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Qlane
Merge PRs with confidence - AI QA that runs your whole app
Most tools sit on one side of a line. AI code-review tools read the diff but never run it, so they guess "this could be null." Black-box E2E tools drive a real browser but see only the DOM. Qlane boots your whole app in a sandbox and tests it from the inside - browser, shell, source, plus connectable logs and database - so it reports only bugs that actually reproduce, and traces each to its root cause. runs your app, not just your code.
Hey Product Hunt - Nazar here, founder of qlane. We built this platform because reading code and running code catch completely different bugs - and almost everything on the market only reads.
On every pull request it clones, builds, and boots your actual app in an ephemeral sandbox - a single repo or your whole multi-service stack via Docker Compose with seeded data. Then an AI agent tests it from the inside, with an engineer's toolkit: a real browser, a shell, read access to your source, and connectable server logs, database, and error tracking.
The difference is where it looks:
- Static code review reads the diff and guesses.
- Black-box test tools click the UI and see the DOM.
- qlane reproduces the bug in the browser, then reads the failing network request, the matching server-log line, the offending DB row, and the source - and posts the root cause as a native GitHub review.
It runs your app, not just your code. Evidence, not opinions. Because it boots the whole stack, it also catches the cross-service bugs that single-component tests structurally can't see.
My question for you: what's the last bug that passed review and tests, then only showed up once the running app hit real data - and how long did it take to trace back to the actual cause?
Thanks for reading - I'll be in the thread all day.
ran a flaky checkout flow through it and the root cause trace pointed straight at a race condition i had been chasing for days, which was a genuinely pleasant surprise
How does it handle flaky tests or external services like third-party APIs when spinning up the whole sandbox?
how does it handle flaky stuff like network requests or third-party api calls when reproducing bugs in the sandbox?
the "boots your whole app and actually runs it" part is the compelling piece, but that cuts both ways - it means the agent can trigger real side effects too, not just observe them. if a PR touches checkout, does the run risk hitting a payment provider's live mode or sending a real email/webhook, or is that sandboxed away by design? feels like the same power that catches real bugs could also cause real damage if it's not scoped carefully.
As a solo dev shipping AI-written code daily, this hits a real pain: unit tests pass but the actual app breaks. Question — can it catch visual regressions? In my product (image processing) the worst bugs are "output looks subtly wrong but nothing throws," and I've only been able to cover those with hand-written pixel-level regression tests.
I like the shift from analyzing code to validating behavior.
As AI writes more software, the harder problem won't be generating code—it'll be proving that the system actually works under real conditions.
Running the app instead of reasoning about diffs feels like a fundamentally different approach.
The bugs that only turn up when the thing is actually running have always been the tedious ones to chase, so this feels like a real weight off before approving anything.
"Reading code and running code catch completely different bugs" is the thesis I'd tattoo on a wall — I verify my own changes by driving the actual app for exactly this reason, because a green typecheck and a working app are different claims. The question that decides how much I'd trust it: fidelity of the sandbox. Booting the stack with Docker Compose and seeded data catches "does the happy path run," but the bugs only running catches usually live in production-shaped state — an auth-token refresh race, a connection pooler that refuses you under a specific condition, a third-party API returning something your seeds never do. Seeded data almost by definition doesn't contain the state that produced the bug. Can Qlane snapshot real (anonymized) state into the ephemeral env, or is it always synthetic seeds? That gap is the difference between "the PR runs" and "the PR is safe."
how does it actually handle test flakiness when the sandbox environment differs from CI, and is that something i have to configure myself or does it handle it under the hood
Tried it on a small project and the fact that it actually boots the app in a sandbox made a difference, caught a race condition two other tools missed. Root cause trace was clear enough to act on.
finally something that doesn't just speculate about my null pointers. watched it catch a real race condition in my db queries by actually running the stack, not just staring at the diff. genuinely useful.
How does this handle apps with heavy external service dependencies, like third-party APIs that need real credentials during a test run?
About Qlane on Product Hunt
“Merge PRs with confidence - AI QA that runs your whole app”
Qlane was submitted on Product Hunt and earned 27 upvotes and 14 comments, placing #16 on the daily leaderboard. Most tools sit on one side of a line. AI code-review tools read the diff but never run it, so they guess "this could be null." Black-box E2E tools drive a real browser but see only the DOM. Qlane boots your whole app in a sandbox and tests it from the inside - browser, shell, source, plus connectable logs and database - so it reports only bugs that actually reproduce, and traces each to its root cause. runs your app, not just your code.
Qlane was featured in SaaS (43.1k followers), Developer Tools (515.9k followers) and Artificial Intelligence (473.7k followers) on Product Hunt. Together, these topics include over 232.9k products, making this a competitive space to launch in.
Who hunted Qlane?
Qlane was hunted by Nazar Kuzmenko. 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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Nazar Kuzmenko