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.

examples/CAPABILITIES.md

@@ -60,29 +60,29 @@ tito module complete 05_losses
 🎯 Achievement: Can evaluate model predictions
 ```
 
-### 🔓 Capability 5: Automatic Differentiation (Module 6)
+### 🔓 Capability 5: Optimization (Module 6)
+**Unlocked**: Advanced training algorithms (SGD, Adam)
+```bash
+tito module complete 06_optimizers
+✅ Integration tests: Optimizer algorithms ready
+🎯 Achievement: Systematic weight updates prepared
+```
+
+### 🔓 Capability 6: Automatic Differentiation (Module 7)
 **Unlocked**: Networks can learn through backpropagation
 ```bash
-tito module complete 06_autograd
+tito module complete 07_autograd
 ✅ Integration tests: Gradient flow through layers
 🎯 Achievement: Solve the XOR Problem (1969)!
 ➡️ RUN: python examples/xor_1969/minsky_xor_problem.py
 ```
 
-### 🔓 Capability 6: Data Loading (Module 7)
-**Unlocked**: Can handle real datasets efficiently
+### 🔓 Capability 7: Complete Training (Module 8)
+**Unlocked**: Full training pipelines with validation
 ```bash
-tito module complete 07_dataloader
-✅ Integration tests: Batching, shuffling, iteration
-🎯 Achievement: Load real-world datasets
-```
-
-### 🔓 Capability 7: Optimization (Module 8)
-**Unlocked**: Advanced training algorithms (SGD, Adam)
-```bash
-tito module complete 08_optimizers
-✅ Integration tests: Optimizer + Autograd + Layers
-🎯 Achievement: Train networks efficiently
+tito module complete 08_training
+✅ Integration tests: Complete training loop
+🎯 Achievement: Train networks end-to-end
 ➡️ RUN: python examples/xor_1969/minsky_xor_problem.py --train
 ```
 
@@ -95,12 +95,12 @@ tito module complete 09_spatial
 ➡️ RUN: python examples/lenet_1998/train_mnist.py
 ```
 
-### 🔓 Capability 9: Complete Training (Module 10)
-**Unlocked**: Full training pipelines with validation
+### 🔓 Capability 9: Data Loading (Module 10)
+**Unlocked**: Can handle real datasets efficiently
 ```bash
-tito module complete 10_training
-✅ Integration tests: Complete training loop
-🎯 Achievement: Train AlexNet-style networks (2012)!
+tito module complete 10_dataloader
+✅ Integration tests: Batching, shuffling, iteration
+🎯 Achievement: Train AlexNet-scale networks (2012)!
 ➡️ RUN: python examples/alexnet_2012/train_cnn.py
 ```