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baoer_signal_grep
A tiny local librarian for AI agents
Give AI agents a clearer way to search local code, docs, notes, and logs. Get focused passages or compact file maps, stable continuation cursors, and explicit coverage limits. Hybrid search separates exact matches from local semantic candidates. Open source, with source navigation for Pi, OMP, and MCP clients such as Codex. GitHub: https://github.com/xcjy8bao/baoer_signal_grep npm: https://www.npmjs.com/package/baoer_signal_grep
Hi Product Hunt, I’m baoer, the maker of baoer_signal_grep.
I built it after watching capable coding agents lose their place while searching a repository: open a file, dump a pile of matches, start over, then sound certain about something they hadn’t finished checking. I wanted to give them a small local librarian with a bookmark.
Small searches return the passage and its location. Broader searches can start with a compact file map. Follow-up requests continue through the retained snapshot, while limits and incomplete coverage stay visible. Hybrid search puts exact evidence first and labels local semantic candidates as leads to inspect.
It works with code, docs, notes, and logs, and includes source navigation for deeper investigations. You can use it through Pi, OMP, or an MCP client such as Codex. The project is open source and available on npm.
One boundary matters: a saved cursor preserves an investigation’s snapshot; it isn’t a live view of later file changes. Agents still need to read the evidence and judge what it supports.
I’d love to hear where your agent loses the thread: long logs, a changing worktree, an awkward search, or a result that looks more conclusive than it should. A small reproduction would be especially helpful.
The gallery artwork is an AI-generated concept illustration, not a product screenshot.
Thanks for taking a look. I’m here to answer questions and learn from the rough edges.
About baoer_signal_grep on Product Hunt
“A tiny local librarian for AI agents”
baoer_signal_grep was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #101 on the daily leaderboard. Give AI agents a clearer way to search local code, docs, notes, and logs. Get focused passages or compact file maps, stable continuation cursors, and explicit coverage limits. Hybrid search separates exact matches from local semantic candidates. Open source, with source navigation for Pi, OMP, and MCP clients such as Codex. GitHub: https://github.com/xcjy8bao/baoer_signal_grep npm: https://www.npmjs.com/package/baoer_signal_grep
On the analytics side, baoer_signal_grep competes within Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how baoer_signal_grep performed against the three products that launched closest to it on the same day.
Who hunted baoer_signal_grep?
baoer_signal_grep was hunted by baoer. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of baoer_signal_grep including community comment highlights and product details, visit the product overview.
baoer
Hi Product Hunt, I’m baoer, the maker of baoer_signal_grep.
I built it after watching capable coding agents lose their place while searching a repository: open a file, dump a pile of matches, start over, then sound certain about something they hadn’t finished checking. I wanted to give them a small local librarian with a bookmark.
Small searches return the passage and its location. Broader searches can start with a compact file map. Follow-up requests continue through the retained snapshot, while limits and incomplete coverage stay visible. Hybrid search puts exact evidence first and labels local semantic candidates as leads to inspect.
It works with code, docs, notes, and logs, and includes source navigation for deeper investigations. You can use it through Pi, OMP, or an MCP client such as Codex. The project is open source and available on npm.
Try it or inspect the source:
GitHub: https://github.com/xcjy8bao/baoer_signal_grep
npm: https://www.npmjs.com/package/baoer_signal_grep
One boundary matters: a saved cursor preserves an investigation’s snapshot; it isn’t a live view of later file changes. Agents still need to read the evidence and judge what it supports.
I’d love to hear where your agent loses the thread: long logs, a changing worktree, an awkward search, or a result that looks more conclusive than it should. A small reproduction would be especially helpful.
The gallery artwork is an AI-generated concept illustration, not a product screenshot.
Thanks for taking a look. I’m here to answer questions and learn from the rough edges.