MLX-CODE: 100% Local AI Coding Assistant for macOS
Discover MLX-CODE, a privacy-focused local AI coding assistant that runs entirely on your Mac using Apple's MLX framework. Free, offline, and powerful with 20+ AI models.
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✍️ Gianluca
🤖 MLX-CODE: 100% Local AI Coding Assistant for macOS
MLX-CODE is a privacy-focused local AI coding assistant that runs entirely on your Mac using Apple's MLX framework. Think of it as a self-hosted alternative to GitHub Copilot or Claude Code, but with full project context awareness and intelligent file handling—completely offline and free.
🔒 What Makes MLX-CODE Special:
- ✅ 100% Local & Private — No data sent to external servers
- ✅ Intelligent Context — Automatically loads project files
- ✅ GPU Accelerated — Uses Apple Silicon GPU for fast inference
- ✅ Auto-Backup — Every file modification is backed up automatically
- ✅ 20+ AI Models — Qwen, DeepSeek, Llama 3, CodeLlama, Mistral
⚡ Key Features
🧠 Intelligent File Reading (V2)
Automatically loads files when you mention them in conversation. Just say "check main.py" and it reads it for you.
📁 Project Awareness
Understands your codebase structure, detects project type (Python, Node.js, Rust), and loads relevant config files.
💾 Automatic Backups
Every file modification creates a timestamped backup. Restore anytime with simple commands.
🎨 Beautiful Diff Previews
See exactly what will change before applying with color-coded diffs (additions, deletions, context).
📋 Smart Templates
Quick workflows for testing, documentation, refactoring, code reviews, and optimization.
⌨️ Advanced Terminal Input
Command history with arrow keys, tab completion, multi-line paste support, and smart Ctrl+C handling.
🔧 Technology Stack
- MLX Framework (Apple) → Metal GPU acceleration optimized for M-series chips
- MLX-LM → High-performance LLM inference library
- Qwen2.5 Coder → State-of-the-art coding models (1.5B/3B/7B/14B/32B variants)
- DeepSeek-V2-Lite → Excellent code quality with 9GB model optimized for 16GB+ RAM
- Python 3.12 → Latest stable Python with performance improvements
🤖 Available AI Models
| Model | Size | Quality | RAM | Recommended For |
|---|---|---|---|---|
| Qwen 1.5B | ~1GB | ⭐⭐ | 4GB | Demo/testing only |
| Qwen 3B | ~1.9GB | ⭐⭐⭐ | 6GB | Light coding |
| Qwen 7B ⭐ | ~4.3GB | ⭐⭐⭐⭐ | 8GB | Daily development (recommended) |
| Qwen 14B | ~8.5GB | ⭐⭐⭐⭐⭐ | 16GB | Advanced projects |
| DeepSeek-V2 ⭐⭐⭐ | ~9GB | ⭐⭐⭐⭐⭐ | 16GB | Best code quality (M1/M2/M3 16GB+) |
📦 System Requirements
- ✅ macOS 13.0 (Ventura) or later
- ✅ Apple Silicon (M1, M2, M3, or M4 chip)
- ✅ Python 3.12 or later
- ✅ 8GB RAM minimum (16GB+ recommended for 7B model)
- ✅ 10GB free disk space (for models and cache)
🚀 Quick Installation
# Install Python 3.12 brew install python@3.12 # Create virtual environment python3.12 -m venv ~/.mlx-env source ~/.mlx-env/bin/activate # Install dependencies pip install mlx-lm prompt-toolkit pillow # Create workspace mkdir -p ~/Projects # Download and setup MLX-CODE # (Copy the mlx-code script to ~/mlx-code) chmod +x ~/mlx-code # Launch! cd ~/Projects/your-project ~/mlx-code
💡 Use Cases
- Code Generation: Create complete files, functions, classes from natural language descriptions
- Refactoring: Modernize legacy code, apply design patterns, improve structure
- Debugging: Intelligent error detection and fix suggestions with full context
- Documentation: Auto-generate docstrings, comments, and README files
- Testing: Create comprehensive unit tests with templates
- Code Review: Get architectural feedback and best practice recommendations
⚡ Performance Insights
M1/M2/M3 (16GB RAM)
- ✅ DeepSeek V2: ~1.8s response time
- ✅ Qwen 7B: ~1.5s response time
- ✅ Qwen 3B: ~0.8s response time
- ⚠️ Qwen 14B: Close other apps
M4 Pro (24GB RAM)
- ✅ 30-40% faster than M1/M2
- ✅ Better memory bandwidth
- ✅ More efficient power usage
- ✅ DeepSeek V2: Excellent stability
🔒 Privacy & Security
Unlike cloud-based AI coding assistants, MLX-CODE processes everything locally:
- No code sent to external servers
- No API keys or subscriptions required
- Works completely offline after model download
- Sandboxed to ~/Projects directory for safety
- All backups stored locally with timestamp
💡 Tips & Best Practices
- Model Selection: Start with Qwen 7B for best quality/speed balance. Try DeepSeek V2 if you have 16GB+ RAM.
- Context Management: Mention files by name for auto-loading. Use /context commands to manage loaded files.
- Templates: Use /template test before writing tests, /template review for code reviews, /template doc for documentation.
- Download Speed: Install git-lfs for 3-5x faster model downloads:
brew install git-lfs && git lfs install - Keyboard Shortcuts: Install prompt-toolkit for arrow key history, tab completion, and better paste support.
🔄 Version Comparison
| Feature | Version 1 | Version 2 |
|---|---|---|
| Write/Edit Files | ✅ Yes | ✅ Yes |
| Auto-load Files | ❌ No | ✅ Yes (intelligent detection) |
| Project Context | ❌ No | ✅ Yes (README, package.json, etc.) |
| Image Support | ❌ No | ✅ Yes (with Pillow) |
| Templates | 6 templates | 8 templates |
| Command History | ❌ No | ✅ Yes (with prompt-toolkit) |
🌟 Competitive Advantages
vs GitHub Copilot
- ✅ 100% local & private
- ✅ No subscription ($10/month saved)
- ✅ Full project context awareness
- ✅ Automatic backups
vs Claude Code
- ✅ Works offline
- ✅ No API costs
- ✅ Template system
- ✅ Colored diff preview
📥 Download & Contribute
MLX-CODE is open source and free. Download it, try it, and help make it better!
⭐ Star the repository if you find it useful! Contributions, bug reports, and feature requests are welcome.
🔗 Resources & Links
Official MLX framework documentation and API reference.
High-performance LLM inference library for Apple Silicon.
Model cards and technical details for Qwen coding models.
What's new in Python 3.12 with performance improvements.
✅ Conclusion
MLX-CODE represents a privacy-first approach to AI-assisted coding. By running entirely on Apple Silicon with the MLX framework, it delivers fast, local inference without sacrificing code quality or compromising your data. Whether you're a professional developer or learning to code, MLX-CODE offers a powerful, free alternative to cloud-based AI coding assistants.
With support for 20+ models, intelligent file context awareness, automatic backups, and a robust template system, MLX-CODE is the complete local AI coding companion for macOS developers.