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.

tinytorch_placeholder/__init__.py

@@ -0,0 +1,32 @@
+"""
+TinyTorch: Build ML Systems from Scratch
+
+🚧 COMING SOON 🚧
+
+TinyTorch is an educational deep learning framework being developed at Harvard University.
+This package is currently under active development.
+
+Full release coming soon with:
+- Complete tensor operations and autograd
+- Neural network layers and optimizers  
+- Educational modules for learning ML systems
+- Production-ready training pipelines
+
+Stay tuned! 🔥
+
+For updates, visit: https://github.com/VJ/TinyTorch
+"""
+
+__version__ = "0.0.1"
+__author__ = "Vijay Janapa Reddi"
+__email__ = "vj@eecs.harvard.edu"
+
+def coming_soon():
+    """Display coming soon message."""
+    print("🔥 TinyTorch: Build ML Systems from Scratch")
+    print("🚧 Coming Soon from Harvard University!")
+    print("📚 Educational deep learning framework in development")
+    print("🌟 Visit https://github.com/VJ/TinyTorch for updates")
+
+# Show message on import
+coming_soon()