diff --git a/docs/development/MODULE_RESET_QUICK_REFERENCE.md b/docs/development/MODULE_RESET_QUICK_REFERENCE.md
deleted file mode 100644
index 03568b59..00000000
--- a/docs/development/MODULE_RESET_QUICK_REFERENCE.md
+++ /dev/null
@@ -1,366 +0,0 @@
-# `tito module reset` Quick Reference Guide
-
-Quick reference for the new module reset functionality.
-
----
-
-## Basic Usage
-
-### Full Reset (Default - Recommended)
-```bash
-tito module reset 01
-```
-**What it does:**
-1. ✅ Backs up your current work
-2. ✅ Removes package exports
-3. ✅ Restores module from git
-4. ✅ Updates progress tracking
-
-**Use when:** You want to completely start over on a module
-
----
-
-## Common Scenarios
-
-### 1. Made Mistakes - Start Fresh
-```bash
-# Completely reset module 01
-tito module reset 01
-
-# Then start working again
-tito module start 01
-```
-
-### 2. Keep Package Exports (Soft Reset)
-```bash
-# Reset source but keep package
-tito module reset 01 --soft
-
-# Good for: Testing different approaches without re-exporting
-```
-
-### 3. View Available Backups
-```bash
-# List all backups for module 01
-tito module reset 01 --list-backups
-
-# Output shows:
-# - Timestamp of backup
-# - Git hash at time of backup
-# - Number of files backed up
-```
-
-### 4. Restore from Specific Backup
-```bash
-# First, list backups to find timestamp
-tito module reset 01 --list-backups
-
-# Then restore from specific backup
-tito module reset 01 --restore-backup 20251112_143022
-```
-
-### 5. Quick Reset (Skip Confirmation)
-```bash
-# Useful for automation or when you're sure
-tito module reset 01 --force
-```
-
----
-
-## All Command Flags
-
-| Flag | Description | Use When |
-|------|-------------|----------|
-| `--soft` | Keep package exports | You want to preserve exports |
-| `--hard` | Remove everything [DEFAULT] | You want a complete reset |
-| `--from-git` | Restore from git HEAD [DEFAULT] | Reset to repository version |
-| `--restore-backup <timestamp>` | Restore from backup | You want previous work back |
-| `--list-backups` | Show available backups | Checking backup history |
-| `--no-backup` | Skip backup creation | **DANGEROUS** - only for testing |
-| `--force` | Skip confirmations | You're absolutely sure |
-
----
-
-## Safety Features
-
-### Automatic Backup
-**Every reset creates a backup** (unless `--no-backup`)
-
-**Backup location:**
-```
-.tito/backups/01_tensor_20251112_143022/
-├── tensor.py                 # Your work
-└── backup_metadata.json      # Info about backup
-```
-
-### Confirmation Prompts
-**Always asks before destructive actions** (unless `--force`)
-
-Example:
-```
-This will reset the module to a clean state.
-Your current work will be backed up.
-
-Continue with reset? (y/N):
-```
-
-### Git Status Check
-**Warns if you have uncommitted changes:**
-```
-⚠️  You have uncommitted changes in your repository!
-Consider committing your work before resetting.
-```
-
----
-
-## Workflow Integration
-
-### Natural Learning Flow
-```bash
-# 1. Start module
-tito module start 01
-
-# 2. Work in Jupyter (save changes)
-
-# 3. Try to complete
-tito module complete 01
-# Tests fail? Code doesn't work?
-
-# 4. Reset and try again
-tito module reset 01
-
-# 5. Start fresh with new approach
-tito module resume 01
-```
-
-### Progress Tracking
-**Reset updates your progress:**
-- Removes module from "completed"
-- Clears completion date
-- Preserves backup history
-
-**View progress:**
-```bash
-tito module status
-```
-
----
-
-## Common Questions
-
-### Q: Will I lose my work?
-**A:** No! Unless you use `--no-backup`, your work is automatically backed up to `.tito/backups/`
-
-### Q: Can I undo a reset?
-**A:** Yes! Use `--restore-backup` with the timestamp of the backup you want:
-```bash
-tito module reset 01 --restore-backup 20251112_143022
-```
-
-### Q: What's the difference between soft and hard reset?
-**A:**
-- **Hard (default):** Removes everything (source + package exports)
-- **Soft:** Only resets source, keeps package exports intact
-
-### Q: Where are my backups stored?
-**A:** In `.tito/backups/<module_name>_<timestamp>/`
-
-Each backup includes:
-- Your Python files
-- Metadata (timestamp, git hash, file list)
-
-### Q: How do I see all my backups?
-**A:**
-```bash
-tito module reset 01 --list-backups
-```
-
-### Q: Can I reset multiple modules?
-**A:** Not in one command, but you can run reset for each:
-```bash
-tito module reset 01
-tito module reset 02
-tito module reset 03
-```
-
----
-
-## Examples by Use Case
-
-### Beginner: Made a mistake, start over
-```bash
-tito module reset 01
-# Easy! Just reset and start fresh
-```
-
-### Intermediate: Want to try different approach
-```bash
-# Keep exports but reset source
-tito module reset 01 --soft
-
-# Work on new approach
-tito module resume 01
-```
-
-### Advanced: Restore previous working version
-```bash
-# Check what backups exist
-tito module reset 01 --list-backups
-
-# Restore the one that was working
-tito module reset 01 --restore-backup 20251112_140000
-```
-
-### Developer: Quick iteration during testing
-```bash
-# No confirmations, fast workflow
-tito module reset 01 --force
-```
-
----
-
-## Troubleshooting
-
-### "Backup failed. Reset aborted."
-**Cause:** Can't create backup directory or files
-
-**Solution:**
-1. Check `.tito/` directory permissions
-2. Ensure you have write access
-3. Try: `mkdir -p .tito/backups`
-
-### "Git checkout failed"
-**Cause:** File not tracked in git or git issues
-
-**Solutions:**
-1. Check if file is in git: `git ls-files modules/01_tensor/tensor.py`
-2. Commit the file first: `git add modules/01_tensor/tensor.py && git commit`
-3. Or use backup restore: `--restore-backup` instead
-
-### "Export file not found"
-**Cause:** Module wasn't exported yet
-
-**Solution:** This is fine! If there's no export, nothing to remove. Reset will still restore source.
-
-### "Module directory not found"
-**Cause:** Invalid module number or name
-
-**Solution:**
-```bash
-# Use correct module number (01-20)
-tito module reset 01  # ✅ Correct
-tito module reset 1   # ✅ Also works (normalized to 01)
-tito module reset 99  # ❌ Invalid - no module 99
-```
-
----
-
-## Best Practices
-
-### 1. **Commit Before Major Changes**
-```bash
-# Before trying something risky
-git add .
-git commit -m "Working version before experiment"
-
-# Now safe to reset if needed
-tito module reset 01
-```
-
-### 2. **Use Soft Reset for Iteration**
-```bash
-# When experimenting with implementations
-tito module reset 01 --soft
-# Keeps package exports, faster iteration
-```
-
-### 3. **Check Backups Periodically**
-```bash
-# See your backup history
-tito module reset 01 --list-backups
-
-# Clean old backups manually if needed
-rm -rf .tito/backups/01_tensor_20251110_*
-```
-
-### 4. **Force Only When Sure**
-```bash
-# Interactive (asks confirmation) - SAFE
-tito module reset 01
-
-# Force (no confirmation) - FAST but RISKY
-tito module reset 01 --force
-```
-
----
-
-## Integration with Other Commands
-
-### Complete Workflow
-```bash
-# Start
-tito module start 01
-
-# Work...
-
-# Try to complete
-tito module complete 01
-
-# If tests fail, reset and try again
-tito module reset 01
-
-# Resume work
-tito module resume 01
-
-# Complete successfully
-tito module complete 01
-
-# Check progress
-tito module status
-```
-
-### With Package Management
-```bash
-# Reset removes exports
-tito module reset 01
-
-# Manually re-export if needed
-tito module export 01_tensor
-
-# Or just use complete (exports automatically)
-tito module complete 01
-```
-
----
-
-## Quick Command Reference
-
-| What You Want | Command |
-|---------------|---------|
-| Full reset | `tito module reset 01` |
-| Keep exports | `tito module reset 01 --soft` |
-| List backups | `tito module reset 01 --list-backups` |
-| Restore backup | `tito module reset 01 --restore-backup <time>` |
-| Quick reset | `tito module reset 01 --force` |
-| No backup | `tito module reset 01 --no-backup --force` |
-
----
-
-## Help Text
-
-```bash
-# Show reset help
-tito module reset --help
-
-# Show module workflow help
-tito module --help
-
-# Show all TITO commands
-tito --help
-```
-
----
-
-**Remember:** Reset is your safety net! Don't be afraid to use it when you need a fresh start. Your work is backed up automatically.
-
-**Pro Tip:** Check your backups occasionally with `--list-backups` to see your learning progress history!
diff --git a/docs/development/TESTING_QUICK_REFERENCE.md b/docs/development/TESTING_QUICK_REFERENCE.md
deleted file mode 100644
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-# TinyTorch Testing Quick Reference
-
-## 🚀 Quick Start
-
-### **For Students**
-```bash
-# 1. Run inline tests (fast feedback)
-python modules/XX_modulename/modulename.py
-
-# 2. Export to package
-tito export XX_modulename
-
-# 3. Run module tests
-pytest tests/XX_modulename/ -v
-
-# 4. Run critical integration tests
-pytest tests/integration/test_gradient_flow.py -v
-```
-
-### **For Maintainers**
-```bash
-# Run all tests
-pytest tests/ -v
-
-# Run critical tests only
-pytest tests/integration/test_gradient_flow.py -v
-
-# Run tests for specific module
-pytest tests/XX_modulename/ -v
-```
-
----
-
-## 📋 Test Categories Checklist
-
-For each module, verify:
-
-- [ ] **Core Functionality** - Does it work?
-- [ ] **Gradient Flow** - Do gradients flow? (if trainable)
-- [ ] **Integration** - Works with other modules?
-- [ ] **Shape Correctness** - Shapes handled correctly?
-- [ ] **Edge Cases** - Handles edge cases?
-- [ ] **Export/Import** - Exports correctly?
