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Flytrap – Debugging tool for fixing production bugs

Hacker News

Flytrap – Debugging tool for fixing production bugs

Hey HN! I’m Rasmus, co-founder of Flytrap. Flytrap is a debugging tool for reproducing and fixing production bugs faster. When launching a product quickly, there is bound to be some bugs. Tools like Sentry might help combat this, but when a bug lands in Sentry, all you see is the Red Carpet of Doom (the stack trace). We were frustrated with the poor developer-experience of current debugging tools, and decided to build Flytrap; a fast debugging tool that quickly allows developers to understand in detail what happened leading up to a bug, and focuses on reproducibility in the developer environment. - Key features 1. Detailed context; See the inputs and outputs of all function calls leading up to the bug 2. Reproducibility; Reproduce the bug on your local development environment in under a minute 3. Security; All capture data encrypted during transit and at rest. - Links Home page: https://www.useflytrap.com Docs: https://docs.useflytrap.com GitHub: https://github.com/useflytrap/flytrap-js What issues have you had with fixing bugs in production? We would love to hear your ideas, thoughts, experiences and feedback!

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Product HuntOn track for Day 1 leaderboard · Strong signals: context · Missing: mac, agents, macos
88%88% 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: ide, io · Missing: https docs, excited, just released
76%76% 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 · Missing: supports, reddit linkedin, podcasting
74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: calls · Missing: plus, platform, intuitive
53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · 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
10%10% 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.

Correct prediction on native model

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