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vibe-git - a frictionless git workflow for vibe coding (with MCP)

Hacker News

vibe-git - a frictionless git workflow for vibe coding (with MCP)

I got tired of context-switching between coding and git operations, so I built vibe-git - an MCP server that automates the entire git workflow when AI assistants make code changes. Here's how it works: - You say "Let's start a vibe session" → AI calls start_vibing(), server creates a branch and starts a file watcher - The watcher detects when AI edits files and auto-commits changes (every second) - You say "Let's stop vibing" → AI calls stop_vibing(), server squashes commits, rebases on latest main, and creates a PR The "vibe" philosophy: you speak naturally about features, AI codes, file watcher handles git seamlessly. Plus you never lose work if/when the agent goes rogue since every change is committed as it happens. I've been dogfooding this by developing vibe-git using Claude Code + vibe-git. The current stack is implemented in Python + FastMCP + beartype + plum + mutmut. My experience so far is it's much easier to stay in the flow when you don't have to context switch to committing / managing git and I've found I micromanage the agent less and instead focus on iterating towards a good looking PR.

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, claude, mcp · Missing: mac, agents, macos
99%99% 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
61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
37%37% 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
25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus, calls · Missing: platform, intuitive, reviews
24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
21%21% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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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