CrashBreak – A new approach for production exceptions and bugs

Hacker News

Hello! Some time ago I was working on an application that had many bugs and reproducing each one was time consuming. I thought about making this process faster and more user-friendly. Recreating bugs manually in the browser by typing data and setting all connected modules in the same way like when the exception occurred can be annoying. Also, there is no certainty that the bug is reproduced in the same way it occurred. The other problem is that many of the bugs are hard to reproduce and connected to different layers of the system. The idea of CrashBreak is to reproduce the exception from the staging server on the programmer’s computer by running a request test. In order to do this the library needs to dump your system piece by piece. Currently we have 3 dumpers: for database, all request data (headers, url, body) and user session. We tried to create the whole system in an adaptive way. It means that you can write your own extensions to adapt the library to your needs. The ruby beta of the service is ready and available at crashbreak.com. Please give it a try and send me your feedback so I can make CrashBreak even better. Thank you kind strangers! Michał Janeczek

Visit product View on Hacker NewsSep 23, 2015

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, computer, new · 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.

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

82%82% predicted probability of success on Indie Hackers, 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

58%58% 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 · Strong signals: way · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

41%41% 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

13%13% 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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