Steadra.dev is for automated QA with no effort

Hacker News

I created Steadra so I wouldn't have to spend effort on writing automated tests anymore. I always thought AI should be smart enough so our software team can rely on it and have automated tests up to date at all times with no effort. Beta is free (no card) because feedback is welcomed. Just add your URL. It will provide you with - Given-When-Then set of scenarios that represent your current state of things - Automated testing against these scenarios that can be run multiple times so developers don’t break existing functionality - Out of the box bug reports like console errors, bad status codes, broken links, etc ... You’ll get scenarios like: ``` Given I am on the pricing page When I click "Start free trial" Then I should land on the signup form ``` Managed to get the costs of using AI way down by working in multiple stages and being specific about what it needs to do for each of them. Feedback is much appreciated, Thank you. btw, you can also write your url on the comments section and I can do a run for you and show you the results for your web application.

Visit product View on Hacker NewsJul 29, 2026

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: using, code · Missing: mac, agents, macos

77%77% 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.

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

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, way · Missing: mobile apps, personal, entrepreneurs

57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

30%30% 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 · Strong signals: smart · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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