Pingsure, test your webapp every hour.

Hacker News

Please give us feedback if you would use such a service. Our Pitch: Website is http://www.pingsure.com We have made “getting integration testcases suite ready” extremely easy. Its as simple as signing up for an account and select the testcases you wish to run against Production server. We write the testcases for you and get them up and running in 48 hrs (max). Our Confusion: Generally everyone believes that testing is never too much. The more you test the better. So such a service should definitely act as a last line of defense. But the response we have got until now has been more on the average to cold. Everyone likes the idea though. But until bought, it does not prove the business model. We did make the mistake of building the product without validating the product need. So please give feedback/criticism.

Visit product View on Hacker NewsMar 29, 2014

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Actual performance

4points

11comments

Did not reach leaderboard

Launch Intel predictions

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

66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

57%57% predicted probability of success on TrustMRR, 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

56%56% 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

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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18%18% 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.

Incorrect prediction on native model

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