Record manual QA flows, get E2E test code that fits your repo

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TLDR: Desktop app for E2E web test generation, built at JetBrains (closed beta). Record the flow in a built-in browser - the agent matches it with your existing codebase, then writes a test that passes, not a draft to debug. Devs use AI to ship more code. That code still needs testing. If your team writes E2E tests by hand, you have a problem - same QA capacity, way more surface to cover. AI agents can write E2E test code, but you're stuck describing flows in text - the agent clicks around via Playwright MCP, takes wrong turns, you re-prompt, retry. 30 minutes for a flow you could click through in 30 seconds. Qure works differently. You record the scenario in Qure's built-in browser by just using your product. The AI turns that recording into code. No prompt engineering, no MCP setup, no explaining your repo in chat - point it at your project and go. Beyond recording, you can also refactor tests, update them, or write new ones from a description. What keeps the AI output grounded: - We match the recording against your codebase - find your page objects, helpers, constants and feed them to the agent instead of hoping it figures out your repo - When agent runs the test, it reads real failure output, fixes with actual error and app context This is a closed beta of an experimental product. Web only, works best with Playwright. If your project has a few dozen tests - Claude Code will honestly get you there. Qure makes a difference on larger codebases with existing test infrastructure. 5-min demo: https://www.youtube.com/watch?v=4CZw4bSSDCE Try the beta: https://quretests.com Happy to answer any questions about the approach, product, or where it breaks - I'm the dev on the Qure team. Egor (@250xp), who leads the project, is in the thread too.

Visit product View on Hacker NewsMar 24, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor

98%98% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, io · Missing: https docs, excited, just released

39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

27%27% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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