Automatically detect visual bugs on responsive websites via ML

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Hi HN! My friend and I just launched the initial version of Responsiveye (https://responsiveye.com) - the first tool that automatically detects visual bugs on responsive websites. Whenever we worked on a web app that was visually intensive and had many users we had trouble with visual bugs slipping through the QA. That is likely because visual testing is very repetitive. For every CSS change, QA needs to scale the browser window many times to check every single element and how it behaves on different screen sizes. I tried a couple of tools to help us with this but all solutions I found focused on visual regression testing which comes with a lot of maintenance. That is where the idea for Responsiveye came from. We were wary if the full automatization of testing was achievable technically but it ended up working quite well. We are still in the alpha stage but we would love to get your feedback and scan your website for visual bugs. You can submit your website for testing (no signup required) on https://responsiveye.com/ and also see examples of websites of well known companies that we found bugs on (Adobe, Dell, etc). If you find this helpful, please let us know and sign up to get access to the latest updates - we are planning to have more bug detections on a weekly basis.

Visit product View on Hacker NewsJul 19, 2022

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

78%78% 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: user, visual, single · Missing: mac, agents, macos

78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

55%55% 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: users · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

38%38% 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

13%13% 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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