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Playrun – Quality Assurance on Autopilot

Hacker News

Playrun – Quality Assurance on Autopilot

Hello HN! I wanted to share what I've been building for a few months now. Playrun https://playrun.dev Playrun is an AI-first QA platform. As an engineering team, ensuring nothing break on every production release either requires the team to manually QA or setup CI pipelines and test automations that are painful to maintain. - I was not happy with these option. So I built Playrun - Our approach was to build a multi-agent AI, that maps out your application flows like a user, generate test for each flow and allow you to setup a schedule to run generated tests. I've personally been using this on a small team of 4 at my 9-5 for about 2 month now and it has significantly reduces how much regressions and bugs gets into production. We'll love to hear your thoughts especially small teams like us who need to move fast without breaking stuff. Thank you.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, user, using · Missing: mac, agents, macos
96%96% 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
91%91% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, month · Missing: mobile apps, ios, entrepreneurs
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: pipe, io · Missing: https docs, excited, just released
42%42% 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: platform · Missing: plus, intuitive, reviews
40%40% 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
26%26% 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.

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