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From 93 to 400 GitHub stars in 8 days — what the numbers actually mean

DEV Community·manoj mallick·19 days ago
#nuiPal7L
#how#ai#productivity#sigmap#github#benchmark
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I built SigMap in April. By May 5 it had 93 stars — growing slowly through organic discovery. Then last week happened. 61K Reddit views. 90.5% upvote ratio. 307 new stars in 8 days. SigMap is now in the top 1.5% of all coding AI repositories on GitHub. Here's what it does and why the numbers moved. The problem it solves When you use Claude Code, Cursor, or Copilot on a large codebase, the tool reads your open files or sends the whole project. On real repos, that's 80,000+ tokens per session. 13 of 18 repos I benchmarked overflow GPT-4o's context window entirely. The AI is working with an incomplete, random picture of your codebase. How SigMap fixes it Instead of sending full source files, SigMap: Extracts function signatures + import graph from your entire codebase Ranks them by TF-IDF against your specific query Sends 200–4,000 query-ranked tokens instead of 80,000 random ones Different question = different context. The AI sees what's relevant, not what's open.…

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