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Ming — Review AI-Written Code
Open-source, local-first review for AI-written code
AI coding agents can change dozens of files in minutes. Ming helps you review those changes with confidence. It is an open-source, local-first review UI that reads your actual Git diff, groups related changes into focused topics, and keeps the diff as the source of truth. No account or backend required.
Hi Product Hunt 👋
I built Ming because AI coding agents have made writing code dramatically faster, but reviewing that code still feels almost the same as before.
After using tools like Codex and Claude Code, I kept running into the same problem: an agent could change 20–30 files in a few minutes, but I still had to understand the result file by file. AI-generated summaries helped a little, but they mostly described what the agent intended to do — not necessarily what actually changed.
Ming is my attempt to improve the review side of the workflow. It reads your local Git diff directly in the browser and can organize related changes into focused review topics, while keeping the actual diff as the source of truth.
I also wanted it to stay simple and developer-friendly:
- open source and MIT licensed
- local-first
- no Ming account
- no application backend
- read-only repository access
- works with local, uncommitted changes
The project is still early, and I’d especially love feedback on the review workflow:
When AI agents make large changes to your codebase, what part of reviewing those changes is the most painful for you today?
Thanks for checking out Ming!
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About Ming — Review AI-Written Code on Product Hunt
“Open-source, local-first review for AI-written code”
Ming — Review AI-Written Code was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #23 on the daily leaderboard. AI coding agents can change dozens of files in minutes. Ming helps you review those changes with confidence. It is an open-source, local-first review UI that reads your actual Git diff, groups related changes into focused topics, and keeps the diff as the source of truth. No account or backend required.
Ming — Review AI-Written Code was featured in Open Source (68.9k followers), Developer Tools (520.4k followers) and GitHub (41.4k followers) on Product Hunt. Together, these topics include over 132.2k products, making this a competitive space to launch in.
Who hunted Ming — Review AI-Written Code?
Ming — Review AI-Written Code was hunted by Yorkie Makoto. 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.
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Yorkie Makoto