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.

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.

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.

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?