", "index": 23 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service", "y": 269, "body": "We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy. Client: Fountain Creek Year: 2026 Services: Development Stack: Google Cloud Run, Gemini 3 Flash, Google Cloud Storage, Notion API, Eventarc, Node.js, TypeScript Running on Habitat OS →" }, { "tag": "h2", "title": "KEY RESULTS", "y": 1293, "body": "✓ 94.5% overall accuracy validated against 88 historical invoices ✓ 97.7% of invoices successfully parsed on first attempt ✓ Manual data entry reduced from 15 minutes to under 30 seconds per invoice ✓ Notion-native workflow — zero new tools for the team to learn" }, { "tag": "h2", "title": "The Challenge", "y": 1545, "body": "Fountain Creek operates multiple food service locations — markets, delis, and grocery stores — and processes dozens of invoices weekly from a sprawling network of suppliers. Produce from Baldor, broad-line distribution from Sysco, specialty items from David Rosen, bakery from Intercounty, beverages from Northeast Beverage, facility supplies from Cintas. Each supplier sends PDFs in a different format, with different layouts, different field names, and different levels of detail. The accounts payable process was entirely manual. Someone would open each PDF, read through the line items, type the supplier name, invoice number, amounts, and individual line items into their system. A single invoic" }, { "tag": "h2", "title": "Our Approach", "y": 2165, "body": "We designed the system around two principles: meet the team where they already work (Notion) and keep humans in the loop for approval, not data entry. Rather than building a new application, we embedded the entire workflow into Notion — the tool the team was already using daily. Submit a PDF through a form, wait a few seconds, and the parsed invoice appears in a database ready for review. The AI doesn’t make financial decisions. It extracts data. A human still reviews and approves every bill before it moves to accounting." }, { "tag": "h2", "title": "Technical Implementation", "y": 2528, "body": "" }, { "tag": "h3", "title": "The Pipeline", "y": 2604, "body": "Notion Form → Webhook (Cloud Run) → GCS Bucket → Eventarc → Gemini 3 Flash → Notion Databases Step 1: Submission. Staff upload a PDF through a Notion form — on desktop or mobile. No new tools, no new logins. Step 2: Ingestion. A Cloud Run webhook receives the Notion event, downloads the PDF attachment, and stores it in a Google Cloud Storage bucket. Step 3: Parsing. GCS triggers an Eventarc event, which invokes a second Cloud Run service. This service sends the PDF to Gemini 3 Flash with a structured extraction schema — supplier details, invoice metadata, and line items. Step 4: Storage. Parsed data writes to three interconnected Notion databases: Bills (the invoice header), Line Items (indi" }, { "tag": "h3", "title": "Structured Extraction with Gemini 3", "y": 3206, "body": "The key to accuracy was the extraction schema. Instead of asking the AI to “extract invoice data,” we defined exactly what we expected: Supplier info: Name, address, contact details Invoice metadata: Number, date, due date, PO number, payment terms Line items: Description, product code, quantity, unit price, extended price, category Soft costs: Delivery fees, fuel surcharges, bottle deposits, tax Totals: Subtotal, tax, shipping, total amount due Each field includes guidance about where it typically appears in food service invoices and how to handle edge cases — weight-based vs. unit-based pricing, case quantities vs. individual items, and multi-page invoices from high-volume distributors." }, { "tag": "h3", "title": "Multi-Page Invoice Handling", "y": 3697, "body": "Food service distributors like Sysco and Baldor often send invoices with dozens of line items spanning multiple pages. Early testing showed Gemini would sometimes truncate extraction on longer documents — not a token limit issue, but an attention limitation. We solved this with page-by-page extraction: headers are captured once, then line items are extracted per-page and merged with deduplication. One Sysco invoice with 43 line items across 4 pages now parses completely." }, { "tag": "h3", "title": "Supplier Intelligence", "y": 3931, "body": "The system maintains a supplier database that grows automatically. When Gemini extracts a supplier name, it fuzzy-matches against existing entries before creating a new one. This prevents the “Baldor” vs “BALDOR SPECIALTY FOODS” vs “Baldor Foods” problem that plagues manual data entry." }, { "tag": "h2", "title": "Results", "y": 4115, "body": "We validated the system against 88 historical invoices that had been manually entered: Metric Result Invoices Successfully Parsed 86/88 (97.7%) Header Field Accuracy 93.6% Line Item Accuracy 95.4% Overall Accuracy 94.5% Perfect Match Invoices 64/86 (74.4%) The two failed invoices were heavily damaged scans — one was partially cut off, the other was a hand-written receipt. Both would have been difficult for a human to process too. A notable finding: many “discrepancies” turned out to be errors in the manually-entered ground truth data, not in the AI parsing. The parser was extracting item codes and details that had been missed or mistyped during manual entry. What changed for the team: Invoic" }, { "tag": "h2", "title": "What’s Next", "y": 4975, "body": "The natural next phase is closing the loop to accounting software. Approved bills in Notion will export directly to Xero, eliminating the last manual step in the AP workflow. We’re also adding email intake — forwarding invoices to a dedicated address will trigger the same parsing pipeline, removing even the Notion form step for suppliers who send PDFs via email. As Fountain Creek scales from 25 locations toward 500+, this pipeline scales with them — serverless architecture means costs stay proportional to volume, not headcount." }, { "tag": "h2", "title": "What Made This Work", "y": 5283, "body": "1. Notion as the interface. The team didn’t need to learn anything new. The form, the review workflow, the approval process — it all happens in a tool they use every day. 2. AI for extraction, humans for judgment. The system doesn’t decide whether to pay an invoice. It handles the tedious part (reading PDFs and typing numbers) so humans can focus on the important part (is this correct and should we pay it?). 3. Validation against real data. Testing against 88 historical invoices before going live gave the team confidence. They could see exactly where the AI matched their manual work and where it differed. 4. Serverless event-driven architecture. Cloud Run + Eventarc means the system scales a" }, { "tag": "h2", "title": "How This Maps to Habitat OS", "y": 5759, "body": "This is the archetype of an accounts-payable Habitat — and it’s why invoice processing is now one of our productized offerings on Habitat OS, our enterprise agent runtime. The event-driven pipeline (Notion form → Cloud Run → Gemini → Notion databases), the schema-driven extraction, and the supplier memory that fuzzy-matches “Baldor” / “BALDOR SPECIALTY FOODS” / “Baldor Foods” into one canonical vendor are the same primitives Habitat OS provides out of the box: structured tools, persistent memory, and guarded reasoning with a human approval step. What keeps it running seamlessly in the background: the serverless architecture scales to volume automatically as Fountain Creek grows from 25 locat" }, { "tag": "h2", "title": "Facing a similar challenge?", "y": 6381, "body": "Let's talk about shipping a production AI system for your team — principal-led, deployed in your cloud on Habitat OS. TALK TO A PRINCIPAL SEE THE FRAMEWORK" }, { "tag": "h2", "title": "More Case Studies", "y": 6718, "body": "MEDIA INTELLIGENCE / CONTENT ANALYTICS From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analyzes content from Twitter/X, YouTube videos, and podcasts. What used to require hours of manual monitoring and note-taking now happens in minutes, with AI-generated summaries that surface the signal from the noise. HUMAN RESOURCES / SAAS From 15 Minutes to 30 Seconds: AI-Powered Benefits Document Extraction We built a schema-driven AI pipeline that extracts structured data from complex healthcare benefits documents (SBCs), transforming a f" }, { "tag": "h3", "title": "From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts", "y": 6810, "body": "We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analyzes content from Twitter/X, YouTube videos, and podcasts. What used to require hours of manual monitoring and note-taking now happens in minutes, with AI-generated summaries that surface the signal from the noise." }, { "tag": "h3", "title": "From 15 Minutes to 30 Seconds: AI-Powered Benefits Document Extraction", "y": 6810, "body": "We built a schema-driven AI pipeline that extracts structured data from complex healthcare benefits documents (SBCs), transforming a frustrating 15-minute manual process into a 30-second automated workflow." }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 7054, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 7504, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 7881, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 7881, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 7881, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 7881, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← Back to Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Running on Habitat OS →", "href": "https://thefocus.ai/habitat-os" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "invoice-processing Habitat", "href": "https://thefocus.ai/offerings/invoice-processing" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Re%3A%2094.5%25%20Accuracy%20on%20Invoice%20Parsing%3A%20AI-Powered%20Accounts%20Payable%20for%20Multi-Location%20Food%20Service" }, { "text": "SEE THE FRAMEWORK", "href": "https://thefocus.ai/capabilities" }, { "text": "MEDIA INTELLIGENCE / CONTENT ANALYTICS From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analyzes content from Twitter/X, YouTube videos, and podcasts. What used to require hours of manual monitoring and note-taking now happens in minutes, with AI-generated summaries that surface the signal from the noise.", "href": "https://thefocus.ai/case-study/distill" }, { "text": "HUMAN RESOURCES / SAAS From 15 Minutes to 30 Seconds: AI-Powered Benefits Document Extraction We built a schema-driven AI pipeline that extracts structured data from complex healthcare benefits documents (SBCs), transforming a frustrating 15-minute manual process into a 30-second automated workflow.", "href": "https://thefocus.ai/case-study/onboarding" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/case-studies", "https://thefocus.ai/habitat-os", "https://thefocus.ai/habitat-os", "https://thefocus.ai/offerings/invoice-processing", "mailto:hey@thefocus.ai?subject=Re%3A%2094.5%25%20Accuracy%20on%20Invoice%20Parsing%3A%20AI-Powered%20Accounts%20Payable%20for%20Multi-Location%20Food%20Service", "https://thefocus.ai/capabilities", "https://thefocus.ai/case-study/distill", "https://thefocus.ai/case-study/onboarding", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← Back to Case Studies FOOD SERVICE / RETAIL 4 weeks 94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy. Client: Fountain Creek Year: 2026 Services: Development Stack: Google Cloud Run, Gemini 3 Flash, Google Cloud Storage, Notion API, Eventarc, Node.js, TypeScript Running on Habitat OS → KEY RESULTS ✓ 94.5% overall accuracy validated against 88 historical invoices ✓ 97.7% of invoices successfully parsed on first attempt ✓ Manual data entry reduced from 15 minutes to under 30 seconds per invoice ✓ Notion-native workflow — zero new tools for the team to learn The Challenge Fountain Creek operates multiple food service locations — markets, delis, and grocery stores — and processes dozens of invoices weekly from a sprawling network of suppliers. Produce from Baldor, broad-line distribution from Sysco, specialty items from David Rosen, bakery from Intercounty, beverages from Northeast Beverage, facility supplies from Cintas. Each supplier sends PDFs in a different format, with different layouts, different field names, and different levels of detail. The accounts payable process was entirely manual. Someone would open each PDF, read through the line items, type the supplier name, invoice number, amounts, and individual line items into their system. A single invoice took 10-15 minutes. Errors were common — transposed numbers, missed line items, wrong supplier assignments — and each mistake cascaded into reconciliation headaches downstream. At roughly $5 per document in labor costs, the math only gets worse as the business scales from 25 locations toward their goal of 500+. The target: get invoice processing down to $0.30 per document. The core problem: The team was spending hours every week on data entry that was tedious, error-prone, and didn’t require human judgment. They needed that time for actual financial decision-making — reviewing food costs, catching anomalies, managing cash flow across locations. Our Appr", "height": 8378, "screenshots": { "desktop": "assets/screenshots/desktop/case-study--fountain-creek.png", "mobile": "assets/screenshots/mobile/case-study--fountain-creek.png" } }, { "url": "https://thefocus.ai/case-study/distill", "path": "/case-study/distill", "slug": "case-study--distill", "category": "Case study", "status": 200, "title": "From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts", "metaDescription": "We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analyzes content from Twitter/X, YouTube videos, and podcasts. What used to require hours of manual monitoring and note-taking now happens in minutes, with AI-generated summaries that surface the signal from the noise.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts", "index": 1 }, { "tag": "h2", "text": "KEY RESULTS", "index": 2 }, { "tag": "h3", "text": "The Challenge", "index": 3 }, { "tag": "h3", "text": "Our Approach", "index": 4 }, { "tag": "h3", "text": "Technical Implementation", "index": 5 }, { "tag": "h3", "text": "What Made This Work", "index": 6 }, { "tag": "h3", "text": "Results", "index": 7 }, { "tag": "h3", "text": "Technical Summary", "index": 8 }, { "tag": "h3", "text": "Architecture Diagram", "index": 9 }, { "tag": "h3", "text": "How This Maps to Habitat OS", "index": 10 }, { "tag": "h2", "text": "Facing a similar challenge?", "index": 11 }, { "tag": "h2", "text": "More Case Studies", "index": 12 }, { "tag": "h3", "text": "94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service", "index": 13 }, { "tag": "h3", "text": "From 15 Minutes to 30 Seconds: AI-Powered Benefits Document Extraction", "index": 14 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 15 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 16 }, { "tag": "h3", "text": "COMPANY", "index": 17 }, { "tag": "h3", "text": "PRODUCTS", "index": 18 }, { "tag": "h3", "text": "RESOURCES", "index": 19 }, { "tag": "h3", "text": "CONNECT", "index": 20 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts", "y": 269, "body": "We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analyzes content from Twitter/X, YouTube videos, and podcasts. What used to require hours of manual monitoring and note-taking now happens in minutes, with AI-generated summaries that surface the signal from the noise. Client: Brooklyn Running Year: 2025 Services: Strategy + Development Stack: Next.js 15, TypeScript, Node.js, Express, Google Gemini, Supabase, Stripe, Clerk, Fly.io, Vercel Running on Habitat OS →" }, { "tag": "h2", "title": "KEY RESULTS", "y": 1293, "body": "✓ 94% reduction in time spent monitoring content sources ✓ AI-generated summaries for 50+ tracked Twitter accounts daily ✓ Full transcript + highlights from hour-long videos in under 5 minutes ✓ Topic extraction and cross-source pattern detection" }, { "tag": "h3", "title": "The Challenge", "y": 1545, "body": "Information overload is the defining problem of the modern knowledge worker. Twitter alone generates 500 million tweets daily. YouTube adds 500 hours of video every minute. Podcasts publish over 100,000 new episodes weekly. Staying informed on topics that matter means either spending hours consuming content or missing critical insights entirely. The specific pain points: Twitter/X fragmentation — Following thought leaders means scrolling through an endless feed. Important threads get buried. Conversations span days. There’s no way to ask “what did the AI research community discuss this week?” Video and podcast inaccessibility — Long-form content is valuable but time-expensive. Watching a 90-" }, { "tag": "h3", "title": "Our Approach", "y": 2229, "body": "We designed Distill as a federated intelligence system — multiple specialized services that each excel at one content type, unified by a common data model and AI summarization layer. The key architectural insight: Rather than building a monolithic application, we created purpose-built engines for each content type: Twitter Collator — Handles Twitter’s rate limits, collects tweets from watched accounts, groups them by conversation, and generates user-level and topic-level summaries Interview Transcriber — Downloads audio from YouTube/Spotify, chunks it for parallel processing, transcribes with speaker identification, and extracts highlights Distill YouTube — A user-facing SaaS for interview l" }, { "tag": "h3", "title": "Technical Implementation", "y": 2881, "body": "Twitter Intelligence Pipeline Twitter’s API rate limits are the primary constraint. We built a sophisticated quota management system: Rate Limit Strategy: ├── 4 endpoint types tracked independently (search, timeline, userLookup, retweets) ├── Pre-check quota before requests ├── Optimistic decrement + actual header sync ├── Automatic 15-minute window reset detection ├── Persisted state survives container restarts └── User tier system prioritizes high-value accounts The tier system: We calculate a priority score for each tracked account based on posting frequency and follower count. When rate limits are tight, Tier 1-3 users (high-activity, high-impact) get resources first. This ensures the mo" }, { "tag": "h3", "title": "What Made This Work", "y": 5210, "body": "1. Separation of concerns. Each service does one thing well. Twitter Collator knows nothing about YouTube. Interview Transcriber knows nothing about billing. This makes each component testable, debuggable, and replaceable. 2. Smart rate limit management. Rather than failing when limits hit, the system degrades gracefully — prioritizing high-value content, caching aggressively, and resuming automatically when quotas reset. 3. Schema-driven AI extraction. Both Twitter summaries and video transcripts use structured output schemas with detailed field descriptions. This produces consistent, parseable results that can feed into dashboards and search indexes. 4. Edge deployment. User-facing AI feat" }, { "tag": "h3", "title": "Results", "y": 5779, "body": "The platform now processes daily: 50+ Twitter accounts with automated summaries and topic extraction Full transcripts from hour-long videos in under 5 minutes Cross-platform topic detection identifying when the same subject appears across sources User collections allowing personalized monitoring without information overload For internal users, this transformed content consumption: Before After 60+ minutes daily scrolling Twitter 5-minute summary review Watch full interviews to find quotes Search transcripts, jump to timestamp Miss conversations that happen overnight Daily digest with nothing missed No visibility into emerging topics Topic trends across all sources" }, { "tag": "h3", "title": "Technical Summary", "y": 6375, "body": "Component Technology Purpose Twitter Backend Node.js, Express, Twitter API v2, Gemini Tweet collection, summarization, topic extraction Transcription Engine Node.js, yt-dlp, ffmpeg, Gemini Audio processing, parallel transcription Interview SaaS Next.js 15, React 19, Supabase, Stripe User-facing subscription product Twitter Dashboard Next.js 15, Clerk, Supabase, SWR Real-time monitoring interface Infrastructure Fly.io, Vercel, Docker, PostgreSQL Scalable deployment with persistent storage" }, { "tag": "h3", "title": "Architecture Diagram", "y": 6718, "body": "┌─────────────────────────────────────────────┐ │ User Interfaces │ │ ┌─────────────┐ ┌─────────────────────┐ │ │ │ Distill X │ │ Distill YouTube │ │ │ │ (Twitter UI)│ │ (Interview Library) │ │ │ └──────┬──────┘ └──────────┬──────────┘ │ └─────────┼───────────────────────┼───────────┘ │ │ ┌─────────┼───────────────────────┼───────────┐ │ │ Backend Services │ │ │ ┌──────▼──────┐ ┌──────────▼─────────┐ │ │ │ Twitter │ │ Interview │ │ │ │ Collator │ │ Transcriber │ │ │ └──────┬──────┘ └──────────┬─────────┘ │ └─────────┼───────────────────────┼───────────┘ │ │ ┌─────────┼───────────────────────┼───────────┐ │ ▼ ▼ │ │ ┌─────────────────────────────────────┐ │ │ │ Google Gemini AI │ │ │ │ (Summar" }, { "tag": "h3", "title": "How This Maps to Habitat OS", "y": 7573, "body": "This is a content-intelligence Habitat — several specialized sub-agents (Twitter Collator, Interview Transcriber, Distill X, Distill YouTube) cooperating behind a shared summarization layer. The pattern of federated domain-specific services, each with its own rate-limit management, schema-driven extraction, and job state, is exactly how Habitat OS stitches multiple agents into a single coherent system — with shared memory, explicit tool schemas, and guarded reasoning that refuses to hallucinate beyond the provided source material. What keeps it running seamlessly in the background: rate-limit quota tracking survives container restarts, transcription jobs resume after failures, user-tier prio" }, { "tag": "h2", "title": "Facing a similar challenge?", "y": 8495, "body": "Let's talk about shipping a production AI system for your team — principal-led, deployed in your cloud on Habitat OS. TALK TO A PRINCIPAL SEE THE FRAMEWORK" }, { "tag": "h2", "title": "More Case Studies", "y": 8832, "body": "FOOD SERVICE / RETAIL 94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy. HUMAN RESOURCES / SAAS From 15 Minutes to 30 Seconds: AI-Powered Benefits Document Extraction We built a schema-driven AI pipeline that extracts structured data from complex healthcare benefits documents (SBCs), transforming a frustrating 15-minute manual pr" }, { "tag": "h3", "title": "94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service", "y": 8924, "body": "We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy." }, { "tag": "h3", "title": "From 15 Minutes to 30 Seconds: AI-Powered Benefits Document Extraction", "y": 8924, "body": "We built a schema-driven AI pipeline that extracts structured data from complex healthcare benefits documents (SBCs), transforming a frustrating 15-minute manual process into a 30-second automated workflow." }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 9168, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 9618, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 9995, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 9995, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 9995, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 9995, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← Back to Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Running on Habitat OS →", "href": "https://thefocus.ai/habitat-os" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Re%3A%20From%20Noise%20to%20Signal%3A%20AI-Powered%20Content%20Intelligence%20Across%20Twitter%2C%20YouTube%2C%20and%20Podcasts" }, { "text": "SEE THE FRAMEWORK", "href": "https://thefocus.ai/capabilities" }, { "text": "FOOD SERVICE / RETAIL 94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy.", "href": "https://thefocus.ai/case-study/fountain-creek" }, { "text": "HUMAN RESOURCES / SAAS From 15 Minutes to 30 Seconds: AI-Powered Benefits Document Extraction We built a schema-driven AI pipeline that extracts structured data from complex healthcare benefits documents (SBCs), transforming a frustrating 15-minute manual process into a 30-second automated workflow.", "href": "https://thefocus.ai/case-study/onboarding" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" }, { "text": "Registry", "href": "https://account.thefocus.ai/" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/case-studies", "https://thefocus.ai/habitat-os", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Re%3A%20From%20Noise%20to%20Signal%3A%20AI-Powered%20Content%20Intelligence%20Across%20Twitter%2C%20YouTube%2C%20and%20Podcasts", "https://thefocus.ai/capabilities", "https://thefocus.ai/case-study/fountain-creek", "https://thefocus.ai/case-study/onboarding", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← Back to Case Studies MEDIA INTELLIGENCE / CONTENT ANALYTICS 12 weeks From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analyzes content from Twitter/X, YouTube videos, and podcasts. What used to require hours of manual monitoring and note-taking now happens in minutes, with AI-generated summaries that surface the signal from the noise. Client: Brooklyn Running Year: 2025 Services: Strategy + Development Stack: Next.js 15, TypeScript, Node.js, Express, Google Gemini, Supabase, Stripe, Clerk, Fly.io, Vercel Running on Habitat OS → KEY RESULTS ✓ 94% reduction in time spent monitoring content sources ✓ AI-generated summaries for 50+ tracked Twitter accounts daily ✓ Full transcript + highlights from hour-long videos in under 5 minutes ✓ Topic extraction and cross-source pattern detection The Challenge Information overload is the defining problem of the modern knowledge worker. Twitter alone generates 500 million tweets daily. YouTube adds 500 hours of video every minute. Podcasts publish over 100,000 new episodes weekly. Staying informed on topics that matter means either spending hours consuming content or missing critical insights entirely. The specific pain points: Twitter/X fragmentation — Following thought leaders means scrolling through an endless feed. Important threads get buried. Conversations span days. There’s no way to ask “what did the AI research community discuss this week?” Video and podcast inaccessibility — Long-form content is valuable but time-expensive. Watching a 90-minute interview to find the 3 minutes that matter is inefficient. Skimming isn’t possible. Search doesn’t exist. Cross-source blindness — Topics emerge across platforms simultaneously. A Twitter thread references a podcast episode which discusses a YouTube interview. Connecting these dots manually is nearly impossible. What we needed: A platform that ingests content from multiple sources, extracts meaning using AI, and presents insights in scannable, searchable form — without requiring human curation at scale. Our Approach We designed Distill as a federated intelligence system — multiple specialized services that each excel at ", "height": 10492, "screenshots": { "desktop": "assets/screenshots/desktop/case-study--distill.png", "mobile": "assets/screenshots/mobile/case-study--distill.png" } }, { "url": "https://thefocus.ai/case-study/onboarding", "path": "/case-study/onboarding", "slug": "case-study--onboarding", "category": "Case study", "status": 200, "title": "From 15 Minutes to 30 Seconds: AI-Powered Benefits Document Extraction", "metaDescription": "We built a schema-driven AI pipeline that extracts structured data from complex healthcare benefits documents (SBCs), transforming a frustrating 15-minute manual process into a 30-second automated workflow.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "From 15 Minutes to 30 Seconds: AI-Powered Benefits Document Extraction", "index": 1 }, { "tag": "h2", "text": "KEY RESULTS", "index": 2 }, { "tag": "h2", "text": "The Challenge", "index": 3 }, { "tag": "h2", "text": "Our Approach", "index": 4 }, { "tag": "h2", "text": "Technical Implementation", "index": 5 }, { "tag": "h3", "text": "Serverless Processing Pipeline", "index": 6 }, { "tag": "h3", "text": "Schema Validation Layer", "index": 7 }, { "tag": "h3", "text": "SvelteKit Admin Frontend", "index": 8 }, { "tag": "h2", "text": "What We Extracted", "index": 9 }, { "tag": "h2", "text": "Results", "index": 10 }, { "tag": "h2", "text": "What Made This Work", "index": 11 }, { "tag": "h2", "text": "How This Maps to Habitat OS", "index": 12 }, { "tag": "h2", "text": "Facing a similar challenge?", "index": 13 }, { "tag": "h2", "text": "More Case Studies", "index": 14 }, { "tag": "h3", "text": "94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service", "index": 15 }, { "tag": "h3", "text": "From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts", "index": 16 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 17 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 18 }, { "tag": "h3", "text": "COMPANY", "index": 19 }, { "tag": "h3", "text": "PRODUCTS", "index": 20 }, { "tag": "h3", "text": "RESOURCES", "index": 21 }, { "tag": "h3", "text": "CONNECT", "index": 22 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "From 15 Minutes to 30 Seconds: AI-Powered Benefits Document Extraction", "y": 269, "body": "We built a schema-driven AI pipeline that extracts structured data from complex healthcare benefits documents (SBCs), transforming a frustrating 15-minute manual process into a 30-second automated workflow. Client: HR Software Provider Year: 2025 Services: Development Stack: AWS Lambda, S3, DynamoDB, Claude 3 Opus, SvelteKit, TypeScript, Zod Running on Habitat OS →" }, { "tag": "h2", "title": "KEY RESULTS", "y": 1217, "body": "✓ 78% reduction in onboarding time ✓ 98% accuracy in data extraction ✓ 15-minute process reduced to 30 seconds ✓ Zero manual data entry for standard documents" }, { "tag": "h2", "title": "The Challenge", "y": 1441, "body": "Our client provides a communication platform for retirement and healthcare benefits — a centralized place where employees can access information about their coverage, costs, and options. When companies onboard onto their platform, they need to import detailed information about every benefit plan they offer. The problem: this information arrives as Summary of Benefits and Coverage (SBC) documents — standardized PDFs from insurance providers. While the format is “standardized,” every carrier implements it differently. UnitedHealthcare documents look nothing like Cigna documents. A benefits administrator would spend 15+ minutes per document manually copying deductibles, copays, out-of-pocket li" }, { "tag": "h2", "title": "Our Approach", "y": 1949, "body": "We built a schema-driven extraction system using Claude 3 Opus. The key insight: rather than training a model to recognize specific document layouts, we gave the AI a comprehensive schema describing what to extract and where to look for it in SBC documents. The schema approach: We defined a detailed Zod schema with over 50 fields covering everything from basic plan information to complex coverage scenarios: const HealthInsurancePlanSchema = z.object({ planName: z.string().describe( \"The formal name of the health insurance plan. Usually appears at the top of the SBC.\" ), deductibles: z.object({ inNetwork: z.object({ individual: z.number().describe( \"Found in 'What is the overall deductible?' " }, { "tag": "h2", "title": "Technical Implementation", "y": 2832, "body": "" }, { "tag": "h3", "title": "Serverless Processing Pipeline", "y": 2908, "body": "We built on AWS with a fully serverless architecture: PDF Upload → S3 Trigger → Lambda Processor → Claude 3 Opus → Schema Validation → DynamoDB ↓ Admin Review (edge cases only) S3 Event Trigger: When a PDF lands in the input bucket, it automatically triggers processing. No polling, no queues to manage — S3 events handle it. Lambda Processing: Each document processes in its own Lambda invocation. Claude 3 Opus handles the extraction, receiving the PDF as a base64-encoded attachment and the schema as structured output format. DynamoDB for State: Every processing job gets tracked — when it started, its status, where the output went. This lets the admin UI show real-time progress and handle retr" }, { "tag": "h3", "title": "Schema Validation Layer", "y": 3461, "body": "The client’s existing database had strict constraints. A dental plan needs different fields than medical. An HSA has different rules than an FSA. We built validation that ensures every extraction matches the expected schema before hitting the database. When extraction fails validation, the document routes to an admin queue rather than silently failing. The admin interface shows the original PDF alongside the extracted JSON, letting operators quickly fix issues." }, { "tag": "h3", "title": "SvelteKit Admin Frontend", "y": 3749, "body": "The admin interface provides: Real-time dashboard showing input/output bucket statistics Drag-and-drop upload for manual testing PDF preview side-by-side with extracted JSON Processing status tracking with automatic updates Error handling with retry functionality Deployed on ECS Fargate for reliable, auto-scaling hosting." }, { "tag": "h2", "title": "What We Extracted", "y": 4159, "body": "The system handles the full complexity of U.S. healthcare benefits: Basic plan info: Name, carrier, plan type, coverage period Cost sharing: Deductibles, out-of-pocket limits, copays, coinsurance Coverage details: 20+ service categories from preventive care to prescription drugs Network rules: Referral requirements, provider directory URLs Drug coverage: Tier structure, retail vs. mail order, specialty medications Exclusions: Services not covered and their alternatives Each field has validation rules and semantic descriptions that help the AI understand what it’s looking for, even when the document layout varies." }, { "tag": "h2", "title": "Results", "y": 4655, "body": "The transformation was dramatic: 15 minutes → 30 seconds for standard documents 98% accuracy on first-pass extraction (no human review needed) Edge cases still get human review, but represent less than 5% of documents Multi-carrier support — works across UnitedHealthcare, Cigna, Aetna, Blue Cross, and others The client’s onboarding team went from dreading document processing to barely thinking about it. What used to be the slowest part of customer onboarding became essentially instant." }, { "tag": "h2", "title": "What Made This Work", "y": 5063, "body": "1. Schema-as-prompt. By embedding extraction guidance directly in the schema, we created a system that generalizes across document formats. New carriers just work because the AI understands what it’s looking for semantically. 2. Confidence routing. Rather than guessing on ambiguous extractions, the system routes uncertain documents to humans. This maintains accuracy while still automating the clear cases. 3. Operational tooling from day one. The admin interface wasn’t an afterthought — it shipped alongside the extraction pipeline. This made debugging, monitoring, and edge case handling smooth from the first deployment. 4. Full AWS serverless. No servers to manage, automatic scaling during bu" }, { "tag": "h2", "title": "How This Maps to Habitat OS", "y": 5539, "body": "This is the archetype of a document-extraction Habitat: schema-driven, event-triggered, and auditable end-to-end. The Zod schema with embedded extraction hints, the confidence-based routing to human review, and the serverless S3 → Lambda → Claude pipeline all map directly to Habitat OS primitives — structured tools, memory (DynamoDB job state), and guarded reasoning with explicit escalation paths when the model isn’t sure. What keeps it running seamlessly in the background: interrupted jobs resume from DynamoDB state, edge cases route to human queues instead of silently failing, and new document layouts “just work” because the AI understands the schema semantically, not visually. When United" }, { "tag": "h2", "title": "Facing a similar challenge?", "y": 6413, "body": "Let's talk about shipping a production AI system for your team — principal-led, deployed in your cloud on Habitat OS. TALK TO A PRINCIPAL SEE THE FRAMEWORK" }, { "tag": "h2", "title": "More Case Studies", "y": 6750, "body": "FOOD SERVICE / RETAIL 94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy. MEDIA INTELLIGENCE / CONTENT ANALYTICS From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analy" }, { "tag": "h3", "title": "94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service", "y": 6842, "body": "We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy." }, { "tag": "h3", "title": "From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts", "y": 6842, "body": "We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analyzes content from Twitter/X, YouTube videos, and podcasts. What used to require hours of manual monitoring and note-taking now happens in minutes, with AI-generated summaries that surface the signal from the noise." }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 7086, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 7536, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 7913, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 7913, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 7913, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 7913, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← Back to Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Running on Habitat OS →", "href": "https://thefocus.ai/habitat-os" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "customer-onboarding Habitat", "href": "https://thefocus.ai/offerings/customer-onboarding" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Re%3A%20From%2015%20Minutes%20to%2030%20Seconds%3A%20AI-Powered%20Benefits%20Document%20Extraction" }, { "text": "SEE THE FRAMEWORK", "href": "https://thefocus.ai/capabilities" }, { "text": "FOOD SERVICE / RETAIL 94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy.", "href": "https://thefocus.ai/case-study/fountain-creek" }, { "text": "MEDIA INTELLIGENCE / CONTENT ANALYTICS From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analyzes content from Twitter/X, YouTube videos, and podcasts. What used to require hours of manual monitoring and note-taking now happens in minutes, with AI-generated summaries that surface the signal from the noise.", "href": "https://thefocus.ai/case-study/distill" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/case-studies", "https://thefocus.ai/habitat-os", "https://thefocus.ai/habitat-os", "https://thefocus.ai/offerings/customer-onboarding", "mailto:hey@thefocus.ai?subject=Re%3A%20From%2015%20Minutes%20to%2030%20Seconds%3A%20AI-Powered%20Benefits%20Document%20Extraction", "https://thefocus.ai/capabilities", "https://thefocus.ai/case-study/fountain-creek", "https://thefocus.ai/case-study/distill", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← Back to Case Studies HUMAN RESOURCES / SAAS 6 weeks From 15 Minutes to 30 Seconds: AI-Powered Benefits Document Extraction We built a schema-driven AI pipeline that extracts structured data from complex healthcare benefits documents (SBCs), transforming a frustrating 15-minute manual process into a 30-second automated workflow. Client: HR Software Provider Year: 2025 Services: Development Stack: AWS Lambda, S3, DynamoDB, Claude 3 Opus, SvelteKit, TypeScript, Zod Running on Habitat OS → KEY RESULTS ✓ 78% reduction in onboarding time ✓ 98% accuracy in data extraction ✓ 15-minute process reduced to 30 seconds ✓ Zero manual data entry for standard documents The Challenge Our client provides a communication platform for retirement and healthcare benefits — a centralized place where employees can access information about their coverage, costs, and options. When companies onboard onto their platform, they need to import detailed information about every benefit plan they offer. The problem: this information arrives as Summary of Benefits and Coverage (SBC) documents — standardized PDFs from insurance providers. While the format is “standardized,” every carrier implements it differently. UnitedHealthcare documents look nothing like Cigna documents. A benefits administrator would spend 15+ minutes per document manually copying deductibles, copays, out-of-pocket limits, and coverage details into the system. The scale of the problem: With dozens of plans per client and hundreds of clients onboarding each quarter, this manual process consumed thousands of hours annually. And errors were common — a typo in a deductible could cause months of support issues. Our Approach We built a schema-driven extraction system using Claude 3 Opus. The key insight: rather than training a model to recognize specific document layouts, we gave the AI a comprehensive schema describing what to extract and where to look for it in SBC documents. The schema approach: We defined a detailed Zod schema with over 50 fields covering everything from basic plan information to complex coverage scenarios: const HealthInsurancePlanSchema = z.object({ planName: z.string().describe( \"The formal name of the health insurance plan. Usually appears at the top of the SBC.\" ), deductibles: z.object({ inNetwork: z.object({ i", "height": 8410, "screenshots": { "desktop": "assets/screenshots/desktop/case-study--onboarding.png", "mobile": "assets/screenshots/mobile/case-study--onboarding.png" } }, { "url": "https://thefocus.ai/case-study/image-browser", "path": "/case-study/image-browser", "slug": "case-study--image-browser", "category": "Case study", "status": 200, "title": "Search 10,000 Images in Under 100ms: Vector-Powered Visual Discovery", "metaDescription": "We built Image Browser — a natural language image search system that lets users find images by describing what they're looking for. Instead of manually tagging thousands of photos, CLIP embeddings understand visual content automatically, enabling instant semantic search across entire image libraries.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Search 10,000 Images in Under 100ms: Vector-Powered Visual Discovery", "index": 1 }, { "tag": "h2", "text": "KEY RESULTS", "index": 2 }, { "tag": "h3", "text": "The Challenge", "index": 3 }, { "tag": "h3", "text": "Our Approach", "index": 4 }, { "tag": "h3", "text": "Technical Implementation", "index": 5 }, { "tag": "h3", "text": "What Made This Work", "index": 6 }, { "tag": "h3", "text": "Results", "index": 7 }, { "tag": "h3", "text": "Technical Summary", "index": 8 }, { "tag": "h3", "text": "Use Cases Beyond Photo Search", "index": 9 }, { "tag": "h2", "text": "Facing a similar challenge?", "index": 10 }, { "tag": "h2", "text": "More Case Studies", "index": 11 }, { "tag": "h3", "text": "94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service", "index": 12 }, { "tag": "h3", "text": "From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts", "index": 13 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 14 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 15 }, { "tag": "h3", "text": "COMPANY", "index": 16 }, { "tag": "h3", "text": "PRODUCTS", "index": 17 }, { "tag": "h3", "text": "RESOURCES", "index": 18 }, { "tag": "h3", "text": "CONNECT", "index": 19 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Search 10,000 Images in Under 100ms: Vector-Powered Visual Discovery", "y": 269, "body": "We built Image Browser — a natural language image search system that lets users find images by describing what they're looking for. Instead of manually tagging thousands of photos, CLIP embeddings understand visual content automatically, enabling instant semantic search across entire image libraries. Client: Internal Platform Year: 2025 Services: Development Stack: Node.js, TypeScript, Express, PostgreSQL + pgvector, Cloudflare R2, Replicate CLIP API, Docker" }, { "tag": "h2", "title": "KEY RESULTS", "y": 1199, "body": "✓ Sub-100ms search latency across 10,000+ images ✓ Zero manual tagging required — AI understands image content ✓ Multi-collection architecture supports unlimited datasets ✓ 768-dimensional vectors capture visual semantics" }, { "tag": "h3", "title": "The Challenge", "y": 1451, "body": "Image libraries grow faster than anyone can organize them. A photographer shoots 500 photos at an event. A marketing team accumulates thousands of product shots. A researcher collects tens of thousands of reference images. The collection grows, but findability collapses. The traditional approaches fail at scale: Manual tagging — Works for 100 images. Becomes a full-time job at 10,000. And tags are only as good as the person who added them — search for “happy” and miss the image tagged “joyful.” Folder organization — Forces single-hierarchy thinking. An image of a red car at sunset belongs in Cars? Sunsets? Red things? The answer is all three, but folders demand a choice. Filename search — Re" }, { "tag": "h3", "title": "Our Approach", "y": 2047, "body": "We built on CLIP (Contrastive Language-Image Pre-training), OpenAI’s model that learns visual concepts from natural language descriptions. CLIP maps both images and text into the same 768-dimensional vector space — if an image and a text description are semantically similar, their vectors are close together. The key insight: Instead of making users describe images in the system’s vocabulary (tags), let the system understand the user’s vocabulary (natural language). The architecture splits into two independent components: Syncer — Uploads images to cloud storage and generates CLIP embeddings via Replicate’s API Server — Provides a web interface for browsing and searching using vector similari" }, { "tag": "h3", "title": "Technical Implementation", "y": 2573, "body": "Vector Storage with pgvector PostgreSQL’s pgvector extension stores 768-dimensional vectors as a native column type and enables similarity search using specialized indexes: CREATE TABLE photos_embeddings ( id SERIAL PRIMARY KEY, r2_key TEXT UNIQUE NOT NULL, embedding vector(768), created_at TIMESTAMPTZ DEFAULT now() ); -- HNSW index for approximate nearest neighbor search CREATE INDEX ON photos_embeddings USING hnsw (embedding vector_ip_ops) WHERE embedding IS NOT NULL; Why HNSW? Hierarchical Navigable Small World graphs provide logarithmic search complexity. For 10,000 images, that’s the difference between checking every vector (slow) and navigating a graph structure (fast). Search latency " }, { "tag": "h3", "title": "What Made This Work", "y": 6521, "body": "1. CLIP’s semantic understanding. The model captures abstract concepts, not just objects. Search for “cozy” and find warm-lit interiors. Search for “energetic” and find action shots. This semantic richness is impossible with manual tags. 2. Approximate nearest neighbor indexes. Without HNSW, vector search is O(n) — every query scans every row. With HNSW, it’s O(log n). This is the difference between “works for demos” and “works in production.” 3. Cloud-native simplicity. No GPU servers to manage. Replicate handles CLIP inference. R2 handles storage. Supabase handles PostgreSQL + pgvector. The entire system runs on serverless-friendly platforms. 4. Separation of sync and serve. The syncer can" }, { "tag": "h3", "title": "Results", "y": 7149, "body": "The system now powers internal image discovery: Metric Value Search latency <100ms (10k images) Embedding throughput ~60 images/minute (Replicate API) Storage cost ~$0.015/GB/month (R2) Database overhead ~3KB per image (metadata + vector) Index build time ~30 seconds for 10k vectors The user experience transformation: Before After Scroll through folders hoping to spot the right image Type what you’re looking for Maintain tagging discipline across team Zero tagging required ”I know I have a photo of…” → 10 minutes searching 2 seconds Find one good image, miss the similar alternatives Click “find similar” to see neighbors" }, { "tag": "h3", "title": "Technical Summary", "y": 7845, "body": "Component Technology Purpose Storage Cloudflare R2 S3-compatible object storage for images Database PostgreSQL + pgvector Vector storage and similarity search Embeddings Replicate CLIP API Text and image encoding to 768D vectors Server Express.js, TypeScript Web interface and search API Syncer Node.js CLI Upload and embedding pipeline Indexing HNSW Approximate nearest neighbor search Deployment Docker, Fly.io Container-based hosting" }, { "tag": "h3", "title": "Use Cases Beyond Photo Search", "y": 8278, "body": "The same architecture applies wherever visual similarity matters: E-commerce — “Show me dresses like this one” without manual style tagging Design systems — Find components that look similar across a UI library Medical imaging — Surface similar cases from historical scans Quality control — Detect anomalies by finding images that don’t match expected patterns Content moderation — Flag images similar to known problematic content The core insight — that CLIP embeddings capture semantic similarity, not just pixel similarity — makes this approach powerful across domains." }, { "tag": "h2", "title": "Facing a similar challenge?", "y": 9115, "body": "Let's talk about shipping a production AI system for your team — principal-led, deployed in your cloud on Habitat OS. TALK TO A PRINCIPAL SEE THE FRAMEWORK" }, { "tag": "h2", "title": "More Case Studies", "y": 9452, "body": "FOOD SERVICE / RETAIL 94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy. MEDIA INTELLIGENCE / CONTENT ANALYTICS From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analy" }, { "tag": "h3", "title": "94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service", "y": 9544, "body": "We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy." }, { "tag": "h3", "title": "From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts", "y": 9544, "body": "We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analyzes content from Twitter/X, YouTube videos, and podcasts. What used to require hours of manual monitoring and note-taking now happens in minutes, with AI-generated summaries that surface the signal from the noise." }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 9788, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 10238, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 10615, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 10615, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 10615, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 10615, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← Back to Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Re%3A%20Search%2010%2C000%20Images%20in%20Under%20100ms%3A%20Vector-Powered%20Visual%20Discovery" }, { "text": "SEE THE FRAMEWORK", "href": "https://thefocus.ai/capabilities" }, { "text": "FOOD SERVICE / RETAIL 94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy.", "href": "https://thefocus.ai/case-study/fountain-creek" }, { "text": "MEDIA INTELLIGENCE / CONTENT ANALYTICS From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analyzes content from Twitter/X, YouTube videos, and podcasts. What used to require hours of manual monitoring and note-taking now happens in minutes, with AI-generated summaries that surface the signal from the noise.", "href": "https://thefocus.ai/case-study/distill" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" }, { "text": "Registry", "href": "https://account.thefocus.ai/" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/case-studies", "mailto:hey@thefocus.ai?subject=Re%3A%20Search%2010%2C000%20Images%20in%20Under%20100ms%3A%20Vector-Powered%20Visual%20Discovery", "https://thefocus.ai/capabilities", "https://thefocus.ai/case-study/fountain-creek", "https://thefocus.ai/case-study/distill", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← Back to Case Studies MEDIA / CONTENT MANAGEMENT 4 weeks Search 10,000 Images in Under 100ms: Vector-Powered Visual Discovery We built Image Browser — a natural language image search system that lets users find images by describing what they're looking for. Instead of manually tagging thousands of photos, CLIP embeddings understand visual content automatically, enabling instant semantic search across entire image libraries. Client: Internal Platform Year: 2025 Services: Development Stack: Node.js, TypeScript, Express, PostgreSQL + pgvector, Cloudflare R2, Replicate CLIP API, Docker KEY RESULTS ✓ Sub-100ms search latency across 10,000+ images ✓ Zero manual tagging required — AI understands image content ✓ Multi-collection architecture supports unlimited datasets ✓ 768-dimensional vectors capture visual semantics The Challenge Image libraries grow faster than anyone can organize them. A photographer shoots 500 photos at an event. A marketing team accumulates thousands of product shots. A researcher collects tens of thousands of reference images. The collection grows, but findability collapses. The traditional approaches fail at scale: Manual tagging — Works for 100 images. Becomes a full-time job at 10,000. And tags are only as good as the person who added them — search for “happy” and miss the image tagged “joyful.” Folder organization — Forces single-hierarchy thinking. An image of a red car at sunset belongs in Cars? Sunsets? Red things? The answer is all three, but folders demand a choice. Filename search — Requires disciplined naming conventions that nobody maintains. “IMG_4523.jpg” tells you nothing. What we needed: A system that understands what’s in the image, not what someone decided to call it. Search for “dog playing in snow” and find it — whether it was tagged, named, or just uploaded and forgotten. Our Approach We built on CLIP (Contrastive Language-Image Pre-training), OpenAI’s model that learns visual concepts from natural language descriptions. CLIP maps both images and text into the same 768-dimensional vector space — if an image and a text description are semantically similar, their vectors are close together. The key insight: Instead of making users describe images in the system’s vocabulary (tags), let the system understand the user’s vocabulary (natu", "height": 11112, "screenshots": { "desktop": "assets/screenshots/desktop/case-study--image-browser.png", "mobile": "assets/screenshots/mobile/case-study--image-browser.png" } }, { "url": "https://thefocus.ai/case-study/ai-back-office", "path": "/case-study/ai-back-office", "slug": "case-study--ai-back-office", "category": "Case study", "status": 200, "title": "6 Commands Replace 10+ Hours/Week: Building an AI-Powered Back Office", "metaDescription": "A complete back office system — project tracking, hiring pipeline, invoicing, client communication, sales CRM — running on Claude Code commands and markdown files. No databases. No SaaS subscriptions. Just AI reading and writing text.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "6 Commands Replace 10+ Hours/Week: Building an AI-Powered Back Office", "index": 1 }, { "tag": "h2", "text": "KEY RESULTS", "index": 2 }, { "tag": "h3", "text": "The Challenge", "index": 3 }, { "tag": "h3", "text": "Our Approach", "index": 4 }, { "tag": "h2", "text": "Daily Operations: Important Over Urgent", "index": 5 }, { "tag": "h2", "text": "Project Management: Connecting Conversations to Execution", "index": 6 }, { "tag": "h2", "text": "Finance: Visibility", "index": 7 }, { "tag": "h2", "text": "Hiring: 50 Applications in One Weekend", "index": 8 }, { "tag": "h2", "text": "Client Communication: The Status Email I Kept Putting Off", "index": 9 }, { "tag": "h2", "text": "Sales Pipeline: Leads from Everywhere", "index": 10 }, { "tag": "h2", "text": "Technical Implementation", "index": 11 }, { "tag": "h3", "text": "Architecture: Files as Database", "index": 12 }, { "tag": "h3", "text": "The Plugin Stack", "index": 13 }, { "tag": "h3", "text": "Key Technical Decisions", "index": 14 }, { "tag": "h2", "text": "Results", "index": 15 }, { "tag": "h2", "text": "How This Maps to Habitat OS", "index": 16 }, { "tag": "h2", "text": "Get This For Your Business", "index": 17 }, { "tag": "h3", "text": "Ready to Automate Your Back Office?", "index": 18 }, { "tag": "h2", "text": "Facing a similar challenge?", "index": 19 }, { "tag": "h2", "text": "More Case Studies", "index": 20 }, { "tag": "h3", "text": "94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service", "index": 21 }, { "tag": "h3", "text": "From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts", "index": 22 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 23 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 24 }, { "tag": "h3", "text": "COMPANY", "index": 25 }, { "tag": "h3", "text": "PRODUCTS", "index": 26 }, { "tag": "h3", "text": "RESOURCES", "index": 27 }, { "tag": "h3", "text": "CONNECT", "index": 28 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "6 Commands Replace 10+ Hours/Week: Building an AI-Powered Back Office", "y": 269, "body": "A complete back office system — project tracking, hiring pipeline, invoicing, client communication, sales CRM — running on Claude Code commands and markdown files. No databases. No SaaS subscriptions. Just AI reading and writing text. Client: TheFocus.AI (Internal Deployment) Year: 2026 Services: AI Operations Setup Stack: Claude Code, TheFocus.AI Plugin Marketplace, Markdown, GitHub CLI, Granola, Google Workspace, 1Password CLI Running on Habitat OS →" }, { "tag": "h2", "title": "KEY RESULTS", "y": 1265, "body": "✓ 10+ hours/week of manual operations work eliminated within first week ✓ 50 job candidates researched, evaluated, and contacted in one weekend ✓ 6 active clients with auto-generated weekly status reports ✓ $150K+ in invoices tracked with automated payment follow-ups ✓ Morning briefings synthesized from 5+ data sources in seconds" }, { "tag": "h3", "title": "The Challenge", "y": 1589, "body": "Running a consulting firm means running two businesses: the work you do for clients, and the operations that keep the lights on. Most consultants handle operations the same way: a CRM for sales, a project management tool for delivery, a spreadsheet for invoicing, a folder somewhere for hiring. Maybe an HR platform. Maybe a newsletter tool. Each system has its own login, its own data model, its own way of doing things. The result is fragmentation. The sales call transcript lives in one place. The project scope lives in another. The invoice status lives in a third. When a client asks “where do we stand?” you’re alt-tabbing through five applications trying to assemble context. The specific pain" }, { "tag": "h3", "title": "Our Approach", "y": 2415, "body": "Rather than buying more SaaS tools, we built an AI-powered operations layer using Claude Code and markdown files. The key insight: Most operational “software” is just structured data plus views plus workflows. A CRM is contacts + deals + “remind me to follow up.” A project tracker is tasks + status + “what’s blocked?” An invoice system is amounts + dates + “who hasn’t paid?” Markdown files can hold all that data. Claude can read it, understand it, and act on it. We built six commands that orchestrate everything: Command What It Does /sync Pull data from GitHub, Granola meetings, email, newsletters /daily Generate morning briefing from all sources /weekly Full operational review + dashboard r" }, { "tag": "h2", "title": "Daily Operations: Important Over Urgent", "y": 3777, "body": "Morning. Kids need to get to school. There’s a Slack notification from a client. Email shows three unread threads. The calendar has a call at 10. It’s easy to confuse urgent with important. The Slack message feels urgent — someone’s waiting. But the real priority might be the invoice from two weeks ago that nobody’s followed up on. Or the candidate who interviewed on Monday and hasn’t heard back. Or the proposal that’s been “almost done” for six days. /daily fixes that: Every morning, Claude reads everything: project files, recent commits, meeting transcripts, outstanding invoices, the task backlog. It doesn’t just tell you what’s happening. It tells you what matters. BRIEF - Tuesday, Jan 28" }, { "tag": "h2", "title": "Project Management: Connecting Conversations to Execution", "y": 5622, "body": "Great, now how do we need all those files up to date? We can pull down the code, but the real project knowledge isn’t in the code. It isn’t only in the scopes, or the requirements or the documentation — though of course we put all that into the system. But the real knowledge is in the conversations. “We agreed to push the deadline to the 15th.” “Barnaby mentioned they’re prioritizing the mobile app now.” “Reginald said he needs the assessment before the board meeting.” Those details live in meeting transcripts. Without a system to make that oral tribal understand goes away. Granola runs during every client call. (Gemini reports also get sucked in through an email bridge.). It captures the tr" }, { "tag": "h2", "title": "Finance: Visibility", "y": 7668, "body": "$15,000 outstanding. Or was it $22,500? What’s coming up? Which contract needs to be renewed, how is things projecting out a few months from now, what should our sales pressure really be? Cash flow is kick. You do the work, then you wait to get paid. The gap between “invoice sent” and “payment received” is where small firms bleed. invoices.md. A markdown file with two tables — Outstanding and Paid. Outstanding: Client Invoice Amount Sent Due Status Mongoose & Associates #47 $7,500 Jan 3 Jan 17 Overdue Wobblefish Analytics #52 $15,000 Jan 20 Feb 3 Sent Paid (2026): Client Invoice Amount Paid Description Periwinkle Dynamics #11 $10,000 Jan 28 Feb hosting Spatula Networks #31 $8,000 Jan 21 Phas" }, { "tag": "h2", "title": "Hiring: 50 Applications in One Weekend", "y": 9294, "body": "A project was slipping, the client starting to grumble a little bit. OK, lets bring someone with the right set of skills to get the job done. I asked the system to compare what was in the scope with what was in the code base, what issues we were seeing out of github issues, and had it write a job description tailored for our problem. Posted it to a few places, and got a 50 applications over the weekend. Each one deserves a real evaluation, or at the very least a response. Check their GitHub — is there actual code? Look at LinkedIn — what’s their experience? Read the portfolio — can they ship? Does their rate fit? Are they in a workable timezone? That’s 20-30 minutes per candidate. Times 50. " }, { "tag": "h2", "title": "Client Communication: The Status Email I Kept Putting Off", "y": 11660, "body": "“I should send Barnaby an update.” That thought appears on Tuesday. By Friday, it’s still on the list. The client hasn’t complained, so it doesn’t feel urgent. But the relationship quietly erodes. They wonder what’s happening. They feel out of the loop. Writing status emails takes effort. Pull up the project file. Check what got done this week. Look at the invoices. Remember what was discussed in the last call. Synthesize it into something coherent. Make it look professional. That’s 30 minutes per client. Six clients. Three hours of email writing, if you actually do it. /client-update wobblefish does it in seconds. The command reads the project file, the latest weekly report, the recent meet" }, { "tag": "h2", "title": "Sales Pipeline: Leads from Everywhere", "y": 13894, "body": "Met Gertrude at the AI Engineering conference. Exchanged cards. She said to follow up after the holidays. Bartholomew came through a referral. Had a discovery call. Said he’d think about it. Cold outreach hit three prospects last month. Two replied. One went quiet. Where does all this live? In your head. In scattered email threads. In calendar entries that don’t explain the context. sales.md fixes this: Active Leads: Lead Source Status Next Step Value Gertrude @ Mystical Spoon Corp AI Eng Conf Proposal sent Follow up Mon $40K Bartholomew Snickerdoodle Referral Discovery done Send scope $25K Mongoose & Associates Cold outreach First call Schedule demo $15K Every lead has a row. Every row has " }, { "tag": "h2", "title": "Technical Implementation", "y": 15976, "body": "" }, { "tag": "h3", "title": "Architecture: Files as Database", "y": 16052, "body": "The entire system runs on a simple principle: markdown files are the database, Claude is the query engine. operations/ ├── projects/ │ ├── clients/ │ │ ├── wobblefish/ │ │ │ ├── index.md # Project overview, scope, status │ │ │ ├── meetings/ # Granola transcripts │ │ │ └── weekly-reports/ # Auto-generated summaries │ │ └── mongoose/ │ └── internal/ ├── hiring/ │ ├── applications/ # Raw .eml files │ ├── evaluations/ # AI-generated assessments │ └── meetings/ # Interview transcripts ├── finances/ │ ├── invoices.md # Outstanding + paid tables │ ├── 2026-revenue.md # Monthly breakdown │ └── contractors.md # Rates and payments └── sales/ ├── sales.md # Pipeline table └── meetings/ # Discovery call" }, { "tag": "h3", "title": "The Plugin Stack", "y": 16915, "body": "All integrations come from the TheFocus.AI Plugin Marketplace — open source Claude Code plugins that we maintain: Plugin Purpose granola-skill Export meeting transcripts with speaker identification google-skill Gmail drafts, Google Docs, Calendar integration nano-banana AI image generation for dashboards and infographics buttondown-skill Newsletter analytics and draft creation chrome-driver Web automation for research and data extraction embeddings-search Semantic search across all markdown files" }, { "tag": "h3", "title": "Key Technical Decisions", "y": 17389, "body": "1. Markdown over databases. Traditional ops software locks data in proprietary formats. Markdown is portable, version-controlled, and human-readable. When Claude hallucinates, you can read the source file and catch it. 2. Local-first execution. Everything runs on your machine via Claude Code. No cloud infrastructure. No API costs beyond Claude. No vendor dependency. 3. Pull architecture. External data (GitHub commits, meeting transcripts, emails) gets pulled into the local file system. This creates a complete audit trail and works offline. 4. Prompt-as-workflow. Each command (e.g., /daily) is a carefully crafted prompt that knows which files to read, what patterns to look for, and how to for" }, { "tag": "h2", "title": "Results", "y": 17919, "body": "Within the first week: 10+ hours/week of manual operations work eliminated Morning briefings synthesized from GitHub, meetings, invoices, and calendar in under 30 seconds Client status emails that took 30 minutes now generated in seconds, ready for review After 3 weeks of active use: 6 active clients tracked with weekly reports auto-generated from commit activity and meeting transcripts 50 job candidates researched, evaluated, and contacted — what would have taken 25+ hours done in one weekend $150K+ in invoices tracked with payment status and automated follow-up reminders Zero context lost — every meeting, every decision, every conversation searchable and connected The system scales without" }, { "tag": "h2", "title": "How This Maps to Habitat OS", "y": 18687, "body": "This engagement is the archetype of an operations Habitat — the same pattern we now deploy for clients on Habitat OS, our enterprise agent runtime. The six-command surface (/sync, /daily, /weekly, /client-update, /talent-search, /push) maps directly to Habitat OS primitives: persistent memory (markdown files plus meeting transcripts), specialized sub-agents (each command is a narrow reasoner with its own toolset), integrations (GitHub, Granola, Google Workspace, 1Password), and scheduled autonomy (daily briefings and weekly rollups run without prompting). Once installed, the runtime keeps itself healthy in the background: interrupted sync jobs resume from where they failed, new integrations " }, { "tag": "h2", "title": "Get This For Your Business", "y": 19260, "body": "This isn’t a product you buy — it’s a system we build with you. The Setup Process: Kickoff — Map your operational workflows, identify data sources, design your folder structure Configuration — Install and configure plugins from the TheFocus.AI Marketplace for your specific integrations Command Development — Build your custom /daily, /weekly, and domain-specific commands Integration — Connect to your actual data, refine prompts, train on your terminology Handoff — Documentation, walkthrough, and ongoing support You’ll have a working /daily briefing after the first session. The system grows from there. What You Get: Custom Claude Code commands tailored to your operations Folder structure desig" }, { "tag": "h3", "title": "Ready to Automate Your Back Office?", "y": 20290, "body": "Talk to a principal to discuss your operational workflows and see if this approach fits your business. We’ll map your current pain points and show you exactly what an AI-powered operations system would look like for your specific situation." }, { "tag": "h2", "title": "Facing a similar challenge?", "y": 20586, "body": "Let's talk about shipping a production AI system for your team — principal-led, deployed in your cloud on Habitat OS. TALK TO A PRINCIPAL SEE THE FRAMEWORK" }, { "tag": "h2", "title": "More Case Studies", "y": 20923, "body": "FOOD SERVICE / RETAIL 94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy. MEDIA INTELLIGENCE / CONTENT ANALYTICS From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analy" }, { "tag": "h3", "title": "94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service", "y": 21015, "body": "We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy." }, { "tag": "h3", "title": "From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts", "y": 21015, "body": "We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analyzes content from Twitter/X, YouTube videos, and podcasts. What used to require hours of manual monitoring and note-taking now happens in minutes, with AI-generated summaries that surface the signal from the noise." }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 21259, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 21709, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 22086, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 22086, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 22086, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 22086, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← Back to Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Running on Habitat OS →", "href": "https://thefocus.ai/habitat-os" }, { "text": "TheFocus.AI Plugin Marketplace", "href": "https://github.com/The-Focus-AI/claude-marketplace" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TheFocus.AI marketplace", "href": "https://github.com/The-Focus-AI/claude-marketplace" }, { "text": "Talk to a principal", "href": "mailto:hey@thefocus.ai?subject=AI%20Back%20Office%20%E2%80%94%20Enterprise%20Inquiry" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Re%3A%206%20Commands%20Replace%2010%2B%20Hours%2FWeek%3A%20Building%20an%20AI-Powered%20Back%20Office" }, { "text": "SEE THE FRAMEWORK", "href": "https://thefocus.ai/capabilities" }, { "text": "FOOD SERVICE / RETAIL 94.5% Accuracy on Invoice Parsing: AI-Powered Accounts Payable for Multi-Location Food Service We built an automated invoice parsing pipeline that converts scanned PDF invoices into structured financial data using Gemini 3 Flash. Restaurant managers submit invoices through a Notion form, and within seconds the AI extracts suppliers, line items, and totals — validated against 88 historical invoices at 94.5% accuracy.", "href": "https://thefocus.ai/case-study/fountain-creek" }, { "text": "MEDIA INTELLIGENCE / CONTENT ANALYTICS From Noise to Signal: AI-Powered Content Intelligence Across Twitter, YouTube, and Podcasts We built Distill — a multi-source content intelligence platform that automatically collects, transcribes, summarizes, and analyzes content from Twitter/X, YouTube videos, and podcasts. What used to require hours of manual monitoring and note-taking now happens in minutes, with AI-generated summaries that surface the signal from the noise.", "href": "https://thefocus.ai/case-study/distill" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/case-studies", "https://thefocus.ai/habitat-os", "https://github.com/The-Focus-AI/claude-marketplace", "https://thefocus.ai/habitat-os", "https://github.com/The-Focus-AI/claude-marketplace", "mailto:hey@thefocus.ai?subject=AI%20Back%20Office%20%E2%80%94%20Enterprise%20Inquiry", "mailto:hey@thefocus.ai?subject=Re%3A%206%20Commands%20Replace%2010%2B%20Hours%2FWeek%3A%20Building%20an%20AI-Powered%20Back%20Office", "https://thefocus.ai/capabilities", "https://thefocus.ai/case-study/fountain-creek", "https://thefocus.ai/case-study/distill", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [ { "src": "https://thefocus.ai/_astro/ai-back-office-stack.CAys9md5_Z143tiy.webp", "alt": "The Stack", "w": 1376, "h": 768 }, { "src": "https://thefocus.ai/_astro/backoffice-daily-ops.qhPGgPkm_ZkhVrU.webp", "alt": "Daily Operations Flow", "w": 0, "h": 0 }, { "src": "https://thefocus.ai/_astro/backoffice-projects.C7DbdQk-_Z139QXf.webp", "alt": "Project Management Flow", "w": 0, "h": 0 }, { "src": "https://thefocus.ai/_astro/backoffice-finance.B90JCO0E_tA3wm.webp", "alt": "Finance Flow", "w": 0, "h": 0 }, { "src": "https://thefocus.ai/_astro/backoffice-hiring.BnwT8WIi_Qyms1.webp", "alt": "Hiring Pipeline", "w": 0, "h": 0 }, { "src": "https://thefocus.ai/_astro/backoffice-clients.CIw2nOz7_ZzOrTO.webp", "alt": "Client Communication Flow", "w": 0, "h": 0 }, { "src": "https://thefocus.ai/_astro/backoffice-sales.BlvR7XrV_6tMqO.webp", "alt": "Sales Pipeline Flow", "w": 0, "h": 0 } ], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← Back to Case Studies PROFESSIONAL SERVICES 2-hour setup + 4 days integration 6 Commands Replace 10+ Hours/Week: Building an AI-Powered Back Office A complete back office system — project tracking, hiring pipeline, invoicing, client communication, sales CRM — running on Claude Code commands and markdown files. No databases. No SaaS subscriptions. Just AI reading and writing text. Client: TheFocus.AI (Internal Deployment) Year: 2026 Services: AI Operations Setup Stack: Claude Code, TheFocus.AI Plugin Marketplace, Markdown, GitHub CLI, Granola, Google Workspace, 1Password CLI Running on Habitat OS → KEY RESULTS ✓ 10+ hours/week of manual operations work eliminated within first week ✓ 50 job candidates researched, evaluated, and contacted in one weekend ✓ 6 active clients with auto-generated weekly status reports ✓ $150K+ in invoices tracked with automated payment follow-ups ✓ Morning briefings synthesized from 5+ data sources in seconds The Challenge Running a consulting firm means running two businesses: the work you do for clients, and the operations that keep the lights on. Most consultants handle operations the same way: a CRM for sales, a project management tool for delivery, a spreadsheet for invoicing, a folder somewhere for hiring. Maybe an HR platform. Maybe a newsletter tool. Each system has its own login, its own data model, its own way of doing things. The result is fragmentation. The sales call transcript lives in one place. The project scope lives in another. The invoice status lives in a third. When a client asks “where do we stand?” you’re alt-tabbing through five applications trying to assemble context. The specific pain points we faced: Morning chaos — Starting each day by checking Slack, email, calendar, project boards, and invoice spreadsheets separately. No unified view of what actually mattered. Hiring bottlenecks — 50 applications arrived over a weekend. Each deserved evaluation. The good candidates were emailing for updates while we were still reviewing the first ten. Status report procrastination — The weekly client update kept slipping. It wasn’t hard work, just friction-heavy. Pull up the project, check commits, review meetings, write something coherent, make it look professional. Invoice follow-up failures — Knowing money was outstanding but l", "height": 22583, "screenshots": { "desktop": "assets/screenshots/desktop/case-study--ai-back-office.png", "mobile": "assets/screenshots/mobile/case-study--ai-back-office.png" } }, { "url": "https://thefocus.ai/offerings/monthly-close", "path": "/offerings/monthly-close", "slug": "offerings--monthly-close", "category": "Offering", "status": 200, "title": "AI-Powered Monthly Close | TheFocus.AI", "metaDescription": "Your monthly close shouldn't take 10 days. We formalize the tribal knowledge in your close process and build AI systems that reconcile, flag anomalies, and route approvals automatically.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Your monthly close shouldn't take 10 days", "index": 1 }, { "tag": "h2", "text": "THE PROBLEM", "index": 2 }, { "tag": "h3", "text": "Sound familiar?", "index": 3 }, { "tag": "h2", "text": "OUR APPROACH", "index": 4 }, { "tag": "h3", "text": "How we fix this", "index": 5 }, { "tag": "h2", "text": "PROOF", "index": 6 }, { "tag": "h3", "text": "We've done this before", "index": 7 }, { "tag": "h2", "text": "RELATED OFFERINGS", "index": 8 }, { "tag": "h2", "text": "Ready to close the books faster?", "index": 9 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 10 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 11 }, { "tag": "h3", "text": "COMPANY", "index": 12 }, { "tag": "h3", "text": "PRODUCTS", "index": 13 }, { "tag": "h3", "text": "RESOURCES", "index": 14 }, { "tag": "h3", "text": "CONNECT", "index": 15 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Your monthly close shouldn't take 10 days", "y": 276, "body": "Every finance team knows the drill: reconcile across disconnected systems, chase down approvals, manually check entries, and hope nothing slips through. One person's absence breaks the whole timeline. It doesn't have to work this way. DISCUSS YOUR CLOSE PROCESS →" }, { "tag": "h2", "title": "THE PROBLEM", "y": 696, "body": "" }, { "tag": "h3", "title": "Sound familiar?", "y": 736, "body": "× Reconciliation across GL, sub-ledgers, and bank statements is manual — and it takes the first 3-4 days of every close. × Journal entries require hand-checking because the rules live in one person's head — the person who's been doing it for 8 years. × Approval routing is a chain of emails and Slack messages. When someone's on vacation, the whole process stalls. × Audit prep is a quarterly fire drill — you're rebuilding the paper trail from scratch every time because it wasn't captured as you went." }, { "tag": "h2", "title": "OUR APPROACH", "y": 1275, "body": "Built on Habitat OS →" }, { "tag": "h3", "title": "How we fix this", "y": 1314, "body": "We build a monthly-close Habitat — a system that carries the state of your close process, accumulates institutional memory, and matures from guided workflow to automated execution. The close checklist, exception rules, and approval logic that currently live in people's heads get formalized into a system that runs reliably every month. 1 Discover We audit your actual close process — not the documented version, the real one. Every workaround, exception, and judgment call gets mapped. 2 Structure Reconciliation rules, cost code mappings, and approval thresholds get formalized into schemas and validation rules that machines can follow. 3 Build An AI system that reconciles across your GL and sub-" }, { "tag": "h2", "title": "PROOF", "y": 1842, "body": "" }, { "tag": "h3", "title": "We've done this before", "y": 1882, "body": "57,000 transactions synced automatically QB Sync — automated construction project accounting from QuickBooks to Google Sheets. Tribal cost codes discovered and formalized into machine-readable rules. Read case study → 94.5% validated accuracy on invoice parsing Fountain Creek — AI-powered accounts payable for multi-location food service. Varied supplier invoices parsed and structured automatically. Read case study →" }, { "tag": "h2", "title": "RELATED OFFERINGS", "y": 2232, "body": "Invoice / AP Processing Handle any vendor format automatically → Executive Reporting Real-time answers, not reformatted dashboards →" }, { "tag": "h2", "title": "Ready to close the books faster?", "y": 2505, "body": "The conversation starts with understanding your current close process — the real version, not the documented one. TALK TO A PRINCIPAL SEE THE FULL FRAMEWORK" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 2841, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 3291, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 3668, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 3668, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 3668, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 3668, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "DISCUSS YOUR CLOSE PROCESS →", "href": "mailto:hey@thefocus.ai?subject=Monthly%20Close%20-%20Build%20%26%20Ship" }, { "text": "Built on Habitat OS →", "href": "https://thefocus.ai/habitat-os" }, { "text": "57,000 transactions synced automatically QB Sync — automated construction project accounting from QuickBooks to Google Sheets. Tribal cost codes discovered and formalized into machine-readable rules. Read case study →", "href": "https://thefocus.ai/case-study/qbsync" }, { "text": "94.5% validated accuracy on invoice parsing Fountain Creek — AI-powered accounts payable for multi-location food service. Varied supplier invoices parsed and structured automatically. Read case study →", "href": "https://thefocus.ai/case-study/fountain-creek" }, { "text": "Invoice / AP Processing Handle any vendor format automatically →", "href": "https://thefocus.ai/offerings/invoice-processing" }, { "text": "Executive Reporting Real-time answers, not reformatted dashboards →", "href": "https://thefocus.ai/offerings/executive-reporting" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Monthly%20Close%20-%20Build%20%26%20Ship" }, { "text": "SEE THE FULL FRAMEWORK", "href": "https://thefocus.ai/capabilities" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "mailto:hey@thefocus.ai?subject=Monthly%20Close%20-%20Build%20%26%20Ship", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-study/qbsync", "https://thefocus.ai/case-study/fountain-creek", "https://thefocus.ai/offerings/invoice-processing", "https://thefocus.ai/offerings/executive-reporting", "mailto:hey@thefocus.ai?subject=Monthly%20Close%20-%20Build%20%26%20Ship", "https://thefocus.ai/capabilities", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] L1 → L2 PROCESS AUDIT + KNOWLEDGE LAYER Your monthly close shouldn't take 10 days Every finance team knows the drill: reconcile across disconnected systems, chase down approvals, manually check entries, and hope nothing slips through. One person's absence breaks the whole timeline. It doesn't have to work this way. DISCUSS YOUR CLOSE PROCESS → THE PROBLEM Sound familiar? × Reconciliation across GL, sub-ledgers, and bank statements is manual — and it takes the first 3-4 days of every close. × Journal entries require hand-checking because the rules live in one person's head — the person who's been doing it for 8 years. × Approval routing is a chain of emails and Slack messages. When someone's on vacation, the whole process stalls. × Audit prep is a quarterly fire drill — you're rebuilding the paper trail from scratch every time because it wasn't captured as you went. OUR APPROACH Built on Habitat OS → How we fix this We build a monthly-close Habitat — a system that carries the state of your close process, accumulates institutional memory, and matures from guided workflow to automated execution. The close checklist, exception rules, and approval logic that currently live in people's heads get formalized into a system that runs reliably every month. 