Giti – Natural Language to Git Commands with Local LLM

Hacker News

Hi HN, I built Giti, a command-line tool that converts plain English into actual Git commands using a fast, local language model (Qwen2.5-Coder, ~1 GB). Example: Input: giti "undo last commit" Output: git reset --soft HEAD~1 No internet required after setup. No API keys. You can also run it in an interactive shell to chain commands naturally. Key features: - Natural language to Git translation - Local LLM powered by Qwen2.5-Coder in GGUF format - Works fully offline after model download - Dry-run mode to preview commands before running - Interactive shell mode for session-based workflows - Context file support to teach Giti your custom Git habits Quick install: - Clone the repo - Install llama-cpp-python - Add giti to your PATH - Download the 1GB model from HuggingFace - Run giti "your query." You can also enhance its accuracy using context files in a simple Q&A format like: USER: How to start new feature? BOT: git checkout main && git pull && git checkout -b feature/<name> This lets Giti learn your workflow and generate project-specific Git commands. Thanks for checking it out.

Visit product View on Hacker NewsJul 22, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, new · Missing: mac, agents, macos

97%97% 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

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: llama, io · Missing: https docs, excited, just released

50%50% 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 · Missing: mobile apps, ios, personal

36%36% 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

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

15%15% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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