ai(32)
- / Date/ Name
- My daily post didn't fail. It just didn't happen.
- AI code needs a receipt
- My file sync committed a delete of 803,100 files. Then it tried to push it everywhere.
- Reconcile-or-refuse: how to trust a number an AI pulled out of a bank statement
- I said my bank-statement parser was 100% accurate. I was grading it against itself.
- I benchmarked three OCR models on real bank statements. The best one flipped with the layout.
- A 51-line parser beat a 3-billion-parameter model at reading bank statements
- Make your AI reviewer argue with itself
- CODY has to prove itself wrong first
- Examples are the spine, not the rulebook
- Finish, don't stage: what I want from an agent on the night shift
- Green on mocks is not done
- I cloned my own voice for my website
- I built a load balancer for my Claude Code subscriptions
- Git worktrees ate my edits, so we switched to dedicated machines for agent isolation
- Building on giants: how Daniel Miessler's PAI became my foundation
- Skills are just the beginning: the four-layer agent stack
- Version-controlling your AI's brain
- PAI: the operating system I built around my AI assistant
- Your CLAUDE.md is probably making your agent worse
- Field-level ensemble OCR: getting 74.8% accuracy from two mediocre vision models
- From 5.6% to 62.3% accuracy: building a self-hosted insurance card OCR service
- Two AI trends changing urgent care in 2026
- The four-line architecture that beat complex AI frameworks
- Building an AI patient chatbot for urgent care with n8n, GPT-4, and Langfuse
- How I built a $2,300/year RAG system that rivals $40K OpenAI setups
- The universal algorithm: how one framework scales from bug fixes to building companies
- Building an enterprise RAG system with local SLMs: Phi-4 and LightRAG
- Building an AI analysis agent in hours: a no-code approach with Lovable and n8n
- Supporting SSE for Model Context Protocol (MCP) in Python: introducing fastapi-mcp-client
- Moving a RAG pipeline onto GPUs: 3.5 hours down to 42 minutes
- Building an enterprise-grade RAG system: three retrieval strategies, fused