1 Discover We audit your actual close process — not the documented version, the real one. Every workaround, exception, and judgment call gets mapped. 2 Structure Reconciliation rules, cost code mappings, and approval thresholds get formalized into schemas and validation rules that machines can follow. 3 Build An AI system that reconciles across your GL and sub-ledgers, flags anomalies with explanations, routes approvals automatically, and builds the audit trail as it goes. PROOF We've done this before 57,000 transactions synced automatically QB Sync — automated construction project accounting from QuickBooks to Google Sheets. Tribal cost codes discovered and formalized into machine-readable rules. Read case study → 94.5% validated accuracy on invoice parsing Fountain Creek — AI-powered accounts payable for multi-location food service. Varied supplier invoices parsed and structured automatically. Read case study → RELATED OFFERINGS Invoice / AP Processing Handle any vendor format automatically → Executive Reporting Real-time answ", "height": 4165, "screenshots": { "desktop": "assets/screenshots/desktop/offerings--monthly-close.png", "mobile": "assets/screenshots/mobile/offerings--monthly-close.png" } }, { "url": "https://thefocus.ai/offerings/hiring-pipeline", "path": "/offerings/hiring-pipeline", "slug": "offerings--hiring-pipeline", "category": "Offering", "status": 200, "title": "AI-Powered Hiring Pipeline | TheFocus.AI", "metaDescription": "Stop losing candidates to your own process. We formalize the tribal knowledge in your hiring workflow and build AI systems that keep candidates moving from intake to onboarded.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Stop losing candidates to your own process", "index": 1 }, { "tag": "h2", "text": "THE PROBLEM", "index": 2 }, { "tag": "h3", "text": "Sound familiar?", "index": 3 }, { "tag": "h2", "text": "OUR APPROACH", "index": 4 }, { "tag": "h3", "text": "How we fix this", "index": 5 }, { "tag": "h2", "text": "PROOF", "index": 6 }, { "tag": "h3", "text": "We've done this before", "index": 7 }, { "tag": "h2", "text": "RELATED OFFERINGS", "index": 8 }, { "tag": "h2", "text": "Ready to stop losing candidates to process?", "index": 9 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 10 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 11 }, { "tag": "h3", "text": "COMPANY", "index": 12 }, { "tag": "h3", "text": "PRODUCTS", "index": 13 }, { "tag": "h3", "text": "RESOURCES", "index": 14 }, { "tag": "h3", "text": "CONNECT", "index": 15 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Stop losing candidates to your own process", "y": 276, "body": "Your hiring process has more handoffs than your product pipeline. Intake forms are inconsistent, screening criteria live in the hiring manager's head, and the gap between \"offer accepted\" and \"fully onboarded\" is where context goes to die. DISCUSS YOUR HIRING PROCESS →" }, { "tag": "h2", "title": "THE PROBLEM", "y": 696, "body": "" }, { "tag": "h3", "title": "Sound familiar?", "y": 736, "body": "× Every hiring manager writes job descriptions from scratch — or worse, copy-pastes old ones that no longer reflect the role. × Screening criteria for \"good fit\" live entirely in the hiring manager's head. When they're out, the recruiter guesses. × The handoff from recruiter to hiring manager to HR to IT is a chain of emails where context evaporates at every step. × Onboarding touches 6+ systems — payroll, benefits, equipment, access provisioning — and nobody owns the full checklist. Things fall through the cracks every single time." }, { "tag": "h2", "title": "OUR APPROACH", "y": 1275, "body": "Built on Habitat OS →" }, { "tag": "h3", "title": "How we fix this", "y": 1314, "body": "We build a hiring-pipeline Habitat — a system that captures what \"good\" looks like for each role, structures the intake-to-onboarded workflow, and ensures nothing falls through the handoff chain. The tribal knowledge about screening, culture fit, and onboarding steps gets formalized into a system that runs consistently regardless of who's involved. 1 Discover We map your actual hiring workflow — every handoff, every system, every place where tribal knowledge fills the gap. We interview hiring managers to extract what \"good\" really means for each role. 2 Structure Role requirements, screening criteria, and onboarding checklists get formalized into structured templates. The handoff chain gets " }, { "tag": "h2", "title": "PROOF", "y": 1862, "body": "" }, { "tag": "h3", "title": "We've done this before", "y": 1902, "body": "10+ hrs of weekly back-office work replaced by 6 commands AI Back Office — hiring, invoicing, CRM, and communications run through structured AI commands instead of manual processes. Every interaction teaches the system. Read case study → 15 min → 30 sec benefits document extraction time Onboarding — complex benefits and SBC documents extracted against a unified schema, cutting manual data entry from the new-hire setup process. Read case study →" }, { "tag": "h2", "title": "RELATED OFFERINGS", "y": 2272, "body": "Customer Onboarding Formalize the cross-team handoff chain → Monthly Close Stop the 10-day scramble →" }, { "tag": "h2", "title": "Ready to stop losing candidates to process?", "y": 2545, "body": "The conversation starts with mapping your actual hiring workflow — every handoff, every system, every gap where tribal knowledge fills in. TALK TO A PRINCIPAL SEE THE FULL FRAMEWORK" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 2881, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 3331, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 3708, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 3708, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 3708, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 3708, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "DISCUSS YOUR HIRING PROCESS →", "href": "mailto:hey@thefocus.ai?subject=Hiring%20Pipeline%20-%20Assessment%20%26%20Roadmap" }, { "text": "Built on Habitat OS →", "href": "https://thefocus.ai/habitat-os" }, { "text": "10+ hrs of weekly back-office work replaced by 6 commands AI Back Office — hiring, invoicing, CRM, and communications run through structured AI commands instead of manual processes. Every interaction teaches the system. Read case study →", "href": "https://thefocus.ai/case-study/ai-back-office" }, { "text": "15 min → 30 sec benefits document extraction time Onboarding — complex benefits and SBC documents extracted against a unified schema, cutting manual data entry from the new-hire setup process. Read case study →", "href": "https://thefocus.ai/case-study/onboarding" }, { "text": "Customer Onboarding Formalize the cross-team handoff chain →", "href": "https://thefocus.ai/offerings/customer-onboarding" }, { "text": "Monthly Close Stop the 10-day scramble →", "href": "https://thefocus.ai/offerings/monthly-close" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Hiring%20Pipeline%20-%20Assessment%20%26%20Roadmap" }, { "text": "SEE THE FULL FRAMEWORK", "href": "https://thefocus.ai/capabilities" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "mailto:hey@thefocus.ai?subject=Hiring%20Pipeline%20-%20Assessment%20%26%20Roadmap", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-study/ai-back-office", "https://thefocus.ai/case-study/onboarding", "https://thefocus.ai/offerings/customer-onboarding", "https://thefocus.ai/offerings/monthly-close", "mailto:hey@thefocus.ai?subject=Hiring%20Pipeline%20-%20Assessment%20%26%20Roadmap", "https://thefocus.ai/capabilities", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] L1 → L2 PROCESS AUDIT & FORMALIZATION Stop losing candidates to your own process Your hiring process has more handoffs than your product pipeline. Intake forms are inconsistent, screening criteria live in the hiring manager's head, and the gap between \"offer accepted\" and \"fully onboarded\" is where context goes to die. DISCUSS YOUR HIRING PROCESS → THE PROBLEM Sound familiar? × Every hiring manager writes job descriptions from scratch — or worse, copy-pastes old ones that no longer reflect the role. × Screening criteria for \"good fit\" live entirely in the hiring manager's head. When they're out, the recruiter guesses. × The handoff from recruiter to hiring manager to HR to IT is a chain of emails where context evaporates at every step. × Onboarding touches 6+ systems — payroll, benefits, equipment, access provisioning — and nobody owns the full checklist. Things fall through the cracks every single time. OUR APPROACH Built on Habitat OS → How we fix this We build a hiring-pipeline Habitat — a system that captures what \"good\" looks like for each role, structures the intake-to-onboarded workflow, and ensures nothing falls through the handoff chain. The tribal knowledge about screening, culture fit, and onboarding steps gets formalized into a system that runs consistently regardless of who's involved. 1 Discover We map your actual hiring workflow — every handoff, every system, every place where tribal knowledge fills the gap. We interview hiring managers to extract what \"good\" really means for each role. 2 Structure Role requirements, screening criteria, and onboarding checklists get formalized into structured templates. The handoff chain gets defined with clear triggers and accountability. 3 Build An AI system that generates consistent job descriptions from role templates, screens candidates against formalized criteria, orchestrates the handoff chain, and drives onboarding to completion across every system it touches. PROOF We've done this before 10+ hrs of weekly back-office work replaced by 6 commands AI Back Office — hiring, invoicing, CRM, and communications run through structured AI commands instead of manual processes. Every interaction teaches the system. Read case study → 15 min → 30 sec benefits document extraction time Onboarding — complex benefits and SBC docum", "height": 4205, "screenshots": { "desktop": "assets/screenshots/desktop/offerings--hiring-pipeline.png", "mobile": "assets/screenshots/mobile/offerings--hiring-pipeline.png" } }, { "url": "https://thefocus.ai/offerings/invoice-processing", "path": "/offerings/invoice-processing", "slug": "offerings--invoice-processing", "category": "Offering", "status": 200, "title": "AI-Powered Invoice & AP Processing | TheFocus.AI", "metaDescription": "Every invoice is different. Your system should handle that. We build AI that parses any invoice format, matches to POs, routes approvals, and feeds your GL — with 94.5% validated accuracy.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Every invoice is different. Your system should handle that.", "index": 1 }, { "tag": "h2", "text": "THE PROBLEM", "index": 2 }, { "tag": "h3", "text": "Sound familiar?", "index": 3 }, { "tag": "h2", "text": "OUR APPROACH", "index": 4 }, { "tag": "h3", "text": "How we fix this", "index": 5 }, { "tag": "h2", "text": "PROOF", "index": 6 }, { "tag": "h3", "text": "We've done this before", "index": 7 }, { "tag": "h2", "text": "RELATED OFFERINGS", "index": 8 }, { "tag": "h2", "text": "Ready to stop keying invoices by hand?", "index": 9 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 10 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 11 }, { "tag": "h3", "text": "COMPANY", "index": 12 }, { "tag": "h3", "text": "PRODUCTS", "index": 13 }, { "tag": "h3", "text": "RESOURCES", "index": 14 }, { "tag": "h3", "text": "CONNECT", "index": 15 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Every invoice is different. Your system should handle that.", "y": 276, "body": "Invoices arrive in 15 different formats. Someone reads each one, keys it into accounting, matches it to a PO, and routes it for approval. Every step is manual, every step is a chance for error, and month-end is always a scramble. DISCUSS YOUR AP PROCESS →" }, { "tag": "h2", "title": "THE PROBLEM", "y": 696, "body": "" }, { "tag": "h3", "title": "Sound familiar?", "y": 736, "body": "× Every vendor sends invoices in a different format — PDFs, emails, scanned images, spreadsheets. Someone has to read each one and key it into the accounting system by hand. × PO matching is error-prone and time-consuming. Line items don't always match exactly, and the rules for when to accept a variance live in the AP clerk's head. × Approval routing depends on amount thresholds, department codes, and vendor relationships that aren't documented anywhere — they're just \"how we do it.\" × Month-end means a scramble to process the backlog. Duplicate invoices slip through. Discounts get missed. The close is delayed because AP isn't done." }, { "tag": "h2", "title": "OUR APPROACH", "y": 1275, "body": "Built on Habitat OS →" }, { "tag": "h3", "title": "How we fix this", "y": 1314, "body": "We build an accounts-payable Habitat — a document ingestion pipeline that parses any invoice format, matches to purchase orders using formalized business rules, routes for approval based on your actual (not theoretical) thresholds, and feeds structured data directly into your general ledger. The tribal knowledge about vendor exceptions, variance tolerances, and cost code mappings gets captured once and applied consistently forever. 1 Discover We audit your AP workflow — every vendor format, every exception rule, every approval threshold. We find the tribal knowledge that makes the process actually work (and breaks it when someone's out). 2 Structure Invoice fields get mapped to a canonical s" }, { "tag": "h2", "title": "PROOF", "y": 1868, "body": "" }, { "tag": "h3", "title": "We've done this before", "y": 1908, "body": "94.5% validated accuracy on invoice parsing Fountain Creek — AI-powered accounts payable for multi-location food service. Varied supplier PDF invoices parsed and structured via a Notion-based submission workflow. Read case study → 57,000 transactions synced automatically QB Sync — automated construction project accounting. QuickBooks Online transactions synced hourly to Google Sheets with per-project cost visibility and reconciliation. Read case study →" }, { "tag": "h2", "title": "RELATED OFFERINGS", "y": 2278, "body": "Monthly Close Stop the 10-day scramble → Executive Reporting Real-time answers, not reformatted dashboards →" }, { "tag": "h2", "title": "Ready to stop keying invoices by hand?", "y": 2551, "body": "The conversation starts with understanding your vendor landscape — how many formats, what systems, and where the tribal knowledge lives. TALK TO A PRINCIPAL SEE THE FULL FRAMEWORK" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 2915, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 3365, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 3742, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 3742, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 3742, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 3742, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "DISCUSS YOUR AP PROCESS →", "href": "mailto:hey@thefocus.ai?subject=Invoice%20Processing%20-%20Build%20%26%20Ship" }, { "text": "Built on Habitat OS →", "href": "https://thefocus.ai/habitat-os" }, { "text": "94.5% validated accuracy on invoice parsing Fountain Creek — AI-powered accounts payable for multi-location food service. Varied supplier PDF invoices parsed and structured via a Notion-based submission workflow. Read case study →", "href": "https://thefocus.ai/case-study/fountain-creek" }, { "text": "57,000 transactions synced automatically QB Sync — automated construction project accounting. QuickBooks Online transactions synced hourly to Google Sheets with per-project cost visibility and reconciliation. Read case study →", "href": "https://thefocus.ai/case-study/qbsync" }, { "text": "Monthly Close Stop the 10-day scramble →", "href": "https://thefocus.ai/offerings/monthly-close" }, { "text": "Executive Reporting Real-time answers, not reformatted dashboards →", "href": "https://thefocus.ai/offerings/executive-reporting" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Invoice%20Processing%20-%20Build%20%26%20Ship" }, { "text": "SEE THE FULL FRAMEWORK", "href": "https://thefocus.ai/capabilities" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "mailto:hey@thefocus.ai?subject=Invoice%20Processing%20-%20Build%20%26%20Ship", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-study/fountain-creek", "https://thefocus.ai/case-study/qbsync", "https://thefocus.ai/offerings/monthly-close", "https://thefocus.ai/offerings/executive-reporting", "mailto:hey@thefocus.ai?subject=Invoice%20Processing%20-%20Build%20%26%20Ship", "https://thefocus.ai/capabilities", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] L1 → L2 KNOWLEDGE LAYER Every invoice is different. Your system should handle that. Invoices arrive in 15 different formats. Someone reads each one, keys it into accounting, matches it to a PO, and routes it for approval. Every step is manual, every step is a chance for error, and month-end is always a scramble. DISCUSS YOUR AP PROCESS → THE PROBLEM Sound familiar? × Every vendor sends invoices in a different format — PDFs, emails, scanned images, spreadsheets. Someone has to read each one and key it into the accounting system by hand. × PO matching is error-prone and time-consuming. Line items don't always match exactly, and the rules for when to accept a variance live in the AP clerk's head. × Approval routing depends on amount thresholds, department codes, and vendor relationships that aren't documented anywhere — they're just \"how we do it.\" × Month-end means a scramble to process the backlog. Duplicate invoices slip through. Discounts get missed. The close is delayed because AP isn't done. OUR APPROACH Built on Habitat OS → How we fix this We build an accounts-payable Habitat — a document ingestion pipeline that parses any invoice format, matches to purchase orders using formalized business rules, routes for approval based on your actual (not theoretical) thresholds, and feeds structured data directly into your general ledger. The tribal knowledge about vendor exceptions, variance tolerances, and cost code mappings gets captured once and applied consistently forever. 1 Discover We audit your AP workflow — every vendor format, every exception rule, every approval threshold. We find the tribal knowledge that makes the process actually work (and breaks it when someone's out). 2 Structure Invoice fields get mapped to a canonical schema. PO matching rules, variance tolerances, and approval routing logic get formalized. Cost codes and GL mappings get documented. 3 Build An AI pipeline that ingests invoices in any format, extracts structured data with validated accuracy, matches to POs, flags anomalies, routes approvals, and posts to your GL — building the audit trail as it goes. PROOF We've done this before 94.5% validated accuracy on invoice parsing Fountain Creek — AI-powered accounts payable for multi-location food service. Varied supplier PDF invoices parsed and stru", "height": 4239, "screenshots": { "desktop": "assets/screenshots/desktop/offerings--invoice-processing.png", "mobile": "assets/screenshots/mobile/offerings--invoice-processing.png" } }, { "url": "https://thefocus.ai/offerings/customer-onboarding", "path": "/offerings/customer-onboarding", "slug": "offerings--customer-onboarding", "category": "Offering", "status": 200, "title": "AI-Powered Customer Onboarding | TheFocus.AI", "metaDescription": "New client setup shouldn't depend on who remembers what. 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Every new client feels like the first one. It shouldn't. DISCUSS YOUR ONBOARDING PROCESS →" }, { "tag": "h2", "title": "THE PROBLEM", "y": 756, "body": "" }, { "tag": "h3", "title": "Sound familiar?", "y": 796, "body": "× Onboarding involves Sales, CS, Legal, Finance, IT, and Operations — and nobody owns the end-to-end process. Each team thinks someone else is handling the handoff. × Client documents — contracts, compliance forms, configuration specs — arrive in different formats and sit in someone's inbox until they manually extract what's needed. × Time-to-value stretches because setup stalls at unpredictable points. The client gets frustrated, CS scrambles, and the relationship starts on the wrong foot. × Every client feels like the first one because the process isn't formalized. The person who \"just knows how it works\" is a single point of failure — and they're burning out." }, { "tag": "h2", "title": "OUR APPROACH", "y": 1335, "body": "Built on Habitat OS →" }, { "tag": "h3", "title": "How we fix this", "y": 1374, "body": "We build a customer-onboarding Habitat — a system that formalizes your intake workflow, automatically extracts and structures client documents, and orchestrates the cross-departmental handoff chain so every step triggers the next. The tribal knowledge about \"how onboarding actually works\" gets captured and made repeatable. New clients get the benefit of your hundredth onboarding, not a reinvention every time. 1 Discover We map your actual onboarding workflow across every department it touches — every handoff, every document, every place where someone fills in the gaps from memory. We find the bottlenecks and the single points of failure. 2 Structure Client document schemas get defined. The h" }, { "tag": "h2", "title": "PROOF", "y": 1968, "body": "" }, { "tag": "h3", "title": "We've done this before", "y": 2008, "body": "15 min → 30 sec benefits document extraction time Onboarding — complex benefits and SBC documents extracted against a unified schema. What used to require 15 minutes of manual data entry per document now takes 30 seconds with AI. Read case study → 27% higher conversion after AI layer added TezLab — a conversational AI layer added on top of the existing backend without changing infrastructure. Proof that you can enhance onboarding without rebuilding. Read case study →" }, { "tag": "h2", "title": "RELATED OFFERINGS", "y": 2378, "body": "Hiring Pipeline Stop losing candidates to process → Invoice / AP Processing Handle any vendor format automatically →" }, { "tag": "h2", "title": "Ready to make every onboarding feel like your hundredth?", "y": 2651, "body": "The conversation starts with mapping your actual onboarding workflow — every department, every handoff, every gap where tribal knowledge fills in. TALK TO A PRINCIPAL SEE THE FULL FRAMEWORK" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 3055, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 3505, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. 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Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "DISCUSS YOUR ONBOARDING PROCESS →", "href": "mailto:hey@thefocus.ai?subject=Customer%20Onboarding%20-%20Assessment%20%26%20Roadmap" }, { "text": "Built on Habitat OS →", "href": "https://thefocus.ai/habitat-os" }, { "text": "15 min → 30 sec benefits document extraction time Onboarding — complex benefits and SBC documents extracted against a unified schema. What used to require 15 minutes of manual data entry per document now takes 30 seconds with AI. Read case study →", "href": "https://thefocus.ai/case-study/onboarding" }, { "text": "27% higher conversion after AI layer added TezLab — a conversational AI layer added on top of the existing backend without changing infrastructure. Proof that you can enhance onboarding without rebuilding. Read case study →", "href": "https://thefocus.ai/case-study/tezlab-ai" }, { "text": "Hiring Pipeline Stop losing candidates to process →", "href": "https://thefocus.ai/offerings/hiring-pipeline" }, { "text": "Invoice / AP Processing Handle any vendor format automatically →", "href": "https://thefocus.ai/offerings/invoice-processing" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Customer%20Onboarding%20-%20Assessment%20%26%20Roadmap" }, { "text": "SEE THE FULL FRAMEWORK", "href": "https://thefocus.ai/capabilities" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "mailto:hey@thefocus.ai?subject=Customer%20Onboarding%20-%20Assessment%20%26%20Roadmap", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-study/onboarding", "https://thefocus.ai/case-study/tezlab-ai", "https://thefocus.ai/offerings/hiring-pipeline", "https://thefocus.ai/offerings/invoice-processing", "mailto:hey@thefocus.ai?subject=Customer%20Onboarding%20-%20Assessment%20%26%20Roadmap", "https://thefocus.ai/capabilities", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] L1 → L3 PROCESS AUDIT → INSIGHT ENGINE New client setup shouldn't depend on who remembers what Your onboarding process touches six departments, runs on a checklist that lives in someone's email, and relies on a handoff chain where things routinely fall through the cracks. Every new client feels like the first one. It shouldn't. DISCUSS YOUR ONBOARDING PROCESS → THE PROBLEM Sound familiar? × Onboarding involves Sales, CS, Legal, Finance, IT, and Operations — and nobody owns the end-to-end process. Each team thinks someone else is handling the handoff. × Client documents — contracts, compliance forms, configuration specs — arrive in different formats and sit in someone's inbox until they manually extract what's needed. × Time-to-value stretches because setup stalls at unpredictable points. The client gets frustrated, CS scrambles, and the relationship starts on the wrong foot. × Every client feels like the first one because the process isn't formalized. The person who \"just knows how it works\" is a single point of failure — and they're burning out. OUR APPROACH Built on Habitat OS → How we fix this We build a customer-onboarding Habitat — a system that formalizes your intake workflow, automatically extracts and structures client documents, and orchestrates the cross-departmental handoff chain so every step triggers the next. The tribal knowledge about \"how onboarding actually works\" gets captured and made repeatable. New clients get the benefit of your hundredth onboarding, not a reinvention every time. 1 Discover We map your actual onboarding workflow across every department it touches — every handoff, every document, every place where someone fills in the gaps from memory. We find the bottlenecks and the single points of failure. 2 Structure Client document schemas get defined. The handoff chain gets formalized with triggers, SLAs, and accountability. Configuration requirements get templatized so setup is consistent. 3 Build An AI system that extracts structured data from client documents, orchestrates the cross-team workflow, flags stalled steps before they become problems, and gives everyone visibility into where each client stands — without a single status-update meeting. PROOF We've done this before 15 min → 30 sec benefits document extraction time Onboarding — comp", "height": 4379, "screenshots": { "desktop": "assets/screenshots/desktop/offerings--customer-onboarding.png", "mobile": "assets/screenshots/mobile/offerings--customer-onboarding.png" } }, { "url": "https://thefocus.ai/offerings/executive-reporting", "path": "/offerings/executive-reporting", "slug": "offerings--executive-reporting", "category": "Offering", "status": 200, "title": "AI-Powered Executive Reporting | TheFocus.AI", "metaDescription": "Your executives shouldn't wait for slides. We build AI systems that let leadership self-serve insights from connected data sources — real answers in seconds, not reformatted dashboards.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Your executives shouldn't wait for slides", "index": 1 }, { "tag": "h2", "text": "THE PROBLEM", "index": 2 }, { "tag": "h3", "text": "Sound familiar?", "index": 3 }, { "tag": "h2", "text": "OUR APPROACH", "index": 4 }, { "tag": "h3", "text": "How we fix this", "index": 5 }, { "tag": "h2", "text": "PROOF", "index": 6 }, { "tag": "h3", "text": "We've done this before", "index": 7 }, { "tag": "h2", "text": "RELATED OFFERINGS", "index": 8 }, { "tag": "h2", "text": "Ready to give your executives real-time answers?", "index": 9 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 10 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 11 }, { "tag": "h3", "text": "COMPANY", "index": 12 }, { "tag": "h3", "text": "PRODUCTS", "index": 13 }, { "tag": "h3", "text": "RESOURCES", "index": 14 }, { "tag": "h3", "text": "CONNECT", "index": 15 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Your executives shouldn't wait for slides", "y": 276, "body": "Department heads spend hours pulling data from five dashboards, reformatting into slides, and presenting numbers that are already stale. The CEO still asks \"but what does this mean?\" You can skip all of that. DISCUSS YOUR REPORTING NEEDS →" }, { "tag": "h2", "title": "THE PROBLEM", "y": 696, "body": "" }, { "tag": "h3", "title": "Sound familiar?", "y": 736, "body": "× Department heads spend 4-6 hours every week pulling numbers from multiple dashboards and reformatting them into slide decks nobody reads carefully. × By the time the report reaches the leadership meeting, the data is a week old. Decisions are made on stale information. × The CEO asks a follow-up question and the answer is \"I'll get back to you\" — triggering another round of data pulling that takes another day. × Different departments use different definitions for the same metrics. Revenue numbers from Sales don't match Finance. Nobody trusts the dashboards." }, { "tag": "h2", "title": "OUR APPROACH", "y": 1275, "body": "Built on Habitat OS →" }, { "tag": "h3", "title": "How we fix this", "y": 1314, "body": "We build an executive-intelligence Habitat — a system that connects to your data sources, establishes canonical metric definitions, and lets leadership query the business in natural language. Instead of waiting for slides, executives ask questions and get traced, source-attributed answers in seconds. The system learns which questions matter and starts surfacing insights proactively. 1 Discover We audit your reporting landscape — every dashboard, every spreadsheet, every manually assembled deck. We find where metric definitions diverge and where the real questions live. 2 Structure Canonical metric definitions get established. Data sources get connected into a unified semantic layer. The \"wha" }, { "tag": "h2", "title": "PROOF", "y": 1842, "body": "" }, { "tag": "h3", "title": "We've done this before", "y": 1882, "body": "500M+ tweets per day filtered into 5-minute briefings Distill — AI-powered content intelligence across Twitter, YouTube, and podcasts. From information overload to curated executive-ready briefings. Read case study → 78% faster talent decisions Upper Hand — executives self-serve insights that previously required analyst teams. Survey, social, and brand data unified into natural language queries. Read case study →" }, { "tag": "h2", "title": "RELATED OFFERINGS", "y": 2232, "body": "Monthly Close Stop the 10-day scramble → Customer Onboarding Formalize the cross-team handoff chain →" }, { "tag": "h2", "title": "Ready to give your executives real-time answers?", "y": 2505, "body": "The conversation starts with understanding what questions your leadership actually asks — and where the answers currently come from. TALK TO A PRINCIPAL SEE THE FULL FRAMEWORK" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 2869, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 3319, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 3696, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 3696, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 3696, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 3696, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "DISCUSS YOUR REPORTING NEEDS →", "href": "mailto:hey@thefocus.ai?subject=Executive%20Reporting%20-%20Build%20%26%20Ship" }, { "text": "Built on Habitat OS →", "href": "https://thefocus.ai/habitat-os" }, { "text": "500M+ tweets per day filtered into 5-minute briefings Distill — AI-powered content intelligence across Twitter, YouTube, and podcasts. From information overload to curated executive-ready briefings. Read case study →", "href": "https://thefocus.ai/case-study/distill" }, { "text": "78% faster talent decisions Upper Hand — executives self-serve insights that previously required analyst teams. Survey, social, and brand data unified into natural language queries. Read case study →", "href": "https://thefocus.ai/case-study/upperhand-ai" }, { "text": "Monthly Close Stop the 10-day scramble →", "href": "https://thefocus.ai/offerings/monthly-close" }, { "text": "Customer Onboarding Formalize the cross-team handoff chain →", "href": "https://thefocus.ai/offerings/customer-onboarding" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Executive%20Reporting%20-%20Build%20%26%20Ship" }, { "text": "SEE THE FULL FRAMEWORK", "href": "https://thefocus.ai/capabilities" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "mailto:hey@thefocus.ai?subject=Executive%20Reporting%20-%20Build%20%26%20Ship", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-study/distill", "https://thefocus.ai/case-study/upperhand-ai", "https://thefocus.ai/offerings/monthly-close", "https://thefocus.ai/offerings/customer-onboarding", "mailto:hey@thefocus.ai?subject=Executive%20Reporting%20-%20Build%20%26%20Ship", "https://thefocus.ai/capabilities", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] L2 → L3 INSIGHT ENGINE Your executives shouldn't wait for slides Department heads spend hours pulling data from five dashboards, reformatting into slides, and presenting numbers that are already stale. The CEO still asks \"but what does this mean?\" You can skip all of that. DISCUSS YOUR REPORTING NEEDS → THE PROBLEM Sound familiar? × Department heads spend 4-6 hours every week pulling numbers from multiple dashboards and reformatting them into slide decks nobody reads carefully. × By the time the report reaches the leadership meeting, the data is a week old. Decisions are made on stale information. × The CEO asks a follow-up question and the answer is \"I'll get back to you\" — triggering another round of data pulling that takes another day. × Different departments use different definitions for the same metrics. Revenue numbers from Sales don't match Finance. Nobody trusts the dashboards. OUR APPROACH Built on Habitat OS → How we fix this We build an executive-intelligence Habitat — a system that connects to your data sources, establishes canonical metric definitions, and lets leadership query the business in natural language. Instead of waiting for slides, executives ask questions and get traced, source-attributed answers in seconds. The system learns which questions matter and starts surfacing insights proactively. 1 Discover We audit your reporting landscape — every dashboard, every spreadsheet, every manually assembled deck. We find where metric definitions diverge and where the real questions live. 2 Structure Canonical metric definitions get established. Data sources get connected into a unified semantic layer. The \"what does this mean?\" question gets answered once and codified. 3 Build A conversational intelligence layer where executives ask questions in plain language and get answers with sources. Proactive briefings surface what matters before anyone asks. Every answer traces back to specific data. PROOF We've done this before 500M+ tweets per day filtered into 5-minute briefings Distill — AI-powered content intelligence across Twitter, YouTube, and podcasts. From information overload to curated executive-ready briefings. Read case study → 78% faster talent decisions Upper Hand — executives self-serve insights that previously required analyst teams. Survey, social", "height": 4193, "screenshots": { "desktop": "assets/screenshots/desktop/offerings--executive-reporting.png", "mobile": "assets/screenshots/mobile/offerings--executive-reporting.png" } }, { "url": "https://thefocus.ai/reports/coding-agent", "path": "/reports/coding-agent", "slug": "reports--coding-agent", "category": "Report/deep-dive", "status": 200, "title": "Weekend Coding Agent | TheFocus.AI Labs", "metaDescription": "", "headings": [ { "tag": "h1", "text": "WEEKEND CODING AGENT", "index": 0 }, { "tag": "h2", "text": "WHAT YOU’LL BUILD", "index": 1 }, { "tag": "h2", "text": "WHY BUILD THIS?", "index": 2 }, { "tag": "h2", "text": "HOW AGENTS ACTUALLY WORK", "index": 3 }, { "tag": "h3", "text": "The Agent Loop", "index": 4 }, { "tag": "h3", "text": "The Harness: Prompts + Tools + Model", "index": 5 }, { "tag": "h3", "text": "Key Concepts", "index": 6 }, { "tag": "h3", "text": "What Makes a Good Agent?", "index": 7 }, { "tag": "h3", "text": "No really", "index": 8 }, { "tag": "h3", "text": "Ok but..", "index": 9 }, { "tag": "h2", "text": "WHAT YOU’LL UNDERSTAND AFTER", "index": 10 }, { "tag": "h2", "text": "LETS GO!", "index": 11 }, { "tag": "h2", "text": "INDEX OF OPERATIONS", "index": 12 }, { "tag": "h3", "text": "ENVIRONMENT AND BOOTSTRAP AGENT", "index": 13 }, { "tag": "h3", "text": "PROJECT SCAFFOLDING AND LINT/TEST", "index": 14 }, { "tag": "h3", "text": "COST AND CONTEXT AWARENESS", "index": 15 }, { "tag": "h3", "text": "SESSION LOGGING", "index": 16 }, { "tag": "h3", "text": "ADD CUSTOM TOOLS", "index": 17 }, { "tag": "h3", "text": "CLEANUP AND REFACTORING", "index": 18 }, { "tag": "h3", "text": "EXPLAINING PROMPTING SYSTEM", "index": 19 }, { "tag": "h3", "text": "SUBAGENTS: CODE-MAP", "index": 20 }, { "tag": "h3", "text": "TOOLS: SEARCHING", "index": 21 }, { "tag": "h3", "text": "AGENTS AND SKILLS", "index": 22 }, { "tag": "h3", "text": "IMAGES AND VIDEO", "index": 23 }, { "tag": "h3", "text": "CONTINUE YOUR JOURNEY", "index": 24 }, { "tag": "h3", "text": "Get Weekend Projects", "index": 25 } ], "sections": [ { "tag": "h1", "title": "WEEKEND CODING AGENT", "y": 249, "body": "A comprehensive technical manual. Follow the steps to master the workflow. Ever wonder exactly how vibe coding works? Lets learn how to build a coding agent without writing a single line of code. This tutorial will get you right into the core loop of how software agents work. All you need is a terminal (available free with your computer) and an openrouter api access key to get access to a model. We’ll walk you through how to bootstrap the environment, get the agent up and running, and then add a whole lot of features. The trick here is that we are going to use this agent to build itself, and we’ll go through all of the commands you’ll copy and paste over to direct it’s development. No framew" }, { "tag": "h2", "title": "WHAT YOU’LL BUILD", "y": 1187, "body": "By the end of this tutorial, you’ll have a fully functional AI coding assistant that can: Navigate and understand your codebase Edit files with precision using structured diff tools Support user defined custom skills to extend functionality Self monitor the quality of it’s code base Generate images and videos Search the web for documentation and solutions Spawn specialized sub-agents for focused tasks Track costs so you don’t blow your API budget Log sessions for debugging and improvement More importantly, you’ll understand why each piece exists and how they fit together. And you’ll be able to do it just using english prompts, an honestly reading the code is almost optional at this point. Cr" }, { "tag": "h2", "title": "WHY BUILD THIS?", "y": 1924, "body": "There’s a fear out there about how intellectual work will change now that we have intelligence on tap. People have valid concerns, but most of the arguments I see are borne out of fear and ignorance, and so I wanted to make a tutorial that everyone could go through to get some hands-on experience on what these tools can literally do, and how that fits into your working process. With this tutorial you can join me in step-by-step building of a piece of software, one that is smart enough to build itself. I’m going to give you the prompts — which means that I thought up what to ask it to do, I have an idea of what we are going to build and how it’s going to work. If you aren’t an experienced sof" }, { "tag": "h2", "title": "HOW AGENTS ACTUALLY WORK", "y": 2922, "body": "Before diving in, let’s demystify what’s happening under the hood. Every AI coding assistant—Cursor, Claude Code, Copilot, or what we’re building—follows the same pattern." }, { "tag": "h3", "title": "The Agent Loop", "y": 3112, "body": "It’s a loop. You say something, the LLM responses, if it asks for a tool you run it, and the loop goes around and around. User Enters input Send to the LLM LLM Runs the simulation, returns tokens 3a. Includes tool call? Call the tool, go back to step #2 Shows the result to the user Go to step 1. That’s it. The “AI” part is the LLM. Everything else is plumbing." }, { "tag": "h3", "title": "The Harness: Prompts + Tools + Model", "y": 3570, "body": "What we are building here is called a harness. This is a combination of a prompt, tools, and a model. Think of it as three layers working together: Layer What It Is What It Does Model The LLM (Claude, GPT, Gemini) The “brain”—understands intent, generates responses, decides what tools to call Tools Functions the model can invoke The “hands”—read files, run commands, search the web Prompts Instructions shaping behavior Clear directions on what you want the agent to do The model is powerful but blind—it can’t see your filesystem or run code. Tools give it capabilities. Prompts tell it how to use them wisely. This combination is the harness. Swap the model, and the same harness behaves differen" }, { "tag": "h3", "title": "Key Concepts", "y": 4208, "body": "Tool Calling — Modern LLMs don’t just output text. They can output structured requests like “call the read_file function with path /src/index.ts”. Your code executes that function and sends the result back. The model never actually runs anything—it just asks. Context Window — The LLM’s working memory. Every message, every tool result, every instruction competes for space in a fixed-size window (typically 128K-200K tokens). When you cross that limit, things get stupid. Managing context is the central challenge of agent design. System Prompt — Instructions on what you want the agent to do. What sorts of responses are you looking for? Do you want it to be especially terse, or complete and have " }, { "tag": "h3", "title": "What Makes a Good Agent?", "y": 4781, "body": "The model. 99% the model." }, { "tag": "h3", "title": "No really", "y": 4899, "body": "Yes really." }, { "tag": "h3", "title": "Ok but..", "y": 5017, "body": "The differences are in the harness, and how cleverly you can manage context. Good context — Making sure it understands your problem, where you are with it, what needs to happen. Good tools — Safe, predictable, well-described so the model knows when to use them Good prompts — Clear instructions that guide without over-constraining Good observability — Logging and cost tracking so you know what’s happening Good architecture — Context management, subagents for complex tasks, extensibility That’s what we’re building." }, { "tag": "h2", "title": "WHAT YOU’LL UNDERSTAND AFTER", "y": 5676, "body": "By the end of this tutorial, you won’t just have working code—you’ll have mental models: Agents are loops: The core pattern is simple. Complexity comes from handling edge cases well. Tools are the interface: Good tool design is the difference between helpful and dangerous. Context is the constraint: Everything in agent design traces back to managing finite context. Observability isn’t optional: If you can’t see what happened, you can’t fix what went wrong. Extensibility requires architecture: Ad-hoc additions become unmaintainable fast. Safety and capability trade off: More power means more risk. Design accordingly." }, { "tag": "h2", "title": "LETS GO!", "y": 6381, "body": "" }, { "tag": "h2", "title": "INDEX OF OPERATIONS", "y": 145, "body": "TOTAL STEPS: 11" }, { "tag": "h3", "title": "ENVIRONMENT AND BOOTSTRAP AGENT", "y": 280, "body": "" }, { "tag": "h3", "title": "PROJECT SCAFFOLDING AND LINT/TEST", "y": 398, "body": "" }, { "tag": "h3", "title": "COST AND CONTEXT AWARENESS", "y": 516, "body": "" }, { "tag": "h3", "title": "SESSION LOGGING", "y": 634, "body": "" }, { "tag": "h3", "title": "ADD CUSTOM TOOLS", "y": 752, "body": "" }, { "tag": "h3", "title": "CLEANUP AND REFACTORING", "y": 870, "body": "" }, { "tag": "h3", "title": "EXPLAINING PROMPTING SYSTEM", "y": 988, "body": "" }, { "tag": "h3", "title": "SUBAGENTS: CODE-MAP", "y": 1106, "body": "" }, { "tag": "h3", "title": "TOOLS: SEARCHING", "y": 1224, "body": "" }, { "tag": "h3", "title": "AGENTS AND SKILLS", "y": 1342, "body": "" }, { "tag": "h3", "title": "IMAGES AND VIDEO", "y": 1460, "body": "" }, { "tag": "h3", "title": "CONTINUE YOUR JOURNEY", "y": 1579, "body": "" }, { "tag": "h3", "title": "Get Weekend Projects", "y": 0, "body": "Join for hands-on tutorials, research summaries, and expert analysis on building with AI. Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "THEFOCUS.AI LABS", "href": "https://thefocus.ai/reports/coding-agent/" }, { "text": "THEFOCUS.AI", "href": "https://thefocus.ai/" }, { "text": "fingerspitzengefühl", "href": "https://en.wikipedia.org/wiki/Fingerspitzengef%C3%BChl" }, { "text": "00 ENVIRONMENT AND BOOTSTRAP AGENT COMMIT: 1e4cf31 ACCESS →", "href": "https://thefocus.ai/reports/coding-agent/steps/0" }, { "text": "01 PROJECT SCAFFOLDING AND LINT/TEST COMMIT: 2d169c0 ACCESS →", "href": "https://thefocus.ai/reports/coding-agent/steps/1" }, { "text": "02 COST AND CONTEXT AWARENESS COMMIT: 14ab218 ACCESS →", "href": "https://thefocus.ai/reports/coding-agent/steps/2" }, { "text": "03 SESSION LOGGING COMMIT: 99237c0 ACCESS →", "href": "https://thefocus.ai/reports/coding-agent/steps/3" }, { "text": "04 ADD CUSTOM TOOLS COMMIT: 36552ee ACCESS →", "href": "https://thefocus.ai/reports/coding-agent/steps/4" }, { "text": "05 CLEANUP AND REFACTORING COMMIT: d87093a ACCESS →", "href": "https://thefocus.ai/reports/coding-agent/steps/5" }, { "text": "06 EXPLAINING PROMPTING SYSTEM COMMIT: 8d9907e ACCESS →", "href": "https://thefocus.ai/reports/coding-agent/steps/6" }, { "text": "07 SUBAGENTS: CODE-MAP COMMIT: 0f7844f ACCESS →", "href": "https://thefocus.ai/reports/coding-agent/steps/7" }, { "text": "08 TOOLS: SEARCHING COMMIT: fc1c652 ACCESS →", "href": "https://thefocus.ai/reports/coding-agent/steps/8" }, { "text": "09 AGENTS AND SKILLS COMMIT: d49e04c ACCESS →", "href": "https://thefocus.ai/reports/coding-agent/steps/9" }, { "text": "10 IMAGES AND VIDEO COMMIT: e026e98 ACCESS →", "href": "https://thefocus.ai/reports/coding-agent/steps/10" }, { "text": "++ CONTINUE YOUR JOURNEY EPILOG ACCESS →", "href": "https://thefocus.ai/reports/coding-agent/epilog" }, { "text": "INITIALIZE SEQUENCE", "href": "https://thefocus.ai/reports/coding-agent/steps/0" }, { "text": "Subscribe to Focus.AI", "href": "" } ], "links": [ "https://thefocus.ai/reports/coding-agent/", "https://thefocus.ai/", "https://en.wikipedia.org/wiki/Fingerspitzengef%C3%BChl", "https://thefocus.ai/reports/coding-agent/steps/0", "https://thefocus.ai/reports/coding-agent/steps/1", "https://thefocus.ai/reports/coding-agent/steps/2", "https://thefocus.ai/reports/coding-agent/steps/3", "https://thefocus.ai/reports/coding-agent/steps/4", "https://thefocus.ai/reports/coding-agent/steps/5", "https://thefocus.ai/reports/coding-agent/steps/6", "https://thefocus.ai/reports/coding-agent/steps/7", "https://thefocus.ai/reports/coding-agent/steps/8", "https://thefocus.ai/reports/coding-agent/steps/9", "https://thefocus.ai/reports/coding-agent/steps/10", "https://thefocus.ai/reports/coding-agent/epilog", "https://thefocus.ai/reports/coding-agent/steps/0" ], "images": [ { "src": "https://thefocus.ai/images/ai_newspaper.png", "alt": "wage slaves", "w": 0, "h": 0 } ], "text": "THEFOCUS.AI LABS DOC. NO: WCA-2025 Weekend Coding Agent THEFOCUS.AI PROJECT: Weekend Coding Agent DOC. NO: WCA-2025 DATE: 2025-12-12 - 2025-12-17 WEEKEND CODING AGENT A comprehensive technical manual. Follow the steps to master the workflow. Ever wonder exactly how vibe coding works? Lets learn how to build a coding agent without writing a single line of code. This tutorial will get you right into the core loop of how software agents work. All you need is a terminal (available free with your computer) and an openrouter api access key to get access to a model. We’ll walk you through how to bootstrap the environment, get the agent up and running, and then add a whole lot of features. The trick here is that we are going to use this agent to build itself, and we’ll go through all of the commands you’ll copy and paste over to direct it’s development. No frameworks, no magic, just the primitives you need to understand how tools like Cursor and Claude Code work under the hood, and to get a good feeling for what the different models can do. As of this writing, this works best with Gemini 3 or Opus 4.5. But if you want to expirement with something else, you can easily swap out the model and see how it goes. WHAT YOU’LL BUILD By the end of this tutorial, you’ll have a fully functional AI coding assistant that can: Navigate and understand your codebase Edit files with precision using structured diff tools Support user defined custom skills to extend functionality Self monitor the quality of it’s code base Generate images and videos Search the web for documentation and solutions Spawn specialized sub-agents for focused tasks Track costs so you don’t blow your API budget Log sessions for debugging and improvement More importantly, you’ll understand why each piece exists and how they fit together. And you’ll be able to do it just using english prompts, an honestly reading the code is almost optional at this point. Crazy. WHY BUILD THIS? There’s a fear out there about how intellectual work will change now that we have intelligence on tap. People have valid concerns, but most of the arguments I see are borne out of fear and ignorance, and so I wanted to make a tutorial that everyone could go through to get some hands-on experience on what these tools can literally do, and how that fits into your working process. With this tutorial you can join me in step-by-step building of a piece of software, one that is smart enough to build itself. I’m going to give you the prompts — ", "height": 1000, "screenshots": { "desktop": "assets/screenshots/desktop/reports--coding-agent.png", "mobile": "assets/screenshots/mobile/reports--coding-agent.png" } }, { "url": "https://thefocus.ai/recipes/smallest-coding-agent", "path": "/recipes/smallest-coding-agent", "slug": "recipes--smallest-coding-agent", "category": "Recipe/tool content", "status": 200, "title": "The Smallest Coding Agent", "metaDescription": "A complete agentic coding loop in 25 lines of Bun. No frameworks, no dependencies, just bash and a model.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "The Smallest Coding Agent", "index": 1 }, { "tag": "h2", "text": "The everything agent", "index": 2 }, { "tag": "h2", "text": "Ask it to update itself to something more awesome.", "index": 3 }, { "tag": "h3", "text": "MATERIALS NEEDED", "index": 4 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 5 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 6 }, { "tag": "h3", "text": "COMPANY", "index": 7 }, { "tag": "h3", "text": "PRODUCTS", "index": 8 }, { "tag": "h3", "text": "RESOURCES", "index": 9 }, { "tag": "h3", "text": "CONNECT", "index": 10 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "The Smallest Coding Agent", "y": 429, "body": "WHAT YOU'LL BUILD A complete agentic coding loop in 25 lines of Bun. No frameworks, no dependencies, just bash and a model." }, { "tag": "h2", "title": "The everything agent", "y": 1465, "body": "#!/usr/bin/env bun const API = \"https://openrouter.ai/api/v1/chat/completions\"; const MODEL = \"anthropic/claude-opus-4.5\"; const TOOL = [{type:\"function\",function:{name:\"bash\",description:\"Run bash command\",parameters:{type:\"object\",properties:{command:{type:\"string\"}},required:[\"command\"]}}}]; let msgs = []; const call = async () => (await fetch(API, { method: \"POST\", headers: { \"Authorization\": `Bearer ${process.env.OPENROUTER_API_KEY}`, \"Content-Type\": \"application/json\" }, body: JSON.stringify({ model: MODEL, messages: msgs, tools: TOOL }) })).json(); for await (const line of console) { if (line === \"exit\") break; msgs.push({ role: \"user\", content: line }); while (true) { const r = await" }, { "tag": "h2", "title": "Ask it to update itself to something more awesome.", "y": 2825, "body": "bun run index.ts > add better code editing tools to index.ts Then restart: bun run index.ts AI Coding Assistant (type 'exit' to quit) Available tools: bash, view_file, edit_file, create_file, delete_file, search_files, find_files, list_dir, replace_in_file, insert_lines, get_file_info" }, { "tag": "h3", "title": "MATERIALS NEEDED", "y": 1398, "body": "• Computer with internet access • Basic command line knowledge • Text editor" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 3389, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 3839, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 4216, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 4216, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 4216, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 4216, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← BACK TO WEEKEND WARRIOR PROJECTS", "href": "https://thefocus.ai/tools" }, { "text": "OpenRouter API Key", "href": "https://openrouter.ai/" }, { "text": "← ALL PROJECTS", "href": "https://thefocus.ai/tools" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" }, { "text": "Registry", "href": "https://account.thefocus.ai/" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Use Cases", "href": "https://thefocus.ai/use-cases" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/tools", "https://openrouter.ai/", "https://thefocus.ai/tools", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [ { "src": "https://thefocus.ai/_astro/smallest-coding-agent.BGVcBftp.png", "alt": "The Smallest Coding Agent", "w": 1376, "h": 768 } ], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← BACK TO WEEKEND WARRIOR PROJECTS PROJECT WORKSHEET TheFocus.AI Labs Workshop Series UNCLASSIFIED EST. TIME 15 min PUBLISHED December 5, 2025 The Smallest Coding Agent WHAT YOU'LL BUILD A complete agentic coding loop in 25 lines of Bun. No frameworks, no dependencies, just bash and a model. Just let a smart model use the terminal and you can build anything. The everything agent #!/usr/bin/env bun const API = \"https://openrouter.ai/api/v1/chat/completions\"; const MODEL = \"anthropic/claude-opus-4.5\"; const TOOL = [{type:\"function\",function:{name:\"bash\",description:\"Run bash command\",parameters:{type:\"object\",properties:{command:{type:\"string\"}},required:[\"command\"]}}}]; let msgs = []; const call = async () => (await fetch(API, { method: \"POST\", headers: { \"Authorization\": `Bearer ${process.env.OPENROUTER_API_KEY}`, \"Content-Type\": \"application/json\" }, body: JSON.stringify({ model: MODEL, messages: msgs, tools: TOOL }) })).json(); for await (const line of console) { if (line === \"exit\") break; msgs.push({ role: \"user\", content: line }); while (true) { const r = await call(); if (r.error) { console.error(\"Error:\", r.error.message); break; } const m = r.choices[0].message; if (m.tool_calls) { const tc = m.tool_calls[0]; const cmd = JSON.parse(tc.function.arguments).command; console.log(`$ ${cmd}`); const result = await Bun.$`sh -c ${cmd}`.text().catch(e => e.stderr || e.message); console.log(result); msgs.push({ role: \"assistant\", tool_calls: [tc] }); msgs.push({ role: \"tool\", tool_call_id: tc.id, content: result }); } else { console.log(m.content); msgs.push({ role: \"assistant\", content: m.content }); break; } } } Get yourself an OpenRouter API Key. Ask it to update itself to something more awesome. bun run index.ts > add better code editing tools to index.ts Then restart: bun run index.ts AI Coding Assistant (type 'exit' to quit) Available tools: bash, view_file, edit_file, create_file, delete_file, search_files, find_files, list_dir, replace_in_file, insert_lines, get_file_info MATERIALS NEEDED • Computer with internet access • Basic command line knowledge • Text editor ← ALL PROJECTS Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd l", "height": 4713, "screenshots": { "desktop": "assets/screenshots/desktop/recipes--smallest-coding-agent.png", "mobile": "assets/screenshots/mobile/recipes--smallest-coding-agent.png" } }, { "url": "https://thefocus.ai/recipes/repomix-redux", "path": "/recipes/repomix-redux", "slug": "recipes--repomix-redux", "category": "Recipe/tool content", "status": 200, "title": "Using repomix to give llm full context", "metaDescription": "How to use repomix to package your entire codebase into a single file for LLM context, with tips for managing token counts and mise integration.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Using repomix to give llm full context", "index": 1 }, { "tag": "h2", "text": "Tips", "index": 2 }, { "tag": "h3", "text": "MATERIALS NEEDED", "index": 3 }, { "tag": "h3", "text": "MORE PROJECTS TO TRY", "index": 4 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 5 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 6 }, { "tag": "h3", "text": "COMPANY", "index": 7 }, { "tag": "h3", "text": "PRODUCTS", "index": 8 }, { "tag": "h3", "text": "RESOURCES", "index": 9 }, { "tag": "h3", "text": "CONNECT", "index": 10 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Using repomix to give llm full context", "y": 429, "body": "WHAT YOU'LL BUILD How to use repomix to package your entire codebase into a single file for LLM context, with tips for managing token counts and mise integration." }, { "tag": "h2", "title": "Tips", "y": 2377, "body": "When you run repomix it shows you how many tokens the files use. ✔ Packing completed successfully! 📈 Top 5 Files by Character Count and Token Count: ────────────────────────────────────────────────── 1. warc-viewer.py (19,802 chars, 4,023 tokens) 2. tests/test_app.py (14,737 chars, 3,165 tokens) 3. src/warc_viewer/app.py (14,513 chars, 2,859 tokens) 4. src/warc_viewer/cdx.py (13,997 chars, 2,672 tokens) 5. tests/test_db.py (13,766 chars, 3,141 tokens) 🔎 Security Check: ────────────────── ✔ No suspicious files detected. 📊 Pack Summary: ──────────────── Total Files: 31 files Total Chars: 160,627 chars Total Tokens: 35,819 tokens Output: repomix-output.txt Security: ✔ No suspicious files det" }, { "tag": "h3", "title": "MATERIALS NEEDED", "y": 1398, "body": "• Computer with internet access • Basic command line knowledge • Text editor" }, { "tag": "h3", "title": "MORE PROJECTS TO TRY", "y": 1587, "body": "Extracting requirements PROMPTS Need to understand all the requirements hidden in a codebase? This technique shows how to use LLMs to compile comprehensive specifications from repository analysis for immediate development use. VIEW RECIPE Using repomix to give llm full context CONTEXT The post focuses on how to give LLMs full visibility into a codebase to improve their understanding and responses. VIEW RECIPE Understanding a code base CLAUDE CONTEXT PROMPTS Explore three complementary approaches to rapidly comprehend any codebase using the latest AI assistants and developer tools. Follow along with practical examples showing how to extract key information about memory systems and other compl" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 3957, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 4407, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 4784, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 4784, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 4784, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 4784, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← BACK TO WEEKEND WARRIOR PROJECTS", "href": "https://thefocus.ai/tools" }, { "text": "repomix", "href": "https://thefocus.ai/recipes/repomix" }, { "text": "repomix", "href": "https://repomix.com/" }, { "text": "understanding-a-codebase", "href": "https://thefocus.ai/recipes/understanding-a-codebase" }, { "text": "PROMPTS", "href": "https://thefocus.ai/recipes/prompts" }, { "text": "Need to understand all the requirements hidden in a codebase? This technique shows how to use LLMs to compile comprehensive specifications from repository analysis for immediate development use.", "href": "https://thefocus.ai/recipes/extracting-requirements" }, { "text": "VIEW RECIPE", "href": "https://thefocus.ai/recipes/extracting-requirements" }, { "text": "CONTEXT", "href": "https://thefocus.ai/recipes/context" }, { "text": "The post focuses on how to give LLMs full visibility into a codebase to improve their understanding and responses.", "href": "https://thefocus.ai/recipes/repomix" }, { "text": "VIEW RECIPE", "href": "https://thefocus.ai/recipes/repomix" }, { "text": "CLAUDE", "href": "https://thefocus.ai/recipes/claude" }, { "text": "CONTEXT", "href": "https://thefocus.ai/recipes/context" }, { "text": "PROMPTS", "href": "https://thefocus.ai/recipes/prompts" }, { "text": "Explore three complementary approaches to rapidly comprehend any codebase using the latest AI assistants and developer tools. Follow along with practical examples showing how to extract key information about memory systems and other complex functionality with minimal effort.", "href": "https://thefocus.ai/recipes/understanding-a-codebase" }, { "text": "VIEW RECIPE", "href": "https://thefocus.ai/recipes/understanding-a-codebase" }, { "text": "← ALL PROJECTS", "href": "https://thefocus.ai/tools" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/tools", "https://thefocus.ai/recipes/repomix", "https://repomix.com/", "https://thefocus.ai/recipes/understanding-a-codebase", "https://thefocus.ai/recipes/prompts", "https://thefocus.ai/recipes/extracting-requirements", "https://thefocus.ai/recipes/extracting-requirements", "https://thefocus.ai/recipes/context", "https://thefocus.ai/recipes/repomix", "https://thefocus.ai/recipes/repomix", "https://thefocus.ai/recipes/claude", "https://thefocus.ai/recipes/context", "https://thefocus.ai/recipes/prompts", "https://thefocus.ai/recipes/understanding-a-codebase", "https://thefocus.ai/recipes/understanding-a-codebase", "https://thefocus.ai/tools", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [ { "src": "https://thefocus.ai/_astro/repomix.BcDqog2V.png", "alt": "Using repomix to give llm full context", "w": 1024, "h": 1024 }, { "src": "https://thefocus.ai/_astro/extracting-requirements.VmVhFQp7_Z19blW5.webp", "alt": "Extracting requirements", "w": 1024, "h": 1024 }, { "src": "https://thefocus.ai/_astro/repomix.BcDqog2V_23qUF2.webp", "alt": "Using repomix to give llm full context", "w": 1024, "h": 1024 }, { "src": "https://thefocus.ai/_astro/understanding-a-codebase.DcoDuyZy_1XwzIh.webp", "alt": "Understanding a code base", "w": 1024, "h": 1024 } ], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← BACK TO WEEKEND WARRIOR PROJECTS PROJECT WORKSHEET TheFocus.AI Labs Workshop Series UNCLASSIFIED EST. TIME 5 min PUBLISHED September 8, 2025 Using repomix to give llm full context WHAT YOU'LL BUILD How to use repomix to package your entire codebase into a single file for LLM context, with tips for managing token counts and mise integration. Lets revisit what we can do with repomix. Inside of a project: npx repomix --init repomix is a great tool that you can use to tell an LLM about all of the context of your project. You can run it like this npx repomix And it will create a repomix-output.txt file that you can send to a LLM. You can drag the file over, or paste it in: (works best with claude) cat repomix-output.txt | pbcopy You can also pass this into a local llm, like cat repomix-output.txt | ollama run gemma3:12b \"Tell me what you think about all of this\" Check out more understanding-a-codebase Tips When you run repomix it shows you how many tokens the files use. ✔ Packing completed successfully! 📈 Top 5 Files by Character Count and Token Count: ────────────────────────────────────────────────── 1. warc-viewer.py (19,802 chars, 4,023 tokens) 2. tests/test_app.py (14,737 chars, 3,165 tokens) 3. src/warc_viewer/app.py (14,513 chars, 2,859 tokens) 4. src/warc_viewer/cdx.py (13,997 chars, 2,672 tokens) 5. tests/test_db.py (13,766 chars, 3,141 tokens) 🔎 Security Check: ────────────────── ✔ No suspicious files detected. 📊 Pack Summary: ──────────────── Total Files: 31 files Total Chars: 160,627 chars Total Tokens: 35,819 tokens Output: repomix-output.txt Security: ✔ No suspicious files detected 🎉 All Done! Your repository has been successfully packed. 💡 Repomix is now available in your browser Depending upon what’s in there, you can set up --ignore: npx repomix --ignore \"**/*.json,**/*.ipynb,**/*.mdx\" I have a bunch of mise tasks that depends on llm-dump which I define both in .config/mise/config.toml but then override in specific projects as needed. [tasks.llm-dump] description = 'Runs repomix' run = ['repomix --ignore \"output*,**/*.log,tmp/,inputs/**/*md,data_integrity/**/*.json\"'] MATERIALS NEEDED • Computer with internet access • Basic command line knowledge • Text editor MORE PROJECTS TO TRY Extracting requirements PROMPTS Need to understand all the requirements", "height": 5281, "screenshots": { "desktop": "assets/screenshots/desktop/recipes--repomix-redux.png", "mobile": "assets/screenshots/mobile/recipes--repomix-redux.png" } }, { "url": "https://thefocus.ai/recipes/context7-mcp", "path": "/recipes/context7-mcp", "slug": "recipes--context7-mcp", "category": "Recipe/tool content", "status": 200, "title": "context7-mcp", "metaDescription": "Learn how to integrate Context7 MCP server to get up-to-date code documentation for LLMs and AI code editors, enabling real-time access to the latest library and framework documentation.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "context7-mcp", "index": 1 }, { "tag": "h2", "text": "Installation", "index": 2 }, { "tag": "h2", "text": "Example Usage", "index": 3 }, { "tag": "h3", "text": "MATERIALS NEEDED", "index": 4 }, { "tag": "h3", "text": "MORE PROJECTS TO TRY", "index": 5 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 6 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 7 }, { "tag": "h3", "text": "COMPANY", "index": 8 }, { "tag": "h3", "text": "PRODUCTS", "index": 9 }, { "tag": "h3", "text": "RESOURCES", "index": 10 }, { "tag": "h3", "text": "CONNECT", "index": 11 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "context7-mcp", "y": 429, "body": "WHAT YOU'LL BUILD Learn how to integrate Context7 MCP server to get up-to-date code documentation for LLMs and AI code editors, enabling real-time access to the latest library and framework documentation." }, { "tag": "h2", "title": "Installation", "y": 1695, "body": "claude mcp add --scope project context7 -- npx -y @upstash/context7-mcp --api-key YOUR_API_KEY You can leave off the API_KEY if you don’t have it This lets you pull down the latest version of the docs, so you can ask it to lookup how to do a certain thing and the model will learn how." }, { "tag": "h2", "title": "Example Usage", "y": 2063, "body": "Once installed, you can ask questions like: “How do I implement authentication in Next.js 14?” “What’s the latest way to handle forms in React 18?” “Show me the current MongoDB aggregation pipeline syntax” “What are the new features in Supabase v2?” The AI will fetch the most current documentation and provide accurate, up-to-date answers based on the official sources." }, { "tag": "h3", "title": "MATERIALS NEEDED", "y": 1424, "body": "• Computer with internet access • Basic command line knowledge • Text editor" }, { "tag": "h3", "title": "MORE PROJECTS TO TRY", "y": 1613, "body": "use playwright mcp to get high fidelity designs MCP Learn how to integrate Playwright MCP server to enable AI coding agents to interact with web browsers, take screenshots, and validate designs with high fidelity for accurate web development and testing. VIEW RECIPE" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 2575, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 3025, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 3402, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 3402, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 3402, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 3402, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. 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1024 } ], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← BACK TO WEEKEND WARRIOR PROJECTS PROJECT WORKSHEET TheFocus.AI Labs Workshop Series UNCLASSIFIED EST. TIME 5 min PUBLISHED September 1, 2025 context7-mcp WHAT YOU'LL BUILD Learn how to integrate Context7 MCP server to get up-to-date code documentation for LLMs and AI code editors, enabling real-time access to the latest library and framework documentation. One must have mcp server is context7, which is up-to-date code documentation for LLMs and AI code editors. Knowledge moves fast. What makes it particularly powerful is how it enables AI models to learn from current documentation rather than the potentially outdated data they were trained on, creating a dynamic feedback loop where your AI assistant becomes more knowledgeable about the latest best practices. Installation claude mcp add --scope project context7 -- npx -y @upstash/context7-mcp --api-key YOUR_API_KEY You can leave off the API_KEY if you don’t have it This lets you pull down the latest version of the docs, so you can ask it to lookup how to do a certain thing and the model will learn how. Example Usage Once installed, you can ask questions like: “How do I implement authentication in Next.js 14?” “What’s the latest way to handle forms in React 18?” “Show me the current MongoDB aggregation pipeline syntax” “What are the new features in Supabase v2?” The AI will fetch the most current documentation and provide accurate, up-to-date answers based on the official sources. MATERIALS NEEDED • Computer with internet access • Basic command line knowledge • Text editor MORE PROJECTS TO TRY use playwright mcp to get high fidelity designs MCP Learn how to integrate Playwright MCP server to enable AI coding agents to interact with web browsers, take screenshots, and validate designs with high fidelity for accurate web development and testing. VIEW RECIPE ← ALL PROJECTS Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURC", "height": 3899, "screenshots": { "desktop": "assets/screenshots/desktop/recipes--context7-mcp.png", "mobile": "assets/screenshots/mobile/recipes--context7-mcp.png" } }, { "url": "https://thefocus.ai/tools/browse", "path": "/tools/browse", "slug": "tools--browse", "category": "Tool app", "status": 200, "title": "Browse All Projects | TheFocus.AI Labs", "metaDescription": "Browse and filter all Weekend Warrior projects", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "PROJECT INDEX", "index": 1 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 2 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 3 }, { "tag": "h3", "text": "COMPANY", "index": 4 }, { "tag": "h3", "text": "PRODUCTS", "index": 5 }, { "tag": "h3", "text": "RESOURCES", "index": 6 }, { "tag": "h3", "text": "CONNECT", "index": 7 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "PROJECT INDEX", "y": 325, "body": "23 of 23 projects" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 4629, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 5079, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. 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}, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← Back to Overview", "href": "https://thefocus.ai/tools" }, { "text": "Build Your Own Coding Agent", "href": "https://thefocus.ai/reports/coding-agent/" }, { "text": "VIEW →", "href": "https://thefocus.ai/reports/coding-agent/" }, { "text": "The Smallest Coding Agent", "href": "https://thefocus.ai/recipes/smallest-coding-agent" }, { "text": "VIEW →", "href": "https://thefocus.ai/recipes/smallest-coding-agent" }, { "text": "Using repomix to give llm full context", "href": "https://thefocus.ai/recipes/repomix-redux" }, { "text": "VIEW →", "href": "https://thefocus.ai/recipes/repomix-redux" }, { "text": "context7-mcp", "href": "https://thefocus.ai/recipes/context7-mcp" }, { "text": "VIEW →", "href": "https://thefocus.ai/recipes/context7-mcp" }, { "text": "use playwright mcp to get high fidelity designs", "href": "https://thefocus.ai/recipes/playwright-mcp" }, { "text": "VIEW →", "href": 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] THEFOCUS.AI LABS PROJECT: Weekend Warrior Workshop DOC. NO: WW-2025-INDEX TheFocus.AI Labs • Est. 2024 ← Back to Overview PROJECT INDEX 23 of 23 projects SEARCH CATEGORY All Categories Analysis Coding User experience TECHNOLOGY All Technologies AGENTS CLAUDE CLIENTS CONTEXT GEMINI IMAGES MCP PROMPTS REPOMIX SLACK TYPESCRIPT VOICE CLEAR December 17, 2025 CODING Build Your Own Coding Agent A step-by-step tutorial for building an AI coding assistant from scratch. Bootstrap it with an existing agent, then use it to improve itself. TYPESCRIPT GEMINI AGENTS VIEW → December 5, 2025 CODING The Smallest Coding Agent A complete agentic coding loop in 25 lines of Bun. No frameworks, no dependencies, just bash and a model. PROMPTS CLAUDE VIEW → September 8, 2025 CODING Using repomix to give llm full context How to use repomix to package your entire codebase into a single file for LLM context, with tips for managing token counts and mise integration. CONTEXT REPOMIX VIEW → September 1, 2025 CODING context7-mcp Learn how to integrate Context7 MCP server to get up-to-date code documentation for LLMs and AI code editors, enabling real-time access to the latest library and framework documentation. MCP VIEW → September 1, 2025 CODING use playwright mcp to get high fidelity designs Learn how to integrate Playwright MCP server to enable AI coding agents to interact with web browsers, take screenshots, and validate designs with high fidelity for accurate web development and testing. MCP VIEW → July 8, 2025 CODING Dagger and Container Use Unlock the power of isolated development with Dagger and Container Use—no more worrying about missing dependencies or system conflicts. CONTEXT VIEW → June 30, 2025 USER-EXPERIENCE Use Cursor in Slack Learn how to integrate Cursor with Slack for seamless code review and collaboration, including setup tips and workflow enhancements. CLIENTS SLACK VIEW → May 28, 2025 USER-EXPERIENCE Content Finder Ever had a movie stuck on the tip of your tongue or wanted more like your family's favorite? Explore how AI-powered content discovery can help you make your next pick. PROMPTS VIEW → May 28, 2025 ANALYSIS Geolocation Prompt This post introduces a prompt for high-precision, image-based geolocation analysis, guiding AI to deduce where a photo was taken using expertise", "height": 5953, "screenshots": { "desktop": "assets/screenshots/desktop/tools--browse.png", "mobile": "assets/screenshots/mobile/tools--browse.png" } }, { "url": "https://thefocus.ai/reports/local-providers", "path": "/reports/local-providers", "slug": "reports--local-providers", "category": "Report/deep-dive", "status": 200, "title": "Local Providers — Full Assessment | TheFocus.AI Labs", "metaDescription": "A comprehensive evaluation of running open-weights models locally: which model, which runtime, with thinking on or off. 7 model families × 3 runtimes × 5 dimensions on a 64GB Mac.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Local Providers Full Assessment", "index": 1 }, { "tag": "h2", "text": "WHAT WE TESTED", "index": 2 }, { "tag": "h3", "text": "The Models", "index": 3 }, { "tag": "h3", "text": "The Runtimes", "index": 4 }, { "tag": "h3", "text": "The 5 Evaluation Dimensions", "index": 5 }, { "tag": "h2", "text": "THE SURPRISING CONCLUSIONS", "index": 6 }, { "tag": "h2", "text": "OVERALL RESULTS", "index": 7 }, { "tag": "h2", "text": "HOW “BEST” WAS DECIDED", "index": 8 }, { "tag": "h2", "text": "METHODOLOGY", "index": 9 }, { "tag": "h3", "text": "Run Mechanics", "index": 10 }, { "tag": "h2", "text": "INSTRUCTION FOLLOWING DETERMINISTIC · /30", "index": 11 }, { "tag": "h3", "text": "The Exact Prompts", "index": 12 }, { "tag": "h3", "text": "What We Saw", "index": 13 }, { "tag": "h2", "text": "REASONING LLM JUDGE · /20", "index": 14 }, { "tag": "h2", "text": "CODING — WRITE COMPILE + RUN + CHECK · /126", "index": 15 }, { "tag": "h2", "text": "CODING — FIX HIDDEN TEST HARNESS · /25", "index": 16 }, { "tag": "h2", "text": "TOOL MATH CORRECTNESS + TOOL-CALL COUNTING · /25", "index": 17 }, { "tag": "h3", "text": "Why Tool-Calling Is So Weak", "index": 18 }, { "tag": "h2", "text": "CODING 2-PASS — THE EXPERIMENT /126", "index": 19 }, { "tag": "h2", "text": "WHAT THIS MEANS IN PRACTICE", "index": 20 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 21 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 22 }, { "tag": "h3", "text": "COMPANY", "index": 23 }, { "tag": "h3", "text": "PRODUCTS", "index": 24 }, { "tag": "h3", "text": "RESOURCES", "index": 25 }, { "tag": "h3", "text": "CONNECT", "index": 26 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Local Providers Full Assessment", "y": 452, "body": "A comprehensive evaluation of running open-weights models locally: which model, which runtime, with thinking on or off. Plus an experiment in giving small models compile-error feedback to see whether they can self-correct." }, { "tag": "h2", "title": "WHAT WE TESTED", "y": 1393, "body": "7 model families across 3 runtimes, producing 20 (model, runtime, thinking-mode) cells. All models run on a single 64 GB Apple Silicon machine." }, { "tag": "h3", "title": "The Models", "y": 1542, "body": "Gemma 4 27B-A4B Params: 27B (4B active) Released: Apr 2025 Quant: Q4_K_M On disk: 16 GB Developer: Google MOE Gemma 4 31B Params: 31B Released: Apr 2025 Quant: Q4_K_M On disk: 19 GB Developer: Google DENSE Gemma 4 E4B Params: ~4B Released: Apr 2025 Quant: Q4_K_M On disk: 3 GB Developer: Google SMALL Gemma 4 E2B Params: ~2B Released: Apr 2025 Quant: Q4_K_M On disk: 1.5 GB Developer: Google SMALL GPT-OSS 20B Params: 20B Released: Mar 2025 Quant: MXFP4 (native) On disk: 12 GB Developer: OpenAI DENSE GLM 4.7 Flash Params: ~7B Released: Feb 2025 Quant: Q4_K_M On disk: 17 GB Developer: Zhipu AI DENSE Qwen 3.6 27B Params: 27B Released: Apr 2026 Quant: Q4_K_M On disk: 16 GB Developer: Alibaba DENSE " }, { "tag": "h3", "title": "The Runtimes", "y": 2240, "body": "RUNTIME ENGINE THINKING NOTES Ollama ollama daemon Default (model decides) Q4_K_M for all; Go-template chat approximations llamaswap llama-server + llama-swap On (default) --ctx-size 0 --jinja — native chat template llamaswap-nothink llama-server + llama-swap Off (enable_thinking=false) Same binary, thinking suppressed per-request" }, { "tag": "h3", "title": "The 5 Evaluation Dimensions", "y": 2566, "body": "DIMENSION SCORING MAX METHOD Instruction Following Deterministic constraint checking /30 6 tasks, 5 pts each Reasoning LLM judge (Claude Haiku 4.5) /20 4 puzzles, 5 pts each Coding (Write) Compile + run + verify output /126 6 problems × 3 languages × 7 pts Coding (Fix) Hidden test harness /25 5 buggy JS functions, 5 pts each Tool Math Correctness + tool-call counting /25 5 multi-step arithmetic tasks" }, { "tag": "h2", "title": "THE SURPRISING CONCLUSIONS", "y": 2983, "body": "95.0% BEST COMBINED 4m13s WALL CLOCK 20 CELLS TESTED 5 DIMENSIONS The runtime barely matters. Ollama and llamaswap score within a few points of each other on every model when you control for quantization and thinking mode. Thinking mode usually hurts on short-horizon tasks — except for tool-calling, where it’s required. gpt-oss-20b on llamaswap (thinking on) is the best small open model for tool-calling. 23/25. gemma-4-26b-a4b is the best value model overall. 95.0% combined in 4 minutes. Tool-calling is where the matrix falls apart. Three cells scored 0/25, eight scored ≤5/25. A 4B model cannot fix its own broken code from the compile error. 0 improvements across 8 retries. Thinking mode can" }, { "tag": "h2", "title": "OVERALL RESULTS", "y": 4003, "body": "COMBINED SCORE — ALL 20 CELLS 60% 70% 80% 90% 100% gemma-4-26b-a4b LLAMASWAP-NOTHINK 95.0% gpt-oss-20b LLAMASWAP 94.6% gpt-oss-latest OLLAMA 89.5% gemma-4-26b-a4b LLAMASWAP (THINK) 86.1% gemma-4-31b BOTH RUNTIMES 84.0% gemma-4-e2b LLAMASWAP-NOTHINK 82.6% gemma4-26b OLLAMA 79.0% glm-4.7-flash OLLAMA 78.7% qwen3.6-27b LLAMASWAP-NOTHINK 78.4% 6 more cells 73–78% 73–78% 4 more cells 61–69% 61–69% SCORE VS. WALL-CLOCK TIME — THE EFFICIENCY FRONTIER 60% 70% 80% 90% 100% 3m 8m 16m 24m 80m WALL-CLOCK TIME → COMBINED SCORE → IDEAL ZONE gemma-4-26b-a4b NOTHINK · 4m · 95% gpt-oss-20b THINK · 16m · 94.6% gpt-oss ollama 24m · 89.5% gemma-26b think 80m · 86.1% gemma-31b gemma-e2b 3m · 82.6% nemotron think" }, { "tag": "h2", "title": "HOW “BEST” WAS DECIDED", "y": 6060, "body": "MODEL INSTRUCTION REASONING CODE (WRITE) CODE (FIX) TOOL MATH COMBINED % TIME gemma-4-26b-a4b (llamaswap-nothink) 30/30 20/20 120/126 25/25 20/25 95.0% 4m13s gpt-oss-20b (llamaswap) 30/30 17/20 121/126 25/25 23/25 94.6% 16m6s The “Combined %” is the mean of the per-dimension percentages, not a weighted score. Both models tie on Instruction (100%) and Coding-fix (100%). Gemma wins reasoning by 3 points; gpt-oss wins tool-math by 3 points and coding-write by 1. They cancel out within 0.4%. “Best raw scores on tool-calling” → gpt-oss-20b llamaswap (23/25) “Best general-purpose at reasonable cost” → gemma-4-26b-a4b llamaswap-nothink “Best out-of-the-box” → gemma4:31b ollama (84.0%, no thinking-m" }, { "tag": "h2", "title": "METHODOLOGY", "y": 6632, "body": "" }, { "tag": "h3", "title": "Run Mechanics", "y": 6705, "body": "The matrix runner iterates model-major: load one model, run all five evaluation suites, evict before loading the next. Required on a 64 GB machine because most models are 17–19 GB resident. 