Test automation that doesn't suck

Hacker News

Hey HN. We’re two devs building Magnitude (magnitude.run), a software testing platform to eliminate brittle and high-maintenance testing. It uses LLM-powered web agents to execute natural language test cases while identifying specific bugs along the way. There’s plenty of buzz around web agents and new approaches to automated testing, so what makes our product different? Our approach is web-native, provides dynamic yet consistent behavior, and is packaged in an all-in-one solution so that you can forget about maintaining internal tools for testing. With Magnitude, you’re able to create test cases in natural language that just work based on what you intend, rather than relying on a particular webpage structure. Instead of brittle test failures becoming white noise, you’ll know that when a test fails, it means real users could be impacted. If you’d like to give this a try, check out our preview. We’d love to hear what you think about it. - Tom & Anders

Visit product View on Hacker NewsJan 31, 2025

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

89%89% 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.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

34%34% 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

20%20% 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.

Incorrect prediction on native model

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