RocketQA – Write Tests in English (Gherkin), Run with Playwright

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We are Eric and Alex and we are the developers behind RocketQA. We built it for our own team because we were tired of writing clunky QA tests. We wanted something that felt natural — write features in Gherkin, run them with Playwright, and actually enjoy reading the tests later. We wanted our manual QA guys to write automated tests without developers' help. Unlike most QA frameworks, RocketQA keeps tests both developer-friendly and business-readable. You don’t have to choose between speed of writing and clarity of output. It’s also lightweight - no complex setup, just plug it into your project and go. It worked well for us, so I decided to make it open source and put it out there in case it helps anyone else. No sales pitch here - just sharing because I know how annoying QA automation can get when it doesn’t fit into your workflow. If anyone’s curious, I’m happy to record a quick video walking through how we use it. Or if you just want to poke around, docs are here: https//rocketqa.ai/docs Would love feedback, ideas, or just to hear how you’re handling QA in your projects.

Visit product View on Hacker NewsSep 19, 2025

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

90%90% 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

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video · 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: open source, ide, io · Missing: https docs, excited, just released

28%28% 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 · Strong signals: friendly · Missing: plus, platform, intuitive

28%28% 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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

Correct prediction on native model

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