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Automated, hosted UI tests with AI test discovery (octomind.dev)

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Automated, hosted UI tests with AI test discovery (octomind.dev)

I am Marc, the co-founder of octomind.dev. I am a former chess player turned machine learner turned entrepreneur. I always wanted to develop products based on the latest AI research. I did it at my previous startup with computer vision and now at octomind.dev with LLMs/agents. Octomind is an AI-powered end-to-end testing platform for web apps. You enter your URL, and we will auto-generate, run, and auto-maintain UI tests. My co-founder Daniel and I used to work together in our previous AI startup. We had never-ending problems with end-to-end testing. We tried many frameworks and tools and even looked into outsourced QA services. Nothing that wasn’t a pain for our dev teams to write and maintain wouldn’t hold back releases or be reasonably priced. We have a decent level of experience in baking AI into software products. It made sense to apply it to e2e testing. Whenever a test target is set, our AI agents discover user flows and interactions leading toward a desired user goal. This serves as the foundation for creating deterministic test cases. We use it for automated test case discovery and are working on expanding it to maintenance so that broken tests can self-heal. We’ve built Octomind on top of Playwright, which is imho the most advanced framework for end-to-end testing today. We don’t need access to your code, and the generated Playwright code is yours. We run and host the tests. We report the test results in our app or directly in PR comments if you integrate Octomind with your CI pipeline. To help with debugging, we’ve integrated Trace Viewer and created Debugtopus - an open-source tool for local debugging. (see https://github.com/OctoMind-dev ). We’re still in an early access mode, so any feedback from the community would be appreciated. Try us out for free. We want to keep the freemium option even as we grow.

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
96%96% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, agent · Missing: macos, cursor, claude
95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: pipe, io · Missing: https docs, excited, just released
52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: apps, way · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews
23%23% 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
16%16% 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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