I came across this project and thought it was worth sharing. It’s called ChunkHound, a local-first, offline code search engine that lets AI assistants (and humans) explore and search large codebases — semantic search, regex, MCP protocol, etc.
After speaking with the developer, I learned something pretty wild.
The entire project — specs, architecture, implementation, even the name — was generated by an AI coding agent. No human coding, just high-level prompting with a factory-style system feeding the agent tasks and specs. The agent handled the full build end-to-end.
In a way, it even indexed itself once it was done.
Hi HN,
I came across this project and thought it was worth sharing. It’s called ChunkHound, a local-first, offline code search engine that lets AI assistants (and humans) explore and search large codebases — semantic search, regex, MCP protocol, etc.
After speaking with the developer, I learned something pretty wild.
The entire project — specs, architecture, implementation, even the name — was generated by an AI coding agent. No human coding, just high-level prompting with a factory-style system feeding the agent tasks and specs. The agent handled the full build end-to-end.
In a way, it even indexed itself once it was done.
* Local-first semantic + regex search (Tree-Sitter + DuckDB)
* MCP server to serve AI agents (Claude, Cursor, VSCode, etc.)
* MIT licensed
Repo: https://github.com/ofriw/chunkhound