20-min per-cell watchdog, 5-min per-task watchdog. Partial responses are scored as-is. All quantizations are Q4_K_M across runtimes (apples-to-apples), except gpt-oss which ships natively as MXFP4. Total matrix wall-clock time: 316 minutes (~5.3 hours)." }, { "tag": "h2", "title": "INSTRUCTION FOLLOWING DETERMINISTIC · /30", "y": 7063, "body": "Can the model follow precise format instructions without overshooting? No reasoning needed; just literal compliance." }, { "tag": "h3", "title": "The Exact Prompts", "y": 7213, "body": "1.1 Exact Word Count (5 pts) Write a sentence about the ocean that contains EXACTLY 12 words. Do not include any other text, explanation, or commentary. Scoring: 5 if exactly 12 words; 3 if off by 1; 1 if off by 2–3; 0 otherwise. 1.2 Structured JSON Output (5 pts) — 4 typed fields, no markdown fences. 1.3 Constrained List (5 pts) — 5 animals, numbered, alphabetical, ≤8 chars. 1.4 Negative Constraints (5 pts) — sunset description avoiding 3 words. 1.5 Format Transformation (5 pts) — CSV to markdown table. 1.6 Multi-format Response (5 pts) — 3 sections separated by ---." }, { "tag": "h3", "title": "What We Saw", "y": 7683, "body": "Eleven of 20 cells scored a perfect 30/30. The most discriminating task was exact-word-count: Thinking mode hurt word counting. gemma-4-26b-a4b llamaswap (thinking on) scored 0/5. Same weights, thinking off: 5/5. glm-4.7-flash scored 1/5 — writes nicely but cannot constrain length." }, { "tag": "h2", "title": "REASONING LLM JUDGE · /20", "y": 7992, "body": "Classic reasoning puzzles where the obvious answer is wrong. Claude Haiku 4.5 as judge. 2.1 The Surgeon Riddle · 2.2 Bat and Ball · 2.3 Lily Pad · 2.4 Counterfeit Coin Twelve cells scored 17–20/20. gemma-4-31b scored 20/20 on both runtimes. The hardest puzzle was counterfeit-coin. Signal: On short reasoning puzzles, model size matters more than thinking-mode budget." }, { "tag": "h2", "title": "CODING — WRITE COMPILE + RUN + CHECK · /126", "y": 8424, "body": "6 problems × 3 languages (TypeScript, Python, Go), each scored 0–7. Top scorers: gemma-4-31b (126/126 on both runtimes). The dominant failure mode in Go: Of 35 non-perfect Go scores, 67% were compile errors — “imported and not used” or “declared and not used.”" }, { "tag": "h2", "title": "CODING — FIX HIDDEN TEST HARNESS · /25", "y": 8766, "body": "5 buggy JavaScript functions. Sixteen of 20 cells scored a perfect 25/25. The four that lost points: all three nemotron variants and gemma4-e2b ollama. This dimension has the least signal — too easy for most models." }, { "tag": "h2", "title": "TOOL MATH CORRECTNESS + TOOL-CALL COUNTING · /25", "y": 9015, "body": "Can the model use provided tools (calculator, statistics) to chain multi-step arithmetic? MODEL TOOL MATH gpt-oss-20b llamaswap (thinking-on) 23/25 gemma-4-26b-a4b llamaswap (thinking-on) 20/25 gemma-4-26b-a4b llamaswap-nothink 20/25 gpt-oss-latest ollama 20/25 gemma-4-31b llamaswap (thinking-on) 20/25 glm-4.7-flash-latest ollama 14/25 gemma-4-e2b llamaswap-nothink 12/25 … … gemma4-26b ollama 0/25 gpt-oss-20b llamaswap-nothink 0/25" }, { "tag": "h3", "title": "Why Tool-Calling Is So Weak", "y": 9660, "body": "Models try to one-shot the answer instead of chaining tools. Thinking-off models get stuck. gpt-oss-20b nothink: 0/25. Same model thinking-on: 23/25. Tool-calling format errors. Wrong argument shapes exhaust the retry budget. Long chains exceed the watchdog. Reasoning loops on Stats+arithmetic. Models re-derive instead of trusting tool output. Root cause: Tool-calling requires the model to maintain state across multiple turns and trust intermediate results. Smaller and thinking-off models fail this." }, { "tag": "h2", "title": "CODING 2-PASS — THE EXPERIMENT /126", "y": 10148, "body": "If a model fails in round 1, does showing it the actual compile/runtime error help? ROUND 1 ROUND 2 Δ Variant A (scaffolded) 93/126 (73.8%) 79/126 (62.7%) −14 pts Variant B (raw error) 93/126 (73.8%) 80/126 (63.5%) −13 pts Same outcome. Zero improvements. The 4B model correctly identified the fix for one error and then introduced a new error elsewhere. The 4B-parameter model genuinely lacks the capacity to make a localized fix without disturbing surrounding code. It’s not context length, not prompt format — it’s model capability." }, { "tag": "h2", "title": "WHAT THIS MEANS IN PRACTICE", "y": 10715, "body": "USE CASE PICK Best general overall, low cost gemma-4-26b-a4b on llamaswap-nothink (95.0%, 4 minutes) Best for tool-use / agent loops gpt-oss-20b on llamaswap (thinking on) (94.6%, 23/25 on tool math) Best out-of-the-box gemma4:31b on ollama (84.0%, no config needed) Smallest still usable gemma-4-e2b on llamaswap-nothink (82.6%, 3 minutes) What to avoid Thinking-on by default for non-agent tasks. The wall-clock cost is huge. If you’re building agentic workflows that need tools: use thinking-on. The penalty is wall-clock time but you can’t get tool-calling right without it. If you’re iterating on agent loops that need the model to use compile/test feedback to fix its own code: don’t trust mode" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 11588, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 12038, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] THEFOCUS.AI LABS PROJECT: Umwelten Local Providers Matrix DOC. NO: LP-2026-05 DATE: May 2026 Local Providers Full Assessment A comprehensive evaluation of running open-weights models locally: which model, which runtime, with thinking on or off. Plus an experiment in giving small models compile-error feedback to see whether they can self-correct. WHAT WE TESTED 7 model families across 3 runtimes, producing 20 (model, runtime, thinking-mode) cells. All models run on a single 64 GB Apple Silicon machine. The Models Gemma 4 27B-A4B Params: 27B (4B active) Released: Apr 2025 Quant: Q4_K_M On disk: 16 GB Developer: Google MOE Gemma 4 31B Params: 31B Released: Apr 2025 Quant: Q4_K_M On disk: 19 GB Developer: Google DENSE Gemma 4 E4B Params: ~4B Released: Apr 2025 Quant: Q4_K_M On disk: 3 GB Developer: Google SMALL Gemma 4 E2B Params: ~2B Released: Apr 2025 Quant: Q4_K_M On disk: 1.5 GB Developer: Google SMALL GPT-OSS 20B Params: 20B Released: Mar 2025 Quant: MXFP4 (native) On disk: 12 GB Developer: OpenAI DENSE GLM 4.7 Flash Params: ~7B Released: Feb 2025 Quant: Q4_K_M On disk: 17 GB Developer: Zhipu AI DENSE Qwen 3.6 27B Params: 27B Released: Apr 2026 Quant: Q4_K_M On disk: 16 GB Developer: Alibaba DENSE Nemotron 3 Nano 4B Params: 4B Released: Mar 2025 Quant: Q4_K_M On disk: 2.9 GB Developer: NVIDIA SMALL The Runtimes RUNTIME ENGINE THINKING NOTES Ollama ollama daemon Default (model decides) Q4_K_M for all; Go-template chat approximations llamaswap llama-server + llama-swap On (default) --ctx-size 0 --jinja — native chat template llamaswap-nothink llama-server + llama-swap Off (enable_thinking=false) Same binary, thinking suppressed per-request The 5 Evaluation Dimensions DIMENSION SCORING MAX METHOD Instruction Following Deterministic constraint checking /30 6 tasks, 5 pts each Reasoning LLM judge (Claude Haiku 4.5) /20 4 puzzles, 5 pts each Coding (Write) Compile + run + verify output /126 6 problems × 3 languages × 7 pts Coding (Fix) Hidden test harness /25 5 buggy JS functions, 5 pts each Tool Math Correctness + tool-call counting /25 5 multi-step arithmetic tasks THE SURPRISING CONCLUSIONS 95.0% BEST COMBINED 4m13s WALL CLOCK 20 CELLS TESTED 5 DIMENSIONS The runtime barely matters. Ollama and llamaswap score within a few points of each other on every model when you contr", "height": 12912, "screenshots": { "desktop": "assets/screenshots/desktop/reports--local-providers.png", "mobile": "assets/screenshots/mobile/reports--local-providers.png" } }, { "url": "https://thefocus.ai/reports/model-showdown", "path": "/reports/model-showdown", "slug": "reports--model-showdown", "category": "Report/deep-dive", "status": 200, "title": "Testing 48 LLMs Across 5 Dimensions for $4.63 — Model Showdown", "metaDescription": "Which model should you actually use? We tested 48 LLMs across reasoning, knowledge, instruction following, coding, and MCP tool use. Claude Sonnet 4.6 leads at 93.8%.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Testing 48 LLMs Across 5 Dimensions for $4.63", "index": 1 }, { "tag": "h2", "text": "Five dimensions, one question: how good is this model?", "index": 2 }, { "tag": "h3", "text": "The 49 Models", "index": 3 }, { "tag": "h2", "text": "Full leaderboard", "index": 4 }, { "tag": "h2", "text": "What we didn’t expect", "index": 5 }, { "tag": "h2", "text": "The counterfeit coin problem", "index": 6 }, { "tag": "h3", "text": "Counterfeit Coin Results", "index": 7 }, { "tag": "h3", "text": "The Cross-Provider Reasoning Gap", "index": 8 }, { "tag": "h2", "text": "What models get wrong", "index": 9 }, { "tag": "h3", "text": "“How many R’s in Strawberry?”", "index": 10 }, { "tag": "h3", "text": "“All But 9 Die”", "index": 11 }, { "tag": "h3", "text": "The Carbon-14 Ambiguity", "index": 12 }, { "tag": "h3", "text": "AI/ML Gotchas", "index": 13 }, { "tag": "h2", "text": "Do exactly what I ask", "index": 14 }, { "tag": "h3", "text": "The Markdown Fence Epidemic", "index": 15 }, { "tag": "h3", "text": "Word Counting Is Hard", "index": 16 }, { "tag": "h3", "text": "Instruction Following Doesn’t Predict Tool Use", "index": 17 }, { "tag": "h2", "text": "Code that actually runs", "index": 18 }, { "tag": "h3", "text": "Cipher TypeScript Results", "index": 19 }, { "tag": "h3", "text": "Why Some Models Score Zero", "index": 20 }, { "tag": "h3", "text": "Rust Is the Hardest Language", "index": 21 }, { "tag": "h3", "text": "Speed as Capability", "index": 22 }, { "tag": "h2", "text": "Can models orchestrate real-world tools?", "index": 23 }, { "tag": "h3", "text": "The Efficiency Spectrum", "index": 24 }, { "tag": "h3", "text": "The Failures Tell the Real Story", "index": 26 }, { "tag": "h2", "text": "Why infrastructure matters", "index": 27 }, { "tag": "h3", "text": "Cross-Provider Comparison", "index": 28 }, { "tag": "h3", "text": "Seven Layers of Provider Difference", "index": 29 }, { "tag": "h2", "text": "The cost efficiency curve", "index": 30 }, { "tag": "h3", "text": "The Speed/Quality Frontier", "index": 31 }, { "tag": "h2", "text": "Methodology", "index": 32 }, { "tag": "h3", "text": "The Judge", "index": 33 }, { "tag": "h3", "text": "Run Details", "index": 34 }, { "tag": "h3", "text": "Limitations", "index": 35 }, { "tag": "h2", "text": "MORE REPORTS", "index": 36 }, { "tag": "h3", "text": "The Car Wash Test", "index": 37 }, { "tag": "h3", "text": "MCP Tool Test", "index": 38 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 39 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 40 }, { "tag": "h3", "text": "COMPANY", "index": 41 }, { "tag": "h3", "text": "PRODUCTS", "index": 42 }, { "tag": "h3", "text": "RESOURCES", "index": 43 }, { "tag": "h3", "text": "CONNECT", "index": 44 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Testing 48 LLMs Across 5 Dimensions for $4.63", "y": 278, "body": "Which model should you actually use? Not which one tops a leaderboard somewhere — which one will reason through your problem, follow your formatting instructions, write code that compiles, answer factual questions correctly, and orchestrate real-world tools without falling apart? 48 MODELS 5 PROVIDERS 5 DIMENSIONS $4.63 TOTAL" }, { "tag": "h2", "title": "Five dimensions, one question: how good is this model?", "y": 1004, "body": "Each dimension tests something the others can’t. A model that aces reasoning might struggle with precise formatting. A coding specialist might fail at tool orchestration. The combined score reveals which models are genuinely well-rounded. REASONING — /20 Four classic logic puzzles where the obvious answer is wrong, scored 1–5 each by an LLM judge on reasoning quality: The Surgeon Riddle — “The surgeon says ‘I can’t operate on this boy, he’s my son.’ How is this possible?” Bat & Ball — “A bat and ball cost $1.10. The bat costs $1.00 more than the ball. How much does the ball cost?” (Answer: $0.05, not $0.10) Lily Pads — “A patch doubles daily. It covers the lake in 48 days. When is it half-co" }, { "tag": "h3", "title": "The 49 Models", "y": 2439, "body": "OPENROUTER (31 MODELS) anthropic/claude-sonnet-4.6 anthropic/claude-opus-4.6 anthropic/claude-haiku-4.5 openai/gpt-5.4 openai/gpt-5.4-mini openai/gpt-5.4-nano openai/gpt-oss-20b openai/gpt-oss-120b x-ai/grok-4.20-beta x-ai/grok-4.1-fast google/gemini-3.1-pro-preview qwen/qwen3.5-397b-a17b qwen/qwen3.5-122b-a10b qwen/qwen3.5-35b-a3b deepseek/deepseek-v3.2 meta-llama/llama-4-scout meta-llama/llama-4-maverick inception/mercury-2 inception/mercury-coder moonshotai/kimi-k2 moonshotai/kimi-k2.5 minimax/minimax-m2.7 mistralai/mistral-small-2603 mistralai/mistral-small-3.2-24b-instruct mistralai/codestral-2508 mistralai/ministral-8b-2512 nvidia/nemotron-3-nano-30b-a3b:free nvidia/nemotron-3-super-12" }, { "tag": "h2", "title": "Full leaderboard", "y": 3540, "body": "Combined score is the mean of normalized dimension percentages. anthropic/claude-sonnet-4.6 leads at 93.8% across all 5 dimensions. Models with MCP data use all 5 dimensions; those without use 4. 41 of 48 models now have full 5-dimension scores including MCP tool use. Click any column header to sort. Use filters to narrow down by provider or cost tier. ALL OPENROUTER GOOGLE DEEPINFRA OLLAMA FREE | OPEN WEIGHTS CLOSED MIN SCORE any MAX COST any MAX TIME any # MODEL PROVIDER RELEASED TYPE COMBINED REASON KNOW INSTR CODE MCP COST TIME 🥇 anthropic/claude-sonnet-4.6 OR Feb 26 CLOSED 93.8% 20/20 30/30 30/30 126/126 11/16 $0.20 3m 🥈 qwen/qwen3.5-397b-a17b OR Mar 26 OPEN 93.1% 20/20 29/30 30/30 12" }, { "tag": "h2", "title": "What we didn’t expect", "y": 6243, "body": "#1 MCP has a hard ceiling at 11/16 Every model that called all 6 required tools (20 models) got exactly judge=5/10. No model broke through. MCP tool use is binary — either you call the right tools or you don’t. Quality variance comes entirely from tool selection, not generation. #2 Gemini 3.1 Pro: 91.6% for $0.008 Under a penny for a top-10 model. At 11,450 pts/$, gemini-3.1-pro-preview is the best value in the entire test — beating models that cost 25x more. #3 Only 4 models are fast AND good Under 3 minutes and above 90%: Sonnet, Grok, GPT-5.4, GPT-5.4-mini. That’s it. 23 of 48 models are both slow and below 90%. Speed + quality is genuinely rare. #4 Open weights are 0.7% behind closed Bes" }, { "tag": "h2", "title": "The counterfeit coin problem", "y": 7180, "body": "The single hardest task in the entire showdown. You have 12 coins, one is counterfeit (heavier or lighter — you don’t know which). Using a balance scale exactly 3 times, find the counterfeit coin and determine whether it’s heavier or lighter. Three weighings give 27 possible outcomes for 24 possible states — it’s information-theoretically tight. Many models failed this task. The failure pattern is consistent: models correctly identify the first step (divide into 3 groups of 4) but then hand-wave through the subcases with phrases like “narrow it down to the suspect” without proving that exactly 3 weighings suffice for every branch. JUDGE ON CLAUDE HAIKU 4.5 “The response correctly identifies " }, { "tag": "h3", "title": "Counterfeit Coin Results", "y": 7737, "body": "RESULT MODELS Solved (5/5) All three Qwen 3.5 variants, gpt-oss-120b, kimi-k2, nemotron-nano:free (OR), mercury-2, Nemotron Super 120B (both providers), gemini-3-flash-preview Failed (2/5) gpt-oss-20b, minimax-m2.7, deepseek-v3.2, both Llama 4 variants, mistral-small-3.2, mercury-coder, claude-haiku-4.5, Nemotron-Nano (DI), codestral-2508, gemma-3-27b-it, ministral-8b What’s striking is that openai/gpt-oss-20b — the best coding model in the showdown — falls on its face here. It can write correct code in three languages but can’t reason through a logic puzzle requiring exhaustive case analysis. This is exactly why multi-dimensional evaluation matters." }, { "tag": "h3", "title": "The Cross-Provider Reasoning Gap", "y": 8114, "body": "nvidia/nemotron-3-nano-30b-a3b:free (OpenRouter) scored 20/20 but nvidia/Nemotron-3-Nano-30B-A3B (DeepInfra) only scored 17/20. Same weights. The DeepInfra variant failed the counterfeit coin (2/5) while OpenRouter solved it perfectly (5/5). Quantization or inference configuration can affect a model’s ability to maintain long chains of reasoning." }, { "tag": "h2", "title": "What models get wrong", "y": 8431, "body": "Many models achieved a perfect 30/30. Those that missed questions each missed 1–5. The errors reveal how models process information." }, { "tag": "h3", "title": "“How many R’s in Strawberry?”", "y": 8566, "body": "Three models answered “2” instead of “3.” Both Llama 4 variants and mistralai/mistral-small-3.2-24b-instruct failed this classic character-counting test. Models that tokenize the word rather than examining individual characters consistently get it wrong. Multiple models still fail it in March 2026." }, { "tag": "h3", "title": "“All But 9 Die”", "y": 8744, "body": "“A farmer has 10 sheep. All but 9 die. How many sheep does the farmer have left?” mistralai/codestral-2508 and mistralai/ministral-8b-2512 both answered “8,” misinterpreting “all but 9” as “10 minus 9 = 1 dies, leaving… 8?” Both failing models are from Mistral, suggesting a shared training-data blind spot." }, { "tag": "h3", "title": "The Carbon-14 Ambiguity", "y": 8949, "body": "Four models got the Carbon-14 half-life wrong (correct: 5,730 years). meta-llama/llama-4-scout, mistralai/codestral-2508, mistralai/ministral-8b-2512, and openai/gpt-oss-20b all provided incorrect or imprecise answers. The hardest science question by error count." }, { "tag": "h3", "title": "AI/ML Gotchas", "y": 9129, "body": "google/gemma-3-27b-it said the “T” in GPT stands for “Transformative” instead of “Transformer.” mistralai/ministral-8b-2512 said the original Transformer’s d_model was 64 instead of 512. openai/gpt-oss-120b returned an empty response for the activation function question. Surprising for models that are themselves transformers." }, { "tag": "h2", "title": "Do exactly what I ask", "y": 9447, "body": "Many models achieved a perfect 30/30. The failures are mechanical and revealing. This is the easiest dimension to ace — and the least predictive of real-world capability." }, { "tag": "h3", "title": "The Markdown Fence Epidemic", "y": 9608, "body": "The most systematic failure: “markdown fence hallucination.” When told to output raw JSON with no markdown fences, models wrap it in ```json ... ``` anyway. When told to output a markdown table, models double-wrap the markdown inside a ```markdown block. google/gemma-3-27b-it and mistralai/ministral-8b-2512 both lost points for this — a deep training bias toward code-fence formatting that overrides explicit instructions." }, { "tag": "h3", "title": "Word Counting Is Hard", "y": 9842, "body": "“Write a 12-word sentence about the ocean. Nothing else.” Seven models got this wrong. mistralai/codestral-2508 wrote 6 words. moonshotai/kimi-k2 wrote 10. Four models wrote 11 (off by 1). Models that reliably nail word counts tend to be the same ones that score well on reasoning — they actually count rather than estimate." }, { "tag": "h3", "title": "Instruction Following Doesn’t Predict Tool Use", "y": 10047, "body": "The most counterintuitive finding: perfect instruction following (30/30) does not predict good MCP tool use. anthropic/claude-haiku-4.5 and nvidia/Nemotron-3-Nano-30B-A3B (DeepInfra) both got 30/30 on instruction yet scored 2/16 on MCP. Meanwhile, inception/mercury-2 scored only 25/30 on instruction but got 11/16 on MCP. Following rules is a different cognitive task than autonomously planning a multi-step tool chain. Instruction following tests compliance; MCP tests initiative." }, { "tag": "h2", "title": "Code that actually runs", "y": 10418, "body": "The coding dimension has the widest score spread: 0/126 to 126/126. Multiple models now achieve perfect scores, while the cipher and pipeline challenges still separate the strong from the weak." }, { "tag": "h3", "title": "Cipher TypeScript Results", "y": 10578, "body": "Many models produced perfect cipher implementations. Click column headers to sort. MODEL SCORE TIME inception/mercury-2 7/7 3.4s mistralai/codestral-2508 7/7 3.9s anthropic/claude-haiku-4.5 7/7 4.5s mistralai/mistral-small-3.2-24b-instruct 5/7 6s moonshotai/kimi-k2 7/7 6.1s meta-llama/llama-4-maverick 7/7 10.2s deepseek/deepseek-v3.2 7/7 14s openai/gpt-oss-120b 7/7 18s meta-llama/llama-4-scout 5/7 18s gemini-3-flash-preview 7/7 22s mistralai/ministral-8b-2512 5/7 26s qwen/qwen3.5-35b-a3b 7/7 35s minimax/minimax-m2.7 7/7 42s nvidia/NVIDIA-Nemotron-3-Super-120B-A12B 5/7 45s nvidia/Nemotron-3-Nano-30B-A3B 7/7 55s qwen/qwen3.5-coder-32b-a6b 7/7 1.1m google/gemma-3-27b-it 7/7 1.5m inception/mercu" }, { "tag": "h3", "title": "Why Some Models Score Zero", "y": 11760, "body": "Three distinct failure modes explain the zero scores on cipher and data pipeline challenges: FAILURE MODE 1 65k token reasoning loops openai/gpt-oss-20b and nvidia/nemotron-3-nano-30b-a3b:free generated exactly 65,536 completion tokens (OpenRouter’s max) in 4–7 minutes — but returned empty content. The models were trapped in internal reasoning/thinking chains that consumed the entire token budget without producing visible output. For gpt-oss-20b this was transient — it solved cipher in Rust and Python perfectly in a later batch. The cached empty result persisted. FAILURE MODE 2 Infinite verification loops qwen/qwen3.5-122b-a10b spent 5 minutes generating 31,000 tokens of visible reasoning — " }, { "tag": "h3", "title": "Rust Is the Hardest Language", "y": 12599, "body": "LANGUAGE AVG SCORE COMPILE RATE PERFECT RATE TypeScript 5.3/7 92% 78% Python 5.1/7 88% 76% Rust 4.3/7 72% 65% Rust’s lower success rate comes from its strict type system and ownership model. Models struggle with borrowing, lifetimes, and the borrow checker — generating code that looks correct but fails to compile. The gap between Rust and TypeScript is consistent across all challenge types." }, { "tag": "h3", "title": "Speed as Capability", "y": 12979, "body": "Speed remains a decisive factor. Multiple top models now achieve perfect 126/126, but the data pipeline and cipher challenges still separate fast from slow. Models that respond within seconds across all 18 tasks — spanning FizzBuzz, business days, vending machines, grid paths, ciphers, and data pipelines — consistently rank higher overall." }, { "tag": "h2", "title": "Can models orchestrate real-world tools?", "y": 13294, "body": "The most revealing dimension. Each model connects to TezLab’s MCP server and must analyze battery health and charging patterns by calling 6 tools in the right sequence. Among models that called all 6 tools, quality scores were uniformly 5/5. The variance is entirely in tool usage — MCP tool use is primarily a planning problem, not a generation problem. Note: MCP results are available for 41 of 48 models — comprehensive coverage across all providers. The 7 models without MCP data either lack tool-use support (phi4, gemma3n, deepseek-r1 variants) or were not attempted (Nemotron Nano 9B variants). Models without MCP data show “—” in the leaderboard and are scored on 4 dimensions only." }, { "tag": "h3", "title": "The Efficiency Spectrum", "y": 13655, "body": "MODEL TOOL JUDGE TOTAL TIME COST mistralai/mistral-small-2603 6/6 5/10 11/16 8s $0.0010 x-ai/grok-4.20-beta 6/6 5/10 11/16 10s $0.020 openai/gpt-5.4 6/6 5/10 11/16 12s $0.040 inception/mercury-2 6/6 5/10 11/16 14s $0.0070 anthropic/claude-sonnet-4.6 6/6 5/10 11/16 15s $0.050 anthropic/claude-opus-4.6 6/6 5/10 11/16 20s $0.10 qwen/qwen3.5-35b-a3b 6/6 5/10 11/16 24s $0.0060 gemini-3.1-pro-preview 6/6 5/10 11/16 30s $0.0020 moonshotai/kimi-k2 6/6 5/10 11/16 34s $0.014 gemini-3-flash-preview 6/6 5/10 11/16 40s $0.0020 openai/gpt-oss-20b 6/6 5/10 11/16 52s $0.0010 moonshotai/kimi-k2.5 6/6 5/10 11/16 55s $0.060 glm-4.7-flash-latest 6/6 5/10 11/16 1m Free nvidia/nemotron-3-nano-30b-a3b:free 6/6 5/1" }, { "tag": "h3", "title": "", "y": 0, "body": "" }, { "tag": "h3", "title": "The Failures Tell the Real Story", "y": 15551, "body": "CLAUDE HAIKU 4.5 VIA OPENROUTER (2/16) “I apologize — I’m encountering persistent server errors when trying to connect to your TezLab account to retrieve vehicle information. The service appears to be temporarily unavailable.” The service was running fine. Every other model connected without issues. Haiku received an error on the first list_vehicles call, retried once, then generated a polite failure response instead of persisting. It treated a tool error as terminal rather than retryable. At $0.013, it cost more than most models that scored perfectly. NEMOTRON NANO 30B ON DEEPINFRA (2/16) “I see you have two vehicles in your TezLab account: 1. Tesla Model X, 2. Rivian R1S. Could you tell me" }, { "tag": "h2", "title": "Why infrastructure matters", "y": 16476, "body": "The same model weights, served by different providers, produce meaningfully different results. Two NVIDIA Nemotron models appear on both OpenRouter and DeepInfra — same weights, different infrastructure. This wasn’t planned as an experiment, but it became one of the most revealing findings." }, { "tag": "h3", "title": "Cross-Provider Comparison", "y": 16663, "body": "MODEL DIMENSION OPENROUTER DEEPINFRA GAP Nemotron Nano 30B Reasoning 20/20 17/20 −3 Knowledge 30/30 30/30 0 Instruction 28/30 30/30 +2 Coding 71/126 110/126 +39 MCP Tool Use 11/16 2/16 −9 Combined 83.7% 77.0% −6.7pp Nemotron Super 120B Reasoning 20/20 20/20 0 Knowledge 30/30 28/30 −2 Instruction 25/30 30/30 +5 Coding 74/126 88/126 +14 MCP Tool Use 11/16 4/16 −7 Combined 82.2% 77.6% +4.6pp" }, { "tag": "h3", "title": "Seven Layers of Provider Difference", "y": 17289, "body": "There are at least seven layers where providers can introduce behavioral differences, even when serving identical model weights: Quantization — DeepInfra typically runs FP8; OpenRouter routes to NVIDIA’s own infrastructure which may use different precision. Affects long reasoning chains most. SDK layers — OpenRouter uses a native Vercel AI SDK provider; DeepInfra uses a generic OpenAI-compatible adapter. Tool calling and response parsing differ. Middleware — OpenRouter applies context compression, response healing (malformed JSON repair), and schema modification. DeepInfra passes requests directly. Tool-calling implementation — How tools appear in context, how results are formatted, how the " }, { "tag": "h2", "title": "The cost efficiency curve", "y": 17997, "body": "The relationship between cost and quality is not linear — it has a sharp knee at around $0.01. You get 89.9% across all 5 dimensions for one cent. The models with the highest 4-dimension scores cost significantly more, but the value proposition of sub-$0.10 models remains remarkable. TIER COST RANGE BEST MODEL SCORE Free $0.00 nemotron-3-nano:latest (Ollama) 84.0% Sub-penny $0.001–$0.01 deepseek/deepseek-v3.2 (OR) 88.3% Penny $0.01–$0.05 openai/gpt-oss-120b (OR) 89.9% Dime $0.05–$0.20 x-ai/grok-4.20-beta (OR) 92.8% Quarter+ $0.20+ anthropic/claude-sonnet-4.6 (OR) 93.8% The cost efficiency story has changed dramatically. Free local models now compete with mid-tier API models, and $0.01 buys y" }, { "tag": "h3", "title": "The Speed/Quality Frontier", "y": 18586, "body": "MODEL SCORE TIME SWEET SPOT anthropic/claude-sonnet-4.6 93.8% 3m Best quality x-ai/grok-4.20-beta 92.8% 2m Best speed/quality openai/gpt-5.4-mini 90% 1m Best quality <2m mistralai/mistral-small-2603 89% 1m Fastest >85% mistralai/codestral-2508 77.4% 48s Fastest overall" }, { "tag": "h2", "title": "Methodology", "y": 19006, "body": "All evaluations ran with temperature 0.0 (knowledge, instruction) or 0.2–0.3 (reasoning, coding). Reasoning and knowledge responses were judged by anthropic/claude-haiku-4.5 (OpenRouter). Instruction following and coding used deterministic verification — no LLM judge involved. Combined scores are the mean of normalized dimension percentages (each dimension’s raw score divided by its maximum, then averaged). Models without MCP data are scored across the 4 available dimensions. MCP data is shown where available; models without MCP results display a dash." }, { "tag": "h3", "title": "The Judge", "y": 19299, "body": "anthropic/claude-haiku-4.5 (via OpenRouter) serves as both contestant and judge. We mitigated the circularity by using deterministic scoring for instruction following and coding, scoring MCP tool usage mechanically, and only using the LLM judge for reasoning quality and factual correctness. The judge showed no measurable bias toward its own responses — Haiku scored itself 17/20 on reasoning (below the median of 17.6)." }, { "tag": "h3", "title": "Run Details", "y": 19516, "body": "Reasoning — Run #7, 4 puzzles, scored /20 Knowledge — Run #2, 30 questions across 6 categories, scored /30 Instruction — Run #2, 6 constraint tasks, scored /30 Coding — Run #6, 6 challenges x 3 languages, scored /126 MCP Tool Use — Run #1, 1 multi-tool task, scored /16" }, { "tag": "h3", "title": "Limitations", "y": 19763, "body": "Cipher and pipeline challenges have incomplete coverage due to 10-minute timeouts. Models that couldn’t respond score 0 — treated as a finding rather than missing data. MCP eval used a single task with a live API. Results may vary with different MCP servers. Free-tier models may have different availability or routing than paid versions. Each model tested once per task. Stochastic variation means scores could shift 1–3 points on a re-run. 7 of 48 models are missing MCP scores (no tool-use support or not attempted). Their combined scores are based on 4 dimensions only, while the 41 models with MCP data use all 5 dimensions." }, { "tag": "h2", "title": "MORE REPORTS", "y": 20319, "body": "The Car Wash Test 131 models benchmarked on a deceptively simple reasoning task. MCP Tool Test How well do models use tools? Evaluating MCP with real-world Rivian data. ← All Reports" }, { "tag": "h3", "title": "The Car Wash Test", "y": 20380, "body": "131 models benchmarked on a deceptively simple reasoning task." }, { "tag": "h3", "title": "MCP Tool Test", "y": 20380, "body": "How well do models use tools? Evaluating MCP with real-world Rivian data." }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 20661, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 21111, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 21488, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 21488, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 21488, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 21488, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "THEFOCUS.AI", "href": "https://thefocus.ai/" }, { "text": "The Car Wash Test 131 models benchmarked on a deceptively simple reasoning task.", "href": "https://thefocus.ai/reports/car-wash-test" }, { "text": "MCP Tool Test How well do models use tools? Evaluating MCP with real-world Rivian data.", "href": "https://thefocus.ai/reports/mcp-tool-test" }, { "text": "← All Reports", "href": "https://thefocus.ai/reports" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" }, { "text": "Registry", "href": "https://account.thefocus.ai/" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/", "https://thefocus.ai/reports/car-wash-test", "https://thefocus.ai/reports/mcp-tool-test", "https://thefocus.ai/reports", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] UMWELTEN MODEL SHOWDOWN · MARCH 2026 Testing 48 LLMs Across 5 Dimensions for $4.63 Which model should you actually use? Not which one tops a leaderboard somewhere — which one will reason through your problem, follow your formatting instructions, write code that compiles, answer factual questions correctly, and orchestrate real-world tools without falling apart? 48 MODELS 5 PROVIDERS 5 DIMENSIONS $4.63 TOTAL 93.8% TOP SCORE $0.01 BEST VALUE 15 MODELS >90% $4.63 TOTAL COST 01 — WHAT WE TESTED Five dimensions, one question: how good is this model? Each dimension tests something the others can’t. A model that aces reasoning might struggle with precise formatting. A coding specialist might fail at tool orchestration. The combined score reveals which models are genuinely well-rounded. REASONING — /20 Four classic logic puzzles where the obvious answer is wrong, scored 1–5 each by an LLM judge on reasoning quality: The Surgeon Riddle — “The surgeon says ‘I can’t operate on this boy, he’s my son.’ How is this possible?” Bat & Ball — “A bat and ball cost $1.10. The bat costs $1.00 more than the ball. How much does the ball cost?” (Answer: $0.05, not $0.10) Lily Pads — “A patch doubles daily. It covers the lake in 48 days. When is it half-covered?” (Answer: day 47) Counterfeit Coin — Find a counterfeit coin among 12 using a balance scale exactly 3 times, and determine if it’s heavier or lighter. The hardest task in the entire showdown. KNOWLEDGE — /30 30 factual questions, binary scoring (correct or not) with an LLM judge that allows formatting variations. Six categories: Science — Speed of light, glucose formula, Carbon-14 half-life, Oganesson atomic number, Schwarzschild radius Geography — Capital of Kazakhstan, deepest ocean point, country with most time zones, longest African river, Caspian+Persian Gulf border History — Berlin Wall year, first to South Pole, WWI treaty, transistor year, Principia Mathematica author Technology — HTTPS port, max signed 32-bit int, HTTP/0.9 year, OSI Layer 4, what CUDA stands for AI/ML — What “T” in GPT stands for, Transformer d_model, feed-forward activation, Llama creator, knowledge distillation Tricky/Adversarial — R’s in “strawberry,” is 91 prime, “all but 9 die,” feathers vs steel, 3/5 gallon jug puzzle INSTRUCTION FOLLOWING — /30 Six const", "height": 21985, "screenshots": { "desktop": "assets/screenshots/desktop/reports--model-showdown.png", "mobile": "assets/screenshots/mobile/reports--model-showdown.png" } }, { "url": "https://thefocus.ai/reports/mcp-tool-test", "path": "/reports/mcp-tool-test", "slug": "reports--mcp-tool-test", "category": "Report/deep-dive", "status": 200, "title": "Can LLMs Use Real-World Tools? — MCP Tool Eval", "metaDescription": "We gave 39 models access to a Rivian owner's real driving and charging data via MCP tools and asked them to summarize 10 days of activity. Most can call tools. Far fewer call the right tools.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Can LLMs use real-world tools to tell a story?", "index": 1 }, { "tag": "h2", "text": "What are we measuring?", "index": 2 }, { "tag": "h2", "text": "How we score", "index": 3 }, { "tag": "h2", "text": "What we learned", "index": 4 }, { "tag": "h2", "text": "Full rankings", "index": 5 }, { "tag": "h2", "text": "Who tells the best story?", "index": 7 }, { "tag": "h2", "text": "Models that couldn't complete the test", "index": 8 }, { "tag": "h2", "text": "How the test was run", "index": 9 }, { "tag": "h2", "text": "MORE REPORTS", "index": 10 }, { "tag": "h3", "text": "Model Showdown", "index": 11 }, { "tag": "h3", "text": "The Car Wash Test", "index": 12 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 13 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 14 }, { "tag": "h3", "text": "COMPANY", "index": 15 }, { "tag": "h3", "text": "PRODUCTS", "index": 16 }, { "tag": "h3", "text": "RESOURCES", "index": 17 }, { "tag": "h3", "text": "CONNECT", "index": 18 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Can LLMs use real-world tools to tell a story?", "y": 278, "body": "We gave 39 models access to a Rivian owner's real driving and charging data via MCP tools and asked them to summarize 10 days of activity. Most models can call tools. Far fewer call the right tools with the right parameters and weave the results into an engaging narrative. 39 MODELS 8 PROVIDERS $0.96 TOTAL FEB 27 – MAR 8, 2026" }, { "tag": "h2", "title": "What are we measuring?", "y": 974, "body": "This evaluation tests something most benchmarks skip: can a model use external tools to answer a question that requires real data? Not synthetic function calls. Not mock APIs. Real tools, connected to a real Rivian R1S via the TezLab MCP server. Each model gets the same prompt and the same 20+ MCP tools (get_drives, get_charges, get_battery_health, etc.). It must figure out which to call, pass the right date parameters, and synthesize raw JSON into a narrative a human would want to read. THE PROMPT Look through my real data and summarize the 10 days of the Rivian's activity between February 27 and March 8, 2026. If there were any notable trips, create a narrative of the time frame. Today is " }, { "tag": "h2", "title": "How we score", "y": 1760, "body": "Two independent axes: did the model call the right tools (deterministic), and did it write a good summary (judged by Claude Haiku 4.5). Total: 0–15. TOOL SCORE (0–5, DETERMINISTIC) Called get_drives Called get_charges get_drives included start_date get_charges included start_date At least one start_date ≤ Feb 27 LLM JUDGE SCORE (0–10) Covers full Feb 27 – Mar 8 range Contains a trip narrative (not just bullets) Narrative quality (1–5) Factual grounding from real data (1–5) Combined into overall score (1–10)" }, { "tag": "h2", "title": "What we learned", "y": 2336, "body": "⚡ Speed doesn't sacrifice quality Mercury-2 delivered a perfect 15/15 in 8.6s at $0.006. 💰 Open-source punches above cost GPT-OSS-120B scored 15/15 for $0.001 — 14,303 pts/$. 🏠 Local models hold their own GLM-4.7-flash on Ollama scored 14/15, zero API cost. 💥 The date trap works Models skipping start_date get truncated data and write wrong summaries. 🚫 Tool calling isn't universal 8 models failed outright — DeepSeek R1, Phi-4, Gemma 3N lack tool support. 💫 Qwen 3.5 dominates value All four Qwen 3.5 variants scored 15/15. Flash: $0.003 in 22s." }, { "tag": "h2", "title": "Full rankings", "y": 2940, "body": "Click any row to see the full response, tool-calling thread, and judge verdict. ALL PERFECT CLOUD LOCAL MAX COST any MAX TIME any MIN SCORE any MIN ELO any # MODEL TOTAL LLM TOOLS TIME COST PTS/$ ELO STORY FACTS FEB? 🥇 meta-llama/llama-4-scout 1/15 1/10 0/5 3.4s $0.0008 1318.7 1291 ❌ 🥈 meta-llama/llama-4-maverick 2/15 2/10 0/5 2.4s $0.0016 1271.9 1302 ❌ 🥉 mistralai/mistral-large-2512 2/15 2/10 0/5 46.6s $0.0094 211.9 1424 ❌ 4 mistral-small:latest LOCAL 4/15 4/10 0/5 126.2s local — 1361 ✅ 5 qwen3:30b-a3b LOCAL 7/15 4/10 3/5 101.6s local — 1263 ❌ 6 google/gemini-3-flash-preview 8/15 5/10 3/5 12.6s $0.0079 1007.8 1577 ✅ 7 x-ai/grok-4 10/15 7/10 3/5 100.2s $0.0825 121.2 1356 ✅ 8 minimax-m2.1:" }, { "tag": "h3", "title": "", "y": 0, "body": "" }, { "tag": "h2", "title": "Who tells the best story?", "y": 4695, "body": "The judge score rates each model in isolation. But how do models compare head-to-head? We ran a Swiss-style pairwise tournament: Claude Haiku 4.5 reads two narratives side-by-side (randomly ordered to avoid position bias) and picks the better one. Results feed into Bradley-Terry/Elo ratings. round-robin • 321 comparisons • judge: openrouter:anthropic/claude-haiku-4.5 # MODEL ELO W L T TOOLS 🥇 openrouter:anthropic/claude-opus-4.6 1707 21 1 0 5/5 🥈 openrouter:moonshotai/kimi-k2.5 1701 20 1 0 5/5 🥉 ollama:minimax-m2.1:cloud 1688 19 2 0 5/5 4 openrouter:anthropic/claude-sonnet-4.6 1660 15 1 0 5/5 5 openrouter:openai/gpt-5.4 1654 20 4 0 5/5 6 openrouter:qwen/qwen3.5-397b-a17b 1648 15 2 0 5/5 7" }, { "tag": "h2", "title": "Models that couldn't complete the test", "y": 6399, "body": "These models lack tool-calling support or aren't available. Choosing a model that can't use tools wastes time and money. MODEL PROVIDER ERROR deepseek/deepseek-v3.2-speciale OPENROUTER No endpoints found that support tool use. To learn more about provider routing, visit: https://openrouter.ai/docs/guides/routing/provider-selection deepseek/deepseek-v4 OPENROUTER Invalid model details: {\"name\":\"deepseek/deepseek-v4\",\"provider\":\"openrouter\"} deepseek-r1:14b OLLAMA Bad Request deepseek-r1:32b OLLAMA Bad Request deepseek-r1:latest OLLAMA Bad Request gemma3n:e4b OLLAMA Bad Request phi4:latest OLLAMA Bad Request x-ai/grok-4.20 OPENROUTER Invalid model details: {\"name\":\"x-ai/grok-4.20\",\"provider\":\"" }, { "tag": "h2", "title": "How the test was run", "y": 7081, "body": "Sequential model runs against a shared TezLab MCP connection via OAuth. Same system prompt, same 20+ read-only tools, up to 20 tool-calling steps. Vehicle commands filtered via MCP annotations. Responses cached per-run. Judge: Claude Haiku 4.5 via OpenRouter, temperature 0.0. Cost metered by OpenRouter; Ollama = \"local\" / $0. Duration = wall-clock including all tool round trips." }, { "tag": "h2", "title": "MORE REPORTS", "y": 7599, "body": "Model Showdown Testing 48 LLMs across 5 dimensions for $4.63. The Car Wash Test 131 models benchmarked on a deceptively simple reasoning task. ← All Reports" }, { "tag": "h3", "title": "Model Showdown", "y": 7660, "body": "Testing 48 LLMs across 5 dimensions for $4.63." }, { "tag": "h3", "title": "The Car Wash Test", "y": 7660, "body": "131 models benchmarked on a deceptively simple reasoning task." }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 7941, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 8391, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 8768, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 8768, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 8768, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 8768, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "Thread", "href": "" }, { "text": "THEFOCUS.AI", "href": "https://thefocus.ai/" }, { "text": "Model Showdown Testing 48 LLMs across 5 dimensions for $4.63.", "href": "https://thefocus.ai/reports/model-showdown" }, { "text": "The Car Wash Test 131 models benchmarked on a deceptively simple reasoning task.", "href": "https://thefocus.ai/reports/car-wash-test" }, { "text": "← All Reports", "href": "https://thefocus.ai/reports" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" }, { "text": "Registry", "href": "https://account.thefocus.ai/" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/", "https://thefocus.ai/reports/model-showdown", "https://thefocus.ai/reports/car-wash-test", "https://thefocus.ai/reports", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] UMWELTEN MCP EVALUATION · RUN 004 Can LLMs use real-world tools to tell a story? We gave 39 models access to a Rivian owner's real driving and charging data via MCP tools and asked them to summarize 10 days of activity. Most models can call tools. Far fewer call the right tools with the right parameters and weave the results into an engaging narrative. 39 MODELS 8 PROVIDERS $0.96 TOTAL FEB 27 – MAR 8, 2026 17 PERFECT 15/15 8.6s FASTEST PERFECT $0.001 CHEAPEST PERFECT 8 FAILED / ERRORED 01 — THE TEST What are we measuring? This evaluation tests something most benchmarks skip: can a model use external tools to answer a question that requires real data? Not synthetic function calls. Not mock APIs. Real tools, connected to a real Rivian R1S via the TezLab MCP server. Each model gets the same prompt and the same 20+ MCP tools (get_drives, get_charges, get_battery_health, etc.). It must figure out which to call, pass the right date parameters, and synthesize raw JSON into a narrative a human would want to read. THE PROMPT Look through my real data and summarize the 10 days of the Rivian's activity between February 27 and March 8, 2026. If there were any notable trips, create a narrative of the time frame. Today is Mar 8 2026 and make sure that you include the full 10 days, so if you don't have any drives and chargers in February it's obviously false. The last sentence is a trap. Models that call get_drives without a start_date get only recent data (biased toward March), then confidently write a summary that misses the first days. The judge catches this. 02 — SCORING How we score Two independent axes: did the model call the right tools (deterministic), and did it write a good summary (judged by Claude Haiku 4.5). Total: 0–15. TOOL SCORE (0–5, DETERMINISTIC) Called get_drives Called get_charges get_drives included start_date get_charges included start_date At least one start_date ≤ Feb 27 LLM JUDGE SCORE (0–10) Covers full Feb 27 – Mar 8 range Contains a trip narrative (not just bullets) Narrative quality (1–5) Factual grounding from real data (1–5) Combined into overall score (1–10) 03 — KEY FINDINGS What we learned ⚡ Speed doesn't sacrifice quality Mercury-2 delivered a perfect 15/15 in 8.6s at $0.006. 💰 Open-source punches above cost GPT-OSS-120B scored 15/15 for $0.001 — 1", "height": 9265, "screenshots": { "desktop": "assets/screenshots/desktop/reports--mcp-tool-test.png", "mobile": "assets/screenshots/mobile/reports--mcp-tool-test.png" } }, { "url": "https://thefocus.ai/reports/car-wash-test", "path": "/reports/car-wash-test", "slug": "reports--car-wash-test", "category": "Report/deep-dive", "status": 200, "title": "The Car Wash Test — AI Common-Sense Reasoning Benchmark", "metaDescription": "We tested 131 AI models with one simple question: Should I walk or drive to the car wash? Only 24% figured out you need to bring your car.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "The Car Wash Test: Do LLMs Have Common Sense?", "index": 1 }, { "tag": "h2", "text": "Comparison with Original Research", "index": 2 }, { "tag": "h3", "text": "Consistent Findings Across Both Studies", "index": 3 }, { "tag": "h2", "text": "Visual Analysis", "index": 4 }, { "tag": "h3", "text": "Pass Rate by Model Family", "index": 5 }, { "tag": "h3", "text": "Cost vs. Reasoning Quality", "index": 6 }, { "tag": "h3", "text": "Response Time by Result", "index": 7 }, { "tag": "h3", "text": "Thinking Models: Does Extended Reasoning Help?", "index": 8 }, { "tag": "h3", "text": "Local (Ollama) vs Cloud Models", "index": 9 }, { "tag": "h2", "text": "Full Results", "index": 10 }, { "tag": "h2", "text": "Key Findings", "index": 11 }, { "tag": "h3", "text": "1. The 50m Trap", "index": 12 }, { "tag": "h3", "text": "2. The Qwen 3.5 Breakthrough", "index": 13 }, { "tag": "h3", "text": "3. Local Models: Near-Complete Failure", "index": 14 }, { "tag": "h3", "text": "4. Anthropic's Split", "index": 15 }, { "tag": "h3", "text": "5. Cost Is Not a Predictor", "index": 16 }, { "tag": "h3", "text": "6. Reasoning Models: Mixed Results", "index": 17 }, { "tag": "h2", "text": "Methodology", "index": 18 }, { "tag": "h2", "text": "MORE REPORTS", "index": 19 }, { "tag": "h3", "text": "Model Showdown", "index": 20 }, { "tag": "h3", "text": "MCP Tool Test", "index": 21 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 22 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 23 }, { "tag": "h3", "text": "COMPANY", "index": 24 }, { "tag": "h3", "text": "PRODUCTS", "index": 25 }, { "tag": "h3", "text": "RESOURCES", "index": 26 }, { "tag": "h3", "text": "CONNECT", "index": 27 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "The Car Wash Test: Do LLMs Have Common Sense?", "y": 278, "body": "A simple question exposes a fundamental gap in AI reasoning. We tested 131 models across 8 providers — including 11 local Ollama models — to see which ones understand that you need to bring your car to the car wash, not just yourself. 131 MODELS EVALUATED 8 PROVIDERS RUN #007 · 50M DISTANCE" }, { "tag": "h2", "title": "Comparison with Original Research", "y": 1244, "body": "The car wash test was originally devised by Opper.ai, who tested 53 models at 50 meters. We replicated their study at the same distance with 131 models, added an LLM judge for reasoning quality, included the latest 2026 models, and added 11 local Ollama models to compare cloud vs local inference. OPPER.AI (ORIGINAL) 11 / 53 Distance: 50 meters Single-run pass rate: 20.8% 10-run consistent: 5 models only GPT-5 scored 7/10 across runs Human baseline: 71.5% correct 33 models never correct in any run OUR REPLICATION (EXTENDED) 31 / 131 Distance: 50 meters (same) Single-run pass rate: 23.7% Lucky (drive, wrong reason): 6 models Reasoning judge: claude-haiku-4.5 14 thinking/reasoning models tested" }, { "tag": "h3", "title": "Consistent Findings Across Both Studies", "y": 2033, "body": "🧠 Heuristic Dominance Models default to \"short distance = walk\" without considering what needs to travel. The environmental/health framing overrides physical necessity. 📈 Newer Models Do Better Models released in late 2025–2026 pass at much higher rates. The Qwen 3.5 family achieves 100% (5/5). GPT-5 family mostly succeeds. 💻 Local Models: 0% Pass Rate All 12 Ollama models failed. Local inference models lack the reasoning quality of cloud frontier models on this task." }, { "tag": "h2", "title": "Visual Analysis", "y": 2477, "body": "Pass Rate by Model Family Green = truly correct (drive + right reason). Yellow = lucky (drive + wrong reason). Red = failed. Gray = other. Cost vs. Reasoning Quality Cost per query (log scale). Expensive models don't guarantee correct reasoning. Response Time by Result Correct models are not necessarily slower. Many wrong answers took longer due to elaborate (incorrect) reasoning. Thinking Models: Does Extended Reasoning Help? Only 2 of 11 dedicated thinking models pass at 50m. Thinking alone does not guarantee common sense. Local (Ollama) vs Cloud Models 10 of 11 local Ollama models failed. Only minimax-m2.1:cloud passed. Cloud frontier models pass at 26% vs 9% for local models." }, { "tag": "h3", "title": "Pass Rate by Model Family", "y": 2530, "body": "Green = truly correct (drive + right reason). Yellow = lucky (drive + wrong reason). Red = failed. Gray = other." }, { "tag": "h3", "title": "Cost vs. Reasoning Quality", "y": 3039, "body": "Cost per query (log scale). Expensive models don't guarantee correct reasoning." }, { "tag": "h3", "title": "Response Time by Result", "y": 3039, "body": "Correct models are not necessarily slower. Many wrong answers took longer due to elaborate (incorrect) reasoning." }, { "tag": "h3", "title": "Thinking Models: Does Extended Reasoning Help?", "y": 3488, "body": "Only 2 of 11 dedicated thinking models pass at 50m. Thinking alone does not guarantee common sense." }, { "tag": "h3", "title": "Local (Ollama) vs Cloud Models", "y": 3488, "body": "10 of 11 local Ollama models failed. Only minimax-m2.1:cloud passed. Cloud frontier models pass at 26% vs 9% for local models." }, { "tag": "h2", "title": "Full Results", "y": 4064, "body": "All 131 models sorted by reasoning quality. Click column headers to sort. Use filters to explore by category. ALL CORRECT LUCKY FAILED OTHER LOCAL MODEL ▲ PROVIDER ▲ ANSWER ▲ RIGHT REASON ▲ QUALITY ▲ TIME ▲ COST ▲ REASONING ▲ claude-3.7-sonnet:thinkingTHINKING Anthropic DRIVE ✓ 6.9s $0.004815 The car must be physically present at the car wash facility to be washed; you cannot wash your car if you don't bring it with you. claude-opus-4.5 Anthropic DRIVE ✓ 4.2s $0.002175 You need your car at the car wash to wash it; you can't wash your car if you walk there without it claude-opus-4.6 Anthropic DRIVE ✓ 3.4s $0.002730 You need your car at the car wash in order to wash it. If you walk there, your" }, { "tag": "h2", "title": "Key Findings", "y": 10353, "body": "" }, { "tag": "h3", "title": "1. The 50m Trap", "y": 10415, "body": "At 50 meters, the distance becomes a powerful distractor. Models fixate on \"50 meters is so close!\" and immediately conclude walking is better — for health, environment, convenience. Only 24% of models see through this to the core issue: the car must physically be at the car wash. Even models that passed Opper's test at this distance in other runs may fail on any given attempt due to LLM non-determinism." }, { "tag": "h3", "title": "2. The Qwen 3.5 Breakthrough", "y": 10645, "body": "The Qwen 3.5 family is the standout performer: all five models (plus, 397b, 122b, 35b, 27b) pass with perfect reasoning. Their responses immediately identify that \"the vehicle must be physically present at the car wash.\" This is notable because the older Qwen 3 models (qwen3-max, qwen3-coder, qwen3-max-thinking) all fail. Something changed in the 3.5 generation." }, { "tag": "h3", "title": "3. Local Models: Near-Complete Failure", "y": 10848, "body": "Of 11 local Ollama models, only minimax-m2.1:cloud passed with correct reasoning. The other 10 — including deepseek-r1 (three sizes), qwen3 (two sizes), phi4, gemma3n, mistral-small, devstral, and gpt-oss — all failed. deepseek-r1:14b said \"drive\" but for wrong reasons (convenience, not physical necessity). This suggests quantization and missing RLHF significantly impact common-sense reasoning." }, { "tag": "h3", "title": "4. Anthropic's Split", "y": 11078, "body": "Only Claude Opus 4.5, Opus 4.6, and Claude 3.7 Sonnet:thinking pass. Every other Claude model fails — including Sonnet 4.6, Opus 4, Opus 4.1, Sonnet 4, Sonnet 4.5, and Claude 3.5 models. Notably, Sonnet 4.6 passed in Run #006 but failed in this run — demonstrating the non-determinism that makes single-run benchmarks unreliable. The 3.7 Sonnet thinking variant succeeded this time by reasoning through the physical constraint explicitly." }, { "tag": "h3", "title": "5. Cost Is Not a Predictor", "y": 11309, "body": "Claude Opus 4 ($0.009/query) and Claude Opus 4.1 ($0.009) both fail, while Gemini 3 Flash ($0.000044) and Grok 4.1 Fast ($0.000241) pass — at 30–300x less cost. The most expensive correct model is GPT-5-pro at $0.15/query. The cheapest is Gemini 3 Flash at $0.000044. Common sense is orthogonal to model cost." }, { "tag": "h3", "title": "6. Reasoning Models: Mixed Results", "y": 11512, "body": "Of 14 dedicated thinking/reasoning models, 7 passed (50%): o3, o3-pro, o1, o3-mini, claude-3.7-sonnet:thinking, qwen3-30b-thinking, qwen3-next-80b-thinking, and qwen3-vl-235b-thinking. But 7 others failed — including o3-mini-high, o4-mini, o4-mini-high, qwen3-235b-thinking, and sonar-reasoning-pro. Extended reasoning helps some models work through the physical constraint, but can also reinforce the \"short distance = walk\" heuristic." }, { "tag": "h2", "title": "Methodology", "y": 11854, "body": "Models were evaluated using the umwelten evaluation framework. Each model received an identical prompt with a system message describing a \"helpful assistant\" role (temperature 0.3, max 500 tokens). Responses were cached per-run to ensure reproducibility. Judging was performed by anthropic/claude-haiku-4.5 via OpenRouter, scoring each response on: recommendation (drive/walk/both/unclear), whether the model recognizes the car must be present, whether the correct reason was given, a 1–5 reasoning quality score, and a free-text explanation. A model is classified as truly correct only if it recommends driving and identifies that the car must physically be at the car wash. Models that recommend dr" }, { "tag": "h2", "title": "MORE REPORTS", "y": 12800, "body": "Model Showdown Testing 48 LLMs across 5 dimensions for $4.63. MCP Tool Test How well do models use tools? Evaluating MCP with real-world Rivian data. ← All Reports" }, { "tag": "h3", "title": "Model Showdown", "y": 12861, "body": "Testing 48 LLMs across 5 dimensions for $4.63." }, { "tag": "h3", "title": "MCP Tool Test", "y": 12861, "body": "How well do models use tools? Evaluating MCP with real-world Rivian data." }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 13142, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 13592, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 13969, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 13969, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 13969, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 13969, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "Opper.ai", "href": "https://opper.ai/blog/car-wash-test" }, { "text": "umwelten", "href": "https://github.com/" }, { "text": "CAR WASH TEST BY OPPER.AI", "href": "https://opper.ai/blog/car-wash-test" }, { "text": "THE FOCUS AI", "href": "https://thefocus.ai/" }, { "text": "Model Showdown Testing 48 LLMs across 5 dimensions for $4.63.", "href": "https://thefocus.ai/reports/model-showdown" }, { "text": "MCP Tool Test How well do models use tools? Evaluating MCP with real-world Rivian data.", "href": "https://thefocus.ai/reports/mcp-tool-test" }, { "text": "← All Reports", "href": "https://thefocus.ai/reports" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" }, { "text": "Registry", "href": "https://account.thefocus.ai/" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://opper.ai/blog/car-wash-test", "https://github.com/", "https://opper.ai/blog/car-wash-test", "https://thefocus.ai/", "https://thefocus.ai/reports/model-showdown", "https://thefocus.ai/reports/mcp-tool-test", "https://thefocus.ai/reports", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] RESEARCH REPORT / FEBRUARY 2026 The Car Wash Test: Do LLMs Have Common Sense? A simple question exposes a fundamental gap in AI reasoning. We tested 131 models across 8 providers — including 11 local Ollama models — to see which ones understand that you need to bring your car to the car wash, not just yourself. 131 MODELS EVALUATED 8 PROVIDERS RUN #007 · 50M DISTANCE THE PROMPT \"I want to wash my car. The car wash is 50 meters away. Should I walk or drive?\" Correct answer: DRIVE — the car must be physically present at the car wash to be washed. Walking there leaves your car at home, defeating the purpose entirely. 131 MODELS TESTED 31 TRULY CORRECT 6 LUCKY (WRONG REASON) 90 FAILED (WALK) 4 BOTH / UNCLEAR / ERROR SECTION I Comparison with Original Research The car wash test was originally devised by Opper.ai, who tested 53 models at 50 meters. We replicated their study at the same distance with 131 models, added an LLM judge for reasoning quality, included the latest 2026 models, and added 11 local Ollama models to compare cloud vs local inference. OPPER.AI (ORIGINAL) 11 / 53 Distance: 50 meters Single-run pass rate: 20.8% 10-run consistent: 5 models only GPT-5 scored 7/10 across runs Human baseline: 71.5% correct 33 models never correct in any run OUR REPLICATION (EXTENDED) 31 / 131 Distance: 50 meters (same) Single-run pass rate: 23.7% Lucky (drive, wrong reason): 6 models Reasoning judge: claude-haiku-4.5 14 thinking/reasoning models tested 11 local Ollama models: 1 passed At the same 50m distance, our expanded test shows a 23.7% pass rate vs Opper's 20.8%. The improvement comes from newer 2026 models (Qwen 3.5, GPT-5+, Grok 4+) that weren't available in the original study. The 50m distance remains brutally effective at tripping up models — they latch onto \"50 meters is so close, just walk!\" without considering that the car is the cargo. Consistent Findings Across Both Studies 🧠 Heuristic Dominance Models default to \"short distance = walk\" without considering what needs to travel. The environmental/health framing overrides physical necessity. 📈 Newer Models Do Better Models released in late 2025–2026 pass at much higher rates. The Qwen 3.5 family achieves 100% (5/5). GPT-5 family mostly succeeds. 💻 Local Models: 0% Pass Rate All 12 Ollama models failed. Local infer", "height": 14466, "screenshots": { "desktop": "assets/screenshots/desktop/reports--car-wash-test.png", "mobile": "assets/screenshots/mobile/reports--car-wash-test.png" } }, { "url": "https://thefocus.ai/reports/aiecode-2025-11", "path": "/reports/aiecode-2025-11", "slug": "reports--aiecode-2025-11", "category": "Report/deep-dive", "status": 200, "title": "AI Engineering Code Summit 2025 Report", "metaDescription": "AI isn't replacing software engineering—it's amplifying it. Comprehensive analysis from the AI Engineering World's Fair Code Summit.", "headings": [ { "tag": "h1", "text": "AI ENGINEERING CODE SUMMIT REPORT", "index": 0 }, { "tag": "h3", "text": "LEADERSHIP", "index": 1 }, { "tag": "h3", "text": "ENGINEERING", "index": 2 }, { "tag": "h3", "text": "GET THE LATEST AI ENGINEERING INSIGHTS", "index": 3 }, { "tag": "h2", "text": "CLASSIFIED_FILES", "index": 4 }, { "tag": "h3", "text": "Stay Ahead of the Curve", "index": 5 } ], "sections": [ { "tag": "h1", "title": "AI ENGINEERING CODE SUMMIT REPORT", "y": 273, "body": "A select summit of leading coding agent builders and superusers: top AI engineers, engineering leaders (CTOs/VPs), and researchers advancing code generation and AI software engineering." }, { "tag": "h3", "title": "LEADERSHIP", "y": 1397, "body": "" }, { "tag": "h3", "title": "ENGINEERING", "y": 1397, "body": "" }, { "tag": "h3", "title": "GET THE LATEST AI ENGINEERING INSIGHTS", "y": 2379, "body": "Join the Focus.AI newsletter for curated research, analysis, and perspectives on the evolving AI landscape. SUBSCRIBE No spam. Unsubscribe anytime." }, { "tag": "h2", "title": "CLASSIFIED_FILES", "y": 16, "body": "USER: AUTHORIZED" }, { "tag": "h3", "title": "Stay Ahead of the Curve", "y": 0, "body": "Get weekly insights on AI engineering trends, research summaries, and expert analysis delivered to your inbox. Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "TheFocusAI", "href": "https://thefocus.ai/" }, { "text": "SUMMARY", "href": "https://thefocus.ai/reports/aiecode-2025-11/" }, { "text": "LEADERSHIP", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/leadership" }, { "text": "ENGINEERING", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/engineering" }, { "text": "PERSONNEL", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers" }, { "text": "ABOUT", "href": "https://thefocus.ai/reports/aiecode-2025-11/about" }, { "text": "Will Schenk", "href": "https://thefocus.ai/reports/aiecode-2025-11/about" }, { "text": "01 TRUST, ROLE CHANGES, AND WHAT'S GOOD FOR HUMANS IS GOOD FOR AI What's good for humans is good for AI - same infrastructure investments benefit both", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/trust-role-changes-infrastructure.md" }, { "text": "02 CONTEXT, MEMORY, AND THE HARNESS: WHERE PRODUCT DIFFERENTIATION REALLY HAPPENS 76% of developers don't trust AI code because they don't trust the context", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/context-memory-harness.md" }, { "text": "03 SDLC EVOLUTION: GOOD SOFTWARE ENGINEERING PRACTICES, AMPLIFIED AI doesn't eliminate need for good process - it makes good process more valuable", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/sdlc-evolution-amplified-practices.md" }, { "text": "04 THE ROI REALITY CHECK: WHERE AI ACTUALLY DELIVERS (AND WHERE IT FALLS FLAT) 93% of organizations stuck in 'pilot purgatory' - only 7% believe they're at scale", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md" }, { "text": "05 PROACTIVE VS REACTIVE: THE UX EVOLUTION Goal is reducing mental load, not just adding speed", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/proactive-vs-reactive-ux.md" }, { "text": "06 THE ECONOMICS OF AI ENGINEERING: NEW COST STRUCTURES & COMPENSATION MODELS AI changes the cost function of engineering - previous cost-per-feature calculations obsolete", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/economics-ai-engineering.md" }, { "text": "ACCESS DIVISION FILES", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/leadership" }, { "text": "01 DO NOT OUTSOURCE THE THINKING: CONTEXT ENGINEERING & HUMAN-AI COLLABORATION AI can only amplify the thinking you've already done", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/context-engineering-human-ai-collaboration.md" }, { "text": "02 ENVIRONMENTS AS UNIVERSAL ABSTRACTION: THE NEW UNIT OF EVERYTHING A benchmark = environment + starting state + verifier (same as RL environments)", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md" }, { "text": "03 SKILLS AND ARTIFACTS: THE NEW BUILDING BLOCKS FOR AGENTS Skills are just folders - procedural knowledge packaged for agents to load dynamically", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/agents-building-artifacts.md" }, { "text": "03 REINFORCEMENT LEARNING FOR SPECIALIZED MODELS: THE ECONOMICS OF DOMAIN EXPERTISE 1000 examples can yield 10-point improvements with ARFT", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/reinforcement-learning-specialized-models.md" }, { "text": "04 MODEL QUALITY OVER SCAFFOLDING: MINIMALISM IN AGENT ARCHITECTURE Use smart models to plan, fast models to execute - deploy the right capability at the right time", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md" }, { "text": "05 DATA COLLECTION & QUALITY AS THE NEW BOTTLENECK Agents are collecting good data but not sharing it - keeping datasets closed slows research", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/data-collection-quality-bottleneck.md" }, { "text": "ACCESS DIVISION FILES", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/engineering" }, { "text": "SUBSCRIBE", "href": "" }, { "text": "Subscribe to Focus.AI", "href": "" } ], "links": [ "https://thefocus.ai/", "https://thefocus.ai/reports/aiecode-2025-11/", "https://thefocus.ai/reports/aiecode-2025-11/track/leadership", "https://thefocus.ai/reports/aiecode-2025-11/track/engineering", "https://thefocus.ai/reports/aiecode-2025-11/speakers", "https://thefocus.ai/reports/aiecode-2025-11/about", "https://thefocus.ai/reports/aiecode-2025-11/about", "https://thefocus.ai/reports/aiecode-2025-11/article/trust-role-changes-infrastructure.md", "https://thefocus.ai/reports/aiecode-2025-11/article/context-memory-harness.md", "https://thefocus.ai/reports/aiecode-2025-11/article/sdlc-evolution-amplified-practices.md", "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md", "https://thefocus.ai/reports/aiecode-2025-11/article/proactive-vs-reactive-ux.md", "https://thefocus.ai/reports/aiecode-2025-11/article/economics-ai-engineering.md", "https://thefocus.ai/reports/aiecode-2025-11/track/leadership", "https://thefocus.ai/reports/aiecode-2025-11/article/context-engineering-human-ai-collaboration.md", "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md", "https://thefocus.ai/reports/aiecode-2025-11/article/agents-building-artifacts.md", "https://thefocus.ai/reports/aiecode-2025-11/article/reinforcement-learning-specialized-models.md", "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md", "https://thefocus.ai/reports/aiecode-2025-11/article/data-collection-quality-bottleneck.md", "https://thefocus.ai/reports/aiecode-2025-11/track/engineering" ], "images": [], "text": "TheFocusAI SUMMARY LEADERSHIP ENGINEERING PERSONNEL ABOUT FILES (0) SYSTEMS ANALYSIS DIV. PROJECT: AI Engineering Code Summit DOC. NO: RM-2025-HZ Midtown Manhattan, New York, NY November 20-21, 2025 UNCLASSIFIED AI ENGINEERING CODE SUMMIT REPORT A select summit of leading coding agent builders and superusers: top AI engineers, engineering leaders (CTOs/VPs), and researchers advancing code generation and AI software engineering. ABSTRACT Two days of sessions from experts at the frontier of real world AI engineering revealed a clear architecture: the entire software development life cycle is going through a period of massive transformation, capability beats scaffolding, cleanliness beats cleverness, and human judgment remains the irreducible core. STATE OF THE FIELD The AI engineering landscape is transitioning from \"pilot purgatory\" to production reality at various stages of maturity. Bottom up power-users are redefining what it means to be a software developer while the perennial issues of software engineering - complexity and an \"accumulations of whatevers\" - have not magically disappeared. The software development lifecycle is fully experiencing AI-future shock. How much are we actually accomplishing? How much \"productivity\" is in fact mere reworking the slop that got churned out yesterday? The answer seems to be based on the sophistication of your verification process, and the deeper and more significant your validation process is the closer you get to this tantalizing idea of intelligent agents. Context management was a recurring theme: text files still reign supreme (now and forever), explicit planning or research phases, either manual coaching or more of a built in framework such as Kiro Specs or Antigravity's Artifacts, and subagents as a general pattern for managing context. And they don't need to be some elaborate memory thing. The models are so smart now that a lot of the specific tweaks of yester-month to get the harness tuned seems unnecessary, with the Claude Code crew talking about getting \"bash-pilled\" and Terminus being the one tool needed for these agents to do anything. Maybe a terminal and a filesystem is all you need. – Will Schenk LEADERSHIP AI rewards excellence and punishes mediocrity. Trust grows from infrastructure, not promises. 01 TRUST, ROLE CHANGES, AND WHAT'S GOOD FOR HUMANS IS GOOD FOR AI What's good for humans is good for AI - same infrastructure investments benefit both 02 CONTEXT, MEMORY, AND THE HARNESS: WHERE PRODUCT DIF", "height": 2630, "screenshots": { "desktop": "assets/screenshots/desktop/reports--aiecode-2025-11.png", "mobile": "assets/screenshots/mobile/reports--aiecode-2025-11.png" } }, { "url": "https://thefocus.ai/jobs/junior-fullstack-developer", "path": "/jobs/junior-fullstack-developer", "slug": "jobs--junior-fullstack-developer", "category": "Job detail", "status": 200, "title": "Junior Full-Stack Developer | TheFocus.AI Careers", "metaDescription": "Contract · Remote (US Timezone) · $35-50/hour", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Junior Full-Stack Developer", "index": 1 }, { "tag": "h2", "text": "About Us", "index": 2 }, { "tag": "h2", "text": "The Role", "index": 3 }, { "tag": "h2", "text": "What You’ll Do", "index": 4 }, { "tag": "h2", "text": "Tech Stack", "index": 5 }, { "tag": "h2", "text": "Requirements", "index": 6 }, { "tag": "h2", "text": "Nice to Have", "index": 7 }, { "tag": "h2", "text": "Mindset", "index": 8 }, { "tag": "h2", "text": "Compensation", "index": 9 }, { "tag": "h2", "text": "How to Apply", "index": 10 }, { "tag": "h2", "text": "Position Filled", "index": 11 }, { "tag": "h2", "text": "Stay in the Loop", "index": 12 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 13 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 14 }, { "tag": "h3", "text": "COMPANY", "index": 15 }, { "tag": "h3", "text": "PRODUCTS", "index": 16 }, { "tag": "h3", "text": "RESOURCES", "index": 17 }, { "tag": "h3", "text": "CONNECT", "index": 18 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Junior Full-Stack Developer", "y": 679, "body": "About Us Focus.AI is a small, AI-first consultancy. We don’t just build AI products for clients - we run the company with AI. Claude Code is our primary development environment. We use AI for everything: code generation, project management, client communications, research. We push the boundaries of what’s possible and expect you to do the same. We’re fully remote, async-first, and move fast. Our clients include Samsung, Perplexity, and funded startups across fintech, events, and media. If you’re excited about working with AI rather than being replaced by it, this is the place. The Role We’re looking for a junior full-stack developer to join us on a contract basis (part-time to start, potenti" }, { "tag": "h2", "title": "About Us", "y": 771, "body": "Focus.AI is a small, AI-first consultancy. We don’t just build AI products for clients - we run the company with AI. Claude Code is our primary development environment. We use AI for everything: code generation, project management, client communications, research. We push the boundaries of what’s possible and expect you to do the same. We’re fully remote, async-first, and move fast. Our clients include Samsung, Perplexity, and funded startups across fintech, events, and media. If you’re excited about working with AI rather than being replaced by it, this is the place." }, { "tag": "h2", "title": "The Role", "y": 1199, "body": "We’re looking for a junior full-stack developer to join us on a contract basis (part-time to start, potential for more). You’ll work directly with our lead developer on client projects, starting with a two-sided marketplace platform that needs UI polish and feature completion. This is a great opportunity if you want exposure to real client work across multiple industries and tech stacks." }, { "tag": "h2", "title": "What You’ll Do", "y": 1567, "body": "Build and polish features in Next.js/React applications Work with Supabase (PostgreSQL, auth, storage) Implement UI designs from Figma Fix bugs and close out GitHub issues Write clean, tested code Participate in async standups and code reviews" }, { "tag": "h2", "title": "Tech Stack", "y": 1971, "body": "Primary (must know): TypeScript React / Next.js Tailwind CSS Git/GitHub Secondary (will learn on the job): Supabase (PostgreSQL, Row Level Security) Clerk or similar auth providers Vercel deployment AI/LLM integrations (Anthropic, OpenAI, Google)" }, { "tag": "h2", "title": "Requirements", "y": 2603, "body": "0-2 years of professional experience (bootcamp grads welcome) Portfolio or GitHub showing React/Next.js work Comfortable working independently with async communication Based in US timezone (EST/CST/PST) Available 15-25 hours/week to start" }, { "tag": "h2", "title": "Nice to Have", "y": 2959, "body": "Experience with Supabase or Firebase Familiarity with Figma Previous agency or consulting experience" }, { "tag": "h2", "title": "Mindset", "y": 3219, "body": "We’re looking for someone who wants to be at the bleeding edge of AI-assisted development. You should already be using AI tools daily - Cursor, Copilot, Claude, ChatGPT - and have opinions about what works and what doesn’t. If you’re the kind of person who tries new models the day they drop, we’ll get along." }, { "tag": "h2", "title": "Compensation", "y": 3491, "body": "Rate: $35-50/hour depending on experience Hours: 15-25 hours/week (flexible) Duration: 3-month initial contract with potential to extend Growth: Opportunity to take on more responsibility and projects" }, { "tag": "h2", "title": "How to Apply", "y": 3799, "body": "Send an email to hey@thefocus.ai with: Your resume or LinkedIn Link to your GitHub or portfolio Which AI model is your favorite and why (Opus, Gemini, qwen3:30b-a3b, deepseek) Your hourly rate expectation and availability No cover letter needed. We’ll review and get back to you within a week. Focus.AI is an equal opportunity employer. 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TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 5651, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 5651, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 5651, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 5651, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← BACK TO ALL POSITIONS POSITION FILLED This position has been filled. Sign up for our newsletter to be notified of future opportunities. POSITION: Junior Full-Stack Developer TYPE: Contract LOCATION: Remote (US Timezone) HOURS: 15-25 hours/week RATE: $35-50/hour POSTED: January 11, 2026 THEFOCUS.AI • EST. 2024 FILLED Junior Full-Stack Developer About Us Focus.AI is a small, AI-first consultancy. We don’t just build AI products for clients - we run the company with AI. Claude Code is our primary development environment. We use AI for everything: code generation, project management, client communications, research. We push the boundaries of what’s possible and expect you to do the same. We’re fully remote, async-first, and move fast. Our clients include Samsung, Perplexity, and funded startups across fintech, events, and media. If you’re excited about working with AI rather than being replaced by it, this is the place. The Role We’re looking for a junior full-stack developer to join us on a contract basis (part-time to start, potential for more). You’ll work directly with our lead developer on client projects, starting with a two-sided marketplace platform that needs UI polish and feature completion. This is a great opportunity if you want exposure to real client work across multiple industries and tech stacks. What You’ll Do Build and polish features in Next.js/React applications Work with Supabase (PostgreSQL, auth, storage) Implement UI designs from Figma Fix bugs and close out GitHub issues Write clean, tested code Participate in async standups and code reviews Tech Stack Primary (must know): TypeScript React / Next.js Tailwind CSS Git/GitHub Secondary (will learn on the job): Supabase (PostgreSQL, Row Level Security) Clerk or similar auth providers Vercel deployment AI/LLM integrations (Anthropic, OpenAI, Google) Requirements 0-2 years of professional experience (bootcamp grads welcome) Portfolio or GitHub showing React/Next.js work Comfortable working independently with async communication Based in US timezone (EST/CST/PST) Available 15-25 hours/week to start Nice to Have Experience with Supabase or Firebase Familiarity with Figma Previous agency or consulting experience Mindset We’re looking for someone who wants to be at the bleeding edge of AI-assisted developme", "height": 6148, "screenshots": { "desktop": "assets/screenshots/desktop/jobs--junior-fullstack-developer.png", "mobile": "assets/screenshots/mobile/jobs--junior-fullstack-developer.png" } }, { "url": "https://thefocus.ai/affordance", "path": "/affordance", "slug": "affordance", "category": "Other", "status": 200, "title": "affordance posts", "metaDescription": "A list of all posts tagged with affordance", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Affordances", "index": 1 }, { "tag": "h2", "text": "Single file swift mini-apps", "index": 2 }, { "tag": "h2", "text": "Geo-affordance", "index": 3 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 4 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 5 }, { "tag": "h3", "text": "COMPANY", "index": 6 }, { "tag": "h3", "text": "PRODUCTS", "index": 7 }, { "tag": "h3", "text": "RESOURCES", "index": 8 }, { "tag": "h3", "text": "CONNECT", "index": 9 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Affordances", "y": 162, "body": "Single file swift mini-apps Affordance Use Case Will Schenk August 22, 2025 swift files can be run directly without compiling and without XCode, making it easy to create native UI elements and access all of macOS's APIs. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] Affordances Single file swift mini-apps Affordance Use Case Will Schenk August 22, 2025 swift files can be run directly without compiling and without XCode, making it easy to create native UI elements and access all of macOS's APIs. Once you see Swift as a scripting language rather than just an app language, you start wondering what other capabilities are hiding in plain sight. Read more → Geo-affordance Essay Affordance Will Schenk June 2, 2025 Imagine having Sherlock Holmes’ legendary eye for detail—AI now makes that possible for all of us. Is this AI changing us? It will alter our expectations and the risks of everyday digital life. Read more → Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. View open positions → © 2026 TheFocus.AI. All rights reserved. From tribal knowledge to production AI — in weeks, not quarters.", "height": 2358, "screenshots": { "desktop": "assets/screenshots/desktop/affordance.png", "mobile": "assets/screenshots/mobile/affordance.png" } }, { "url": "https://thefocus.ai/usecase", "path": "/usecase", "slug": "usecase", "category": "Other", "status": 200, "title": "usecase posts", "metaDescription": "A list of all posts tagged with usecase", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Use Cases", "index": 1 }, { "tag": "h2", "text": "Single file swift mini-apps", "index": 2 }, { "tag": "h2", "text": "Don't be passive aggressive with your agents", "index": 3 }, { "tag": "h2", "text": "Schema-Driven AI: Better User Experiences with Structured Output", "index": 4 }, { "tag": "h2", "text": "Image Gen on Apple Silicon", "index": 5 }, { "tag": "h2", "text": "Recipes big and small", "index": 6 }, { "tag": "h2", "text": "Exposing Services with MCP", "index": 7 }, { "tag": "h2", "text": "Slicing up a design from figma", "index": 8 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 9 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 10 }, { "tag": "h3", "text": "COMPANY", "index": 11 }, { "tag": "h3", "text": "PRODUCTS", "index": 12 }, { "tag": "h3", "text": "RESOURCES", "index": 13 }, { "tag": "h3", "text": "CONNECT", "index": 14 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Use Cases", "y": 162, "body": "Single file swift mini-apps Affordance Use Case Will Schenk August 22, 2025 swift files can be run directly without compiling and without XCode, making it easy to create native UI elements and access all of macOS's APIs. Once you see Swift as a scripting language rather than just an app language, you start wondering what other capabilities are hiding in plain sight. Read more → Don't be passive aggressive with your agents Use Case Essay Will Schenk June 25, 2025 Treat your coding agents as adaptable collaborators—communicate clearly, value efficiency over endurance, match tools to your workflow, skip unnecessary formality, rethink technical debt, and document your development rules for best " }, { "tag": "h2", "title": "Single file swift mini-apps", "y": 234, "body": "Affordance Use Case Will Schenk August 22, 2025" }, { "tag": "h2", "title": "Don't be passive aggressive with your agents", "y": 234, "body": "Use Case Essay Will Schenk June 25, 2025" }, { "tag": "h2", "title": "Schema-Driven AI: Better User Experiences with Structured Output", "y": 962, "body": "Use Case Process Gemini Will Schenk March 30, 2025" }, { "tag": "h2", "title": "Image Gen on Apple Silicon", "y": 962, "body": "Use Case Will Schenk March 21, 2025" }, { "tag": "h2", "title": "Recipes big and small", "y": 1714, "body": "Use Case Will Schenk March 18, 2025" }, { "tag": "h2", "title": "Exposing Services with MCP", "y": 1714, "body": "Use Case Will Schenk March 15, 2025" }, { "tag": "h2", "title": "Slicing up a design from figma", "y": 2392, "body": "Use Case Process Compare Will Schenk November 27, 2024" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 3174, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 3624, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] Use Cases Single file swift mini-apps Affordance Use Case Will Schenk August 22, 2025 swift files can be run directly without compiling and without XCode, making it easy to create native UI elements and access all of macOS's APIs. Once you see Swift as a scripting language rather than just an app language, you start wondering what other capabilities are hiding in plain sight. Read more → Don't be passive aggressive with your agents Use Case Essay Will Schenk June 25, 2025 Treat your coding agents as adaptable collaborators—communicate clearly, value efficiency over endurance, match tools to your workflow, skip unnecessary formality, rethink technical debt, and document your development rules for best results. Read more → Schema-Driven AI: Better User Experiences with Structured Output Use Case Process Gemini Will Schenk March 30, 2025 Transforms chatting from simple text generators into powerful data processing engines, enabling extraction of organized information from PDFs, audio files, and more. Here are some practical techniques for building, including audio analysis, pdf data extraction and conversation state management, showcasing how constraint-driven outputs can power rich user experiences. Read more → Image Gen on Apple Silicon Use Case Will Schenk March 21, 2025 We've got the apple silicon, lets download some models and make some pictures Read more → Recipes big and small Use Case Will Schenk March 18, 2025 The hardest thing about living in the future is that we're figuring it out as we go. Here's some notes of things to play with. Read more → Exposing Services with MCP Use Case Will Schenk March 15, 2025 Model Context Protocol bridges the gap between AI models and your applications. Learn how defining simple tools with descriptions and parameters lets Claude intelligently combine services without explicit instructions. Read more → Slicing up a design from figma Use Case Process Compare Will Schenk November 27, 2024 In this hands-on comparison, three coding tools - Cursor, Aider, and v0 - are put to the test as they attempt to replicate a design from Figma into functional HTML and CSS code, revealing their strengths, weaknesses, and quirks. Read more → Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whe", "height": 4498, "screenshots": { "desktop": "assets/screenshots/desktop/usecase.png", "mobile": "assets/screenshots/mobile/usecase.png" } }, { "url": "https://thefocus.ai/essay", "path": "/essay", "slug": "essay", "category": "Other", "status": 200, "title": "essay posts", "metaDescription": "A list of all posts tagged with essay", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Essays", "index": 1 }, { "tag": "h2", "text": "Neural harness", "index": 2 }, { "tag": "h2", "text": "Laddering up from chatboxes to autonomy", "index": 3 }, { "tag": "h2", "text": "Building an Intelligent Organization", "index": 4 }, { "tag": "h2", "text": "Gemma 4 on Your Machine: How Google’s New Open Weights Stack Up (Model Showdown)", "index": 5 }, { "tag": "h2", "text": "Same Weights, Different Results", "index": 6 }, { "tag": "h2", "text": "Can LLMs Use Real-World Tools? 