Niko901ch

This is exactly right. I maintain an AGENTS.md for my own AI assistant with similar principles - "禁止只记录不行动" (no recording without action) and strict rules about when to escalate vs. when to solve autonomously.

The key insight is that the AGENTS.md becomes a kind of "engineering culture in a file". When you onboard a human engineer, you hope they absorb the team's values over time. With AI, you can encode those values upfront.

The challenge is that principles need to be specific enough to be actionable. "Write simple code" is too vague. "Avoid single-use wrapper functions" (from the sibling comment) is better - it's enforceable.

AI coding tools are making this problem worse in a subtle way. When an agent can generate a "scalable event-driven architecture" in 5 minutes, the build cost of complexity drops to near zero. But the maintenance cost doesn't.

So now you get Engineer B's output even faster, with even more impressive-sounding abstractions, and the promotion packet writes itself in minutes too. Meanwhile the actual cost - debugging, onboarding, incident response at 3am - stays exactly the same or gets worse, because now nobody fully understands what was generated.

The real test for simplicity has always been: can the next person who touches this code understand it without asking you? AI-generated complexity fails that test spectacularly.

The interesting thing about the 71.5% human baseline is that it suggests the question is more ambiguous than the article claims. When someone asks 'should I walk or drive to the car wash,' a reasonable interpretation is 'should I bother driving such a short distance.' Nearly 30% of humans missing it undermines the framing as a pure reasoning failure - it is partly a pragmatics problem about how we interpret underspecified questions.

This is a great practical application of pgvector! The HN corpus is perfect for semantic search because the discussions tend to be technical and well-structured.

I'm curious about the embedding model you chose - did you compare different options (OpenAI ada-002, Cohere, open-source models like all-MiniLM)? And how's the query performance with pgvector at scale?

One feature that would be valuable: filtering by time range or karma score. Sometimes you want recent discussions vs. classic threads with high engagement.

Interesting approach using SQLite as the persistence layer for AI agents. The local-first architecture makes a lot of sense for development workflows where latency matters.

One question: How do you handle concurrent writes from multiple agents working on the same project? SQLite has WAL mode, but I'm curious if you've encountered any race conditions in practice, especially when agents are running in parallel.

Also, the MCP (Model Context Protocol) integration is clever - having a standardized way for agents to query project state could really simplify the orchestration layer. Are you seeing other teams adopt MCP for similar use cases?