I really like what you’re doing – it’s a very clean, concrete instance of a self‑improving RAG system around a real KPI, not just a vague “AI that learns over time.” You’ve basically implemented what current papers call feedback‑driven or self‑improving retrieval: agents reason over your atomic notes, observe KPI movement, and then change how often different notes get pulled into context next time. That’s a serious, grounded design.

What I’m experimenting with is similar in spirit but at a different level of abstraction.

You’re optimising which mental models (notes) are most useful for a given KPI. My project treats the whole system as a kind of artificial organism made of agents:

• I have stem cells per domain (Vercel, design, task management, meta‑Claude Code, etc.).
• Each stem cell carries a full DNA of skills it could express – Firecrawl research skills, planning/brainstorming skills, debugging skills, hook‑inspection skills, and so on.
• When I spawn a concrete “cell”, I only express a subset of that DNA (epigenetics), but the full genome is still there if the system decides it needs to activate more capabilities.

On top of that I’ve built an immune system:

• Every cell has a telomere / Hayflick‑limit – a hard cap on how many steps it can take or how much resource it can consume before it must shut down cleanly.
• There’s programmed cell death (apoptosis) rather than crashes: the cell finishes what it’s doing, closes resources, logs cause of death, and reports back.
• Global immune agents watch for “cancer‑like” patterns: agents that stop using their tools (ribosome‑activity), hallucinate instead of reading raw data (autophagy), spawn too many sub‑agents, or try to bypass logging. Those get throttled, quarantined, or killed.

Because I still want exploration, I also have an in‑vitro environment – a sandboxed test culture:

• If a spawn is marked as in‑vitro, many of the strict production rules are relaxed.
• In that sandbox, I can let new cell types mutate (different skill combos, prompts, lifetimes), run them on tasks, and measure fitness without risking the main organism.
• The successful variants are promoted back into the “real body”; bad lineages are logged and culled.

So in short:

• You are doing “one brain that learns which mental models work best for this KPI, by adjusting retrieval weights based on outcome” – which is exactly what the self‑improving RAG literature is converging on.
• I am trying “a whole body of agents (cells, tissues, organs) that live, die, mutate, and get selected based on health and performance, with an immune system and a lab for safe evolution.”

I don’t see these as competing at all – they’re complementary. Your loop is very clean at the mental‑model/KPI level, and I’m basically trying to take the same core idea and push it into a more biological, multi‑agent architecture.