👋 Hey Product Hunt, I'm Gal, founder and CEO of Checksum.
A few years ago at my last startup, I watched our team lose entire sprints to test maintenance. Every time the product changed, someone had to go update selectors, re-triage failures, and figure out which broken tests were real bugs and which were just noise. I'd spent years before that building ML models to detect suspicious activity from satellite data—pattern recognition at scale—and it nagged me that software testing was the same kind of problem.
AI coding tools solved generation and teams can now ship far more code than ever. But they didn't solve verification; every PR still needs to be tested and trusted before it ships. Counterpart, an agentic insurance platform, runs a 10x QA team on Checksum at less than half the cost of one offshore developer, and hasn't had a production outage since. Their engineering manager Ron Alexssen put it this way: "For less than half the salary cost of an offshore developer, I have the impact of a full QA team."
That’s why we built Checksum. Our agentic loop runs in two parts:
🔁 Generate and maintain. On every pull request, an agent spins up in a sandbox, detects what changed, and generates or updates your End-to-end and API tests automatically. No written selectors by hand.
🔁 Run, report, fix. Trigger your suite from a PR, the API, or MCP. When something fails, a second agent triages it: real bug, or broken test by a product change? Real bugs route to Jira, Linear, or Slack. Broken tests get fixed autonomously. Söderberg & Partners went from zero to full coverage in weeks and now reclaims 90 hours of manual testing a month. Postilize cut bugs by 70% and sped up engineering cycles by 30%, with zero flaky tests.
Everything ships as standard Playwright code committed to your own repo. No proprietary format, no lock-in. And the agent doesn't just chase easy passing tests, it goes after the hard cases too: auth boundaries, edge flows, the stuff that's tedious to test manually and easy for AI to skip if you let it.
🎁 Product Hunt community gets a free 30-day trial with code PHLAUNCH
We're here all day, ask us anything. 🙏
PH 用户
The interesting part of AI coding isn't just generating more code—it's being confident that the code actually works. Anything that closes that verification gap has huge potential for modern engineering teams.
PH 用户
AI-generated code is making development faster, but it also makes verification more important than ever. Tools that help developers catch problems before they reach production feel like a natural next step.
PH 用户
The stale test detection is the part that caught my attention. AI generated code is only useful if the tests dont become another maintenance job.
PH 用户
I work at checksum so grain of salt, but my take:
What surprised me: I came in assuming the hard problem was generating tests. It isn't — models will write plausible-looking tests all day. The hard problem is that "plausible" and "green" are both terrible proxies for "actually proves the feature works." A big part of my job turned out to be making the system distrust its own passing tests.
The moment that sold me: a test broke after a routine frontend change, and the obvious move — the one every human on every team makes ten times a week — was to update the assertion to match the new behavior and get back to green. The agent refused. It walked the diff, decided the new behavior wasn't an intentional change but a regression, and filed it as a product bug instead of "fixing" the test. It was right. A human reviewer would have rubber-stamped the assertion update, the suite would have gone green, and the bug would have shipped with a passing test standing guard over it.
Why verification is the unglamorous problem: when it works, nothing visible happens. There's no demo moment for "this green check is real." So everyone builds the flashy generation demo and quietly ships suites that decay into checkmarks nobody trusts. But an untrusted suite is worse than no suite — you keep paying the maintenance cost and get none of the confidence. Solving that is the actual product, and it's the part nobody wants to put on stage.
A few years ago at my last startup, I watched our team lose entire sprints to test maintenance. Every time the product changed, someone had to go update selectors, re-triage failures, and figure out which broken tests were real bugs and which were just noise. I'd spent years before that building ML models to detect suspicious activity from satellite data—pattern recognition at scale—and it nagged me that software testing was the same kind of problem.
AI coding tools solved generation and teams can now ship far more code than ever. But they didn't solve verification; every PR still needs to be tested and trusted before it ships. Counterpart, an agentic insurance platform, runs a 10x QA team on Checksum at less than half the cost of one offshore developer, and hasn't had a production outage since. Their engineering manager Ron Alexssen put it this way: "For less than half the salary cost of an offshore developer, I have the impact of a full QA team."
That’s why we built Checksum. Our agentic loop runs in two parts:
🔁 Generate and maintain. On every pull request, an agent spins up in a sandbox, detects what changed, and generates or updates your End-to-end and API tests automatically. No written selectors by hand.
🔁 Run, report, fix. Trigger your suite from a PR, the API, or MCP. When something fails, a second agent triages it: real bug, or broken test by a product change? Real bugs route to Jira, Linear, or Slack. Broken tests get fixed autonomously. Söderberg & Partners went from zero to full coverage in weeks and now reclaims 90 hours of manual testing a month. Postilize cut bugs by 70% and sped up engineering cycles by 30%, with zero flaky tests.
Everything ships as standard Playwright code committed to your own repo. No proprietary format, no lock-in. And the agent doesn't just chase easy passing tests, it goes after the hard cases too: auth boundaries, edge flows, the stuff that's tedious to test manually and easy for AI to skip if you let it.
🎁 Product Hunt community gets a free 30-day trial with code PHLAUNCH
We're here all day, ask us anything. 🙏
What surprised me: I came in assuming the hard problem was generating tests. It isn't — models will write plausible-looking tests all day. The hard problem is that "plausible" and "green" are both terrible proxies for "actually proves the feature works." A big part of my job turned out to be making the system distrust its own passing tests.
The moment that sold me: a test broke after a routine frontend change, and the obvious move — the one every human on every team makes ten times a week — was to update the assertion to match the new behavior and get back to green. The agent refused. It walked the diff, decided the new behavior wasn't an intentional change but a regression, and filed it as a product bug instead of "fixing" the test. It was right. A human reviewer would have rubber-stamped the assertion update, the suite would have gone green, and the bug would have shipped with a passing test standing guard over it.
Why verification is the unglamorous problem: when it works, nothing visible happens. There's no demo moment for "this green check is real." So everyone builds the flashy generation demo and quietly ships suites that decay into checkmarks nobody trusts. But an untrusted suite is worse than no suite — you keep paying the maintenance cost and get none of the confidence. Solving that is the actual product, and it's the part nobody wants to put on stage.