Most AI review tools flag issues. LiveReview promises production results: fewer incidents, fewer rollbacks, less senior reviewer load. Every PR is risk-scored. The lowest-risk 30-50% are safely auto-approved; the risky ones go straight to the engineers who should see them. The approach is inspired by Meta's highly successful internal AI review system, where production incidents fell 50x and rollbacks 3x. LLM + incident-trained ML + code-graph analysis. Free and self-hostable.
In February, you helped git-lrc reach #3 Product of the Day. Thank you.
git-lrc fixed one problem: developers committing AI-written code they hadn't read.
As teams adopted it, a bigger problem showed up at the pull request.
AI made code cheap to write. Reviews stayed expensive.
Every PR waited in the same queue for the same few senior engineers.
A renamed variable and a rewritten payment flow got the same attention.
Seniors became the bottleneck. They skimmed. RISKY changes slipped into production.
Most AI review tools add more comments to the thread.
We measure review by production outcomes: fewer incidents, fewer rollbacks, less senior reviewer time.
LiveReview follows the approach behind Meta's highly successful internal AI review system, RADAR: score every change for risk, fast-track the safe ones, and send the risky ones to the right reviewers.
At Meta, this led to 50× fewer production incidents, 3× fewer rollbacks and 35% faster reviews.
Here's how LiveReview does it:
- Auto-approve: the lowest-risk 30–50% are approved instantly. The rest ALWAYS get a human reviewer.
- Risk-first review: high-risk PRs go to the top of the queue with comments sorted by blast radius, sent to the engineers for review.
- Cognitive Coverage: walk through a PR as slides or a summary, or quiz yourself on it before it merges. Your team still understands the code AI writes.
- Livi Assistant: ask about your engineering data in plain English, on the web or in Slack, Teams and Discord. Track adoption, ROI and the teams that need attention across 50+ actionable graphs.
- Integrations: GitHub, GitLab, Bitbucket, Gitea and Azure DevOps.
- Any AI model: reuse the Claude or OpenAI key you already pay for, or any of 15+ AI providers, or use the LiveReview model.
LiveReview is FREE and SELF-HOSTABLE. Run it on your own infra.
If someone chops a high-risk change into a stack of small PRs that each look safe — a rename, then a helper, and the last one flips the flag — does the risk score see that whole stack and the branch history? Or does each one get scored on its own and come back looking harmless?
I'd definitely want to try this. We built our own review setup and are still improving it. I like that LiveReview promises production results, not more comments.
Two things I keep running into. With everyone on our team running 10+ coding agents in parallel, the rebase, re-review, rebase loop burns a lot of time and tokens. And it's hard to give the reviewer enough context to see the whole architecture, not just the diff. @shrsv how does LiveReview handle these?
This is marvellous effort towards making review a religious practice for large organisation and the most important thing which I like the most is that user spend less time reviewing still focussing wider code level review. Loved git lrc back in days. Can't wait to try this. Congrats on launch!!!
Honestly, half the PRs I see barely need a review these days. This makes a lot of sense.
Interesting! So How does it work? Is it like a graphify kind of tool? that looks at blast radius and then does industry standard heuristic bench marks on what it finds etc? I assume no AI since its free and self hostable?
Does Cognitive Coverage also run on the PRs that get auto-approved, or only on the ones routed to a reviewer?
most review bots just spam nits on every pr and people stop reading them. risk scoring first feels like the right order. does the auto-approve play nice with branch protection or do you still need a human check to merge?
Free and self-hostable with my own Claude key is the part that would actually get me to try it — I don't love routing my code through another hosted reviewer. As a solo dev, the Cognitive Coverage bit is what caught my eye: walking a PR as slides before merging is a nice answer to the 'AI wrote it, I skimmed it' problem.
auto-approving the low-risk PRs is the useful part. when it routes a riskier one, does the engineer see the risk score breakdown or just a flag?
how does an incident get linked back to a PR? ours show up days after the deploy, in Sentry or in a saturated db pool, and rarely point at one commit. do you read Sentry/PagerDuty or do we tag reverts by hand?
Congrats on the launch. Can we customise some particular kind of diffs so that they go to the reviewer despite having lower risk score?
before an org has recorded any reverts or incidents, how much weight does the custom ml model get in the risk score?
Auto approving the lowest risk third and sending everything else to the right engineer is the version of this that would survive a real Monday, since another tool that flags fifty things a day just trains us to ignore it.
congrats on the launch shrijith. auto approving low risk prs is where i would be careful. a tiny pricing or config change can look harmless in code but still affect customers. can those be marked as risky too?
cool launch, congrats!! if a one-line config change hits the payments path, does LiveReview flag it from the code graph or could it get auto approved as low risk?
About LiveReview on Product Hunt
“Free, Risk-Aware Code Review: Auto Approve Low-Risk Changes”
LiveReview launched on Product Hunt on November 7th, 2026 and earned 252 upvotes and 52 comments, earning #2 Product of the Day. Most AI review tools flag issues. LiveReview promises production results: fewer incidents, fewer rollbacks, less senior reviewer load. Every PR is risk-scored. The lowest-risk 30-50% are safely auto-approved; the risky ones go straight to the engineers who should see them. The approach is inspired by Meta's highly successful internal AI review system, where production incidents fell 50x and rollbacks 3x. LLM + incident-trained ML + code-graph analysis. Free and self-hostable.
LiveReview was featured in SaaS (45.3k followers), Developer Tools (523.1k followers), Artificial Intelligence (483.7k followers) and GitHub (41.5k followers) on Product Hunt. Together, these topics include over 327.5k products, making this a competitive space to launch in.
Who hunted LiveReview?
LiveReview was hunted by Shrijith Venkatramana. 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 LiveReview stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Shrijith Venkatramana
In February, you helped git-lrc reach #3 Product of the Day. Thank you.
git-lrc fixed one problem: developers committing AI-written code they hadn't read.
As teams adopted it, a bigger problem showed up at the pull request.
AI made code cheap to write. Reviews stayed expensive.
Every PR waited in the same queue for the same few senior engineers.
A renamed variable and a rewritten payment flow got the same attention.
Seniors became the bottleneck. They skimmed. RISKY changes slipped into production.
Most AI review tools add more comments to the thread.
We measure review by production outcomes: fewer incidents, fewer rollbacks, less senior reviewer time.
LiveReview follows the approach behind Meta's highly successful internal AI review system, RADAR: score every change for risk, fast-track the safe ones, and send the risky ones to the right reviewers.
At Meta, this led to 50× fewer production incidents, 3× fewer rollbacks and 35% faster reviews.
Here's how LiveReview does it:
- Auto-approve: the lowest-risk 30–50% are approved instantly. The rest ALWAYS get a human reviewer.
- Risk-first review: high-risk PRs go to the top of the queue with comments sorted by blast radius, sent to the engineers for review.
- Cognitive Coverage: walk through a PR as slides or a summary, or quiz yourself on it before it merges. Your team still understands the code AI writes.
- Livi Assistant: ask about your engineering data in plain English, on the web or in Slack, Teams and Discord. Track adoption, ROI and the teams that need attention across 50+ actionable graphs.
- Integrations: GitHub, GitLab, Bitbucket, Gitea and Azure DevOps.
- Any AI model: reuse the Claude or OpenAI key you already pay for, or any of 15+ AI providers, or use the LiveReview model.
LiveReview is FREE and SELF-HOSTABLE. Run it on your own infra.
See how it works, then hit Try Free: https://hexmos.com/livereview/
If you like what we're building, star it to help devs discover LiveReview: https://github.com/HexmosTech/LiveReview