Launch Intel
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I built a Git client that auto-backs up your code while using AI tools

Hacker News

I built a Git client that auto-backs up your code while using AI tools

Like many developers, I found myself losing good code while experimenting with different LLM suggestions. Copy-paste a new approach from an LLM, and your previous solution is gone. I built Xferro to solve this. It's a Git client that automatically creates backup branches and commits your changes as you work. Every time you make a change, it's committed to the backup branch and pushed to remote - creating a complete history of your development process, including all those AI experiments. I wrote a blog post about how the "LLM Experimentation Trap" impacts developers and how Xferro addresses this: [ https://www.xferro.ai/post/the-ai-suggestion-vortex ] We're currently collecting waitlist signups for our beta launch. I'd appreciate any feedback on the approach or other scenarios where you find yourself losing work. Join the waitlist [ https://xferro.ai ]

Visit productView on Hacker NewsMar 25, 2025
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, using, code · Missing: mac, agents, macos
73%73% 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: ios, including · Missing: supports, reddit linkedin, podcasting
60%60% 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
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io, including · Missing: https docs, excited, just released
29%29% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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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