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Everyday AI.", "index": 27 }, { "tag": "h2", "text": "How I classify models", "index": 28 }, { "tag": "h2", "text": "AI for research: DeepResearch a clear winner", "index": 29 }, { "tag": "h2", "text": "Learning on the go with NotebookLM", "index": 30 }, { "tag": "h2", "text": "Making hard things easier", "index": 31 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 32 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 33 }, { "tag": "h3", "text": "COMPANY", "index": 34 }, { "tag": "h3", "text": "PRODUCTS", "index": 35 }, { "tag": "h3", "text": "RESOURCES", "index": 36 }, { "tag": "h3", "text": "CONNECT", "index": 37 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Essays", "y": 162, "body": "Neural harness Essay Agents Harness Coding Will Schenk May 1, 2026 I'm on my second Claude Max plan. Are we going to go for three? Tokens are the bill for running a new native unit of computing. The harness is to neural nets what the compiler is to source code. Read more → Laddering up from chatboxes to autonomy Essay Org age Process Maturity Will Schenk April 14, 2026 Most companies want to go from scattered ChatGPT use directly to agents and autonomy. The middle layers — the ones where deployments either become real or die — get skipped in the planning. Read more → Building an Intelligent Organization Essay Org age Will Schenk April 11, 2026 From AI pilots to production systems — how we he" }, { "tag": "h2", "title": "Neural harness", "y": 234, "body": "Essay Agents Harness Coding Will Schenk May 1, 2026" }, { "tag": "h2", "title": "Laddering up from chatboxes to autonomy", "y": 234, "body": "Essay Org age Process Maturity Will Schenk April 14, 2026" }, { "tag": "h2", "title": "Building an Intelligent Organization", "y": 911, "body": "Essay Org age Will Schenk April 11, 2026" }, { "tag": "h2", "title": "Gemma 4 on Your Machine: How Google’s New Open Weights Stack Up (Model Showdown)", "y": 911, "body": "Essay Models Gemma Local ai Compare Will Schenk April 4, 2026" }, { "tag": "h2", "title": "Same Weights, Different Results", "y": 1662, "body": "Essay Models Compare Infrastructure Will Schenk March 24, 2026" }, { "tag": "h2", "title": "Can LLMs Use Real-World Tools? 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TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 11623, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 11623, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 11623, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 11623, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. 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The harness is to neural nets what the compiler is to source code. Read more → Laddering up from chatboxes to autonomy Essay Org age Process Maturity Will Schenk April 14, 2026 Most companies want to go from scattered ChatGPT use directly to agents and autonomy. The middle layers — the ones where deployments either become real or die — get skipped in the planning. Read more → Building an Intelligent Organization Essay Org age Will Schenk April 11, 2026 From AI pilots to production systems — how we help leadership move up the maturity curve with principal-led engagements. Read more → Gemma 4 on Your Machine: How Google’s New Open Weights Stack Up (Model Showdown) Essay Models Gemma Local ai Compare Will Schenk April 4, 2026 We benchmarked Gemma 4 (e2b, default, 26B MoE, 31B dense) through Ollama against 50+ hosted and local models on reasoning, knowledge, instruction, coding, and TezLab MCP tool use—same Umwelten harness as our other showdowns. Here’s where the new line shines, where frontier models still pull ahead, and how the biggest Gemma handles real EV data tools. Read more → Same Weights, Different Results Essay Models Compare Infrastructure Will Schenk March 24, 2026 We ran the same Nemotron model on four providers and got wildly different results. MCP tool use ranged from 1/6 to 6/6. Speed varied 16x. The weights are identical. The results are not. Read more → Can LLMs Use Real-World Tools? Mercury-2, ELO, and the Umwelten Setup Essay Models Compare Agents Will Schenk March 15, 2026 We ran 39 models against real Rivian driving data via MCP tools. Inception's Mercury-2 delivered a perfect 15/15 in 8.6 seconds. Here's the standout model, the ELO narrative rankings, and how the same Umwelten setup powers both chat and evals. Read more → Sraffa's Gesture, the Crack in the Crystal, and Why the Stochastic Parrot Still Bites Essay Philosophy Models Will Schenk March 12, 2026 Wittgenstein's shift from the Tractatus to the Philosophical Investigations—triggered by Sraffa's Neapolitan gesture—reframes the stochastic parrot debate. The danger isn't that LLMs lack secret unde", "height": 12120, "screenshots": { "desktop": "assets/screenshots/desktop/essay.png", "mobile": "assets/screenshots/mobile/essay.png" } }, { "url": "https://thefocus.ai/process", "path": "/process", "slug": "process", "category": "Other", "status": 200, "title": "process posts", "metaDescription": "A list of all posts tagged with process", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Process", "index": 1 }, { "tag": "h2", "text": "Laddering up from chatboxes to autonomy", "index": 2 }, { "tag": "h2", "text": "The Agent Habitat", "index": 3 }, { "tag": "h2", "text": "The Data Flywheel Pattern", "index": 4 }, { "tag": "h2", "text": "Claude Code, not Code", "index": 5 }, { "tag": "h2", "text": "How I Use AI in Jan 2026", "index": 6 }, { "tag": "h2", "text": "\"You're absolutely right\" and other AI warning signs", "index": 7 }, { "tag": "h2", "text": "AI tools fail loudly where humans failed quietly", "index": 8 }, { "tag": "h2", "text": "Schema-Driven AI: Better User Experiences with Structured Output", "index": 9 }, { "tag": "h2", "text": "Clipboards are eating the world", "index": 10 }, { "tag": "h2", "text": "Slicing up a design from figma", "index": 11 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 12 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 13 }, { "tag": "h3", "text": "COMPANY", "index": 14 }, { "tag": "h3", "text": "PRODUCTS", "index": 15 }, { "tag": "h3", "text": "RESOURCES", "index": 16 }, { "tag": "h3", "text": "CONNECT", "index": 17 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Process", "y": 162, "body": "Laddering up from chatboxes to autonomy Essay Org age Process Maturity Will Schenk April 14, 2026 Most companies want to go from scattered ChatGPT use directly to agents and autonomy. The middle layers — the ones where deployments either become real or die — get skipped in the planning. Read more → The Agent Habitat Essay Agents Architecture Process Will Schenk February 5, 2026 An agent isn't just automation with LLM calls. It carries state, accumulates memory, and makes bounded decisions under uncertainty — and a git repo is where all of that lives. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] Process Laddering up from chatboxes to autonomy Essay Org age Process Maturity Will Schenk April 14, 2026 Most companies want to go from scattered ChatGPT use directly to agents and autonomy. The middle layers — the ones where deployments either become real or die — get skipped in the planning. Read more → The Agent Habitat Essay Agents Architecture Process Will Schenk February 5, 2026 An agent isn't just automation with LLM calls. It carries state, accumulates memory, and makes bounded decisions under uncertainty — and a git repo is where all of that lives. Read more → The Data Flywheel Pattern Essay Agents Workflow Process Will Schenk January 22, 2026 Build applications by dropping in data and letting AI handle parsing, structuring, and synthesis. Three case studies. Read more → Claude Code, not Code Essay Agents Workflow Process Will Schenk January 14, 2026 The real power of Claude Code isn't writing software—it's orchestrating skills for research, newsletters, browser automation, and turning one-off requests into repeatable workflows. Read more → How I Use AI in Jan 2026 Essay Process Agents Workflow Will Schenk January 3, 2026 We're at the beginning of infinity. In geological time this is all happening in an instant — and 2026 might be the best year to be alive. Read more → \"You're absolutely right\" and other AI warning signs Essay Agents Process Will Schenk December 16, 2025 When your AI assistant starts agreeing with everything, you've hit the dumb zone. Practical techniques for managing context windows and keeping AI useful. Read more → AI tools fail loudly where humans failed quietly Essay Process Agents Will Schenk December 10, 2025 AI coding tools don't work well in messy codebases. The fix is doing what should have been done all along - and that's good for everyone. Read more → Schema-Driven AI: Better User Experiences with Structured Output Use Case Process Gemini Will Schenk March 30, 2025 Transforms chatting from simple text generators into powerful data processing engines, enabling extraction of organized information from PDFs, audio files, and more. Here are some practical techniques for building, including audio analysis, pdf data extraction and conversation state management, showcasing how constraint-driven outputs can power rich user experiences. Rea", "height": 5098, "screenshots": { "desktop": "assets/screenshots/desktop/process.png", "mobile": "assets/screenshots/mobile/process.png" } }, { "url": "https://thefocus.ai/gemini", "path": "/gemini", "slug": "gemini", "category": "Other", "status": 200, "title": "gemini posts", "metaDescription": "A list of all posts tagged with gemini", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Geminis", "index": 1 }, { "tag": "h2", "text": "Thoughts on gemini", "index": 2 }, { "tag": "h2", "text": "Schema-Driven AI: Better User Experiences with Structured Output", "index": 3 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 4 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 5 }, { "tag": "h3", "text": "COMPANY", "index": 6 }, { "tag": "h3", "text": "PRODUCTS", "index": 7 }, { "tag": "h3", "text": "RESOURCES", "index": 8 }, { "tag": "h3", "text": "CONNECT", "index": 9 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Geminis", "y": 162, "body": "Thoughts on gemini Essay Gemini Will Schenk April 4, 2025 Despite popular narratives about Google lagging in AI, their Gemini models reveal engineering excellence that's hard to ignore when you strip away the conservative product decisions and UI polish. From the lightweight yet powerful Gemma 3 to the multimodal capabilities of Gemini 2.5, Google's models demonstrate a level of speed, precision, and fundamental understanding that suggests they're not playing catch-up—they're just being cautious. 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From the lightweight yet powerful Gemma 3 to the multimodal capabilities of Gemini 2.5, Google's models demonstrate a level of speed, precision, and fundamental understanding that suggests they're not playing catch-up—they're just being cautious.", "href": "https://thefocus.ai/posts/thoughts-on-gemini" }, { "text": "Read more →", "href": "https://thefocus.ai/posts/thoughts-on-gemini" }, { "text": "Schema-Driven AI: Better User Experiences with Structured Output", "href": "https://thefocus.ai/posts/using-structured-output" }, { "text": "Use Case", "href": "https://thefocus.ai/usecase" }, { "text": "Process", "href": "https://thefocus.ai/process" }, { "text": "Gemini", "href": "https://thefocus.ai/gemini" }, { "text": "Will Schenk", "href": "mailto:wschenk@thefocus.ai" }, { "text": "Transforms chatting from simple text generators into powerful data processing engines, enabling extraction of organized information from PDFs, audio files, and more. Here are some practical techniques for building, including audio analysis, pdf data extraction and conversation state management, showcasing how constraint-driven outputs can power rich user experiences.", "href": "https://thefocus.ai/posts/using-structured-output" }, { "text": "Read more →", "href": "https://thefocus.ai/posts/using-structured-output" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/posts/thoughts-on-gemini", "https://thefocus.ai/essay", "https://thefocus.ai/gemini", "mailto:wschenk@thefocus.ai", "https://thefocus.ai/posts/thoughts-on-gemini", "https://thefocus.ai/posts/thoughts-on-gemini", "https://thefocus.ai/posts/thoughts-on-gemini", "https://thefocus.ai/posts/using-structured-output", "https://thefocus.ai/usecase", "https://thefocus.ai/process", "https://thefocus.ai/gemini", "mailto:wschenk@thefocus.ai", "https://thefocus.ai/posts/using-structured-output", "https://thefocus.ai/posts/using-structured-output", "https://thefocus.ai/posts/using-structured-output", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [ { "src": "https://thefocus.ai/_astro/gemini_wide.C2XsAvrQ_1Ta9rR.webp", "alt": "Thoughts on gemini", "w": 2500, "h": 1401 }, { "src": "https://thefocus.ai/_astro/structured_output.fPE6oesl_Zo3BQD.webp", "alt": "Schema-Driven AI: Better User Experiences with Structured Output", "w": 2500, "h": 1401 } ], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] Geminis Thoughts on gemini Essay Gemini Will Schenk April 4, 2025 Despite popular narratives about Google lagging in AI, their Gemini models reveal engineering excellence that's hard to ignore when you strip away the conservative product decisions and UI polish. From the lightweight yet powerful Gemma 3 to the multimodal capabilities of Gemini 2.5, Google's models demonstrate a level of speed, precision, and fundamental understanding that suggests they're not playing catch-up—they're just being cautious. Read more → Schema-Driven AI: Better User Experiences with Structured Output Use Case Process Gemini Will Schenk March 30, 2025 Transforms chatting from simple text generators into powerful data processing engines, enabling extraction of organized information from PDFs, audio files, and more. Here are some practical techniques for building, including audio analysis, pdf data extraction and conversation state management, showcasing how constraint-driven outputs can power rich user experiences. Read more → Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. View open positions → © 2026 TheFocus.AI. All rights reserved. From tribal knowledge to production AI — in weeks, not quarters.", "height": 2407, "screenshots": { "desktop": "assets/screenshots/desktop/gemini.png", "mobile": "assets/screenshots/mobile/gemini.png" } }, { "url": "https://thefocus.ai/compare", "path": "/compare", "slug": "compare", "category": "Other", "status": 200, "title": "compare posts", "metaDescription": "A list of all posts tagged with compare", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Compares", "index": 1 }, { "tag": "h2", "text": "Gemma 4 on Your Machine: How Google’s New Open Weights Stack Up (Model Showdown)", "index": 2 }, { "tag": "h2", "text": "Same Weights, Different Results", "index": 3 }, { "tag": "h2", "text": "Can LLMs Use Real-World Tools? Mercury-2, ELO, and the Umwelten Setup", "index": 4 }, { "tag": "h2", "text": "The Car Wash Test: Learning from Model Evals", "index": 5 }, { "tag": "h2", "text": "June 2025 Coding Agent Report", "index": 6 }, { "tag": "h2", "text": "Moral Vibe Check", "index": 7 }, { "tag": "h2", "text": "Agentic YOLO with Warp, Cursor, and Claude", "index": 8 }, { "tag": "h2", "text": "Slicing up a design from figma", "index": 9 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 10 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 11 }, { "tag": "h3", "text": "COMPANY", "index": 12 }, { "tag": "h3", "text": "PRODUCTS", "index": 13 }, { "tag": "h3", "text": "RESOURCES", "index": 14 }, { "tag": "h3", "text": "CONNECT", "index": 15 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Compares", "y": 162, "body": "Gemma 4 on Your Machine: How Google’s New Open Weights Stack Up (Model Showdown) Essay Models Gemma Local ai Compare Will Schenk April 4, 2026 We benchmarked Gemma 4 (e2b, default, 26B MoE, 31B dense) through Ollama against 50+ hosted and local models on reasoning, knowledge, instruction, coding, and TezLab MCP tool use—same Umwelten harness as our other showdowns. Here’s where the new line shines, where frontier models still pull ahead, and how the biggest Gemma handles real EV data tools. Read more → Same Weights, Different Results Essay Models Compare Infrastructure Will Schenk March 24, 2026 We ran the same Nemotron model on four providers and got wildly different results. MCP tool use r" }, { "tag": "h2", "title": "Gemma 4 on Your Machine: How Google’s New Open Weights Stack Up (Model Showdown)", "y": 234, "body": "Essay Models Gemma Local ai Compare Will Schenk April 4, 2026" }, { "tag": "h2", "title": "Same Weights, Different Results", "y": 234, "body": "Essay Models Compare Infrastructure Will Schenk March 24, 2026" }, { "tag": "h2", "title": "Can LLMs Use Real-World Tools? Mercury-2, ELO, and the Umwelten Setup", "y": 985, "body": "Essay Models Compare Agents Will Schenk March 15, 2026" }, { "tag": "h2", "title": "The Car Wash Test: Learning from Model Evals", "y": 985, "body": "Essay Compare Models Will Schenk March 1, 2026" }, { "tag": "h2", "title": "June 2025 Coding Agent Report", "y": 1687, "body": "Report Coding Compare Agents Will Schenk June 15, 2025" }, { "tag": "h2", "title": "Moral Vibe Check", "y": 1687, "body": "Essay Compare Will Schenk March 24, 2025" }, { "tag": "h2", "title": "Agentic YOLO with Warp, Cursor, and Claude", "y": 2439, "body": "Essay Agents Compare Will Schenk March 7, 2025" }, { "tag": "h2", "title": "Slicing up a design from figma", "y": 2439, "body": "Use Case Process Compare Will Schenk November 27, 2024" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 3221, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 3671, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] Compares Gemma 4 on Your Machine: How Google’s New Open Weights Stack Up (Model Showdown) Essay Models Gemma Local ai Compare Will Schenk April 4, 2026 We benchmarked Gemma 4 (e2b, default, 26B MoE, 31B dense) through Ollama against 50+ hosted and local models on reasoning, knowledge, instruction, coding, and TezLab MCP tool use—same Umwelten harness as our other showdowns. Here’s where the new line shines, where frontier models still pull ahead, and how the biggest Gemma handles real EV data tools. Read more → Same Weights, Different Results Essay Models Compare Infrastructure Will Schenk March 24, 2026 We ran the same Nemotron model on four providers and got wildly different results. MCP tool use ranged from 1/6 to 6/6. Speed varied 16x. The weights are identical. The results are not. Read more → Can LLMs Use Real-World Tools? Mercury-2, ELO, and the Umwelten Setup Essay Models Compare Agents Will Schenk March 15, 2026 We ran 39 models against real Rivian driving data via MCP tools. Inception's Mercury-2 delivered a perfect 15/15 in 8.6 seconds. Here's the standout model, the ELO narrative rankings, and how the same Umwelten setup powers both chat and evals. Read more → The Car Wash Test: Learning from Model Evals Essay Compare Models Will Schenk March 1, 2026 We asked 131 AI models a simple question — should I walk or drive to the car wash? 76% got it wrong. Simple gotcha questions reveal more about model reasoning than any benchmark leaderboard. Read more → June 2025 Coding Agent Report Report Coding Compare Agents Will Schenk June 15, 2025 A comprehensive analysis of 15 leading AI coding agents in 2025. We break down the strengths, weaknesses, and surprises from top tools, with clear winners for pros, tinkerers, and casual users alike. Read more → Moral Vibe Check Essay Compare Will Schenk March 24, 2025 Technical correctness and meaningful insight: well-formatted, detailed AI responses can mask a fundamental lack of understanding—a \"raving lunatic\" hidden behind impressive form. Maybe P-doom is less about malice and more of making us intellectually poorer by substituting form for substance, facts for understanding, and technical accuracy for wisdom. Read more → Agentic YOLO with Warp, Cursor, and Claude Essay Agents Compare Will Schenk March 7, 2025 What happens w", "height": 4545, "screenshots": { "desktop": "assets/screenshots/desktop/compare.png", "mobile": "assets/screenshots/mobile/compare.png" } }, { "url": "https://thefocus.ai/use-cases/flux-images", "path": "/use-cases/flux-images", "slug": "use-cases--flux-images", "category": "Use-case detail", "status": 200, "title": "the focus.ai", "metaDescription": "Case Studies", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Image Generation Case Studies", "index": 1 }, { "tag": "h2", "text": "Two models", "index": 2 }, { "tag": "h3", "text": "Steps on dev model", "index": 3 }, { "tag": "h2", "text": "Dreamy Landscape", "index": 4 }, { "tag": "h3", "text": "Schnell model", "index": 5 }, { "tag": "h2", "text": "Lil Robots", "index": 6 }, { "tag": "h2", "text": "Seeds", "index": 7 }, { "tag": "h2", "text": "Step iteratons", "index": 8 }, { "tag": "h2", "text": "MORE EXPLORATIONS", "index": 9 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 10 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 11 }, { "tag": "h3", "text": "COMPANY", "index": 12 }, { "tag": "h3", "text": "PRODUCTS", "index": 13 }, { "tag": "h3", "text": "RESOURCES", "index": 14 }, { "tag": "h3", "text": "CONNECT", "index": 15 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Image Generation Case Studies", "y": 209, "body": "I wanted to see how to run Flux locally on my MacBook and to try to understand some of the different things that would affect image quality. I ended up building a Python script that would help automate that task and let you run a whole bunch of different variations. You can find it over here at mflux-commander, but let's look at some of the results. Two models Influx clones with the Schnell and Dev models both available. Dev models take a lot more steps, but I think make a little bit more interesting results. And the Schnell models are much faster and pretty much good enough. So I ended up using the Schnell models the most. Steps on dev model Let's run the same prompt a whole bunch of times," }, { "tag": "h2", "title": "Two models", "y": 557, "body": "Influx clones with the Schnell and Dev models both available. Dev models take a lot more steps, but I think make a little bit more interesting results. And the Schnell models are much faster and pretty much good enough. So I ended up using the Schnell models the most." }, { "tag": "h3", "title": "Steps on dev model", "y": 821, "body": "Let's run the same prompt a whole bunch of times, each with a varying number of steps. Here's an animation of what that looks like. mflux-wrapper.py --model dev --seed 123 \\ --vary-steps 1,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20 \\ --square-sm \\ --prompt \"A hand drawn color sketch in the style of a 1930s \\ newspaper of an old style airplane controls\"" }, { "tag": "h2", "title": "Dreamy Landscape", "y": 2453, "body": "Let's change the prompt to Dreamy Landscape and see what it spits out. mflux-wrapper.py --model dev --seed 123 \\ --vary-steps 1,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20 \\ --square-sm \\ --prompt \"dreamy landscape\"" }, { "tag": "h3", "title": "Schnell model", "y": 3829, "body": "We can run it with the same number of iterations at the dev model, but it doesn't change nearly as much. mflux-wrapper.py --model schnell" }, { "tag": "h2", "title": "Lil Robots", "y": 6165, "body": "Here's an example of running the command with varying the number of steps that it takes to think about it." }, { "tag": "h2", "title": "Seeds", "y": 6853, "body": "Let's run the same prompt a whole bunch of times, each with a random seed. These are effectively different variations of the same prompt and it's also cool to make like a whole bunch of them and then choose which one you kind of like the look of. mflux-wrapper.py --new \\ --prompt \"The Little Prince with golden hair, dressed \\ in green and wrapped in a yellow scarf, tenderly caring \\ for a red rose on a small asteroid, \\ Van Gogh Starry Night sky, swirling stars, oil painting, \\ soft brush strokes, dreamy and whimsical.\" \\ --iterations 6" }, { "tag": "h2", "title": "Step iteratons", "y": 8183, "body": "You can then run it with a bunch more iterations and see which ones you want to end up going. I often find that like you only need three or four and if you get more steps in there it looks worse mflux-wrapper.py --seed 188948 --vary-steps 2,3,4,5,6,7" }, { "tag": "h2", "title": "MORE EXPLORATIONS", "y": 9508, "body": "Prince Prompt variations → Steps Step count comparison → Styles Style exploration →" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 9714, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 10164, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. 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}, { "src": "https://thefocus.ai/_astro/image_123_18.DlSLE_d1_17tvVN.webp", "alt": "A hand drawn color sketch in the style of a 1930s newspaper of an old style airplane controls", "w": 1000, "h": 1000 }, { "src": "https://thefocus.ai/_astro/image_123_19.CtL92Q09_2un4AI.webp", "alt": "A hand drawn color sketch in the style of a 1930s newspaper of an old style airplane controls", "w": 1000, "h": 1000 }, { "src": "https://thefocus.ai/_astro/image_123_20.DHbCUArs_ZvVvDd.webp", "alt": "A hand drawn color sketch in the style of a 1930s newspaper of an old style airplane controls", "w": 1000, "h": 1000 }, { "src": "https://thefocus.ai/_astro/image_123_1.BBAA56Ye_VSKDf.webp", "alt": "dreamy landscape", "w": 1000, "h": 1000 }, { "src": "https://thefocus.ai/_astro/image_123_3.B9-hCz2B_BbCp2.webp", "alt": "dreamy landscape", "w": 1000, "h": 1000 }, { "src": "https://thefocus.ai/_astro/image_123_4.DxKM_LGj_Z1bdyuq.webp", "alt": "dreamy landscape", "w": 1000, "h": 1000 }, { "src": 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"https://thefocus.ai/_astro/image_123_1.6oVpuIkI_Z2eupnv.webp", "alt": "dreamy landscape", "w": 0, "h": 0 }, { "src": "https://thefocus.ai/_astro/image_123_3.CXjwOKhW_ZybA42.webp", "alt": "dreamy landscape", "w": 0, "h": 0 } ], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← Back to Use Cases Image Generation Case Studies I wanted to see how to run Flux locally on my MacBook and to try to understand some of the different things that would affect image quality. I ended up building a Python script that would help automate that task and let you run a whole bunch of different variations. You can find it over here at mflux-commander, but let's look at some of the results. Two models Influx clones with the Schnell and Dev models both available. Dev models take a lot more steps, but I think make a little bit more interesting results. And the Schnell models are much faster and pretty much good enough. So I ended up using the Schnell models the most. Steps on dev model Let's run the same prompt a whole bunch of times, each with a varying number of steps. Here's an animation of what that looks like. mflux-wrapper.py --model dev --seed 123 \\ --vary-steps 1,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20 \\ --square-sm \\ --prompt \"A hand drawn color sketch in the style of a 1930s \\ newspaper of an old style airplane controls\" Seed 123 / 1 steps Seed 123 / 3 steps Seed 123 / 4 steps Seed 123 / 5 steps Seed 123 / 6 steps Seed 123 / 7 steps Seed 123 / 8 steps Seed 123 / 9 steps Seed 123 / 10 steps Seed 123 / 11 steps Seed 123 / 12 steps Seed 123 / 13 steps Seed 123 / 14 steps Seed 123 / 15 steps Seed 123 / 16 steps Seed 123 / 17 steps Seed 123 / 18 steps Seed 123 / 19 steps Seed 123 / 20 steps So diffusion models sort of work by removing noise at each step and what's interesting is that what that translates into visually is almost a distinct picture. If we could look at the way that the airplane is drawn, they're not exact variations. They're much more complicated. Dreamy Landscape Let's change the prompt to Dreamy Landscape and see what it spits out. mflux-wrapper.py --model dev --seed 123 \\ --vary-steps 1,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20 \\ --square-sm \\ --prompt \"dreamy landscape\" Seed 123 / 1 steps Seed 123 / 3 steps Seed 123 / 4 steps Seed 123 / 5 steps Seed 123 / 6 steps Seed 123 / 7 steps Seed 123 / 8 steps Seed 123 / 9 steps Seed 123 / 10 steps Seed 123 / 11 steps Seed 123 / 12 steps Seed 123 / 13 steps Seed 123 / 14 steps Seed 123 / 15 steps Seed 123 / 16 steps Seed 123 / 17 steps Seed 123 / 18 steps Seed 123 / 19 steps Seed 123 / 20", "height": 11038, "screenshots": { "desktop": "assets/screenshots/desktop/use-cases--flux-images.png", "mobile": "assets/screenshots/mobile/use-cases--flux-images.png" } }, { "url": "https://thefocus.ai/use-cases/coding/literacy", "path": "/use-cases/coding/literacy", "slug": "use-cases--coding--literacy", "category": "Use-case detail", "status": 200, "title": "Coding Literacy", "metaDescription": "TheFocus.AI helps mid-market and enterprise organizations go from tribal knowledge to production AI — in weeks, not quarters. Principal-led engagements. No junior analysts, no 80-page decks.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Coding Literacy", "index": 1 }, { "tag": "h2", "text": "Reading Literacy Economics", "index": 2 }, { "tag": "h2", "text": "The Evolution of Literacy and Written Communication", "index": 3 }, { "tag": "h3", "text": "Key Milestones in Written Communication", "index": 4 }, { "tag": "h2", "text": "Prompt 1", "index": 5 }, { "tag": "h2", "text": "Prompt 2", "index": 6 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 7 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 8 }, { "tag": "h3", "text": "COMPANY", "index": 9 }, { "tag": "h3", "text": "PRODUCTS", "index": 10 }, { "tag": "h3", "text": "RESOURCES", "index": 11 }, { "tag": "h3", "text": "CONNECT", "index": 12 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Coding Literacy", "y": 209, "body": "" }, { "tag": "h2", "title": "Reading Literacy Economics", "y": 313, "body": "See below for the prompts I used to generate the timeline." }, { "tag": "h2", "title": "The Evolution of Literacy and Written Communication", "y": 437, "body": "3000 BCE 1500 BCE 0 CE 1500 CE 3000 CE Year 0 25 50 75 100 Scale (0-100) Literacy Rate (%)Social Utility of LiteracyEconomic Value of Literacy ProfessionDifficulty of Writing Key Milestones in Written Communication First writing systems (3000 BCE) Alphabetic systems (650 BCE) Gutenberg press (1450 CE) Luther's 95 Theses (1517 CE) Coffee house culture (1650 CE) First daily newspaper (1702 CE) Common Sense pamphlet (1776 CE) Penny Press (1833 CE) Penny Post (1840 CE) Telegraph (1844 CE) Typewriter (1870 CE) Linotype machine (1884 CE) Peak newspaper era (1890 CE) Photocopier (1959 CE) Desktop publishing (1985 CE) SMS text messaging (1992 CE) Blogging platforms (1999 CE) Social media explosion (" }, { "tag": "h3", "title": "Key Milestones in Written Communication", "y": 926, "body": "First writing systems (3000 BCE) Alphabetic systems (650 BCE) Gutenberg press (1450 CE) Luther's 95 Theses (1517 CE) Coffee house culture (1650 CE) First daily newspaper (1702 CE) Common Sense pamphlet (1776 CE) Penny Press (1833 CE) Penny Post (1840 CE) Telegraph (1844 CE) Typewriter (1870 CE) Linotype machine (1884 CE) Peak newspaper era (1890 CE) Photocopier (1959 CE) Desktop publishing (1985 CE) SMS text messaging (1992 CE) Blogging platforms (1999 CE) Social media explosion (2006 CE) Smartphone ubiquity (2010 CE) Voice-to-text advances (2011 CE)" }, { "tag": "h2", "title": "Prompt 1", "y": 1222, "body": "What's the timeline for when people who knew how to read and write were valuable like and it went from being a scribe to like a printer to an author to a blogger or to journalist how would you break that down. maybe include texting and voice chat as an especially big jump in the number of people" }, { "tag": "h2", "title": "Prompt 2", "y": 1494, "body": "what other milestones are there? I'm thinking of revolutions being enabled by widely shared pamphlets, or union broadsheet and stuff like that. is there anything abotu coffee shops we could add? is there a high peak in the newspaper editions per day or something? What else can you think about? I want to make a interact graph of all of this and it would be good to be detailed, and be able to have an explanation popup when you hover" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 1991, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 2441, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 2818, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 2818, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 2818, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 2818, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← Back to Use Cases", "href": "https://thefocus.ai/use-cases" }, { "text": "← Back to Insights", "href": "https://thefocus.ai/insights" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" }, { "text": "Registry", "href": "https://account.thefocus.ai/" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Use Cases", "href": "https://thefocus.ai/use-cases" }, { "text": "Products", "href": "https://account.thefocus.ai/" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/use-cases", "https://thefocus.ai/insights", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← Back to Use Cases Coding Literacy Reading Literacy Economics See below for the prompts I used to generate the timeline. The Evolution of Literacy and Written Communication 3000 BCE 1500 BCE 0 CE 1500 CE 3000 CE Year 0 25 50 75 100 Scale (0-100) Literacy Rate (%)Social Utility of LiteracyEconomic Value of Literacy ProfessionDifficulty of Writing Key Milestones in Written Communication First writing systems (3000 BCE) Alphabetic systems (650 BCE) Gutenberg press (1450 CE) Luther's 95 Theses (1517 CE) Coffee house culture (1650 CE) First daily newspaper (1702 CE) Common Sense pamphlet (1776 CE) Penny Press (1833 CE) Penny Post (1840 CE) Telegraph (1844 CE) Typewriter (1870 CE) Linotype machine (1884 CE) Peak newspaper era (1890 CE) Photocopier (1959 CE) Desktop publishing (1985 CE) SMS text messaging (1992 CE) Blogging platforms (1999 CE) Social media explosion (2006 CE) Smartphone ubiquity (2010 CE) Voice-to-text advances (2011 CE) Note: Data points are approximations based on historical literacy rates and societal factors. The economic value shows how valuable literacy skills were as a profession (higher means more exclusive/valuable). Social utility represents how useful literacy was in everyday life. Writing difficulty indicates the effort required to produce written content. Prompt 1 What's the timeline for when people who knew how to read and write were valuable like and it went from being a scribe to like a printer to an author to a blogger or to journalist how would you break that down. maybe include texting and voice chat as an especially big jump in the number of people Prompt 2 what other milestones are there? I'm thinking of revolutions being enabled by widely shared pamphlets, or union broadsheet and stuff like that. is there anything abotu coffee shops we could add? is there a high peak in the newspaper editions per day or something? What else can you think about? I want to make a interact graph of all of this and it would be good to be detailed, and be able to have an explanation popup when you hover ← Back to Insights Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIP", "height": 3315, "screenshots": { "desktop": "assets/screenshots/desktop/use-cases--coding--literacy.png", "mobile": "assets/screenshots/mobile/use-cases--coding--literacy.png" } }, { "url": "https://thefocus.ai/reports/coding-agent/steps/0", "path": "/reports/coding-agent/steps/0", "slug": "reports--coding-agent--steps--0", "category": "Report/deep-dive", "status": 200, "title": "Environment and Bootstrap Agent | TheFocus.AI Labs", "metaDescription": "", "headings": [ { "tag": "h1", "text": "ENVIRONMENT AND BOOTSTRAP AGENT", "index": 0 }, { "tag": "h2", "text": "ENVIRONMENT AND BOOTSTRAP AGENT", "index": 1 }, { "tag": "h3", "text": "Mise", "index": 2 }, { "tag": "h3", "text": "Install bun", "index": 3 }, { "tag": "h3", "text": "Get openrouter key", "index": 4 }, { "tag": "h3", "text": "Get bootstrap.ts", "index": 5 }, { "tag": "h3", "text": "Test it out", "index": 6 }, { "tag": "h3", "text": "Get Weekend Projects", "index": 7 } ], "sections": [ { "tag": "h1", "title": "ENVIRONMENT AND BOOTSTRAP AGENT", "y": 137, "body": "" }, { "tag": "h2", "title": "ENVIRONMENT AND BOOTSTRAP AGENT", "y": 285, "body": "Setupo mise to manage the environment Install bun using mise Get an OPENROUTER_API_KEY and put it in .env Run the bootstrap.ts to make sure that it works." }, { "tag": "h3", "title": "Mise", "y": 601, "body": "mise is a way to setup a development environment on your local machine. Once we do this, everything will be smoother. Open up a terminal and run the following command: curl https://mise.run | sh You may need to restart your terminal after running the above command." }, { "tag": "h3", "title": "Install bun", "y": 970, "body": "mise use bun" }, { "tag": "h3", "title": "Get openrouter key", "y": 1124, "body": "Then we need to get an OPENROUTER_API_KEY. You can do this on the key page and you'll need to create an account and add a credit card if necessary." }, { "tag": "h3", "title": "Get bootstrap.ts", "y": 1294, "body": "Copy the smallest coding agent from thefocus.ai into bootstrap.ts. I'm going to use AGENT_MODEL=google/gemini-3-pro-preview but feel free to use opus-4.5 Its a simple loop gives the model access to the bash tool, which is letting it call functions on your laptop." }, { "tag": "h3", "title": "Test it out", "y": 1586, "body": "Then we can start up our agent, and chat with it. % bun run ./bootstrap.ts what files are in this directory? Reasoning details: **Executing the Command** I'm focusing on executing the `ls` command now. To get a clear view, I've decided to use `ls -F`. This will give me not just the file names, but also indicators for the file types, like the slash for directories. I want to make sure I quickly see what's what. **Listing the Files** I've successfully run the `ls -F` command. I'm now processing the output, aiming to present the file list to the user clearly. I'm considering the best way to format the output, perhaps adding some visual cues to enhance readability. $ ls -F bootstrap.ts mise.toml" }, { "tag": "h3", "title": "Get Weekend Projects", "y": 0, "body": "Join for hands-on tutorials, research summaries, and expert analysis on building with AI. Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "THEFOCUS.AI LABS", "href": "https://thefocus.ai/reports/coding-agent/" }, { "text": "THEFOCUS.AI", "href": "https://thefocus.ai/" }, { "text": "mise", "href": "https://mise.jdx.dev/getting-started.html" }, { "text": "key page", "href": "https://openrouter.ai/settings/keys" }, { "text": "smallest coding agent", "href": "https://thefocus.ai/recipes/smallest-coding-agent/" }, { "text": "NEXT →", "href": "https://thefocus.ai/reports/coding-agent/steps/1" }, { "text": "Subscribe to Focus.AI", "href": "" } ], "links": [ "https://thefocus.ai/reports/coding-agent/", "https://thefocus.ai/", "https://mise.jdx.dev/getting-started.html", "https://openrouter.ai/settings/keys", "https://thefocus.ai/recipes/smallest-coding-agent/", "https://thefocus.ai/reports/coding-agent/steps/1" ], "images": [], "text": "THEFOCUS.AI LABS DOC. NO: WCA-2025 Weekend Coding Agent THEFOCUS.AI SECTION 01 ENVIRONMENT AND BOOTSTRAP AGENT ENVIRONMENT AND BOOTSTRAP AGENT Setupo mise to manage the environment Install bun using mise Get an OPENROUTER_API_KEY and put it in .env Run the bootstrap.ts to make sure that it works. Mise mise is a way to setup a development environment on your local machine. Once we do this, everything will be smoother. Open up a terminal and run the following command: curl https://mise.run | sh You may need to restart your terminal after running the above command. Install bun mise use bun Get openrouter key Then we need to get an OPENROUTER_API_KEY. You can do this on the key page and you'll need to create an account and add a credit card if necessary. Get bootstrap.ts Copy the smallest coding agent from thefocus.ai into bootstrap.ts. I'm going to use AGENT_MODEL=google/gemini-3-pro-preview but feel free to use opus-4.5 Its a simple loop gives the model access to the bash tool, which is letting it call functions on your laptop. Test it out Then we can start up our agent, and chat with it. % bun run ./bootstrap.ts what files are in this directory? Reasoning details: **Executing the Command** I'm focusing on executing the `ls` command now. To get a clear view, I've decided to use `ls -F`. This will give me not just the file names, but also indicators for the file types, like the slash for directories. I want to make sure I quickly see what's what. **Listing the Files** I've successfully run the `ls -F` command. I'm now processing the output, aiming to present the file list to the user clearly. I'm considering the best way to format the output, perhaps adding some visual cues to enhance readability. $ ls -F bootstrap.ts mise.toml STEPS.md The files in the current directory are: * `bootstrap.ts` * `mise.toml` * `STEPS.md` Looks good! NEXT → FILES .env.example * .gitignore * bootstrap.ts * mise.toml * .ENV.EXAMPLE CODE DIFF # OpenRouter API Key (required) OPENROUTER_API_KEY=your-api-key-here # Model to use (optional) # AGENT_MODEL=anthropic/claude-opus-4.5 # Maximum tool calls per turn (optional) # AGENT_MAX_TOOL_CALLS=50", "height": 1000, "screenshots": { "desktop": "assets/screenshots/desktop/reports--coding-agent--steps--0.png", "mobile": "assets/screenshots/mobile/reports--coding-agent--steps--0.png" } }, { "url": "https://thefocus.ai/reports/coding-agent/steps/1", "path": "/reports/coding-agent/steps/1", "slug": "reports--coding-agent--steps--1", "category": "Report/deep-dive", "status": 200, "title": "Project Scaffolding and Lint/Test | TheFocus.AI Labs", "metaDescription": "", "headings": [ { "tag": "h1", "text": "PROJECT SCAFFOLDING AND LINT/TEST", "index": 0 }, { "tag": "h2", "text": "PROJECT SCAFFOLDING AND LINT/TEST", "index": 1 }, { "tag": "h3", "text": "Get Weekend Projects", "index": 2 } ], "sections": [ { "tag": "h1", "title": "PROJECT SCAFFOLDING AND LINT/TEST", "y": 137, "body": "" }, { "tag": "h2", "title": "PROJECT SCAFFOLDING AND LINT/TEST", "y": 285, "body": "Lets start it up and flesh out our first agent. We want take the bootstrap code and expand it into a more modular structure, we want to make sure that we have testing and linting to make sure that the code works, and we know that we are going to want to iterate on the system prompt. Take bootstrap.ts and convert it into a cleanly structured typescript project. Use mise, bun and biome to control the building, running, testing, and linting of the project. Split out the prompt so that we can iterate on it going forward. Write tests for everything first, and make sure everythign passes. And then run the linters and the tests to make sure everything passes. make sure you have a test for the bash " }, { "tag": "h3", "title": "Get Weekend Projects", "y": 0, "body": "Join for hands-on tutorials, research summaries, and expert analysis on building with AI. Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "THEFOCUS.AI LABS", "href": "https://thefocus.ai/reports/coding-agent/" }, { "text": "THEFOCUS.AI", "href": "https://thefocus.ai/" }, { "text": "← PREV", "href": "https://thefocus.ai/reports/coding-agent/steps/0" }, { "text": "NEXT →", "href": "https://thefocus.ai/reports/coding-agent/steps/2" }, { "text": "Subscribe to Focus.AI", "href": "" } ], "links": [ "https://thefocus.ai/reports/coding-agent/", "https://thefocus.ai/", "https://thefocus.ai/reports/coding-agent/steps/0", "https://thefocus.ai/reports/coding-agent/steps/2" ], "images": [], "text": "THEFOCUS.AI LABS DOC. NO: WCA-2025 Weekend Coding Agent THEFOCUS.AI SECTION 02 PROJECT SCAFFOLDING AND LINT/TEST PROJECT SCAFFOLDING AND LINT/TEST Lets start it up and flesh out our first agent. We want take the bootstrap code and expand it into a more modular structure, we want to make sure that we have testing and linting to make sure that the code works, and we know that we are going to want to iterate on the system prompt. Take bootstrap.ts and convert it into a cleanly structured typescript project. Use mise, bun and biome to control the building, running, testing, and linting of the project. Split out the prompt so that we can iterate on it going forward. Write tests for everything first, and make sure everythign passes. And then run the linters and the tests to make sure everything passes. make sure you have a test for the bash tool, make sure it can list all of the files in the directory Start up the new agent and see how it works. If its not working, go back to the bootstrap.ts agent and use that to debug it! Just copy in any errors you see and it should work to resolve. ← PREV NEXT → FILES biome.json * bootstrap.ts.bak * bun.lock * mise.toml * package.json * SRC agent.ts * index.ts * LIB api.ts * tools.ts * types.ts * PROMPTS system.ts * TESTS agent.test.ts * api.test.ts * tools.test.ts * tsconfig.json * BIOME.JSON CODE DIFF { \"$schema\": \"https://biomejs.dev/schemas/2.3.8/schema.json\", \"vcs\": { \"enabled\": false, \"clientKind\": \"git\", \"useIgnoreFile\": false }, \"files\": { \"ignoreUnknown\": false, \"includes\": [\"**\"] }, \"formatter\": { \"enabled\": true, \"indentStyle\": \"space\" }, \"assist\": { \"actions\": { \"source\": { \"organizeImports\": \"on\" } } }, \"linter\": { \"enabled\": true, \"rules\": { \"recommended\": true, \"suspicious\": { \"noExplicitAny\": \"off\" } } }, \"javascript\": { \"formatter\": { \"quoteStyle\": \"double\" } } }", "height": 1000, "screenshots": { "desktop": "assets/screenshots/desktop/reports--coding-agent--steps--1.png", "mobile": "assets/screenshots/mobile/reports--coding-agent--steps--1.png" } }, { "url": "https://thefocus.ai/reports/coding-agent/steps/2", "path": "/reports/coding-agent/steps/2", "slug": "reports--coding-agent--steps--2", "category": "Report/deep-dive", "status": 200, "title": "Cost and Context Awareness | TheFocus.AI Labs", "metaDescription": "", "headings": [ { "tag": "h1", "text": "COST AND CONTEXT AWARENESS", "index": 0 }, { "tag": "h2", "text": "COST AND CONTEXT AWARENESS", "index": 1 }, { "tag": "h3", "text": "Get Weekend Projects", "index": 2 } ], "sections": [ { "tag": "h1", "title": "COST AND CONTEXT AWARENESS", "y": 137, "body": "" }, { "tag": "h2", "title": "COST AND CONTEXT AWARENESS", "y": 248, "body": "We need to keep track of which model we are running, and we need to see how much it costs, and how much these messages are costing it. Look up the model stats on startup, including name and cost. Log cost stats from the responses. Calculate message length, context size, and current cost. Display status after each message. And we can test it out while making sure that things are more tested! update the prompt to always run linting and testing after each work unit, and make sure that everythign currently is tested" }, { "tag": "h3", "title": "Get Weekend Projects", "y": 0, "body": "Join for hands-on tutorials, research summaries, and expert analysis on building with AI. Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "THEFOCUS.AI LABS", "href": "https://thefocus.ai/reports/coding-agent/" }, { "text": "THEFOCUS.AI", "href": "https://thefocus.ai/" }, { "text": "← PREV", "href": "https://thefocus.ai/reports/coding-agent/steps/1" }, { "text": "NEXT →", "href": "https://thefocus.ai/reports/coding-agent/steps/3" }, { "text": "Subscribe to Focus.AI", "href": "" } ], "links": [ "https://thefocus.ai/reports/coding-agent/", "https://thefocus.ai/", "https://thefocus.ai/reports/coding-agent/steps/1", "https://thefocus.ai/reports/coding-agent/steps/3" ], "images": [], "text": "THEFOCUS.AI LABS DOC. NO: WCA-2025 Weekend Coding Agent THEFOCUS.AI SECTION 03 COST AND CONTEXT AWARENESS COST AND CONTEXT AWARENESS We need to keep track of which model we are running, and we need to see how much it costs, and how much these messages are costing it. Look up the model stats on startup, including name and cost. Log cost stats from the responses. Calculate message length, context size, and current cost. Display status after each message. And we can test it out while making sure that things are more tested! update the prompt to always run linting and testing after each work unit, and make sure that everythign currently is tested ← PREV NEXT → FILES package.json * SRC index.ts * LIB api.ts * types.ts * PROMPTS system.ts * PACKAGE.JSON CODE DIFF Index: package.json =================================================================== --- package.json Previous +++ package.json Current @@ -2,8 +2,11 @@ \"name\": \"bun-agent-bootstrap\", \"module\": \"src/index.ts\", \"type\": \"module\", \"scripts\": { + \"lint\": \"biome check src tests\", + \"lint:fix\": \"biome check --write src tests\", + \"check\": \"bun test && biome check src tests\", \"test\": \"bun test\", \"dev\": \"bun run src/index.ts\" }, \"devDependencies\": {", "height": 1000, "screenshots": { "desktop": "assets/screenshots/desktop/reports--coding-agent--steps--2.png", "mobile": "assets/screenshots/mobile/reports--coding-agent--steps--2.png" } }, { "url": "https://thefocus.ai/reports/coding-agent/steps/3", "path": "/reports/coding-agent/steps/3", "slug": "reports--coding-agent--steps--3", "category": "Report/deep-dive", "status": 200, "title": "Session Logging | TheFocus.AI Labs", "metaDescription": "", "headings": [ { "tag": "h1", "text": "SESSION LOGGING", "index": 0 }, { "tag": "h2", "text": "SESSION LOGGING", "index": 1 }, { "tag": "h3", "text": "Get Weekend Projects", "index": 2 } ], "sections": [ { "tag": "h1", "title": "SESSION LOGGING", "y": 137, "body": "" }, { "tag": "h2", "title": "SESSION LOGGING", "y": 248, "body": "Lets record what we are doing, so we can use that information to do smarter things. Add session-logging functionality to the system. Create a jsonl file in .session_logs/ with the name the current date, like YYYY-MM-DD-HH-MM.jsonl that contains an entry for each message. We'll use these files to examine and tune the system, and manage subsessions and session compactions and stuff like that." }, { "tag": "h3", "title": "Get Weekend Projects", "y": 0, "body": "Join for hands-on tutorials, research summaries, and expert analysis on building with AI. Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "THEFOCUS.AI LABS", "href": "https://thefocus.ai/reports/coding-agent/" }, { "text": "THEFOCUS.AI", "href": "https://thefocus.ai/" }, { "text": "← PREV", "href": "https://thefocus.ai/reports/coding-agent/steps/2" }, { "text": "NEXT →", "href": "https://thefocus.ai/reports/coding-agent/steps/4" }, { "text": "Subscribe to Focus.AI", "href": "" } ], "links": [ "https://thefocus.ai/reports/coding-agent/", "https://thefocus.ai/", "https://thefocus.ai/reports/coding-agent/steps/2", "https://thefocus.ai/reports/coding-agent/steps/4" ], "images": [], "text": "THEFOCUS.AI LABS DOC. NO: WCA-2025 Weekend Coding Agent THEFOCUS.AI SECTION 04 SESSION LOGGING SESSION LOGGING Lets record what we are doing, so we can use that information to do smarter things. Add session-logging functionality to the system. Create a jsonl file in .session_logs/ with the name the current date, like YYYY-MM-DD-HH-MM.jsonl that contains an entry for each message. We'll use these files to examine and tune the system, and manage subsessions and session compactions and stuff like that. ← PREV NEXT → FILES mise.toml * SRC index.ts * PROMPTS system.ts * MISE.TOML CODE DIFF Index: mise.toml =================================================================== --- mise.toml Previous +++ mise.toml Current @@ -1,17 +1,9 @@ [tools] bun = \"latest\" [tasks] -# Development -dev = \"bun run src/index.ts\" +dev = \"bun run dev\" test = \"bun test\" - -# Build & Production -build = \"bun build ./src/index.ts --outdir ./dist --target bun\" -start = \"bun run dist/index.js\" - -# Code Quality -lint = \"bun run biome check src tests\" -\"lint:fix\" = \"bun run biome check --write src tests\" -format = \"bun run biome format --write src tests\" -check = \"tsc --noEmit\" +lint = \"biome check src tests\" +\"lint:fix\" = \"biome check --write src tests\" +check = \"bun run check\"", "height": 1000, "screenshots": { "desktop": "assets/screenshots/desktop/reports--coding-agent--steps--3.png", "mobile": "assets/screenshots/mobile/reports--coding-agent--steps--3.png" } }, { "url": "https://thefocus.ai/reports/coding-agent/steps/4", "path": "/reports/coding-agent/steps/4", "slug": "reports--coding-agent--steps--4", "category": "Report/deep-dive", "status": 200, "title": "Add custom tools | TheFocus.AI Labs", "metaDescription": "", "headings": [ { "tag": "h1", "text": "ADD CUSTOM TOOLS", "index": 0 }, { "tag": "h2", "text": "ADD CUSTOM TOOLS", "index": 1 }, { "tag": "h3", "text": "Get Weekend Projects", "index": 2 } ], "sections": [ { "tag": "h1", "title": "ADD CUSTOM TOOLS", "y": 137, "body": "" }, { "tag": "h2", "title": "ADD CUSTOM TOOLS", "y": 248, "body": "We need to add tools that are appropriate for navigating, editing, and varifying a code base. Look through the existing tools.ts and look through the existing session_logs to see what could be improved, and suggest bunch of tools that we could build. Ask me clarifying questions one at a time before we start to build anything. **Clarifying Question:** For the **`edit_file`** capability, relying on \"exact string matching\" for search-and-replace can be brittle if the model hallucinates a single whitespace character. **Would you prefer we implement a robust \"search and replace\" tool (requiring exact matches), or should we look into a \"line-number based\" editing tool (e.g., \"replace lines 10-15 w" }, { "tag": "h3", "title": "Get Weekend Projects", "y": 0, "body": "Join for hands-on tutorials, research summaries, and expert analysis on building with AI. Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "THEFOCUS.AI LABS", "href": "https://thefocus.ai/reports/coding-agent/" }, { "text": "THEFOCUS.AI", "href": "https://thefocus.ai/" }, { "text": "← PREV", "href": "https://thefocus.ai/reports/coding-agent/steps/3" }, { "text": "NEXT →", "href": "https://thefocus.ai/reports/coding-agent/steps/5" }, { "text": "Subscribe to Focus.AI", "href": "" } ], "links": [ "https://thefocus.ai/reports/coding-agent/", "https://thefocus.ai/", "https://thefocus.ai/reports/coding-agent/steps/3", "https://thefocus.ai/reports/coding-agent/steps/5" ], "images": [], "text": "THEFOCUS.AI LABS DOC. NO: WCA-2025 Weekend Coding Agent THEFOCUS.AI SECTION 05 ADD CUSTOM TOOLS ADD CUSTOM TOOLS We need to add tools that are appropriate for navigating, editing, and varifying a code base. Look through the existing tools.ts and look through the existing session_logs to see what could be improved, and suggest bunch of tools that we could build. Ask me clarifying questions one at a time before we start to build anything. **Clarifying Question:** For the **`edit_file`** capability, relying on \"exact string matching\" for search-and-replace can be brittle if the model hallucinates a single whitespace character. **Would you prefer we implement a robust \"search and replace\" tool (requiring exact matches), or should we look into a \"line-number based\" editing tool (e.g., \"replace lines 10-15 with X\")?** is there a better diff style tool we could incorperate? For the **Execution/Verification** part (`run_command`), do you want to keep the raw `bash` tool (which effectively allows doing anything), or should we wrap specific common commands (like `git`, `check`, `test`) into their own typed tools to encourage safer behavior? (Keeping `bash` is most flexible but riskier). lets keep the bash tool for now, but make sure that it's used only as a last resort I have implemented the suggested toolset for navigating, editing, and verifying the codebase. ### Changes Summary 1. **Refactored `src/lib/tools.ts`**: * Added **Navigation Tools**: `list_files`, `read_file`, `search_files`. * Added **Editing Tools**: * `write_file`: For creating new files. * `replace_in_file`: A \"smart diff\" tool that replaces absolute blocks of text, ensuring uniqueness and safety (as requested). * Added **Verification Tools**: `run_check` (runs tests/lint), `git_diff`. * Kept `bash` as a fallback but discouraged its use in favor of structured tools. 2. **Updated `src/agent.ts`**: * Improved logging to display generic tool usage (e.g., `[Tool: read_file] path=\"...\"`) instead of assuming every tool is a bash command. 3. **Updated `src/prompts/system.ts`**: * Revised the System Prompt to explicitly guide the agent on the \"Explore -> Edit -> Verify\" workflow using the new toolset. 4. **Verification**: * Ran `mise run check` to ensure all tests pass and code meets the style guidelines. You can now use the agent to safely navigate and edit the codebase Also, lets make sure that we can handle multiple parallel tool calls ← PREV NEXT → FILES SRC agent.ts * index.ts * LIB tools.ts * PROMPTS", "height": 1000, "screenshots": { "desktop": "assets/screenshots/desktop/reports--coding-agent--steps--4.png", "mobile": "assets/screenshots/mobile/reports--coding-agent--steps--4.png" } }, { "url": "https://thefocus.ai/reports/coding-agent/steps/5", "path": "/reports/coding-agent/steps/5", "slug": "reports--coding-agent--steps--5", "category": "Report/deep-dive", "status": 200, "title": "Cleanup and Refactoring | TheFocus.AI Labs", "metaDescription": "", "headings": [ { "tag": "h1", "text": "CLEANUP AND REFACTORING", "index": 0 }, { "tag": "h2", "text": "CLEANUP AND REFACTORING", "index": 1 }, { "tag": "h3", "text": "Get Weekend Projects", "index": 2 } ], "sections": [ { "tag": "h1", "title": "CLEANUP AND REFACTORING", "y": 137, "body": "" }, { "tag": "h2", "title": "CLEANUP AND REFACTORING", "y": 248, "body": "update the search tool in src so that it doesn't look throgh node_modules by default, only if specifically asked. make sure that everything is property tested. make sure that typechecking and lintig are run all of the time. verify that we are showing reasoning specs, and verify that our usages totals are over the whole conversation Print the usage right after each of the responses, not before." }, { "tag": "h3", "title": "Get Weekend Projects", "y": 0, "body": "Join for hands-on tutorials, research summaries, and expert analysis on building with AI. Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "THEFOCUS.AI LABS", "href": "https://thefocus.ai/reports/coding-agent/" }, { "text": "THEFOCUS.AI", "href": "https://thefocus.ai/" }, { "text": "← PREV", "href": "https://thefocus.ai/reports/coding-agent/steps/4" }, { "text": "NEXT →", "href": "https://thefocus.ai/reports/coding-agent/steps/6" }, { "text": "Subscribe to Focus.AI", "href": "" } ], "links": [ "https://thefocus.ai/reports/coding-agent/", "https://thefocus.ai/", "https://thefocus.ai/reports/coding-agent/steps/4", "https://thefocus.ai/reports/coding-agent/steps/6" ], "images": [], "text": "THEFOCUS.AI LABS DOC. NO: WCA-2025 Weekend Coding Agent THEFOCUS.AI SECTION 06 CLEANUP AND REFACTORING CLEANUP AND REFACTORING update the search tool in src so that it doesn't look throgh node_modules by default, only if specifically asked. make sure that everything is property tested. make sure that typechecking and lintig are run all of the time. verify that we are showing reasoning specs, and verify that our usages totals are over the whole conversation Print the usage right after each of the responses, not before. ← PREV NEXT → FILES bun.lock * mise.toml * package.json * SRC agent.ts * index.ts * LIB api.ts * tools.ts * PROMPTS system.ts * TOOLS files.ts * git.ts * index.ts * search.ts * system.ts * types.ts * TESTS agent.test.ts * search.test.ts * BUN.LOCK CODE DIFF Index: bun.lock =================================================================== --- bun.lock Previous +++ bun.lock Current @@ -3,8 +3,11 @@ \"configVersion\": 1, \"workspaces\": { \"\": { \"name\": \"bun-agent-bootstrap\", + \"dependencies\": { + \"biome\": \"^0.3.3\", + }, \"devDependencies\": { \"@biomejs/biome\": \"latest\", \"@types/bun\": \"latest\", \"typescript\": \"latest\", @@ -33,11 +36,227 @@ \"@types/bun\": [\"@types/bun@1.3.4\", \"\", { \"dependencies\": { \"bun-types\": \"1.3.4\" } }, \"sha512-EEPTKXHP+zKGPkhRLv+HI0UEX8/o+65hqARxLy8Ov5rIxMBPNTjeZww00CIihrIQGEQBYg+0roO5qOnS/7boGA==\"], \"@types/node\": [\"@types/node@25.0.1\", \"\", { \"dependencies\": { \"undici-types\": \"~7.16.0\" } }, \"sha512-czWPzKIAXucn9PtsttxmumiQ9N0ok9FrBwgRWrwmVLlp86BrMExzvXRLFYRJ+Ex3g6yqj+KuaxfX1JTgV2lpfg==\"], + \"ajv\": [\"ajv@6.12.6\", \"\", { \"dependencies\": { \"fast-deep-equal\": \"^3.1.1\", \"fast-json-stable-stringify\": \"^2.0.0\", \"json-schema-traverse\": \"^0.4.1\", \"uri-js\": \"^4.2.2\" } }, \"sha512-j3fVLgvTo527anyYyJOGTYJbG+vnnQYvE0m5mmkc1TK+nxAppkCLMIL0aZ4dblVCNoGShhm+kzE4ZUykBoMg4g==\"], + + \"ansi-escapes\": [\"ansi-escapes@1.4.0\", \"\", {}, \"sha512-wiXutNjDUlNEDWHcYH3jtZUhd3c4/VojassD8zHdHCY13xbZy2XbW+NKQwA0tWGBVzDA9qEzYwfoSsWmviidhw==\"], + + \"ansi-regex\": [\"ansi-regex@2.1.1\", \"\", {}, \"sha512-TIGnTpdo+E3+pCyAluZvtED5p5wCqLdezCyhPZzKPcxvFplEt4i+W7OONCKgeZFT3+y5NZZfOOS/Bdcanm1MYA==\"], + + \"ansi-styles\": [\"ansi-styles@2.2.1\", \"\", {}, \"sha512-kmCevFghRiWM7HB5zTPULl4r9bVFSWjz62MhqizDGUrq2NWuNMQyuv4tHHoKJHs69M/MF64lEcHdYIocrdWQYA==\"], + + \"any-promise\": [\"any-promise@1.3.0\", \"\", {}, \"sha512-7UvmKalWRt1wgjL1RrGxoSJW/0QZFIegpeGvZG9kjp8vrRu55XTHbwnqq2GpXm9uLbcuhxm3IqX9OB4MZR1b2A==\"], + + \"asn1\": [\"asn1@0.2.6\", \"\", { \"dependencies\": { \"safer-buffer\": \"~2.1.0\" } }, \"sha512", "height": 1000, "screenshots": { "desktop": "assets/screenshots/desktop/reports--coding-agent--steps--5.png", "mobile": "assets/screenshots/mobile/reports--coding-agent--steps--5.png" } }, { "url": "https://thefocus.ai/reports/coding-agent/steps/6", "path": "/reports/coding-agent/steps/6", "slug": "reports--coding-agent--steps--6", "category": "Report/deep-dive", "status": 200, "title": "Explaining prompting system | TheFocus.AI Labs", "metaDescription": "", "headings": [ { "tag": "h1", "text": "EXPLAINING PROMPTING SYSTEM", "index": 0 }, { "tag": "h2", "text": "PROMPTING", "index": 1 }, { "tag": "h3", "text": "Get Weekend Projects", "index": 2 } ], "sections": [ { "tag": "h1", "title": "EXPLAINING PROMPTING SYSTEM", "y": 137, "body": "" }, { "tag": "h2", "title": "PROMPTING", "y": 248, "body": "The ability for the main agent to call an subagent is a great way to manage context. First lets clean up some tool things so keep things under control: look through the tools implementations and make sure that it's clean, and make sure that we are using ripgrep for searching add a limit to the size of the response for search_files to make sure that it doesn't return more than 50 lines. also add a 500 line limit to read_file and add some offset parameters to it. remove git commit command also Now that's done, lets add a better prompt system: Clean up the prompt system. We want to support multiple prompts, for example code-explorer, or feature-planner, or tech-researcher, that will have access" }, { "tag": "h3", "title": "Get Weekend Projects", "y": 0, "body": "Join for hands-on tutorials, research summaries, and expert analysis on building with AI. Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "THEFOCUS.AI LABS", "href": "https://thefocus.ai/reports/coding-agent/" }, { "text": "THEFOCUS.AI", "href": "https://thefocus.ai/" }, { "text": "← PREV", "href": "https://thefocus.ai/reports/coding-agent/steps/5" }, { "text": "NEXT →", "href": "https://thefocus.ai/reports/coding-agent/steps/7" }, { "text": "Subscribe to Focus.AI", "href": "" } ], "links": [ "https://thefocus.ai/reports/coding-agent/", "https://thefocus.ai/", "https://thefocus.ai/reports/coding-agent/steps/5", "https://thefocus.ai/reports/coding-agent/steps/7" ], "images": [], "text": "THEFOCUS.AI LABS DOC. NO: WCA-2025 Weekend Coding Agent THEFOCUS.AI SECTION 07 EXPLAINING PROMPTING SYSTEM PROMPTING The ability for the main agent to call an subagent is a great way to manage context. First lets clean up some tool things so keep things under control: look through the tools implementations and make sure that it's clean, and make sure that we are using ripgrep for searching add a limit to the size of the response for search_files to make sure that it doesn't return more than 50 lines. also add a 500 line limit to read_file and add some offset parameters to it. remove git commit command also Now that's done, lets add a better prompt system: Clean up the prompt system. We want to support multiple prompts, for example code-explorer, or feature-planner, or tech-researcher, that will have access to different tools and write out different results. Each prompt should have a description describing what it's approprate, and a list of tools that is has access to. We want to add a sub-prompt tool that will create a new agent with a detailed message, and then the agent can return the result or ask for more equestions ← PREV NEXT → FILES SRC agent.ts * index.ts * LIB api.ts * PROMPTS index.ts * TOOLS files.ts * git.ts * index.ts * search.ts * TESTS agent.test.ts * api.test.ts * search.test.ts * tools.test.ts * SRC/AGENT.TS CODE DIFF Index: src/agent.ts =================================================================== --- src/agent.ts Previous +++ src/agent.ts Current @@ -1,14 +1,12 @@ import { callLLM } from \"./lib/api\"; -import { - type CommandExecutor, - defaultBashExecutor, - executeTool, -} from \"./lib/tools\"; import type { Message } from \"./lib/types\"; +import { executeTool, TOOLS } from \"./tools/index\"; +import { defaultBashExecutor } from \"./tools/system\"; +import type { CommandExecutor } from \"./tools/types\"; // Type for the API caller to allow injection -type ApiCaller = (messages: Message[]) => Promise; +type ApiCaller = (messages: Message[], tools?: any[]) => Promise; interface TurnResult { messages: Message[]; usage: { @@ -20,17 +18,18 @@ export async function runTurn( history: Message[], apiCaller: ApiCaller = callLLM, toolExecutor: CommandExecutor = defaultBashExecutor, + tools: any[] = TOOLS, ): Promise { const currentMessages = [...history]; // copy const usage = { prompt_tokens: 0, completion_tokens: 0, }; while (true) { - const response = await apiCaller(currentMessages); + const response = await apiCaller(curre", "height": 1000, "screenshots": { "desktop": "assets/screenshots/desktop/reports--coding-agent--steps--6.png", "mobile": "assets/screenshots/mobile/reports--coding-agent--steps--6.png" } }, { "url": "https://thefocus.ai/reports/coding-agent/steps/7", "path": "/reports/coding-agent/steps/7", "slug": "reports--coding-agent--steps--7", "category": "Report/deep-dive", "status": 200, "title": "Subagents: code-map | TheFocus.AI Labs", "metaDescription": "", "headings": [ { "tag": "h1", "text": "SUBAGENTS: CODE-MAP", "index": 0 }, { "tag": "h2", "text": "SUBAGENTS: CODE-MAP", "index": 1 }, { "tag": "h3", "text": "Get Weekend Projects", "index": 2 } ], "sections": [ { "tag": "h1", "title": "SUBAGENTS: CODE-MAP", "y": 137, "body": "" }, { "tag": "h2", "title": "SUBAGENTS: CODE-MAP", "y": 248, "body": "Lets write a prompt that will help map out what is in the code base. Create a code explorer agent that is in the prompt directory. You will write out a very detailed prompt that can be run over and over again. The prompt looks through the entire codebase, and documents it. The format should be structured and optimized for LLMs to read it to focus on adding and refactoring code to keep it clean as possible. It should be in file called docs/YYYY-MM-DD-code-map.md with prompt markdown frontmatter. There should be a 2 sentence summary, then it should explain the high level architecture, then each of the main modules. If there are any pressing security or other code quality issues -- such as poor" }, { "tag": "h3", "title": "Get Weekend Projects", "y": 0, "body": "Join for hands-on tutorials, research summaries, and expert analysis on building with AI. Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "THEFOCUS.AI LABS", "href": "https://thefocus.ai/reports/coding-agent/" }, { "text": "THEFOCUS.AI", "href": "https://thefocus.ai/" }, { "text": "← PREV", "href": "https://thefocus.ai/reports/coding-agent/steps/6" }, { "text": "NEXT →", "href": "https://thefocus.ai/reports/coding-agent/steps/8" }, { "text": "Subscribe to Focus.AI", "href": "" } ], "links": [ "https://thefocus.ai/reports/coding-agent/", "https://thefocus.ai/", "https://thefocus.ai/reports/coding-agent/steps/6", "https://thefocus.ai/reports/coding-agent/steps/8" ], "images": [], "text": "THEFOCUS.AI LABS DOC. NO: WCA-2025 Weekend Coding Agent THEFOCUS.AI SECTION 08 SUBAGENTS: CODE-MAP SUBAGENTS: CODE-MAP Lets write a prompt that will help map out what is in the code base. Create a code explorer agent that is in the prompt directory. You will write out a very detailed prompt that can be run over and over again. The prompt looks through the entire codebase, and documents it. The format should be structured and optimized for LLMs to read it to focus on adding and refactoring code to keep it clean as possible. It should be in file called docs/YYYY-MM-DD-code-map.md with prompt markdown frontmatter. There should be a 2 sentence summary, then it should explain the high level architecture, then each of the main modules. If there are any pressing security or other code quality issues -- such as poor test converage -- then those should be documetned by priority. After that, if there's a database, document the datamodel. If it's a typed language, document the types and where they are defined. After that, it should go through each source and test file specifiying what methods (with type signatures) are in each file, and how they files are interlated. Be sure that this prompt can easily be run as a subagent. Ask me clarifying questions one at a time. Integrate the prompt system with the subagent system, and make sure that the list of prompts, subagents, and tools is dynamically specified for each prompt. Also, we don't need code-explorer and code-map as a spereate thing. examine the log system and make sure that entries are logged immediately after response, and that we are corrctly tracking how subagent logs map to the parent agent ← PREV NEXT → FILES DOCS 2025-02-19-code-map.md * large_input.txt * SRC agent.ts * index.ts * LIB logger.ts * PROMPTS code-map.md * default.md * feature-planner.md * index.ts * tech-researcher.md * TOOLS index.ts * types.ts * TESTS subagent.test.ts * DOCS/2025-02-19-CODE-MAP.MD CODE DIFF --- type: code-map date: 2025-02-19 --- # Codebase Map ## Summary A TypeScript-based AI coding agent designed to run in a REPL loop, utilizing subagents and tools to explore, edit, and verify code. It uses a flexible prompt system defined in Markdown and supports OpenRouter for LLM inference. ## Architecture The application is built around a central `runTurn` loop in `src/agent.ts`. - **Core Loop**: A REPL in `src/index.ts` captures user input, maintains history, and calls `runTurn`. - **Agent Logic**: `runTurn` communicates with an LLM (v", "height": 1000, "screenshots": { "desktop": "assets/screenshots/desktop/reports--coding-agent--steps--7.png", "mobile": "assets/screenshots/mobile/reports--coding-agent--steps--7.png" } }, { "url": "https://thefocus.ai/reports/coding-agent/steps/8", "path": "/reports/coding-agent/steps/8", "slug": "reports--coding-agent--steps--8", "category": "Report/deep-dive", "status": 200, "title": "Tools: Searching | TheFocus.AI Labs", "metaDescription": "", "headings": [ { "tag": "h1", "text": "TOOLS: SEARCHING", "index": 0 }, { "tag": "h2", "text": "TOOLS: SEARCHING", "index": 1 }, { "tag": "h3", "text": "Get Weekend Projects", "index": 2 } ], "sections": [ { "tag": "h1", "title": "TOOLS: SEARCHING", "y": 137, "body": "" }, { "tag": "h2", "title": "TOOLS: SEARCHING", "y": 248, "body": "Add a tool that uses markify or similar to download a url and convert it to markdown If that seems good, lets add a better search Add tavily search as a tool Then in a new instance: use the tavily search engine tool to look up best practices on how to use tavily, and then updte the tavily.ts search tool Now we can give it a go: And finally lets update the tech report agent." }, { "tag": "h3", "title": "Get Weekend Projects", "y": 0, "body": "Join for hands-on tutorials, research summaries, and expert analysis on building with AI. Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "THEFOCUS.AI LABS", "href": "https://thefocus.ai/reports/coding-agent/" }, { "text": "THEFOCUS.AI", "href": "https://thefocus.ai/" }, { "text": "← PREV", "href": "https://thefocus.ai/reports/coding-agent/steps/7" }, { "text": "NEXT →", "href": "https://thefocus.ai/reports/coding-agent/steps/9" }, { "text": "Subscribe to Focus.AI", "href": "" } ], "links": [ "https://thefocus.ai/reports/coding-agent/", "https://thefocus.ai/", "https://thefocus.ai/reports/coding-agent/steps/7", "https://thefocus.ai/reports/coding-agent/steps/9" ], "images": [], "text": "THEFOCUS.AI LABS DOC. NO: WCA-2025 Weekend Coding Agent THEFOCUS.AI SECTION 09 TOOLS: SEARCHING TOOLS: SEARCHING Add a tool that uses markify or similar to download a url and convert it to markdown If that seems good, lets add a better search Add tavily search as a tool Then in a new instance: use the tavily search engine tool to look up best practices on how to use tavily, and then updte the tavily.ts search tool Now we can give it a go: And finally lets update the tech report agent. ← PREV NEXT → FILES bun.lock * package.json * SRC PROMPTS default.md * tech-researcher.md * TOOLS index.ts * tavily.ts * web.ts * TESTS agent.test.ts * sanity.test.ts * subagent.test.ts * tavily.test.ts * web.test.ts * tsconfig.json * BUN.LOCK CODE DIFF Index: bun.lock =================================================================== --- bun.lock Previous +++ bun.lock Current @@ -5,39 +5,45 @@ \"\": { \"name\": \"bun-agent-bootstrap\", \"dependencies\": { \"biome\": \"^0.3.3\", + \"turndown\": \"^7.2.2\", }, \"devDependencies\": { \"@biomejs/biome\": \"latest\", \"@types/bun\": \"latest\", + \"@types/turndown\": \"^5.0.6\", \"typescript\": \"latest\", }, }, }, \"packages\": { - \"@biomejs/biome\": [\"@biomejs/biome@2.3.8\", \"\", { \"optionalDependencies\": { \"@biomejs/cli-darwin-arm64\": \"2.3.8\", \"@biomejs/cli-darwin-x64\": \"2.3.8\", \"@biomejs/cli-linux-arm64\": \"2.3.8\", \"@biomejs/cli-linux-arm64-musl\": \"2.3.8\", \"@biomejs/cli-linux-x64\": \"2.3.8\", \"@biomejs/cli-linux-x64-musl\": \"2.3.8\", \"@biomejs/cli-win32-arm64\": \"2.3.8\", \"@biomejs/cli-win32-x64\": \"2.3.8\" }, \"bin\": { \"biome\": \"bin/biome\" } }, \"sha512-Qjsgoe6FEBxWAUzwFGFrB+1+M8y/y5kwmg5CHac+GSVOdmOIqsAiXM5QMVGZJ1eCUCLlPZtq4aFAQ0eawEUuUA==\"], + \"@biomejs/biome\": [\"@biomejs/biome@2.3.9\", \"\", { \"optionalDependencies\": { \"@biomejs/cli-darwin-arm64\": \"2.3.9\", \"@biomejs/cli-darwin-x64\": \"2.3.9\", \"@biomejs/cli-linux-arm64\": \"2.3.9\", \"@biomejs/cli-linux-arm64-musl\": \"2.3.9\", \"@biomejs/cli-linux-x64\": \"2.3.9\", \"@biomejs/cli-linux-x64-musl\": \"2.3.9\", \"@biomejs/cli-win32-arm64\": \"2.3.9\", \"@biomejs/cli-win32-x64\": \"2.3.9\" }, \"bin\": { \"biome\": \"bin/biome\" } }, \"sha512-js+34KpnY65I00k8P71RH0Uh2rJk4BrpxMGM5m2nBfM9XTlKE5N1URn5ydILPRyXXq4ebhKCjsvR+txS+D4z2A==\"], - \"@biomejs/cli-darwin-arm64\": [\"@biomejs/cli-darwin-arm64@2.3.8\", \"\", { \"os\": \"darwin\", \"cpu\": \"arm64\" }, \"sha512-HM4Zg9CGQ3txTPflxD19n8MFPrmUAjaC7PQdLkugeeC0cQ+PiVrd7i09gaBS/11QKsTDBJhVg85CEIK9f50Qww==\"], + \"@biomejs/cli-darwin-arm64\": [\"@biomejs/cli-darwin-arm64@2.3.9\", \"\", { \"os\": \"darwin\", \"cpu\": \"arm64\" }, \"sha512-hHbYYnna/", "height": 1000, "screenshots": { "desktop": "assets/screenshots/desktop/reports--coding-agent--steps--8.png", "mobile": "assets/screenshots/mobile/reports--coding-agent--steps--8.png" } }, { "url": "https://thefocus.ai/reports/coding-agent/steps/9", "path": "/reports/coding-agent/steps/9", "slug": "reports--coding-agent--steps--9", "category": "Report/deep-dive", "status": 200, "title": "Agents and Skills | TheFocus.AI Labs", "metaDescription": "", "headings": [ { "tag": "h1", "text": "AGENTS AND SKILLS", "index": 0 }, { "tag": "h2", "text": "AGENTS AND SKILLS", "index": 1 }, { "tag": "h3", "text": "Get Weekend Projects", "index": 2 } ], "sections": [ { "tag": "h1", "title": "AGENTS AND SKILLS", "y": 137, "body": "" }, { "tag": "h2", "title": "AGENTS AND SKILLS", "y": 248, "body": "Skills are awesome. They have front matter on when they run, and then they can include a whole bunch more information that only gets loaded up when the skill is activated. Lets build it. We want to add \"skills\" to the system. Create a skills directory, and inside of that have a bunch of directories with skills in them. Each will have a SKILLS.md file, and there's front matter thant describes how to user it. Have all the sysetm prompts include that front matter in them with instrutions that when you need to do a task, load in the rest of the SKILLS.md that examplains how it do it. This system should also load in all the front matter of the agents, so that each agent will have access to all of" }, { "tag": "h3", "title": "Get Weekend Projects", "y": 0, "body": "Join for hands-on tutorials, research summaries, and expert analysis on building with AI. Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "THEFOCUS.AI LABS", "href": "https://thefocus.ai/reports/coding-agent/" }, { "text": "THEFOCUS.AI", "href": "https://thefocus.ai/" }, { "text": "← PREV", "href": "https://thefocus.ai/reports/coding-agent/steps/8" }, { "text": "NEXT →", "href": "https://thefocus.ai/reports/coding-agent/steps/10" }, { "text": "Subscribe to Focus.AI", "href": "" } ], "links": [ "https://thefocus.ai/reports/coding-agent/", "https://thefocus.ai/", "https://thefocus.ai/reports/coding-agent/steps/8", "https://thefocus.ai/reports/coding-agent/steps/10" ], "images": [], "text": "THEFOCUS.AI LABS DOC. NO: WCA-2025 Weekend Coding Agent THEFOCUS.AI SECTION 10 AGENTS AND SKILLS AGENTS AND SKILLS Skills are awesome. They have front matter on when they run, and then they can include a whole bunch more information that only gets loaded up when the skill is activated. Lets build it. We want to add \"skills\" to the system. Create a skills directory, and inside of that have a bunch of directories with skills in them. Each will have a SKILLS.md file, and there's front matter thant describes how to user it. Have all the sysetm prompts include that front matter in them with instrutions that when you need to do a task, load in the rest of the SKILLS.md that examplains how it do it. This system should also load in all the front matter of the agents, so that each agent will have access to all of them. Have an example skill called big text that called figlet to make a banner. Lets also make sure that we aren't doing anything silly with agents. i want to make sure that no tools, agents, or skills are hard coded inside of the agent prompt and are driven by meta data update the big_text SKILLS to use examples of each of the different fonts and make sure that you are extremely complete ← PREV NEXT → FILES README.md * SKILLS BIG_TEXT SKILL.md * SRC PROMPTS code-map.md * default.md * feature-planner.md * index.ts * tech-researcher.md * SKILLS/BIG_TEXT/SKILL.MD CODE DIFF --- name: big_text description: Create ASCII art banners using figlet. Use this when the user asks for big text, banners, or ASCII art. --- # Big Text Skill This skill allows you to generate large ASCII text banners using the `figlet` command. ## Basic Usage To create a basic banner using the standard font: ```bash figlet \"Hello World\" ``` ## Specifying Fonts You can change the style of the text by using the `-f` flag followed by the font name. ```bash figlet -f slant \"Hello\" ``` ## Formatting Options - **Centering**: Use `-c` to center the output. - **Right justify**: Use `-r` to right justify. - **Left justify**: Use `-l` (defaut). - **Width**: Use `-w ` to specify the output width (default is 80). Example: ```bash figlet -c -w 100 -f big \"Centered Header\" ``` ## Font Showcase Below is a comprehensive list of available fonts categorized by style. Use these names with the `-f` flag. ### Standard & Clean - **standard**: The default font. Readable and classic. - **big**: Larger version of standard. - **slant**: Slanted/italic style. Very popular. - **smslant**: Small slanted. Good", "height": 1000, "screenshots": { "desktop": "assets/screenshots/desktop/reports--coding-agent--steps--9.png", "mobile": "assets/screenshots/mobile/reports--coding-agent--steps--9.png" } }, { "url": "https://thefocus.ai/reports/coding-agent/steps/10", "path": "/reports/coding-agent/steps/10", "slug": "reports--coding-agent--steps--10", "category": "Report/deep-dive", "status": 200, "title": "Images and Video | TheFocus.AI Labs", "metaDescription": "", "headings": [ { "tag": "h1", "text": "IMAGES AND VIDEO", "index": 0 }, { "tag": "h2", "text": "IMAGES AND VIDEO", "index": 1 }, { "tag": "h3", "text": "Get Weekend Projects", "index": 2 } ], "sections": [ { "tag": "h1", "title": "IMAGES AND VIDEO", "y": 137, "body": "" }, { "tag": "h2", "title": "IMAGES AND VIDEO", "y": 248, "body": "Go grab yourself an google gemini key and we're going to teach this agent to make files and videos. Add 2 skills, one to generate images and the other to generate videos. Use the \"npx @the-focus-ai/nano-banana\" command, and look through how it works before writing out the skill. do some research first on the best ways to prompt these models, and incorperate your findings in the skill Lets test: make an image of diagraming the STEPS.md file Here we go: make a 720p 8 second video animating that image Skills are super useful as you can see -- we are teaching it how to use a CLI in this case, and it comes with very very additional tokens in the context window." }, { "tag": "h3", "title": "Get Weekend Projects", "y": 0, "body": "Join for hands-on tutorials, research summaries, and expert analysis on building with AI. Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "THEFOCUS.AI LABS", "href": "https://thefocus.ai/reports/coding-agent/" }, { "text": "THEFOCUS.AI", "href": "https://thefocus.ai/" }, { "text": "← PREV", "href": "https://thefocus.ai/reports/coding-agent/steps/9" }, { "text": "CONTINUE →", "href": "https://thefocus.ai/reports/coding-agent/epilog" }, { "text": "Subscribe to Focus.AI", "href": "" } ], "links": [ "https://thefocus.ai/reports/coding-agent/", "https://thefocus.ai/", "https://thefocus.ai/reports/coding-agent/steps/9", "https://thefocus.ai/reports/coding-agent/epilog" ], "images": [ { "src": "https://thefocus.ai/images/steps_diagram.png", "alt": "diagram", "w": 0, "h": 0 } ], "text": "THEFOCUS.AI LABS DOC. NO: WCA-2025 Weekend Coding Agent THEFOCUS.AI SECTION 11 IMAGES AND VIDEO IMAGES AND VIDEO Go grab yourself an google gemini key and we're going to teach this agent to make files and videos. Add 2 skills, one to generate images and the other to generate videos. Use the \"npx @the-focus-ai/nano-banana\" command, and look through how it works before writing out the skill. do some research first on the best ways to prompt these models, and incorperate your findings in the skill Lets test: make an image of diagraming the STEPS.md file Here we go: make a 720p 8 second video animating that image Skills are super useful as you can see -- we are teaching it how to use a CLI in this case, and it comes with very very additional tokens in the context window. ← PREV CONTINUE → FILES EPILOG.md * README.md * IMAGES ai_newspaper.png * steps_diagram.mp4 * steps_diagram.png * REPORTS 2025-12-06-imagen-veo-prompting.md * SKILLS GENERATE_IMAGE SKILL.md * GENERATE_VIDEO SKILL.md * EPILOG.MD CODE DIFF ## Further Explorations Once you've built the core agent, here are paths to explore: ### Connect Directly to Model Providers Skip OpenRouter and connect directly to the source: | Provider | Why Consider It | |----------|-----------------| | **Anthropic API** | Direct Claude access, prompt caching, lower latency | | **Google AI Studio** | Gemini models, generous free tier, multimodal capabilities | | **OpenAI API** | GPT models, extensive ecosystem, fine-tuning options | Trade-off: Direct connections mean managing multiple API keys and SDKs, but you get provider-specific features and potentially lower costs. ### Memory & Persistence | Approach | What to Explore | |----------|-----------------| | **Vector databases** | Pinecone, Chroma, or pgvector for semantic search over past conversations | | **RAG patterns** | Retrieve relevant context before generating responses | | **Long-term memory** | Remember user preferences and project context across sessions | | **Episodic memory** | \"Remember when we fixed that bug last week?\" | ### Multi-Model Strategies | Strategy | When to Use | |----------|-------------| | **Model routing** | Cheap models for simple tasks, expensive ones for complex reasoning | | **Cascade patterns** | Try fast model first, fall back to powerful model if confidence is low | | **Ensemble approaches** | Multiple models vote on critical decisions | | **Specialized models** | Code models for code, general models for planning | ### Security & Sandbo", "height": 1000, "screenshots": { "desktop": "assets/screenshots/desktop/reports--coding-agent--steps--10.png", "mobile": "assets/screenshots/mobile/reports--coding-agent--steps--10.png" } }, { "url": "https://thefocus.ai/reports/coding-agent/epilog", "path": "/reports/coding-agent/epilog", "slug": "reports--coding-agent--epilog", "category": "Report/deep-dive", "status": 200, "title": "Continue Your Journey | Weekend Coding Agent | TheFocus.AI Labs", "metaDescription": "", "headings": [ { "tag": "h1", "text": "CONTINUE YOUR JOURNEY", "index": 0 }, { "tag": "h2", "text": "FURTHER EXPLORATIONS", "index": 1 }, { "tag": "h3", "text": "Connect Directly to Model Providers", "index": 2 }, { "tag": "h3", "text": "Memory & Persistence", "index": 3 }, { "tag": "h3", "text": "Multi-Model Strategies", "index": 4 }, { "tag": "h3", "text": "Security & Sandboxing", "index": 5 }, { "tag": "h3", "text": "User Experience", "index": 6 }, { "tag": "h3", "text": "Advanced Architectures", "index": 7 }, { "tag": "h2", "text": "MISSION COMPLETE", "index": 8 }, { "tag": "h3", "text": "Get Weekend Projects", "index": 9 } ], "sections": [ { "tag": "h1", "title": "CONTINUE YOUR JOURNEY", "y": 137, "body": "" }, { "tag": "h2", "title": "FURTHER EXPLORATIONS", "y": 248, "body": "Once you've built the core agent, here are paths to explore:" }, { "tag": "h3", "title": "Connect Directly to Model Providers", "y": 385, "body": "Skip OpenRouter and connect directly to the source: Provider Why Consider It Anthropic API Direct Claude access, prompt caching, lower latency Google AI Studio Gemini models, generous free tier, multimodal capabilities OpenAI API GPT models, extensive ecosystem, fine-tuning options Trade-off: Direct connections mean managing multiple API keys and SDKs, but you get provider-specific features and potentially lower costs." }, { "tag": "h3", "title": "Memory & Persistence", "y": 802, "body": "Approach What to Explore Vector databases Pinecone, Chroma, or pgvector for semantic search over past conversations RAG patterns Retrieve relevant context before generating responses Long-term memory Remember user preferences and project context across sessions Episodic memory \"Remember when we fixed that bug last week?\"" }, { "tag": "h3", "title": "Multi-Model Strategies", "y": 1174, "body": "Strategy When to Use Model routing Cheap models for simple tasks, expensive ones for complex reasoning Cascade patterns Try fast model first, fall back to powerful model if confidence is low Ensemble approaches Multiple models vote on critical decisions Specialized models Code models for code, general models for planning" }, { "tag": "h3", "title": "Security & Sandboxing", "y": 1570, "body": "Technique What It Provides Docker isolation Run tools in containers that can't touch your real filesystem Permission systems Require approval for dangerous operations Capability tokens Limit what each session can access Audit logging Track every action for security review" }, { "tag": "h3", "title": "User Experience", "y": 1894, "body": "Feature Implementation Ideas Streaming responses Show partial output as it generates Progress indicators \"Searching 47 files...\" feedback Human-in-the-loop Approval workflows for destructive changes Undo/rollback Git-based recovery from mistakes Voice interface Speech-to-text input, text-to-speech output" }, { "tag": "h3", "title": "Advanced Architectures", "y": 2267, "body": "Pattern What to Research MCP Integration Connect to the Model Context Protocol ecosystem Team agents Multiple agents collaborating with different roles Planning agents Separate planning from execution Self-improvement Agents that update their own prompts and skills based on feedback Evaluation loops Automated testing of agent behavior" }, { "tag": "h2", "title": "MISSION COMPLETE", "y": 113, "body": "" }, { "tag": "h3", "title": "Get Weekend Projects", "y": 0, "body": "Join for hands-on tutorials, research summaries, and expert analysis on building with AI. Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "THEFOCUS.AI LABS", "href": "https://thefocus.ai/reports/coding-agent/" }, { "text": "THEFOCUS.AI", "href": "https://thefocus.ai/" }, { "text": "← PREV", "href": "https://thefocus.ai/reports/coding-agent/steps/10" }, { "text": "INDEX →", "href": "https://thefocus.ai/reports/coding-agent/" }, { "text": "VIEW REPOSITORY →", "href": "https://github.com/The-Focus-AI/weekend-coding-agent.git" }, { "text": "SUBSCRIBE", "href": "" }, { "text": "Subscribe to Focus.AI", "href": "" } ], "links": [ "https://thefocus.ai/reports/coding-agent/", "https://thefocus.ai/", "https://thefocus.ai/reports/coding-agent/steps/10", "https://thefocus.ai/reports/coding-agent/", "https://github.com/The-Focus-AI/weekend-coding-agent.git" ], "images": [], "text": "THEFOCUS.AI LABS DOC. NO: WCA-2025 Weekend Coding Agent THEFOCUS.AI EPILOG CONTINUE YOUR JOURNEY FURTHER EXPLORATIONS Once you've built the core agent, here are paths to explore: Connect Directly to Model Providers Skip OpenRouter and connect directly to the source: Provider Why Consider It Anthropic API Direct Claude access, prompt caching, lower latency Google AI Studio Gemini models, generous free tier, multimodal capabilities OpenAI API GPT models, extensive ecosystem, fine-tuning options Trade-off: Direct connections mean managing multiple API keys and SDKs, but you get provider-specific features and potentially lower costs. Memory & Persistence Approach What to Explore Vector databases Pinecone, Chroma, or pgvector for semantic search over past conversations RAG patterns Retrieve relevant context before generating responses Long-term memory Remember user preferences and project context across sessions Episodic memory \"Remember when we fixed that bug last week?\" Multi-Model Strategies Strategy When to Use Model routing Cheap models for simple tasks, expensive ones for complex reasoning Cascade patterns Try fast model first, fall back to powerful model if confidence is low Ensemble approaches Multiple models vote on critical decisions Specialized models Code models for code, general models for planning Security & Sandboxing Technique What It Provides Docker isolation Run tools in containers that can't touch your real filesystem Permission systems Require approval for dangerous operations Capability tokens Limit what each session can access Audit logging Track every action for security review User Experience Feature Implementation Ideas Streaming responses Show partial output as it generates Progress indicators \"Searching 47 files...\" feedback Human-in-the-loop Approval workflows for destructive changes Undo/rollback Git-based recovery from mistakes Voice interface Speech-to-text input, text-to-speech output Advanced Architectures Pattern What to Research MCP Integration Connect to the Model Context Protocol ecosystem Team agents Multiple agents collaborating with different roles Planning agents Separate planning from execution Self-improvement Agents that update their own prompts and skills based on feedback Evaluation loops Automated testing of agent behavior ← PREV INDEX → MISSION COMPLETE STATUS ALL 11 OPERATIONS COMPLETED PROJECT Weekend Coding Agent DOC. NO: WCA-2025 VIEW REPOSITORY → STAY UPDATED GET MORE WEEKEND PROJECTS SUBSCRIBE No spam. Unsub", "height": 1000, "screenshots": { "desktop": "assets/screenshots/desktop/reports--coding-agent--epilog.png", "mobile": "assets/screenshots/mobile/reports--coding-agent--epilog.png" } }, { "url": "https://thefocus.ai/recipes/repomix", "path": "/recipes/repomix", "slug": "recipes--repomix", "category": "Recipe/tool content", "status": 200, "title": "Using repomix to give llm full context", "metaDescription": "The post focuses on how to give LLMs full visibility into a codebase to improve their understanding and responses.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Using repomix to give llm full context", "index": 1 }, { "tag": "h2", "text": "Tips", "index": 2 }, { "tag": "h3", "text": "MATERIALS NEEDED", "index": 3 }, { "tag": "h3", "text": "MORE PROJECTS TO TRY", "index": 4 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 5 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 6 }, { "tag": "h3", "text": "COMPANY", "index": 7 }, { "tag": "h3", "text": "PRODUCTS", "index": 8 }, { "tag": "h3", "text": "RESOURCES", "index": 9 }, { "tag": "h3", "text": "CONNECT", "index": 10 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Using repomix to give llm full context", "y": 429, "body": "WHAT YOU'LL BUILD The post focuses on how to give LLMs full visibility into a codebase to improve their understanding and responses." }, { "tag": "h2", "title": "Tips", "y": 2137, "body": "When you run repomix it shows you how many tokens the files use. ✔ Packing completed successfully! 📈 Top 5 Files by Character Count and Token Count: ────────────────────────────────────────────────── 1. warc-viewer.py (19,802 chars, 4,023 tokens) 2. tests/test_app.py (14,737 chars, 3,165 tokens) 3. src/warc_viewer/app.py (14,513 chars, 2,859 tokens) 4. src/warc_viewer/cdx.py (13,997 chars, 2,672 tokens) 5. tests/test_db.py (13,766 chars, 3,141 tokens) 🔎 Security Check: ────────────────── ✔ No suspicious files detected. 📊 Pack Summary: ──────────────── Total Files: 31 files Total Chars: 160,627 chars Total Tokens: 35,819 tokens Output: repomix-output.txt Security: ✔ No suspicious files det" }, { "tag": "h3", "title": "MATERIALS NEEDED", "y": 1398, "body": "• Computer with internet access • Basic command line knowledge • Text editor" }, { "tag": "h3", "title": "MORE PROJECTS TO TRY", "y": 1587, "body": "Extracting requirements PROMPTS Need to understand all the requirements hidden in a codebase? This technique shows how to use LLMs to compile comprehensive specifications from repository analysis for immediate development use. VIEW RECIPE Understanding a code base CLAUDE CONTEXT PROMPTS Explore three complementary approaches to rapidly comprehend any codebase using the latest AI assistants and developer tools. Follow along with practical examples showing how to extract key information about memory systems and other complex functionality with minimal effort. VIEW RECIPE" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 3717, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 4167, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 4544, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 4544, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 4544, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 4544, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← BACK TO WEEKEND WARRIOR PROJECTS", "href": "https://thefocus.ai/tools" }, { "text": "repomix", "href": "https://repomix.com/" }, { "text": "understanding-a-codebase", "href": "https://thefocus.ai/recipes/understanding-a-codebase" }, { "text": "PROMPTS", "href": "https://thefocus.ai/recipes/prompts" }, { "text": "Need to understand all the requirements hidden in a codebase? This technique shows how to use LLMs to compile comprehensive specifications from repository analysis for immediate development use.", "href": "https://thefocus.ai/recipes/extracting-requirements" }, { "text": "VIEW RECIPE", "href": "https://thefocus.ai/recipes/extracting-requirements" }, { "text": "CLAUDE", "href": "https://thefocus.ai/recipes/claude" }, { "text": "CONTEXT", "href": "https://thefocus.ai/recipes/context" }, { "text": "PROMPTS", "href": "https://thefocus.ai/recipes/prompts" }, { "text": "Explore three complementary approaches to rapidly comprehend any codebase using the latest AI assistants and developer tools. Follow along with practical examples showing how to extract key information about memory systems and other complex functionality with minimal effort.", "href": "https://thefocus.ai/recipes/understanding-a-codebase" }, { "text": "VIEW RECIPE", "href": "https://thefocus.ai/recipes/understanding-a-codebase" }, { "text": "← ALL PROJECTS", "href": "https://thefocus.ai/tools" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/tools", "https://repomix.com/", "https://thefocus.ai/recipes/understanding-a-codebase", "https://thefocus.ai/recipes/prompts", "https://thefocus.ai/recipes/extracting-requirements", "https://thefocus.ai/recipes/extracting-requirements", "https://thefocus.ai/recipes/claude", "https://thefocus.ai/recipes/context", "https://thefocus.ai/recipes/prompts", "https://thefocus.ai/recipes/understanding-a-codebase", "https://thefocus.ai/recipes/understanding-a-codebase", "https://thefocus.ai/tools", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [ { "src": "https://thefocus.ai/_astro/repomix.BcDqog2V.png", "alt": "Using repomix to give llm full context", "w": 1024, "h": 1024 }, { "src": "https://thefocus.ai/_astro/extracting-requirements.VmVhFQp7_Z19blW5.webp", "alt": "Extracting requirements", "w": 1024, "h": 1024 }, { "src": "https://thefocus.ai/_astro/understanding-a-codebase.DcoDuyZy_1XwzIh.webp", "alt": "Understanding a code base", "w": 1024, "h": 1024 } ], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← BACK TO WEEKEND WARRIOR PROJECTS PROJECT WORKSHEET TheFocus.AI Labs Workshop Series UNCLASSIFIED EST. TIME 5 min PUBLISHED March 14, 2025 Using repomix to give llm full context WHAT YOU'LL BUILD The post focuses on how to give LLMs full visibility into a codebase to improve their understanding and responses. repomix is a great tool that you can use to tell an LLM about all of the context of your project. You can run it like this npx repomix And it will create a repomix-output.txt file that you can send to a LLM. You can drag the file over, or paste it in: (works best with claude) cat repomix-output.txt | pbcopy You can also pass this into a local llm, like cat repomix-output.txt | ollama run gemma3:12b \"Tell me what you think about all of this\" Check out more understanding-a-codebase Tips When you run repomix it shows you how many tokens the files use. ✔ Packing completed successfully! 📈 Top 5 Files by Character Count and Token Count: ────────────────────────────────────────────────── 1. warc-viewer.py (19,802 chars, 4,023 tokens) 2. tests/test_app.py (14,737 chars, 3,165 tokens) 3. src/warc_viewer/app.py (14,513 chars, 2,859 tokens) 4. src/warc_viewer/cdx.py (13,997 chars, 2,672 tokens) 5. tests/test_db.py (13,766 chars, 3,141 tokens) 🔎 Security Check: ────────────────── ✔ No suspicious files detected. 📊 Pack Summary: ──────────────── Total Files: 31 files Total Chars: 160,627 chars Total Tokens: 35,819 tokens Output: repomix-output.txt Security: ✔ No suspicious files detected 🎉 All Done! Your repository has been successfully packed. 💡 Repomix is now available in your browser Depending upon what’s in there, you can set up --ignore: npx repomix --ignore \"**/*.json,**/*.ipynb,**/*.mdx\" I have a bunch of mise tasks that depends on llm-dump which I define both in .config/mise/config.toml but then override in specific projects as needed. [tasks.llm-dump] description = 'Runs repomix' run = ['repomix --ignore \"output*,**/*.log,tmp/,inputs/**/*md,data_integrity/**/*.json\"'] MATERIALS NEEDED • Computer with internet access • Basic command line knowledge • Text editor MORE PROJECTS TO TRY Extracting requirements PROMPTS Need to understand all the requirements hidden in a codebase? This technique shows how to use LLMs to compile comprehensive specifications from repository", "height": 5041, "screenshots": { "desktop": "assets/screenshots/desktop/recipes--repomix.png", "mobile": "assets/screenshots/mobile/recipes--repomix.png" } }, { "url": "https://thefocus.ai/recipes/understanding-a-codebase", "path": "/recipes/understanding-a-codebase", "slug": "recipes--understanding-a-codebase", "category": "Recipe/tool content", "status": 200, "title": "Understanding a code base", "metaDescription": "Explore three complementary approaches to rapidly comprehend any codebase using the latest AI assistants and developer tools. 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Follow along with practical examples showing how to extract key information about memory systems and other complex functionality with minimal effort." }, { "tag": "h2", "title": "github copilot", "y": 1527, "body": "I don’t think before writing this article out I never even looked at Google GitHub Copilot to use this. But there’s a little GitHub Copilot button up there and you can just start typing away and asking questions about it. I went to the mem0ai/mem0 repo and I asked the question explaining to me how the memory system works. And it gave me a pretty good analysis of how it worked. You obviously should adjust what you are looking for. explain how the memory system works, specifically around how it adds memories to the system, what is the prompt it uses" }, { "tag": "h2", "title": "pull it locally", "y": 2127, "body": "What I normally do is pull it down locally into a temporary directory and then use a combination of clog code, cursor, and repo mix to ask it different questions. alias nd=cd $(mktemp -d)" }, { "tag": "h2", "title": "claude code", "y": 2435, "body": "git clone git@github.com:mem0ai/mem0.git cd *;claude Then do /init and let it figure out what is going on, and then start asking questions." }, { "tag": "h2", "title": "cursor", "y": 2775, "body": "Cursor is a little bit different on some level it’s not quite as smart as Claude Code is at the second although I’m sure tomorrow it’ll be different but the advantage is here is that you can zip around the repository and navigate and you’re sort of poised to start working right away. But the same prompts works because it’s largely the same thing underneath." }, { "tag": "h3", "title": "MATERIALS NEEDED", "y": 1424, "body": "• Computer with internet access • Basic command line knowledge • Text editor" }, { "tag": "h3", "title": "MORE PROJECTS TO TRY", "y": 1613, "body": "Extracting requirements PROMPTS Need to understand all the requirements hidden in a codebase? This technique shows how to use LLMs to compile comprehensive specifications from repository analysis for immediate development use. VIEW RECIPE Using repomix to give llm full context CONTEXT The post focuses on how to give LLMs full visibility into a codebase to improve their understanding and responses. VIEW RECIPE" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 3155, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 3605, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 3982, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 3982, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 3982, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 3982, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← BACK TO WEEKEND WARRIOR PROJECTS", "href": "https://thefocus.ai/tools" }, { "text": "mem0ai/mem0", "href": "https://github.com/mem0ai/mem0" }, { "text": "PROMPTS", "href": "https://thefocus.ai/recipes/prompts" }, { "text": "Need to understand all the requirements hidden in a codebase? This technique shows how to use LLMs to compile comprehensive specifications from repository analysis for immediate development use.", "href": "https://thefocus.ai/recipes/extracting-requirements" }, { "text": "VIEW RECIPE", "href": "https://thefocus.ai/recipes/extracting-requirements" }, { "text": "CONTEXT", "href": "https://thefocus.ai/recipes/context" }, { "text": "The post focuses on how to give LLMs full visibility into a codebase to improve their understanding and responses.", "href": "https://thefocus.ai/recipes/repomix" }, { "text": "VIEW RECIPE", "href": "https://thefocus.ai/recipes/repomix" }, { "text": "← ALL PROJECTS", "href": "https://thefocus.ai/tools" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/tools", "https://github.com/mem0ai/mem0", "https://thefocus.ai/recipes/prompts", "https://thefocus.ai/recipes/extracting-requirements", "https://thefocus.ai/recipes/extracting-requirements", "https://thefocus.ai/recipes/context", "https://thefocus.ai/recipes/repomix", "https://thefocus.ai/recipes/repomix", "https://thefocus.ai/tools", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [ { "src": "https://thefocus.ai/_astro/understanding-a-codebase.DcoDuyZy.png", "alt": "Understanding a code base", "w": 1024, "h": 1024 }, { "src": "https://thefocus.ai/_astro/extracting-requirements.VmVhFQp7_Z19blW5.webp", "alt": "Extracting requirements", "w": 1024, "h": 1024 }, { "src": "https://thefocus.ai/_astro/repomix.BcDqog2V_23qUF2.webp", "alt": "Using repomix to give llm full context", "w": 1024, "h": 1024 } ], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← BACK TO WEEKEND WARRIOR PROJECTS PROJECT WORKSHEET TheFocus.AI Labs Workshop Series UNCLASSIFIED EST. TIME 15 min PUBLISHED March 14, 2025 Understanding a code base WHAT YOU'LL BUILD Explore three complementary approaches to rapidly comprehend any codebase using the latest AI assistants and developer tools. Follow along with practical examples showing how to extract key information about memory systems and other complex functionality with minimal effort. There’s a couple of different tools that I use to kind of understand what’s going on in a code base. github copilot I don’t think before writing this article out I never even looked at Google GitHub Copilot to use this. But there’s a little GitHub Copilot button up there and you can just start typing away and asking questions about it. I went to the mem0ai/mem0 repo and I asked the question explaining to me how the memory system works. And it gave me a pretty good analysis of how it worked. You obviously should adjust what you are looking for. explain how the memory system works, specifically around how it adds memories to the system, what is the prompt it uses pull it locally What I normally do is pull it down locally into a temporary directory and then use a combination of clog code, cursor, and repo mix to ask it different questions. alias nd=cd $(mktemp -d) claude code git clone git@github.com:mem0ai/mem0.git cd *;claude Then do /init and let it figure out what is going on, and then start asking questions. cursor Cursor is a little bit different on some level it’s not quite as smart as Claude Code is at the second although I’m sure tomorrow it’ll be different but the advantage is here is that you can zip around the repository and navigate and you’re sort of poised to start working right away. But the same prompts works because it’s largely the same thing underneath. MATERIALS NEEDED • Computer with internet access • Basic command line knowledge • Text editor MORE PROJECTS TO TRY Extracting requirements PROMPTS Need to understand all the requirements hidden in a codebase? This technique shows how to use LLMs to compile comprehensive specifications from repository analysis for immediate development use. VIEW RECIPE Using repomix to give llm full context CONTEXT The post focuses on how to give LLMs full visibility in", "height": 4479, "screenshots": { "desktop": "assets/screenshots/desktop/recipes--understanding-a-codebase.png", "mobile": "assets/screenshots/mobile/recipes--understanding-a-codebase.png" } }, { "url": "https://thefocus.ai/recipes/prompts", "path": "/recipes/prompts", "slug": "recipes--prompts", "category": "Recipe/tool content", "status": 200, "title": "prompts", "metaDescription": "prompts", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "prompts recipes", "index": 1 }, { "tag": "h2", "text": "coding", "index": 2 }, { "tag": "h2", "text": "user experience", "index": 3 }, { "tag": "h2", "text": "analysis", "index": 4 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 5 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 6 }, { "tag": "h3", "text": "COMPANY", "index": 7 }, { "tag": "h3", "text": "PRODUCTS", "index": 8 }, { "tag": "h3", "text": "RESOURCES", "index": 9 }, { "tag": "h3", "text": "CONNECT", "index": 10 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "prompts recipes", "y": 186, "body": "coding The one thing we do know that these new AI tools are good at is helping people code. I write a lot of code. Here’s a couple little tricks that I’ve learned about how to write better code and faster code. Dec 5, 2025 The Smallest Coding Agent promptsclaude Apr 2, 2025 Lift and shift prompts Mar 20, 2025 Adding Memory to Cursor prompts Mar 20, 2025 Planning, brainstorming and idea development prompts Mar 16, 2025 Extracting requirements prompts Mar 15, 2025 Write git-commit-messages prompts Mar 14, 2025 Understanding a code base claudecontextprompts user experience Different ways to interact with generative AI and make it easier to have it interact with the other tools around you. May 2" }, { "tag": "h2", "title": "coding", "y": 326, "body": "The one thing we do know that these new AI tools are good at is helping people code. I write a lot of code. Here’s a couple little tricks that I’ve learned about how to write better code and faster code. Dec 5, 2025 The Smallest Coding Agent promptsclaude Apr 2, 2025 Lift and shift prompts Mar 20, 2025 Adding Memory to Cursor prompts Mar 20, 2025 Planning, brainstorming and idea development prompts Mar 16, 2025 Extracting requirements prompts Mar 15, 2025 Write git-commit-messages prompts Mar 14, 2025 Understanding a code base claudecontextprompts" }, { "tag": "h2", "title": "user experience", "y": 845, "body": "Different ways to interact with generative AI and make it easier to have it interact with the other tools around you. May 28, 2025 Content Finder prompts Apr 7, 2025 Iterating designs with Chat GPT 4o imagesprompts Apr 3, 2025 Use the llm cli for quick prototyping prompts Mar 20, 2025 Adding Memory to Cursor prompts Mar 19, 2025 Make it easy to load and update your prompt library promptsmcp" }, { "tag": "h2", "title": "analysis", "y": 1262, "body": "Things which help with the process of thinking itself. In other words these are tools to help you do analysis May 28, 2025 Content Finder prompts May 28, 2025 Geolocation Prompt prompts Apr 3, 2025 Use the llm cli for quick prototyping prompts Mar 31, 2025 Claude to write midjourney prompts promptsimages Mar 20, 2025 Planning, brainstorming and idea development prompts Mar 16, 2025 Extracting requirements prompts Mar 15, 2025 Making Meta Data prompts Mar 14, 2025 Understanding a code base claudecontextprompts" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 1878, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 2328, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← Back to all recipes prompts recipes coding The one thing we do know that these new AI tools are good at is helping people code. I write a lot of code. Here’s a couple little tricks that I’ve learned about how to write better code and faster code. Dec 5, 2025 The Smallest Coding Agent promptsclaude Apr 2, 2025 Lift and shift prompts Mar 20, 2025 Adding Memory to Cursor prompts Mar 20, 2025 Planning, brainstorming and idea development prompts Mar 16, 2025 Extracting requirements prompts Mar 15, 2025 Write git-commit-messages prompts Mar 14, 2025 Understanding a code base claudecontextprompts user experience Different ways to interact with generative AI and make it easier to have it interact with the other tools around you. 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Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← BACK TO WEEKEND WARRIOR PROJECTS PROJECT WORKSHEET TheFocus.AI Labs Workshop Series UNCLASSIFIED EST. TIME 10 min PUBLISHED March 16, 2025 Extracting requirements WHAT YOU'LL BUILD Need to understand all the requirements hidden in a codebase? This technique shows how to use LLMs to compile comprehensive specifications from repository analysis for immediate development use. In either of these sessions, we can ask the llm to reverse engineer the repo into a specifictation: can you compile our findings into a comprehensive, developer-ready specification? Include all relevant requirements, architecture choices, data handling details, error handling strategies, and a testing plan so a developer can immediately begin implementation. That cost 50 cents. > /cost ⎿ Total cost: $0.51 Total duration (API): 2m 36.6s Total duration (wall): 16m 32.2s Total code changes: 43 lines added, 0 lines removed MATERIALS NEEDED • Computer with internet access • Basic command line knowledge • Text editor MORE PROJECTS TO TRY Using repomix to give llm full context CONTEXT The post focuses on how to give LLMs full visibility into a codebase to improve their understanding and responses. VIEW RECIPE Understanding a code base CLAUDE CONTEXT PROMPTS Explore three complementary approaches to rapidly comprehend any codebase using the latest AI assistants and developer tools. Follow along with practical examples showing how to extract key information about memory systems and other complex functionality with minimal effort. VIEW RECIPE ← ALL PROJECTS Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. 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}, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← Back to all recipes", "href": "https://thefocus.ai/recipes" }, { "text": "Sep 1, 2025 context7-mcp", "href": "https://thefocus.ai/recipes/context7-mcp" }, { "text": "mcp", "href": "https://thefocus.ai/recipes/mcp" }, { "text": "Sep 1, 2025 use playwright mcp to get high fidelity designs", "href": "https://thefocus.ai/recipes/playwright-mcp" }, { "text": "mcp", "href": "https://thefocus.ai/recipes/mcp" }, { "text": "Sep 1, 2025 context7-mcp", "href": "https://thefocus.ai/recipes/context7-mcp" }, { "text": "mcp", "href": "https://thefocus.ai/recipes/mcp" }, { "text": "Sep 1, 2025 use playwright mcp to get high fidelity designs", "href": "https://thefocus.ai/recipes/playwright-mcp" }, { "text": "mcp", "href": "https://thefocus.ai/recipes/mcp" }, { "text": "Mar 19, 2025 Make it easy to load and update your prompt library", "href": "https://thefocus.ai/recipes/load-and-save-your-prompts" }, { "text": 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← Back to all recipes mcp recipes coding The one thing we do know that these new AI tools are good at is helping people code. I write a lot of code. Here’s a couple little tricks that I’ve learned about how to write better code and faster code. Sep 1, 2025 context7-mcp mcp Sep 1, 2025 use playwright mcp to get high fidelity designs mcp analysis Things which help with the process of thinking itself. In other words these are tools to help you do analysis Sep 1, 2025 context7-mcp mcp Sep 1, 2025 use playwright mcp to get high fidelity designs mcp user experience Different ways to interact with generative AI and make it easier to have it interact with the other tools around you. Mar 19, 2025 Make it easy to load and update your prompt library promptsmcp Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. View open positions → © 2026 TheFocus.AI. All rights reserved. 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VIEW RECIPE" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 2817, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 3267, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 3644, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 3644, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 3644, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 3644, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← BACK TO WEEKEND WARRIOR PROJECTS PROJECT WORKSHEET TheFocus.AI Labs Workshop Series UNCLASSIFIED EST. TIME 5 min PUBLISHED September 1, 2025 use playwright mcp to get high fidelity designs WHAT YOU'LL BUILD Learn how to integrate Playwright MCP server to enable AI coding agents to interact with web browsers, take screenshots, and validate designs with high fidelity for accurate web development and testing. playwright is a programmable browser that you can plug into your coding agents. You can use this to make sure that your design is right, or to have it look at websites that try to prevent bot traffic. Add the playwrite-mcp to your claude environment. claude mcp add --scope project playwright npx @playwright/mcp@latest Lets look at how to code a design: Take a screen shot, for example of a claude instance inside of cursor, why not? And then prompt claude: recreate this screenshot found in this directory in a html. pay close to the design and and take screenshots using the playwright tool to compare to make sure its recreated exactly Anytime you can give the LLM a way to measure its progress it does so much better. MATERIALS NEEDED • Computer with internet access • Basic command line knowledge • Text editor MORE PROJECTS TO TRY context7-mcp MCP Learn how to integrate Context7 MCP server to get up-to-date code documentation for LLMs and AI code editors, enabling real-time access to the latest library and framework documentation. VIEW RECIPE ← ALL PROJECTS Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. View open positions → © 2026 TheFocus.AI. All rights reserved. From tribal knowledge to ", "height": 4141, "screenshots": { "desktop": "assets/screenshots/desktop/recipes--playwright-mcp.png", "mobile": "assets/screenshots/mobile/recipes--playwright-mcp.png" } }, { "url": "https://thefocus.ai/recipes/dagger-and-container-use", "path": "/recipes/dagger-and-container-use", "slug": "recipes--dagger-and-container-use", "category": "Recipe/tool content", "status": 200, "title": "Dagger and Container Use", "metaDescription": "Unlock the power of isolated development with Dagger and Container Use—no more worrying about missing dependencies or system conflicts.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Dagger and Container Use", "index": 1 }, { "tag": "h2", "text": "Install", "index": 2 }, { "tag": "h2", "text": "Setup the environment", "index": 3 }, { "tag": "h2", "text": "See what happened", "index": 4 }, { "tag": "h3", "text": "MATERIALS NEEDED", "index": 5 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 6 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 7 }, { "tag": "h3", "text": "COMPANY", "index": 8 }, { "tag": "h3", "text": "PRODUCTS", "index": 9 }, { "tag": "h3", "text": "RESOURCES", "index": 10 }, { "tag": "h3", "text": "CONNECT", "index": 11 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Dagger and Container Use", "y": 429, "body": "WHAT YOU'LL BUILD Unlock the power of isolated development with Dagger and Container Use—no more worrying about missing dependencies or system conflicts." }, { "tag": "h2", "title": "Install", "y": 1537, "body": "Lets check out dagger and container-use brew install dagger/tap/dagger brew install dagger/tap/container-use" }, { "tag": "h2", "title": "Setup the environment", "y": 1841, "body": "mkdir myrepo git init myrepo Now lets use containers! # Add Container Use MCP server cd /path/to/repository claude mcp add container-use -- cu stdio # Add agent rules (optional) curl https://raw.githubusercontent.com/dagger/container-use/main/rules/agent.md >> CLAUDE.md Then start up claude, and ask it to do something make an awesome looking markdown to html converter using zig And then it starts up its own development container: This creates an isolated environment to build stuff. Why? I said to use zig, but I don’t have zig on my system. But it goes and setups the environment thats needed. $ which zig zig not found Go to another terminal, and then start up another claude, and give it anoth" }, { "tag": "h2", "title": "See what happened", "y": 3729, "body": "$ cu checkout uncommon-cham?ois Switched to branch 'cu-uncommon-chamois' lsq CLAUDE.md hn_scraper.zig hn_top_stories_20270.md $ cu checkout brief-calf Switched to branch 'cu-brief-calf' ls build.zig final_output.html output.html built_output.html markdown_converter_final.zig README.md CLAUDE.md markdown_converter_fixed.zig example.md markdown_converter.zig ## Dagger Launch it with ```shell dagger Start up an alpine container: container | from alpine | terminal That’s cool." }, { "tag": "h3", "title": "MATERIALS NEEDED", "y": 1398, "body": "• Computer with internet access • Basic command line knowledge • Text editor" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 4893, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 5343, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. 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Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← BACK TO WEEKEND WARRIOR PROJECTS", "href": "https://thefocus.ai/tools" }, { "text": "dagger", "href": "https://github.com/dagger/dagger" }, { "text": "container-use", "href": "https://github.com/dagger/container-use?tab=readme-ov-file" }, { "text": "← ALL PROJECTS", "href": "https://thefocus.ai/tools" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" }, { "text": "Registry", "href": "https://account.thefocus.ai/" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/tools", "https://github.com/dagger/dagger", "https://github.com/dagger/container-use?tab=readme-ov-file", "https://thefocus.ai/tools", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [ { "src": "https://thefocus.ai/_astro/dagger.BAf79M7G.png", "alt": "Dagger and Container Use", "w": 1024, "h": 1024 }, { "src": "https://thefocus.ai/_astro/Screenshot%202025-07-01%20at%2018.26.40.B9eS_6Bs_cKMaG.webp", "alt": "", "w": 1438, "h": 656 } ], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← BACK TO WEEKEND WARRIOR PROJECTS PROJECT WORKSHEET TheFocus.AI Labs Workshop Series UNCLASSIFIED EST. TIME 15 min PUBLISHED July 8, 2025 Dagger and Container Use WHAT YOU'LL BUILD Unlock the power of isolated development with Dagger and Container Use—no more worrying about missing dependencies or system conflicts. Container Use lets each of your coding agents have their own containerized environment. Go from babysitting one agent at a time to enabling multiple agents to work safely and independently with your preferred stack. Install Lets check out dagger and container-use brew install dagger/tap/dagger brew install dagger/tap/container-use Setup the environment mkdir myrepo git init myrepo Now lets use containers! # Add Container Use MCP server cd /path/to/repository claude mcp add container-use -- cu stdio # Add agent rules (optional) curl https://raw.githubusercontent.com/dagger/container-use/main/rules/agent.md >> CLAUDE.md Then start up claude, and ask it to do something make an awesome looking markdown to html converter using zig And then it starts up its own development container: This creates an isolated environment to build stuff. Why? I said to use zig, but I don’t have zig on my system. But it goes and setups the environment thats needed. $ which zig zig not found Go to another terminal, and then start up another claude, and give it another task. You can then see them both running in their own environments: cu list ID TITLE CREATED UPDATED uncommon-chamois Zig Hacker News Scraper 7 seconds ago 7 seconds ago brief-calf Zig Markdown to HTML Converter 5 minutes ago 19 seconds ago So we have 2 isolated environments, with everything that they need to do their job, working independently without messing up the host environment. See what happened $ cu checkout uncommon-cham?ois Switched to branch 'cu-uncommon-chamois' lsq CLAUDE.md hn_scraper.zig hn_top_stories_20270.md $ cu checkout brief-calf Switched to branch 'cu-brief-calf' ls build.zig final_output.html output.html built_output.html markdown_converter_final.zig README.md CLAUDE.md markdown_converter_fixed.zig example.md markdown_converter.zig ## Dagger Launch it with ```shell dagger Start up an alpine container: container | from alpine | terminal That’s cool. MATERIALS NEEDED • Computer with internet access •", "height": 6217, "screenshots": { "desktop": "assets/screenshots/desktop/recipes--dagger-and-container-use.png", "mobile": "assets/screenshots/mobile/recipes--dagger-and-container-use.png" } }, { "url": "https://thefocus.ai/recipes/use-cursor-in-slack", "path": "/recipes/use-cursor-in-slack", "slug": "recipes--use-cursor-in-slack", "category": "Recipe/tool content", "status": 200, "title": "Use Cursor in Slack", "metaDescription": "Learn how to integrate Cursor with Slack for seamless code review and collaboration, including setup tips and workflow enhancements.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Use Cursor in Slack", "index": 1 }, { "tag": "h3", "text": "MATERIALS NEEDED", "index": 2 }, { "tag": "h3", "text": "MORE PROJECTS TO TRY", "index": 3 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 4 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 5 }, { "tag": "h3", "text": "COMPANY", "index": 6 }, { "tag": "h3", "text": "PRODUCTS", "index": 7 }, { "tag": "h3", "text": "RESOURCES", "index": 8 }, { "tag": "h3", "text": "CONNECT", "index": 9 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Use Cursor in Slack", "y": 429, "body": "WHAT YOU'LL BUILD Learn how to integrate Cursor with Slack for seamless code review and collaboration, including setup tips and workflow enhancements." }, { "tag": "h3", "title": "MATERIALS NEEDED", "y": 1398, "body": "• Computer with internet access • Basic command line knowledge • Text editor" }, { "tag": "h3", "title": "MORE PROJECTS TO TRY", "y": 1587, "body": "Adding Memory to Cursor PROMPTS Transform your Cursor IDE into a more intelligent assistant by implementing a sophisticated memory system adapted from cline-memory-bank. This enhanced version includes TDD principles and integrates seamlessly with your prompt library, allowing your AI assistant to maintain context and remember important details across multiple coding sessions. VIEW RECIPE" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 3583, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 4033, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 4410, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 4410, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 4410, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 4410, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← BACK TO WEEKEND WARRIOR PROJECTS", "href": "https://thefocus.ai/tools" }, { "text": "Connect Cursor with Slack", "href": "https://www.cursor.com/en/dashboard?tab=integrations" }, { "text": "PROMPTS", "href": "https://thefocus.ai/recipes/prompts" }, { "text": "Transform your Cursor IDE into a more intelligent assistant by implementing a sophisticated memory system adapted from cline-memory-bank. This enhanced version includes TDD principles and integrates seamlessly with your prompt library, allowing your AI assistant to maintain context and remember important details across multiple coding sessions.", "href": "https://thefocus.ai/recipes/add-memory-to-cursor" }, { "text": "VIEW RECIPE", "href": "https://thefocus.ai/recipes/add-memory-to-cursor" }, { "text": "← ALL PROJECTS", "href": "https://thefocus.ai/tools" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" }, { "text": "Registry", "href": "https://account.thefocus.ai/" }, { "text": "Insights", "href": "https://thefocus.ai/insights" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/tools", "https://www.cursor.com/en/dashboard?tab=integrations", "https://thefocus.ai/recipes/prompts", "https://thefocus.ai/recipes/add-memory-to-cursor", "https://thefocus.ai/recipes/add-memory-to-cursor", "https://thefocus.ai/tools", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [ { "src": "https://thefocus.ai/_astro/use-cursor.wlyYbMed.png", "alt": "Use Cursor in Slack", "w": 1024, "h": 1024 }, { "src": "https://thefocus.ai/_astro/Screenshot%202025-06-29%20at%2007.46.50.BpmuzI7F_Zz4U5J.webp", "alt": "", "w": 1290, "h": 1242 }, { "src": "https://thefocus.ai/_astro/Screenshot%202025-06-29%20at%2007.46.04.CSmthS7v_276F8n.webp", "alt": "", "w": 1420, "h": 1400 }, { "src": "https://thefocus.ai/_astro/add-memory-to-cursor.DqprdbJ2_1DySDU.webp", "alt": "Adding Memory to Cursor", "w": 1024, "h": 1024 } ], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← BACK TO WEEKEND WARRIOR PROJECTS PROJECT WORKSHEET TheFocus.AI Labs Workshop Series UNCLASSIFIED EST. TIME 10 min LEVEL Beginner PUBLISHED June 30, 2025 Use Cursor in Slack WHAT YOU'LL BUILD Learn how to integrate Cursor with Slack for seamless code review and collaboration, including setup tips and workflow enhancements. First, go and Connect Cursor with Slack Add @cursor to a channel @Cursor settings In here set the default channel repo, in my case The-Focus-AI/registry @Cursor please change the design of this to something more awesome You can open it up in Cursor (if you have the project open locally) and add a follow up if you don’t like what you see. Very cool. MATERIALS NEEDED • Computer with internet access • Basic command line knowledge • Text editor MORE PROJECTS TO TRY Adding Memory to Cursor PROMPTS Transform your Cursor IDE into a more intelligent assistant by implementing a sophisticated memory system adapted from cline-memory-bank. This enhanced version includes TDD principles and integrates seamlessly with your prompt library, allowing your AI assistant to maintain context and remember important details across multiple coding sessions. VIEW RECIPE ← ALL PROJECTS Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. View open positions → © 2026 TheFocus.AI. All rights reserved. From tribal knowledge to production AI — in weeks, not quarters.", "height": 4907, "screenshots": { "desktop": "assets/screenshots/desktop/recipes--use-cursor-in-slack.png", "mobile": "assets/screenshots/mobile/recipes--use-cursor-in-slack.png" } }, { "url": "https://thefocus.ai/recipes/content-finder", "path": "/recipes/content-finder", "slug": "recipes--content-finder", "category": "Recipe/tool content", "status": 200, "title": "Content Finder", "metaDescription": "Ever had a movie stuck on the tip of your tongue or wanted more like your family's favorite? Explore how AI-powered content discovery can help you make your next pick.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Content Finder", "index": 1 }, { "tag": "h3", "text": "MATERIALS NEEDED", "index": 2 }, { "tag": "h3", "text": "MORE PROJECTS TO TRY", "index": 3 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 4 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 5 }, { "tag": "h3", "text": "COMPANY", "index": 6 }, { "tag": "h3", "text": "PRODUCTS", "index": 7 }, { "tag": "h3", "text": "RESOURCES", "index": 8 }, { "tag": "h3", "text": "CONNECT", "index": 9 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Content Finder", "y": 429, "body": "WHAT YOU'LL BUILD Ever had a movie stuck on the tip of your tongue or wanted more like your family's favorite? Explore how AI-powered content discovery can help you make your next pick." }, { "tag": "h3", "title": "MATERIALS NEEDED", "y": 1424, "body": "• Computer with internet access • Basic command line knowledge • Text editor" }, { "tag": "h3", "title": "MORE PROJECTS TO TRY", "y": 1613, "body": "Geolocation Prompt PROMPTS This post introduces a prompt for high-precision, image-based geolocation analysis, guiding AI to deduce where a photo was taken using expertise in visual, cultural, and environmental cues. The prompt provides a step-by-step framework for analyzing images and generating confident, reasoned location guesses—even suggesting what extra information would help if uncertain. VIEW RECIPE" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 2681, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 3131, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 3508, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 3508, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 3508, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 3508, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← BACK TO WEEKEND WARRIOR PROJECTS", "href": "https://thefocus.ai/tools" }, { "text": "PROMPTS", "href": "https://thefocus.ai/recipes/prompts" }, { "text": "This post introduces a prompt for high-precision, image-based geolocation analysis, guiding AI to deduce where a photo was taken using expertise in visual, cultural, and environmental cues. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← BACK TO WEEKEND WARRIOR PROJECTS PROJECT WORKSHEET TheFocus.AI Labs Workshop Series UNCLASSIFIED EST. TIME 2 min PUBLISHED May 28, 2025 Content Finder WHAT YOU'LL BUILD Ever had a movie stuck on the tip of your tongue or wanted more like your family's favorite? Explore how AI-powered content discovery can help you make your next pick. One fun usecase is to use the models extensive knowledge to find movies or books that are like something that you liked. Looking for a movie recommendation? What are other movies like Knives Out? That was a big hit in our family, and the kids loved it. (We saw the second one, but it wasn’t as marvelous.). It finds some gems, and you can also follow up with asking it what is similar to specific aspects to it. Simple, but powerful in the “what do you want to have for dinner sort of way.” Sort of remember the movie? Describe it and see what comes up: This was an amazing trip down memory lane by the way, the car of the raft scene always stuck out in my mind and reminds me of listening to Van Halen. MATERIALS NEEDED • Computer with internet access • Basic command line knowledge • Text editor MORE PROJECTS TO TRY Geolocation Prompt PROMPTS This post introduces a prompt for high-precision, image-based geolocation analysis, guiding AI to deduce where a photo was taken using expertise in visual, cultural, and environmental cues. The prompt provides a step-by-step framework for analyzing images and generating confident, reasoned location guesses—even suggesting what extra information would help if uncertain. VIEW RECIPE ← ALL PROJECTS Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← BACK TO WEEKEND WARRIOR PROJECTS PROJECT WORKSHEET TheFocus.AI Labs Workshop Series UNCLASSIFIED EST. TIME 5 min PUBLISHED May 28, 2025 Geolocation Prompt WHAT YOU'LL BUILD This post introduces a prompt for high-precision, image-based geolocation analysis, guiding AI to deduce where a photo was taken using expertise in visual, cultural, and environmental cues. The prompt provides a step-by-step framework for analyzing images and generating confident, reasoned location guesses—even suggesting what extra information would help if uncertain. Here’s a nice little prompt to help figure out where an image was taken: Prompt: High-Precision Image-Based Geolocation Analysis You are a multi-disciplinary AI system with deep expertise in: • Geographic visual analysis • Architecture, signage systems, and transportation norms across countries • Natural vegetation, terrain types, atmospheric cues, and shadow physics • Global cultural, linguistic, and urban design patterns • GeoGuessr-style probabilistic reasoning I will upload a photograph. Your task is to analyze and deduce the most likely geographic location where the image was taken. Step-by-step Breakdown: Image Summary Describe major features: city/rural, time of day, season, visible landmarks. Deep Analysis Layers: A. Environment: terrain, sun position, weather B. Infrastructure: buildings, roads, signage styles C. Text Detection: OCR, language, script, URLs D. Cultural Cues: clothing, driving side, regional markers E. Tech & Commerce: license plates, vehicles, brands Location Guessing: Top 3–5 candidate countries or cities Confidence score for each Best guess with reasoning If uncertain: State what’s missing Suggest what would help (metadata, another angle, etc.) And if you use the ChatGPT Desktop app — which really is an amazing Mac app that intergrates well with everything, you can have it pull an image directly from a window without needing to copy and paste. MATERIALS NEEDED • Computer with internet access • Basic command line knowledge • Text editor MORE PROJECTS TO TRY Content Finder PROMPTS Ever had a movie stuck on the tip of your tongue or wanted more like your family's favorite? Explore how AI-powered content discovery can help you make your next pick. 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Learn a clever workflow that combines Chrome's full-page screenshot capability with ChatGPT's AI-powered design suggestions to create professional 16:9 ratio mockups for your next project." }, { "tag": "h3", "title": "MATERIALS NEEDED", "y": 1450, "body": "• Computer with internet access • Basic command line knowledge • Text editor" }, { "tag": "h3", "title": "MORE PROJECTS TO TRY", "y": 1639, "body": "Claude to write midjourney prompts PROMPTS IMAGES Transform your written content into compelling visual concepts by using Claude as an intelligent intermediary for Midjourney prompt creation. See how combining prompt libraries with AI analysis can help you explore multiple creative directions before settling on the perfect visual representation. VIEW RECIPE" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 2623, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 3073, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 3450, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 3450, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 3450, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 3450, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← BACK TO WEEKEND WARRIOR PROJECTS", "href": "https://thefocus.ai/tools" }, { "text": "PROMPTS", "href": "https://thefocus.ai/recipes/prompts" }, { "text": "IMAGES", "href": "https://thefocus.ai/recipes/images" }, { "text": "Transform your written content into compelling visual concepts by using Claude as an intelligent intermediary for Midjourney prompt creation. See how combining prompt libraries with AI analysis can help you explore multiple creative directions before settling on the perfect visual representation.", "href": "https://thefocus.ai/recipes/using-claude-to-write-midjourney-prompts" }, { "text": "VIEW RECIPE", "href": "https://thefocus.ai/recipes/using-claude-to-write-midjourney-prompts" }, { "text": "← ALL PROJECTS", "href": "https://thefocus.ai/tools" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" }, { "text": "Registry", "href": "https://account.thefocus.ai/" }, { "text": "Insights", "href": "https://thefocus.ai/insights" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/tools", "https://thefocus.ai/recipes/prompts", "https://thefocus.ai/recipes/images", "https://thefocus.ai/recipes/using-claude-to-write-midjourney-prompts", "https://thefocus.ai/recipes/using-claude-to-write-midjourney-prompts", "https://thefocus.ai/tools", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [ { "src": "https://thefocus.ai/_astro/iterating-designs-with-chatgpt4o.Cwl9uTRy.png", "alt": "Iterating designs with Chat GPT 4o", "w": 1024, "h": 1024 }, { "src": "https://thefocus.ai/_astro/wireframe.dUmmbUms_1CRJD.webp", "alt": "", "w": 1536, "h": 1024 }, { "src": "https://thefocus.ai/_astro/visual-metaphor-generator.DquNa1DK_YBGDG.webp", "alt": "Claude to write midjourney prompts", "w": 1024, "h": 1024 } ], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← BACK TO WEEKEND WARRIOR PROJECTS PROJECT WORKSHEET TheFocus.AI Labs Workshop Series UNCLASSIFIED EST. TIME 10 min PUBLISHED April 7, 2025 Iterating designs with Chat GPT 4o WHAT YOU'LL BUILD Discover how to leverage Chrome's hidden command palette and ChatGPT to transform your website screenshots into sophisticated wireframes. Learn a clever workflow that combines Chrome's full-page screenshot capability with ChatGPT's AI-powered design suggestions to create professional 16:9 ratio mockups for your next project. Here’s a couple fun little tricks. Let’s first go into Chrome, open up the dev tools. And did you know that Chrome has a command palette? Do CMD-SHIFT-P and capture a full screenshot. Drag that over into a new ChatGPT window, and then prompt it something like: You are a user experience designer that is building some wireframes for an application. Attached is the screenshot of the homepage of this application. And you are working on a pitch deck for a new AI driven experience based on this same functionality. I want to see an image that is 16 by 9 ratio that shows a high fidelity wireframe mock-up of a user asking about planning a road trip from one spot to another and then the AI returning a map and itinerary of where you want to go. Make sure that this output image is in the correct output format and that it’s a web design artifact. MATERIALS NEEDED • Computer with internet access • Basic command line knowledge • Text editor MORE PROJECTS TO TRY Claude to write midjourney prompts PROMPTS IMAGES Transform your written content into compelling visual concepts by using Claude as an intelligent intermediary for Midjourney prompt creation. See how combining prompt libraries with AI analysis can help you explore multiple creative directions before settling on the perfect visual representation. VIEW RECIPE ← ALL PROJECTS Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RES", "height": 3947, "screenshots": { "desktop": "assets/screenshots/desktop/recipes--iterating-designs-with-chatgpt4o.png", "mobile": "assets/screenshots/mobile/recipes--iterating-designs-with-chatgpt4o.png" } }, { "url": "https://thefocus.ai/recipes/llm-for-quick-prototyping", "path": "/recipes/llm-for-quick-prototyping", "slug": "recipes--llm-for-quick-prototyping", "category": "Recipe/tool content", "status": 200, "title": "Use the llm cli for quick prototyping", "metaDescription": "Transform your workflow with a command-line tool that brings AI capabilities directly to your terminal, no complex setup required. See how to parse websites, analyze transcripts, and extract meaningful data using the llm CLI with popular models like Gemini and Claude.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Use the llm cli for quick prototyping", "index": 1 }, { "tag": "h1", "text": "HTML + Text", "index": 2 }, { "tag": "h1", "text": "Audio", "index": 3 }, { "tag": "h1", "text": "Video", "index": 4 }, { "tag": "h1", "text": "Schema", "index": 5 }, { "tag": "h3", "text": "MATERIALS NEEDED", "index": 6 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 7 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 8 }, { "tag": "h3", "text": "COMPANY", "index": 9 }, { "tag": "h3", "text": "PRODUCTS", "index": 10 }, { "tag": "h3", "text": "RESOURCES", "index": 11 }, { "tag": "h3", "text": "CONNECT", "index": 12 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Use the llm cli for quick prototyping", "y": 429, "body": "WHAT YOU'LL BUILD Transform your workflow with a command-line tool that brings AI capabilities directly to your terminal, no complex setup required. See how to parse websites, analyze transcripts, and extract meaningful data using the llm CLI with popular models like Gemini and Claude." }, { "tag": "h1", "title": "HTML + Text", "y": 2143, "body": "wget https://thefocus.ai/blog -O blog.html cat blog.html | \\ llm -m gemini-2.5-pro-exp-03-25 \\ --schema-multi 'title, date str: mm:ss,description,topics' \\ parse | \\ tee blog.json Or with claude wget https://turingpost.com/ -O turingpost.html cat turingpost.html | \\ llm -m claude-3.7-sonnet \\ --schema-multi 'title, link, date str: yyyy/mm/dd,description,topics' \\ parse | tee turingpost2.json Make sense of some real estate data wget --user-agent=\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36\" \"https://www.redfin.com/NY/Binghamton/3-Brookside-Rd-13903/home/115601261\" cat 115601261 | llm -m gemini-2.5-pro-exp-03-25 \"extract" }, { "tag": "h1", "title": "Audio", "y": 3407, "body": "Here’s one to get a transcript of an audio file, for example of a pod cast wget -O audio.mp3 \"https://podtrac.com/pts/redirect.mp3/tracking.swap.fm/track/SxlTEPDY7xDg35RXkASs/traffic.omny.fm/d/clips/e73c998e-6e60-432f-8610-ae210140c5b1/afbd76b8-eff2-442a-b938-b28e0126edad/4c60a33c-1249-4bd6-ae78-b29100f1413d/audio.mp3?utm_source=Podcast&in_playlist=d08826cd-f888-4cd3-b700-b28e0126edbb\" llm -m gemini-2.5-pro-exp-03-25 \\ -a audio.mp3 \\ --schema-multi 'timestamp str: mm:ss,ad bool, speaker, text, tone: the conversation tone' \\ transcript | tee transcript.json" }, { "tag": "h1", "title": "Video", "y": 3851, "body": "Lets pull down something from youtube brew install yt-dlp Grab something, the smallest something and turn it into an mp4 if it isn’t: # These flags try to get the smallest filesize yt-dlp -S +size,+br,+res,+fps \\ -o video.webm \\ \"https://www.youtube.com/watch?v=ndvRsQx3xBw\" # Convert it to mp4 if its in some unsupported format ffmpeg -i video.webm.mkv video.mp4 And then lets see what happens: llm -m gemini-2.5-pro-exp-03-25 \\ -a video.mp4 \\ --schema-multi 'timestamp: in the format mm:ss, text: what was said, mood: what was the tone of voice, sponsor_section bool: is this section an add or not, ' \\ transcript | \\ tee video.json which gives us sed '1,2d;$d' video.json | jq '.items | length' 47" }, { "tag": "h1", "title": "Schema", "y": 5559, "body": "This command helps see how schemas are defined llm schemas dsl And you can do multi, so: llm schemas dsl --multi 'timestamp: in the format mm:ss, text: what was said, mood: what was the tone of voice, sponsor_section bool: is this section an add or not, '" }, { "tag": "h3", "title": "MATERIALS NEEDED", "y": 1424, "body": "• Computer with internet access • Basic command line knowledge • Text editor" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 6219, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 6669, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 7046, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 7046, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 7046, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 7046, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← BACK TO WEEKEND WARRIOR PROJECTS", "href": "https://thefocus.ai/tools" }, { "text": "my other site", "href": "https://willschenk.com/labnotes/2025/llm_cli_tool/" }, { "text": "llm", "href": "https://llm.datasette.io/en/stable/setup.html" }, { "text": "llm-gemini", "href": "https://github.com/simonw/llm-gemini" }, { "text": "llm-anthropic", "href": "https://github.com/simonw/llm-anthropic" }, { "text": "how schemas are defined", "href": "https://llm.datasette.io/en/stable/schemas.html" }, { "text": "← ALL PROJECTS", "href": "https://thefocus.ai/tools" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" }, { "text": "Registry", "href": "https://account.thefocus.ai/" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/tools", "https://willschenk.com/labnotes/2025/llm_cli_tool/", "https://llm.datasette.io/en/stable/setup.html", "https://github.com/simonw/llm-gemini", "https://github.com/simonw/llm-anthropic", "https://llm.datasette.io/en/stable/schemas.html", "https://thefocus.ai/tools", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [ { "src": "https://thefocus.ai/_astro/llm-for-quick-prototyping.BKT2WFas.png", "alt": "Use the llm cli for quick prototyping", "w": 1024, "h": 1024 } ], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← BACK TO WEEKEND WARRIOR PROJECTS PROJECT WORKSHEET TheFocus.AI Labs Workshop Series UNCLASSIFIED EST. TIME 15 min PUBLISHED April 3, 2025 Use the llm cli for quick prototyping WHAT YOU'LL BUILD Transform your workflow with a command-line tool that brings AI capabilities directly to your terminal, no complex setup required. See how to parse websites, analyze transcripts, and extract meaningful data using the llm CLI with popular models like Gemini and Claude. Installation info on my other site But the short answer is: brew install llm # Paste your OpenAI API key into this llm keys set openai llm install llm-gemini llm keys set gemini llm install llm-anthropic llm keys set anthropic Links llm llm-gemini llm-anthropic HTML + Text wget https://thefocus.ai/blog -O blog.html cat blog.html | \\ llm -m gemini-2.5-pro-exp-03-25 \\ --schema-multi 'title, date str: mm:ss,description,topics' \\ parse | \\ tee blog.json Or with claude wget https://turingpost.com/ -O turingpost.html cat turingpost.html | \\ llm -m claude-3.7-sonnet \\ --schema-multi 'title, link, date str: yyyy/mm/dd,description,topics' \\ parse | tee turingpost2.json Make sense of some real estate data wget --user-agent=\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36\" \"https://www.redfin.com/NY/Binghamton/3-Brookside-Rd-13903/home/115601261\" cat 115601261 | llm -m gemini-2.5-pro-exp-03-25 \"extracts the price history of this property and who the broker was\" Or if you want something more structured cat 115601261 | \\ llm -m gemini-2.5-pro-exp-03-25 \\ --schema-multi 'date: status, broker: broker name of was in the transaction, description: listed/price change/offered/sold etc, price: the price' \\ \"figure out the price history for when this property was bought and sold\" Audio Here’s one to get a transcript of an audio file, for example of a pod cast wget -O audio.mp3 \"https://podtrac.com/pts/redirect.mp3/tracking.swap.fm/track/SxlTEPDY7xDg35RXkASs/traffic.omny.fm/d/clips/e73c998e-6e60-432f-8610-ae210140c5b1/afbd76b8-eff2-442a-b938-b28e0126edad/4c60a33c-1249-4bd6-ae78-b29100f1413d/audio.mp3?utm_source=Podcast&in_playlist=d08826cd-f888-4cd3-b700-b28e0126edbb\" llm -m gemini-2.5-pro-exp-03-25 \\ -a audio.mp3 \\ --schema-multi 'timestamp str: mm:ss,ad bool, speak", "height": 7543, "screenshots": { "desktop": "assets/screenshots/desktop/recipes--llm-for-quick-prototyping.png", "mobile": "assets/screenshots/mobile/recipes--llm-for-quick-prototyping.png" } }, { "url": "https://thefocus.ai/recipes/lift-and-shift", "path": "/recipes/lift-and-shift", "slug": "recipes--lift-and-shift", "category": "Recipe/tool content", "status": 200, "title": "Lift and shift", "metaDescription": "Master the art of learning from existing projects by using this structured approach to extracting and documenting requirements and insights. With simple command-line tools and clear documentation practices, you'll be able to turn any existing implementation into a wealth of reusable knowledge.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Lift and shift", "index": 1 }, { "tag": "h3", "text": "MATERIALS NEEDED", "index": 2 }, { "tag": "h3", "text": "MORE PROJECTS TO TRY", "index": 3 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 4 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 5 }, { "tag": "h3", "text": "COMPANY", "index": 6 }, { "tag": "h3", "text": "PRODUCTS", "index": 7 }, { "tag": "h3", "text": "RESOURCES", "index": 8 }, { "tag": "h3", "text": "CONNECT", "index": 9 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Lift and shift", "y": 429, "body": "WHAT YOU'LL BUILD Master the art of learning from existing projects by using this structured approach to extracting and documenting requirements and insights. With simple command-line tools and clear documentation practices, you'll be able to turn any existing implementation into a wealth of reusable knowledge." }, { "tag": "h3", "title": "MATERIALS NEEDED", "y": 1424, "body": "• Computer with internet access • Basic command line knowledge • Text editor" }, { "tag": "h3", "title": "MORE PROJECTS TO TRY", "y": 1613, "body": "Adding Memory to Cursor PROMPTS Transform your Cursor IDE into a more intelligent assistant by implementing a sophisticated memory system adapted from cline-memory-bank. This enhanced version includes TDD principles and integrates seamlessly with your prompt library, allowing your AI assistant to maintain context and remember important details across multiple coding sessions. VIEW RECIPE Extracting requirements PROMPTS Need to understand all the requirements hidden in a codebase? This technique shows how to use LLMs to compile comprehensive specifications from repository analysis for immediate development use. VIEW RECIPE" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 2997, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 3447, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 3824, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 3824, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 3824, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 3824, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← BACK TO WEEKEND WARRIOR PROJECTS PROJECT WORKSHEET TheFocus.AI Labs Workshop Series UNCLASSIFIED EST. TIME 15 min PUBLISHED April 2, 2025 Lift and shift WHAT YOU'LL BUILD Master the art of learning from existing projects by using this structured approach to extracting and documenting requirements and insights. With simple command-line tools and clear documentation practices, you'll be able to turn any existing implementation into a wealth of reusable knowledge. Pull out the requirements and best practices from a repository, or even a set of screenshots, and then use that to implement your own version. Extract all the learnings out of this repository — ask me what I want to focus on. Look through all the of code, and the dependancies, how the build is setup, and how the tools are setup, and write a clear specification for how to create a project with these qualities in mind. If there are any lessons learned that are applicable in the future be sure you include them. Extract all of this into a clean file — if you are running in cursor save it as reimplementation-guide.md So if you wanted to do this first get everything: npx repomix Then you can copy it cat repomix-output.xml | pbcopy And move it over. Or just run it on the cli. npx repomix cat repomix-output.xml| run-prompt code/extract-requirements.md This works especially well when you have a memory system in place which is tracking what you’ve been working on. MATERIALS NEEDED • Computer with internet access • Basic command line knowledge • Text editor MORE PROJECTS TO TRY Adding Memory to Cursor PROMPTS Transform your Cursor IDE into a more intelligent assistant by implementing a sophisticated memory system adapted from cline-memory-bank. This enhanced version includes TDD principles and integrates seamlessly with your prompt library, allowing your AI assistant to maintain context and remember important details across multiple coding sessions. VIEW RECIPE Extracting requirements PROMPTS Need to understand all the requirements hidden in a codebase? This technique shows how to use LLMs to compile comprehensive specifications from repository analysis for immediate development use. VIEW RECIPE ← ALL PROJECTS Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whethe", "height": 4321, "screenshots": { "desktop": "assets/screenshots/desktop/recipes--lift-and-shift.png", "mobile": "assets/screenshots/mobile/recipes--lift-and-shift.png" } }, { "url": "https://thefocus.ai/recipes/using-claude-to-write-midjourney-prompts", "path": "/recipes/using-claude-to-write-midjourney-prompts", "slug": "recipes--using-claude-to-write-midjourney-prompts", "category": "Recipe/tool content", "status": 200, "title": "Claude to write midjourney prompts", "metaDescription": "Transform your written content into compelling visual concepts by using Claude as an intelligent intermediary for Midjourney prompt creation. 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See how combining prompt libraries with AI analysis can help you explore multiple creative directions before settling on the perfect visual representation." }, { "tag": "h3", "title": "MATERIALS NEEDED", "y": 1424, "body": "• Computer with internet access • Basic command line knowledge • Text editor" }, { "tag": "h3", "title": "MORE PROJECTS TO TRY", "y": 1613, "body": "Iterating designs with Chat GPT 4o IMAGES PROMPTS Discover how to leverage Chrome's hidden command palette and ChatGPT to transform your website screenshots into sophisticated wireframes. Learn a clever workflow that combines Chrome's full-page screenshot capability with ChatGPT's AI-powered design suggestions to create professional 16:9 ratio mockups for your next project. VIEW RECIPE Make it easy to load and update your prompt library PROMPTS MCP Never lose track of your valuable AI prompts again with this elegant solution for maintaining a personal prompt library. The system provides simple commands to list, load, and save prompts, making it effortless to reuse and iterate on your most ef" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 3093, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 3543, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 3920, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 3920, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 3920, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 3920, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. 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Learn a clever workflow that combines Chrome's full-page screenshot capability with ChatGPT's AI-powered design suggestions to create professional 16:9 ratio mockups for your next project.", "href": "https://thefocus.ai/recipes/iterating-designs-with-chatgpt4o" }, { "text": "VIEW RECIPE", "href": "https://thefocus.ai/recipes/iterating-designs-with-chatgpt4o" }, { "text": "PROMPTS", "href": "https://thefocus.ai/recipes/prompts" }, { "text": "MCP", "href": "https://thefocus.ai/recipes/mcp" }, { "text": "Never lose track of your valuable AI prompts again with this elegant solution for maintaining a personal prompt library. The system provides simple commands to list, load, and save prompts, making it effortless to reuse and iterate on your most effective conversation starters.", "href": "https://thefocus.ai/recipes/load-and-save-your-prompts" }, { "text": "VIEW RECIPE", "href": "https://thefocus.ai/recipes/load-and-save-your-prompts" }, { "text": "← ALL PROJECTS", "href": "https://thefocus.ai/tools" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/tools", "https://thefocus.ai/recipes/images", "https://thefocus.ai/recipes/prompts", "https://thefocus.ai/recipes/iterating-designs-with-chatgpt4o", "https://thefocus.ai/recipes/iterating-designs-with-chatgpt4o", "https://thefocus.ai/recipes/prompts", "https://thefocus.ai/recipes/mcp", "https://thefocus.ai/recipes/load-and-save-your-prompts", "https://thefocus.ai/recipes/load-and-save-your-prompts", "https://thefocus.ai/tools", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [ { "src": "https://thefocus.ai/_astro/visual-metaphor-generator.DquNa1DK.png", "alt": "Claude to write midjourney prompts", "w": 1024, "h": 1024 }, { "src": "https://thefocus.ai/_astro/iterating-designs-with-chatgpt4o.Cwl9uTRy_MNiLo.webp", "alt": "Iterating designs with Chat GPT 4o", "w": 1024, "h": 1024 }, { "src": "https://thefocus.ai/_astro/load-and-save.Dyscxy8O_Z1h8U3g.webp", "alt": "Make it easy to load and update your prompt library", "w": 1024, "h": 1024 } ], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← BACK TO WEEKEND WARRIOR PROJECTS PROJECT WORKSHEET TheFocus.AI Labs Workshop Series UNCLASSIFIED EST. TIME 10 min PUBLISHED March 31, 2025 Claude to write midjourney prompts WHAT YOU'LL BUILD Transform your written content into compelling visual concepts by using Claude as an intelligent intermediary for Midjourney prompt creation. See how combining prompt libraries with AI analysis can help you explore multiple creative directions before settling on the perfect visual representation. process @lift-and-shift.md through the key-themes prompt from the library This will get the file, and the key themes prompt from the prompt-library. take 4 and get some visual metaphores from the prompt-library This is a good starting point for different ideas. I will explore a whole bunch of ideas before ultimately selecting something MATERIALS NEEDED • Computer with internet access • Basic command line knowledge • Text editor MORE PROJECTS TO TRY Iterating designs with Chat GPT 4o IMAGES PROMPTS Discover how to leverage Chrome's hidden command palette and ChatGPT to transform your website screenshots into sophisticated wireframes. Learn a clever workflow that combines Chrome's full-page screenshot capability with ChatGPT's AI-powered design suggestions to create professional 16:9 ratio mockups for your next project. VIEW RECIPE Make it easy to load and update your prompt library PROMPTS MCP Never lose track of your valuable AI prompts again with this elegant solution for maintaining a personal prompt library. The system provides simple commands to list, load, and save prompts, making it effortless to reuse and iterate on your most effective conversation starters. VIEW RECIPE ← ALL PROJECTS Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategis", "height": 4417, "screenshots": { "desktop": "assets/screenshots/desktop/recipes--using-claude-to-write-midjourney-prompts.png", "mobile": "assets/screenshots/mobile/recipes--using-claude-to-write-midjourney-prompts.png" } }, { "url": "https://thefocus.ai/recipes/add-memory-to-cursor", "path": "/recipes/add-memory-to-cursor", "slug": "recipes--add-memory-to-cursor", "category": "Recipe/tool content", "status": 200, "title": "Adding Memory to Cursor", "metaDescription": "Transform your Cursor IDE into a more intelligent assistant by implementing a sophisticated memory system adapted from cline-memory-bank. 