This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Acrity reviews pull requests through multiple AI specialists—architecture, spec validation, QA/behavior, and security—then challenges findings and synthesizes one traceable verdict with cost, token, and timing visibility.
Hey Product Hunt 👋
I’m Auguimar, maker of Acrity.
I built Acrity because code review is becoming a real bottleneck for engineering teams.
Teams need to ship faster, but every PR still needs careful review: architecture, requirements, regressions, edge cases, security, and production risk. Manual review is valuable, but it is hard to scale deeply across every change.
Acrity takes an adversarial approach to AI code review.
Instead of one model giving one opinion, each pull request is reviewed through multiple specialized lenses:
🧠 Architect — architecture, layers, coupling, and project rules
📋 Spec Validator — diff vs task and acceptance criteria
🧪 QA / Behavior — regressions, risky behavior, and edge cases
🔐 Security — security-sensitive changes and risky patterns
Then a Chairman consolidates the findings, removes noise, resolves conflicts, and produces one traceable verdict with evidence.
A few things I care a lot about:
• findings should be challenged before becoming decisions
• review should produce evidence, not generic comments
• teams should see time, tokens, model calls, and cost per PR
• different models/providers can be used across rounds to reduce blind spots
• the review should fit the workflow teams already use
Acrity connects with GitHub, GitLab, Bitbucket, Azure DevOps, Jira, Linear, ClickUp, and native issue trackers.
There is a trial available today.
I’d be very grateful for honest feedback from engineers, tech leads, founders, and teams reviewing real PRs every day.
What feels useful?
What feels noisy?
What is missing?
What would make this trustworthy enough for your team?
Thanks for checking it out — happy to answer anything.
How does it handle merge conflicts or diffs that span multiple files when the specialists disagree on severity?
How does the challenge step actually work in practice. Do the specialists push back on each other until they agree, or does a separate model arbitrate when they disagree on something like whether a finding is critical or just a nit.
ran it on a messy legacy pr i had lying around and the security specialist actually caught a token leak i had missed in two manual reviews, nice.
About Acrity on Product Hunt
“AI code review with adversarial consensus”
Acrity was submitted on Product Hunt and earned 0 upvotes and 4 comments, placing #118 on the daily leaderboard. Acrity reviews pull requests through multiple AI specialists—architecture, spec validation, QA/behavior, and security—then challenges findings and synthesizes one traceable verdict with cost, token, and timing visibility.
Acrity was featured in Software Engineering (42.7k followers), Developer Tools (515.4k followers) and Artificial Intelligence (473.1k followers) on Product Hunt. Together, these topics include over 187.4k products, making this a competitive space to launch in.
Who hunted Acrity?
Acrity was hunted by Acrity. 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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Acrity