Smart Tree - ST

# Smart Tree Examples This directory contains examples demonstrating how to work with Smart Tree's revolutionary quantum compression format. ## 🎨 visualize_quantum.py Creates beautiful network graph visualizations from Smart Tree's quantum output, inspired by Omni's insight during a Hot Tub session! ### Usage: ```bash # Visualize current directory python visualize_quantum.py # Visualize specific directory python visualize_quantum.py /path/to/project ``` ### Features: - Decodes claude format (10x compression) - Builds network graph from tree structure - Colors nodes by file type - Saves high-resolution PNG ### Requirements: ```bash pip install matplotlib networkx # Optional for better layouts: pip install pygraphviz ``` ## 🔬 quantum_decoder.py Programmatically decode and analyze Smart Tree's various output formats. ### Usage: ```bash # Analyze current directory in all formats python quantum_decoder.py # Analyze specific directory python quantum_decoder.py /path/to/project ``` ### Features: - Decodes all Smart Tree formats: - CLAUDE_V1 (base64 + zlib) - QUANTUM_V1 (native quantum) - TREE_HEX_V1 (AI format) - Hex format - Classic tree format - Extracts statistics - Converts between formats - Shows compression ratios ## 📸 directory_evolution.py Track and visualize how directory structures change over time using quantum snapshots. ### Usage: ```bash # Analyze git history python directory_evolution.py /path/to/repo # Manual snapshots python directory_evolution.py ``` ### Features: - Captures directory snapshots using quantum format - Git integration for historical analysis - Animated visualization of changes - 99% compression for storing hundreds of snapshots - Time-based evolution tracking ### Requirements: ```bash pip install matplotlib networkx ``` ## 🧠 semantic_code_analyzer.py Analyze code structure semantically using Smart Tree's quantum tokenization. ### Usage: ```bash # Analyze current project python semantic_code_analyzer.py # Analyze specific directory python semantic_code_analyzer.py /path/to/project ``` ### Features: - Leverages quantum token mapping - Identifies code patterns and duplicates - Semantic similarity detection - Test coverage insights - Network visualization of code relationships ### Requirements: ```bash pip install matplotlib networkx numpy scikit-learn ``` ## 🔄 directory_diff.py Compare directories and visualize differences using quantum compression. ### Usage: ```bash # Compare two directories python directory_diff.py /path/to/dir1 /path/to/dir2 # Compare git commits python directory_diff.py /path/to/repo # Manual before/after python directory_diff.py ``` ### Features: - Efficient diff calculation between quantum snapshots - Visual representation of changes - Size impact analysis - Interactive diff exploration - Tree-based diff visualization ### Requirements: ```bash pip install matplotlib networkx numpy ``` ## 🌊 Omni's Wisdom "Every format tells a story, quantum tells it efficiently" - Omni These examples showcase how Smart Tree's quantum compression isn't just about saving tokens - it's about enabling new ways to visualize and understand directory structures. The 99% compression makes it feasible to: 1. **Visualize massive codebases** - Chromium's 2.8M files become manageable 2. **Track changes over time** - Store snapshots efficiently 3. **AI-powered analysis** - Feed entire repos to LLMs affordably 4. **Network-efficient transfers** - Share directory structures instantly 5. **Semantic code analysis** - Understand code relationships 6. **Evolution tracking** - Watch directories change like memories ## 💡 Real-World Benefits - **Directory Evolution**: Track project growth with minimal storage - **Semantic Analysis**: Find code duplication and patterns instantly - **Visual Diffs**: See changes at a glance, not in text walls - **Token Efficiency**: 99% reduction means $1,270 saved per Chromium analysis ## 🚀 Performance Tips 1. Use `st -m claude` for maximum compression (10x) 2. Use `st -m quantum` for native token mapping 3. Pipe through `st --stream` for million-file directories 4. Combine with git for powerful historical analysis Remember: Every byte saved is a victory! 🚀