New milestone 05 demo that shows students the model learning to "talk":
- Live dashboard with epoch-by-epoch response progression
- Systems stats panel (tokens/sec, batch time, memory)
- 3 test prompts with full history displayed
- Smaller model (110K params) for ~2 minute training time
🤖 Generated with [Claude Code](https://claude.com/claude-code)
- Create professional examples directory showcasing TinyTorch as real ML framework
- Add examples: XOR, MNIST, CIFAR-10, text generation, autograd demo, optimizer comparison
- Fix import paths in exported modules (training.py, dense.py)
- Update training module with autograd integration for loss functions
- Add progressive integration tests for all 16 modules
- Document framework capabilities and usage patterns
This commit establishes the examples gallery that demonstrates TinyTorch
works like PyTorch/TensorFlow, validating the complete framework.
MILESTONE SYSTEM REDESIGN:
- Reduced from 5 to 3 meaningful milestones based on student effort
- Better spacing: Module 6 → Module 11 → Module 16
- More exciting progression: Numbers → Objects → Code
NEW MILESTONE STRUCTURE:
1. 'Machines Can See' (Module 05): MLP achieves 85%+ MNIST accuracy
2. 'I Can Train Real AI' (Module 11): CNN achieves 65%+ CIFAR-10 accuracy
3. 'I Built GPT' (Module 16): Generate Python functions from natural language
CONFIGURATION SYSTEM:
- Created dedicated milestones/ directory
- Added milestones.yml for consistent configuration
- Added comprehensive README with implementation philosophy
- Updated milestone system to load from YAML config
- Proper module exercise tracking and requirements
IMPROVED USER EXPERIENCE:
- Fixed milestone count displays (0/3 instead of 0/5)
- Updated timeline views for 3 milestones
- Maintained all existing CLI functionality
- Better error handling and fallback configs
Each milestone now represents a major capability leap with proper
spacing that honors the substantial work students put into modules.
Major changes:
- Renamed entire system from "milestone" to "checkpoint" for academic framing
- Checkpoints are now positioned as academic progress markers in learning journey
- Implemented enhanced Rich CLI timeline with progress bars and connecting lines
- Added overall progress tracking (16/16 modules = 100%)
Enhanced timeline visualization:
- Horizontal view shows progress bar with filled/unfilled segments
- Visual connecting lines between checkpoints showing completion status
- Color-coded progress: green (complete), yellow (in-progress), dim (future)
- Percentage indicators for each checkpoint and overall progress
CLI improvements:
- `tito checkpoint status` - Shows overall and per-checkpoint progress
- `tito checkpoint timeline --horizontal` - Rich visual progress line
- `tito checkpoint timeline` - Vertical tree view with module details
- Better progress indicators with filled bars and connecting lines
Documentation updates:
- Renamed milestone-system.md to checkpoint-system.md
- Updated all references from milestone to checkpoint terminology
- Emphasized academic checkpoint philosophy and progress markers
- Added descriptions of new Rich CLI visualizations
Benefits:
- More academic framing aligns with educational context
- Visual progress bars provide immediate feedback on learning journey
- Checkpoint terminology is more familiar to students
- Rich CLI visualizations make progress tracking engaging
Features implemented:
- Complete milestone tracking system with Foundation → Architecture → Training → Inference → Serving progression
- Rich CLI visualization with status, timeline (horizontal/vertical), and progress tracking
- Ticker-based granular progress within each milestone showing module completion
- Comprehensive documentation explaining the pedagogical approach and system benefits
- Integration with existing tito CLI infrastructure and module detection
Key capabilities:
- `tito milestone status` - shows current progress and capabilities unlocked
- `tito milestone timeline` - visual progress timeline with multiple views
- `tito milestone test/unlock` - placeholder for future capability testing
- Automatic module detection and progress calculation
- Clear capability statements for each milestone achievement
Benefits:
- Transforms learning from "completing modules" to "building capabilities"
- Provides clear motivation through visual progress and capability unlocks
- Aligns with real ML engineering workflow: Foundation → Architecture → Training → Inference → Serving
- Gives students concrete sense of progress toward complete ML framework
