A better way to monitor web apps

Hacker News

Hey guys, We're excited to reach Beta milestone with our web app monitoring service. By posting here, we're seeking to get some feedback from you. So, why QABot? Why another monitoring service? We believe in Continuous Regression Testing. We are sure that continuous app validation reduces downtime and helps avoid disasters. QABot uses real browser and gets the monitoring as close as possible to real usage. At it's core, QABot is based on Selenium, an open source web browser automation tool. When we say monitoring, we mean more than checking http statuses and response times, we mean Continuous Validation of web apps, of rules defined by you using Selenium IDE. To get started you need an account and a valid Selenium IDE test case (*.html file). Once it's uploaded we'll run it for you and notify if anything goes wrong. URL: http://qabot.net Thank you.

Visit product View on Hacker NewsMar 7, 2013

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

85%85% 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: excited, open source, ide · Missing: https docs, just released, exist

62%62% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

19%19% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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