Auto-file bugs to GitHub issues with console logs and network requests

Hacker News

Hi HN, my team and I have been working on a new tool to improve how bugs are reported to engineers. I used to be a developer, and I thought it was ridiculous the little amount of bug repro details I would get in JIRA tickets. Then I became a product manager, and I realized how time consuming and tedious it was to write good tickets for developers (and then understood why most tickets lack enough detail!) That’s why my team and I decided to build a browser extension for anyone to create bug reports that auto-include: console logs, network requests, session replay, timestamp, url, browser, OS and device specs, and wifi speed. With this extension, it’s faster than the old-school way to report bugs (a few clicks, plus no switching tabs). And, for the developer receiving the bug reports, it should be faster to debug because all the information is right there. We started with a Chrome extension and soon we’re going to build extensions for other browsers too. (Which should we add next? We’re thinking Firefox). We built this in react, typescript, mobx, and graphql. It’s privacy-focused: everything happens locally on your browser until you choose to share, and you can even select which specific websites you want the extension to run on in settings. Today we shipped an integration with GitHub - meaning it’s now just a few seconds to create a really good GitHub issue. I hope you check it out and I hope it helps bring about the end of bad tickets for you and your team! If you have any suggestions or questions, please let me know here!

Visit product View on Hacker NewsNov 9, 2022

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

96%96% 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: started · Missing: supports, reddit linkedin, podcasting

67%67% 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

65%65% 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 · Strong signals: plus, soon · Missing: platform, intuitive, reviews

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

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

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

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