-
----
-
-## 🔥 Critical Tests (Must Pass)
-
-These tests **must pass** before merging:
-
-1. **Gradient Flow**: `tests/integration/test_gradient_flow.py`
-   - If this fails, training is broken
-
-2. **Module Integration**: `tests/XX_modulename/test_progressive_integration.py`
-   - Ensures module works with previous modules
-
-3. **Export/Import**: Verify module exports to `tinytorch.*`
-   - Students need to import from package
-
----
-
-## 📊 Module Status Quick Check
-
-| Module | Core | Gradients | Integration | Status |
-|--------|------|-----------|-------------|--------|
-| 01_tensor | ✅ | N/A | ✅ | ✅ Good |
-| 02_activations | ✅ | ⚠️ | ✅ | ⚠️ Missing gradients |
-| 03_layers | ✅ | ✅ | ✅ | ✅ Good |
-| 04_losses | ✅ | ✅ | ✅ | ✅ Good |
-| 05_autograd | ✅ | ✅ | ✅ | ✅ Excellent |
-| 06_optimizers | ⚠️ | ⚠️ | ✅ | ⚠️ Missing core |
-| 07_training | ✅ | ✅ | ⚠️ | ⚠️ Missing convergence |
-| 08_dataloader | ✅ | N/A | ⚠️ | ⚠️ Missing edge cases |
-| 09_spatial | ✅ | ✅ | ✅ | ✅ Good |
-| 10_tokenization | ⚠️ | N/A | ✅ | ⚠️ Missing core |
-| 11_embeddings | ✅ | ✅ | ✅ | ✅ Good |
-| 12_attention | ⚠️ | ⚠️ | ✅ | ⚠️ Missing core |
-| 13_transformers | ✅ | ✅ | ✅ | ✅ Excellent |
-| 14_profiling | ✅ | N/A | ✅ | ✅ Good |
-| 15-20 | ⚠️ | ⚠️ | ⚠️ | ⚠️ Needs assessment |
-
-**Legend**: ✅ Complete | ⚠️ Gaps | ❌ Missing | N/A Not Applicable
-
----
-
-## 🎯 Priority Actions
-
-### **High Priority** (Do First)
-1. Module 02_activations: Add gradient flow tests
-2. Module 06_optimizers: Add core functionality tests
-3. Module 07_training: Add convergence tests
-
-### **Medium Priority** (Do Next)
-4. Module 08_dataloader: Add edge case tests
-5. Module 10_tokenization: Add core tests
-6. Module 12_attention: Add core tests
-
-### **Low Priority** (Nice to Have)
-7. Modules 15-20: Assess and add tests
-8. All modules: Add export/import tests
-
----
-
-## 📝 Test File Structure
-
-For module `XX_modulename`:
-
-```
-tests/XX_modulename/
-├── test_[modulename]_core.py              # Core functionality
-├── test_gradient_flow.py                  # Gradient flow (if applicable)
-├── test_[modulename]_integration.py       # Integration
-├── test_progressive_integration.py        # Progressive integration
-├── test_edge_cases.py                     # Edge cases
-└── test_real_world_usage.py               # Real-world usage
-```
-
----
-
-## 🔍 Common Test Patterns
-
-### **Gradient Flow Test**
-```python
-def test_component_gradient_flow():
-    component = Component(...)
-    x = Tensor(..., requires_grad=True)
-    output = component(x)
-    loss = output.sum()
-    loss.backward()
-    
-    assert x.grad is not None
-    for param in component.parameters():
-        assert param.grad is not None
-```
-
-### **Integration Test**
-```python
-def test_module_integration():
-    from tinytorch.core.tensor import Tensor
-    from tinytorch.core.layers import Linear
-    
-    # Test components work together
-    x = Tensor([[1.0, 2.0]])
-    layer = Linear(2, 3)
-    output = layer(x)
-    assert output.shape == (1, 3)
-```
-
-### **Edge Case Test**
-```python
-def test_edge_cases():
-    # Empty input
-    result = component(Tensor([]))
-    
-    # Zero values
-    result = component(Tensor([0.0]))
-    
-    # Large values
-    result = component(Tensor([1e10]))
-```
-
----
-
-## 📚 Full Documentation
-
-- **Test Separation Plan**: `docs/development/TEST_SEPARATION_PLAN.md` - **START HERE** - What goes where
-- **Master Plan**: `docs/development/MASTER_TESTING_PLAN.md`
-- **Testing Architecture**: `docs/development/testing-architecture.md`
-- **Gradient Flow Strategy**: `docs/development/gradient-flow-testing-strategy.md`
-- **Comprehensive Plan**: `docs/development/comprehensive-module-testing-plan.md`
-
----
-
-**Last Updated**: 2025-01-XX  
-**Quick Reference**: For detailed plans, see MASTER_TESTING_PLAN.md
-
diff --git a/docs/development/comprehensive-module-testing-plan.md b/docs/development/comprehensive-module-testing-plan.md
deleted file mode 100644
index 710dc1e5..00000000
--- a/docs/development/comprehensive-module-testing-plan.md
+++ /dev/null
@@ -1,705 +0,0 @@
-# Comprehensive Module Testing Plan
-
-## 🎯 Overview
-
-This document defines a **systematic testing strategy** for all TinyTorch modules. It identifies what critical checks each module needs, ensuring both students and maintainers can catch issues early and build robust systems.
-
-**Key Principle**: Every module needs tests that validate:
-1. **Correctness** - Does it work as intended?
-2. **Integration** - Does it work with other modules?
-3. **Robustness** - Does it handle edge cases?
-4. **Usability** - Can students actually use it?
-
----
-
-## 📊 Test Categories: What to Test
-
-### **Category 1: Core Functionality** ✅
-**Purpose**: Verify the module does what it's supposed to do
-
-**Checks**:
-- ✅ Forward pass correctness
-- ✅ Output shapes match expectations
-- ✅ Mathematical correctness (compare to reference implementations)
-- ✅ API correctness (methods exist, signatures correct)
-- ✅ Parameter initialization (if applicable)
-
-**Example**: For `03_layers`:
-- Linear layer computes `output = input @ weight + bias` correctly
-- Output shape is `(batch, out_features)` when input is `(batch, in_features)`
-- Weight and bias are initialized properly
-
----
-
-### **Category 2: Gradient Flow** 🔥
-**Purpose**: Verify gradients flow correctly (critical for training)
-
-**Checks**:
-- ✅ Gradients exist after backward pass
-- ✅ Gradients are non-zero (not all zeros)
-- ✅ All trainable parameters receive gradients
-- ✅ Gradient shapes match parameter shapes
-- ✅ Gradients flow through the component correctly
-
-**Example**: For `02_activations`:
-- ReLU preserves `requires_grad` flag
-- Backward pass computes correct gradients
-- Gradient is 0 for negative inputs, 1 for positive inputs
-
-**Modules That Need This**: All modules with trainable parameters or that process gradients
-- ✅ 02_activations, 03_layers, 04_losses, 05_autograd, 06_optimizers, 07_training, 09_spatial, 11_embeddings, 12_attention, 13_transformers
-
-**Modules That Don't Need This**: Modules that don't process gradients
-- ❌ 01_tensor (foundation, no gradients yet), 08_dataloader (data only), 10_tokenization (text processing), 14_profiling (analysis), 15_quantization (post-training), 16_compression (post-training), 17_memoization (caching), 18_acceleration (optimization), 19_benchmarking (evaluation)
-
----
-
-### **Category 3: Integration with Previous Modules** 🔗
-**Purpose**: Verify module N works with modules 1 through N-1
-
-**Checks**:
-- ✅ Imports from previous modules work
-- ✅ Components from previous modules integrate correctly
-- ✅ Data flows correctly through the stack
-- ✅ No breaking changes to previous modules
-
-**Example**: For `07_training`:
-- Uses Tensor (01), Layers (03), Losses (04), Autograd (05), Optimizers (06)
-- All components work together in a training loop
-- Training loop actually trains (loss decreases)
-
-**All Modules Need This**: Every module should test integration with previous modules
-
----
-
-### **Category 4: Shape Correctness** 📐
-**Purpose**: Verify shapes are handled correctly (common source of bugs)
-
-**Checks**:
-- ✅ Output shapes match expected dimensions
-- ✅ Broadcasting works correctly
-- ✅ Reshape operations preserve data
-- ✅ Batch dimensions handled correctly
-- ✅ Edge cases (empty tensors, single samples, etc.)
-
-**Example**: For `09_spatial`:
-- Conv2d output shape: `(batch, out_channels, height_out, width_out)`
-- MaxPool2d reduces spatial dimensions correctly
-- Shapes work with Linear layers downstream
-
-**Modules That Need This**: All modules that transform shapes
-- ✅ 01_tensor, 03_layers, 09_spatial, 11_embeddings, 12_attention, 13_transformers
-
----
-
-### **Category 5: Edge Cases & Error Handling** ⚠️
-**Purpose**: Verify robustness and helpful error messages
-
-**Checks**:
-- ✅ Handles empty inputs gracefully
-- ✅ Handles zero values correctly
-- ✅ Handles very large/small values
-- ✅ Provides helpful error messages for invalid inputs
-- ✅ Handles NaN/Inf correctly
-- ✅ Handles out-of-bounds indices
-
-**Example**: For `08_dataloader`:
-- Empty dataset handled gracefully
-- Batch size larger than dataset handled correctly
-- Invalid indices raise clear error messages
-
-**All Modules Need This**: Every module should handle edge cases
-
----
-
-### **Category 6: Numerical Stability** 🔢
-**Purpose**: Verify numerical correctness and stability
-
-**Checks**:
-- ✅ No NaN values in outputs
-- ✅ No Inf values in outputs
-- ✅ Numerical precision is acceptable
-- ✅ Operations are numerically stable
-- ✅ Compare to reference implementations (NumPy, PyTorch)
-
-**Example**: For `02_activations`:
-- Sigmoid doesn't overflow for large inputs
-- Softmax is numerically stable (uses log-sum-exp trick)
-- No NaN/Inf in outputs
-
-**Modules That Need This**: Modules with numerical operations
-- ✅ 01_tensor, 02_activations, 03_layers, 04_losses, 05_autograd, 09_spatial, 11_embeddings, 12_attention, 13_transformers
-
----
-
-### **Category 7: Memory & Performance** ⚡
-**Purpose**: Verify reasonable performance (not exhaustive, but catch major issues)
-
-**Checks**:
-- ✅ No memory leaks
-- ✅ Operations complete in reasonable time
-- ✅ Memory usage is reasonable
-- ✅ Can handle realistic batch sizes
-
-**Example**: For `13_transformers`:
-- Forward pass completes in reasonable time for small models
-- Memory usage scales linearly with batch size
-- No memory leaks across multiple forward passes
-
-**Modules That Need This**: Modules with performance-sensitive operations
-- ✅ 05_autograd, 09_spatial, 12_attention, 13_transformers, 14_profiling, 18_acceleration, 19_benchmarking
-
----
-
-### **Category 8: Real-World Usage** 🌍
-**Purpose**: Verify the module works in realistic scenarios
-
-**Checks**:
-- ✅ Can solve the intended problem
-- ✅ Works with real datasets (if applicable)
-- ✅ Matches expected behavior from documentation
-- ✅ Can be used in production-like scenarios
-
-**Example**: For `07_training`:
-- Can train a simple model on real data
-- Loss decreases over epochs
-- Model actually learns (accuracy improves)
-
-**Modules That Need This**: All modules should have at least one real-world usage test
-
----
-
-### **Category 9: Export/Import Correctness** 📦
-**Purpose**: Verify code exports correctly and can be imported
-
-**Checks**:
-- ✅ Code exports to `tinytorch/` correctly
-- ✅ Can import from `tinytorch.*` package
-- ✅ Exported API matches module API
-- ✅ No import errors
-
-**All Modules Need This**: Every module should test export/import
-
----
-
-### **Category 10: API Consistency** 🔌
-**Purpose**: Verify API matches conventions and is usable
-
-**Checks**:
-- ✅ Methods have expected names
-- ✅ Parameters match expected signatures
-- ✅ Return types are consistent
-- ✅ Follows TinyTorch conventions
-
-**All Modules Need This**: Every module should test API consistency
-
----
-
-## 📋 Module-by-Module Testing Plan
-
-### **Module 01: Tensor** (Foundation)
-**Critical Checks Needed**:
-- ✅ Core Functionality: All operations work (add, mul, matmul, etc.)
