i built an AI code reviewer for github (used it on itself during dev)
I’ve spent the last 2.5 months building a product that runs LLM-powered code reviews on my pull requests — and I just launched it. The tool is built specifically for solo developers. You install it on your repo, trigger a scan by creating a pull request, and it leaves structured review comments using OpenAI under the hood. Funnily enough, I used the dev version of this app to review its own pull requests while building it. It helped me spot bugs, simplify structure, and keep quality high — all with minimal need for another human in the loop. Things I want to try out in the next months : -…
What it does
In the maker’s words, at launch
I’ve spent the last 2.5 months building a product that runs LLM-powered code reviews on my pull requests — and I just launched it. The tool is built specifically for solo developers. You install it on your repo, trigger a scan by creating a pull request, and it leaves structured review comments using OpenAI under the hood. Funnily enough, I used the dev version of this app to review its own pull requests while building it. It helped me spot bugs, simplify structure, and keep quality high — all with minimal need for another human in the loop. Things I want to try out in the next months : - Model integrations beyond OpenAI: Claude, Gemini, DeepSeek, LLaMA - Bring Your Own Model (BYOM) - Smarter review agents: repo memory, config-awareness, custom rules Right now I’m focused on getting feedback from devs and evolving based on what actually helps. Try it here: [https://codii.dev] I’d love feedback on: - Are the reviews actually useful for you or noisy? - What would make this fit into your workflow? - Anything obviously missing, confusing, or broken? Happy to answer any questions! (feel free to dm me on X https://x.com/cepstrum9)
Does the same job
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