AI agents to automate running and maintaining regression tests

Hacker News

In a nutshell: 1. We built a web app and had to do lots of repeated manual testing after each push to prod to make sure that we didn't break any existing features. 2. Coding up tests early on just isn’t worth it, especially with the amount of updates they would need as the UI changes so frequently. BUT we have to test. We built an LLM-based tool that lets you write, run, and maintain tests for web applications in natural language. E.g., if you want to test the sign in flow, you would simply type "sign in with email test@example.com and password Passw0rd!" and it would execute it for you. Curious to hear thoughts here - is manual regression testing as much of a pain for other teams as it is for us? If you're interested in trying it out, our free MVP is ready to use and super quick to set up. We would love to hear feedback / questions. - Masha

Visit product View on Hacker NewsFeb 23, 2024

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

94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

33%33% predicted probability of success on AppSumo, 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

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

nativeThis product was originally launched on this platform.

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

21%21% 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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