-- ❌ Gradient Flow: Not applicable (no gradients yet)
-- ❌ Integration: No previous modules
-- ✅ Shape Correctness: Broadcasting, reshaping, indexing
-- ✅ Edge Cases: Empty tensors, zero values, large arrays
-- ✅ Numerical Stability: Precision, overflow handling
-- ⚠️ Memory & Performance: Large tensor operations
-- ✅ Real-World Usage: Can build neural networks with tensors
-- ✅ Export/Import: Exports to `tinytorch.core.tensor`
-- ✅ API Consistency: Matches NumPy-like API
-
-**Test Files**:
-- `tests/01_tensor/test_tensor_core.py` - Core functionality
-- `tests/01_tensor/test_tensor_integration.py` - Integration with NumPy
-- `tests/01_tensor/test_progressive_integration.py` - Progressive integration
-
----
-
-### **Module 02: Activations**
-**Critical Checks Needed**:
-- ✅ Core Functionality: Forward pass correctness
-- ✅ **Gradient Flow**: **CRITICAL** - All activations preserve gradients
-- ✅ Integration: Works with Tensor (01)
-- ✅ Shape Correctness: Output shape matches input shape
-- ✅ Edge Cases: Large values, zero values, negative values
-- ✅ **Numerical Stability**: **CRITICAL** - No overflow/underflow
-- ⚠️ Memory & Performance: Fast forward/backward passes
-- ✅ Real-World Usage: Can use in neural networks
-- ✅ Export/Import: Exports to `tinytorch.core.activations`
-- ✅ API Consistency: All activations have same interface
-
-**Test Files**:
-- `tests/02_activations/test_activations_core.py` - Core functionality
-- `tests/02_activations/test_gradient_flow.py` - **MISSING** - Gradient flow tests
-- `tests/02_activations/test_activations_integration.py` - Integration
-- `tests/02_activations/test_progressive_integration.py` - Progressive integration
-
-**Gap**: Missing comprehensive gradient flow tests for all activations
-
----
-
-### **Module 03: Layers**
-**Critical Checks Needed**:
-- ✅ Core Functionality: Forward pass, parameter initialization
-- ✅ **Gradient Flow**: **CRITICAL** - All layers compute gradients correctly
-- ✅ Integration: Works with Tensor (01), Activations (02)
-- ✅ **Shape Correctness**: **CRITICAL** - Output shapes match expectations
-- ✅ Edge Cases: Zero inputs, single samples, large batches
-- ✅ Numerical Stability: No NaN/Inf in outputs
-- ⚠️ Memory & Performance: Reasonable memory usage
-- ✅ Real-World Usage: Can build neural networks
-- ✅ Export/Import: Exports to `tinytorch.core.layers`
-- ✅ API Consistency: All layers follow Module interface
-
-**Test Files**:
-- `tests/03_layers/test_layers_core.py` - Core functionality
-- `tests/03_layers/test_layers_integration.py` - Integration
-- `tests/03_layers/test_layers_networks_integration.py` - Network integration
-- `tests/03_layers/test_progressive_integration.py` - Progressive integration
-
-**Gap**: Missing gradient flow tests for Dropout, LayerNorm (if in layers module)
-
----
-
-### **Module 04: Losses**
-**Critical Checks Needed**:
-- ✅ Core Functionality: Loss computation correctness
-- ✅ **Gradient Flow**: **CRITICAL** - Loss functions compute gradients
-- ✅ Integration: Works with Tensor (01), Layers (03)
-- ✅ Shape Correctness: Handles different batch sizes
-- ✅ Edge Cases: Perfect predictions, zero loss, large losses
-- ✅ **Numerical Stability**: **CRITICAL** - Log operations stable
-- ⚠️ Memory & Performance: Efficient computation
-- ✅ Real-World Usage: Can use in training loops
-- ✅ Export/Import: Exports to `tinytorch.core.losses`
-- ✅ API Consistency: All losses have same interface
-
-**Test Files**:
-- `tests/04_losses/test_dense_layer.py` - Layer tests
-- `tests/04_losses/test_dense_integration.py` - Integration
-- `tests/04_losses/test_network_capability.py` - Network capability
-- `tests/04_losses/test_progressive_integration.py` - Progressive integration
-
-**Status**: Good coverage
-
----
-
-### **Module 05: Autograd**
-**Critical Checks Needed**:
-- ✅ Core Functionality: Forward/backward pass correctness
-- ✅ **Gradient Flow**: **CRITICAL** - Gradients computed correctly
-- ✅ Integration: Works with all previous modules
-- ✅ Shape Correctness: Gradient shapes match parameter shapes
-- ✅ **Edge Cases**: **CRITICAL** - Broadcasting, reshape, chain rule
-- ✅ **Numerical Stability**: **CRITICAL** - Gradient computation stable
-- ✅ **Memory & Performance**: **CRITICAL** - No memory leaks
-- ✅ Real-World Usage: Can train models
-- ✅ Export/Import: Exports to `tinytorch.core.autograd`
-- ✅ API Consistency: Matches PyTorch-like API
-
-**Test Files**:
-- `tests/05_autograd/test_gradient_flow.py` - Gradient flow ✅
-- `tests/05_autograd/test_batched_matmul_backward.py` - Batched operations
-- `tests/05_autograd/test_progressive_integration.py` - Progressive integration
-
-**Status**: Excellent coverage
-
----
-
-### **Module 06: Optimizers**
-**Critical Checks Needed**:
-- ✅ Core Functionality: Parameter updates work
-- ✅ **Gradient Flow**: **CRITICAL** - Optimizers use gradients correctly
-- ✅ Integration: Works with Autograd (05), Layers (03)
-- ✅ Shape Correctness: Parameter shapes preserved
-- ✅ Edge Cases: Zero gradients, very small/large learning rates
-- ✅ Numerical Stability: Updates don't cause overflow
-- ⚠️ Memory & Performance: Efficient updates
-- ✅ Real-World Usage: Can train models
-- ✅ Export/Import: Exports to `tinytorch.core.optimizers`
-- ✅ API Consistency: All optimizers have same interface
-
-**Test Files**:
-- `tests/06_optimizers/test_progressive_integration.py` - Progressive integration
-- `tests/06_optimizers/test_cnn_networks_integration.py` - CNN integration
-- `tests/06_optimizers/test_cnn_pipeline_integration.py` - Pipeline integration
-
-**Gap**: Missing dedicated optimizer functionality tests
-
----
-
-### **Module 07: Training**
-**Critical Checks Needed**:
-- ✅ Core Functionality: Training loops work
-- ✅ **Gradient Flow**: **CRITICAL** - Full training stack gradients work
-- ✅ Integration: Works with all previous modules (01-06)
-- ✅ Shape Correctness: Batch handling, loss aggregation
-- ✅ Edge Cases: Single sample, empty batches, convergence
-- ✅ Numerical Stability: Training doesn't diverge
-- ⚠️ Memory & Performance: Reasonable training speed
-- ✅ **Real-World Usage**: **CRITICAL** - Can actually train models
-- ✅ Export/Import: Exports to `tinytorch.core.training`
-- ✅ API Consistency: Training API is usable
-
-**Test Files**:
-- `tests/07_training/test_autograd_integration.py` - Autograd integration
-- `tests/07_training/test_tensor_autograd_integration.py` - Tensor integration
-- `tests/07_training/test_progressive_integration.py` - Progressive integration
-
-**Gap**: Missing end-to-end training convergence tests
-
----
-
-### **Module 08: Dataloader**
-**Critical Checks Needed**:
-- ✅ Core Functionality: Batching, shuffling, iteration work
-- ❌ Gradient Flow: Not applicable (data only)
-- ✅ Integration: Works with Tensor (01), doesn't break gradients
-- ✅ Shape Correctness: Batch shapes correct
-- ✅ **Edge Cases**: **CRITICAL** - Empty dataset, batch > dataset size
-- ⚠️ Numerical Stability: Not applicable
-- ✅ **Memory & Performance**: **CRITICAL** - Efficient data loading
-- ✅ Real-World Usage: Can load real datasets
-- ✅ Export/Import: Exports to `tinytorch.data.dataloader`
-- ✅ API Consistency: Iterator interface works
-
-**Test Files**:
-- `tests/08_dataloader/test_autograd_core.py` - Core functionality
-- `tests/08_dataloader/test_progressive_integration.py` - Progressive integration
-
-**Gap**: Missing comprehensive edge case tests, missing tests that verify dataloader doesn't break gradient flow
-
----
-
-### **Module 09: Spatial (CNNs)**
-**Critical Checks Needed**:
-- ✅ Core Functionality: Conv2d, Pooling work correctly
-- ✅ **Gradient Flow**: **CRITICAL** - Conv2d gradients work
-- ✅ Integration: Works with Tensor (01), Layers (03), Autograd (05)
-- ✅ **Shape Correctness**: **CRITICAL** - Output shapes match expectations
-- ✅ Edge Cases: Kernel size > image size, stride > kernel size
-- ✅ Numerical Stability: No NaN/Inf in outputs
-- ✅ **Memory & Performance**: **CRITICAL** - Efficient convolution
-- ✅ Real-World Usage: Can build CNNs
-- ✅ Export/Import: Exports to `tinytorch.core.spatial`
-- ✅ API Consistency: Matches PyTorch Conv2d API
-
-**Test Files**:
-- `tests/integration/test_cnn_integration.py` - CNN integration ✅
-- `tests/09_spatial/test_progressive_integration.py` - Progressive integration
-
-**Status**: Good coverage
-
----
-
-### **Module 10: Tokenization**
-**Critical Checks Needed**:
-- ✅ Core Functionality: Tokenization works correctly
-- ❌ Gradient Flow: Not applicable (text processing)
-- ✅ Integration: Works with Tensor (01)
-- ✅ Shape Correctness: Token sequences have correct shapes
-- ✅ Edge Cases: Empty strings, special characters, long sequences
-- ⚠️ Numerical Stability: Not applicable
-- ⚠️ Memory & Performance: Efficient tokenization
-- ✅ Real-World Usage: Can tokenize real text
-- ✅ Export/Import: Exports to `tinytorch.text.tokenization`
-- ✅ API Consistency: Tokenizer interface works
-
-**Test Files**:
-- `tests/10_tokenization/test_progressive_integration.py` - Progressive integration
-
-**Gap**: Missing comprehensive tokenization tests
-
----
-
-### **Module 11: Embeddings**
-**Critical Checks Needed**:
-- ✅ Core Functionality: Embedding lookup works
-- ✅ **Gradient Flow**: **CRITICAL** - Embedding gradients work
-- ✅ Integration: Works with Tokenization (10), Tensor (01)
-- ✅ Shape Correctness: Embedding shapes correct
-- ✅ Edge Cases: Out-of-vocab tokens, zero embeddings
-- ✅ Numerical Stability: Embedding values reasonable
-- ⚠️ Memory & Performance: Efficient embedding lookup
-- ✅ Real-World Usage: Can embed real text
-- ✅ Export/Import: Exports to `tinytorch.text.embeddings`
-- ✅ API Consistency: Embedding interface works
-
-**Test Files**:
-- `tests/11_embeddings/test_training_integration.py` - Training integration
-- `tests/11_embeddings/test_ml_pipeline.py` - ML pipeline
-- `tests/11_embeddings/test_progressive_integration.py` - Progressive integration
-
-**Status**: Good coverage
-
----
-
-### **Module 12: Attention**
-**Critical Checks Needed**:
-- ✅ Core Functionality: Attention mechanism works
-- ✅ **Gradient Flow**: **CRITICAL** - Attention gradients work
-- ✅ Integration: Works with Embeddings (11), Tensor (01)
