Obsessive monitoring

Hacker News

Hi all! We have been working for over two years on a data analysis product which gives you alerts (via phone/sms/e-mail) whenever input data triggers them. We built a server monitoring product with it which uses data to determine if your server is down/slow/broken etc. The server monitoring part is written in Perl and open source; it is an example of how you can input your own data. A Windows version is in the making (an alpha user already made an example on codeproject.com but I wouldn't consider that production ready). If you have some lazy sunday afternoon time, I hope you can give us some feedback. Bad or good, all welcome! Sign up on: http://observu.com

Visit product View on Hacker NewsFeb 10, 2013

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Actual performance

2points

1comments

Did not reach leaderboard

Launch Intel predictions

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

65%65% 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, code, open · Missing: mac, agents, macos

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

51%51% 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 · Missing: mobile apps, ios, personal

42%42% 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

18%18% 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

14%14% 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.

Incorrect prediction on native model

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