Vibe Backup – A time machine for your Vibe Coding projects

Hacker News

I built an app to stop that mini heart attack you get when you realize you’ve lost code. It's called Vibe Backup, and it's made just for us. It automatically saves versions of your Vibe Coding projects in the background. Think of it as a simple time machine for your code. It works great with Git—saving a snapshot after each commit—and has a hotkey for those "hold my beer" moments before a big refactor. Restoring is just a click. Key features: Trigger backups instantly with KEYBOARD SHORTCUTS — without leaving your IDE. Set it and FORGET IT — Schedule backups on a flexible timer or automatically trigger them after every Git commit Your project's Time Machine Restore to any Version in Seconds Securely encrypted and syncs to iCloud Drive, Dropbox, Google Drive, or OneDrive automatically

Visit product View on Hacker NewsSep 26, 2025

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

92%92% 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

51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

33%33% 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: google · Missing: mobile apps, ios, personal

20%20% 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

12%12% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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