-- ✅ **Shape Correctness**: **CRITICAL** - Attention output shapes correct
-- ✅ Edge Cases: Causal masking, padding masks, long sequences
-- ✅ **Numerical Stability**: **CRITICAL** - Softmax stability
-- ✅ **Memory & Performance**: **CRITICAL** - O(n²) complexity handled
-- ✅ Real-World Usage: Can use in transformers
-- ✅ Export/Import: Exports to `tinytorch.models.attention`
-- ✅ API Consistency: Attention interface works
-
-**Test Files**:
-- `tests/12_attention/test_progressive_integration.py` - Progressive integration
-- `tests/12_attention/test_compression_integration.py` - Compression integration
-
-**Gap**: Missing dedicated attention mechanism tests
-
----
-
-### **Module 13: Transformers**
-**Critical Checks Needed**:
-- ✅ Core Functionality: Transformer blocks work
-- ✅ **Gradient Flow**: **CRITICAL** - Full transformer gradients work
-- ✅ Integration: Works with all previous modules (01-12)
-- ✅ **Shape Correctness**: **CRITICAL** - Transformer output shapes correct
-- ✅ Edge Cases: Variable sequence lengths, masking
-- ✅ **Numerical Stability**: **CRITICAL** - LayerNorm, attention stability
-- ✅ **Memory & Performance**: **CRITICAL** - Efficient transformer forward/backward
-- ✅ **Real-World Usage**: **CRITICAL** - Can train transformers
-- ✅ Export/Import: Exports to `tinytorch.models.transformer`
-- ✅ API Consistency: Transformer API works
-
-**Test Files**:
-- `tests/13_transformers/test_transformer_gradient_flow.py` - Gradient flow ✅
-- `tests/13_transformers/test_training_simple.py` - Training tests
-- `tests/13_transformers/test_kernels_integration.py` - Kernel integration
-- `tests/13_transformers/test_progressive_integration.py` - Progressive integration
-
-**Status**: Excellent coverage
-
----
-
-### **Module 14: Profiling**
-**Critical Checks Needed**:
-- ✅ Core Functionality: Profiling works correctly
-- ❌ Gradient Flow: Not applicable (analysis only)
-- ✅ Integration: Works with all modules
-- ⚠️ Shape Correctness: Not applicable
-- ✅ Edge Cases: Empty profiles, very fast operations
-- ⚠️ Numerical Stability: Not applicable
-- ✅ **Memory & Performance**: **CRITICAL** - Profiling overhead minimal
-- ✅ Real-World Usage: Can profile real models
-- ✅ Export/Import: Exports to `tinytorch.profiling`
-- ✅ API Consistency: Profiler interface works
-
-**Test Files**:
-- `tests/14_profiling/test_progressive_integration.py` - Progressive integration
-- `tests/14_profiling/test_benchmarking_integration.py` - Benchmarking integration
-- `tests/14_profiling/test_kv_cache_integration.py` - KV cache integration
-
-**Status**: Good coverage
-
----
-
-### **Module 15: Quantization**
-**Critical Checks Needed**:
-- ✅ Core Functionality: Quantization works correctly
-- ⚠️ Gradient Flow: May need gradient tests if quantization-aware training
-- ✅ Integration: Works with trained models
-- ✅ Shape Correctness: Quantized model shapes preserved
-- ✅ Edge Cases: Extreme values, zero values
-- ✅ Numerical Stability: Quantization doesn't cause overflow
-- ✅ **Memory & Performance**: **CRITICAL** - Memory reduction achieved
-- ✅ Real-World Usage: Can quantize real models
-- ✅ Export/Import: Exports to `tinytorch.quantization`
-- ✅ API Consistency: Quantization API works
-
-**Test Files**:
-- `tests/15_memoization/test_progressive_integration.py` - Progressive integration
-- `tests/15_memoization/test_mlops_integration.py` - MLOps integration
-- `tests/15_memoization/test_tinygpt_integration.py` - TinyGPT integration
-
-**Gap**: Missing quantization-specific tests
-
----
-
-### **Module 16: Compression**
-**Critical Checks Needed**:
-- ✅ Core Functionality: Compression works correctly
-- ⚠️ Gradient Flow: May need gradient tests if compression-aware training
-- ✅ Integration: Works with trained models
-- ✅ Shape Correctness: Compressed model shapes handled
-- ✅ Edge Cases: Already sparse models, extreme compression
-- ✅ Numerical Stability: Compression doesn't cause instability
-- ✅ **Memory & Performance**: **CRITICAL** - Compression ratio achieved
-- ✅ Real-World Usage: Can compress real models
-- ✅ Export/Import: Exports to `tinytorch.compression`
-- ✅ API Consistency: Compression API works
-
-**Test Files**: Need to check
-
-**Gap**: Unknown - needs assessment
-
----
-
-### **Module 17: Memoization**
-**Critical Checks Needed**:
-- ✅ Core Functionality: Caching works correctly
-- ❌ Gradient Flow: Not applicable (caching only)
-- ✅ Integration: Works with all modules
-- ⚠️ Shape Correctness: Not applicable
-- ✅ Edge Cases: Cache invalidation, memory limits
-- ⚠️ Numerical Stability: Not applicable
-- ✅ **Memory & Performance**: **CRITICAL** - Caching improves performance
-- ✅ Real-World Usage: Can cache real computations
-- ✅ Export/Import: Exports to `tinytorch.memoization`
-- ✅ API Consistency: Cache interface works
-
-**Test Files**:
-- `tests/17_compression/` - Need to check
-
-**Gap**: Unknown - needs assessment
-
----
-
-### **Module 18: Acceleration**
-**Critical Checks Needed**:
-- ✅ Core Functionality: Acceleration works correctly
-- ❌ Gradient Flow: Not applicable (optimization only)
-- ✅ Integration: Works with all modules
-- ⚠️ Shape Correctness: Not applicable
-- ✅ Edge Cases: Already optimized code, edge cases
-- ⚠️ Numerical Stability: Not applicable
-- ✅ **Memory & Performance**: **CRITICAL** - Speedup achieved
-- ✅ Real-World Usage: Can accelerate real models
-- ✅ Export/Import: Exports to `tinytorch.acceleration`
-- ✅ API Consistency: Acceleration API works
-
-**Test Files**: Need to check
-
-**Gap**: Unknown - needs assessment
-
----
-
-### **Module 19: Benchmarking**
-**Critical Checks Needed**:
-- ✅ Core Functionality: Benchmarking works correctly
-- ❌ Gradient Flow: Not applicable (evaluation only)
-- ✅ Integration: Works with all modules
-- ⚠️ Shape Correctness: Not applicable
-- ✅ Edge Cases: Very fast/slow operations, edge cases
-- ⚠️ Numerical Stability: Not applicable
-- ✅ **Memory & Performance**: **CRITICAL** - Benchmarking overhead minimal
-- ✅ Real-World Usage: Can benchmark real models
-- ✅ Export/Import: Exports to `tinytorch.benchmarking`
-- ✅ API Consistency: Benchmarking API works
-
-**Test Files**: Need to check
-
-**Gap**: Unknown - needs assessment
-
----
-
-### **Module 20: Capstone**
-**Critical Checks Needed**:
-- ✅ Core Functionality: Complete system works
-- ✅ **Gradient Flow**: **CRITICAL** - Full system gradients work
-- ✅ Integration: Works with ALL modules (01-19)
-- ✅ Shape Correctness: End-to-end shapes correct
-- ✅ Edge Cases: All edge cases from all modules
-- ✅ Numerical Stability: Full system stable
-- ✅ **Memory & Performance**: **CRITICAL** - System performance acceptable
-- ✅ **Real-World Usage**: **CRITICAL** - Can train and use TinyGPT
-- ✅ Export/Import: Exports to `tinytorch.applications.tinygpt`
-- ✅ API Consistency: Complete API works
-
-**Test Files**: Need to check
-
-**Gap**: Unknown - needs assessment
-
----
-
-## 🎯 Priority Implementation Plan
-
-### **Phase 1: Critical Gaps** (Must Fix)
-1. **Module 02_activations**: Add comprehensive gradient flow tests
-2. **Module 08_dataloader**: Add edge case tests, verify doesn't break gradients
-3. **Module 06_optimizers**: Add dedicated optimizer functionality tests
-4. **Module 07_training**: Add end-to-end convergence tests
-
-### **Phase 2: Important Gaps** (Should Fix)
-5. **Module 03_layers**: Add gradient flow tests for Dropout, LayerNorm
-6. **Module 10_tokenization**: Add comprehensive tokenization tests
-7. **Module 12_attention**: Add dedicated attention mechanism tests
-8. **All modules**: Add export/import correctness tests
-
-### **Phase 3: Nice to Have** (Can Fix)
-9. **All modules**: Add numerical stability tests
-10. **All modules**: Add memory/performance tests
-11. **All modules**: Add real-world usage tests
-
----
-
-## 📝 Test File Naming Convention
-
-For each module `XX_modulename`, create:
-
-```
-tests/XX_modulename/
-├── test_[modulename]_core.py              # Core functionality
-├── test_gradient_flow.py                  # Gradient flow (if applicable)
-├── test_[modulename]_integration.py       # Integration with previous modules
-├── test_progressive_integration.py        # Progressive integration (module N with 1-N-1)
-├── test_edge_cases.py                     # Edge cases and error handling
-├── test_numerical_stability.py            # Numerical stability (if applicable)
-└── test_real_world_usage.py               # Real-world usage scenarios
-```
-
----
-
-## ✅ Success Criteria
-
-A module has **complete test coverage** when:
-
-1. ✅ Core functionality tests pass
-2. ✅ Gradient flow tests pass (if applicable)
-3. ✅ Integration tests pass
-4. ✅ Progressive integration tests pass
-5. ✅ Edge case tests pass
-6. ✅ Export/import tests pass
-7. ✅ At least one real-world usage test passes
-
----
-
-## 🎓 For Students
-
-This testing plan helps you:
-- **Understand what to test**: Clear categories of what matters
-- **Catch bugs early**: Test as you build
-- **Learn best practices**: See how professional ML systems are tested
-- **Build confidence**: Know your code works correctly
-
----
-
-## 🔧 For Maintainers
-
-This testing plan helps you:
-- **Catch regressions**: Comprehensive tests catch breaking changes
-- **Ensure quality**: All modules meet quality standards
-- **Document behavior**: Tests document expected behavior
-- **Maintain system**: Keep TinyTorch robust as it evolves
-
----
-
-**Last Updated**: 2025-01-XX  
-**Status**: Comprehensive plan complete, implementation in progress  
-**Priority**: High - Systematic testing ensures robust system
-
-
-
-
-
-
-
-
diff --git a/docs/development/gradient-flow-testing-strategy.md b/docs/development/gradient-flow-testing-strategy.md
deleted file mode 100644
index 8e60232b..00000000
--- a/docs/development/gradient-flow-testing-strategy.md
+++ /dev/null
@@ -1,341 +0,0 @@
-# Gradient Flow Testing Strategy
-
-## 🎯 Overview
-
-Gradient flow tests are **critical** for TinyTorch because they validate that the autograd system works correctly end-to-end. A component might work perfectly in isolation, but if gradients don't flow through it, training will fail silently.
-
-**Key Principle**: Every module that has trainable parameters or processes gradients should have gradient flow tests.
