Videobug – The time travel debugger for JVM

Hacker News

Hi All, this is Shardul here - I am the co-founder of Videobug. https://bug.video We are super excited to share Videobug with you. Videobug records run time code execution so that developers can watch it line by line as frequently as they want, right in their IDE. It takes away the pain of recreating exact conditions that led to a bug and saves developer time in every bug squash. Parth (my co-founder) and I have worked on multiple production grade applications in startups and enterprises. We used Logrocket, Sentry, and Datadog for logging and found ourselves adding more logs after a bug is found. Adding accurate logs required disciplined engineering and collaboration across teams. Time to connect these logs to what’s wrong in the code, took quite sometime. Videobug logs everything automatically. We are super psyched to launch our offline version. We plan to launch a fully self hosted version in 4 weeks from now, which will allow you to record and replay code executions in your staging and production environments. We are looking for product feedback. Here is a 2 min demo explaining how to use Videobug. https://www.youtube.com/watch?v=U53IQifMt54 Join our discord channel to share your feedback or in case you need support. https://discord.gg/Hhwvay8uTa P.S. Please note that we collect the following analytics data from your IDE: Your computer hostname, debugging events such as “started debugger”, “fetched exceptions” and “searched for code execution”.

Visit product View on Hacker NewsMay 6, 2022

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Product HuntOn track for Day 1 leaderboard · Strong signals: computer, code, plain · 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

67%67% 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: video, way · Missing: mobile apps, ios, personal

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

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

26%26% 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

16%16% 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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