4 Commits

Author	SHA1	Message	Date
Vijay Janapa Reddi	a4b806156e	Improve module-developer guidelines and fix all module issues ... - Added progressive complexity guidelines (Foundation/Intermediate/Advanced) - Added measurement function consolidation to prevent information overload - Fixed all diagnostic issues in losses_dev.py - Fixed markdown formatting across all modules - Consolidated redundant analysis functions in foundation modules - Fixed syntax errors and unused variables - Ensured all educational content is in proper markdown cells for Jupyter	2025-09-28 09:42:25 -04:00
Vijay Janapa Reddi	4aec4ba297	Major reorganization: Remove setup module, renumber all modules, add tito setup command and numeric shortcuts ... - Removed 01_setup module (archived to archive/setup_module) - Renumbered all modules: tensor is now 01, activations is 02, etc. - Added tito setup command for environment setup and package installation - Added numeric shortcuts: tito 01, tito 02, etc. for quick module access - Fixed view command to find dev files correctly - Updated module dependencies and references - Improved user experience: immediate ML learning instead of boring setup	2025-09-28 07:02:08 -04:00
Vijay Janapa Reddi	56f374efa3	FOUNDATION: Establish AI Engineering as a discipline through TinyTorch ... 🎯 NORTH STAR VISION DOCUMENTED: 'Don't Just Import It, Build It' - Training AI Engineers, not just ML users AI Engineering emerges as a foundational discipline like Computer Engineering, bridging algorithms and systems to build the AI infrastructure of the future. 🧪 ROBUST TESTING FRAMEWORK ESTABLISHED: - Created tests/regression/ for sandbox integrity tests - Implemented test-driven bug prevention workflow - Clear separation: student tests (pedagogical) vs system tests (robustness) - Every bug becomes a test to prevent recurrence ✅ KEY IMPLEMENTATIONS: - NORTH_STAR.md: Vision for AI Engineering discipline - Testing best practices: Focus on robust student sandbox - Git workflow standards: Professional development practices - Regression test suite: Prevent infrastructure issues - Conv->Linear dimension tests (found CNN bug) - Transformer reshaping tests (found GPT bug) 🏗️ SANDBOX INTEGRITY: Students need a solid, predictable environment where they focus on ML concepts, not debugging framework issues. The framework must be invisible. 📚 EDUCATIONAL PHILOSOPHY: TinyTorch isn't just teaching a framework - it's founding the AI Engineering discipline by training engineers who understand how to BUILD ML systems. This establishes the foundation for training the first generation of true AI Engineers who will define this emerging discipline.	2025-09-25 11:16:28 -04:00
Vijay Janapa Reddi	753ae52ae0	MAJOR: Implement beautiful module progression through strategic reordering ... This commit implements the pedagogically optimal "inevitable discovery" module progression based on expert validation and educational design principles. ## Module Reordering Summary **Previous Order (Problems)**: - 05_losses → 06_autograd → 07_dataloader → 08_optimizers → 09_spatial → 10_training - Issues: Autograd before optimizers, DataLoader before training, scattered dependencies **New Order (Beautiful Progression)**: - 05_losses → 06_optimizers → 07_autograd → 08_training → 09_spatial → 10_dataloader - Benefits: Each module creates inevitable need for the next ## Pedagogical Flow Achieved **05_losses** → "Need systematic weight updates" → **06_optimizers** **06_optimizers** → "Need automatic gradients" → **07_autograd** **07_autograd** → "Need systematic training" → **08_training** **08_training** → "MLPs hit limits on images" → **09_spatial** **09_spatial** → "Training is too slow" → **10_dataloader** ## Technical Changes ### Module Directory Renaming - `06_autograd` → `07_autograd` - `07_dataloader` → `10_dataloader` - `08_optimizers` → `06_optimizers` - `10_training` → `08_training` - `09_spatial` → `09_spatial` (no change) ### System Integration Updates - **MODULE_TO_CHECKPOINT mapping**: Updated in tito/commands/export.py - **Test directories**: Renamed module_XX directories to match new numbers - **Documentation**: Updated all references in MD files and agent configurations - **CLI integration**: Updated next-steps suggestions for proper flow ### Agent Configuration Updates - **Quality Assurance**: Updated module audit status with new numbers - **Module Developer**: Updated work tracking with new sequence - **Documentation**: Updated MASTER_PLAN_OF_RECORD.md with beautiful progression ## Educational Benefits 1. **Inevitable Discovery**: Each module naturally leads to the next 2. **Cognitive Load**: Concepts introduced exactly when needed 3. **Motivation**: Students understand WHY each tool is necessary 4. **Synthesis**: Everything flows toward complete ML systems understanding 5. **Professional Alignment**: Matches real ML engineering workflows ## Quality Assurance - ✅ All CLI commands still function - ✅ Checkpoint system mappings updated - ✅ Documentation consistency maintained - ✅ Test directory structure aligned - ✅ Agent configurations synchronized **Impact**: This reordering transforms TinyTorch from a collection of modules into a coherent educational journey where each step naturally motivates the next, creating optimal conditions for deep learning systems understanding.	2025-09-24 15:56:47 -04:00