Stop Writing Boring Commit Messages. Let a Local AI Do It for You.

Vlad — Sun, 31 May 2026 02:03:57 +0000

How many times have you typed git commit -m "fix stuff" or git commit -m "wip" because you just wanted to push your code and grab a coffee?

Writing great, meaningful commit messages is a hallmark of a good developer. It helps your team, makes code reviews easier, and keeps your project history clean. But let’s be honest: when you’re deep in the zone, writing a perfect Conventional Commit is the last thing you want to spend brainpower on.

Enter komit — an AI-powered git commit message generator that runs 100% locally on your machine. No API keys, no internet required, and completely private.

🧠 Why `komit`?

There are plenty of AI commit generators out there, but most of them send your proprietary diffs to external APIs (like OpenAI or Anthropic). If you are working on a closed-source enterprise project, that’s a massive security no-no.

komit bridges the gap by leveraging Ollama to run lightweight, powerful open-source LLMs right on your laptop.

✨ Key Features:

🔒 Pure Privacy: Everything stays on your machine via Ollama. No data leaks.
🌿 Smart Branch Inference: It automatically reads your active git branch (e.g., feat/auth-login) to extract the exact Conventional Commit type (feat) and scope (auth) before the AI even starts writing!
📝 Multiple Styles: Choose between conventional, simple, or detailed commit formats depending on your team's workflow.
⚡ Safe & Interactive: It previews the message, letting you accept it, reject it, regenerate a new one, or open your default terminal editor ($EDITOR) to tweak it before finalizing.
🪟 Universal & Fast: Works seamlessly across Linux, macOS (including Apple Silicon), and Windows.

🛠️ The Interactive Developer Experience

When you run komit, you get a beautiful, clean terminal interface:

┬──────────────────────── Staged files (3) ────────────────────────┬
│  • src/auth.py                                                   │
│  • tests/test_auth.py                                            │
│  • README.md                                                     │
┴──────────────────────────────────────────────────────────────────┴
Branch name: feature/auth-tokens
Model: qwen2.5:7b · Style: conventional

⠋ Generating commit message...

┬──────────────────── Suggested commit message ────────────────────┬
│ feat(auth): add JWT authentication token engine [feature/auth-t… │
┴──────────────────────────────────────────────────────────────────┴

» Choose an action: (y)es, (n)o, (e)dit, (r)egenerate [y]:

What do those options do?

y (Yes) $\rightarrow$ Instantly runs git commit -m "..." for you.
n (No) $\rightarrow$ Aborts the mission without touching your code.
e (Edit) $\rightarrow$ Launches your environment editor (git commit -m ... -e) so you can perfect the AI's draft.
r (Regenerate) $\rightarrow$ Feeds the diff back to Ollama for a fresh perspective.

🚀 Getting Started in 60 Seconds

1. Prerequisites

You just need Ollama installed and a local model pulled down. We recommend any of these depending on your system specs:

# High quality (Recommended)
ollama pull qwen2.5:7b

# Great balance
ollama pull mistral:7b

# Blazing fast / Resource friendly
ollama pull llama3.2:3b

2. Installation

You can install komit using your preferred method:

Via pipx (Recommended for Python CLI tools):

pipx install komit

Via Shell Script (Linux/macOS):

curl -fsSL https://raw.githubusercontent.com/glemiu6/komit/master/scripts/install.sh | bash

Via PowerShell (Windows):

irm https://raw.githubusercontent.com/glemiu6/komit/master/scripts/install.ps1 | iex

3. Pro-Tip: Create a Git Alias

Make your life even easier by aliasing it directly in git:

git config --global alias.ai '!komit'

Now, your workflow becomes:

git add .
git ai

⚙️ Customization & Styles

By running komit init, the tool sets up a global configuration file (~/.config/komit/config.toml) where you can tweak your default model, timeout limits, and preferred output style.

Here is what the different styles look like:

Conventional (Default)

feat(auth): add user authentication [feature/login]

Simple

Add user authentication [feature/login]

Detailed

feat(auth): add user authentication [feature/login]
- Add JWT token generation pipeline
- Implement password security layers using bcrypt primitives
- Append rotation verification routes

🛡️ Open Source & Contributing

komit is fully open-source under the Apache License 2.0. If you want to check out the code, report a bug, or add a feature, feel free to drop by the repository!

👉 Check out komit on GitHub

Give it a spin, and let me know in the comments: Do you trust AI to write your commit messages, or are you a commit-message purist? 👇

I published my first Python package — a reusable RAG core library

Vlad — Fri, 01 May 2026 23:29:37 +0000

If you've ever built a RAG (Retrieval-Augmented Generation) system, you know the pain: every new project means rewriting the same boilerplate — vector store setup, embeddings, chunking, LLM wiring. I got tired of it, so I packaged it up.
pyragcore is a modular RAG library built on FAISS and Ollama. The idea is simple: give you a solid foundation so you can focus on your actual use case instead of reinventing the plumbing every time.
What I'm most proud of is that it runs entirely locally — no external APIs, no data leaving your machine. Just Ollama for the LLM and SentenceTransformers for embeddings.

What's inside

FAISS vector store with persistence, deduplication, and metadata filtering
Semantic search with MMR support
Local LLM inference via Ollama
Modular installs — grab only what you need
Abstract base classes so you can extend it your way

How to install it:

pip install pyragcore[all]

from pyragcore import BasePipeline

class MyPipeline(BasePipeline):
    def ingest(self, source: str) -> str:
        # your ingestion logic here
        ...

pipeline = MyPipeline(persist_dir="./memory", output_folder="./output")
source_id = pipeline.ingest("./my_document.pdf")
answer = pipeline.ask("What is this document about?", source_id=source_id)
print(answer)

It's still early days (v0.1.11) and I'm actively working on it, but it's already powering a couple of projects I built — a document chat bot.

PyPI Link
GitHub Repo

Would love any feedback, ideas, or contributions. If you build something with it, let me know!

DEV Community: Vlad