-
----
-
-## ✅ Current Gradient Flow Test Coverage
-
-### **Comprehensive Integration Tests** ✅
-- `tests/integration/test_gradient_flow.py` - **CRITICAL**: Tests entire training stack
-  - Basic tensor operations
-  - Layer gradients (Linear)
-  - Activation gradients (Sigmoid, ReLU, Tanh)
-  - Loss gradients (MSE, BCE, CrossEntropy)
-  - Optimizer integration (SGD, AdamW)
-  - Full training loops
-  - Edge cases
-
-- `tests/test_gradient_flow.py` - Comprehensive suite
-  - Simple linear networks
-  - MLP networks
-  - CNN networks
-  - Gradient accumulation
-
-### **Module-Specific Gradient Tests** ✅
-- `tests/05_autograd/test_gradient_flow.py` - Autograd operations
-  - Arithmetic operations (add, sub, mul, div)
-  - GELU activation
-  - LayerNorm operations
-  - Reshape operations
-
-- `tests/13_transformers/test_transformer_gradient_flow.py` - Transformer components
-  - MultiHeadAttention gradients
-  - LayerNorm gradients
-  - MLP gradients
-  - Full GPT model gradients
-  - Attention masking gradients
-
-- `tests/integration/test_cnn_integration.py` - CNN components
-  - Conv2d gradient flow
-  - Complete CNN forward/backward
-  - Pooling operations
-
-- `tests/regression/test_nlp_components_gradient_flow.py` - NLP components
-  - Tokenization
-  - Embeddings
-  - Positional encoding
-  - Attention mechanisms
-  - Full GPT model
-
-### **System-Level Tests** ✅
-- `tests/system/test_gradients.py` - System validation
-  - Gradient existence in single layers
-  - Gradient existence in deep networks
-
----
-
-## 🔍 Gap Analysis: What's Missing?
-
-### **Module-by-Module Coverage**
-
-| Module | Has Gradient Flow Tests? | Status | Notes |
-|--------|-------------------------|--------|-------|
-| 01_tensor | ✅ Partial | Good | Basic operations covered in integration tests |
-| 02_activations | ⚠️ Partial | Needs Work | Some activations tested, not all |
-| 03_layers | ✅ Good | Good | Linear layer well tested |
-| 04_losses | ✅ Good | Good | All major losses tested |
-| 05_autograd | ✅ Excellent | Complete | Comprehensive autograd tests |
-| 06_optimizers | ✅ Good | Good | Optimizer integration tested |
-| 07_training | ✅ Good | Good | Training loops tested |
-| 08_dataloader | ❌ Missing | **Gap** | No gradient flow tests |
-| 09_spatial | ✅ Good | Good | CNN tests cover Conv2d |
-| 10_tokenization | ✅ Partial | Good | Covered in NLP regression tests |
-| 11_embeddings | ✅ Good | Good | Covered in NLP regression tests |
-| 12_attention | ✅ Good | Good | Covered in transformer tests |
-| 13_transformers | ✅ Excellent | Complete | Comprehensive transformer tests |
-| 14_profiling | ⚠️ N/A | N/A | Profiling doesn't need gradients |
-| 15_memoization | ⚠️ N/A | N/A | Caching doesn't need gradients |
-| 16_quantization | ⚠️ Unknown | Needs Check | Quantization might need gradient tests |
-| 17_compression | ⚠️ Unknown | Needs Check | Compression might need gradient tests |
-| 18_acceleration | ⚠️ N/A | N/A | Acceleration doesn't need gradients |
-| 19_benchmarking | ⚠️ N/A | N/A | Benchmarking doesn't need gradients |
-
-### **Specific Gaps Identified**
-
-1. **Module 02_activations** - Not all activations have gradient tests
-   - ✅ Sigmoid tested
-   - ✅ ReLU tested (partial)
-   - ⚠️ Tanh not fully tested
-   - ⚠️ GELU tested in autograd but not in activations module
-   - ⚠️ Softmax not tested
-
-2. **Module 08_dataloader** - No gradient flow tests
-   - Dataloader doesn't have trainable parameters, but should test:
-     - Data doesn't break gradient flow
-     - Batched operations preserve gradients
-
-3. **Module 03_layers** - Missing some layer types
-   - ✅ Linear well tested
-   - ⚠️ Dropout not tested
-   - ⚠️ BatchNorm not tested (if exists)
-   - ⚠️ LayerNorm tested in transformers but not in layers module
-
-4. **Edge Cases** - Some gaps
-   - ⚠️ Vanishing gradients detection
-   - ⚠️ Exploding gradients detection
-   - ⚠️ Gradient clipping
-   - ⚠️ Mixed precision (if applicable)
-
----
-
-## 📋 Recommended Test Structure
-
-### **For Each Module with Trainable Parameters**
-
-Create: `tests/XX_modulename/test_gradient_flow.py`
-
-**Template**:
-```python
-"""
-Gradient Flow Tests for Module XX: [Module Name]
-
-Tests that gradients flow correctly through all components in this module.
-"""
-
-def test_[component]_gradient_flow():
-    """Test that [Component] preserves gradient flow."""
-    # 1. Create component
-    component = Component(...)
-    
-    # 2. Forward pass
-    x = Tensor(..., requires_grad=True)
-    output = component(x)
-    
-    # 3. Backward pass
-    loss = output.sum()
-    loss.backward()
-    
-    # 4. Verify gradients exist
-    assert x.grad is not None, "Input should have gradients"
-    
-    # 5. Verify component parameters have gradients (if trainable)
-    if hasattr(component, 'parameters'):
-        for param in component.parameters():
-            assert param.grad is not None, f"{param} should have gradient"
-            assert np.abs(param.grad).max() > 1e-10, "Gradient should be non-zero"
-
-def test_[component]_with_previous_modules():
-    """Test that [Component] works with modules 01 through XX-1."""
-    # Use previous modules
-    from tinytorch.core.tensor import Tensor
-    from tinytorch.core.layers import Linear  # if applicable
-    
-    # Test integration
-    ...
-```
-
-### **Critical Checks for Every Module**
-
-1. **Gradient Existence**: Do gradients exist after backward?
-2. **Gradient Non-Zero**: Are gradients actually computed (not all zeros)?
-3. **Parameter Coverage**: Do all trainable parameters receive gradients?
-4. **Shape Correctness**: Do gradient shapes match parameter shapes?
-5. **Integration**: Does it work with previous modules?
-
----
-
-## 🎯 Priority Recommendations
-
-### **High Priority** (Must Have)
-
-1. **Complete Module 02_activations gradient tests**
-   - Create `tests/02_activations/test_gradient_flow.py`
-   - Test all activations: Sigmoid, ReLU, Tanh, GELU, Softmax
-   - Verify gradients are correct (not just exist)
-
-2. **Add Module 08_dataloader gradient flow tests**
-   - Create `tests/08_dataloader/test_gradient_flow.py`
-   - Test that dataloader doesn't break gradient flow
-   - Test batched operations preserve gradients
-
-3. **Complete Module 03_layers gradient tests**
-   - Add Dropout gradient tests
-   - Add LayerNorm gradient tests (if in layers module)
-   - Add BatchNorm gradient tests (if exists)
-
-### **Medium Priority** (Should Have)
-
-4. **Add vanishing/exploding gradient detection**
-   - Create `tests/debugging/test_gradient_vanishing.py`
-   - Create `tests/debugging/test_gradient_explosion.py`
-   - Provide helpful error messages for students
-
-5. **Add per-module progressive integration gradient tests**
-   - Each module should test: "Do gradients flow through module N with modules 1-N-1?"
-   - Example: `tests/07_training/test_gradient_flow_progressive.py`
-
-### **Low Priority** (Nice to Have)
-
-6. **Add numerical stability gradient tests**
-   - Test with very small values
-   - Test with very large values
-   - Test with NaN/Inf handling
-
-7. **Add gradient accumulation tests per module**
-   - Test that gradients accumulate correctly
-   - Test zero_grad() works correctly
-
----
-
-## 🔧 Implementation Plan
-
-### **Step 1: Create Missing Module Gradient Flow Tests**
-
-For each module missing gradient flow tests:
-
-```bash
-# Create test file
-touch tests/XX_modulename/test_gradient_flow.py
-
-# Add template with:
-# - Component gradient flow tests
-# - Integration with previous modules
-# - Edge cases
-```
-
-### **Step 2: Enhance Existing Tests**
-
-For modules with partial coverage:
-
-1. Review existing tests
-2. Identify missing components
-3. Add tests for missing components
-4. Ensure all trainable parameters are tested
-
-### **Step 3: Add Debugging Tests**
-
-Create helpful debugging tests:
-
-```python
-# tests/debugging/test_gradient_vanishing.py
-def test_detect_vanishing_gradients():
-    """Detect and diagnose vanishing gradients."""
-    # Deep network
-    # Check gradient magnitudes
-    # Provide helpful error message
-```
-
-### **Step 4: Add Progressive Integration Gradient Tests**
-
-For each module, add:
-
-```python
-# tests/XX_modulename/test_gradient_flow_progressive.py
-def test_module_N_gradients_with_all_previous():
-    """Test that module N gradients work with modules 1 through N-1."""
-    # Use all previous modules
-    # Test gradient flow through complete stack
-```
-
----
-
-## 📊 Test Execution Strategy
-
-### **During Development**
-```bash
-# Test specific module gradient flow
-pytest tests/XX_modulename/test_gradient_flow.py -v
-
-# Test integration gradient flow
-pytest tests/integration/test_gradient_flow.py -v
-
-# Test all gradient flow tests
-pytest tests/ -k "gradient" -v
-```
-
-### **Before Committing**
-```bash
-# Run all gradient flow tests
-pytest tests/integration/test_gradient_flow.py tests/*/test_gradient_flow.py -v
-
-# Critical: Must pass before merging
-pytest tests/integration/test_gradient_flow.py -v
-```
-
-### **CI/CD Integration**
-- Add gradient flow tests to CI pipeline
-- Fail build if critical gradient flow tests fail
-- Report gradient flow test coverage
-
----
-
-## ✅ Success Criteria
-
-A module has **complete gradient flow coverage** when:
-
-1. ✅ All trainable components have gradient flow tests
-2. ✅ All activations preserve gradient flow
-3. ✅ Integration with previous modules is tested
-4. ✅ Edge cases are covered (zero gradients, small values, etc.)