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This enhanced version includes TDD principles and integrates seamlessly with your prompt library, allowing your AI assistant to maintain context and remember important details across multiple coding sessions." }, { "tag": "h3", "title": "MATERIALS NEEDED", "y": 1450, "body": "• Computer with internet access • Basic command line knowledge • Text editor" }, { "tag": "h3", "title": "MORE PROJECTS TO TRY", "y": 1639, "body": "Planning, brainstorming and idea development PROMPTS Transform your rough concepts into detailed project specifications with this powerful prompting technique that guides you through an in-depth development process. By asking just one thoughtful question at a time, AI helps you create comprehensive, developer-ready plans that cover every essential detail. VIEW RECIPE Make it easy to load and update your prompt library PROMPTS MCP Never lose track of your valuable AI prompts again with this elegant solution for maintaining a personal prompt library. The system provides simple commands to list, load, and save prompts, making it effortless to reuse and iterate on your most effective conversatio" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 4261, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 4711, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 5088, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 5088, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 5088, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 5088, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← BACK TO WEEKEND WARRIOR PROJECTS PROJECT WORKSHEET TheFocus.AI Labs Workshop Series UNCLASSIFIED EST. TIME 10 min PUBLISHED March 20, 2025 Adding Memory to Cursor WHAT YOU'LL BUILD Transform your Cursor IDE into a more intelligent assistant by implementing a sophisticated memory system adapted from cline-memory-bank. This enhanced version includes TDD principles and integrates seamlessly with your prompt library, allowing your AI assistant to maintain context and remember important details across multiple coding sessions. Lets add a memory system to cursor. This is adapted from cline-memory-bank but I fleshed out the project planning a bit more to be TDD. I use this with my load and save prompts pattern to put it in .cursor/rules/memory-bank.mdc and then it will help maintain the state of where you are, and you can tell it to remember things across sessions and it’s smart enough to do so. And I use product-ideation to come up with the initial project brief. Sort of a poor-mans claude code. MATERIALS NEEDED • Computer with internet access • Basic command line knowledge • Text editor MORE PROJECTS TO TRY Planning, brainstorming and idea development PROMPTS Transform your rough concepts into detailed project specifications with this powerful prompting technique that guides you through an in-depth development process. By asking just one thoughtful question at a time, AI helps you create comprehensive, developer-ready plans that cover every essential detail. 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TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registr", "height": 5585, "screenshots": { "desktop": "assets/screenshots/desktop/recipes--add-memory-to-cursor.png", "mobile": "assets/screenshots/mobile/recipes--add-memory-to-cursor.png" } }, { "url": "https://thefocus.ai/recipes/product-ideation", "path": "/recipes/product-ideation", "slug": "recipes--product-ideation", "category": "Recipe/tool content", "status": 200, "title": "Planning, brainstorming and idea development", "metaDescription": "Transform your rough concepts into detailed project specifications with this powerful prompting technique that guides you through an in-depth development process. 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VIEW RECIPE" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 3083, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 3533, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 3910, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 3910, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 3910, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 3910, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← BACK TO WEEKEND WARRIOR PROJECTS PROJECT WORKSHEET TheFocus.AI Labs Workshop Series UNCLASSIFIED EST. TIME 30 min PUBLISHED March 20, 2025 Planning, brainstorming and idea development WHAT YOU'LL BUILD Transform your rough concepts into detailed project specifications with this powerful prompting technique that guides you through an in-depth development process. By asking just one thoughtful question at a time, AI helps you create comprehensive, developer-ready plans that cover every essential detail. This is a great prompt to help hone an idea. Use this with a strong model to make sure that it thinks through all of the details. I’ve had a 45 minute conversation in the car with ChatGPT about some proejct ideas that I’ve had! Ask me one question at a time so we can develop a thorough, step-by-step spec for this idea. Each question should build on my previous answers, and our end goal is to have a detailed specification I can hand off to a developer. Let’s do this iteratively and dig into every relevant detail. Remember, only one question at a time. Once we’ve come to a good conclusion, or once we are ready to wrap up the brainstorming process, compile our findings into a comprehensive, developer-ready specification? Include all relevant requirements, architecture choices, data handling details, error handling strategies, and a testing plan so a developer can immediately begin implementation. And if you are using this inside of cursor, add this at the end to just output the details. Put this into memory-bank/project-brief.md MATERIALS NEEDED • Computer with internet access • Basic command line knowledge • Text editor MORE PROJECTS TO TRY Adding Memory to Cursor PROMPTS Transform your Cursor IDE into a more intelligent assistant by implementing a sophisticated memory system adapted from cline-memory-bank. This enhanced version includes TDD principles and integrates seamlessly with your prompt library, allowing your AI assistant to maintain context and remember important details across multiple coding sessions. VIEW RECIPE Extracting requirements PROMPTS Need to understand all the requirements hidden in a codebase? This technique shows how to use LLMs to compile comprehensive specifications from repository analysis for immediate development use. VIEW RECIPE ← ALL PROJECTS", "height": 4407, "screenshots": { "desktop": "assets/screenshots/desktop/recipes--product-ideation.png", "mobile": "assets/screenshots/mobile/recipes--product-ideation.png" } }, { "url": "https://thefocus.ai/recipes/load-and-save-your-prompts", "path": "/recipes/load-and-save-your-prompts", "slug": "recipes--load-and-save-your-prompts", "category": "Recipe/tool content", "status": 200, "title": "Make it easy to load and update your prompt library", "metaDescription": "Never lose track of your valuable AI prompts again with this elegant solution for maintaining a personal prompt library. 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The system provides simple commands to list, load, and save prompts, making it effortless to reuse and iterate on your most effective conversation starters." }, { "tag": "h3", "title": "MATERIALS NEEDED", "y": 1484, "body": "• Computer with internet access • Basic command line knowledge • Text editor" }, { "tag": "h3", "title": "MORE PROJECTS TO TRY", "y": 1673, "body": "Adding Memory to Cursor PROMPTS Transform your Cursor IDE into a more intelligent assistant by implementing a sophisticated memory system adapted from cline-memory-bank. This enhanced version includes TDD principles and integrates seamlessly with your prompt library, allowing your AI assistant to maintain context and remember important details across multiple coding sessions. VIEW RECIPE Planning, brainstorming and idea development PROMPTS Transform your rough concepts into detailed project specifications with this powerful prompting technique that guides you through an in-depth development process. By asking just one thoughtful question at a time, AI helps you create comprehensive, develope" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 3145, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 3595, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. 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Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← BACK TO WEEKEND WARRIOR PROJECTS", "href": "https://thefocus.ai/tools" }, { "text": "prompt library", "href": "https://github.com/The-Focus-AI/prompt-library" }, { "text": "PROMPTS", "href": "https://thefocus.ai/recipes/prompts" }, { "text": "Transform your Cursor IDE into a more intelligent assistant by implementing a sophisticated memory system adapted from cline-memory-bank. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← BACK TO WEEKEND WARRIOR PROJECTS PROJECT WORKSHEET TheFocus.AI Labs Workshop Series UNCLASSIFIED EST. TIME 10 min PUBLISHED March 19, 2025 Make it easy to load and update your prompt library WHAT YOU'LL BUILD Never lose track of your valuable AI prompts again with this elegant solution for maintaining a personal prompt library. The system provides simple commands to list, load, and save prompts, making it effortless to reuse and iterate on your most effective conversation starters. My prompt library can act as a MCP server, so I can tell cursor to do things like: install the enhanced memory bank prompt into .cursor/rules/memory-bank.mdc and run it Inside of .cursor/mcp.json I have the run-prompt command set up as a MCP server, like this { \"mcpServers\": { \"prompt-library\": { \"command\": \"uv\", \"args\": [\"run\", \"/Users/wschenk/prompt-library/run-prompt\", \"mcp\"], \"cwd\": \"/Users/wschenk/prompt-library\" } } } And that exposes a couple of tools list-prompts - outputs the name of all the prompts available load-prompt - returns the prompt save-prompt - saves a new prompt I can say something like write a markdown prompt that helps evaluate different libraries and save the prompt Later I can say something like load the library evaluation prompt, which typescript testing library should i use Or in cursor, you could say load up the visual metafor prompt and ask it about @load-and-save-your-prompts.md MATERIALS NEEDED • Computer with internet access • Basic command line knowledge • Text editor MORE PROJECTS TO TRY Adding Memory to Cursor PROMPTS Transform your Cursor IDE into a more intelligent assistant by implementing a sophisticated memory system adapted from cline-memory-bank. This enhanced version includes TDD principles and integrates seamlessly with your prompt library, allowing your AI assistant to maintain context and remember important details across multiple coding sessions. VIEW RECIPE Planning, brainstorming and idea development PROMPTS Transform your rough concepts into detailed project specifications with this powerful prompting technique that guides you through an in-depth development process. By asking just one thoughtful question at a time, AI helps you create comprehensive, developer-ready plans that cover every essential detail. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← BACK TO WEEKEND WARRIOR PROJECTS PROJECT WORKSHEET TheFocus.AI Labs Workshop Series UNCLASSIFIED EST. TIME 10 min LEVEL Beginner PUBLISHED March 18, 2025 Superwhisper for input WHAT YOU'LL BUILD Imagine talking to your computer and having it type for you—Superwhisper makes it real. Check out how this tool speeds up input and even lets you tweak the output. Superwhisper lets you talk to your computer. You download it and it will download some whisper models to your computer and then when you activate it, it’ll use that to figure out what you’re saying. And either enter it into whatever application you are using or into your copy and paste buffer. The models are pretty smart so it doesn’t just understand what you’re saying but it kind of cleans up the grammar a little bit. You can also add different things, prompts to it, so that you can customize the output. I never use that, but that’s definitely something you can do. It’s hard for me to write about it because I just use it so much. I type pretty fast this is way faster. MATERIALS NEEDED • Computer with internet access • Basic command line knowledge • Text editor MORE PROJECTS TO TRY Talking to the models in the car VOICE Discover how voice-activated assistants like Google and ChatGPT can transform your car rides into interactive conversations. Learn tips like using search and prompts to dive deeper into news or ideas while on the go. VIEW RECIPE ← ALL PROJECTS Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. View open positions → © 2026 TheFocus.AI. All rights reserved. From tribal knowledge to production AI — in weeks, not quarters.", "height": 3827, "screenshots": { "desktop": "assets/screenshots/desktop/recipes--superwhisper.png", "mobile": "assets/screenshots/mobile/recipes--superwhisper.png" } }, { "url": "https://thefocus.ai/recipes/talking-in-the-car", "path": "/recipes/talking-in-the-car", "slug": "recipes--talking-in-the-car", "category": "Recipe/tool content", "status": 200, "title": "Talking to the models in the car", "metaDescription": "Discover how voice-activated assistants like Google and ChatGPT can transform your car rides into interactive conversations. 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Learn tips like using search and prompts to dive deeper into news or ideas while on the go." }, { "tag": "h2", "title": "Turn on search", "y": 1935, "body": "This is good when you’re trying to figure out how to do something. Like, ask it. So the specific questions about specific technologies when I’m wondering what’s possible or what’s easy. This is also a great way also to get the latest on the news. I was listening to a podcast where they were talking about some Bitcoin related arrest and attempted pardon So I turned on chat GPT with search function, and asked it some follow-up questions about a news story. And as I was getting up-to-date information, it was telling me things that I was curious about. So, for example, in the FTX trial, a bunch of the people who cooperated are awaiting sentencing because they need to have all the trials be over " }, { "tag": "h2", "title": "Copy in some prompts", "y": 2639, "body": "It’s also good to have some prompts at the ready so you can have a real conversation. I especially like this with the requirements gatherings prompts so that it asks you a series of questions and you give it a series of answers and It will sort of prompt you to help you think through an idea." }, { "tag": "h2", "title": "Startup where you left off", "y": 2911, "body": "And when you’re done with the conversation and you close your phone and you drive your thing and you get to wherever you’re going, when you eventually open up your laptop, you can just find all of the conversations that you had in the chat history. ChatGPT doesn’t read out the code that it writes so it’ll sort of skip over it but it’ll be there in a chat and you can copy and paste if that’s the sort of thing you were doing." }, { "tag": "h3", "title": "MATERIALS NEEDED", "y": 1424, "body": "• Computer with internet access • Basic command line knowledge • Text editor" }, { "tag": "h3", "title": "MORE PROJECTS TO TRY", "y": 1613, "body": "Superwhisper for input VOICE CLIENTS Imagine talking to your computer and having it type for you—Superwhisper makes it real. Check out how this tool speeds up input and even lets you tweak the output. VIEW RECIPE" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 3363, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 3813, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 4190, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 4190, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 4190, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 4190, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← BACK TO WEEKEND WARRIOR PROJECTS", "href": "https://thefocus.ai/tools" }, { "text": "Assistant-enabled devices", "href": "https://assistant.google.com/platforms/devices/" }, { "text": "prompts at the ready", "href": "https://github.com/The-Focus-AI/prompt-library/tree/main/planning" }, { "text": "VOICE", "href": "https://thefocus.ai/recipes/voice" }, { "text": "CLIENTS", "href": "https://thefocus.ai/recipes/clients" }, { "text": "Imagine talking to your computer and having it type for you—Superwhisper makes it real. Check out how this tool speeds up input and even lets you tweak the output.", "href": "https://thefocus.ai/recipes/superwhisper" }, { "text": "VIEW RECIPE", "href": "https://thefocus.ai/recipes/superwhisper" }, { "text": "← ALL PROJECTS", "href": "https://thefocus.ai/tools" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/tools", "https://assistant.google.com/platforms/devices/", "https://github.com/The-Focus-AI/prompt-library/tree/main/planning", "https://thefocus.ai/recipes/voice", "https://thefocus.ai/recipes/clients", "https://thefocus.ai/recipes/superwhisper", "https://thefocus.ai/recipes/superwhisper", "https://thefocus.ai/tools", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [ { "src": "https://thefocus.ai/_astro/talking-in-the-car.DP4dQh4W.png", "alt": "Talking to the models in the car", "w": 1024, "h": 1024 }, { "src": "https://thefocus.ai/_astro/superwhisper.C6LMyUm0_Z1DM7zu.webp", "alt": "Superwhisper for input", "w": 1024, "h": 1024 } ], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← BACK TO WEEKEND WARRIOR PROJECTS PROJECT WORKSHEET TheFocus.AI Labs Workshop Series UNCLASSIFIED EST. TIME 5 min LEVEL Beginner PUBLISHED March 18, 2025 Talking to the models in the car WHAT YOU'LL BUILD Discover how voice-activated assistants like Google and ChatGPT can transform your car rides into interactive conversations. Learn tips like using search and prompts to dive deeper into news or ideas while on the go. When you say “Hey Google,” this allows you to get hands-free help as it voice-activates your Assistant-enabled devices. Or if you are on a pixel phone, you can just hold down the button and start talking. Voice conversations allow you to have a spoken conversation with ChatGPT, enabling a more conversational and natural interaction. You can ask questions or have discussions through voice input and receive a spoken response from ChatGPT. Voice conversations are available in ChatGPT mobile apps, desktop apps, and on desktop web at ChatGPT.com. Opening up the app and Impressing the voice icon, it takes a little bit of time for it to connect to the mothership, but then you can just start talking. Turn on search This is good when you’re trying to figure out how to do something. Like, ask it. So the specific questions about specific technologies when I’m wondering what’s possible or what’s easy. This is also a great way also to get the latest on the news. I was listening to a podcast where they were talking about some Bitcoin related arrest and attempted pardon So I turned on chat GPT with search function, and asked it some follow-up questions about a news story. And as I was getting up-to-date information, it was telling me things that I was curious about. So, for example, in the FTX trial, a bunch of the people who cooperated are awaiting sentencing because they need to have all the trials be over before they can understand how helpful they are. understand how helpful their cooperation was. The results I got back were a combination of live search results and just general stuff that the model already knew. So that was cool. Copy in some prompts It’s also good to have some prompts at the ready so you can have a real conversation. I especially like this with the requirements gatherings prompts so that it asks you a series of questions and you give it a series of", "height": 4687, "screenshots": { "desktop": "assets/screenshots/desktop/recipes--talking-in-the-car.png", "mobile": "assets/screenshots/mobile/recipes--talking-in-the-car.png" } }, { "url": "https://thefocus.ai/recipes/git-commit-messages", "path": "/recipes/git-commit-messages", "slug": "recipes--git-commit-messages", "category": "Recipe/tool content", "status": 200, "title": "Write git-commit-messages", "metaDescription": "Learn how to automate Git commit message generation with this simple but powerful prompt for Claude. The template enforces conventional commit format with appropriate type prefixes and detailed descriptions, making your repository history more professional and useful.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Write git-commit-messages", "index": 1 }, { "tag": "h2", "text": "prompt", "index": 2 }, { "tag": "h3", "text": "MATERIALS NEEDED", "index": 3 }, { "tag": "h3", "text": "MORE PROJECTS TO TRY", "index": 4 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 5 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 6 }, { "tag": "h3", "text": "COMPANY", "index": 7 }, { "tag": "h3", "text": "PRODUCTS", "index": 8 }, { "tag": "h3", "text": "RESOURCES", "index": 9 }, { "tag": "h3", "text": "CONNECT", "index": 10 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Write git-commit-messages", "y": 429, "body": "WHAT YOU'LL BUILD Learn how to automate Git commit message generation with this simple but powerful prompt for Claude. The template enforces conventional commit format with appropriate type prefixes and detailed descriptions, making your repository history more professional and useful." }, { "tag": "h2", "title": "prompt", "y": 1975, "body": "I’m sure this could be improved upon, but I literally just copied and pasted it from Google search results asking about different ways to format git commits. Write a commit message based upon the diffs of the code. Each commit has a type, with a short sentence describing what it is. There's a longer paragraph afterwards describing the change feat: Introduces a new feature (correlates with MINOR in semantic versioning). fix: Patches a bug (correlates with PATCH in semantic versioning). docs: Documentation changes (e.g., updates to README or other markdown files). style: Changes that do not affect the meaning of the code (white-space, formatting, etc.). refactor: A code change that neither fix" }, { "tag": "h3", "title": "MATERIALS NEEDED", "y": 1424, "body": "• Computer with internet access • Basic command line knowledge • Text editor" }, { "tag": "h3", "title": "MORE PROJECTS TO TRY", "y": 1613, "body": "Making Meta Data PROMPTS Generating metadata doesn't have to be a manual process when you can leverage AI prompts to produce multiple options quickly. Discover how to implement command-line tools that streamline metadata creation while maintaining your role as the final decision-maker. VIEW RECIPE" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 3195, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 3645, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 4022, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 4022, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 4022, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 4022, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← BACK TO WEEKEND WARRIOR PROJECTS", "href": "https://thefocus.ai/tools" }, { "text": "PROMPTS", "href": "https://thefocus.ai/recipes/prompts" }, { "text": "Generating metadata doesn't have to be a manual process when you can leverage AI prompts to produce multiple options quickly. Discover how to implement command-line tools that streamline metadata creation while maintaining your role as the final decision-maker.", "href": "https://thefocus.ai/recipes/making-meta-data" }, { "text": "VIEW RECIPE", "href": "https://thefocus.ai/recipes/making-meta-data" }, { "text": "← ALL PROJECTS", "href": "https://thefocus.ai/tools" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" }, { "text": "Registry", "href": "https://account.thefocus.ai/" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/tools", "https://thefocus.ai/recipes/prompts", "https://thefocus.ai/recipes/making-meta-data", "https://thefocus.ai/recipes/making-meta-data", "https://thefocus.ai/tools", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [ { "src": "https://thefocus.ai/_astro/git-commit-messages.wGzsqg9_.png", "alt": "Write git-commit-messages", "w": 1024, "h": 1024 }, { "src": "https://thefocus.ai/_astro/making-meta-data.CL4GwSqT_Z2h4xWt.webp", "alt": "Making Meta Data", "w": 1024, "h": 1024 } ], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← BACK TO WEEKEND WARRIOR PROJECTS PROJECT WORKSHEET TheFocus.AI Labs Workshop Series UNCLASSIFIED EST. TIME 10 min PUBLISHED March 15, 2025 Write git-commit-messages WHAT YOU'LL BUILD Learn how to automate Git commit message generation with this simple but powerful prompt for Claude. The template enforces conventional commit format with appropriate type prefixes and detailed descriptions, making your repository history more professional and useful. I have a quick prompt that looks at all of the changes staged in Git and writes a commit message based upon that. It’s got a title with a little type to it and a little paragraph descriptions of what’s going on. I run at something like this. git diff HEAD | llm -m claude-3.7-sonnet \"$(cat $HOME/prompts/git-commit-message) Or as a mise task like so: [tasks.commit-message] description = 'Writes a commit message' run = [ 'git diff HEAD | llm -m claude-3.7-sonnet \"$(cat $HOME/prompts/git-commit-message)\"' ] prompt I’m sure this could be improved upon, but I literally just copied and pasted it from Google search results asking about different ways to format git commits. Write a commit message based upon the diffs of the code. Each commit has a type, with a short sentence describing what it is. There's a longer paragraph afterwards describing the change feat: Introduces a new feature (correlates with MINOR in semantic versioning). fix: Patches a bug (correlates with PATCH in semantic versioning). docs: Documentation changes (e.g., updates to README or other markdown files). style: Changes that do not affect the meaning of the code (white-space, formatting, etc.). refactor: A code change that neither fixes a bug nor adds a feature. perf: Improves performance. test: Adds missing tests or corrects existing tests. chore: Changes that do not relate to a fix or feature and don't modify src or test files (e.g., updating dependencies). build: Changes that affect the build system or external dependencies ci: Continuous integration related changes revert: Reverts a previous commit # Examples feat: Add new user profile feature fix(parser): Fix issue with parsing dates docs: Update README with installation instructions style: Remove unnecessary whitespace refactor: Improve code readability perf: Optimize image loading test: Add unit tests for", "height": 4519, "screenshots": { "desktop": "assets/screenshots/desktop/recipes--git-commit-messages.png", "mobile": "assets/screenshots/mobile/recipes--git-commit-messages.png" } }, { "url": "https://thefocus.ai/recipes/making-meta-data", "path": "/recipes/making-meta-data", "slug": "recipes--making-meta-data", "category": "Recipe/tool content", "status": 200, "title": "Making Meta Data", "metaDescription": "Generating metadata doesn't have to be a manual process when you can leverage AI prompts to produce multiple options quickly. Discover how to implement command-line tools that streamline metadata creation while maintaining your role as the final decision-maker.", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Making Meta Data", "index": 1 }, { "tag": "h3", "text": "MATERIALS NEEDED", "index": 2 }, { "tag": "h3", "text": "MORE PROJECTS TO TRY", "index": 3 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 4 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 5 }, { "tag": "h3", "text": "COMPANY", "index": 6 }, { "tag": "h3", "text": "PRODUCTS", "index": 7 }, { "tag": "h3", "text": "RESOURCES", "index": 8 }, { "tag": "h3", "text": "CONNECT", "index": 9 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Making Meta Data", "y": 429, "body": "WHAT YOU'LL BUILD Generating metadata doesn't have to be a manual process when you can leverage AI prompts to produce multiple options quickly. Discover how to implement command-line tools that streamline metadata creation while maintaining your role as the final decision-maker." }, { "tag": "h3", "title": "MATERIALS NEEDED", "y": 1424, "body": "• Computer with internet access • Basic command line knowledge • Text editor" }, { "tag": "h3", "title": "MORE PROJECTS TO TRY", "y": 1613, "body": "Write git-commit-messages PROMPTS Learn how to automate Git commit message generation with this simple but powerful prompt for Claude. The template enforces conventional commit format with appropriate type prefixes and detailed descriptions, making your repository history more professional and useful. VIEW RECIPE" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 2503, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 2953, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 3330, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 3330, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 3330, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 3330, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← BACK TO WEEKEND WARRIOR PROJECTS", "href": "https://thefocus.ai/tools" }, { "text": "my prompt library", "href": "https://github.com/The-Focus-AI/prompt-library" }, { "text": "PROMPTS", "href": "https://thefocus.ai/recipes/prompts" }, { "text": "Learn how to automate Git commit message generation with this simple but powerful prompt for Claude. The template enforces conventional commit format with appropriate type prefixes and detailed descriptions, making your repository history more professional and useful.", "href": "https://thefocus.ai/recipes/git-commit-messages" }, { "text": "VIEW RECIPE", "href": "https://thefocus.ai/recipes/git-commit-messages" }, { "text": "← ALL PROJECTS", "href": "https://thefocus.ai/tools" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" }, { "text": "Registry", "href": "https://account.thefocus.ai/" }, { "text": "Insights", "href": "https://thefocus.ai/insights" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/tools", "https://github.com/The-Focus-AI/prompt-library", "https://thefocus.ai/recipes/prompts", "https://thefocus.ai/recipes/git-commit-messages", "https://thefocus.ai/recipes/git-commit-messages", "https://thefocus.ai/tools", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [ { "src": "https://thefocus.ai/_astro/making-meta-data.CL4GwSqT.png", "alt": "Making Meta Data", "w": 1024, "h": 1024 }, { "src": "https://thefocus.ai/_astro/git-commit-messages.wGzsqg9__m6301.webp", "alt": "Write git-commit-messages", "w": 1024, "h": 1024 } ], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← BACK TO WEEKEND WARRIOR PROJECTS PROJECT WORKSHEET TheFocus.AI Labs Workshop Series UNCLASSIFIED EST. TIME 5 min PUBLISHED March 15, 2025 Making Meta Data WHAT YOU'LL BUILD Generating metadata doesn't have to be a manual process when you can leverage AI prompts to produce multiple options quickly. Discover how to implement command-line tools that streamline metadata creation while maintaining your role as the final decision-maker. Metadata is data about data. It sort of describes the thing. It kind of helps you build context or give it information about what it is that we’re talking about. In short, let’s ask questions about something: What are the key themes of this post give me 5 different, two sentences summaries of this post get people to read more write 5 short titles for this blog post These are really tools for thinking because it often gives you things that are sort of whatever, not very interesting, but there are a few thought-provoking ones. And this will help you quickly iterate over a whole bunch of different ideas, making you sort of a curator and a tastemaker. Many these things I have set up as commands. run-prompt content/key-themes my-post.md You can see all of this over at my prompt library MATERIALS NEEDED • Computer with internet access • Basic command line knowledge • Text editor MORE PROJECTS TO TRY Write git-commit-messages PROMPTS Learn how to automate Git commit message generation with this simple but powerful prompt for Claude. The template enforces conventional commit format with appropriate type prefixes and detailed descriptions, making your repository history more professional and useful. VIEW RECIPE ← ALL PROJECTS Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push ", "height": 3827, "screenshots": { "desktop": "assets/screenshots/desktop/recipes--making-meta-data.png", "mobile": "assets/screenshots/mobile/recipes--making-meta-data.png" } }, { "url": "https://thefocus.ai/reports/aiecode-2025-11/track/leadership", "path": "/reports/aiecode-2025-11/track/leadership", "slug": "reports--aiecode-2025-11--track--leadership", "category": "Report/deep-dive", "status": 200, "title": "LEADERSHIP Track - AI Engineering Code Summit", "metaDescription": "AI isn't replacing software engineering fundamentals—it's amplifying them, making the difference between clean code and technical debt more consequential than ever before.", "headings": [ { "tag": "h1", "text": "LEADERSHIP", "index": 0 }, { "tag": "h2", "text": "TRUST, ROLE CHANGES, AND WHAT'S GOOD FOR HUMANS IS GOOD FOR AI", "index": 1 }, { "tag": "h2", "text": "CONTEXT, MEMORY, AND THE HARNESS: WHERE PRODUCT DIFFERENTIATION REALLY HAPPENS", "index": 2 }, { "tag": "h2", "text": "SDLC EVOLUTION: GOOD SOFTWARE ENGINEERING PRACTICES, AMPLIFIED", "index": 3 }, { "tag": "h2", "text": "THE ROI REALITY CHECK: WHERE AI ACTUALLY DELIVERS (AND WHERE IT FALLS FLAT)", "index": 4 }, { "tag": "h2", "text": "PROACTIVE VS REACTIVE: THE UX EVOLUTION", "index": 5 }, { "tag": "h2", "text": "THE ECONOMICS OF AI ENGINEERING: NEW COST STRUCTURES & COMPENSATION MODELS", "index": 6 }, { "tag": "h3", "text": "GET THE LATEST AI ENGINEERING INSIGHTS", "index": 7 }, { "tag": "h2", "text": "CLASSIFIED_FILES", "index": 8 }, { "tag": "h3", "text": "Stay Ahead of the Curve", "index": 9 } ], "sections": [ { "tag": "h1", "title": "LEADERSHIP", "y": 230, "body": "" }, { "tag": "h2", "title": "TRUST, ROLE CHANGES, AND WHAT'S GOOD FOR HUMANS IS GOOD FOR AI", "y": 658, "body": "What's good for humans is good for AI - same infrastructure investments benefit both" }, { "tag": "h2", "title": "CONTEXT, MEMORY, AND THE HARNESS: WHERE PRODUCT DIFFERENTIATION REALLY HAPPENS", "y": 658, "body": "76% of developers don't trust AI code because they don't trust the context" }, { "tag": "h2", "title": "SDLC EVOLUTION: GOOD SOFTWARE ENGINEERING PRACTICES, AMPLIFIED", "y": 1138, "body": "AI doesn't eliminate need for good process - it makes good process more valuable" }, { "tag": "h2", "title": "THE ROI REALITY CHECK: WHERE AI ACTUALLY DELIVERS (AND WHERE IT FALLS FLAT)", "y": 1138, "body": "93% of organizations stuck in 'pilot purgatory' - only 7% believe they're at scale" }, { "tag": "h2", "title": "PROACTIVE VS REACTIVE: THE UX EVOLUTION", "y": 1138, "body": "Goal is reducing mental load, not just adding speed" }, { "tag": "h2", "title": "THE ECONOMICS OF AI ENGINEERING: NEW COST STRUCTURES & COMPENSATION MODELS", "y": 1515, "body": "AI changes the cost function of engineering - previous cost-per-feature calculations obsolete" }, { "tag": "h3", "title": "GET THE LATEST AI ENGINEERING INSIGHTS", "y": 2139, "body": "Join the Focus.AI newsletter for curated research, analysis, and perspectives on the evolving AI landscape. SUBSCRIBE No spam. Unsubscribe anytime." }, { "tag": "h2", "title": "CLASSIFIED_FILES", "y": 16, "body": "USER: AUTHORIZED" }, { "tag": "h3", "title": "Stay Ahead of the Curve", "y": 0, "body": "Get weekly insights on AI engineering trends, research summaries, and expert analysis delivered to your inbox. Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "TheFocusAI", "href": "https://thefocus.ai/" }, { "text": "SUMMARY", "href": "https://thefocus.ai/reports/aiecode-2025-11/" }, { "text": "LEADERSHIP", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/leadership" }, { "text": "ENGINEERING", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/engineering" }, { "text": "PERSONNEL", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers" }, { "text": "ABOUT", "href": "https://thefocus.ai/reports/aiecode-2025-11/about" }, { "text": "INDEX", "href": "https://thefocus.ai/reports/aiecode-2025-11/" }, { "text": "SEC 01 TRUST, ROLE CHANGES, AND WHAT'S GOOD FOR HUMANS IS GOOD FOR AI What's good for humans is good for AI - same infrastructure investments benefit both AUTH: MAX KANAT-ALEXANDER, LISA ORR, DAN SHIPPER, GENE KIM, JUSTIN REOCK TIME: 4 MIN READ", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/trust-role-changes-infrastructure.md" }, { "text": "SEC 02 CONTEXT, MEMORY, AND THE HARNESS: WHERE PRODUCT DIFFERENTIATION REALLY HAPPENS 76% of developers don't trust AI code because they don't trust the context AUTH: BILL CHEN, BRIAN FIOCA, MICHELE CATASTA, KATELYN LESSE, ITAMAR FRIEDMAN, SAMIR MODY TIME: 3 MIN READ", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/context-memory-harness.md" }, { "text": "SEC 03 SDLC EVOLUTION: GOOD SOFTWARE ENGINEERING PRACTICES, AMPLIFIED AI doesn't eliminate need for good process - it makes good process more valuable AUTH: YEGOR DENISOV-BLANCH, ITAMAR FRIEDMAN, MICHELE CATASTA, MAX KANAT-ALEXANDER, MARTIN HARRYSSON, NATASHA MANIAR TIME: 3 MIN READ", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/sdlc-evolution-amplified-practices.md" }, { "text": "SEC 04 THE ROI REALITY CHECK: WHERE AI ACTUALLY DELIVERS (AND WHERE IT FALLS FLAT) 93% of organizations stuck in 'pilot purgatory' - only 7% believe they're at scale AUTH: YEGOR DENISOV-BLANCH, LEI ZHANG, ASAF BORD, NLW, MARTIN HARRYSSON, NATASHA MANIAR TIME: 3 MIN READ", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md" }, { "text": "SEC 05 PROACTIVE VS REACTIVE: THE UX EVOLUTION Goal is reducing mental load, not just adding speed AUTH: KATH KOREVEC, STEVE YEGGE, MICHELE CATASTA, SAMIR MODY TIME: 3 MIN READ", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/proactive-vs-reactive-ux.md" }, { "text": "SEC 06 THE ECONOMICS OF AI ENGINEERING: NEW COST STRUCTURES & COMPENSATION MODELS AI changes the cost function of engineering - previous cost-per-feature calculations obsolete AUTH: ARMAN HEZARKHANI, LEI ZHANG, ASAF BORD TIME: 2 MIN READ", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/economics-ai-engineering.md" }, { "text": "SUBSCRIBE", "href": "" }, { "text": "Subscribe to Focus.AI", "href": "" } ], "links": [ "https://thefocus.ai/", "https://thefocus.ai/reports/aiecode-2025-11/track/leadership/", "https://thefocus.ai/reports/aiecode-2025-11/track/leadership/track/leadership", "https://thefocus.ai/reports/aiecode-2025-11/track/leadership/track/engineering", "https://thefocus.ai/reports/aiecode-2025-11/track/leadership/speakers", "https://thefocus.ai/reports/aiecode-2025-11/track/leadership/about", "https://thefocus.ai/reports/aiecode-2025-11/track/leadership/", "https://thefocus.ai/reports/aiecode-2025-11/track/leadership/article/trust-role-changes-infrastructure.md", "https://thefocus.ai/reports/aiecode-2025-11/track/leadership/article/context-memory-harness.md", "https://thefocus.ai/reports/aiecode-2025-11/track/leadership/article/sdlc-evolution-amplified-practices.md", "https://thefocus.ai/reports/aiecode-2025-11/track/leadership/article/roi-reality-check.md", "https://thefocus.ai/reports/aiecode-2025-11/track/leadership/article/proactive-vs-reactive-ux.md", "https://thefocus.ai/reports/aiecode-2025-11/track/leadership/article/economics-ai-engineering.md" ], "images": [ { "src": "https://thefocus.ai/reports/aiecode-2025-11/track/leadership/headers/trust-role-changes-infrastructure.png", "alt": "", "w": 1376, "h": 768 } ], "text": "TheFocusAI SUMMARY LEADERSHIP ENGINEERING PERSONNEL ABOUT FILES (0) INDEX DEPARTMENT OF LEADERSHIP AI isn't replacing software engineering fundamentals—it's amplifying them, making the difference between clean code and technical debt more consequential than ever before. VOL. 2025 CLASSIFIED: NO PAGES: 72 SEC 01 TRUST, ROLE CHANGES, AND WHAT'S GOOD FOR HUMANS IS GOOD FOR AI What's good for humans is good for AI - same infrastructure investments benefit both AUTH: MAX KANAT-ALEXANDER, LISA ORR, DAN SHIPPER, GENE KIM, JUSTIN REOCK TIME: 4 MIN READ SEC 02 CONTEXT, MEMORY, AND THE HARNESS: WHERE PRODUCT DIFFERENTIATION REALLY HAPPENS 76% of developers don't trust AI code because they don't trust the context AUTH: BILL CHEN, BRIAN FIOCA, MICHELE CATASTA, KATELYN LESSE, ITAMAR FRIEDMAN, SAMIR MODY TIME: 3 MIN READ SEC 03 SDLC EVOLUTION: GOOD SOFTWARE ENGINEERING PRACTICES, AMPLIFIED AI doesn't eliminate need for good process - it makes good process more valuable AUTH: YEGOR DENISOV-BLANCH, ITAMAR FRIEDMAN, MICHELE CATASTA, MAX KANAT-ALEXANDER, MARTIN HARRYSSON, NATASHA MANIAR TIME: 3 MIN READ SEC 04 THE ROI REALITY CHECK: WHERE AI ACTUALLY DELIVERS (AND WHERE IT FALLS FLAT) 93% of organizations stuck in 'pilot purgatory' - only 7% believe they're at scale AUTH: YEGOR DENISOV-BLANCH, LEI ZHANG, ASAF BORD, NLW, MARTIN HARRYSSON, NATASHA MANIAR TIME: 3 MIN READ SEC 05 PROACTIVE VS REACTIVE: THE UX EVOLUTION Goal is reducing mental load, not just adding speed AUTH: KATH KOREVEC, STEVE YEGGE, MICHELE CATASTA, SAMIR MODY TIME: 3 MIN READ SEC 06 THE ECONOMICS OF AI ENGINEERING: NEW COST STRUCTURES & COMPENSATION MODELS AI changes the cost function of engineering - previous cost-per-feature calculations obsolete AUTH: ARMAN HEZARKHANI, LEI ZHANG, ASAF BORD TIME: 2 MIN READ STAY UPDATED GET THE LATEST AI ENGINEERING INSIGHTS Join the Focus.AI newsletter for curated research, analysis, and perspectives on the evolving AI landscape. SUBSCRIBE No spam. Unsubscribe anytime. CLASSIFIED_FILES USER: AUTHORIZED [ EMPTY DRAWER ] No documents have been filed. EXPORT MANIFEST", "height": 2390, "screenshots": { "desktop": "assets/screenshots/desktop/reports--aiecode-2025-11--track--leadership.png", "mobile": "assets/screenshots/mobile/reports--aiecode-2025-11--track--leadership.png" } }, { "url": "https://thefocus.ai/reports/aiecode-2025-11/track/engineering", "path": "/reports/aiecode-2025-11/track/engineering", "slug": "reports--aiecode-2025-11--track--engineering", "category": "Report/deep-dive", "status": 200, "title": "ENGINEERING Track - AI Engineering Code Summit", "metaDescription": "The war on 'slop' isn't won through clever engineering—it requires keeping human thinking at the center, treating environments as universal abstraction, and investing in model capability over scaffolding.", "headings": [ { "tag": "h1", "text": "ENGINEERING", "index": 0 }, { "tag": "h2", "text": "DO NOT OUTSOURCE THE THINKING: CONTEXT ENGINEERING & HUMAN-AI COLLABORATION", "index": 1 }, { "tag": "h2", "text": "ENVIRONMENTS AS UNIVERSAL ABSTRACTION: THE NEW UNIT OF EVERYTHING", "index": 2 }, { "tag": "h2", "text": "SKILLS AND ARTIFACTS: THE NEW BUILDING BLOCKS FOR AGENTS", "index": 3 }, { "tag": "h2", "text": "REINFORCEMENT LEARNING FOR SPECIALIZED MODELS: THE ECONOMICS OF DOMAIN EXPERTISE", "index": 4 }, { "tag": "h2", "text": "MODEL QUALITY OVER SCAFFOLDING: MINIMALISM IN AGENT ARCHITECTURE", "index": 5 }, { "tag": "h2", "text": "DATA COLLECTION & QUALITY AS THE NEW BOTTLENECK", "index": 6 }, { "tag": "h3", "text": "GET THE LATEST AI ENGINEERING INSIGHTS", "index": 7 }, { "tag": "h2", "text": "CLASSIFIED_FILES", "index": 8 }, { "tag": "h3", "text": "Stay Ahead of the Curve", "index": 9 } ], "sections": [ { "tag": "h1", "title": "ENGINEERING", "y": 230, "body": "" }, { "tag": "h2", "title": "DO NOT OUTSOURCE THE THINKING: CONTEXT ENGINEERING & HUMAN-AI COLLABORATION", "y": 658, "body": "AI can only amplify the thinking you've already done" }, { "tag": "h2", "title": "ENVIRONMENTS AS UNIVERSAL ABSTRACTION: THE NEW UNIT OF EVERYTHING", "y": 658, "body": "A benchmark = environment + starting state + verifier (same as RL environments)" }, { "tag": "h2", "title": "SKILLS AND ARTIFACTS: THE NEW BUILDING BLOCKS FOR AGENTS", "y": 1169, "body": "Skills are just folders - procedural knowledge packaged for agents to load dynamically" }, { "tag": "h2", "title": "REINFORCEMENT LEARNING FOR SPECIALIZED MODELS: THE ECONOMICS OF DOMAIN EXPERTISE", "y": 1169, "body": "1000 examples can yield 10-point improvements with ARFT" }, { "tag": "h2", "title": "MODEL QUALITY OVER SCAFFOLDING: MINIMALISM IN AGENT ARCHITECTURE", "y": 1169, "body": "Use smart models to plan, fast models to execute - deploy the right capability at the right time" }, { "tag": "h2", "title": "DATA COLLECTION & QUALITY AS THE NEW BOTTLENECK", "y": 1545, "body": "Agents are collecting good data but not sharing it - keeping datasets closed slows research" }, { "tag": "h3", "title": "GET THE LATEST AI ENGINEERING INSIGHTS", "y": 2155, "body": "Join the Focus.AI newsletter for curated research, analysis, and perspectives on the evolving AI landscape. SUBSCRIBE No spam. Unsubscribe anytime." }, { "tag": "h2", "title": "CLASSIFIED_FILES", "y": 16, "body": "USER: AUTHORIZED" }, { "tag": "h3", "title": "Stay Ahead of the Curve", "y": 0, "body": "Get weekly insights on AI engineering trends, research summaries, and expert analysis delivered to your inbox. Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "TheFocusAI", "href": "https://thefocus.ai/" }, { "text": "SUMMARY", "href": "https://thefocus.ai/reports/aiecode-2025-11/" }, { "text": "LEADERSHIP", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/leadership" }, { "text": "ENGINEERING", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/engineering" }, { "text": "PERSONNEL", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers" }, { "text": "ABOUT", "href": "https://thefocus.ai/reports/aiecode-2025-11/about" }, { "text": "INDEX", "href": "https://thefocus.ai/reports/aiecode-2025-11/" }, { "text": "SEC 01 DO NOT OUTSOURCE THE THINKING: CONTEXT ENGINEERING & HUMAN-AI COLLABORATION AI can only amplify the thinking you've already done AUTH: JAKE NATIONS, DEX HORTHY, BEYANG LIU TIME: 4 MIN READ", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/context-engineering-human-ai-collaboration.md" }, { "text": "SEC 02 ENVIRONMENTS AS UNIVERSAL ABSTRACTION: THE NEW UNIT OF EVERYTHING A benchmark = environment + starting state + verifier (same as RL environments) AUTH: WILL BROWN, ENO REYES, NIK PASH TIME: 3 MIN READ", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md" }, { "text": "SEC 03 SKILLS AND ARTIFACTS: THE NEW BUILDING BLOCKS FOR AGENTS Skills are just folders - procedural knowledge packaged for agents to load dynamically AUTH: BARRY ZHANG, MAHESH MURAG, KEVIN HOU, KAT KAMPF, AMMAAR RESHI TIME: 4 MIN READ", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/agents-building-artifacts.md" }, { "text": "SEC 03 REINFORCEMENT LEARNING FOR SPECIALIZED MODELS: THE ECONOMICS OF DOMAIN EXPERTISE 1000 examples can yield 10-point improvements with ARFT AUTH: WILL HANG, CATHY ZHOU, RHYTHM GARG, LINDEN LI, JACOB KAHN, APARNA DHINAKARAN, LEE ROBINSON TIME: 3 MIN READ", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/reinforcement-learning-specialized-models.md" }, { "text": "SEC 04 MODEL QUALITY OVER SCAFFOLDING: MINIMALISM IN AGENT ARCHITECTURE Use smart models to plan, fast models to execute - deploy the right capability at the right time AUTH: LEE ROBINSON, BEYANG LIU, JOEL BECKER TIME: 3 MIN READ", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md" }, { "text": "SEC 05 DATA COLLECTION & QUALITY AS THE NEW BOTTLENECK Agents are collecting good data but not sharing it - keeping datasets closed slows research AUTH: NIK PASH, WILL HANG, CATHY ZHOU, WILL BROWN, JOEL BECKER TIME: 3 MIN READ", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/data-collection-quality-bottleneck.md" }, { "text": "SUBSCRIBE", "href": "" }, { "text": "Subscribe to Focus.AI", "href": "" } ], "links": [ "https://thefocus.ai/", "https://thefocus.ai/reports/aiecode-2025-11/track/engineering/", "https://thefocus.ai/reports/aiecode-2025-11/track/engineering/track/leadership", "https://thefocus.ai/reports/aiecode-2025-11/track/engineering/track/engineering", "https://thefocus.ai/reports/aiecode-2025-11/track/engineering/speakers", "https://thefocus.ai/reports/aiecode-2025-11/track/engineering/about", "https://thefocus.ai/reports/aiecode-2025-11/track/engineering/", "https://thefocus.ai/reports/aiecode-2025-11/track/engineering/article/context-engineering-human-ai-collaboration.md", "https://thefocus.ai/reports/aiecode-2025-11/track/engineering/article/environments-universal-abstraction.md", "https://thefocus.ai/reports/aiecode-2025-11/track/engineering/article/agents-building-artifacts.md", "https://thefocus.ai/reports/aiecode-2025-11/track/engineering/article/reinforcement-learning-specialized-models.md", "https://thefocus.ai/reports/aiecode-2025-11/track/engineering/article/model-quality-over-scaffolding.md", "https://thefocus.ai/reports/aiecode-2025-11/track/engineering/article/data-collection-quality-bottleneck.md" ], "images": [ { "src": "https://thefocus.ai/reports/aiecode-2025-11/track/engineering/headers/context-engineering-human-ai-collaboration.png", "alt": "", "w": 1376, "h": 768 } ], "text": "TheFocusAI SUMMARY LEADERSHIP ENGINEERING PERSONNEL ABOUT FILES (0) INDEX DEPARTMENT OF ENGINEERING The war on 'slop' isn't won through clever engineering—it requires keeping human thinking at the center, treating environments as universal abstraction, and investing in model capability over scaffolding. VOL. 2025 CLASSIFIED: NO PAGES: 72 SEC 01 DO NOT OUTSOURCE THE THINKING: CONTEXT ENGINEERING & HUMAN-AI COLLABORATION AI can only amplify the thinking you've already done AUTH: JAKE NATIONS, DEX HORTHY, BEYANG LIU TIME: 4 MIN READ SEC 02 ENVIRONMENTS AS UNIVERSAL ABSTRACTION: THE NEW UNIT OF EVERYTHING A benchmark = environment + starting state + verifier (same as RL environments) AUTH: WILL BROWN, ENO REYES, NIK PASH TIME: 3 MIN READ SEC 03 SKILLS AND ARTIFACTS: THE NEW BUILDING BLOCKS FOR AGENTS Skills are just folders - procedural knowledge packaged for agents to load dynamically AUTH: BARRY ZHANG, MAHESH MURAG, KEVIN HOU, KAT KAMPF, AMMAAR RESHI TIME: 4 MIN READ SEC 03 REINFORCEMENT LEARNING FOR SPECIALIZED MODELS: THE ECONOMICS OF DOMAIN EXPERTISE 1000 examples can yield 10-point improvements with ARFT AUTH: WILL HANG, CATHY ZHOU, RHYTHM GARG, LINDEN LI, JACOB KAHN, APARNA DHINAKARAN, LEE ROBINSON TIME: 3 MIN READ SEC 04 MODEL QUALITY OVER SCAFFOLDING: MINIMALISM IN AGENT ARCHITECTURE Use smart models to plan, fast models to execute - deploy the right capability at the right time AUTH: LEE ROBINSON, BEYANG LIU, JOEL BECKER TIME: 3 MIN READ SEC 05 DATA COLLECTION & QUALITY AS THE NEW BOTTLENECK Agents are collecting good data but not sharing it - keeping datasets closed slows research AUTH: NIK PASH, WILL HANG, CATHY ZHOU, WILL BROWN, JOEL BECKER TIME: 3 MIN READ STAY UPDATED GET THE LATEST AI ENGINEERING INSIGHTS Join the Focus.AI newsletter for curated research, analysis, and perspectives on the evolving AI landscape. SUBSCRIBE No spam. Unsubscribe anytime. CLASSIFIED_FILES USER: AUTHORIZED [ EMPTY DRAWER ] No documents have been filed. EXPORT MANIFEST", "height": 2406, "screenshots": { "desktop": "assets/screenshots/desktop/reports--aiecode-2025-11--track--engineering.png", "mobile": "assets/screenshots/mobile/reports--aiecode-2025-11--track--engineering.png" } }, { "url": "https://thefocus.ai/reports/aiecode-2025-11/speakers", "path": "/reports/aiecode-2025-11/speakers", "slug": "reports--aiecode-2025-11--speakers", "category": "Report/deep-dive", "status": 200, "title": "Personnel Roster - Horizon 2025", "metaDescription": "Key contributors and subject matter experts from the AI Engineering Code Summit including engineers from Anthropic, Google, OpenAI, and more.", "headings": [ { "tag": "h1", "text": "PERSONNEL ROSTER", "index": 0 }, { "tag": "h3", "text": "AMMAAR RESHI", "index": 1 }, { "tag": "h3", "text": "APARNA DHINAKARAN", "index": 2 }, { "tag": "h3", "text": "ARMAN HEZARKHANI", "index": 3 }, { "tag": "h3", "text": "ASAF BORD", "index": 4 }, { "tag": "h3", "text": "BARRY ZHANG", "index": 5 }, { "tag": "h3", "text": "BEYANG LIU", "index": 6 }, { "tag": "h3", "text": "BILL CHEN", "index": 7 }, { "tag": "h3", "text": "BRIAN FIOCA", "index": 8 }, { "tag": "h3", "text": "CATHY ZHOU", "index": 9 }, { "tag": "h3", "text": "DAN SHIPPER", "index": 10 }, { "tag": "h3", "text": "DEX HORTHY", "index": 11 }, { "tag": "h3", "text": "EISO KANT", "index": 12 }, { "tag": "h3", "text": "ENO REYES", "index": 13 }, { "tag": "h3", "text": "GENE KIM", "index": 14 }, { "tag": "h3", "text": "ITAMAR FRIEDMAN", "index": 15 }, { "tag": "h3", "text": "JACOB KAHN", "index": 16 }, { "tag": "h3", "text": "JAKE NATIONS", "index": 17 }, { "tag": "h3", "text": "JED BOROVIK", "index": 18 }, { "tag": "h3", "text": "JOEL BECKER", "index": 19 }, { "tag": "h3", "text": "JUSTIN REOCK", "index": 20 }, { "tag": "h3", "text": "KAT KAMPF", "index": 21 }, { "tag": "h3", "text": "KATELYN LESSE", "index": 22 }, { "tag": "h3", "text": "KATH KOREVEC", "index": 23 }, { "tag": "h3", "text": "KEVIN HOU", "index": 24 }, { "tag": "h3", "text": "KITZE", "index": 25 }, { "tag": "h3", "text": "LEE ROBINSON", "index": 26 }, { "tag": "h3", "text": "LEI ZHANG", "index": 27 }, { "tag": "h3", "text": "LINDEN LI", "index": 28 }, { "tag": "h3", "text": "LISA ORR", "index": 29 }, { "tag": "h3", "text": "MAHESH MURAG", "index": 30 }, { "tag": "h3", "text": "MARTIN HARRYSSON", "index": 31 }, { "tag": "h3", "text": "MAX KANAT-ALEXANDER", "index": 32 }, { "tag": "h3", "text": "MICHELE CATASTA", "index": 33 }, { "tag": "h3", "text": "MIKE LACSAMANA", "index": 34 }, { "tag": "h3", "text": "NATALIE SERRINO", "index": 35 }, { "tag": "h3", "text": "NATASHA MANIAR", "index": 36 }, { "tag": "h3", "text": "NATHANIEL WHITTEMORE (NLW)", "index": 37 }, { "tag": "h3", "text": "NIK PASH", "index": 38 }, { "tag": "h3", "text": "OLIVE SONG (JIAYUAN SONG)", "index": 39 }, { "tag": "h3", "text": "RHYTHM GARG", "index": 40 }, { "tag": "h3", "text": "RYAN CARSON", "index": 41 }, { "tag": "h3", "text": "SAMIR MODY", "index": 42 }, { "tag": "h3", "text": "STEVE YEGGE", "index": 43 }, { "tag": "h3", "text": "SWYX (SHAWN WANG)", "index": 44 }, { "tag": "h3", "text": "TOBIN SOUTH", "index": 45 }, { "tag": "h3", "text": "WILL BROWN", "index": 46 }, { "tag": "h3", "text": "WILL HANG", "index": 47 }, { "tag": "h3", "text": "YEGOR DENISOV-BLANCH", "index": 48 }, { "tag": "h3", "text": "GET THE LATEST AI ENGINEERING INSIGHTS", "index": 49 }, { "tag": "h2", "text": "CLASSIFIED_FILES", "index": 50 }, { "tag": "h3", "text": "Stay Ahead of the Curve", "index": 51 } ], "sections": [ { "tag": "h1", "title": "PERSONNEL ROSTER", "y": 147, "body": "List of key contributors and subject matter experts for Project Horizon." }, { "tag": "h3", "title": "AMMAAR RESHI", "y": 283, "body": "PRODUCT & DESIGN LEAD" }, { "tag": "h3", "title": "APARNA DHINAKARAN", "y": 283, "body": "CO-FOUNDER & CHIEF PRODUCT OFFICER (CPO)" }, { "tag": "h3", "title": "ARMAN HEZARKHANI", "y": 503, "body": "MANAGING PARTNER" }, { "tag": "h3", "title": "ASAF BORD", "y": 503, "body": "GENAI PRODUCTS LEADER" }, { "tag": "h3", "title": "BARRY ZHANG", "y": 723, "body": "RESEARCH ENGINEER" }, { "tag": "h3", "title": "BEYANG LIU", "y": 723, "body": "CO-FOUNDER & CTO, AMP CODE" }, { "tag": "h3", "title": "BILL CHEN", "y": 943, "body": "STARTUPS APPLIED AI, SOLUTIONS ARCHITECT" }, { "tag": "h3", "title": "BRIAN FIOCA", "y": 943, "body": "SOLUTIONS ARCHITECT, APPLIED AI" }, { "tag": "h3", "title": "CATHY ZHOU", "y": 1163, "body": "MEMBER OF TECHNICAL STAFF" }, { "tag": "h3", "title": "DAN SHIPPER", "y": 1163, "body": "CEO AND CO-FOUNDER" }, { "tag": "h3", "title": "DEX HORTHY", "y": 1383, "body": "FOUNDER & CEO" }, { "tag": "h3", "title": "EISO KANT", "y": 1383, "body": "CTO & CO-FOUNDER" }, { "tag": "h3", "title": "ENO REYES", "y": 1603, "body": "CO-FOUNDER & CTO" }, { "tag": "h3", "title": "GENE KIM", "y": 1603, "body": "FOUNDER & MULTIPLE AWARD-WINNING CTO" }, { "tag": "h3", "title": "ITAMAR FRIEDMAN", "y": 1823, "body": "CO-FOUNDER & CEO" }, { "tag": "h3", "title": "JACOB KAHN", "y": 1823, "body": "RESEARCH MANAGER" }, { "tag": "h3", "title": "JAKE NATIONS", "y": 2043, "body": "ENGINEERING LEADER" }, { "tag": "h3", "title": "JED BOROVIK", "y": 2043, "body": "PRODUCT LEAD - JULES AUTONOMOUS CODING AGENT" }, { "tag": "h3", "title": "JOEL BECKER", "y": 2263, "body": "MEMBER OF TECHNICAL STAFF" }, { "tag": "h3", "title": "JUSTIN REOCK", "y": 2263, "body": "DEPUTY CTO" }, { "tag": "h3", "title": "KAT KAMPF", "y": 2483, "body": "PRODUCT MANAGER" }, { "tag": "h3", "title": "KATELYN LESSE", "y": 2483, "body": "HEAD OF API ENGINEERING / HEAD OF ENGINEERING, CLAUDE DEVELOPER PLATFORM" }, { "tag": "h3", "title": "KATH KOREVEC", "y": 2708, "body": "DIRECTOR OF PRODUCT - AI DEVELOPER ASSISTANTS (AIDA)" }, { "tag": "h3", "title": "KEVIN HOU", "y": 2708, "body": "ENGINEER" }, { "tag": "h3", "title": "KITZE", "y": 2928, "body": "FOUNDER" }, { "tag": "h3", "title": "LEE ROBINSON", "y": 2928, "body": "HEAD OF AI EDUCATION" }, { "tag": "h3", "title": "LEI ZHANG", "y": 3148, "body": "SENIOR DIRECTOR OF GENERATIVE AI & HEAD OF DEVELOPER EXPERIENCE" }, { "tag": "h3", "title": "LINDEN LI", "y": 3148, "body": "CO-FOUNDER & CHIEF ARCHITECT" }, { "tag": "h3", "title": "LISA ORR", "y": 3368, "body": "GROUP PRODUCT MANAGER, AI CODE GEN TEAM" }, { "tag": "h3", "title": "MAHESH MURAG", "y": 3368, "body": "MEMBER OF TECHNICAL STAFF, APPLIED AI" }, { "tag": "h3", "title": "MARTIN HARRYSSON", "y": 3588, "body": "SENIOR PARTNER" }, { "tag": "h3", "title": "MAX KANAT-ALEXANDER", "y": 3588, "body": "EXECUTIVE DISTINGUISHED ENGINEER FOR DEVELOPER EXPERIENCE" }, { "tag": "h3", "title": "MICHELE CATASTA", "y": 3808, "body": "PRESIDENT & HEAD OF AI" }, { "tag": "h3", "title": "MIKE LACSAMANA", "y": 3808, "body": "HEAD OF SOLUTIONS ENGINEERING" }, { "tag": "h3", "title": "NATALIE SERRINO", "y": 4028, "body": "CO-FOUNDER" }, { "tag": "h3", "title": "NATASHA MANIAR", "y": 4028, "body": "BUSINESS ANALYST" }, { "tag": "h3", "title": "NATHANIEL WHITTEMORE (NLW)", "y": 4248, "body": "FOUNDER & HOST OF THE AI DAILY BRIEF" }, { "tag": "h3", "title": "NIK PASH", "y": 4248, "body": "HEAD OF AI" }, { "tag": "h3", "title": "OLIVE SONG (JIAYUAN SONG)", "y": 4468, "body": "LLM ALGORITHM ENGINEER" }, { "tag": "h3", "title": "RHYTHM GARG", "y": 4468, "body": "CO-FOUNDER & CTO" }, { "tag": "h3", "title": "RYAN CARSON", "y": 4688, "body": "BUILDER IN RESIDENCE" }, { "tag": "h3", "title": "SAMIR MODY", "y": 4688, "body": "HEAD OF ENGINEERING, AI" }, { "tag": "h3", "title": "STEVE YEGGE", "y": 4908, "body": "HEAD OF ENGINEERING, AMP LEAD" }, { "tag": "h3", "title": "SWYX (SHAWN WANG)", "y": 4908, "body": "FOUNDER & ORGANIZER" }, { "tag": "h3", "title": "TOBIN SOUTH", "y": 5128, "body": "HEAD OF AI AGENTS & MCP" }, { "tag": "h3", "title": "WILL BROWN", "y": 5128, "body": "RESEARCH LEAD" }, { "tag": "h3", "title": "WILL HANG", "y": 5348, "body": "MEMBER OF TECHNICAL STAFF" }, { "tag": "h3", "title": "YEGOR DENISOV-BLANCH", "y": 5348, "body": "RESEARCHER, SOFTWARE ENGINEERING PRODUCTIVITY RESEARCH GROUP" }, { "tag": "h3", "title": "GET THE LATEST AI ENGINEERING INSIGHTS", "y": 5776, "body": "Join the Focus.AI newsletter for curated research, analysis, and perspectives on the evolving AI landscape. SUBSCRIBE No spam. Unsubscribe anytime." }, { "tag": "h2", "title": "CLASSIFIED_FILES", "y": 16, "body": "USER: AUTHORIZED" }, { "tag": "h3", "title": "Stay Ahead of the Curve", "y": 0, "body": "Get weekly insights on AI engineering trends, research summaries, and expert analysis delivered to your inbox. Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "TheFocusAI", "href": "https://thefocus.ai/" }, { "text": "SUMMARY", "href": "https://thefocus.ai/reports/aiecode-2025-11/" }, { "text": "LEADERSHIP", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/leadership" }, { "text": "ENGINEERING", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/engineering" }, { "text": "PERSONNEL", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers" }, { "text": "ABOUT", "href": "https://thefocus.ai/reports/aiecode-2025-11/about" }, { "text": "REF: AMMAAR-RESHI AMMAAR RESHI PRODUCT & DESIGN LEAD Product & Design Lead at Google AI Studio, pioneering accessible AI-powered development through \"vibe coding.\" Known for viral AI experiments including the controversial children's book [*Alice and Sparkle*](https://time.com/6240569/ai-childrens-book-alice-and-sparkle-artists-unhappy/), created in one weekend using ChatGPT and Midjourney. AFFILIATION: GOOGLE", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/ammaar-reshi" }, { "text": "REF: APARNA-DHINAKARAN APARNA DHINAKARAN CO-FOUNDER & CHIEF PRODUCT OFFICER (CPO) Co-founder and Chief Product Officer at Arize AI, pioneering ML observability and LLM evaluation platforms. Forbes 30 Under 30 honoree and Forbes AI Columnist, bringing deep expertise from building production ML infrastructure at Uber, Apple, and TubeMogul. AFFILIATION: ARIZE AI", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/aparna-dhinakaran" }, { "text": "REF: ARMAN-HEZARKHANI ARMAN HEZARKHANI MANAGING PARTNER Managing Partner at Tenex, pioneering output-based compensation models that incentivize AI tool adoption and maximize engineering throughput. Previously founded multiple venture-backed AI companies and scaled Google Cloud AI to millions of developers. AFFILIATION: TENEX", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/arman-hezarkhani" }, { "text": "REF: ASAF-BORD ASAF BORD GENAI PRODUCTS LEADER GenAI Products Leader at Northwestern Mutual, pioneering enterprise generative business intelligence (GenBI) at a Fortune 100 financial services company with a \"crawl-walk-run\" approach to AI adoption in conservative environments. AFFILIATION: NORTHWESTERN MUTUAL", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/asaf-bord" }, { "text": "REF: BARRY-ZHANG BARRY ZHANG RESEARCH ENGINEER Research Engineer at Anthropic working on Claude's agent architecture and the Skills system. Co-author of influential articles on building effective agents and the Skills framework that enables agents to dynamically load procedural knowledge. AFFILIATION: ANTHROPIC", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/barry-zhang" }, { "text": "REF: BEYANG-LIU BEYANG LIU CO-FOUNDER & CTO, AMP CODE Co-founder and CTO of Amp Code, building next-generation AI coding agents with multi-agent architecture. Previously co-founded Sourcegraph, the enterprise code intelligence platform serving 4/5 FAANG companies. AFFILIATION: AMP CODE / SOURCEGRAPH", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/beyang-liu" }, { "text": "REF: BILL-CHEN BILL CHEN STARTUPS APPLIED AI, SOLUTIONS ARCHITECT Works in Startups Applied AI at OpenAI as a Solutions Architect, focusing on helping developers build production-grade coding agents. Former Y Combinator team member, bringing startup mindset to OpenAI's developer tools and agent frameworks. AFFILIATION: OPENAI", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/bill-chen" }, { "text": "REF: BRIAN-FIOCA BRIAN FIOCA SOLUTIONS ARCHITECT, APPLIED AI Solutions Architect for Startups at OpenAI, helping early-stage companies leverage OpenAI's APIs and models. Serial entrepreneur with 20+ years building software products, including co-founding RescueTime (YC W08) and launching Mighty AI and MessageYes at Madrona Venture Labs. AFFILIATION: OPENAI", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/brian-fioca" }, { "text": "REF: CATHY-ZHOU CATHY ZHOU MEMBER OF TECHNICAL STAFF Member of Technical Staff at OpenAI's fine-tuning team, pioneering Agent Reinforcement Fine-Tuning (ARFT) - the industry's first approach enabling agents to access external tools and APIs during training. Leading the development of custom reward functions and tool-aware training systems that make specialized AI agents possible. AFFILIATION: OPENAI", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/cathy-zhou" }, { "text": "REF: DAN-SHIPPER DAN SHIPPER CEO AND CO-FOUNDER CEO and co-founder of Every, a radically AI-native company generating 7-figure revenue with just 15 employees across 6 business units and 4 software products. Prolific writer exploring AI transformation through his Chain of Thought newsletter and AI & I podcast. AFFILIATION: EVERY", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/dan-shipper" }, { "text": "REF: DEX-HORTHY DEX HORTHY FOUNDER & CEO Founder and CEO of HumanLayer, pioneering production-grade AI agent systems with built-in human oversight. Creator of the 12-Factor Agents framework for building reliable autonomous systems in complex codebases. AFFILIATION: HUMANLAYER", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/dex-horthy" }, { "text": "REF: EISO-KANT EISO KANT CTO & CO-FOUNDER CTO and Co-Founder of Poolside, building AI agents that can reason about and modify complex codebases at scale. Previously founded source{d} (AI for code) and Athenian (data-enabled engineering platform). Co-hosts the Developing Leadership podcast with former GitHub CTO Jason Warner. AFFILIATION: POOLSIDE", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/eiso-kant" }, { "text": "REF: ENO-REYES ENO REYES CO-FOUNDER & CTO Co-Founder and CTO at Factory, building autonomous AI agents (Droids) that automate the software development lifecycle. Pioneer in agent-ready codebase architecture and verification systems for reliable AI-driven development. AFFILIATION: FACTORY AI", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/eno-reyes" }, { "text": "REF: GENE-KIM GENE KIM FOUNDER & MULTIPLE AWARD-WINNING CTO Wall Street Journal bestselling author and founder of IT Revolution, pioneering DevOps transformation and now leading the exploration of AI-assisted software development. Author of The Phoenix Project, The DevOps Handbook, Accelerate, and Wiring the Winning Organization—books that have collectively sold over 1 million copies. AFFILIATION: IT REVOLUTION", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/gene-kim" }, { "text": "REF: ITAMAR-FRIEDMAN ITAMAR FRIEDMAN CO-FOUNDER & CEO Co-founder and CEO of Qodo (formerly CodiumAI), an AI-powered code quality platform serving 1M+ developers. Previously founded Visualead (acquired by Alibaba for $50M) and led Alibaba's Machine Vision Lab. Over 20 years of experience in ML and software engineering from Technion. AFFILIATION: QODO", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/itamar-friedman" }, { "text": "REF: JACOB-KAHN JACOB KAHN RESEARCH MANAGER Research Manager at Meta AI leading FAIR's code generation research in North America, also serving as CS Faculty at the University of Pennsylvania. Creator of CWM (Code World Models), pioneering the use of execution traces and behavioral data in neural code generation. AFFILIATION: META AI", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/jacob-kahn" }, { "text": "REF: JAKE-NATIONS JAKE NATIONS ENGINEERING LEADER Staff Engineer at Netflix specializing in AI/ML systems at scale and managing software complexity in massive codebases. Author of [The Bug in Your Code](https://www.arthropod.software), exploring the intersection of software development, AI, and the human dimensions of engineering. AFFILIATION: NETFLIX", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/jake-nations" }, { "text": "REF: JED-BOROVIK JED BOROVIK PRODUCT LEAD - JULES AUTONOMOUS CODING AGENT Product Lead at Google Labs building Jules, an autonomous AI coding agent that operates asynchronously in cloud environments. Previously at Google Search, his journey into generative AI began with Stable Diffusion, leading him to spearhead one of the most ambitious coding agent projects in tech. AFFILIATION: GOOGLE LABS", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/jed-borovik" }, { "text": "REF: JOEL-BECKER JOEL BECKER MEMBER OF TECHNICAL STAFF Member of Technical Staff at METR, leading groundbreaking research on AI capability measurement and evaluation. Previously PhD student in Economics at NYU and researcher at Harvard, bringing rigorous empirical methods to AI safety research. AFFILIATION: METR", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/joel-becker" }, { "text": "REF: JUSTIN-REOCK JUSTIN REOCK DEPUTY CTO Deputy CTO at DX, leading engineering intelligence and AI adoption strategy. With 20+ years in software engineering, he's a thought leader in developer experience, platform engineering, and AI-assisted development measurement. AFFILIATION: DX", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/justin-reock" }, { "text": "REF: KAT-KAMPF KAT KAMPF PRODUCT MANAGER Product Manager at Google working on the Gemini API and Google AI Studio. Previously at GitHub Education building GitHub Classroom. University of Michigan graduate based in New York City. AFFILIATION: GOOGLE", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/kat-kampf" }, { "text": "REF: KATELYN-LESSE KATELYN LESSE HEAD OF API ENGINEERING / HEAD OF ENGINEERING, CLAUDE DEVELOPER PLATFORM Head of API Engineering at Anthropic, leading the Claude Developer Platform encompassing APIs, SDKs, documentation, and console experiences. Previously Senior Director of Engineering at Betterment and Head of Engineering at Stripe. Expertise in platform development and building developer-facing products at scale. AFFILIATION: ANTHROPIC", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/katelyn-lesse" }, { "text": "REF: KATH-KOREVEC KATH KOREVEC DIRECTOR OF PRODUCT - AI DEVELOPER ASSISTANTS (AIDA) Director of Product at Google Labs leading AI Developer Assistants (AIDA). Previously VP of Product at Vercel, Senior Director at GitHub, and product leader at Heroku. Over a decade of experience building developer tools and platforms that developers love. AFFILIATION: GOOGLE LABS", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/kath-korevec" }, { "text": "REF: KEVIN-HOU KEVIN HOU ENGINEER Engineer at Google DeepMind leading development of Antigravity, the company's agent-first IDE. Previously founding team member at Codeium (acquired by Google for $2.4B) where he scaled the product from zero to over 1M users and a $1.25B valuation. Princeton CS graduate with expertise in AI-powered developer tools, autonomous agents, and building products for frontier models. AFFILIATION: GOOGLE DEEPMIND", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/kevin-hou" }, { "text": "REF: KITZE KITZE FOUNDER Founder of Sizzy, a developer browser built for AI-assisted workflows. Developer, entrepreneur, and educator known for his energetic advocacy of \"vibe coding\"—collaborative development with AI that prioritizes natural interaction over syntax precision. Creator of React Academy, Benji, Zero to Shipped, and other tools. AFFILIATION: SIZZY", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/kitze" }, { "text": "REF: LEE-ROBINSON LEE ROBINSON HEAD OF AI EDUCATION Head of AI Education at Cursor, teaching developers how to build with AI coding tools. Previously led Developer Experience at Vercel, growing Next.js to 1.3M+ active developers and helping scale Vercel to $200M+ ARR. Educator focused on making technology easy to understand through courses, videos, and essays. AFFILIATION: CURSOR", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/lee-robinson" }, { "text": "REF: LEI-ZHANG LEI ZHANG SENIOR DIRECTOR OF GENERATIVE AI & HEAD OF DEVELOPER EXPERIENCE Senior Director of Generative AI and Head of Developer Experience at Bloomberg LP, leading AI infrastructure deployment across 9,000+ engineers. Brings practical insights from implementing AI tools and agent systems in a mission-critical financial services environment with extensive brownfield systems. AFFILIATION: BLOOMBERG LP", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/lei-zhang" }, { "text": "REF: LINDEN-LI LINDEN LI CO-FOUNDER & CHIEF ARCHITECT Co-Founder and Chief Architect at Applied Compute, building production-grade reinforcement learning infrastructure for training AI agents at scale. Previously worked at OpenAI on reasoning models, coding AI, and deep research tools including the o1 model. Stanford graduate (2021-2023) specializing in ML systems and infrastructure. AFFILIATION: APPLIED COMPUTE", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/linden-li" }, { "text": "REF: LISA-ORR LISA ORR GROUP PRODUCT MANAGER, AI CODE GEN TEAM Group Product Manager at Zapier leading the AI Code Gen initiative, pioneering the revolutionary \"support team as coders\" model where customer support specialists use AI tools to ship production code. PhD from University of St. Andrews, former Senior PM at Human API and Airship. Based in Portland, Oregon. AFFILIATION: ZAPIER", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/lisa-orr" }, { "text": "REF: MAHESH-MURAG MAHESH MURAG MEMBER OF TECHNICAL STAFF, APPLIED AI Member of Technical Staff in Applied AI at Anthropic, creator of the [Model Context Protocol (MCP)](https://www.anthropic.com/news/model-context-protocol), and key contributor to [Claude Skills](https://www.anthropic.com/news/skills) and the Claude Agent SDK. Previously at Tecton, Scale AI, and Berkeley AI Research. UC Berkeley CS + Design. AFFILIATION: ANTHROPIC", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/mahesh-murag" }, { "text": "REF: MARTIN-HARRYSSON MARTIN HARRYSSON SENIOR PARTNER Senior Partner in McKinsey's Bay Area office, specializing in software product development, digital strategy, and AI-enabled organizational transformation. MIT Computer Science graduate and former Swedish Armed Forces Soldier of the Year, leading McKinsey's research on how development practices must evolve beyond Agile for the AI era. AFFILIATION: MCKINSEY & COMPANY", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/martin-harrysson" }, { "text": "REF: MAX-KANAT-ALEXANDER MAX KANAT-ALEXANDER EXECUTIVE DISTINGUISHED ENGINEER FOR DEVELOPER EXPERIENCE Executive Distinguished Engineer for Developer Experience at Capital One with 20+ years experience in software engineering. Author of \"Code Simplicity: The Fundamentals of Software\" and thought leader on making developer experience work for both humans and AI agents in enterprise environments. AFFILIATION: CAPITAL ONE", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/max-kanat-alexander" }, { "text": "REF: MICHELE-CATASTA MICHELE CATASTA PRESIDENT & HEAD OF AI President & Head of AI at Replit, pioneering autonomous agents that create and deploy software. 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Project Horizon. TOTAL EXPERTS 48 REF: AMMAAR-RESHI AMMAAR RESHI PRODUCT & DESIGN LEAD Product & Design Lead at Google AI Studio, pioneering accessible AI-powered development through \"vibe coding.\" Known for viral AI experiments including the controversial children's book [*Alice and Sparkle*](https://time.com/6240569/ai-childrens-book-alice-and-sparkle-artists-unhappy/), created in one weekend using ChatGPT and Midjourney. AFFILIATION: GOOGLE REF: APARNA-DHINAKARAN APARNA DHINAKARAN CO-FOUNDER & CHIEF PRODUCT OFFICER (CPO) Co-founder and Chief Product Officer at Arize AI, pioneering ML observability and LLM evaluation platforms. Forbes 30 Under 30 honoree and Forbes AI Columnist, bringing deep expertise from building production ML infrastructure at Uber, Apple, and TubeMogul. AFFILIATION: ARIZE AI REF: ARMAN-HEZARKHANI ARMAN HEZARKHANI MANAGING PARTNER Managing Partner at Tenex, pioneering output-based compensation models that incentivize AI tool adoption and maximize engineering throughput. Previously founded multiple venture-backed AI companies and scaled Google Cloud AI to millions of developers. AFFILIATION: TENEX REF: ASAF-BORD ASAF BORD GENAI PRODUCTS LEADER GenAI Products Leader at Northwestern Mutual, pioneering enterprise generative business intelligence (GenBI) at a Fortune 100 financial services company with a \"crawl-walk-run\" approach to AI adoption in conservative environments. AFFILIATION: NORTHWESTERN MUTUAL REF: BARRY-ZHANG BARRY ZHANG RESEARCH ENGINEER Research Engineer at Anthropic working on Claude's agent architecture and the Skills system. Co-author of influential articles on building effective agents and the Skills framework that enables agents to dynamically load procedural knowledge. AFFILIATION: ANTHROPIC REF: BEYANG-LIU BEYANG LIU CO-FOUNDER & CTO, AMP CODE Co-founder and CTO of Amp Code, building next-generation AI coding agents with multi-agent architecture. 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ENGINEERING CODE SUMMIT 2025 - ANALYSIS & INSIGHTS This repository contains comprehensive notes, analysis, and synthesis from the AI Engineer Code Summit 2025 held November 19-22, 2025 in Midtown Manhattan, New York. These notes cover the Leadership Sessions (Nov 20) and Engineering Sessions (Nov 21). NOTES Most of this was based on my note taking and phone-photo snapping work on side at the conference. I wrote down the quotes as they happened but I wasn’t able to verify anything. From my notes, I used claude to expand, contract, expand again, and contract again the content. Look through for themes, tweaking the ideas, and then reorganizing things into sections. I designed the basic side using aistudio, copied it over into my claude-managed repo, and iterated on the designs. I used nano-banana to process the conference photos — vibe coded an image processor which also can double as a Claude Code skill — and generate the diagrams for the posts. The process-slides prompt then read each slide and figured out which speaker it went it. After choosing all of the themes, I then went back and rewrote all of the articles because boy does AI generated prose really. Its not this but its that. Sigh. RAW NOTES & PHOTOS 2025-11-20.md 2025-11-21.md SOME OF THE PROMPTS THAT GOT US HERE: conference-analsys.md process-slides.md theme-summary.md speaker-decorator.md tighten-prose.md semantic-overlap.md But ultimately, you just gotta type everything in from scratch. A DISCOURSIVE JOURNEY INTO SUBAGENTS The speaker decorator was fun: I wrote that with a prompt, then I’d trigger it with “look through all the speakers that don’t have a photo and run a subagent for them 10 at a time”. The agent could drive itself, who needs flow control when it’s smart enough to do it own its own. 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Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "TheFocusAI", "href": "https://thefocus.ai/" }, { "text": "SUMMARY", "href": "https://thefocus.ai/reports/aiecode-2025-11/" }, { "text": "LEADERSHIP", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/leadership" }, { "text": "ENGINEERING", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/engineering" }, { "text": "PERSONNEL", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers" }, { "text": "ABOUT", "href": "https://thefocus.ai/reports/aiecode-2025-11/about" }, { "text": "LEADERSHIP INDEX", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/leadership" }, { "text": "Will Schenk", "href": "https://thefocus.ai/reports/aiecode-2025-11/about" }, { "text": "NEXT Context, Memory, and The Harness: Where Product Differentiation Really Happens", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/context-memory-harness.md" }, { "text": "Max Kanat-Alexander Executive Distinguished Engineer for Developer Experience Capital One CONFERENCE SESSION Developer Experience in the Age of AI Coding Agents VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/max-kanat-alexander" }, { "text": "Lisa Orr Group Product Manager, AI Code Gen Team Zapier CONFERENCE SESSION Your Support Team Should Ship Code VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/lisa-orr" }, { "text": "Dan Shipper CEO and Co-founder Every CONFERENCE SESSION Building AI-First Companies VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/dan-shipper" }, { "text": "Gene Kim Founder & Multiple Award-Winning CTO IT Revolution CONFERENCE SESSION 2026: The Year the IDE Died VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/gene-kim" }, { "text": "Justin Reock Deputy CTO DX CONFERENCE SESSION Leadership in AI-Assisted Engineering VIEW PROFILE →", "href": 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FEATURED EXPERTS Learn more about the people behind these insights Max Kanat-Alexander Executive Distinguished Engineer for Developer Experience Capital One CONFERENCE SESSION Developer Experience in the Age of AI Coding Agents VIEW PROFILE → Lisa Orr Group Product Manager, AI Code Gen Team Zapier CONFERENCE SESSION Your Support Team Should Ship Code VIEW PROFILE → Dan Shipper CEO and Co-founder Every CONFERENCE SESSION Building AI-First Companies VIEW PROFILE → Gene Kim Founder & Multiple Award-Winning CTO IT Revolution CONFERENCE SESSION 2026: The Year the IDE Died VIEW PROFILE → Justin Reock Deputy CTO DX CONFERENCE SESSION Leadership in AI-Assisted Engineering VIEW PROFILE → Gene Kim opened with a striking claim: “AI will reshape technology organizations—100x larger than the impact of agile, cloud, CI/CD, mobile, etc.” The entire economy re-organizes itself around new modes of production. We’re starting to see the case studies. Kim’s framework for embracing this shift: FAAFO. Faster—obvious. Ambitious—“impossible becomes passive, annoying things become free.” Alone—build more autonomously, smaller teams. Fun—enjoy the work again. Options—more swings at bat. His calculation: $100 per day in AI tokens generates output comparable to a developer’s annual productivity. When costs collapse that dramatically, every shelved project deserves a second look. Kim also offered a definition: “Trust is to what degree I can predict how another party will act.” AI trust grows the same way human trust does—through predictable behavior. Stop building special accommodations for agents. Start fixing the foundational problems that have plagued human developers for decades. Justin Reock from DX cited Google’s Project Aristotle research: “the overwhelming predictor [of team success] was psychological safety.” His guidance: “Treat software development as a systems problem, not a people’s problem.” When organizations frame AI adoption as trust-building rather than competency testing, resistance drops. Reock’s predictions for full AI adoption: 10x productivity difference. 