Turning local agents into self-optimizing agents

I was experimenting with a self-optimizing agentic pipeline to climb the benchmark leaderboard (TerminalBench). On a 10-task subset, I got the performance to rise from ~30% → ~90%. That loop worked, so I asked: can the same reflect-and-rewrite step run continuously against everyday chats instead of a benchmark? How it works Every chat with your local LLM goes through a small proxy and is logged. autoswarm reflect has the same local model review those logs, distill concrete lessons, and write them to skills.yaml. Lessons auto-inject into the system prompt of future chats.