-5. ✅ Tests verify gradients are non-zero (not just exist)
-6. ✅ Tests verify gradient shapes match parameter shapes
-7. ✅ Tests provide helpful error messages when they fail
-
----
-
-## 🎓 Educational Value
-
-Gradient flow tests teach students:
-
-1. **Gradient flow is critical**: Components must preserve gradients
-2. **Integration matters**: Components must work together
-3. **Debugging skills**: How to diagnose gradient flow issues
-4. **Best practices**: Proper gradient handling patterns
-
----
-
-## 📚 References
-
-- **Critical Test**: `tests/integration/test_gradient_flow.py` - Must pass before merging
-- **Comprehensive Suite**: `tests/test_gradient_flow.py` - Full coverage
-- **Module Tests**: `tests/XX_modulename/test_gradient_flow.py` - Per-module coverage
-- **Transformer Tests**: `tests/13_transformers/test_transformer_gradient_flow.py` - Example of comprehensive module tests
-
----
-
-**Last Updated**: 2025-01-XX  
-**Status**: Analysis complete, implementation in progress  
-**Priority**: High - Gradient flow is critical for training to work
-
-
-
-
-
-
-
-
diff --git a/docs/development/nbgrader-standardized-testing.md b/docs/development/nbgrader-standardized-testing.md
deleted file mode 100644
index 095060be..00000000
--- a/docs/development/nbgrader-standardized-testing.md
+++ /dev/null
@@ -1,188 +0,0 @@
-# NBGrader Standardized Testing Framework
-
-## 🎯 The Perfect Solution
-
-Your suggestion to use **dedicated, locked NBGrader cells** for testing is brilliant! This approach provides:
-
-✅ **Protected Infrastructure** - Students can't break the testing framework  
-✅ **Consistent Placement** - Same location in every module (before final summary)  
-✅ **Educational Flow** - Learn → Implement → Test → Reflect  
-✅ **Professional Standards** - Mirrors real software development practices  
-✅ **Quality Assurance** - Ensures comprehensive validation of all student work  
-
-## 📋 Module Structure
-
-Every TinyTorch module follows this standardized structure:
-
-```
-1. 📖 Educational Content & Implementation Guidance
-2. 💻 Student Implementation Sections (unlocked)
-3. 🧪 Standardized Testing (LOCKED NBGrader cell)
-4. 🎯 Module Summary & Takeaways
-```
-
-## 🔒 The Locked Testing Cell
-
-### NBGrader Configuration
-```python
-# %% nbgrader={"grade": false, "grade_id": "standardized-testing", "locked": true, "schema_version": 3, "solution": false, "task": false}
-```
-
-### Key Settings Explained:
-- **`grade: false`** - Testing cell is not graded (provides feedback only)
-- **`locked: true`** - Students cannot modify this cell
-- **`solution: false`** - This is not a solution cell
-- **`task: false`** - This is not a task for students to complete
-
-### Cell Structure:
-```python
-# =============================================================================
-# STANDARDIZED MODULE TESTING - DO NOT MODIFY
-# This cell is locked to ensure consistent testing across all TinyTorch modules
-# =============================================================================
-
-from tinytorch.utils.testing import create_test_runner
-
-def test_core_functionality():
-    """Test core module functionality."""
-    # Module-specific tests here
-    print("✅ Core functionality tests passed!")
-
-def test_edge_cases():
-    """Test edge cases and error handling."""
-    # Edge case tests here
-    print("✅ Edge case tests passed!")
-
-def test_ml_integration():
-    """Test integration with ML workflows."""
-    # Integration tests here
-    print("✅ ML integration tests passed!")
-
-# Execute standardized testing
-if __name__ == "__main__":
-    test_runner = create_test_runner("ModuleName")
-    
-    test_runner.register_test("Core Functionality", test_core_functionality)
-    test_runner.register_test("Edge Cases", test_edge_cases)
-    test_runner.register_test("ML Integration", test_ml_integration)
-    
-    success = test_runner.run_all_tests()
-```
-
-## 🎭 Consistent Student Experience
-
-Every module produces **identical testing output**:
-
-```
-🔬 Running ModuleName Module Tests...
-==================================================
-🧪 Testing Core Functionality... ✅ PASSED
-🧪 Testing Edge Cases... ✅ PASSED
-🧪 Testing ML Integration... ✅ PASSED
-
-============================================================
-🎯 MODULENAME MODULE TESTING COMPLETE
-============================================================
-🎉 CONGRATULATIONS! All tests passed!
-
-✅ ModuleName Module Status: 3/3 tests passed (100%)
-
-📊 Detailed Results:
-   Core Functionality: ✅ PASSED
-   Edge Cases: ✅ PASSED
-   ML Integration: ✅ PASSED
-
-📈 Progress: ModuleName Module ✓ COMPLETE
-
-🚀 Ready for the next module!
-```
-
-## 📚 Educational Benefits
-
-### For Students:
-1. **Consistent Experience** - Same testing format across all modules
-2. **Immediate Feedback** - Clear validation of their implementations
-3. **Professional Exposure** - Experience with real testing practices
-4. **Protected Learning** - Cannot accidentally break testing infrastructure
-5. **Quality Confidence** - Assurance their implementations work correctly
-
-### For Instructors:
-1. **Standardized Quality** - Consistent validation across all modules
-2. **Protected Infrastructure** - Testing framework cannot be compromised
-3. **Easy Maintenance** - Single source of truth for testing format
-4. **Educational Focus** - More time on content, less on testing logistics
-5. **Scalable Assessment** - Efficient evaluation of student progress
-
-## 🔄 Module Flow
-
-### 1. Educational Introduction
-```markdown
-# Module X: Topic Name
-Learn about [concept] and its importance in ML systems...
-```
-
-### 2. Implementation Guidance
-```python
-# Student implementation sections (UNLOCKED)
-# Clear TODOs and guidance for student work
-```
-
-### 3. Testing Validation (LOCKED)
-```markdown
-## 🧪 Module Testing
-Time to test your implementation! This section is locked to ensure consistency.
-```
-
-### 4. Learning Summary
-```markdown
-## 🎯 Module Summary: Topic Mastery!
-Congratulations! You've successfully implemented...
-```
-
-## 🏗️ Implementation Strategy
-
-### Phase 1: Infrastructure
-- ✅ **Shared testing utilities** - `tinytorch.utils.testing` module
-- ✅ **NBGrader template** - Standardized cell structure
-- ✅ **Documentation** - Clear guidelines for implementation
-
-### Phase 2: Module Migration
-1. **Add testing section** to each module before final summary
-2. **Lock testing cells** with NBGrader configuration
-3. **Register module tests** with shared test runner
-4. **Validate consistency** across all modules
-
-### Phase 3: Quality Assurance
-1. **Test each module** individually for correctness
-2. **Verify consistent output** across all modules
-3. **Ensure NBGrader compatibility** with locked cells
-4. **Document any module-specific considerations**
-
-## 🎯 Benefits Achieved
-
-### Technical Benefits:
-- **Zero Code Duplication** - Shared testing infrastructure
-- **Perfect Consistency** - Identical output format across modules
-- **Protected Quality** - Testing framework cannot be broken
-- **Easy Maintenance** - Single point of update for improvements
-
-### Educational Benefits:
-- **Professional Standards** - Real-world software development practices
-- **Immediate Feedback** - Clear validation of student implementations
-- **Consistent Experience** - Same quality across all learning modules
-- **Focus on Learning** - Students focus on concepts, not testing setup
-
-### Assessment Benefits:
-- **Standardized Evaluation** - Consistent criteria across modules
-- **Automated Validation** - Reliable testing of student implementations
-- **Quality Assurance** - Comprehensive coverage of learning objectives
-- **Scalable Grading** - Efficient instructor workflow
-
-## 🚀 Next Steps
-
-1. **Apply template** to all existing modules
-2. **Test NBGrader integration** with locked cells
-3. **Validate student experience** across all modules
-4. **Document module-specific testing** requirements
-
-This NBGrader standardized testing framework provides the **perfect balance** of consistency, protection, and educational value! 
\ No newline at end of file
diff --git a/docs/development/nbgrader-testing-template.md b/docs/development/nbgrader-testing-template.md
deleted file mode 100644
index 0091065d..00000000
--- a/docs/development/nbgrader-testing-template.md
+++ /dev/null
@@ -1,174 +0,0 @@
-# NBGrader Testing Cell Template
-
-## 🎯 Standardized Module Structure
-
-Every TinyTorch module should follow this structure for consistent testing:
-
-### 1. Educational Content
-```python
-# %% [markdown]
-"""
-# Module X: Topic Name
-[Educational content, implementation guidance, etc.]
-"""
-
-# %%
-#| default_exp core.module_name
-
-# Student implementation sections...
-# [Student code here]
-```
-
-### 2. Individual Test Functions
-```python
-# Test functions that students can run during development
-def test_feature_1_comprehensive():
-    """Test feature 1 functionality comprehensively."""
-    # Detailed test implementation
-    assert feature_works()
-    print("✅ Feature 1 tests passed!")
-
-def test_feature_2_integration():
-    """Test feature 2 integration with other components."""
-    # Integration test implementation
-    assert integration_works()
-    print("✅ Feature 2 integration tests passed!")
-
-def test_module_integration():
-    """Test overall module integration."""
-    # Overall integration tests
-    assert module_works()
-    print("✅ Module integration tests passed!")
-```
-
-### 3. Dedicated Testing Section (Auto-Discovery)
-```python
-# %% [markdown]
-"""
-## 🧪 Module Testing
-
-Time to test your implementation! This section uses TinyTorch's standardized testing framework with **automatic test discovery**.
-
-**This testing section is locked** - it provides consistent feedback across all modules and cannot be modified.
-"""
-
-# %% nbgrader={"grade": false, "grade_id": "standardized-testing", "locked": true, "schema_version": 3, "solution": false, "task": false}
-# =============================================================================
-# STANDARDIZED MODULE TESTING - DO NOT MODIFY
-# This cell is locked to ensure consistent testing across all TinyTorch modules
-# =============================================================================
-
-if __name__ == "__main__":
-    from tinytorch.utils.testing import run_module_tests_auto
-    
-    # Automatically discover and run all tests in this module
-    success = run_module_tests_auto("ModuleName")
-```
-
-### 4. Module Summary (After Testing)
-```python
-# %% [markdown]
-"""
-## 🎯 Module Summary: [Topic] Mastery!
-
-Congratulations! You've successfully implemented [module topic]:
-
-### What You've Accomplished
-✅ **Feature 1**: Description of what was implemented
-✅ **Feature 2**: Description of what was implemented
-✅ **Integration**: How features work together
-
-### Key Concepts You've Learned
-- **Concept 1**: Explanation
-- **Concept 2**: Explanation
-- **Concept 3**: Explanation
-
-### Next Steps
-1. **Export your code**: `tito package nbdev --export module_name`
-2. **Test your implementation**: `tito test module_name`
-3. **Move to next module**: Brief description of what's next
-"""
-```
-
-## 🎯 **Critical: Correct Section Ordering**
-
-The order of sections **must** follow this logical flow:
-
-1. **Educational Content** - Students learn the concepts
-2. **Implementation Sections** - Students build the functionality
-3. **🧪 Module Testing** - Students verify their implementation works
-4. **🎯 Module Summary** - Students celebrate success and move forward
-
-### ❌ **Wrong Order (Confusing)**:
-```
-Implementation → Summary ("Congratulations!") → Testing → "Wait, did it work?"
-```
-
-### ✅ **Correct Order (Natural)**:
-```
-Implementation → Testing → Summary ("Congratulations! It works!") → Next Steps
-```
-
-**Why This Matters**: 
-- Testing **validates** the implementation before celebrating
-- Summary **confirms** success after verification
-- Natural flow: Build → Test → Celebrate → Advance
-- Mirrors real software development practices
-
-## 🔍 Automatic Test Discovery
-
-The new testing framework **automatically discovers** test functions, eliminating manual registration:
-
-### ✅ **Discovered Test Patterns**
-The system automatically finds and runs functions matching these patterns:
-- `test_*_comprehensive`: Comprehensive testing of individual features
-- `test_*_integration`: Integration testing with other components
-- `test_*_activation`: Specific activation function tests (ReLU, Sigmoid, etc.)
-
-### ✅ **Benefits**
-- **Zero Manual Work**: No need to register functions manually
-- **Error Prevention**: Won't miss test functions
-- **Consistent Naming**: Enforces good test naming conventions
-- **Automatic Ordering**: Tests run in alphabetical order
-- **Clean Output**: Standardized reporting format
-
-### ✅ **Example Output**
-```
-🔍 Auto-discovered 4 test functions
-
-🧪 Running Tensor Module Tests...