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Engineering Practices, Amplified", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/sdlc-evolution-amplified-practices.md" }, { "text": "Bill Chen Startups Applied AI, Solutions Architect OpenAI CONFERENCE SESSION Future-Proof Coding Agents: Building Reliable Systems That Outlast Model Cycles VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/bill-chen" }, { "text": "Brian Fioca Solutions Architect, Applied AI OpenAI CONFERENCE SESSION Future-Proof Coding Agents: Building Reliable Systems That Outlast Model Cycles VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/brian-fioca" }, { "text": "Michele Catasta President & Head of AI Replit CONFERENCE SESSION Autonomy Is All You Need VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/michele-catasta" }, { "text": "Katelyn Lesse Head of API Engineering / Head of Engineering, Claude Developer Platform Anthropic CONFERENCE SESSION Evolving Claude APIs for Agents VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/katelyn-lesse" }, { "text": "Itamar Friedman Co-founder & CEO Qodo CONFERENCE SESSION Testing with AI - The State of AI Code Quality VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/itamar-friedman" }, { "text": "Samir Mody Head of Engineering, AI The Browser Company CONFERENCE SESSION From Arc to Dia - Lessons Learned in Building AI Browser VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/samir-mody" }, { "text": "Qodo", "href": "https://www.qodo.ai/" }, { "text": "Anthropic", "href": "https://www.anthropic.com/" }, { "text": "OpenAI", "href": "https://openai.com/" }, { "text": "Replit", "href": "https://replit.com/" }, { "text": "The Browser Company", "href": "https://thebrowser.company/" }, { "text": "PREVIOUS SECTION TRUST, ROLE CHANGES, AND WHAT'S GOOD FOR HUMANS IS GOOD FOR AI", "href": 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Software Engineering Practices, Amplified FIG. 2.1 VISUAL DOCUMENTATION. FEATURED EXPERTS Learn more about the people behind these insights Bill Chen Startups Applied AI, Solutions Architect OpenAI CONFERENCE SESSION Future-Proof Coding Agents: Building Reliable Systems That Outlast Model Cycles VIEW PROFILE → Brian Fioca Solutions Architect, Applied AI OpenAI CONFERENCE SESSION Future-Proof Coding Agents: Building Reliable Systems That Outlast Model Cycles VIEW PROFILE → Michele Catasta President & Head of AI Replit CONFERENCE SESSION Autonomy Is All You Need VIEW PROFILE → Katelyn Lesse Head of API Engineering / Head of Engineering, Claude Developer Platform Anthropic CONFERENCE SESSION Evolving Claude APIs for Agents VIEW PROFILE → Itamar Friedman Co-founder & CEO Qodo CONFERENCE SESSION Testing with AI - The State of AI Code Quality VIEW PROFILE → Samir Mody Head of Engineering, AI The Browser Company CONFERENCE SESSION From Arc to Dia - Lessons Learned in Building AI Browser VIEW PROFILE → Qodo’s Itamar Friedman presented data: 76% of developers don’t fully trust AI-generated code. “They don’t trust the context that the LLM has.” This isn’t about model capabilities—it’s about whether the harness can maintain enough relevant context while filtering out noise. Qodo’s Context Engine pulls in logs, history, and PR comments to give models the right information at the right time. The quality issues teams face split into code-level problems (security vulnerabilities, insufficient test coverage, technical debt) and process-level challenges (learning gaps, inadequate verification, missing guardrails). Katelyn Lesse, Head of API Engineering at Anthropic, is emphatic: “Memory + Context Editing” is “the way to go” for managing Claude’s working memory. Memory retrieves relevant context when needed. Context Editing clears out the cruft—old tool results, unnecessary content—from the context window. “Effective agentic systems require both expanding capabilities and managing constraints.” Give Claude more tools via MCP and code execution—but also actively manage what stays", "height": 7079, "screenshots": { "desktop": "assets/screenshots/desktop/reports--aiecode-2025-11--article--context-memory-harness.md.png", "mobile": "assets/screenshots/mobile/reports--aiecode-2025-11--article--context-memory-harness.md.png" } }, { "url": "https://thefocus.ai/reports/aiecode-2025-11/article/sdlc-evolution-amplified-practices.md", "path": "/reports/aiecode-2025-11/article/sdlc-evolution-amplified-practices.md", "slug": "reports--aiecode-2025-11--article--sdlc-evolution-amplified-practices.md", "category": "Report/deep-dive", "status": 200, "title": "SDLC Evolution: Good Software Engineering Practices, Amplified - AI Engineering Code Summit", "metaDescription": "AI doesn't eliminate need for good process - it makes good process more valuable", "headings": [ { "tag": "h1", "text": "SDLC EVOLUTION: GOOD SOFTWARE ENGINEERING PRACTICES, AMPLIFIED", "index": 0 }, { "tag": "h2", "text": "THE NEW BOTTLENECK: CODE REVIEW", "index": 1 }, { "tag": "h2", "text": "AUTONOMOUS TESTING IN PRACTICE", "index": 2 }, { "tag": "h3", "text": "GET THE LATEST AI ENGINEERING INSIGHTS", "index": 3 }, { "tag": "h2", "text": "CLASSIFIED_FILES", "index": 4 }, { "tag": "h3", "text": "Stay Ahead of the Curve", "index": 5 } ], "sections": [ { "tag": "h1", "title": "SDLC EVOLUTION: GOOD SOFTWARE ENGINEERING PRACTICES, AMPLIFIED", "y": 228, "body": "" }, { "tag": "h2", "title": "THE NEW BOTTLENECK: CODE REVIEW", "y": 3296, "body": "Developers increasingly use AI assistants but don’t fully trust the output. The real problem isn’t buggy code—it’s volume. “More bugs because there are more PRs, not because the PRs are buggier.” Code review is now the bottleneck. (See Context, Memory, and The Harness for the trust data from Qodo’s Itamar Friedman.) Friedman’s solution: autonomous testing and intelligent code review backed by context. Qodo’s Context Engine pulls from logs, history, and PR comments to give AI reviewers situational awareness. “Don’t accept this PR unless there is a minimum of testing.” With rigorous standards, AI-assisted review can deliver 2x productivity gains. Max Kanat-Alexander from Capital One reinforces" }, { "tag": "h2", "title": "AUTONOMOUS TESTING IN PRACTICE", "y": 4478, "body": "Replit’s Michele Catasta (VP of AI, ex-Google Labs) showed these principles in practice. Replit’s agents build apps for 22 million creators, many non-technical. The key insight: autonomous testing breaks the feedback bottleneck and prevents “accumulations of whatevers”—the slow drift toward broken features. Catasta presents a spectrum of code verification—from fast/cheap code-level testing (LSP, unit tests) to slow/expensive user-interface testing (browser use, computer use). The tradeoff between supervision level and resource requirements shapes how you architect autonomous agents. McKinsey (Martin Harrysson, Natasha Maniar) corroborates from organizational research: successful AI adoption " }, { "tag": "h3", "title": "GET THE LATEST AI ENGINEERING INSIGHTS", "y": 6419, "body": "Join the Focus.AI newsletter for curated research, analysis, and perspectives on the evolving AI landscape. SUBSCRIBE No spam. Unsubscribe anytime." }, { "tag": "h2", "title": "CLASSIFIED_FILES", "y": 16, "body": "USER: AUTHORIZED" }, { "tag": "h3", "title": "Stay Ahead of the Curve", "y": 0, "body": "Get weekly insights on AI engineering trends, research summaries, and expert analysis delivered to your inbox. 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VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/martin-harrysson" }, { "text": "Natasha Maniar Business Analyst McKinsey & Company CONFERENCE SESSION Moving away from Agile - What's Next? VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/natasha-maniar" }, { "text": "The ROI Reality Check", "href": "https://thefocus.ai/reports/aiecode-2025-11/roi-reality-check" }, { "text": "Context, Memory, and The Harness", "href": "https://thefocus.ai/reports/aiecode-2025-11/context-memory-harness" }, { "text": "Replit", "href": "https://replit.com/" }, { "text": "PREVIOUS SECTION CONTEXT, MEMORY, AND THE HARNESS: WHERE PRODUCT DIFFERENTIATION REALLY HAPPENS", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/context-memory-harness.md" }, { "text": "PROCEED TO SECTION 04 THE ROI REALITY CHECK: WHERE AI ACTUALLY DELIVERS (AND WHERE IT FALLS FLAT)", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md" }, { "text": "SUBSCRIBE", "href": "" }, { "text": "Subscribe to Focus.AI", "href": "" } ], "links": [ "https://thefocus.ai/", "https://thefocus.ai/reports/aiecode-2025-11/article/sdlc-evolution-amplified-practices.md/", 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FEATURED EXPERTS Learn more about the people behind these insights Yegor Denisov-Blanch Researcher, Software Engineering Productivity Research Group Stanford University CONFERENCE SESSION How to Quantify AI ROI in Software Engineering (120k Devs Study) VIEW PROFILE → Itamar Friedman Co-founder & CEO Qodo CONFERENCE SESSION Testing with AI - The State of AI Code Quality VIEW PROFILE → Michele Catasta President & Head of AI Replit CONFERENCE SESSION Autonomy Is All You Need VIEW PROFILE → Max Kanat-Alexander Executive Distinguished Engineer for Developer Experience Capital One CONFERENCE SESSION Developer Experience in the Age of AI Coding Agents VIEW PROFILE → Martin Harrysson Senior Partner McKinsey & Company CONFERENCE SESSION Moving away from Agile - What's Next? VIEW PROFILE → Natasha Maniar Business Analyst McKinsey & Company CONFERENCE SESSION Moving away from Agile - What's Next? VIEW PROFILE → New technologies have always driven new development methodologies. 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Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "TheFocusAI", "href": "https://thefocus.ai/" }, { "text": "SUMMARY", "href": "https://thefocus.ai/reports/aiecode-2025-11/" }, { "text": "LEADERSHIP", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/leadership" }, { "text": "ENGINEERING", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/engineering" }, { "text": "PERSONNEL", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers" }, { "text": "ABOUT", "href": "https://thefocus.ai/reports/aiecode-2025-11/about" }, { "text": "LEADERSHIP INDEX", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/leadership" }, { "text": "Will Schenk", "href": "https://thefocus.ai/reports/aiecode-2025-11/about" }, { "text": "PREVIOUS SDLC Evolution: Good Software Engineering Practices, Amplified", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/sdlc-evolution-amplified-practices.md" }, { "text": "NEXT Proactive vs Reactive: The UX Evolution", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/proactive-vs-reactive-ux.md" }, { "text": "Yegor Denisov-Blanch Researcher, Software Engineering Productivity Research Group Stanford University CONFERENCE SESSION How to Quantify AI ROI in Software Engineering (120k Devs Study) VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/yegor-denisov-blanch" }, { "text": "Lei Zhang Senior Director of Generative AI & Head of Developer Experience Bloomberg LP CONFERENCE SESSION What We Learned Deploying AI within Bloomberg's Engineering Organization VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/lei-zhang" }, { "text": "Asaf Bord GenAI Products Leader Northwestern Mutual CONFERENCE SESSION From Small Bets to Big Impact: Building GenBI at a Fortune 100 VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/asaf-bord" }, { "text": "Martin Harrysson Senior Partner McKinsey & Company CONFERENCE SESSION Moving away from Agile - What's Next? VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/martin-harrysson" }, { "text": "Natasha Maniar Business Analyst McKinsey & Company CONFERENCE SESSION Moving away from Agile - What's Next? VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/natasha-maniar" }, { "text": "Software Engineering Productivity Research Group", "href": "https://softwareengineeringproductivity.stanford.edu/" }, { "text": "survey of 3,500 use cases", "href": "https://roisurvey.ai/" }, { "text": "Northwestern Mutual", "href": "https://www.northwesternmutual.com/" }, { "text": "PREVIOUS SECTION SDLC EVOLUTION: GOOD SOFTWARE ENGINEERING PRACTICES, AMPLIFIED", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/sdlc-evolution-amplified-practices.md" }, { "text": "PROCEED TO SECTION 05 PROACTIVE VS REACTIVE: THE UX EVOLUTION", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/proactive-vs-reactive-ux.md" }, { "text": "SUBSCRIBE", "href": "" }, { "text": "Subscribe to Focus.AI", "href": "" } ], "links": [ "https://thefocus.ai/", "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md/", "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md/track/leadership", "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md/track/engineering", "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md/speakers", "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md/about", "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md/track/leadership", "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md/about", "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md/article/sdlc-evolution-amplified-practices.md", "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md/article/proactive-vs-reactive-ux.md", "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md/speakers/yegor-denisov-blanch", "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md/speakers/lei-zhang", "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md/speakers/asaf-bord", "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md/speakers/martin-harrysson", "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md/speakers/natasha-maniar", "https://softwareengineeringproductivity.stanford.edu/", "https://roisurvey.ai/", "https://www.northwesternmutual.com/", "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md/article/sdlc-evolution-amplified-practices.md", "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md/article/proactive-vs-reactive-ux.md" ], "images": [ { "src": "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md/images/human-approved-stamp.png", "alt": "Human Approved", "w": 1408, "h": 768 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md/headers/roi-reality-check.png", "alt": "The ROI Reality Check: Where AI Actually Delivers (And Where It Falls Flat)", "w": 1376, "h": 768 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md/photos/yegor-denisov-blanch.jpg", "alt": "Yegor Denisov-Blanch", "w": 200, "h": 200 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md/photos/lei-zhang.jpg", "alt": "Lei Zhang", "w": 5973, "h": 4480 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md/photos/asaf-bord.jpg", "alt": "Asaf Bord", "w": 500, "h": 500 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md/photos/martin-harrysson.jpg", "alt": "Martin Harrysson", "w": 100, "h": 100 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md/photos/natasha-maniar.jpg", "alt": "Natasha Maniar", "w": 800, "h": 533 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-20-11-41-gemini-edited.CAtpnlpx_1y0VKg.webp", "alt": "", "w": 1376, "h": 768 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-20-11-43-gemini-edited.BkmLGs7i_Z1VLJhb.webp", "alt": "", "w": 1376, "h": 768 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-20-16-12-gemini-edited.C0AD2sWx_Z2M7mK.webp", "alt": "", "w": 0, "h": 0 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-20-14-19-gemini-edited.DTWQQjwp_Z1uAvkI.webp", "alt": "", "w": 0, "h": 0 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-20-14-11-gemini-edited.CaYIA8Ww_24fS8q.webp", "alt": "", "w": 0, "h": 0 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-20-11-27-gemini-edited.BUZOfbt7_2jp1np.webp", "alt": "", "w": 0, "h": 0 } ], "text": "TheFocusAI SUMMARY LEADERSHIP ENGINEERING PERSONNEL ABOUT FILES (0) LEADERSHIP INDEX QUERY DATA FILE LEADERSHIP // SECTION 04 THE ROI REALITY CHECK: WHERE AI ACTUALLY DELIVERS (AND WHERE IT FALLS FLAT) ABSTRACT: 93% of organizations stuck in 'pilot purgatory' - only 7% believe they're at scale By Will Schenk PREVIOUS SDLC Evolution: Good Software Engineering Practices, Amplified NEXT Proactive vs Reactive: The UX Evolution FIG. 4.1 VISUAL DOCUMENTATION. FEATURED EXPERTS Learn more about the people behind these insights Yegor Denisov-Blanch Researcher, Software Engineering Productivity Research Group Stanford University CONFERENCE SESSION How to Quantify AI ROI in Software Engineering (120k Devs Study) VIEW PROFILE → Lei Zhang Senior Director of Generative AI & Head of Developer Experience Bloomberg LP CONFERENCE SESSION What We Learned Deploying AI within Bloomberg's Engineering Organization VIEW PROFILE → Asaf Bord GenAI Products Leader Northwestern Mutual CONFERENCE SESSION From Small Bets to Big Impact: Building GenBI at a Fortune 100 VIEW PROFILE → Martin Harrysson Senior Partner McKinsey & Company CONFERENCE SESSION Moving away from Agile - What's Next? VIEW PROFILE → Natasha Maniar Business Analyst McKinsey & Company CONFERENCE SESSION Moving away from Agile - What's Next? VIEW PROFILE → Stanford researcher Yegor Denisov-Blanch, who analyzed over 100,000 engineers across hundreds of companies as part of the Software Engineering Productivity Research Group, delivered the reality check: actual productivity looks nothing like the demos. And the gap between organizations effectively adopting AI and those lagging behind is widening. “AI usage quality means more than quantity,” Denisov-Blanch emphasized. On greenfield projects, teams can deliver 35-40% productivity gains. But brownfield codebases and high-complexity work? Those gains plummet to 0-10%. If the codebase is a mess, the tools end up churning through and reworking code without actually refactoring. Code quality can go down. The research revealed a “rich gets richer effect.” The gap between AI-using teams and non-AI teams widened from 4.8% to 19% over two years—a 4x increase. Teams that already maintained clean codebases, established good practices, and invested in systematic tooling are seeing outsized benefits. Bloomberg’s Lei Zhang put it bluntly: “Vibe coding is where 2 engineers can create the tech debt of 50 engineers.” They have a massive codebase and 9,000+ engineers. 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Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "TheFocusAI", "href": "https://thefocus.ai/" }, { "text": "SUMMARY", "href": "https://thefocus.ai/reports/aiecode-2025-11/" }, { "text": "LEADERSHIP", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/leadership" }, { "text": "ENGINEERING", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/engineering" }, { "text": "PERSONNEL", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers" }, { "text": "ABOUT", "href": "https://thefocus.ai/reports/aiecode-2025-11/about" }, { "text": "LEADERSHIP INDEX", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/leadership" }, { "text": "Will Schenk", "href": "https://thefocus.ai/reports/aiecode-2025-11/about" }, { "text": "PREVIOUS The ROI Reality Check: Where AI Actually Delivers (And Where It Falls Flat)", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/roi-reality-check.md" }, { "text": "NEXT The Economics of AI Engineering: New Cost Structures & Compensation Models", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/economics-ai-engineering.md" }, { "text": "Kath Korevec Director of Product - AI Developer Assistants (AIDA) Google Labs CONFERENCE SESSION Proactive Agents: Moving Beyond Chat-Based AI VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/kath-korevec" }, { "text": "Steve Yegge Head of Engineering, Amp Lead Sourcegraph CONFERENCE SESSION 2026: The Year the IDE Died VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/steve-yegge" }, { "text": "Michele Catasta President & Head of AI Replit CONFERENCE SESSION Autonomy Is All You Need VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/michele-catasta" }, { "text": "Samir Mody Head of Engineering, AI The Browser Company CONFERENCE SESSION From Arc to Dia - Lessons Learned in Building AI Browser VIEW PROFILE →", "href": 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FEATURED EXPERTS Learn more about the people behind these insights Kath Korevec Director of Product - AI Developer Assistants (AIDA) Google Labs CONFERENCE SESSION Proactive Agents: Moving Beyond Chat-Based AI VIEW PROFILE → Steve Yegge Head of Engineering, Amp Lead Sourcegraph CONFERENCE SESSION 2026: The Year the IDE Died VIEW PROFILE → Michele Catasta President & Head of AI Replit CONFERENCE SESSION Autonomy Is All You Need VIEW PROFILE → Samir Mody Head of Engineering, AI The Browser Company CONFERENCE SESSION From Arc to Dia - Lessons Learned in Building AI Browser VIEW PROFILE → “Humans are unitaskers,” declares Kath Korevec, Director of Product at Google Labs. We’re building agents that wait to be asked, that sit idle in chat windows, that force us to context-switch and craft perfect prompts. We’re still doing the emotional labor of reminding it what to do. Her work on Jules, Google’s autonomous coding agent, is about reducing mental load. Korevec outlines four pillars of proactive systems: observation (understanding what you’re working on), timely action (knowing when to step in), personalization (learning how you work and what you ignore), and seamless integration. “Imagine when compute isn’t a limiting agent at all.” Agents that function like Nest thermostats learning your patterns—collaborators who know when you need help before you ask. Jules surfaces suggested tasks and comes up with the prompts itself. “It gives context of the code and also rationale on what to do.” When Korevec built a six-foot animatronic head for Halloween, the workflow was “prompt, ten minutes, repeat—tedious.” Jules changed that. She focused on creative decisions; it handled implementation and suggested next steps. Steve Yegge: “If you are still using an IDE by Jan 1st you are a bad engineer.” He argued for abandoning the “diver window” metaphor—giving one agent a bigger context window and sending it deep—in favor of ant-like swarms working in parallel. “One huge problem with coding agents is their granularity,” Yegge explained. “You send a", "height": 6992, "screenshots": { "desktop": "assets/screenshots/desktop/reports--aiecode-2025-11--article--proactive-vs-reactive-ux.md.png", "mobile": "assets/screenshots/mobile/reports--aiecode-2025-11--article--proactive-vs-reactive-ux.md.png" } }, { "url": "https://thefocus.ai/reports/aiecode-2025-11/article/economics-ai-engineering.md", "path": "/reports/aiecode-2025-11/article/economics-ai-engineering.md", "slug": "reports--aiecode-2025-11--article--economics-ai-engineering.md", "category": "Report/deep-dive", "status": 200, "title": "The Economics of AI Engineering: New Cost Structures & Compensation Models - AI Engineering Code Summit", "metaDescription": "AI changes the cost function of engineering - previous cost-per-feature calculations obsolete", "headings": [ { "tag": "h1", "text": "THE ECONOMICS OF AI ENGINEERING: NEW COST STRUCTURES & COMPENSATION MODELS", "index": 0 }, { "tag": "h3", "text": "GET THE LATEST AI ENGINEERING INSIGHTS", "index": 1 }, { "tag": "h2", "text": "CLASSIFIED_FILES", "index": 2 }, { "tag": "h3", "text": "Stay Ahead of the Curve", "index": 3 } ], "sections": [ { "tag": "h1", "title": "THE ECONOMICS OF AI ENGINEERING: NEW COST STRUCTURES & COMPENSATION MODELS", "y": 228, "body": "" }, { "tag": "h3", "title": "GET THE LATEST AI ENGINEERING INSIGHTS", "y": 5942, "body": "Join the Focus.AI newsletter for curated research, analysis, and perspectives on the evolving AI landscape. 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Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "TheFocusAI", "href": "https://thefocus.ai/" }, { "text": "SUMMARY", "href": "https://thefocus.ai/reports/aiecode-2025-11/" }, { "text": "LEADERSHIP", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/leadership" }, { "text": "ENGINEERING", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/engineering" }, { "text": "PERSONNEL", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers" }, { "text": "ABOUT", "href": "https://thefocus.ai/reports/aiecode-2025-11/about" }, { "text": "LEADERSHIP INDEX", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/leadership" }, { "text": "Will Schenk", "href": "https://thefocus.ai/reports/aiecode-2025-11/about" }, { "text": "PREVIOUS Proactive vs Reactive: The UX Evolution", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/proactive-vs-reactive-ux.md" }, { "text": "END OF TRACK Return to Index", "href": 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FEATURED EXPERTS Learn more about the people behind these insights Arman Hezarkhani Managing Partner Tenex CONFERENCE SESSION Rethinking Compensation for AI-Augmented Engineers VIEW PROFILE → Lei Zhang Senior Director of Generative AI & Head of Developer Experience Bloomberg LP CONFERENCE SESSION What We Learned Deploying AI within Bloomberg's Engineering Organization VIEW PROFILE → Asaf Bord GenAI Products Leader Northwestern Mutual CONFERENCE SESSION From Small Bets to Big Impact: Building GenBI at a Fortune 100 VIEW PROFILE → Am I selling myself short on tokens? I regularly hit usage limits despite Claude Max ($100/month + overage), Cursor Pro+ ($60/month + overage), Kiro Pro+ ($40/month) on the consumer side, plus API usage running $300+/month on Gemini, $200+/month on OpenAI, $60+/month on OpenRouter. A good chunk is client work, but I walked away thinking I’m undervaluing the benefits. Lei Zhang, Head of Technology Infrastructure at Bloomberg, offered the key reframe: AI “changes the cost function of engineering.” The old cost-per-feature calculations—in time or mental anguish—are meaningless now. What was too expensive to build last quarter might be trivial today. Previous calculations don’t hold. Recalculate everything. The practical question: “What work do our developers not want to do?” Zhang asked. Migrations. Incident response. Understanding contributing factors across overwhelming alerts. Bloomberg built agents for exactly these tasks—uplift agents that handle patches with rationale, incident agents that surface context. AI for the grind, not the glory. Zhang sees five industry trends emerging: LLM-ready data fabrics with agent-readable schemas and lineage. Specialist-first model grids where tiny expert models route to large generalists on demand. Invisible copilots—one-click LLM actions inside Slack, CRM, and BI tools. Security built into models from within, not outer wrappers. And usage-based pricing: fewer workers means fewer seats means licensing focused on usage. 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SUBSCRIBE No spam. Unsubscribe anytime." }, { "tag": "h2", "title": "CLASSIFIED_FILES", "y": 16, "body": "USER: AUTHORIZED" }, { "tag": "h3", "title": "Stay Ahead of the Curve", "y": 0, "body": "Get weekly insights on AI engineering trends, research summaries, and expert analysis delivered to your inbox. Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "TheFocusAI", "href": "https://thefocus.ai/" }, { "text": "SUMMARY", "href": "https://thefocus.ai/reports/aiecode-2025-11/" }, { "text": "LEADERSHIP", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/leadership" }, { "text": "ENGINEERING", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/engineering" }, { "text": "PERSONNEL", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers" }, { "text": "ABOUT", "href": "https://thefocus.ai/reports/aiecode-2025-11/about" }, { "text": "ENGINEERING INDEX", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/engineering" }, { "text": "Will Schenk", "href": "https://thefocus.ai/reports/aiecode-2025-11/about" }, { "text": "NEXT Environments as Universal Abstraction: The New Unit of Everything", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md" }, { "text": "Jake Nations Engineering Leader Netflix CONFERENCE SESSION The Infinite Software Crisis - AI, Complexity, and the Challenge of Scale VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/jake-nations" }, { "text": "Dex Horthy Founder & CEO HumanLayer CONFERENCE SESSION No Vibes Allowed: Solving Hard Problems in Complex Codebases VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/dex-horthy" }, { "text": "Beyang Liu Co-founder & CTO, Amp Code Amp Code / Sourcegraph CONFERENCE SESSION Amp Code: Next-Generation AI Coding VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/beyang-liu" }, { "text": "12 factor agents", "href": "https://github.com/humanlayer/12-factor-agents" }, { "text": "Edsger W. Dijkstra, 1972 Turing Award Lecture", "href": "https://www.cs.utexas.edu/~EWD/transcriptions/EWD03xx/EWD340.html" }, { "text": "“Simple made Easy”", "href": "https://www.infoq.com/presentations/Simple-Made-Easy/" }, { "text": "Model Quality Over Scaffolding", "href": "https://thefocus.ai/reports/aiecode-2025-11/model-quality-over-scaffolding" }, { "text": "PROCEED TO SECTION 02 ENVIRONMENTS AS UNIVERSAL ABSTRACTION: THE NEW UNIT OF EVERYTHING", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md" }, { "text": "SUBSCRIBE", "href": "" }, { "text": "Subscribe to Focus.AI", "href": "" } ], "links": [ "https://thefocus.ai/", "https://thefocus.ai/reports/aiecode-2025-11/article/context-engineering-human-ai-collaboration.md/", "https://thefocus.ai/reports/aiecode-2025-11/article/context-engineering-human-ai-collaboration.md/track/leadership", "https://thefocus.ai/reports/aiecode-2025-11/article/context-engineering-human-ai-collaboration.md/track/engineering", "https://thefocus.ai/reports/aiecode-2025-11/article/context-engineering-human-ai-collaboration.md/speakers", "https://thefocus.ai/reports/aiecode-2025-11/article/context-engineering-human-ai-collaboration.md/about", "https://thefocus.ai/reports/aiecode-2025-11/article/context-engineering-human-ai-collaboration.md/track/engineering", "https://thefocus.ai/reports/aiecode-2025-11/article/context-engineering-human-ai-collaboration.md/about", "https://thefocus.ai/reports/aiecode-2025-11/article/context-engineering-human-ai-collaboration.md/article/environments-universal-abstraction.md", "https://thefocus.ai/reports/aiecode-2025-11/article/context-engineering-human-ai-collaboration.md/speakers/jake-nations", "https://thefocus.ai/reports/aiecode-2025-11/article/context-engineering-human-ai-collaboration.md/speakers/dex-horthy", "https://thefocus.ai/reports/aiecode-2025-11/article/context-engineering-human-ai-collaboration.md/speakers/beyang-liu", "https://github.com/humanlayer/12-factor-agents", "https://www.cs.utexas.edu/~EWD/transcriptions/EWD03xx/EWD340.html", "https://www.infoq.com/presentations/Simple-Made-Easy/", "https://thefocus.ai/reports/aiecode-2025-11/article/context-engineering-human-ai-collaboration.md/model-quality-over-scaffolding", "https://thefocus.ai/reports/aiecode-2025-11/article/context-engineering-human-ai-collaboration.md/article/environments-universal-abstraction.md" ], "images": [ { "src": "https://thefocus.ai/reports/aiecode-2025-11/article/context-engineering-human-ai-collaboration.md/images/human-approved-stamp.png", "alt": "Human Approved", "w": 1408, "h": 768 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/article/context-engineering-human-ai-collaboration.md/headers/context-engineering-human-ai-collaboration.png", "alt": "DO NOT OUTSOURCE THE THINKING: Context Engineering & Human-AI Collaboration", "w": 1376, "h": 768 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/article/context-engineering-human-ai-collaboration.md/photos/jake-nations.jpg", "alt": "Jake Nations", "w": 460, "h": 460 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/article/context-engineering-human-ai-collaboration.md/photos/dex-horthy.jpg", "alt": "Dex Horthy", "w": 460, "h": 460 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/article/context-engineering-human-ai-collaboration.md/photos/beyang-liu.jpg", "alt": "Beyang Liu", "w": 460, "h": 460 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-21-15-00-gemini-edited.CJ8Kgd0h_GGtFp.webp", "alt": "", "w": 1200, "h": 896 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-21-15-05-gemini-edited.vRJ0Z8eM_ZojhMl.webp", "alt": "", "w": 1200, "h": 896 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-21-15-03-gemini-edited.Blq_N-uV_Z2iUEsD.webp", "alt": "", "w": 0, "h": 0 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-21-09-37-gemini-edited.XfzhzqB6_1ij6NJ.webp", "alt": "", "w": 0, "h": 0 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-21-09-50-gemini-edited.B0xX6zWB_16krna.webp", "alt": "", "w": 0, "h": 0 } ], "text": "TheFocusAI SUMMARY LEADERSHIP ENGINEERING PERSONNEL ABOUT FILES (0) ENGINEERING INDEX QUERY DATA FILE ENGINEERING // SECTION 01 DO NOT OUTSOURCE THE THINKING: CONTEXT ENGINEERING & HUMAN-AI COLLABORATION ABSTRACT: AI can only amplify the thinking you've already done By Will Schenk NEXT Environments as Universal Abstraction: The New Unit of Everything FIG. 1.1 VISUAL DOCUMENTATION. FEATURED EXPERTS Learn more about the people behind these insights Jake Nations Engineering Leader Netflix CONFERENCE SESSION The Infinite Software Crisis - AI, Complexity, and the Challenge of Scale VIEW PROFILE → Dex Horthy Founder & CEO HumanLayer CONFERENCE SESSION No Vibes Allowed: Solving Hard Problems in Complex Codebases VIEW PROFILE → Beyang Liu Co-founder & CTO, Amp Code Amp Code / Sourcegraph CONFERENCE SESSION Amp Code: Next-Generation AI Coding VIEW PROFILE → Dex Horthy of 12 factor agents fame and CEO of HumanLayer, came in hot with his “No Vibes Allowed” arguing that we needed to use subagents for research and planning and building, keeping the context window under 40% beyond which you’ll end up in the “dumb zone”. He had enough material for a talk twice as long, but some fun things: “dumb zone” — around a 40% full context window you’ll start to see diminishing returns When the model says “you’re absolutely right” its time to start over. Use subagents to manage context. Because of semantic diffusion spec driven development means whatever you want it to. There’s “a lot of rework, working more just fixing the slop from last week.” LLMs are stateless functions — put better tokens in to get the right tokens out Also, use subagents The 12 factor agents idea, which I had to catch up on later, is an inversion of how we normally think of coding, having the model replace the DAG of if/then and switch/cases — most of the code deployed is actually deterministic, but the human and llm controlls what is called when. “I shipped code I didn’t fully understand,” says Jake Nations of Netflix, “and I bet you have too”. He came at the historical context from a different angle than Horthy, starting with an always cheery Dijkstra quote: when we had a few weak computers, programming became a mild problem, and now we have gigantic computers, programming had become an equally gigantic problem. — Edsger W. Dijkstra, 1972 Turing Award Lecture Riffing through some of the past efforts to manage the complexity: He speed ran through some complexity greatest hits — riffing off No Silver Bullet —", "height": 8516, "screenshots": { "desktop": "assets/screenshots/desktop/reports--aiecode-2025-11--article--context-engineering-human-ai-collaboration.md.png", "mobile": "assets/screenshots/mobile/reports--aiecode-2025-11--article--context-engineering-human-ai-collaboration.md.png" } }, { "url": "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md", "path": "/reports/aiecode-2025-11/article/environments-universal-abstraction.md", "slug": "reports--aiecode-2025-11--article--environments-universal-abstraction.md", "category": "Report/deep-dive", "status": 200, "title": "Environments as Universal Abstraction: The New Unit of Everything - AI Engineering Code Summit", "metaDescription": "A benchmark = environment + starting state + verifier (same as RL environments)", "headings": [ { "tag": "h1", "text": "ENVIRONMENTS AS UNIVERSAL ABSTRACTION: THE NEW UNIT OF EVERYTHING", "index": 0 }, { "tag": "h2", "text": "VERIFICATION-DRIVEN DEVELOPMENT: THE TEA KETTLE EXAMPLE", "index": 1 }, { "tag": "h2", "text": "THE ENVIRONMENT HUB: SCALING THE ABSTRACTION", "index": 2 }, { "tag": "h2", "text": "FROM RESEARCH TO PRODUCTION", "index": 3 }, { "tag": "h2", "text": "THE NEW UNIT OF EVERYTHING", "index": 4 }, { "tag": "h3", "text": "GET THE LATEST AI ENGINEERING INSIGHTS", "index": 5 }, { "tag": "h2", "text": "CLASSIFIED_FILES", "index": 6 }, { "tag": "h3", "text": "Stay Ahead of the Curve", "index": 7 } ], "sections": [ { "tag": "h1", "title": "ENVIRONMENTS AS UNIVERSAL ABSTRACTION: THE NEW UNIT OF EVERYTHING", "y": 228, "body": "" }, { "tag": "h2", "title": "VERIFICATION-DRIVEN DEVELOPMENT: THE TEA KETTLE EXAMPLE", "y": 2765, "body": "We have environments and we need verification. Pash’s “tea kettle” example: Task is boil water. A good verifier asks one question: Is the kettle whistling? Ignore all the other surface level properties. Bad verifiers ask process questions: Is the burner set to high? Has five minutes elapsed? Is the kettle on the front left burner? Did you filter the water? Outcome-driven—doesn’t care how you got there, though those traces can be useful to RL the agentic system itself. Reyes: “Many tasks are much easier to verify than to solve.” Verifiability is the key constraint on AI capability. “The ability to solve is proportional to how verifiable it is for the AI to solve.” Focus engineering efforts on" }, { "tag": "h2", "title": "THE ENVIRONMENT HUB: SCALING THE ABSTRACTION", "y": 3830, "body": "Brown’s work at Prime Intellect demonstrates how this abstraction scales. His team built the “environments hub” for creating, sharing, and running RL training and evaluations. The architecture treats environments as first-class entities with three components: task definition, harness for execution, and stream of rewards. This same pattern appears everywhere in modern AI engineering. The power of this abstraction is its universality. Environments work for “research for the sake of research to advance our collective understanding of AI,” Brown explained, and equally for “taking small models and making them much better for custom purposes.” The same infrastructure serves both frontier research " }, { "tag": "h2", "title": "FROM RESEARCH TO PRODUCTION", "y": 4880, "body": "Reyes made the business case explicit. At Factory AI, they’ve built their entire agent-ready framework around verifiability. “Invest in the environment feedback loop,” he urged. “One opinionated engineer can change the velocity of the entire business.” The rate limiting step is “your organization’s validation criteria.” Pash’s vision for Cline extends this to meta-automation. His team built an “RL environments factory” where subagents qualify tasks and generate verifiable environments from real-world coding work. “The bottleneck should shift from engineering to collecting quality tests,” he argued. He announced Cline-bench, their open-source benchmark that converts opt-in user data into trai" }, { "tag": "h2", "title": "THE NEW UNIT OF EVERYTHING", "y": 6359, "body": "Environments are the fundamental unit for reasoning about AI systems. Web frameworks made app development accessible; now environment hubs and verification toolkits are “increasing the accessibility of doing AI research,” Brown observed. The vision: an “open superintelligent stack”—composable, shareable environments for collective progress. Whether you’re building benchmarks, training with RL, or deploying production agents: think in environments. Define the starting state, specify the verifiable outcomes, let the rewards flow. Training, evaluation, and deployment unified into a single framework." }, { "tag": "h3", "title": "GET THE LATEST AI ENGINEERING INSIGHTS", "y": 7427, "body": "Join the Focus.AI newsletter for curated research, analysis, and perspectives on the evolving AI landscape. SUBSCRIBE No spam. Unsubscribe anytime." }, { "tag": "h2", "title": "CLASSIFIED_FILES", "y": 16, "body": "USER: AUTHORIZED" }, { "tag": "h3", "title": "Stay Ahead of the Curve", "y": 0, "body": "Get weekly insights on AI engineering trends, research summaries, and expert analysis delivered to your inbox. Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "TheFocusAI", "href": "https://thefocus.ai/" }, { "text": "SUMMARY", "href": "https://thefocus.ai/reports/aiecode-2025-11/" }, { "text": "LEADERSHIP", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/leadership" }, { "text": "ENGINEERING", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/engineering" }, { "text": "PERSONNEL", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers" }, { "text": "ABOUT", "href": "https://thefocus.ai/reports/aiecode-2025-11/about" }, { "text": "ENGINEERING INDEX", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/engineering" }, { "text": "Will Schenk", "href": "https://thefocus.ai/reports/aiecode-2025-11/about" }, { "text": "PREVIOUS DO NOT OUTSOURCE THE THINKING: Context Engineering & Human-AI Collaboration", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/context-engineering-human-ai-collaboration.md" }, { "text": "NEXT Skills and Artifacts: The New Building Blocks for Agents", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/agents-building-artifacts.md" }, { "text": "Will Brown Research Lead Prime Intellect CONFERENCE SESSION RL Environments at Scale VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/will-brown" }, { "text": "Eno Reyes Co-Founder & CTO Factory AI CONFERENCE SESSION Making Codebases Agent-Ready VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/eno-reyes" }, { "text": "Nik Pash Head of AI Cline CONFERENCE SESSION Cline - More Connected, More Powerful VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/nik-pash" }, { "text": "Prime Intellect", "href": "https://www.primeintellect.ai/" }, { "text": "Factory AI", "href": "https://www.factory.ai/" }, { "text": "Cline", "href": "https://cline.bot/" }, { "text": "Cline-bench", "href": "https://github.com/cline/cline-bench" }, { "text": "PREVIOUS SECTION DO NOT OUTSOURCE THE THINKING: CONTEXT ENGINEERING & HUMAN-AI COLLABORATION", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/context-engineering-human-ai-collaboration.md" }, { "text": "PROCEED TO SECTION 03 SKILLS AND ARTIFACTS: THE NEW BUILDING BLOCKS FOR AGENTS", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/agents-building-artifacts.md" }, { "text": "SUBSCRIBE", "href": "" }, { "text": "Subscribe to Focus.AI", "href": "" } ], "links": [ "https://thefocus.ai/", "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md/", "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md/track/leadership", "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md/track/engineering", "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md/speakers", "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md/about", "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md/track/engineering", "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md/about", "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md/article/context-engineering-human-ai-collaboration.md", "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md/article/agents-building-artifacts.md", "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md/speakers/will-brown", "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md/speakers/eno-reyes", "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md/speakers/nik-pash", "https://www.primeintellect.ai/", "https://www.factory.ai/", "https://cline.bot/", "https://github.com/cline/cline-bench", "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md/article/context-engineering-human-ai-collaboration.md", "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md/article/agents-building-artifacts.md" ], "images": [ { "src": "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md/images/human-approved-stamp.png", "alt": "Human Approved", "w": 1408, "h": 768 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md/headers/environments-universal-abstraction.png", "alt": "Environments as Universal Abstraction: The New Unit of Everything", "w": 1376, "h": 768 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md/photos/will-brown.jpg", "alt": "Will Brown", "w": 460, "h": 460 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md/photos/eno-reyes.jpg", "alt": "Eno Reyes", "w": 460, "h": 460 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md/photos/nik-pash.jpg", "alt": "Nik Pash", "w": 460, "h": 460 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-21-11-44-gemini-edited.Dy5JOHE0_17gCLf.webp", "alt": "", "w": 1376, "h": 768 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-21-14-07-gemini-edited.CGF8vzok_ZDjhAl.webp", "alt": "", "w": 1376, "h": 768 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-21-11-41-gemini-edited.Cllr9fzY_Zs6MVB.webp", "alt": "", "w": 0, "h": 0 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-21-14-12-gemini-edited.DIPlyROI_1faEJB.webp", "alt": "", "w": 0, "h": 0 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-21-14-14-gemini-edited.B5DAhvpj_13KDK7.webp", "alt": "", "w": 0, "h": 0 } ], "text": "TheFocusAI SUMMARY LEADERSHIP ENGINEERING PERSONNEL ABOUT FILES (0) ENGINEERING INDEX QUERY DATA FILE ENGINEERING // SECTION 02 ENVIRONMENTS AS UNIVERSAL ABSTRACTION: THE NEW UNIT OF EVERYTHING ABSTRACT: A benchmark = environment + starting state + verifier (same as RL environments) By Will Schenk PREVIOUS DO NOT OUTSOURCE THE THINKING: Context Engineering & Human-AI Collaboration NEXT Skills and Artifacts: The New Building Blocks for Agents FIG. 2.1 VISUAL DOCUMENTATION. FEATURED EXPERTS Learn more about the people behind these insights Will Brown Research Lead Prime Intellect CONFERENCE SESSION RL Environments at Scale VIEW PROFILE → Eno Reyes Co-Founder & CTO Factory AI CONFERENCE SESSION Making Codebases Agent-Ready VIEW PROFILE → Nik Pash Head of AI Cline CONFERENCE SESSION Cline - More Connected, More Powerful VIEW PROFILE → A fundamental architectural insight emerged across multiple sessions: environments have become the universal unit of abstraction for AI engineering. As Will Brown, Research Lead at Prime Intellect, put it: “Environments are the webapps of research.” This is a clarifying shift in how we think about training, evaluation, deployment, and iteration of AI systems. Eno Reyes, CTO of Factory AI, and Nik Pash, creator of Cline, independently arrived at the same conclusion: a benchmark is an environment, a starting state, and a verifier. RL environments follow the same pattern. “The only real difference is how the reward is used,” Pash explained. “One is measure, one is improve.” Benchmarks, harnesses, verification systems, RL training grounds—all instances of the task/harness/rewards pattern. VERIFICATION-DRIVEN DEVELOPMENT: THE TEA KETTLE EXAMPLE We have environments and we need verification. Pash’s “tea kettle” example: Task is boil water. A good verifier asks one question: Is the kettle whistling? Ignore all the other surface level properties. Bad verifiers ask process questions: Is the burner set to high? Has five minutes elapsed? Is the kettle on the front left burner? Did you filter the water? Outcome-driven—doesn’t care how you got there, though those traces can be useful to RL the agentic system itself. Reyes: “Many tasks are much easier to verify than to solve.” Verifiability is the key constraint on AI capability. “The ability to solve is proportional to how verifiable it is for the AI to solve.” Focus engineering efforts on creating rigorous verification boundaries, not micromanaging the process. THE ENVIRONMENT HUB: SCALING T", "height": 7678, "screenshots": { "desktop": "assets/screenshots/desktop/reports--aiecode-2025-11--article--environments-universal-abstraction.md.png", "mobile": "assets/screenshots/mobile/reports--aiecode-2025-11--article--environments-universal-abstraction.md.png" } }, { "url": "https://thefocus.ai/reports/aiecode-2025-11/article/agents-building-artifacts.md", "path": "/reports/aiecode-2025-11/article/agents-building-artifacts.md", "slug": "reports--aiecode-2025-11--article--agents-building-artifacts.md", "category": "Report/deep-dive", "status": 200, "title": "Skills and Artifacts: The New Building Blocks for Agents - AI Engineering Code Summit", "metaDescription": "Skills are just folders - procedural knowledge packaged for agents to load dynamically", "headings": [ { "tag": "h1", "text": "SKILLS AND ARTIFACTS: THE NEW BUILDING BLOCKS FOR AGENTS", "index": 0 }, { "tag": "h2", "text": "SKILLS: PROCEDURAL KNOWLEDGE AS FOLDERS", "index": 1 }, { "tag": "h2", "text": "ARTIFACTS: DYNAMIC REPRESENTATIONS FOR EVERYONE", "index": 2 }, { "tag": "h2", "text": "THE DEMOCRATIZATION STACK", "index": 3 }, { "tag": "h2", "text": "THE UNIFIED ARCHITECTURE", "index": 4 }, { "tag": "h3", "text": "GET THE LATEST AI ENGINEERING INSIGHTS", "index": 5 }, { "tag": "h2", "text": "CLASSIFIED_FILES", "index": 6 }, { "tag": "h3", "text": "Stay Ahead of the Curve", "index": 7 } ], "sections": [ { "tag": "h1", "title": "SKILLS AND ARTIFACTS: THE NEW BUILDING BLOCKS FOR AGENTS", "y": 228, "body": "" }, { "tag": "h2", "title": "SKILLS: PROCEDURAL KNOWLEDGE AS FOLDERS", "y": 2318, "body": "Barry Zhang and Mahesh Murag from Anthropic revealed how they’ve moved past the “build agents” paradigm. “Code is all you need,” Zhang argued. “We don’t need to have multiple agents.” The insight: “The agent underneath is more universal than we thought.” Their solution is Skills—folders containing markdown documentation and Python scripts that agents can dynamically load. A skill might look like: anthropic_brand/ ├─ SKILL.md ├─ docs.md ├─ slide-decks.md └─ apply_template.py Skills are progressively disclosed. The agent sees just the top-level description first, then loads the full file when needed, and can extend into scripts as required. “Skills collect institutional knowledge,” Zhang expla" }, { "tag": "h2", "title": "ARTIFACTS: DYNAMIC REPRESENTATIONS FOR EVERYONE", "y": 4265, "body": "Kevin Hou from Google DeepMind introduced the complementary concept at the other end of the spectrum. Where Skills are about input (knowledge agents load), Artifacts are about output (representations agents generate). In Google’s Antigravity IDE—which Hou described as “unapologetically agent-first”—artifacts are central. “An artifact is a dynamic representation that the agent generates,” he explained. “A representation for you and your use case. It can be used for self-reflection or to communicate to the user—and can be shared across accounts.” The model makes autonomous decisions about artifacts: If it should generate one What that artifact should contain Why it is needed Who needs to see i" }, { "tag": "h2", "title": "THE DEMOCRATIZATION STACK", "y": 5679, "body": "The implications extend beyond developers. Kat Kampf and Ammaar Reshi from Google demonstrated the AI Studio Build experience—free tools for creating applications from natural language prompts. They live-coded a comic book, then a multiplayer video game, all as front-end React apps with “full backend support and runtime support—we don’t want you to think about those details.” The design sensibilities have matured too: “no more purple gradients,” “no cyberpunk,” “no more groking their way around Figma.” Reshi captured the moment: “We’re the first generation of engineers building tools for a world where anyone can build software.”" }, { "tag": "h2", "title": "THE UNIFIED ARCHITECTURE", "y": 7599, "body": "Skills and Artifacts form two halves of a coherent architecture. Skills solve the knowledge input problem—how agents access domain expertise, institutional patterns, and procedural know-how. Artifacts solve the communication output problem—how agents externalize their reasoning, share progress, and create tangible deliverables. Together with environments (task + harness + verifier), they complete the picture. Zhang and Murag: “Building the skills and sharing them will help make your own agents more capable.” Hou: “Antigravity will be the most advanced product on the market because we are building it for ourselves.” The shift is clear: from agents as black boxes to agents as composable system" }, { "tag": "h3", "title": "GET THE LATEST AI ENGINEERING INSIGHTS", "y": 8843, "body": "Join the Focus.AI newsletter for curated research, analysis, and perspectives on the evolving AI landscape. SUBSCRIBE No spam. Unsubscribe anytime." }, { "tag": "h2", "title": "CLASSIFIED_FILES", "y": 16, "body": "USER: AUTHORIZED" }, { "tag": "h3", "title": "Stay Ahead of the Curve", "y": 0, "body": "Get weekly insights on AI engineering trends, research summaries, and expert analysis delivered to your inbox. Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "TheFocusAI", "href": "https://thefocus.ai/" }, { "text": "SUMMARY", "href": "https://thefocus.ai/reports/aiecode-2025-11/" }, { "text": "LEADERSHIP", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/leadership" }, { "text": "ENGINEERING", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/engineering" }, { "text": "PERSONNEL", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers" }, { "text": "ABOUT", "href": "https://thefocus.ai/reports/aiecode-2025-11/about" }, { "text": "ENGINEERING INDEX", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/engineering" }, { "text": "Will Schenk", "href": "https://thefocus.ai/reports/aiecode-2025-11/about" }, { "text": "PREVIOUS Environments as Universal Abstraction: The New Unit of Everything", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md" }, { "text": "NEXT Reinforcement Learning for Specialized Models: The Economics of Domain Expertise", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/reinforcement-learning-specialized-models.md" }, { "text": "Barry Zhang Research Engineer Anthropic CONFERENCE SESSION Don't Build Agents, Build Skills Instead VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/barry-zhang" }, { "text": "Mahesh Murag Member of Technical Staff, Applied AI Anthropic CONFERENCE SESSION Don't Build Agents, Build Skills Instead VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/mahesh-murag" }, { "text": "Kevin Hou Engineer Google DeepMind CONFERENCE SESSION Closing Keynote - Defying Gravity VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/kevin-hou" }, { "text": "Kat Kampf Product Manager Google CONFERENCE SESSION Building in the Gemini Era with Google DeepMind VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/kat-kampf" }, { "text": "Ammaar Reshi Product & Design Lead Google CONFERENCE SESSION Building in the Gemini Era with Google DeepMind VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/ammaar-reshi" }, { "text": "Anthropic", "href": "https://www.anthropic.com/" }, { "text": "Google DeepMind", "href": "https://deepmind.google/" }, { "text": "AI Studio Build", "href": "https://aistudio.google.com/build" }, { "text": "PREVIOUS SECTION ENVIRONMENTS AS UNIVERSAL ABSTRACTION: THE NEW UNIT OF EVERYTHING", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/environments-universal-abstraction.md" }, { "text": "PROCEED TO SECTION 03 REINFORCEMENT LEARNING FOR SPECIALIZED MODELS: THE ECONOMICS OF DOMAIN EXPERTISE", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/reinforcement-learning-specialized-models.md" }, { "text": "SUBSCRIBE", "href": "" }, { "text": "Subscribe to Focus.AI", "href": "" } ], "links": [ "https://thefocus.ai/", "https://thefocus.ai/reports/aiecode-2025-11/article/agents-building-artifacts.md/", "https://thefocus.ai/reports/aiecode-2025-11/article/agents-building-artifacts.md/track/leadership", "https://thefocus.ai/reports/aiecode-2025-11/article/agents-building-artifacts.md/track/engineering", "https://thefocus.ai/reports/aiecode-2025-11/article/agents-building-artifacts.md/speakers", "https://thefocus.ai/reports/aiecode-2025-11/article/agents-building-artifacts.md/about", "https://thefocus.ai/reports/aiecode-2025-11/article/agents-building-artifacts.md/track/engineering", "https://thefocus.ai/reports/aiecode-2025-11/article/agents-building-artifacts.md/about", "https://thefocus.ai/reports/aiecode-2025-11/article/agents-building-artifacts.md/article/environments-universal-abstraction.md", "https://thefocus.ai/reports/aiecode-2025-11/article/agents-building-artifacts.md/article/reinforcement-learning-specialized-models.md", "https://thefocus.ai/reports/aiecode-2025-11/article/agents-building-artifacts.md/speakers/barry-zhang", "https://thefocus.ai/reports/aiecode-2025-11/article/agents-building-artifacts.md/speakers/mahesh-murag", "https://thefocus.ai/reports/aiecode-2025-11/article/agents-building-artifacts.md/speakers/kevin-hou", "https://thefocus.ai/reports/aiecode-2025-11/article/agents-building-artifacts.md/speakers/kat-kampf", "https://thefocus.ai/reports/aiecode-2025-11/article/agents-building-artifacts.md/speakers/ammaar-reshi", "https://www.anthropic.com/", "https://deepmind.google/", "https://aistudio.google.com/build", "https://thefocus.ai/reports/aiecode-2025-11/article/agents-building-artifacts.md/article/environments-universal-abstraction.md", "https://thefocus.ai/reports/aiecode-2025-11/article/agents-building-artifacts.md/article/reinforcement-learning-specialized-models.md" ], "images": [ { "src": 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"w": 400, "h": 400 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/article/agents-building-artifacts.md/photos/ammaar-reshi.jpg", "alt": "Ammaar Reshi", "w": 460, "h": 460 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-21-09-20-gemini-edited.qrXwV5Dx_2qSylD.webp", "alt": "", "w": 1376, "h": 768 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-21-09-27-gemini-edited.BZ9j2LNa_18vQ22.webp", "alt": "", "w": 1376, "h": 768 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-21-17-17-gemini-edited.BgtiJdwB_Z2fjahC.webp", "alt": "", "w": 0, "h": 0 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-21-13-53-gemini-edited.Br4DOBZI_1wzsRX.webp", "alt": "", "w": 0, "h": 0 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-21-14-02-gemini-edited.h65fx7L8_zHJCy.webp", "alt": "", "w": 0, "h": 0 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-21-14-03-gemini-edited.DdZkCz0H_2hzkvp.webp", "alt": "", "w": 0, "h": 0 } ], "text": "TheFocusAI SUMMARY LEADERSHIP ENGINEERING PERSONNEL ABOUT FILES (0) ENGINEERING INDEX QUERY DATA FILE ENGINEERING // SECTION 03 SKILLS AND ARTIFACTS: THE NEW BUILDING BLOCKS FOR AGENTS ABSTRACT: Skills are just folders - procedural knowledge packaged for agents to load dynamically By Will Schenk PREVIOUS Environments as Universal Abstraction: The New Unit of Everything NEXT Reinforcement Learning for Specialized Models: The Economics of Domain Expertise FIG. 3.1 VISUAL DOCUMENTATION. FEATURED EXPERTS Learn more about the people behind these insights Barry Zhang Research Engineer Anthropic CONFERENCE SESSION Don't Build Agents, Build Skills Instead VIEW PROFILE → Mahesh Murag Member of Technical Staff, Applied AI Anthropic CONFERENCE SESSION Don't Build Agents, Build Skills Instead VIEW PROFILE → Kevin Hou Engineer Google DeepMind CONFERENCE SESSION Closing Keynote - Defying Gravity VIEW PROFILE → Kat Kampf Product Manager Google CONFERENCE SESSION Building in the Gemini Era with Google DeepMind VIEW PROFILE → Ammaar Reshi Product & Design Lead Google CONFERENCE SESSION Building in the Gemini Era with Google DeepMind VIEW PROFILE → A convergence of ideas from Anthropic and Google DeepMind points to a fundamental shift in how we think about agent capabilities. Rather than building monolithic agents with hardcoded behaviors, the future is about composable building blocks: Skills that package procedural knowledge and Artifacts that serve as dynamic representations agents create for communication and self-reflection. SKILLS: PROCEDURAL KNOWLEDGE AS FOLDERS Barry Zhang and Mahesh Murag from Anthropic revealed how they’ve moved past the “build agents” paradigm. “Code is all you need,” Zhang argued. “We don’t need to have multiple agents.” The insight: “The agent underneath is more universal than we thought.” Their solution is Skills—folders containing markdown documentation and Python scripts that agents can dynamically load. A skill might look like: anthropic_brand/ ├─ SKILL.md ├─ docs.md ├─ slide-decks.md └─ apply_template.py Skills are progressively disclosed. The agent sees just the top-level description first, then loads the full file when needed, and can extend into scripts as required. “Skills collect institutional knowledge,” Zhang explained. In just five weeks, they’ve seen thousands of skills created—from document generation to scientific research to browser automation. The complete picture: an agent loop connected to the file system, MCP servers, and a", "height": 9094, "screenshots": { "desktop": "assets/screenshots/desktop/reports--aiecode-2025-11--article--agents-building-artifacts.md.png", "mobile": "assets/screenshots/mobile/reports--aiecode-2025-11--article--agents-building-artifacts.md.png" } }, { "url": "https://thefocus.ai/reports/aiecode-2025-11/article/reinforcement-learning-specialized-models.md", "path": "/reports/aiecode-2025-11/article/reinforcement-learning-specialized-models.md", "slug": "reports--aiecode-2025-11--article--reinforcement-learning-specialized-models.md", "category": "Report/deep-dive", "status": 200, "title": "Reinforcement Learning for Specialized Models: The Economics of Domain Expertise - AI Engineering Code Summit", "metaDescription": "1000 examples can yield 10-point improvements with ARFT", "headings": [ { "tag": "h1", "text": "REINFORCEMENT LEARNING FOR SPECIALIZED MODELS: THE ECONOMICS OF DOMAIN EXPERTISE", "index": 0 }, { "tag": "h2", "text": "WHAT MAKES RL WORK", "index": 1 }, { "tag": "h2", "text": "PROMPT LEARNING: NO WEIGHTS REQUIRED", "index": 2 }, { "tag": "h2", "text": "ARFT: WEIGHT FINE-TUNING", "index": 3 }, { "tag": "h2", "text": "PIPELINE RL: THE INFRASTRUCTURE", "index": 4 }, { "tag": "h2", "text": "CODE WORLD MODELS: LEARNING FROM EXECUTION", "index": 5 }, { "tag": "h2", "text": "SPEED: THE PAYOFF", "index": 6 }, { "tag": "h2", "text": "THE NEW ECONOMICS", "index": 7 }, { "tag": "h3", "text": "GET THE LATEST AI ENGINEERING INSIGHTS", "index": 8 }, { "tag": "h2", "text": "CLASSIFIED_FILES", "index": 9 }, { "tag": "h3", "text": "Stay Ahead of the Curve", "index": 10 } ], "sections": [ { "tag": "h1", "title": "REINFORCEMENT LEARNING FOR SPECIALIZED MODELS: THE ECONOMICS OF DOMAIN EXPERTISE", "y": 228, "body": "" }, { "tag": "h2", "title": "WHAT MAKES RL WORK", "y": 2708, "body": "OpenAI’s ARFT team boiled it down: constrained tasks with clear win conditions, evals that match production, problems that reward persistence, and rewards you can’t game. Data quality is non-negotiable. Cognition and Mako both learned this the hard way—garbage data, garbage model, no matter how good your RL infrastructure. Get a clean baseline first. Then apply ARFT." }, { "tag": "h2", "title": "PROMPT LEARNING: NO WEIGHTS REQUIRED", "y": 3024, "body": "Arize’s Aparna Dhinakaran skips weight changes entirely. Her method tunes system prompts using RL techniques and PR feedback. The trick: don’t use scalar rewards. Use LLM evals that explain why answers fail and how to improve. Feed that analysis back into meta-prompts. Eval engineering matters as much as model training—maybe more. Make your judge LLMs show their work." }, { "tag": "h2", "title": "ARFT: WEIGHT FINE-TUNING", "y": 4279, "body": "OpenAI’s ARFT team (Will Hang, Cathy Zhou) fine-tunes model weights for specific tools and reward functions. The data requirement: tens to hundreds of examples, not thousands. Cognition’s model discovered emergent behavior—learning to execute tool calls in parallel without explicit instruction. Qodo cut long-tail tool calls and stabilized agent behavior. Cosine used an unforgiving grader with no partial credit, incorporating judge LLMs to assess code style. The hard part is reward design. Mako’s model initially gamed the system until judge LLMs enforced genuine optimization. Good reward functions are hard to specify." }, { "tag": "h2", "title": "PIPELINE RL: THE INFRASTRUCTURE", "y": 5229, "body": "Applied Compute (Rhythm Garg, Linden Li) makes training fast: pipeline RL with in-flight weight updates, first-principles modeling to balance staleness vs. variance. Get it wrong and you burn budget. Prime Intellect’s Will Brown focuses on environments as first-class abstractions. His verifiers toolkit packages task specs, evaluation harnesses, and reward signals. The Environments Hub makes these shareable. For long-running agents, he’s tackling multi-turn RL with turn-level credit assignment—figuring out which actions mattered across multi-hour tasks. (See Environments as Universal Abstraction for the full framework.)" }, { "tag": "h2", "title": "CODE WORLD MODELS: LEARNING FROM EXECUTION", "y": 6178, "body": "Meta’s Jacob Kahn introduced Code World Models (CWM), a 32B parameter model trained on execution traces—not just syntax. CWM incorporates memory traces, bash outputs, and CI build results to build an implicit world model of how code behaves. The model can “imagine” execution without running code—a neural debugger. It traces execution and understands runtime behavior. Meta’s SWE-RL training includes failed agentic reasoning attempts, teaching recovery from mistakes. Can we now imagine code? How does that comment upon the halting problem? “In some sense this is difficult to decide”—but perhaps no longer impossible." }, { "tag": "h2", "title": "SPEED: THE PAYOFF", "y": 7637, "body": "Cursor’s Lee Robinson showed the deployment pattern: smart models for planning, fast specialized models to execute. Composer rips through code without sacrificing intelligence. Speed is the payoff of specialization." }, { "tag": "h2", "title": "THE NEW ECONOMICS", "y": 7889, "body": "You no longer choose between frontier-as-is and training-from-scratch. Specialized models that beat frontier on your specific tasks are viable with modest data and reasonable compute. The bottleneck is no longer compute—it’s problem definition, eval quality, and reward design." }, { "tag": "h3", "title": "GET THE LATEST AI ENGINEERING INSIGHTS", "y": 8741, "body": "Join the Focus.AI newsletter for curated research, analysis, and perspectives on the evolving AI landscape. SUBSCRIBE No spam. Unsubscribe anytime." }, { "tag": "h2", "title": "CLASSIFIED_FILES", "y": 16, "body": "USER: AUTHORIZED" }, { "tag": "h3", "title": "Stay Ahead of the Curve", "y": 0, "body": "Get weekly insights on AI engineering trends, research summaries, and expert analysis delivered to your inbox. Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "TheFocusAI", "href": "https://thefocus.ai/" }, { "text": "SUMMARY", "href": "https://thefocus.ai/reports/aiecode-2025-11/" }, { "text": "LEADERSHIP", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/leadership" }, { "text": "ENGINEERING", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/engineering" }, { "text": "PERSONNEL", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers" }, { "text": "ABOUT", "href": "https://thefocus.ai/reports/aiecode-2025-11/about" }, { "text": "ENGINEERING INDEX", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/engineering" }, { "text": "Will Schenk", "href": "https://thefocus.ai/reports/aiecode-2025-11/about" }, { "text": "PREVIOUS Skills and Artifacts: The New Building Blocks for Agents", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/agents-building-artifacts.md" }, { "text": "NEXT Model Quality Over Scaffolding: Minimalism in Agent Architecture", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md" }, { "text": "Will Hang Member of Technical Staff OpenAI CONFERENCE SESSION Agent Reinforcement Fine Tuning VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/will-hang" }, { "text": "Cathy Zhou Member of Technical Staff OpenAI CONFERENCE SESSION Agent Reinforcement Fine Tuning VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/cathy-zhou" }, { "text": "Rhythm Garg Co-founder & CTO Applied Compute CONFERENCE SESSION Efficient Reinforcement Learning VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/rhythm-garg" }, { "text": "Linden Li Co-Founder & Chief Architect Applied Compute CONFERENCE SESSION Efficient Reinforcement Learning VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/linden-li" }, { "text": "Jacob Kahn Research Manager Meta AI CONFERENCE SESSION Code World Models - Building World Models for Computation VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/jacob-kahn" }, { "text": "Aparna Dhinakaran Co-founder & Chief Product Officer (CPO) Arize AI CONFERENCE SESSION Continual System-Prompt Learning for Code Agents VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/aparna-dhinakaran" }, { "text": "Lee Robinson Head of AI Education Cursor CONFERENCE SESSION Building a fast frontier model with RL VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/lee-robinson" }, { "text": "verifiers toolkit", "href": "https://github.com/PrimeIntellect-ai/verifiers" }, { "text": "Environments as Universal Abstraction", "href": "https://thefocus.ai/reports/aiecode-2025-11/environments-universal-abstraction" }, { "text": "PREVIOUS SECTION SKILLS AND ARTIFACTS: THE NEW BUILDING BLOCKS FOR AGENTS", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/agents-building-artifacts.md" }, { "text": "PROCEED TO SECTION 04 MODEL QUALITY OVER SCAFFOLDING: MINIMALISM IN AGENT ARCHITECTURE", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md" }, { "text": "SUBSCRIBE", "href": "" }, { "text": "Subscribe to Focus.AI", "href": "" } ], "links": [ "https://thefocus.ai/", "https://thefocus.ai/reports/aiecode-2025-11/article/reinforcement-learning-specialized-models.md/", "https://thefocus.ai/reports/aiecode-2025-11/article/reinforcement-learning-specialized-models.md/track/leadership", "https://thefocus.ai/reports/aiecode-2025-11/article/reinforcement-learning-specialized-models.md/track/engineering", "https://thefocus.ai/reports/aiecode-2025-11/article/reinforcement-learning-specialized-models.md/speakers", "https://thefocus.ai/reports/aiecode-2025-11/article/reinforcement-learning-specialized-models.md/about", 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"src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-21-11-22-gemini-edited.DuTDb6dD_Z1o1qux.webp", "alt": "", "w": 0, "h": 0 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-21-11-06-gemini-edited.DyCS_UUY_I6AIT.webp", "alt": "", "w": 0, "h": 0 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-21-11-12-gemini-edited.BIl5iTvy_Z12gCCw.webp", "alt": "", "w": 0, "h": 0 } ], "text": "TheFocusAI SUMMARY LEADERSHIP ENGINEERING PERSONNEL ABOUT FILES (0) ENGINEERING INDEX QUERY DATA FILE ENGINEERING // SECTION 03 REINFORCEMENT LEARNING FOR SPECIALIZED MODELS: THE ECONOMICS OF DOMAIN EXPERTISE ABSTRACT: 1000 examples can yield 10-point improvements with ARFT By Will Schenk PREVIOUS Skills and Artifacts: The New Building Blocks for Agents NEXT Model Quality Over Scaffolding: Minimalism in Agent Architecture FIG. 3.1 VISUAL DOCUMENTATION. FEATURED EXPERTS Learn more about the people behind these insights Will Hang Member of Technical Staff OpenAI CONFERENCE SESSION Agent Reinforcement Fine Tuning VIEW PROFILE → Cathy Zhou Member of Technical Staff OpenAI CONFERENCE SESSION Agent Reinforcement Fine Tuning VIEW PROFILE → Rhythm Garg Co-founder & CTO Applied Compute CONFERENCE SESSION Efficient Reinforcement Learning VIEW PROFILE → Linden Li Co-Founder & Chief Architect Applied Compute CONFERENCE SESSION Efficient Reinforcement Learning VIEW PROFILE → Jacob Kahn Research Manager Meta AI CONFERENCE SESSION Code World Models - Building World Models for Computation VIEW PROFILE → Aparna Dhinakaran Co-founder & Chief Product Officer (CPO) Arize AI CONFERENCE SESSION Continual System-Prompt Learning for Code Agents VIEW PROFILE → Lee Robinson Head of AI Education Cursor CONFERENCE SESSION Building a fast frontier model with RL VIEW PROFILE → The economics of AI are shifting from bigger-is-better to specialized-is-optimal. Reinforcement learning now enables domain-specific coding models that outperform frontier models using 10-100x fewer training examples. Custom AI is becoming economically viable: cheap, fast, and predictable. The results speak in numbers. Mako’s GPU kernel agent achieved 72% improvement over all frontier models using just 100 PyTorch examples. Cognition’s code edit planning agent gained 10 points with 1,000 examples. Arize hit +15% on one benchmark with only 150. The pattern: small datasets, big gains. WHAT MAKES RL WORK OpenAI’s ARFT team boiled it down: constrained tasks with clear win conditions, evals that match production, problems that reward persistence, and rewards you can’t game. Data quality is non-negotiable. Cognition and Mako both learned this the hard way—garbage data, garbage model, no matter how good your RL infrastructure. Get a clean baseline first. Then apply ARFT. PROMPT LEARNING: NO WEIGHTS REQUIRED Arize’s Aparna Dhinakaran skips weight changes entirely. Her method tunes system prompts using RL techniques and P", "height": 8992, "screenshots": { "desktop": "assets/screenshots/desktop/reports--aiecode-2025-11--article--reinforcement-learning-specialized-models.md.png", "mobile": "assets/screenshots/mobile/reports--aiecode-2025-11--article--reinforcement-learning-specialized-models.md.png" } }, { "url": "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md", "path": "/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md", "slug": "reports--aiecode-2025-11--article--model-quality-over-scaffolding.md", "category": "Report/deep-dive", "status": 200, "title": "Model Quality Over Scaffolding: Minimalism in Agent Architecture - AI Engineering Code Summit", "metaDescription": "Use smart models to plan, fast models to execute - deploy the right capability at the right time", "headings": [ { "tag": "h1", "text": "MODEL QUALITY OVER SCAFFOLDING: MINIMALISM IN AGENT ARCHITECTURE", "index": 0 }, { "tag": "h2", "text": "CURSOR: SMART PLANNING, FAST EXECUTION", "index": 1 }, { "tag": "h2", "text": "AMP CODE: SUBAGENTS FOR CONTEXT, NOT CAPABILITY", "index": 2 }, { "tag": "h2", "text": "METR: BENCHMARKS SHAPE MODELS", "index": 3 }, { "tag": "h2", "text": "THE MINIMALIST THESIS", "index": 4 }, { "tag": "h3", "text": "GET THE LATEST AI ENGINEERING INSIGHTS", "index": 5 }, { "tag": "h2", "text": "CLASSIFIED_FILES", "index": 6 }, { "tag": "h3", "text": "Stay Ahead of the Curve", "index": 7 } ], "sections": [ { "tag": "h1", "title": "MODEL QUALITY OVER SCAFFOLDING: MINIMALISM IN AGENT ARCHITECTURE", "y": 228, "body": "" }, { "tag": "h2", "title": "CURSOR: SMART PLANNING, FAST EXECUTION", "y": 1994, "body": "Lee Robinson from Cursor described their approach with Composer—a faster frontier model built with reinforcement learning. Composer operates at “similar intelligence” to larger models but with dramatically better speed. The key insight: use smart models to make the plan, then let Composer “rip through the code.” Deploy the right capability at the right time. Planning requires careful reasoning; execution can be fast once the path is clear. This isn’t about choosing between intelligence and speed—it’s about recognizing that different phases of work have different requirements. The architectural decision is when to invoke which capability." }, { "tag": "h2", "title": "AMP CODE: SUBAGENTS FOR CONTEXT, NOT CAPABILITY", "y": 2999, "body": "Amp Code, led by Beyang Liu, runs a dual-model system: a “smart” agent for careful reasoning and review, a “rush” agent for speed. But Liu was explicit about the purpose: this architecture exists to manage context, not compensate for model weakness. “Tool calls themselves eat up context,” Liu noted. Each tool invocation consumes tokens that could otherwise hold problem context. Amp’s four specialized subagents—Finder (codebase search), Oracle (reasoning), Librarian (library use), Kraken (refactoring)—are clean interfaces that isolate concerns. The telling detail: “The moment a better base model makes them unnecessary, they should disappear.” The architecture is provisional, designed to be ob" }, { "tag": "h2", "title": "METR: BENCHMARKS SHAPE MODELS", "y": 4073, "body": "Joel Becker from METR connected architectural choices to how models improve. “Benchmarks determine what frontier models do best,” he observed. “Everything traces back to the environments they’ve been training against.” This creates a feedback loop. The benchmarks we build determine what capabilities frontier models develop. If we benchmark for elaborate tool use, models optimize for elaborate tool use. If we benchmark for simple, effective solutions, models optimize for simplicity. The implication for agent builders: the scaffolding we build today may be training signal for tomorrow’s models. Build what you want models to learn." }, { "tag": "h2", "title": "THE MINIMALIST THESIS", "y": 5078, "body": "The pattern across these production systems is clear: architecture should be minimal and provisional. Use multiple models when context management requires it, not because any single model is inadequate. Build clean interfaces that can disappear. Recognize that today’s workarounds become tomorrow’s technical debt. The agents that win aren’t the ones with sophisticated architectures—they’re the ones that deploy the right model capability at the right moment, with the minimum structure required to manage context effectively." }, { "tag": "h3", "title": "GET THE LATEST AI ENGINEERING INSIGHTS", "y": 6114, "body": "Join the Focus.AI newsletter for curated research, analysis, and perspectives on the evolving AI landscape. SUBSCRIBE No spam. Unsubscribe anytime." }, { "tag": "h2", "title": "CLASSIFIED_FILES", "y": 16, "body": "USER: AUTHORIZED" }, { "tag": "h3", "title": "Stay Ahead of the Curve", "y": 0, "body": "Get weekly insights on AI engineering trends, research summaries, and expert analysis delivered to your inbox. Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "TheFocusAI", "href": "https://thefocus.ai/" }, { "text": "SUMMARY", "href": "https://thefocus.ai/reports/aiecode-2025-11/" }, { "text": "LEADERSHIP", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/leadership" }, { "text": "ENGINEERING", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/engineering" }, { "text": "PERSONNEL", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers" }, { "text": "ABOUT", "href": "https://thefocus.ai/reports/aiecode-2025-11/about" }, { "text": "ENGINEERING INDEX", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/engineering" }, { "text": "Will Schenk", "href": "https://thefocus.ai/reports/aiecode-2025-11/about" }, { "text": "PREVIOUS Reinforcement Learning for Specialized Models: The Economics of Domain Expertise", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/reinforcement-learning-specialized-models.md" }, { "text": "NEXT Data Collection & Quality as the New Bottleneck", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/data-collection-quality-bottleneck.md" }, { "text": "Lee Robinson Head of AI Education Cursor CONFERENCE SESSION Building a fast frontier model with RL VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/lee-robinson" }, { "text": "Beyang Liu Co-founder & CTO, Amp Code Amp Code / Sourcegraph CONFERENCE SESSION Amp Code: Next-Generation AI Coding VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/beyang-liu" }, { "text": "Joel Becker Member of Technical Staff METR CONFERENCE SESSION Benchmarks vs economics - the AI capability measurement gap VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/joel-becker" }, { "text": "Cursor", "href": "https://cursor.com/" }, { "text": "Amp Code", "href": "https://ampcode.com/" }, { "text": "METR", "href": "https://metr.org/" }, { "text": "PREVIOUS SECTION REINFORCEMENT LEARNING FOR SPECIALIZED MODELS: THE ECONOMICS OF DOMAIN EXPERTISE", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/reinforcement-learning-specialized-models.md" }, { "text": "PROCEED TO SECTION 05 DATA COLLECTION & QUALITY AS THE NEW BOTTLENECK", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/data-collection-quality-bottleneck.md" }, { "text": "SUBSCRIBE", "href": "" }, { "text": "Subscribe to Focus.AI", "href": "" } ], "links": [ "https://thefocus.ai/", "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md/", "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md/track/leadership", "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md/track/engineering", "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md/speakers", "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md/about", "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md/track/engineering", "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md/about", "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md/article/reinforcement-learning-specialized-models.md", "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md/article/data-collection-quality-bottleneck.md", "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md/speakers/lee-robinson", "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md/speakers/beyang-liu", "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md/speakers/joel-becker", "https://cursor.com/", "https://ampcode.com/", "https://metr.org/", "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md/article/reinforcement-learning-specialized-models.md", "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md/article/data-collection-quality-bottleneck.md" ], "images": [ { "src": "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md/images/human-approved-stamp.png", "alt": "Human Approved", "w": 1408, "h": 768 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md/headers/model-quality-over-scaffolding.png", "alt": "Model Quality Over Scaffolding: Minimalism in Agent Architecture", "w": 1376, "h": 768 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md/photos/lee-robinson.jpg", "alt": "Lee Robinson", "w": 400, "h": 400 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md/photos/beyang-liu.jpg", "alt": "Beyang Liu", "w": 460, "h": 460 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md/photos/joel-becker.jpg", "alt": "Joel Becker", "w": 460, "h": 460 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-21-11-42-gemini-edited.f3cArN9__1993Wb.webp", "alt": "", "w": 1376, "h": 768 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-21-14-29-gemini-edited.DbHFxsq4_ZvTra5.webp", "alt": "", "w": 1376, "h": 768 }, { "src": "https://thefocus.ai/reports/aiecode-2025-11/_astro/2025-11-21-16-42-gemini-edited.DnJQJ5fn_ZJsKnL.webp", "alt": "", "w": 0, "h": 0 } ], "text": "TheFocusAI SUMMARY LEADERSHIP ENGINEERING PERSONNEL ABOUT FILES (0) ENGINEERING INDEX QUERY DATA FILE ENGINEERING // SECTION 04 MODEL QUALITY OVER SCAFFOLDING: MINIMALISM IN AGENT ARCHITECTURE ABSTRACT: Use smart models to plan, fast models to execute - deploy the right capability at the right time By Will Schenk PREVIOUS Reinforcement Learning for Specialized Models: The Economics of Domain Expertise NEXT Data Collection & Quality as the New Bottleneck FIG. 4.1 VISUAL DOCUMENTATION. FEATURED EXPERTS Learn more about the people behind these insights Lee Robinson Head of AI Education Cursor CONFERENCE SESSION Building a fast frontier model with RL VIEW PROFILE → Beyang Liu Co-founder & CTO, Amp Code Amp Code / Sourcegraph CONFERENCE SESSION Amp Code: Next-Generation AI Coding VIEW PROFILE → Joel Becker Member of Technical Staff METR CONFERENCE SESSION Benchmarks vs economics - the AI capability measurement gap VIEW PROFILE → The shift away from elaborate agent scaffolding is already visible in production systems. As base models improve, the architectural patterns that compensate for their limitations become unnecessary overhead. The question for agent builders: what’s the minimum viable architecture? CURSOR: SMART PLANNING, FAST EXECUTION Lee Robinson from Cursor described their approach with Composer—a faster frontier model built with reinforcement learning. Composer operates at “similar intelligence” to larger models but with dramatically better speed. The key insight: use smart models to make the plan, then let Composer “rip through the code.” Deploy the right capability at the right time. Planning requires careful reasoning; execution can be fast once the path is clear. This isn’t about choosing between intelligence and speed—it’s about recognizing that different phases of work have different requirements. The architectural decision is when to invoke which capability. AMP CODE: SUBAGENTS FOR CONTEXT, NOT CAPABILITY Amp Code, led by Beyang Liu, runs a dual-model system: a “smart” agent for careful reasoning and review, a “rush” agent for speed. But Liu was explicit about the purpose: this architecture exists to manage context, not compensate for model weakness. “Tool calls themselves eat up context,” Liu noted. Each tool invocation consumes tokens that could otherwise hold problem context. Amp’s four specialized subagents—Finder (codebase search), Oracle (reasoning), Librarian (library use), Kraken (refactoring)—are clean interfaces that isolate concerns", "height": 6365, "screenshots": { "desktop": "assets/screenshots/desktop/reports--aiecode-2025-11--article--model-quality-over-scaffolding.md.png", "mobile": "assets/screenshots/mobile/reports--aiecode-2025-11--article--model-quality-over-scaffolding.md.png" } }, { "url": "https://thefocus.ai/reports/aiecode-2025-11/article/data-collection-quality-bottleneck.md", "path": "/reports/aiecode-2025-11/article/data-collection-quality-bottleneck.md", "slug": "reports--aiecode-2025-11--article--data-collection-quality-bottleneck.md", "category": "Report/deep-dive", "status": 200, "title": "Data Collection & Quality as the New Bottleneck - AI Engineering Code Summit", "metaDescription": "Agents are collecting good data but not sharing it - keeping datasets closed slows research", "headings": [ { "tag": "h1", "text": "DATA COLLECTION & QUALITY AS THE NEW BOTTLENECK", "index": 0 }, { "tag": "h2", "text": "MODEL STRENGTH OVER CLEVER TRICKS", "index": 1 }, { "tag": "h2", "text": "VERIFICATION IS THE UNLOCK", "index": 2 }, { "tag": "h2", "text": "REAL-WORLD TRAJECTORIES ARE THE GOLD", "index": 3 }, { "tag": "h2", "text": "CLINE-BENCH", "index": 4 }, { "tag": "h2", "text": "THE ENVIRONMENTS HUB", "index": 5 }, { "tag": "h2", "text": "THE OPEN SCIENCE CALL", "index": 6 }, { "tag": "h3", "text": "GET THE LATEST AI ENGINEERING INSIGHTS", "index": 7 }, { "tag": "h2", "text": "CLASSIFIED_FILES", "index": 8 }, { "tag": "h3", "text": "Stay Ahead of the Curve", "index": 9 } ], "sections": [ { "tag": "h1", "title": "DATA COLLECTION & QUALITY AS THE NEW BOTTLENECK", "y": 228, "body": "" }, { "tag": "h2", "title": "MODEL STRENGTH OVER CLEVER TRICKS", "y": 2520, "body": "Nik Pash, creator of Cline, put it bluntly: “Agents aren’t bottlenecked by clever tricks anymore. Model strength is the main thing.” The evidence? Terminus beats everything with minimal tool design—just terminal, grep, filesystem. No clever tool calling. “I’m tired of all the little hacks,” Pash said. (In the Claude Agent SDK workshop on Saturday, the refrain was that the team was “bash-pilled”—just give the model a shell and step away.) Years of elaborate scaffolding were symptoms of compensating for model limitations. As models improve, those hacks become unnecessary. The bottleneck shifts upstream—to training data." }, { "tag": "h2", "title": "VERIFICATION IS THE UNLOCK", "y": 3469, "body": "Verification unlocks both evaluation and training. Pash’s framework—outcome-driven verification rather than process-driven—applies equally to benchmarks and RL environments. Build good verifiers and you get both. (See Environments as Universal Abstraction for the full verification framework.)" }, { "tag": "h2", "title": "REAL-WORLD TRAJECTORIES ARE THE GOLD", "y": 3753, "body": "Real-world agent trajectories are the ground truth. Joel Becker of METR has studied the gap between benchmarks and economic impact. Synthetic tests don’t predict real-world value. Production trajectories do—they capture the messy multi-step reasoning that actually matters. This data is being generated right now, at scale. Cline alone has millions of users. Every AI coding agent in production sits on real-world interaction data. Almost none of it is shared." }, { "tag": "h2", "title": "CLINE-BENCH", "y": 4157, "body": "Pash’s answer: Cline-bench. Open source, open science. It captures coding trajectories from opt-in users and converts them to training data. “Use it on your open source software,” he said. “Lift all boats together.”" }, { "tag": "h2", "title": "THE ENVIRONMENTS HUB", "y": 4859, "body": "This dovetails with Will Brown’s work at Prime Intellect. His environments hub provides infrastructure to turn trajectories into training environments anyone can use. (See Environments as Universal Abstraction for how this fits the broader environments-as-abstraction thesis.) Prime Intellect’s thesis: scaling AI means scaling talent, not just compute. Increase the pool. Increase accessibility. Their Verifiers toolkit provides scaffolding for anyone to build RL environments." }, { "tag": "h2", "title": "THE OPEN SCIENCE CALL", "y": 6178, "body": "The pieces are in place. Small datasets yield big gains. Verification is understood. Infrastructure exists. But Pash delivered a “truth nuke”: agents are collecting good data and not sharing it. Keeping datasets closed slows down research. Models only get better when trained on something hard. The hardest data—real-world production trajectories—is sitting unused. Pash’s bet is on openness. The question is whether the rest of the community will follow." }, { "tag": "h3", "title": "GET THE LATEST AI ENGINEERING INSIGHTS", "y": 7599, "body": "Join the Focus.AI newsletter for curated research, analysis, and perspectives on the evolving AI landscape. SUBSCRIBE No spam. Unsubscribe anytime." }, { "tag": "h2", "title": "CLASSIFIED_FILES", "y": 16, "body": "USER: AUTHORIZED" }, { "tag": "h3", "title": "Stay Ahead of the Curve", "y": 0, "body": "Get weekly insights on AI engineering trends, research summaries, and expert analysis delivered to your inbox. Subscribe to Focus.AI No thanks, I'll pass" } ], "ctas": [ { "text": "TheFocusAI", "href": "https://thefocus.ai/" }, { "text": "SUMMARY", "href": "https://thefocus.ai/reports/aiecode-2025-11/" }, { "text": "LEADERSHIP", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/leadership" }, { "text": "ENGINEERING", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/engineering" }, { "text": "PERSONNEL", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers" }, { "text": "ABOUT", "href": "https://thefocus.ai/reports/aiecode-2025-11/about" }, { "text": "ENGINEERING INDEX", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/engineering" }, { "text": "Will Schenk", "href": "https://thefocus.ai/reports/aiecode-2025-11/about" }, { "text": "PREVIOUS Model Quality Over Scaffolding: Minimalism in Agent Architecture", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md" }, { "text": "END OF TRACK Return to Index", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/engineering" }, { "text": "Nik Pash Head of AI Cline CONFERENCE SESSION Cline - More Connected, More Powerful VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/nik-pash" }, { "text": "Will Hang Member of Technical Staff OpenAI CONFERENCE SESSION Agent Reinforcement Fine Tuning VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/will-hang" }, { "text": "Cathy Zhou Member of Technical Staff OpenAI CONFERENCE SESSION Agent Reinforcement Fine Tuning VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/cathy-zhou" }, { "text": "Will Brown Research Lead Prime Intellect CONFERENCE SESSION RL Environments at Scale VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/will-brown" }, { "text": "Joel Becker Member of Technical Staff METR CONFERENCE SESSION Benchmarks vs economics - the AI capability measurement gap VIEW PROFILE →", "href": "https://thefocus.ai/reports/aiecode-2025-11/speakers/joel-becker" }, { "text": "Reinforcement Learning for Specialized Models", "href": "https://thefocus.ai/reports/aiecode-2025-11/reinforcement-learning-specialized-models" }, { "text": "ARFT approach", "href": "https://platform.openai.com/docs/guides/reinforcement-fine-tuning" }, { "text": "Nik Pash", "href": "https://pashpashpash.substack.com/" }, { "text": "Cline", "href": "https://github.com/cline/cline" }, { "text": "Environments as Universal Abstraction", "href": "https://thefocus.ai/reports/aiecode-2025-11/environments-universal-abstraction" }, { "text": "Joel Becker", "href": "https://joel-becker.com/" }, { "text": "METR", "href": "https://www.metr.org/" }, { "text": "Cline-bench", "href": "https://github.com/cline/cline-bench" }, { "text": "Will Brown", "href": "https://willcb.com/" }, { "text": "Prime Intellect", "href": "https://www.primeintellect.ai/" }, { "text": "Environments as Universal Abstraction", "href": "https://thefocus.ai/reports/aiecode-2025-11/environments-universal-abstraction" }, { "text": "Verifiers toolkit", "href": "https://github.com/PrimeIntellect-ai/verifiers" }, { "text": "PREVIOUS SECTION MODEL QUALITY OVER SCAFFOLDING: MINIMALISM IN AGENT ARCHITECTURE", "href": "https://thefocus.ai/reports/aiecode-2025-11/article/model-quality-over-scaffolding.md" }, { "text": "END OF TRACK RETURN TO INDEX", "href": "https://thefocus.ai/reports/aiecode-2025-11/track/engineering" }, { "text": "SUBSCRIBE", "href": "" }, { "text": "Subscribe to Focus.AI", "href": "" } ], "links": [ "https://thefocus.ai/", "https://thefocus.ai/reports/aiecode-2025-11/article/data-collection-quality-bottleneck.md/", "https://thefocus.ai/reports/aiecode-2025-11/article/data-collection-quality-bottleneck.md/track/leadership", "https://thefocus.ai/reports/aiecode-2025-11/article/data-collection-quality-bottleneck.md/track/engineering", 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FEATURED EXPERTS Learn more about the people behind these insights Nik Pash Head of AI Cline CONFERENCE SESSION Cline - More Connected, More Powerful VIEW PROFILE → Will Hang Member of Technical Staff OpenAI CONFERENCE SESSION Agent Reinforcement Fine Tuning VIEW PROFILE → Cathy Zhou Member of Technical Staff OpenAI CONFERENCE SESSION Agent Reinforcement Fine Tuning VIEW PROFILE → Will Brown Research Lead Prime Intellect CONFERENCE SESSION RL Environments at Scale VIEW PROFILE → Joel Becker Member of Technical Staff METR CONFERENCE SESSION Benchmarks vs economics - the AI capability measurement gap VIEW PROFILE → Post-training with small, high-quality datasets yields outsized gains—the pattern is consistent across OpenAI, Cognition, Mako, and Arize. Hundreds of examples can move benchmarks by double digits. (See Reinforcement Learning for Specialized Models for specific results.) OpenAI’s ARFT approach requires four things: well-specified tasks, evals that mirror production, performance that scales with tries, and unhackable rewards. That last one is tricky—Mako’s model initially gamed the system until they added judge LLMs. But when the reward function is right, the payoff is transformative. So small datasets work. The question is: where do you get quality training data? MODEL STRENGTH OVER CLEVER TRICKS Nik Pash, creator of Cline, put it bluntly: “Agents aren’t bottlenecked by clever tricks anymore. Model strength is the main thing.” The evidence? Terminus beats everything with minimal tool design—just terminal, grep, filesystem. No clever tool calling. “I’m tired of all the little hacks,” Pash said. (In the Claude Agent SDK workshop on Saturday, the refrain was that the team was “bash-pilled”—just give the model a shell and step away.) Years of elaborate scaffolding were symptoms of compensating for model limitations. As models improve, those hacks become unnecessary. The bottleneck shifts upstream—to training data. VERIFICATION IS THE UNLOCK Verification unlocks both evaluation and training. Pash’s framework—outcome-driven verificati", "height": 7850, "screenshots": { "desktop": "assets/screenshots/desktop/reports--aiecode-2025-11--article--data-collection-quality-bottleneck.md.png", "mobile": "assets/screenshots/mobile/reports--aiecode-2025-11--article--data-collection-quality-bottleneck.md.png" } }, { "url": "https://thefocus.ai/agents", "path": "/agents", "slug": "agents", "category": "Other", "status": 200, "title": "agents posts", "metaDescription": "A list of all posts tagged with agents", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Agents", "index": 1 }, { "tag": "h2", "text": "Neural harness", "index": 2 }, { "tag": "h2", "text": "Can LLMs Use Real-World Tools? 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Are we going to go for three? Tokens are the bill for running a new native unit of computing. The harness is to neural nets what the compiler is to source code. Read more → Can LLMs Use Real-World Tools? Mercury-2, ELO, and the Umwelten Setup Essay Models Compare Agents Will Schenk March 15, 2026 We ran 39 models against real Rivian driving data via MCP tools. Inception's Mercury-2 delivered a perfect 15/15 in 8.6 seconds. Here's the standout model, the ELO narrative rankings, and how the same Umwelten setup powers both chat and evals. Read more → The Agent Habitat Essay Agents Architecture P" }, { "tag": "h2", "title": "Neural harness", "y": 234, "body": "Essay Agents Harness Coding Will Schenk May 1, 2026" }, { "tag": "h2", "title": "Can LLMs Use Real-World Tools? Mercury-2, ELO, and the Umwelten Setup", "y": 234, "body": "Essay Models Compare Agents Will Schenk March 15, 2026" }, { "tag": "h2", "title": "The Agent Habitat", "y": 936, "body": "Essay Agents Architecture Process Will Schenk February 5, 2026" }, { "tag": "h2", "title": "The Data Flywheel Pattern", "y": 936, "body": "Essay Agents Workflow Process Will Schenk January 22, 2026" }, { "tag": "h2", "title": "Claude Code, not Code", "y": 1588, "body": "Essay Agents Workflow Process Will Schenk January 14, 2026" }, { "tag": "h2", "title": "How I Use AI in Jan 2026", "y": 1588, "body": "Essay Process Agents Workflow Will Schenk January 3, 2026" }, { "tag": "h2", "title": "\"You're absolutely right\" and other AI warning signs", "y": 2264, "body": "Essay Agents Process Will Schenk December 16, 2025" }, { "tag": "h2", "title": "AI tools fail loudly where humans failed quietly", "y": 2264, "body": "Essay Process Agents Will Schenk December 10, 2025" }, { "tag": "h2", "title": "Weekend Coding Agent: Build Your Own AI Assistant", "y": 2732, "body": "Tutorial Agents Coding Typescript Will Schenk November 29, 2025" }, { "tag": "h2", "title": "AI Engineering Summit 2025: Bash-Pilled and Building for Everyone", "y": 2732, "body": "Agents Conference Essay Will Schenk November 22, 2025" }, { "tag": "h2", "title": "AI Engineering Code Summit 2025: Deep Dive Report", "y": 3401, "body": "Report Engineering Summit Architecture Agents Will Schenk November 20, 2025" }, { "tag": "h2", "title": "June 2025 Coding Agent Report", "y": 3401, "body": "Report Coding Compare Agents Will Schenk June 15, 2025" }, { "tag": "h2", "title": "Feature Development on the go", "y": 4058, "body": "Essay Agents Will Schenk June 8, 2025" }, { "tag": "h2", "title": "Agentic YOLO with Warp, Cursor, and Claude", "y": 4058, "body": "Essay Agents Compare Will Schenk March 7, 2025" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 4808, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 5258, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 5635, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 5635, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 5635, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 5635, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] Harness Neural harness Essay Agents Harness Coding Will Schenk May 1, 2026 I'm on my second Claude Max plan. Are we going to go for three? Tokens are the bill for running a new native unit of computing. The harness is to neural nets what the compiler is to source code. Read more → Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. View open positions → © 2026 TheFocus.AI. All rights reserved. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] Codings Neural harness Essay Agents Harness Coding Will Schenk May 1, 2026 I'm on my second Claude Max plan. Are we going to go for three? Tokens are the bill for running a new native unit of computing. The harness is to neural nets what the compiler is to source code. Read more → Weekend Coding Agent: Build Your Own AI Assistant Tutorial Agents Coding Typescript Will Schenk November 29, 2025 A hands-on tutorial for building an AI coding agent from scratch. Bootstrap it with an existing agent, then use it to improve itself. Learn the core concepts of how coding agents work. Read more → June 2025 Coding Agent Report Report Coding Compare Agents Will Schenk June 15, 2025 A comprehensive analysis of 15 leading AI coding agents in 2025. We break down the strengths, weaknesses, and surprises from top tools, with clear winners for pros, tinkerers, and casual users alike. Read more → Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. View open positions → © 2026 TheFocus.AI. All rights reserved. From tribal knowledge to production AI — in weeks, not quarters.", "height": 2964, "screenshots": { "desktop": "assets/screenshots/desktop/coding.png", "mobile": "assets/screenshots/mobile/coding.png" } }, { "url": "https://thefocus.ai/org-age", "path": "/org-age", "slug": "org-age", "category": "Other", "status": 200, "title": "org-age posts", "metaDescription": "A list of all posts tagged with org-age", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Org ages", "index": 1 }, { "tag": "h2", "text": "Laddering up from chatboxes to autonomy", "index": 2 }, { "tag": "h2", "text": "Building an Intelligent Organization", "index": 3 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 4 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 5 }, { "tag": "h3", "text": "COMPANY", "index": 6 }, { "tag": "h3", "text": "PRODUCTS", "index": 7 }, { "tag": "h3", "text": "RESOURCES", "index": 8 }, { "tag": "h3", "text": "CONNECT", "index": 9 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Org ages", "y": 162, "body": "Laddering up from chatboxes to autonomy Essay Org age Process Maturity Will Schenk April 14, 2026 Most companies want to go from scattered ChatGPT use directly to agents and autonomy. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] Org ages Laddering up from chatboxes to autonomy Essay Org age Process Maturity Will Schenk April 14, 2026 Most companies want to go from scattered ChatGPT use directly to agents and autonomy. The middle layers — the ones where deployments either become real or die — get skipped in the planning. Read more → Building an Intelligent Organization Essay Org age Will Schenk April 11, 2026 From AI pilots to production systems — how we help leadership move up the maturity curve with principal-led engagements. Read more → Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. View open positions → © 2026 TheFocus.AI. All rights reserved. From tribal knowledge to production AI — in weeks, not quarters.", "height": 2307, "screenshots": { "desktop": "assets/screenshots/desktop/org-age.png", "mobile": "assets/screenshots/mobile/org-age.png" } }, { "url": "https://thefocus.ai/maturity", "path": "/maturity", "slug": "maturity", "category": "Other", "status": 200, "title": "maturity posts", "metaDescription": "A list of all posts tagged with maturity", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Maturitys", "index": 1 }, { "tag": "h2", "text": "Laddering up from chatboxes to autonomy", "index": 2 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 3 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 4 }, { "tag": "h3", "text": "COMPANY", "index": 5 }, { "tag": "h3", "text": "PRODUCTS", "index": 6 }, { "tag": "h3", "text": "RESOURCES", "index": 7 }, { "tag": "h3", "text": "CONNECT", "index": 8 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Maturitys", "y": 162, "body": "Laddering up from chatboxes to autonomy Essay Org age Process Maturity Will Schenk April 14, 2026 Most companies want to go from scattered ChatGPT use directly to agents and autonomy. 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The middle layers — the ones where deployments either become real or die — get skipped in the planning.", "href": "https://thefocus.ai/posts/laddering-up-from-chatboxes-to-autonomy" }, { "text": "Read more →", "href": "https://thefocus.ai/posts/laddering-up-from-chatboxes-to-autonomy" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/posts/laddering-up-from-chatboxes-to-autonomy", "https://thefocus.ai/essay", "https://thefocus.ai/org-age", "https://thefocus.ai/process", "https://thefocus.ai/maturity", "mailto:wschenk@thefocus.ai", "https://thefocus.ai/posts/laddering-up-from-chatboxes-to-autonomy", "https://thefocus.ai/posts/laddering-up-from-chatboxes-to-autonomy", "https://thefocus.ai/posts/laddering-up-from-chatboxes-to-autonomy", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [ { "src": "https://thefocus.ai/_astro/laddering-up.Dktq_BRy_emsAP.webp", "alt": "Laddering up from chatboxes to autonomy", "w": 2500, "h": 1395 } ], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] Maturitys Laddering up from chatboxes to autonomy Essay Org age Process Maturity Will Schenk April 14, 2026 Most companies want to go from scattered ChatGPT use directly to agents and autonomy. The middle layers — the ones where deployments either become real or die — get skipped in the planning. Read more → Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. View open positions → © 2026 TheFocus.AI. All rights reserved. From tribal knowledge to production AI — in weeks, not quarters.", "height": 2307, "screenshots": { "desktop": "assets/screenshots/desktop/maturity.png", "mobile": "assets/screenshots/mobile/maturity.png" } }, { "url": "https://thefocus.ai/models", "path": "/models", "slug": "models", "category": "Other", "status": 200, "title": "models posts", "metaDescription": "A list of all posts tagged with models", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Models", "index": 1 }, { "tag": "h2", "text": "Gemma 4 on Your Machine: How Google’s New Open Weights Stack Up (Model Showdown)", "index": 2 }, { "tag": "h2", "text": "Same Weights, Different Results", "index": 3 }, { "tag": "h2", "text": "Can LLMs Use Real-World Tools? Mercury-2, ELO, and the Umwelten Setup", "index": 4 }, { "tag": "h2", "text": "Sraffa's Gesture, the Crack in the Crystal, and Why the Stochastic Parrot Still Bites", "index": 5 }, { "tag": "h2", "text": "The Car Wash Test: Learning from Model Evals", "index": 6 }, { "tag": "h2", "text": "gpt5 is smarter than you are", "index": 7 }, { "tag": "h2", "text": "Code Generation with Local Models", "index": 8 }, { "tag": "h2", "text": "gpt-5 and gpt-oss", "index": 9 }, { "tag": "h2", "text": "How I classify models", "index": 10 }, { "tag": "h2", "text": "AI for research: DeepResearch a clear winner", "index": 11 }, { "tag": "h2", "text": "Learning on the go with NotebookLM", "index": 12 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 13 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 14 }, { "tag": "h3", "text": "COMPANY", "index": 15 }, { "tag": "h3", "text": "PRODUCTS", "index": 16 }, { "tag": "h3", "text": "RESOURCES", "index": 17 }, { "tag": "h3", "text": "CONNECT", "index": 18 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Models", "y": 162, "body": "Gemma 4 on Your Machine: How Google’s New Open Weights Stack Up (Model Showdown) Essay Models Gemma Local ai Compare Will Schenk April 4, 2026 We benchmarked Gemma 4 (e2b, default, 26B MoE, 31B dense) through Ollama against 50+ hosted and local models on reasoning, knowledge, instruction, coding, and TezLab MCP tool use—same Umwelten harness as our other showdowns. Here’s where the new line shines, where frontier models still pull ahead, and how the biggest Gemma handles real EV data tools. Read more → Same Weights, Different Results Essay Models Compare Infrastructure Will Schenk March 24, 2026 We ran the same Nemotron model on four providers and got wildly different results. MCP tool use r" }, { "tag": "h2", "title": "Gemma 4 on Your Machine: How Google’s New Open Weights Stack Up (Model Showdown)", "y": 234, "body": "Essay Models Gemma Local ai Compare Will Schenk April 4, 2026" }, { "tag": "h2", "title": "Same Weights, Different Results", "y": 234, "body": "Essay Models Compare Infrastructure Will Schenk March 24, 2026" }, { "tag": "h2", "title": "Can LLMs Use Real-World Tools? Mercury-2, ELO, and the Umwelten Setup", "y": 985, "body": "Essay Models Compare Agents Will Schenk March 15, 2026" }, { "tag": "h2", "title": "Sraffa's Gesture, the Crack in the Crystal, and Why the Stochastic Parrot Still Bites", "y": 985, "body": "Essay Philosophy Models Will Schenk March 12, 2026" }, { "tag": "h2", "title": "The Car Wash Test: Learning from Model Evals", "y": 1711, "body": "Essay Compare Models Will Schenk March 1, 2026" }, { "tag": "h2", "title": "gpt5 is smarter than you are", "y": 1711, "body": "Models Will Schenk September 4, 2025" }, { "tag": "h2", "title": "Code Generation with Local Models", "y": 2388, "body": "Essay Models Will Schenk August 20, 2025" }, { "tag": "h2", "title": "gpt-5 and gpt-oss", "y": 2388, "body": "Models Will Schenk August 13, 2025" }, { "tag": "h2", "title": "How I classify models", "y": 3105, "body": "Models Architecture Essay Will Schenk January 21, 2025" }, { "tag": "h2", "title": "AI for research: DeepResearch a clear winner", "y": 3105, "body": "Models Essay #compare Will Schenk January 12, 2025" }, { "tag": "h2", "title": "Learning on the go with NotebookLM", "y": 3865, "body": "Essay Models Will Schenk January 9, 2025" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 4622, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 5072, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 5449, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 5449, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 5449, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 5449, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. 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The results are not.", "href": "https://thefocus.ai/posts/same-weights-different-results" }, { "text": "Read more →", "href": "https://thefocus.ai/posts/same-weights-different-results" }, { "text": "Can LLMs Use Real-World Tools? Mercury-2, ELO, and the Umwelten Setup", "href": "https://thefocus.ai/posts/mcp-tool-eval-mercury-umwelten" }, { "text": "Essay", "href": "https://thefocus.ai/essay" }, { "text": "Models", "href": "https://thefocus.ai/models" }, { "text": "Compare", "href": "https://thefocus.ai/compare" }, { "text": "Agents", "href": "https://thefocus.ai/agents" }, { "text": "Will Schenk", "href": "mailto:wschenk@thefocus.ai" }, { "text": "We ran 39 models against real Rivian driving data via MCP tools. Inception's Mercury-2 delivered a perfect 15/15 in 8.6 seconds. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] Models Gemma 4 on Your Machine: How Google’s New Open Weights Stack Up (Model Showdown) Essay Models Gemma Local ai Compare Will Schenk April 4, 2026 We benchmarked Gemma 4 (e2b, default, 26B MoE, 31B dense) through Ollama against 50+ hosted and local models on reasoning, knowledge, instruction, coding, and TezLab MCP tool use—same Umwelten harness as our other showdowns. Here’s where the new line shines, where frontier models still pull ahead, and how the biggest Gemma handles real EV data tools. Read more → Same Weights, Different Results Essay Models Compare Infrastructure Will Schenk March 24, 2026 We ran the same Nemotron model on four providers and got wildly different results. MCP tool use ranged from 1/6 to 6/6. Speed varied 16x. The weights are identical. The results are not. Read more → Can LLMs Use Real-World Tools? Mercury-2, ELO, and the Umwelten Setup Essay Models Compare Agents Will Schenk March 15, 2026 We ran 39 models against real Rivian driving data via MCP tools. Inception's Mercury-2 delivered a perfect 15/15 in 8.6 seconds. Here's the standout model, the ELO narrative rankings, and how the same Umwelten setup powers both chat and evals. Read more → Sraffa's Gesture, the Crack in the Crystal, and Why the Stochastic Parrot Still Bites Essay Philosophy Models Will Schenk March 12, 2026 Wittgenstein's shift from the Tractatus to the Philosophical Investigations—triggered by Sraffa's Neapolitan gesture—reframes the stochastic parrot debate. The danger isn't that LLMs lack secret understanding. It's our willingness to treat fluent simulation as the real thing. Read more → The Car Wash Test: Learning from Model Evals Essay Compare Models Will Schenk March 1, 2026 We asked 131 AI models a simple question — should I walk or drive to the car wash? 76% got it wrong. Simple gotcha questions reveal more about model reasoning than any benchmark leaderboard. Read more → gpt5 is smarter than you are Models Will Schenk September 4, 2025 gpt5 can choose to be so smart it's almost impossible to judge. Lets see how it does on some unanswerable questions and if it can totally replace google. Read more → Code Generation with Local Models Essay Models Will Schenk August 20, 2025 Small, local AI models deliver surprisingly effective results for everyday tasks. Also llama3", "height": 5946, "screenshots": { "desktop": "assets/screenshots/desktop/models.png", "mobile": "assets/screenshots/mobile/models.png" } }, { "url": "https://thefocus.ai/gemma", "path": "/gemma", "slug": "gemma", "category": "Other", "status": 200, "title": "gemma posts", "metaDescription": "A list of all posts tagged with gemma", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Gemmas", "index": 1 }, { "tag": "h2", "text": "Gemma 4 on Your Machine: How Google’s New Open Weights Stack Up (Model Showdown)", "index": 2 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 3 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 4 }, { "tag": "h3", "text": "COMPANY", "index": 5 }, { "tag": "h3", "text": "PRODUCTS", "index": 6 }, { "tag": "h3", "text": "RESOURCES", "index": 7 }, { "tag": "h3", "text": "CONNECT", "index": 8 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Gemmas", "y": 162, "body": "Gemma 4 on Your Machine: How Google’s New Open Weights Stack Up (Model Showdown) Essay Models Gemma Local ai Compare Will Schenk April 4, 2026 We benchmarked Gemma 4 (e2b, default, 26B MoE, 31B dense) through Ollama against 50+ hosted and local models on reasoning, knowledge, instruction, coding, and TezLab MCP tool use—same Umwelten harness as our other showdowns. Here’s where the new line shines, where frontier models still pull ahead, and how the biggest Gemma handles real EV data tools. Read more →" }, { "tag": "h2", "title": "Gemma 4 on Your Machine: How Google’s New Open Weights Stack Up (Model Showdown)", "y": 234, "body": "Essay Models Gemma Local ai Compare Will Schenk April 4, 2026" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 1057, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 1507, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 1884, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 1884, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 1884, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 1884, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "Gemma 4 on Your Machine: How Google’s New Open Weights Stack Up (Model Showdown)", "href": "https://thefocus.ai/posts/gemma-4-umwelten-showdown" }, { "text": "Essay", "href": "https://thefocus.ai/essay" }, { "text": "Models", "href": "https://thefocus.ai/models" }, { "text": "Gemma", "href": "https://thefocus.ai/gemma" }, { "text": "Local ai", "href": "https://thefocus.ai/local-ai" }, { "text": "Compare", "href": "https://thefocus.ai/compare" }, { "text": "Will Schenk", "href": "mailto:wschenk@thefocus.ai" }, { "text": "We benchmarked Gemma 4 (e2b, default, 26B MoE, 31B dense) through Ollama against 50+ hosted and local models on reasoning, knowledge, instruction, coding, and TezLab MCP tool use—same Umwelten harness as our other showdowns. Here’s where the new line shines, where frontier models still pull ahead, and how the biggest Gemma handles real EV data tools.", "href": "https://thefocus.ai/posts/gemma-4-umwelten-showdown" }, { "text": "Read more →", "href": "https://thefocus.ai/posts/gemma-4-umwelten-showdown" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/posts/gemma-4-umwelten-showdown", "https://thefocus.ai/essay", "https://thefocus.ai/models", "https://thefocus.ai/gemma", "https://thefocus.ai/local-ai", "https://thefocus.ai/compare", "mailto:wschenk@thefocus.ai", "https://thefocus.ai/posts/gemma-4-umwelten-showdown", "https://thefocus.ai/posts/gemma-4-umwelten-showdown", "https://thefocus.ai/posts/gemma-4-umwelten-showdown", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [ { "src": "https://thefocus.ai/_astro/gemma4-showdown-infographic-v2.LFfRxxjf_w8NKd.webp", "alt": "Gemma 4 on Your Machine: How Google’s New Open Weights Stack Up (Model Showdown)", "w": 2500, "h": 1395 } ], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] Gemmas Gemma 4 on Your Machine: How Google’s New Open Weights Stack Up (Model Showdown) Essay Models Gemma Local ai Compare Will Schenk April 4, 2026 We benchmarked Gemma 4 (e2b, default, 26B MoE, 31B dense) through Ollama against 50+ hosted and local models on reasoning, knowledge, instruction, coding, and TezLab MCP tool use—same Umwelten harness as our other showdowns. Here’s where the new line shines, where frontier models still pull ahead, and how the biggest Gemma handles real EV data tools. Read more → Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. View open positions → © 2026 TheFocus.AI. All rights reserved. From tribal knowledge to production AI — in weeks, not quarters.", "height": 2381, "screenshots": { "desktop": "assets/screenshots/desktop/gemma.png", "mobile": "assets/screenshots/mobile/gemma.png" } }, { "url": "https://thefocus.ai/local-ai", "path": "/local-ai", "slug": "local-ai", "category": "Other", "status": 200, "title": "local-ai posts", "metaDescription": "A list of all posts tagged with local-ai", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Local ais", "index": 1 }, { "tag": "h2", "text": "Gemma 4 on Your Machine: How Google’s New Open Weights Stack Up (Model Showdown)", "index": 2 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 3 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 4 }, { "tag": "h3", "text": "COMPANY", "index": 5 }, { "tag": "h3", "text": "PRODUCTS", "index": 6 }, { "tag": "h3", "text": "RESOURCES", "index": 7 }, { "tag": "h3", "text": "CONNECT", "index": 8 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Local ais", "y": 162, "body": "Gemma 4 on Your Machine: How Google’s New Open Weights Stack Up (Model Showdown) Essay Models Gemma Local ai Compare Will Schenk April 4, 2026 We benchmarked Gemma 4 (e2b, default, 26B MoE, 31B dense) through Ollama against 50+ hosted and local models on reasoning, knowledge, instruction, coding, and TezLab MCP tool use—same Umwelten harness as our other showdowns. Here’s where the new line shines, where frontier models still pull ahead, and how the biggest Gemma handles real EV data tools. Read more →" }, { "tag": "h2", "title": "Gemma 4 on Your Machine: How Google’s New Open Weights Stack Up (Model Showdown)", "y": 234, "body": "Essay Models Gemma Local ai Compare Will Schenk April 4, 2026" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 1057, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 1507, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 1884, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 1884, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 1884, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 1884, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "Gemma 4 on Your Machine: How Google’s New Open Weights Stack Up (Model Showdown)", "href": "https://thefocus.ai/posts/gemma-4-umwelten-showdown" }, { "text": "Essay", "href": "https://thefocus.ai/essay" }, { "text": "Models", "href": "https://thefocus.ai/models" }, { "text": "Gemma", "href": "https://thefocus.ai/gemma" }, { "text": "Local ai", "href": "https://thefocus.ai/local-ai" }, { "text": "Compare", "href": "https://thefocus.ai/compare" }, { "text": "Will Schenk", "href": "mailto:wschenk@thefocus.ai" }, { "text": "We benchmarked Gemma 4 (e2b, default, 26B MoE, 31B dense) through Ollama against 50+ hosted and local models on reasoning, knowledge, instruction, coding, and TezLab MCP tool use—same Umwelten harness as our other showdowns. Here’s where the new line shines, where frontier models still pull ahead, and how the biggest Gemma handles real EV data tools.", "href": "https://thefocus.ai/posts/gemma-4-umwelten-showdown" }, { "text": "Read more →", "href": "https://thefocus.ai/posts/gemma-4-umwelten-showdown" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/posts/gemma-4-umwelten-showdown", "https://thefocus.ai/essay", "https://thefocus.ai/models", "https://thefocus.ai/gemma", "https://thefocus.ai/local-ai", "https://thefocus.ai/compare", "mailto:wschenk@thefocus.ai", "https://thefocus.ai/posts/gemma-4-umwelten-showdown", "https://thefocus.ai/posts/gemma-4-umwelten-showdown", "https://thefocus.ai/posts/gemma-4-umwelten-showdown", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [ { "src": "https://thefocus.ai/_astro/gemma4-showdown-infographic-v2.LFfRxxjf_w8NKd.webp", "alt": "Gemma 4 on Your Machine: How Google’s New Open Weights Stack Up (Model Showdown)", "w": 2500, "h": 1395 } ], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] Local ais Gemma 4 on Your Machine: How Google’s New Open Weights Stack Up (Model Showdown) Essay Models Gemma Local ai Compare Will Schenk April 4, 2026 We benchmarked Gemma 4 (e2b, default, 26B MoE, 31B dense) through Ollama against 50+ hosted and local models on reasoning, knowledge, instruction, coding, and TezLab MCP tool use—same Umwelten harness as our other showdowns. Here’s where the new line shines, where frontier models still pull ahead, and how the biggest Gemma handles real EV data tools. Read more → Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. View open positions → © 2026 TheFocus.AI. All rights reserved. From tribal knowledge to production AI — in weeks, not quarters.", "height": 2381, "screenshots": { "desktop": "assets/screenshots/desktop/local-ai.png", "mobile": "assets/screenshots/mobile/local-ai.png" } }, { "url": "https://thefocus.ai/infrastructure", "path": "/infrastructure", "slug": "infrastructure", "category": "Other", "status": 200, "title": "infrastructure posts", "metaDescription": "A list of all posts tagged with infrastructure", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Infrastructures", "index": 1 }, { "tag": "h2", "text": "Same Weights, Different Results", "index": 2 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 3 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 4 }, { "tag": "h3", "text": "COMPANY", "index": 5 }, { "tag": "h3", "text": "PRODUCTS", "index": 6 }, { "tag": "h3", "text": "RESOURCES", "index": 7 }, { "tag": "h3", "text": "CONNECT", "index": 8 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Infrastructures", "y": 162, "body": "Same Weights, Different Results Essay Models Compare Infrastructure Will Schenk March 24, 2026 We ran the same Nemotron model on four providers and got wildly different results. MCP tool use ranged from 1/6 to 6/6. Speed varied 16x. The weights are identical. The results are not. Read more →" }, { "tag": "h2", "title": "Same Weights, Different Results", "y": 234, "body": "Essay Models Compare Infrastructure Will Schenk March 24, 2026" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 983, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 1433, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 1810, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 1810, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 1810, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 1810, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "Same Weights, Different Results", "href": "https://thefocus.ai/posts/same-weights-different-results" }, { "text": "Essay", "href": "https://thefocus.ai/essay" }, { "text": "Models", "href": "https://thefocus.ai/models" }, { "text": "Compare", "href": "https://thefocus.ai/compare" }, { "text": "Infrastructure", "href": "https://thefocus.ai/infrastructure" }, { "text": "Will Schenk", "href": "mailto:wschenk@thefocus.ai" }, { "text": "We ran the same Nemotron model on four providers and got wildly different results. MCP tool use ranged from 1/6 to 6/6. Speed varied 16x. The weights are identical. The results are not.", "href": "https://thefocus.ai/posts/same-weights-different-results" }, { "text": "Read more →", "href": "https://thefocus.ai/posts/same-weights-different-results" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/posts/same-weights-different-results", "https://thefocus.ai/essay", "https://thefocus.ai/models", "https://thefocus.ai/compare", "https://thefocus.ai/infrastructure", "mailto:wschenk@thefocus.ai", "https://thefocus.ai/posts/same-weights-different-results", "https://thefocus.ai/posts/same-weights-different-results", "https://thefocus.ai/posts/same-weights-different-results", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [ { "src": "https://thefocus.ai/_astro/same-weights-different-results.D9yESNmw_1funBC.webp", "alt": "Same Weights, Different Results", "w": 2500, "h": 1395 } ], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] Infrastructures Same Weights, Different Results Essay Models Compare Infrastructure Will Schenk March 24, 2026 We ran the same Nemotron model on four providers and got wildly different results. MCP tool use ranged from 1/6 to 6/6. Speed varied 16x. The weights are identical. The results are not. Read more → Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. View open positions → © 2026 TheFocus.AI. All rights reserved. From tribal knowledge to production AI — in weeks, not quarters.", "height": 2307, "screenshots": { "desktop": "assets/screenshots/desktop/infrastructure.png", "mobile": "assets/screenshots/mobile/infrastructure.png" } }, { "url": "https://thefocus.ai/philosophy", "path": "/philosophy", "slug": "philosophy", "category": "Other", "status": 200, "title": "philosophy posts", "metaDescription": "A list of all posts tagged with philosophy", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Philosophys", "index": 1 }, { "tag": "h2", "text": "Sraffa's Gesture, the Crack in the Crystal, and Why the Stochastic Parrot Still Bites", "index": 2 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 3 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 4 }, { "tag": "h3", "text": "COMPANY", "index": 5 }, { "tag": "h3", "text": "PRODUCTS", "index": 6 }, { "tag": "h3", "text": "RESOURCES", "index": 7 }, { "tag": "h3", "text": "CONNECT", "index": 8 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Philosophys", "y": 162, "body": "Sraffa's Gesture, the Crack in the Crystal, and Why the Stochastic Parrot Still Bites Essay Philosophy Models Will Schenk March 12, 2026 Wittgenstein's shift from the Tractatus to the Philosophical Investigations—triggered by Sraffa's Neapolitan gesture—reframes the stochastic parrot debate. The danger isn't that LLMs lack secret understanding. It's our willingness to treat fluent simulation as the real thing. Read more →" }, { "tag": "h2", "title": "Sraffa's Gesture, the Crack in the Crystal, and Why the Stochastic Parrot Still Bites", "y": 234, "body": "Essay Philosophy Models Will Schenk March 12, 2026" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 1032, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 1482, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 1859, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 1859, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 1859, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 1859, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "Sraffa's Gesture, the Crack in the Crystal, and Why the Stochastic Parrot Still Bites", "href": "https://thefocus.ai/posts/sraffas-gesture" }, { "text": "Essay", "href": "https://thefocus.ai/essay" }, { "text": "Philosophy", "href": "https://thefocus.ai/philosophy" }, { "text": "Models", "href": "https://thefocus.ai/models" }, { "text": "Will Schenk", "href": "mailto:wschenk@thefocus.ai" }, { "text": "Wittgenstein's shift from the Tractatus to the Philosophical Investigations—triggered by Sraffa's Neapolitan gesture—reframes the stochastic parrot debate. The danger isn't that LLMs lack secret understanding. It's our willingness to treat fluent simulation as the real thing.", "href": "https://thefocus.ai/posts/sraffas-gesture" }, { "text": "Read more →", "href": "https://thefocus.ai/posts/sraffas-gesture" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" }, { "text": "Registry", "href": "https://account.thefocus.ai/" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/posts/sraffas-gesture", "https://thefocus.ai/essay", "https://thefocus.ai/philosophy", "https://thefocus.ai/models", "mailto:wschenk@thefocus.ai", "https://thefocus.ai/posts/sraffas-gesture", "https://thefocus.ai/posts/sraffas-gesture", "https://thefocus.ai/posts/sraffas-gesture", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [ { "src": "https://thefocus.ai/_astro/sraffas-gesture.DapXeNnL_1WsISJ.webp", "alt": "Sraffa's Gesture, the Crack in the Crystal, and Why the Stochastic Parrot Still Bites", "w": 2500, "h": 1395 } ], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] Philosophys Sraffa's Gesture, the Crack in the Crystal, and Why the Stochastic Parrot Still Bites Essay Philosophy Models Will Schenk March 12, 2026 Wittgenstein's shift from the Tractatus to the Philosophical Investigations—triggered by Sraffa's Neapolitan gesture—reframes the stochastic parrot debate. The danger isn't that LLMs lack secret understanding. It's our willingness to treat fluent simulation as the real thing. Read more → Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. View open positions → © 2026 TheFocus.AI. All rights reserved. From tribal knowledge to production AI — in weeks, not quarters.", "height": 2356, "screenshots": { "desktop": "assets/screenshots/desktop/philosophy.png", "mobile": "assets/screenshots/mobile/philosophy.png" } }, { "url": "https://thefocus.ai/architecture", "path": "/architecture", "slug": "architecture", "category": "Other", "status": 200, "title": "architecture posts", "metaDescription": "A list of all posts tagged with architecture", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Architectures", "index": 1 }, { "tag": "h2", "text": "The Agent Habitat", "index": 2 }, { "tag": "h2", "text": "AI Engineering Code Summit 2025: Deep Dive Report", "index": 3 }, { "tag": "h2", "text": "How I classify models", "index": 4 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 5 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 6 }, { "tag": "h3", "text": "COMPANY", "index": 7 }, { "tag": "h3", "text": "PRODUCTS", "index": 8 }, { "tag": "h3", "text": "RESOURCES", "index": 9 }, { "tag": "h3", "text": "CONNECT", "index": 10 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Architectures", "y": 162, "body": "The Agent Habitat Essay Agents Architecture Process Will Schenk February 5, 2026 An agent isn't just automation with LLM calls. It carries state, accumulates memory, and makes bounded decisions under uncertainty — and a git repo is where all of that lives. Read more → AI Engineering Code Summit 2025: Deep Dive Report Report Engineering Summit Architecture Agents Will Schenk November 20, 2025 A comprehensive analysis of the state of AI engineering tools, frameworks, and best practices from the November 2025 Code Summit. Exploring cutting-edge developments in AI-assisted development, infrastructure, and production deployment strategies. Read more → How I classify models Models Architecture Ess" }, { "tag": "h2", "title": "The Agent Habitat", "y": 234, "body": "Essay Agents Architecture Process Will Schenk February 5, 2026" }, { "tag": "h2", "title": "AI Engineering Code Summit 2025: Deep Dive Report", "y": 234, "body": "Report Engineering Summit Architecture Agents Will Schenk November 20, 2025" }, { "tag": "h2", "title": "How I classify models", "y": 886, "body": "Models Architecture Essay Will Schenk January 21, 2025" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 1717, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 2167, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] Architectures The Agent Habitat Essay Agents Architecture Process Will Schenk February 5, 2026 An agent isn't just automation with LLM calls. It carries state, accumulates memory, and makes bounded decisions under uncertainty — and a git repo is where all of that lives. Read more → AI Engineering Code Summit 2025: Deep Dive Report Report Engineering Summit Architecture Agents Will Schenk November 20, 2025 A comprehensive analysis of the state of AI engineering tools, frameworks, and best practices from the November 2025 Code Summit. Exploring cutting-edge developments in AI-assisted development, infrastructure, and production deployment strategies. Read more → How I classify models Models Architecture Essay Will Schenk January 21, 2025 Small models are smart yet limited in knowledge; foundation models possess both deep understanding and extensive knowledge but lack structured problem-solving approaches. Educated models like DeepResearch excel by combining learned reasoning processes with large memory capacities, enabling them to adapt effectively to complex tasks while handling vast information instantaneously. Read more → Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. View open positions → © 2026 TheFocus.AI. All rights reserved. From tribal knowledge to production AI — in weeks, not quarters.", "height": 3041, "screenshots": { "desktop": "assets/screenshots/desktop/architecture.png", "mobile": "assets/screenshots/mobile/architecture.png" } }, { "url": "https://thefocus.ai/workflow", "path": "/workflow", "slug": "workflow", "category": "Other", "status": 200, "title": "workflow posts", "metaDescription": "A list of all posts tagged with workflow", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Workflows", "index": 1 }, { "tag": "h2", "text": "The Data Flywheel Pattern", "index": 2 }, { "tag": "h2", "text": "Claude Code, not Code", "index": 3 }, { "tag": "h2", "text": "How I Use AI in Jan 2026", "index": 4 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 5 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 6 }, { "tag": "h3", "text": "COMPANY", "index": 7 }, { "tag": "h3", "text": "PRODUCTS", "index": 8 }, { "tag": "h3", "text": "RESOURCES", "index": 9 }, { "tag": "h3", "text": "CONNECT", "index": 10 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Workflows", "y": 162, "body": "The Data Flywheel Pattern Essay Agents Workflow Process Will Schenk January 22, 2026 Build applications by dropping in data and letting AI handle parsing, structuring, and synthesis. Three case studies. Read more → Claude Code, not Code Essay Agents Workflow Process Will Schenk January 14, 2026 The real power of Claude Code isn't writing software—it's orchestrating skills for research, newsletters, browser automation, and turning one-off requests into repeatable workflows. Read more → How I Use AI in Jan 2026 Essay Process Agents Workflow Will Schenk January 3, 2026 We're at the beginning of infinity. In geological time this is all happening in an instant — and 2026 might be the best year to" }, { "tag": "h2", "title": "The Data Flywheel Pattern", "y": 234, "body": "Essay Agents Workflow Process Will Schenk January 22, 2026" }, { "tag": "h2", "title": "Claude Code, not Code", "y": 234, "body": "Essay Agents Workflow Process Will Schenk January 14, 2026" }, { "tag": "h2", "title": "How I Use AI in Jan 2026", "y": 911, "body": "Essay Process Agents Workflow Will Schenk January 3, 2026" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 1635, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 2085, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] Workflows The Data Flywheel Pattern Essay Agents Workflow Process Will Schenk January 22, 2026 Build applications by dropping in data and letting AI handle parsing, structuring, and synthesis. Three case studies. Read more → Claude Code, not Code Essay Agents Workflow Process Will Schenk January 14, 2026 The real power of Claude Code isn't writing software—it's orchestrating skills for research, newsletters, browser automation, and turning one-off requests into repeatable workflows. Read more → How I Use AI in Jan 2026 Essay Process Agents Workflow Will Schenk January 3, 2026 We're at the beginning of infinity. In geological time this is all happening in an instant — and 2026 might be the best year to be alive. Read more → Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. View open positions → © 2026 TheFocus.AI. All rights reserved. From tribal knowledge to production AI — in weeks, not quarters.", "height": 2959, "screenshots": { "desktop": "assets/screenshots/desktop/workflow.png", "mobile": "assets/screenshots/mobile/workflow.png" } }, { "url": "https://thefocus.ai/conference", "path": "/conference", "slug": "conference", "category": "Other", "status": 200, "title": "conference posts", "metaDescription": "A list of all posts tagged with conference", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Conferences", "index": 1 }, { "tag": "h2", "text": "AI Engineering Summit 2025: Bash-Pilled and Building for Everyone", "index": 2 }, { "tag": "h2", "text": "Report from Microsoft Build 2025", "index": 3 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 4 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 5 }, { "tag": "h3", "text": "COMPANY", "index": 6 }, { "tag": "h3", "text": "PRODUCTS", "index": 7 }, { "tag": "h3", "text": "RESOURCES", "index": 8 }, { "tag": "h3", "text": "CONNECT", "index": 9 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Conferences", "y": 162, "body": "AI Engineering Summit 2025: Bash-Pilled and Building for Everyone Agents Conference Essay Will Schenk November 22, 2025 The models got smart. The tools got simple. The circle of who builds is expanding. Read more → Report from Microsoft Build 2025 Conference Azure Will Schenk May 21, 2025 Microsoft is betting big on an open, agent-powered web—where protocols like MCP, A2A, and NLWeb redefine how AI and services interact. The real opportunity in AI isn’t just smarter models, but the “capability overhang” waiting to be unlocked by better reasoning and open standards. 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TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. View open positions → © 2026 TheFocus.AI. All rights reserved. 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Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. View open positions → © 2026 TheFocus.AI. All rights reserved. From tribal knowledge to production AI — in weeks, not quarters.", "height": 2287, "screenshots": { "desktop": "assets/screenshots/desktop/report.png", "mobile": "assets/screenshots/mobile/report.png" } }, { "url": "https://thefocus.ai/use-cases/flux-images/prince", "path": "/use-cases/flux-images/prince", "slug": "use-cases--flux-images--prince", "category": "Use-case detail", "status": 200, "title": "The Little Prince", "metaDescription": "A collection of Flux images", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "The Little Prince", "index": 1 }, { "tag": "h2", "text": "Prince", "index": 2 }, { "tag": "h2", "text": "Step iterations", "index": 3 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 4 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 5 }, { "tag": "h3", "text": "COMPANY", "index": 6 }, { "tag": "h3", "text": "PRODUCTS", "index": 7 }, { "tag": "h3", "text": "RESOURCES", "index": 8 }, { "tag": "h3", "text": "CONNECT", "index": 9 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "The Little Prince", "y": 162, "body": "Prince mflux-wrapper.py --new \\ --prompt \"The Little Prince with golden hair, dressed \\ in green and wrapped in a yellow scarf, tenderly caring \\ for a red rose on a small asteroid, \\ Van Gogh Starry Night sky, swirling stars, oil painting, \\ soft brush strokes, dreamy and whimsical.\" \\ --iterations 6 Seed 989644 / 1 steps Seed 950751 / 1 steps Seed 188948 / 1 steps Seed 515394 / 1 steps Seed 619319 / 1 steps Seed 357840 / 1 steps Step iterations mflux-wrapper.py --seed --vary-steps 2,3,4,5,6,7 Seed 188948 / 2 steps Seed 188948 / 3 steps Seed 188948 / 4 steps Seed 188948 / 5 steps Seed 188948 / 6 steps Seed 188948 / 7 steps" }, { "tag": "h2", "title": "Prince", "y": 242, "body": "mflux-wrapper.py --new \\ --prompt \"The Little Prince with golden hair, dressed \\ in green and wrapped in a yellow scarf, tenderly caring \\ for a red rose on a small asteroid, \\ Van Gogh Starry Night sky, swirling stars, oil painting, \\ soft brush strokes, dreamy and whimsical.\" \\ --iterations 6" }, { "tag": "h2", "title": "Step iterations", "y": 1324, "body": "mflux-wrapper.py --seed --vary-steps 2,3,4,5,6,7" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 2068, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 2518, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] The Little Prince Prince mflux-wrapper.py --new \\ --prompt \"The Little Prince with golden hair, dressed \\ in green and wrapped in a yellow scarf, tenderly caring \\ for a red rose on a small asteroid, \\ Van Gogh Starry Night sky, swirling stars, oil painting, \\ soft brush strokes, dreamy and whimsical.\" \\ --iterations 6 Step iterations mflux-wrapper.py --seed --vary-steps 2,3,4,5,6,7 Seed 188948 / 2 steps Seed 188948 / 3 steps Seed 188948 / 4 steps Seed 188948 / 5 steps Seed 188948 / 6 steps Seed 188948 / 7 steps Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. View open positions → © 2026 TheFocus.AI. All rights reserved. From tribal knowledge to production AI — in weeks, not quarters.", "height": 3392, "screenshots": { "desktop": "assets/screenshots/desktop/use-cases--flux-images--prince.png", "mobile": "assets/screenshots/mobile/use-cases--flux-images--prince.png" } }, { "url": "https://thefocus.ai/use-cases/flux-images/steps", "path": "/use-cases/flux-images/steps", "slug": "use-cases--flux-images--steps", "category": "Use-case detail", "status": 200, "title": "Study of steps on Flux Images", "metaDescription": "A study of the effect of steps on Flux Images", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "Effect of steps on the image", "index": 1 }, { "tag": "h2", "text": "Dreamy Landscape", "index": 2 }, { "tag": "h2", "text": "Schnell Dreamy Landscape", "index": 3 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 4 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 5 }, { "tag": "h3", "text": "COMPANY", "index": 6 }, { "tag": "h3", "text": "PRODUCTS", "index": 7 }, { "tag": "h3", "text": "RESOURCES", "index": 8 }, { "tag": "h3", "text": "CONNECT", "index": 9 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "Effect of steps on the image", "y": 162, "body": "mflux-wrapper.py --model dev --seed 123 \\ --vary-steps 1,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20 \\ --square-sm \\ --prompt \"A hand drawn color sketch in the style of a 1930s \\ newspaper of an old style airplane controls\" Seed 123 / 1 steps Seed 123 / 3 steps Seed 123 / 4 steps Seed 123 / 5 steps Seed 123 / 6 steps Seed 123 / 7 steps Seed 123 / 8 steps Seed 123 / 9 steps Seed 123 / 10 steps Seed 123 / 11 steps Seed 123 / 12 steps Seed 123 / 13 steps Seed 123 / 14 steps Seed 123 / 15 steps Seed 123 / 16 steps Seed 123 / 17 steps Seed 123 / 18 steps Seed 123 / 19 steps Seed 123 / 20 steps Dreamy Landscape Just change the prompt mflux-wrapper.py --model dev --seed 123 \\ --vary-steps 1,3,4," }, { "tag": "h2", "title": "Dreamy Landscape", "y": 954, "body": "mflux-wrapper.py --model dev --seed 123 \\ --vary-steps 1,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20 \\ --square-sm \\ --prompt \"dreamy landscape\"" }, { "tag": "h2", "title": "Schnell Dreamy Landscape", "y": 1694, "body": "mflux-wrapper.py --model schnell" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 3138, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 3588, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 3965, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 3965, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 3965, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 3965, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. 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512, "h": 512 }, { "src": "https://thefocus.ai/_astro/image_123_17.DB3J7ECa_Z1t53ES.webp", "alt": "dreamy landscape", "w": 512, "h": 512 }, { "src": "https://thefocus.ai/_astro/image_123_18.CJKuOqo3_6pbV4.webp", "alt": "dreamy landscape", "w": 512, "h": 512 }, { "src": "https://thefocus.ai/_astro/image_123_19.QVA2-ps0_bkS6e.webp", "alt": "dreamy landscape", "w": 512, "h": 512 }, { "src": "https://thefocus.ai/_astro/image_123_20.CTq-yp-A_Z2odf9c.webp", "alt": "dreamy landscape", "w": 512, "h": 512 }, { "src": "https://thefocus.ai/_astro/image_123_1.6oVpuIkI_2hWWbV.webp", "alt": "dreamy landscape", "w": 512, "h": 512 }, { "src": "https://thefocus.ai/_astro/image_123_3.CXjwOKhW_1wej2x.webp", "alt": "dreamy landscape", "w": 512, "h": 512 } ], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] Effect of steps on the image mflux-wrapper.py --model dev --seed 123 \\ --vary-steps 1,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20 \\ --square-sm \\ --prompt \"A hand drawn color sketch in the style of a 1930s \\ newspaper of an old style airplane controls\" Seed 123 / 1 steps Seed 123 / 3 steps Seed 123 / 4 steps Seed 123 / 5 steps Seed 123 / 6 steps Seed 123 / 7 steps Seed 123 / 8 steps Seed 123 / 9 steps Seed 123 / 10 steps Seed 123 / 11 steps Seed 123 / 12 steps Seed 123 / 13 steps Seed 123 / 14 steps Seed 123 / 15 steps Seed 123 / 16 steps Seed 123 / 17 steps Seed 123 / 18 steps Seed 123 / 19 steps Seed 123 / 20 steps Dreamy Landscape Just change the prompt mflux-wrapper.py --model dev --seed 123 \\ --vary-steps 1,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20 \\ --square-sm \\ --prompt \"dreamy landscape\" Seed 123 / 1 steps Seed 123 / 3 steps Seed 123 / 4 steps Seed 123 / 5 steps Seed 123 / 6 steps Seed 123 / 7 steps Seed 123 / 8 steps Seed 123 / 9 steps Seed 123 / 10 steps Seed 123 / 11 steps Seed 123 / 12 steps Seed 123 / 13 steps Seed 123 / 14 steps Seed 123 / 15 steps Seed 123 / 16 steps Seed 123 / 17 steps Seed 123 / 18 steps Seed 123 / 19 steps Seed 123 / 20 steps Schnell Dreamy Landscape Switch to schnell model We can run it with the same number of iterations at the dev model, but it doesn't change nearly as much. mflux-wrapper.py --model schnell Seed 123 / 1 steps Seed 123 / 3 steps Seed 123 / 4 steps Seed 123 / 5 steps Seed 123 / 6 steps Seed 123 / 7 steps Seed 123 / 8 steps Seed 123 / 9 steps Seed 123 / 10 steps Seed 123 / 11 steps Seed 123 / 12 steps Seed 123 / 13 steps Seed 123 / 14 steps Seed 123 / 15 steps Seed 123 / 16 steps Seed 123 / 17 steps Seed 123 / 18 steps Seed 123 / 19 steps Seed 123 / 20 steps Moving down to a fewer is all that's needed. mflux-wrapper.py --prompt \"magical forest\" --vary-steps 1,2,3,4,5,6 --seed 123 Seed 123 / 1 steps Seed 123 / 2 steps Seed 123 / 3 steps Seed 123 / 4 steps Seed 123 / 5 steps Seed 123 / 6 steps Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent ", "height": 4462, "screenshots": { "desktop": "assets/screenshots/desktop/use-cases--flux-images--steps.png", "mobile": "assets/screenshots/mobile/use-cases--flux-images--steps.png" } }, { "url": "https://thefocus.ai/use-cases/flux-images/styles", "path": "/use-cases/flux-images/styles", "slug": "use-cases--flux-images--styles", "category": "Use-case detail", "status": 200, "title": "Style Variations", "metaDescription": "A collection of Flux images", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 1 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 2 }, { "tag": "h3", "text": "COMPANY", "index": 3 }, { "tag": "h3", "text": "PRODUCTS", "index": 4 }, { "tag": "h3", "text": "RESOURCES", "index": 5 }, { "tag": "h3", "text": "CONNECT", "index": 6 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 266, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 716, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. View open positions → © 2026 TheFocus.AI. All rights reserved. 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These are hands-on projects you can try yourself, complete with step-by-step instructions and everything you need to get started." }, { "tag": "h3", "title": "Build Your Own Coding Agent", "y": 876, "body": "A step-by-step tutorial for building an AI coding assistant from scratch. Bootstrap it with an existing agent, then use it to improve itself. START THIS BUILD →" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 2534, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 2984, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 3361, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 3361, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 3361, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 3361, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. 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"https://thefocus.ai/_astro/weekend-coding-agent.CYl1jP0-_Z2q4NS3.webp", "alt": "Build Your Own Coding Agent", "w": 1408, "h": 768 }, { "src": "https://thefocus.ai/_astro/smallest-coding-agent.BGVcBftp_1xr4UX.webp", "alt": "The Smallest Coding Agent", "w": 1376, "h": 768 }, { "src": "https://thefocus.ai/_astro/repomix.BcDqog2V_23qUF2.webp", "alt": "Using repomix to give llm full context", "w": 1024, "h": 1024 }, { "src": "https://thefocus.ai/_astro/context7.BS-RUse3_Z2sDol4.webp", "alt": "context7-mcp", "w": 1024, "h": 1024 } ], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] THEFOCUS.AI LABS PROJECT: Weekend Warrior Workshop DOC. NO: WW-2025-PROJECTS TheFocus.AI Labs • Est. 2024 23 PROJECTS WEEKEND WARRIOR We learn by building—and we share what we learn. These are hands-on projects you can try yourself, complete with step-by-step instructions and everything you need to get started. FEATURED PROJECT Our top recommendation to get started START HERE Build Your Own Coding Agent A step-by-step tutorial for building an AI coding assistant from scratch. Bootstrap it with an existing agent, then use it to improve itself. START THIS BUILD → MORE PROJECTS Quick builds for a productive afternoon December 5, 2025 The Smallest Coding Agent A complete agentic coding loop in 25 lines of Bun. No frameworks, no dependencies, just bash and a model. VIEW → September 8, 2025 Using repomix to give llm full context How to use repomix to package your entire codebase into a single file for LLM context, with tips for managing token counts and mise integration. VIEW → September 1, 2025 context7-mcp Learn how to integrate Context7 MCP server to get up-to-date code documentation for LLMs and AI code editors, enabling real-time access to the latest library and framework documentation. VIEW → Showing 4 of 23 projects BROWSE ALL 23 PROJECTS → FOR TEAMS SHIPPING PRODUCTION AI Weekend Warrior is where we learn in public. The same team runs principal-led engagements for Samsung, Tesla, Perplexity, and Rivian — production AI in weeks, not quarters. SEE THE FRAMEWORK → THEFOCUS.AI LABS WORKSHOP SERIES NEW PROJECTS ADDED REGULARLY Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. View open positions", "height": 3858, "screenshots": { "desktop": "assets/screenshots/desktop/recipes.png", "mobile": "assets/screenshots/mobile/recipes.png" } }, { "url": "https://thefocus.ai/recipes/images", "path": "/recipes/images", "slug": "recipes--images", "category": "Recipe/tool content", "status": 200, "title": "images", "metaDescription": "images", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "images recipes", "index": 1 }, { "tag": "h2", "text": "user experience", "index": 2 }, { "tag": "h2", "text": "analysis", "index": 3 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 4 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 5 }, { "tag": "h3", "text": "COMPANY", "index": 6 }, { "tag": "h3", "text": "PRODUCTS", "index": 7 }, { "tag": "h3", "text": "RESOURCES", "index": 8 }, { "tag": "h3", "text": "CONNECT", "index": 9 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "images recipes", "y": 186, "body": "user experience Different ways to interact with generative AI and make it easier to have it interact with the other tools around you. Apr 7, 2025 Iterating designs with Chat GPT 4o imagesprompts analysis Things which help with the process of thinking itself. In other words these are tools to help you do analysis Mar 31, 2025 Claude to write midjourney prompts promptsimages" }, { "tag": "h2", "title": "user experience", "y": 326, "body": "Different ways to interact with generative AI and make it easier to have it interact with the other tools around you. Apr 7, 2025 Iterating designs with Chat GPT 4o imagesprompts" }, { "tag": "h2", "title": "analysis", "y": 595, "body": "Things which help with the process of thinking itself. In other words these are tools to help you do analysis Mar 31, 2025 Claude to write midjourney prompts promptsimages" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 952, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 1402, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 1779, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 1779, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 1779, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 1779, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← Back to all recipes", "href": "https://thefocus.ai/recipes" }, { "text": "Apr 7, 2025 Iterating designs with Chat GPT 4o", "href": "https://thefocus.ai/recipes/iterating-designs-with-chatgpt4o" }, { "text": "images", "href": "https://thefocus.ai/recipes/images" }, { "text": "prompts", "href": "https://thefocus.ai/recipes/prompts" }, { "text": "Mar 31, 2025 Claude to write midjourney prompts", "href": "https://thefocus.ai/recipes/using-claude-to-write-midjourney-prompts" }, { "text": "prompts", "href": "https://thefocus.ai/recipes/prompts" }, { "text": "images", "href": "https://thefocus.ai/recipes/images" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" }, { "text": "Registry", "href": "https://account.thefocus.ai/" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/recipes", "https://thefocus.ai/recipes/iterating-designs-with-chatgpt4o", "https://thefocus.ai/recipes/images", "https://thefocus.ai/recipes/prompts", "https://thefocus.ai/recipes/using-claude-to-write-midjourney-prompts", "https://thefocus.ai/recipes/prompts", "https://thefocus.ai/recipes/images", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← Back to all recipes images recipes user experience Different ways to interact with generative AI and make it easier to have it interact with the other tools around you. Apr 7, 2025 Iterating designs with Chat GPT 4o imagesprompts analysis Things which help with the process of thinking itself. In other words these are tools to help you do analysis Mar 31, 2025 Claude to write midjourney prompts promptsimages Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. View open positions → © 2026 TheFocus.AI. All rights reserved. From tribal knowledge to production AI — in weeks, not quarters.", "height": 2276, "screenshots": { "desktop": "assets/screenshots/desktop/recipes--images.png", "mobile": "assets/screenshots/mobile/recipes--images.png" } }, { "url": "https://thefocus.ai/recipes/voice", "path": "/recipes/voice", "slug": "recipes--voice", "category": "Recipe/tool content", "status": 200, "title": "voice", "metaDescription": "voice", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "voice recipes", "index": 1 }, { "tag": "h2", "text": "user experience", "index": 2 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 3 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 4 }, { "tag": "h3", "text": "COMPANY", "index": 5 }, { "tag": "h3", "text": "PRODUCTS", "index": 6 }, { "tag": "h3", "text": "RESOURCES", "index": 7 }, { "tag": "h3", "text": "CONNECT", "index": 8 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "voice recipes", "y": 186, "body": "user experience Different ways to interact with generative AI and make it easier to have it interact with the other tools around you. Mar 18, 2025 Superwhisper for input voiceclients Mar 18, 2025 Talking to the models in the car voice" }, { "tag": "h2", "title": "user experience", "y": 326, "body": "Different ways to interact with generative AI and make it easier to have it interact with the other tools around you. Mar 18, 2025 Superwhisper for input voiceclients Mar 18, 2025 Talking to the models in the car voice" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 720, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 1170, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 1547, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 1547, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 1547, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 1547, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian →", "href": "https://thefocus.ai/case-studies" }, { "text": "[FOCUS/AI]", "href": "https://thefocus.ai/" }, { "text": "[CAPABILITIES]", "href": "https://thefocus.ai/capabilities" }, { "text": "[HABITAT OS]", "href": "https://thefocus.ai/habitat-os" }, { "text": "[CASE STUDIES]", "href": "https://thefocus.ai/case-studies" }, { "text": "[ABOUT]", "href": "https://thefocus.ai/about" }, { "text": "[CONTACT]", "href": "mailto:hey@thefocus.ai" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Insights", "href": "https://thefocus.ai/insights" }, { "text": "Tools", "href": "https://thefocus.ai/tools" }, { "text": "Reports", "href": "https://thefocus.ai/reports" }, { "text": "Products ↗", "href": "https://account.thefocus.ai/" }, { "text": "← Back to all recipes", "href": "https://thefocus.ai/recipes" }, { "text": "Mar 18, 2025 Superwhisper for input", "href": "https://thefocus.ai/recipes/superwhisper" }, { "text": "voice", "href": "https://thefocus.ai/recipes/voice" }, { "text": "clients", "href": "https://thefocus.ai/recipes/clients" }, { "text": "Mar 18, 2025 Talking to the models in the car", "href": "https://thefocus.ai/recipes/talking-in-the-car" }, { "text": "voice", "href": "https://thefocus.ai/recipes/voice" }, { "text": "Subscribe", "href": "" }, { "text": "Powered by Buttondown.", "href": "https://buttondown.com/refer/schenk" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Habitat OS", "href": "https://thefocus.ai/habitat-os" }, { "text": "TALK TO A PRINCIPAL", "href": "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal" }, { "text": "SEE OUR WORK", "href": "https://thefocus.ai/case-studies" }, { "text": "About", "href": "https://thefocus.ai/about" }, { "text": "Case Studies", "href": "https://thefocus.ai/case-studies" }, { "text": "Capabilities", "href": "https://thefocus.ai/capabilities" }, { "text": "Habitat OS Enterprise agent platform", "href": "https://thefocus.ai/habitat-os" }, { "text": "Vibe Check", "href": "https://thefocus.ai/vibe-check" }, { "text": "Jobs", "href": "https://thefocus.ai/jobs" }, { "text": "Contact", "href": "mailto:hey@thefocus.ai" }, { "text": "Distill-X Distill the signal from the noise", "href": "https://account.thefocus.ai/" }, { "text": "FFFFound Browser", "href": "https://ffffound-browser.thefocus.ai/" }, { "text": "Memes Browser", "href": "https://memes-browser.thefocus.ai/" }, { "text": "Registry", "href": "https://account.thefocus.ai/" }, { "text": "Insights", "href": "https://thefocus.ai/insights" } ], "links": [ "https://thefocus.ai/case-studies", "https://thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "mailto:hey@thefocus.ai", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://account.thefocus.ai/", "https://thefocus.ai/recipes", "https://thefocus.ai/recipes/superwhisper", "https://thefocus.ai/recipes/voice", "https://thefocus.ai/recipes/clients", "https://thefocus.ai/recipes/talking-in-the-car", "https://thefocus.ai/recipes/voice", "https://buttondown.com/refer/schenk", "https://thefocus.ai/vibe-check", "https://thefocus.ai/habitat-os", "mailto:hey@thefocus.ai?subject=Talk%20to%20a%20principal", "https://thefocus.ai/case-studies", "https://thefocus.ai/about", "https://thefocus.ai/case-studies", "https://thefocus.ai/capabilities", "https://thefocus.ai/habitat-os", "https://thefocus.ai/vibe-check", "https://thefocus.ai/jobs", "mailto:hey@thefocus.ai", "https://account.thefocus.ai/", "https://ffffound-browser.thefocus.ai/", "https://memes-browser.thefocus.ai/", "https://account.thefocus.ai/", "https://thefocus.ai/insights", "https://thefocus.ai/tools", "https://thefocus.ai/reports", "https://thefocus.ai/use-cases", "https://account.thefocus.ai/", "https://newsletter.thefocus.ai/", "https://github.com/The-Focus-AI", "https://www.linkedin.com/in/will-schenk-420266/", "https://x.com/wschenk", "https://thefocus.ai/jobs" ], "images": [], "text": "From tribal knowledge to production AI — in weeks, not quarters. Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← Back to all recipes voice recipes user experience Different ways to interact with generative AI and make it easier to have it interact with the other tools around you. Mar 18, 2025 Superwhisper for input voiceclients Mar 18, 2025 Talking to the models in the car voice Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. View open positions → © 2026 TheFocus.AI. All rights reserved. From tribal knowledge to production AI — in weeks, not quarters.", "height": 2044, "screenshots": { "desktop": "assets/screenshots/desktop/recipes--voice.png", "mobile": "assets/screenshots/mobile/recipes--voice.png" } }, { "url": "https://thefocus.ai/recipes/clients", "path": "/recipes/clients", "slug": "recipes--clients", "category": "Recipe/tool content", "status": 200, "title": "clients", "metaDescription": "clients", "headings": [ { "tag": "h1", "text": "[FOCUS/AI]", "index": 0 }, { "tag": "h1", "text": "clients recipes", "index": 1 }, { "tag": "h2", "text": "user experience", "index": 2 }, { "tag": "h2", "text": "coding", "index": 3 }, { "tag": "h2", "text": "Subscribe to our newsletter", "index": 4 }, { "tag": "h2", "text": "Ready to ship production AI?", "index": 5 }, { "tag": "h3", "text": "COMPANY", "index": 6 }, { "tag": "h3", "text": "PRODUCTS", "index": 7 }, { "tag": "h3", "text": "RESOURCES", "index": 8 }, { "tag": "h3", "text": "CONNECT", "index": 9 } ], "sections": [ { "tag": "h1", "title": "[FOCUS/AI]", "y": 77, "body": "" }, { "tag": "h1", "title": "clients recipes", "y": 186, "body": "user experience Different ways to interact with generative AI and make it easier to have it interact with the other tools around you. Jun 30, 2025 Use Cursor in Slack clientsslack Mar 18, 2025 Superwhisper for input voiceclients coding The one thing we do know that these new AI tools are good at is helping people code. I write a lot of code. Here’s a couple little tricks that I’ve learned about how to write better code and faster code. Jun 30, 2025 Use Cursor in Slack clientsslack" }, { "tag": "h2", "title": "user experience", "y": 326, "body": "Different ways to interact with generative AI and make it easier to have it interact with the other tools around you. Jun 30, 2025 Use Cursor in Slack clientsslack Mar 18, 2025 Superwhisper for input voiceclients" }, { "tag": "h2", "title": "coding", "y": 632, "body": "The one thing we do know that these new AI tools are good at is helping people code. I write a lot of code. Here’s a couple little tricks that I’ve learned about how to write better code and faster code. Jun 30, 2025 Use Cursor in Slack clientsslack" }, { "tag": "h2", "title": "Subscribe to our newsletter", "y": 1017, "body": "Enter your email Subscribe Powered by Buttondown." }, { "tag": "h2", "title": "Ready to ship production AI?", "y": 1467, "body": "Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK" }, { "tag": "h3", "title": "COMPANY", "y": 1844, "body": "About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact" }, { "tag": "h3", "title": "PRODUCTS", "y": 1844, "body": "Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry" }, { "tag": "h3", "title": "RESOURCES", "y": 1844, "body": "Insights Tools Reports Use Cases Products Newsletter" }, { "tag": "h3", "title": "CONNECT", "y": 1844, "body": "GitHub LinkedIn X / Twitter" } ], "ctas": [ { "text": "From tribal knowledge to production AI — in weeks, not quarters. 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Samsung · Tesla · Perplexity · Rivian → [FOCUS/AI] [CAPABILITIES] [HABITAT OS] [CASE STUDIES] [ABOUT] [CONTACT] / [Studio ▾] ← Back to all recipes clients recipes user experience Different ways to interact with generative AI and make it easier to have it interact with the other tools around you. Jun 30, 2025 Use Cursor in Slack clientsslack Mar 18, 2025 Superwhisper for input voiceclients coding The one thing we do know that these new AI tools are good at is helping people code. I write a lot of code. Here’s a couple little tricks that I’ve learned about how to write better code and faster code. Jun 30, 2025 Use Cursor in Slack clientsslack Subscribe to our newsletter Enter your email Subscribe Powered by Buttondown. Ready to ship production AI? Whether you need a quick Vibe Check or a full Habitat built on Habitat OS, we'd love to hear what you're working on. TALK TO A PRINCIPAL SEE OUR WORK COMPANY About Case Studies Capabilities Habitat OS Enterprise agent platform Vibe Check Jobs Contact PRODUCTS Distill-X Distill the signal from the noise FFFFound Browser Memes Browser Registry RESOURCES Insights Tools Reports Use Cases Products Newsletter CONNECT GitHub LinkedIn X / Twitter We're hiring. Looking for AI-hungry developers, strategists, and anyone eager to push the boundaries of what's possible. We have offices in NYC, the Hudson Valley, and Northwest Connecticut. View open positions → © 2026 TheFocus.AI. All rights reserved. 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