I'm building Thinker, a knowledge cache for coding agents. On every fresh session, agents rebuild their understanding of your codebase from scratch, then throw it away. Thinker keeps it.
It distills past agent sessions and PRs into short notes about how your code works, e.g. all DB writes use `/sql-utils.py`. A small local model ranks the notes and injects the relevant ones into new sessions, so the agent spends less time exploring. Notes are rescored and updated as PRs merge, so stale context gets demoted.
On 50 PRs from open-source repos, agents using Thinker (Fable 5.1, GPT-6 Astra, Gemini Flash 3.8) used 18% fewer tokens and 10% faster on average with equal correctness using LLM-as-a-judge. Per-model results and the eval harness: https://t.co/au6kgLKjmF.
Repo: https://t.co/jnGrzgAITH. Install: `npx @zerotimedev/thinker`
Unlike AGENTS.md or built-in memory, notes are generated and maintained automatically. Next I want to work on rigor, not just efficiency.