I Built a QA Agent to Detect Broken Web App Flows – E2E Test/Regression

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Hi everyone! I’m Jose, the solo founder of AlertDown, an automated user flow monitoring tool for web apps. It notifies you when something breaks—before your users do. Imagine end-to-end tests with real-time monitoring, catching regressions and silent failures without: • Writing a single line of code • Installing any dependencies Just add your URL, and AlertDown will: 1. Extract all possible user actions (clicks, inputs, dropdowns, etc.) 2. Automatically test each flow, branching out dynamically 3. Detect silent issues like: • Missing dropdowns • Unresponsive buttons • Third-party API failures Unlike tools like Sentry or Datadog, these errors often don’t show up as obvious failures, which can lead to lost revenue. Why I built this: While working at a cash-constrained startup (not mine), we lost $1,347 in revenue due to a misconfigured feature flag at step 7 of our onboarding. We didn’t realize it until 3 days later—after a user reported it. :( I’ve seen this issue repeatedly over the years: • “Non-breaking” code silently causing regressions • Third-party services failing unexpectedly • Testing fatigue—running the same flows over and over manually I thought an automated solution like this wasn’t possible, but after an initial POC and 5 months of work, it’s finally live and working! Tech Stack: • Remix, TypeScript, Vite • Supabase • Docker on Google Cloud Run & Compute Engine Temporal.io for orchestration What’s Next: I’d love your feedback and ideas as I continue improving: • Handling login screens • Slack integrations for alerts • Custom viewport testing Try it out now (for free)! I'm looking for some early adopters that would like to pilot the product. You can access without paying (just head to the login page)—it’s currently in public testing! Looking forward to your thoughts and feedback. P.S: And if you are part of a company or building your SaaS, I want to work close with you to craft a unique experience.

Visit product View on Hacker NewsJan 21, 2025

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

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nativeThis product was originally launched on this platform.

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

34%34% 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

24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: revenue, saas · Missing: arr, mrr, profit

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