-==================================================
-✅ Tensor Arithmetic: PASSED
-✅ Tensor Creation: PASSED
-✅ Tensor Integration: PASSED
-✅ Tensor Properties: PASSED
-==================================================
-🎉 All tests passed! (4/4)
-✅ Tensor module is working correctly!
-```
-
-### ✅ **Safety Features**
-- **Pattern Matching**: Only discovers functions matching expected patterns
-- **Protected Framework**: NBGrader locked cells prevent student modifications
-- **Fallback Support**: Manual registration still available if needed
-- **Error Handling**: Graceful handling of malformed test functions
-
-## 📝 Implementation Notes
-
-### Test Function Requirements
-1. **Naming Convention**: Must start with `test_` and contain expected patterns
-2. **Self-Contained**: Each test should be independent
-3. **Clear Output**: Print success messages for educational feedback
-4. **Proper Assertions**: Use assert statements for validation
-
-### Module Integration
-1. **Single Entry Point**: Each module has one standardized testing entry
-2. **Consistent Interface**: Same API across all modules
-3. **CLI Integration**: `tito test module_name` uses the auto-discovery
-4. **Development Workflow**: Students can run individual tests during development
-
-### Educational Benefits
-1. **Immediate Feedback**: Students see results as they develop
-2. **Professional Practices**: Mirrors real software development workflows
-3. **Consistent Experience**: Same testing approach across all modules
-4. **Assessment Ready**: NBGrader can evaluate student implementations 
\ No newline at end of file
diff --git a/docs/development/testing-architecture.md b/docs/development/testing-architecture.md
deleted file mode 100644
index 6696f6ed..00000000
--- a/docs/development/testing-architecture.md
+++ /dev/null
@@ -1,425 +0,0 @@
-# TinyTorch Testing Architecture
-
-## 🎯 Overview: Two-Tier Testing Strategy
-
-TinyTorch uses a **two-tier testing approach** that separates component validation from system integration:
-
-1. **Inline Tests** (`modules/`) - Component validation, unit tests
-2. **Integration Tests** (`tests/`) - Inter-module integration, edge cases, system tests
-
-This separation follows ML engineering best practices: validate components in isolation, then test how they work together.
-
----
-
-## 📋 Tier 1: Inline Tests (Component Validation)
-
-### **Location**: `modules/XX_modulename/*.py`
-
-### **Purpose**:
-- ✅ Validate individual components work correctly **in isolation**
-- ✅ Test single module functionality
-- ✅ Provide immediate feedback during development
-- ✅ Educate students about expected behavior
-- ✅ Fast execution for rapid iteration
-
-### **What Gets Tested**:
-- Individual class/function correctness
-- Mathematical operations (forward passes)
-- Shape transformations
-- Basic edge cases and error handling
-- Component-level functionality
-
-### **Test Pattern**:
-```python
-def test_unit_componentname():
-    """🧪 Unit Test: Component Name
-    
-    **This is a unit test** - it tests [component] in isolation.
-    """
-    print("🔬 Unit Test: Component...")
-    
-    # Test implementation
-    assert condition, "✅ Component works"
-    
-    print("✅ Component test passed")
-```
-
-### **Example**: `modules/01_tensor/tensor.py`
-- `test_unit_tensor_creation()` - Tests tensor creation
-- `test_unit_arithmetic_operations()` - Tests +, -, *, /
-- `test_unit_matrix_multiplication()` - Tests @ operator
-- `test_unit_shape_manipulation()` - Tests reshape, transpose
-- `test_unit_reduction_operations()` - Tests sum, mean, max
-
-### **Execution**:
-```bash
-# Run inline tests only
-tito test 01_tensor --inline-only
-
-# Tests run when you execute the module file
-python modules/01_tensor/tensor.py
-```
-
-### **Key Characteristics**:
-- ✅ **Fast**: Run during development for immediate feedback
-- ✅ **Isolated**: No dependencies on other modules
-- ✅ **Educational**: Shows students what "correct" looks like
-- ✅ **Component-focused**: Tests one thing at a time
-
----
-
-## 📊 Tier 2: Integration Tests (`tests/` Directory)
-
-### **Location**: `tests/`
-
-### **Purpose**:
-- ✅ Test how **multiple modules work together**
-- ✅ Validate cross-module dependencies
-- ✅ Test realistic workflows and use cases
-- ✅ Ensure system-level correctness
-- ✅ Catch bugs that unit tests miss
-- ✅ Test edge cases and corner scenarios
-- ✅ Validate exported code (`tinytorch/`) works correctly
-
-### **Key Insight**: 
-**Component correctness ≠ System correctness**
-
-A tensor might work perfectly in isolation, but fail when gradients flow through layers → activations → losses → optimizers. Integration tests catch these "seam" bugs.
-
----
-
-## 🗂️ Structure of `tests/` Directory
-
-### 1. **Module-Specific Integration Tests** (`tests/XX_modulename/`)
-
-**Purpose**: Test that module N works correctly **with all previous modules** (1 through N-1)
-
-**Example**: `tests/05_autograd/test_progressive_integration.py`
-- Tests autograd with Tensor (01), Activations (02), Layers (03), Losses (04)
-- Validates that gradients flow correctly through the entire stack built so far
-
-**Pattern**: Progressive integration
-```python
-# tests/05_autograd/test_progressive_integration.py
-def test_autograd_with_all_previous_modules():
-    # Uses real Tensor, real Layers, real Activations, real Losses
-    # Then tests Autograd (05) with all of them
-    x = Tensor([[1.0, 2.0]], requires_grad=True)
-    layer = Linear(2, 3)
-    activation = ReLU()
-    loss_fn = MSELoss()
-    
-    output = activation(layer(x))
-    loss = loss_fn(output, target)
-    loss.backward()
-    
-    assert x.grad is not None  # Gradient flowed through everything!
-```
-
-**Why This Matters**:
-- Catches integration bugs early
-- Ensures modules don't break previous functionality
-- Validates the "seams" between modules
-
----
-
-### 2. **Cross-Module Integration Tests** (`tests/integration/`)
-
-**Purpose**: Test **multiple modules working together** in realistic scenarios
-
-**Key Files**:
-- `test_gradient_flow.py` - **CRITICAL**: Validates gradients flow through entire training stack
-- `test_end_to_end_training.py` - Full training loops
-- `test_module_compatibility.py` - Module interfaces
-
-**Example**: `tests/integration/test_gradient_flow.py`
-```python
-def test_complete_training_stack():
-    """Test that gradients flow through: Tensor → Layers → Activations → Loss → Autograd → Optimizer"""
-    # Uses modules 01, 02, 03, 04, 05, 06, 07
-    # Validates the entire training pipeline works
-```
-
-**Why This Matters**:
-- Catches bugs that unit tests miss
-- Validates the "seams" between modules
-- Ensures training actually works end-to-end
-- Tests realistic ML workflows
-
----
-
-### 3. **Edge Cases & Stress Tests** (`tests/05_autograd/`, `tests/debugging/`)
-
-**Purpose**: Test **corner cases** and **common pitfalls**
-
-**Examples**:
-- `tests/05_autograd/test_broadcasting.py` - Broadcasting gradient bugs
-- `tests/05_autograd/test_computation_graph.py` - Graph construction edge cases
-- `tests/debugging/test_gradient_vanishing.py` - Detect vanishing gradients
-- `tests/debugging/test_common_mistakes.py` - "Did you forget backward()?" style tests
-
-**Philosophy**: When these tests fail, the error message should **teach the student** what went wrong and how to fix it.
-
-**Why This Matters**:
-- Catches numerical stability issues
-- Tests edge cases that break in production
-- Pedagogical: teaches debugging skills
-
----
-
-### 4. **Regression Tests** (`tests/regression/`)
-
-**Purpose**: Ensure **previously fixed bugs don't come back**
-
-**Pattern**: Each bug gets a test file
-- `test_issue_20241125_conv_fc_shapes.py` - Tests a specific bug that was fixed
-- Documents the bug, root cause, fix, and prevention
-
-**Why This Matters**:
-- Prevents regressions
-- Documents historical bugs
-- Ensures fixes persist
-
----
-
-### 5. **Performance Tests** (`tests/performance/`)
-
-**Purpose**: Validate **systems performance** characteristics
-
-**Examples**:
-- Memory profiling
-- Speed benchmarks
-- Scalability tests
-
-**Why This Matters**:
-- Ensures implementations are efficient
-- Validates performance characteristics
-- Catches performance regressions
-
----
-
-### 6. **System Tests** (`tests/system/`)
-
-**Purpose**: Test **entire system workflows**
-
-**Examples**:
-- End-to-end training pipelines
-- Model export/import
-- Checkpoint system tests
-
-**Why This Matters**:
-- Validates complete workflows
-- Tests production scenarios
-- Ensures system-level correctness
-
----
-
-### 7. **Checkpoint Tests** (`tests/checkpoints/`)
-
-**Purpose**: Validate **milestone capabilities**
-
-**Examples**:
-- `checkpoint_01_foundation.py` - Tensor operations mastered
-- `checkpoint_05_learning.py` - Autograd working correctly
-
-**Why This Matters**:
-- Validates student progress
-- Ensures milestones are met
-- Provides clear success criteria
-
----
-
-## 🔄 Code Flow: Development → Export → Testing
-
-```
-┌─────────────────────────────────────────────────────────────┐
-│                    DEVELOPMENT WORKFLOW                      │
-└─────────────────────────────────────────────────────────────┘
-
-1. DEVELOP in modules/
-   └─> modules/01_tensor/tensor.py
-       ├─> Write code
-       ├─> Write inline tests (test_unit_*)
-       └─> Run: python modules/01_tensor/tensor.py
-
-2. EXPORT to tinytorch/
-   └─> tito export 01_tensor
-       └─> Code exported to tinytorch/core/tensor.py
-
-3. TEST integration
-   └─> tests/01_tensor/test_progressive_integration.py
-       ├─> Imports from tinytorch.core.tensor (exported code!)
-       ├─> Tests module works with previous modules
-       └─> Run: pytest tests/01_tensor/
-
-4. TEST cross-module
-   └─> tests/integration/test_gradient_flow.py
-       ├─> Imports from tinytorch.* (all exported modules)
-       ├─> Tests multiple modules working together
-       └─> Run: pytest tests/integration/
-```
-
----
-
-## 🎯 Decision Tree: Where Should This Test Go?
-
-```
-Is it testing a single component in isolation?
-├─ YES → modules/XX_modulename/*.py (inline test_unit_*)
-│
-└─ NO → Is it testing module N with previous modules?
-    ├─ YES → tests/XX_modulename/test_progressive_integration.py
-    │
-    └─ NO → Is it testing multiple modules together?
-        ├─ YES → tests/integration/test_*.py
-        │
-        └─ NO → Is it an edge case or stress test?
-            ├─ YES → tests/XX_modulename/test_*_edge_cases.py
-            │         OR tests/debugging/test_*.py
-            │
-            └─ NO → Is it a regression test?
-                ├─ YES → tests/regression/test_issue_*.py
-                │
-                └─ NO → Is it a performance test?
-                    ├─ YES → tests/performance/test_*.py
-                    │
-                    └─ NO → Is it a system test?
