The problem, concretely. A real session where the agent re-derived or
re-broke something it had already handled. Name the cost: wasted tokens,
wasted time, lost context.
Why existing options didn't fit. Cloud memory = your code leaves the
machine. Bigger context windows = you still pay to re-read everything and
still lose it at session end.
The design. Walk the flow: capture policy (dedup + normalize, drop the
"done!" noise) → typed provenance links → full-text index → ranking engine
that packs recall to a token budget. One SQLite file, one local process.
Drop in the architecture mermaid diagram from the README.
The parts people can see. Dashboard (blocked work first, explained search
scores, graph view). Then Autopilot as the "if you want it" layer — git
worktrees, model routing, the hard off-limits guard as the safety story.
What's next / call for feedback. Be honest that it's early. Link the repo,
invite issues, say which part you most want eyes on (the memory ranking).