Vibeshub – Git for your vibe code transcripts

Hacker News

My colleagues all use Claude code to generate the PRs, but while reviewing I only have a large block of code diff, and a single comment making it tough for me to make sense out of the diff. Moreover, there was no shared knowledge base of vibes amongst the team, nor a way to revisit own reasoning later. I realised that just the code is not enough context, and thats why built Vibeshub. It's a Claude Code plugin which auto uploads the transcript, and posts the comment on the PR with a link pointing towards it on vibeshub. The link contains a much more readable version of your chat, making it easy to skim, review, knowledge share for your teammates. The repo used to build this ( https://github.com/vibeshub/vibeshub ) is the first customer of this product. Feel free to explore some PRs and traces here https://vibeshub.ai/vibeshub/vibeshub Would love to hear from folks if you've faced similar issues when vibe-collaborating, and if this fits into your current workflow. Happy to answer any questions here, or shoot them my way at bhavya@vibeshub.ai

Visit product View on Hacker NewsMay 26, 2026

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

94%94% 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 NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

37%37% 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

35%35% 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

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

26%26% predicted probability of success on Indie Hackers, 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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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