Generic AI reviewers only see the pull request. Revix understands the entire repository. It builds a living knowledge base of your architecture, coding patterns, internal libraries, and engineering standards to deliver reviews that feel like feedback from a teammate who knows your codebase. Every merge helps Revix refine its understanding, making future reviews more accurate, consistent, and aligned with how your team actually builds software.
Hey everyone! 👋
I'm Oriol, one of the founders of Revix.
First of all, thanks for stopping by. It honestly means a lot.
Revix started from something that kept bothering us. Every AI code review tool we tried was good at reading code, but none of them really understood the repository they were reviewing. They would suggest generic refactors, miss project-specific conventions, or comment on things that were intentional.
After enough of those experiences, we thought there had to be a better way.
Instead of trying to make a bigger prompt, we started building a system that actually learns how each repository works. When you connect a project, Revix analyzes its architecture, conventions, internal libraries, and patterns. As the repository evolves, so does that understanding, so reviews become more relevant over time instead of feeling like the same generic advice on every pull request.
One thing that surprised us while building it is that every engineering team has its own unwritten rules. Two projects can use the exact same stack and still have completely different expectations for what "good code" looks like. That became one of the core ideas behind Revix.
We're still at the beginning of the journey, and this launch is a chance to learn as much as possible from other engineers. If you try Revix, we'd genuinely love to hear what you think. Tell us what feels useful, what doesn't, and what you'd build differently. We'll be around all day to answer questions and chat.
Thanks again for checking us out ❤️
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About Revix AI on Product Hunt
“AI code reviews that learn your engineering standards”
Revix AI launched on Product Hunt on October 14th, 2026 and earned 0 upvotes and 1 comments, placing #42 on the daily leaderboard. Generic AI reviewers only see the pull request. Revix understands the entire repository. It builds a living knowledge base of your architecture, coding patterns, internal libraries, and engineering standards to deliver reviews that feel like feedback from a teammate who knows your codebase. Every merge helps Revix refine its understanding, making future reviews more accurate, consistent, and aligned with how your team actually builds software.
Revix AI was featured in Developer Tools (521.5k followers), Artificial Intelligence (481.6k followers) and GitHub (41.4k followers) on Product Hunt. Together, these topics include over 252.4k products, making this a competitive space to launch in.
Who hunted Revix AI?
Revix AI was hunted by Oriol Viladrosa. 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.
Want to see how Revix AI stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Oriol Viladrosa