_ _
__| | ___ __ _ __| |_______ _ __ ___
/ _` |/ _ \/ _` |/ _` |_ / _ \| '_ \ / _ \
| (_| | __/ (_| | (_| |/ / (_) | | | | __/
\__,_|\___|\__,_|\__,_/___\___/|_| |_|\___|
> semantic doc search. local file. no cloud. no key.
> you ask in english. it answers in snippets.Status. Vector search wired end-to-end. MCP over stdio. One binary, Linux + macOS, zero telemetry. See releases for the latest tag and the roadmap for in-flight work. The scraper is still the messy half — #64 is honest about it.
The pitch, in one paragraph
Your AI client says "how do I register a tool?". The doc says AddTool. A grep-based index shrugs; a vector index doesn't. Deadzone is the vector index — nomic-embed-text-v1.5 over Turso's native cosine distance, wrapped in a Go binary that speaks MCP over stdio and keeps every byte on your laptop. It is, roughly, Context7 with the internet turned off.
Rules of the deadzone
- One binary.
deadzone. Subcommands for everything. Nopip install, nonpm i, nodocker compose up. - The index never leaves. Local Turso file. No account. No API key. No egress on the hot path.
- Natural language first. Embeddings over cosine.
FTS5is not invited. - The binary is the version. The DB is pinned to the binary. Upgrade the binary, the DB follows; don't, and it won't.
- Fail loudly or not at all.
DEADZONE_DB_OFFLINE=1refuses to guess. Verification failures in the scraper drop the doc, not the run.
