Gg

Ggc – A Git CLI tool written in Go with interactive UI

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Ggc – A Git CLI tool written in Go with interactive UI

A while ago I shared an early version of ggc, a Git helper I built in Go. Since then the project has grown quite a bit, and I’d love to share the latest updates (v6.0). Repo: https://github.com/bmf-san/ggc Install: - macOS/Linux: `brew install ggc` - Go: `go install github.com/bmf-san/ggc/v6@latest` - Homebrew: `brew install ggc` - Or grab binaries: https://github.com/bmf-san/ggc/releases Features: Dual modes: Traditional CLI commands (ggc add, etc.) and interactive mode (launch with just ggc) Intuitive command structure: Simplified interface for common Git operations Incremental search UI: Quickly find and execute commands with real-time filtering Fast and lightweight: Implemented in Go with minimal dependencies Shell completions: Included for Bash, Zsh, and Fish Custom aliases: Chain multiple commands with user-defined aliases Cross-platform: Works on macOS, Linux, and Windows Technical details: Built with Go standard library and minimal external packages Supports 50+ Git operations (add, commit, branch, pull, etc.) I'd appreciate any feedback or contributions!

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Actual performance

44points
1comments
Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, user · Missing: agents, agent, cursor
80%80% 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: supports · Missing: reddit linkedin, podcasting, created
62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: platform, intuitive, interface · Missing: plus, reviews, host
60%60% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
26%26% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
13%13% 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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