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Tracking AI Code with Git AI

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Git AI is a side project I created to track AI-generated code in our repos from development, through PRs, and into production. It does not just count lines, it keeps track of them as your code evolves, gets refactored and the git history gets rewritten.Think 'git blame' but for AI code. There's a lot about how it works in the post, but wanted to share how it's been impacting me + my team:- I find I review AI code very differently than human code. Being able to see the prompts my colleagues used, what the AI wrote, and where they stepped in to override has been extraordinarily helpful. This is still very manual today, but hope to build more UI around it soon.- “Why is this here?” — more than once I’ve giving my coding agent access to the past prompts that generated code I’m looking at, which lets the Agent know what my colleague was thinking when they made the change. Engineers talk to AI all day now…their prompts are sort of like a log of thoughts :)- I pay a lot of attention to the lines generated for every 1 accepted ratio. If it gets up over 4 or 5 it means I’m well outside the AI’s distribution or prompting poorly — either way, it’s a good cause for reflection and I’ve learned a lot about collaborating with LLMs.This has been really fun to build, especially because some amazing contributors who were working on similar projects came together and directed their efforts towards Git AI shine. We hope you like it.

HN · launched Nov 10, 2025 · 6 upvotes (top 73%) · 0 views

Niche: git management and workflow tools · 221 launches, 10 in the last 7 days ▼ -23% · Explore this niche →

Category and tags

  • Horizontal
  • Compliance & governance
  • Developer
  • AI feature
  • Hobby / open-source project

Visit website ↗ Source listing ↗

Could you build this?

Partial Building an initial dashboard or git hook is straightforward, but tracking token/line provenance across rebases, squash merges, manual edits, and refactors requires sophisticated AST-aware line tracking algorithms.

What it would actually take: A production tool needs IDE extensions (VS Code, JetBrains) that log fine-grained insertion telemetry (keystroke vs. AI completion vs. prompt hash) into local event streams. The core engine attaches metadata (via custom Git notes or commit trailers) and runs custom Git history and tree diffing engines that survive git blame rewriting, refactoring, and merge conflicts using AST node-level diffing algorithms rather than standard line-based heuristics.

Discussion

4 comments analyzed.

Feature requests: Feed back consistently reedited code into context window to reduce token usage, Integrate with MCP (Model Context Protocol) for agent IQ improvement

Competitors

Other products that read as similar to this one : 840 launches clear the similarity bar, closest 8 shown.

Attention rank: #693 of 841 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 12 days after the earliest competitor.

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