BlameAI
Your commit history, drawn against every AI release
BlameAI launched on July 22, 2026.
What it does
Drop in any GitHub username and blameai.dev plots their public commits since 2021 against every major AI release - ChatGPT, GPT-4, Claude, Claude Code, Cursor - so you can actually see how the AI era shows up in real commit graphs. No login to start; sign in to count private repos too (read-only). Every graph becomes a share card, plus a not-too-serious badge for your pattern.
Plot any developer's GitHub contributions since 2021 against every major AI release. Did AI change how much you ship?
Plot any developer's contributions since 2021 against every major AI release. Start with a username, or sign in for your account-visible view. Loads contribution counts visible to your own account. Standard GitHub OAuth, read-only ( read:user ). We never touch your code or repo contents. Username gives you GitHub-visible activity instantly. Signing in uses the contribution calendar available to your own account. Senior SRE at ANNA Money — building cloud infrastructure with Terraform and Go. AI-era ×1.2 = avg. monthly contributions in the AI era vs. before No login to get going. Sign in only to load contribution counts visible to your account. ChatGPT, Copilot, GPT-4, Claude and Cursor,…from blameai.dev
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Git for AI AgentsMay 2026 · github.com · ▲129hi guys. been working on something i think is fundamentally missing in today's workflow with ai agents. vcs. i find myself struggling with questions that agents can't answer like "why did you do it?", "when did u delete this folder? why?", etc. or trying to /rewind (after a /compact...) or basically `bisect` to find when and why something was done by the agent in the current / previous session. just like git did for code, i think we are the same core capabilities with ai agents so... i developed an open source solution for that (currently supporting claude code) would love to…
git-lrcFeb 2026 · hexmos.com · ▲300Hi HN, I'm the author of git-lrc, would appreciate some feedback from the community Last year my team started using AI coding tools more heavily, and we found ourselves generating tons of code, but spending less time looking at the stuff that's been generated. We felt like we were losing a bit of grip/understanding of what we were building. Regressions occasionally slipped through. Sometimes changes made it all the way to production only to be reverted later. We tried several AI code review tools, but most operate at PR time. That felt too late. I wanted review to happen while the…

GitHub Browser Plugin for AI Contribution Blame in Pull RequestsFeb 2026 · blog.rbby.dev · ▲62An extension of Refined GitHub browser plugin for AI contribution tracking capabilities in GitHub pull requests.
ShadowGit2025 · shadowgit.com · ▲20Hey HN, I built ShadowGit a while back to automatically commit code every minute to a hidden git repo (.shadowgit.git). Original goal was to easily rollback when AI tools break things. But I discovered something interesting: this minute-by-minute history is perfect context for AI assistants. So I built an MCP server that lets Claude/Cursor query this history using native git commands. The results surprised me: Before: Claude would read my entire codebase repeatedly, burning 15,000+ tokens to debug issues. After: Claude runs `git log --grep="drag"` finds when drag-and-drop worked,…
What % of your commits were written by AI?Mar 2026 · technically-your-name-is-on-it.btao.org · ▲5Hi HN, I've been using Claude Code etc. for nearly all my work lately, and I wanted to see how many of my commits it was actually co-authoring. So I made this little tool to visualize my usage. You log in with Github (read-only), it scans your commits from the last year, and visualizes how many came from Claude, Cursor, or any tool that adds Co-Authored-By trailer to the commit message. Caveat: not all tools add this trailer, so this doesn't include e.g. Codex. Still, hope you find it interesting!
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A few days ago I found myself trying out GLM 5.2 and was really positively impressed. The capabilities and security I was getting from this LLM are similar to those I've gotten from models like Claude or GPT, and this really surprised me. But then I thought, "I wonder how it would work on a normal computer like mine," and above all, "I wonder if it would work without going into OOM on a computer like mine." So I started working with the help of agents to test this possibility. I started converting the model to int4, understanding MTP usage, and if possible implementing DSA for long context.…
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