-                        └─ YES → tests/system/test_*.py
-```
-
----
-
-## 📝 Best Practices
-
-### **DO**:
-✅ Write inline tests immediately after implementing a component  
-✅ Test one thing per inline test function  
-✅ Use descriptive test function names (`test_unit_sigmoid`, not `test1`)  
-✅ Add integration tests when combining multiple modules  
-✅ Run inline tests frequently during development  
-✅ Run full test suite before committing  
-✅ Test exported code (`tinytorch/`), not development code (`modules/`)  
-✅ Write tests that catch real bugs you've encountered  
-
-### **DON'T**:
-❌ Mix inline and integration test concerns  
-❌ Test implementation details in integration tests  
-❌ Skip inline tests and jump to integration  
-❌ Test mocked/fake components (use real ones)  
-❌ Create dependencies between test files  
-❌ Test code in `modules/` directly in `tests/` (test `tinytorch/` instead)  
-❌ Duplicate inline tests in `tests/` directory  
-
----
-
-## 🔍 Key Distinctions
-
-| Aspect | Inline Tests (`modules/`) | Integration Tests (`tests/`) |
-|--------|-------------------------|----------------------------|
-| **Location** | `modules/XX_name/*.py` | `tests/XX_name/` or `tests/integration/` |
-| **Scope** | Single component | Multiple modules |
-| **Dependencies** | None (isolated) | Previous modules |
-| **Speed** | Fast | Slower |
-| **Purpose** | Component correctness | System correctness |
-| **When to run** | During development | Before commit/export |
-| **What gets tested** | `modules/` code directly | `tinytorch/` exported code |
-| **Example** | `test_unit_tensor_creation()` | `test_tensor_with_layers()` |
-
----
-
-## 🚀 Testing Workflow
-
-### For Students:
-
-```bash
-# 1. Work on module
-cd modules/01_tensor
-vim tensor.py
-
-# 2. Run inline tests (fast feedback)
-python tensor.py
-# or
-tito test 01_tensor --inline-only
-
-# 3. Export to package
-tito export 01_tensor
-
-# 4. Run integration tests (full validation)
-tito test 01_tensor
-# or
-pytest tests/01_tensor/
-
-# 5. Run cross-module tests (ensure nothing broke)
-pytest tests/integration/
-```
-
-### For Instructors:
-
-```bash
-# Comprehensive test suite
-tito test --comprehensive
-
-# Specific module deep dive
-tito test 05_autograd --detailed
-
-# All inline tests only (quick check)
-tito test --all --inline-only
-
-# Critical integration tests
-pytest tests/integration/test_gradient_flow.py -v
-```
-
----
-
-## 💡 Why This Architecture?
-
-### **Separation of Concerns**:
-- **Inline tests** = "Does this component work?"
-- **Integration tests** = "Do these components work together?"
-
-### **Educational Value**:
-- Students learn component testing first
-- Then learn integration testing
-- Mirrors professional ML engineering workflows
-
-### **Practical Benefits**:
-- Fast feedback during development (inline tests)
-- Comprehensive validation before commit (integration tests)
-- Catches bugs at the right level
-- Clear mental model: component vs. system
-
-### **Real-World Alignment**:
-- Professional ML teams use this pattern
-- Unit tests for components
-- Integration tests for pipelines
-- System tests for workflows
-
----
-
-## 📚 Summary
-
-**Think of `tests/` as the "system validation layer":**
-
-1. **`modules/` inline tests** = "Does my component work?"
-2. **`tests/XX_modulename/`** = "Does my module work with previous modules?"
-3. **`tests/integration/`** = "Do multiple modules work together?"
-4. **`tests/debugging/`** = "Are there edge cases I'm missing?"
-5. **`tests/regression/`** = "Did I break something that was working?"
-6. **`tests/performance/`** = "Is my implementation efficient?"
-7. **`tests/system/`** = "Does the entire system work?"
-
-**The key insight**: `tests/` validates that exported code (`tinytorch/`) works correctly in realistic scenarios, catching bugs that isolated unit tests miss.
-
----
-
-**Last Updated**: 2025-01-XX  
-**Test Infrastructure**: Complete (20/20 modules have test directories)  
-**Philosophy**: Component correctness ≠ System correctness
-
-
-
-
-
-
-
-
diff --git a/instructor/guides/README_analyzer.md b/instructor/guides/README_analyzer.md
deleted file mode 100644
index b3d0bffb..00000000
--- a/instructor/guides/README_analyzer.md
+++ /dev/null
@@ -1,213 +0,0 @@
-# TinyTorch Module Analyzer & Report Card Generator
-
-A comprehensive, reusable tool for analyzing educational quality and generating actionable report cards for TinyTorch modules.
-
-## 🎯 Purpose
-
-This tool automatically analyzes TinyTorch modules to:
-- **Identify student overwhelm points** (complexity cliffs, long cells, missing guidance)
-- **Grade educational scaffolding** (A-F grades for different aspects)
-- **Generate actionable recommendations** for improvement
-- **Compare modules** to find best and worst practices
-- **Track progress** over time with quantitative metrics
-
-## 🚀 Quick Start
-
-```bash
-# Analyze a single module
-python tinytorch_module_analyzer.py --module 02_activations
-
-# Analyze all modules
-python tinytorch_module_analyzer.py --all
-
-# Compare specific modules
-python tinytorch_module_analyzer.py --compare 01_tensor 02_activations 03_layers
-
-# Generate and save detailed reports
-python tinytorch_module_analyzer.py --module 02_activations --save
-```
-
-## 📊 What It Analyzes
-
-### Educational Quality Metrics
-- **Scaffolding Quality** (1-5): How well the module supports student learning
-- **Complexity Distribution**: Percentage of high-complexity cells
-- **Learning Progression**: Whether difficulty increases smoothly
-- **Implementation Support**: Ratio of hints to TODO items
-
-### Content Metrics
-- **Module Length**: Total lines and cells
-- **Cell Length**: Average lines per cell
-- **Concept Density**: Concepts introduced per cell
-- **Test Coverage**: Number of test files
-
-### Student Experience Factors
-- **Overwhelm Points**: Specific issues that could frustrate students
-- **Complexity Cliffs**: Sudden difficulty jumps
-- **Missing Guidance**: Implementation cells without hints
-- **Long Cells**: Cells that exceed cognitive load limits
-
-## 📈 Report Card Grades
-
-### Overall Grade (A-F)
-- **A**: Excellent scaffolding, smooth progression, student-friendly
-- **B**: Good structure with minor issues
-- **C**: Adequate but needs improvement
-- **D**: Significant scaffolding problems
-- **F**: Major issues, likely to overwhelm students
-
-### Category Grades
-- **Scaffolding**: Quality of learning support and guidance
-- **Complexity**: Appropriateness of difficulty progression
-- **Cell_Length**: Whether cells are digestible chunks
-
-## 🎯 Target Metrics
-
-The analyzer compares modules against these educational best practices:
-
-| Metric | Target | Why It Matters |
-|--------|--------|----------------|
-| Module Length | 200-400 lines | Manageable scope for students |
-| Cell Length | ≤30 lines | Fits cognitive working memory |
-| High-Complexity Cells | ≤30% | Prevents overwhelm |
-| Scaffolding Quality | ≥4/5 | Ensures student support |
-| Hint Ratio | ≥80% | Implementation guidance |
-
-## 🔍 Sample Output
-
-```
-🔍 Analyzing module: 02_activations
-
-📊 Report Card for 02_activations:
-Overall Grade: C
-Scaffolding Quality: 3/5
-Critical Issues: 2
-```
-
-### Critical Issues Detected
-- Too many high-complexity cells (77%)
-- Implementation cells lack guidance (40% without hints)
-- Sudden complexity jumps will overwhelm students
-- 3 cells are too long (>50 lines)
-
-### Recommendations
-- Add implementation ladders: break complex functions into 3 progressive steps
-- Add concept bridges: connect new ideas to familiar concepts
-- Split 3 long cells into smaller, focused cells
-- Add hints to 4 implementation cells
-
-## 📁 Output Formats
-
-### JSON Format (for programmatic use)
-```json
-{
-  "module_name": "02_activations",
-  "overall_grade": "C",
-  "scaffolding_quality": 3,
-  "critical_issues": [...],
-  "recommendations": [...],
-  "cell_analyses": [...]
-}
-```
-
-### HTML Format (for human reading)
-Beautiful, interactive report cards with:
-- Color-coded grades and metrics
-- Cell-by-cell analysis with complexity indicators
-- Visual progress indicators
-- Actionable recommendations
-
-## 🔄 Workflow Integration
-
-### Before Making Changes
-```bash
-# Get baseline metrics
-python tinytorch_module_analyzer.py --module 02_activations --save
-```
-
-### After Improvements
-```bash
-# Check improvement
-python tinytorch_module_analyzer.py --module 02_activations --save
-# Compare with previous reports to track progress
-```
-
-### Continuous Monitoring
-```bash
-# Check all modules regularly
-python tinytorch_module_analyzer.py --all --save
-```
-
-## 🎓 Educational Framework
-
-The analyzer is based on proven educational principles:
-
-### Rule of 3s
-- Max 3 complexity levels per module
-- Max 3 new concepts per cell  
-- Max 30 lines per implementation cell
-
-### Progressive Scaffolding
-- **Concept bridges**: Connect unfamiliar to familiar
-- **Implementation ladders**: Break complex tasks into steps
-- **Confidence builders**: Early wins build momentum
-
-### Cognitive Load Theory
-- **Chunking**: Information in digestible pieces
-- **Progressive disclosure**: Introduce complexity gradually
-- **Support structures**: Hints and guidance when needed
-
-## 🛠️ Customization
-
-### Modify Target Metrics
-Edit the `target_metrics` in the `TinyTorchModuleAnalyzer` class:
-
-```python
-self.target_metrics = {
-    'ideal_lines': (200, 400),
-    'max_cell_lines': 30,
-    'max_complexity_ratio': 0.3,
-    'min_scaffolding_quality': 4,
-    'max_concepts_per_cell': 3,
-    'min_hint_ratio': 0.8
-}
-```
-
-### Add Custom Analysis
-Extend the analyzer with domain-specific metrics for your educational context.
-
-## 📊 Use Cases
-
-### For Instructors
-- **Quality assurance**: Ensure modules meet educational standards
-- **Continuous improvement**: Track scaffolding quality over time
-- **Comparison**: Find best practices across modules
-- **Student feedback**: Predict where students might struggle
-
-### For Course Developers
-- **Design validation**: Check if new modules follow best practices
-- **Refactoring guidance**: Identify specific improvement areas
-- **Progress tracking**: Measure improvement after changes
-- **Standardization**: Ensure consistent quality across modules
-
-### For Researchers
-- **Educational analytics**: Study what makes effective ML education
-- **A/B testing**: Compare different scaffolding approaches
-- **Longitudinal studies**: Track student outcomes vs. module quality
-- **Best practice identification**: Find patterns in successful modules
-
-## 🎯 Success Stories
-
-After applying analyzer recommendations:
-- **01_tensor**: Improved from C to B grade with better scaffolding
-- **02_activations**: Reduced overwhelm points from 8 to 2
-- **03_layers**: Increased hint ratio from 40% to 85%
-
-The analyzer transforms gut feelings about educational quality into actionable, data-driven improvements.
-
----
-
-**Ready to improve your educational content? Start with:**
-```bash
-python tinytorch_module_analyzer.py --all
-``` 
\ No newline at end of file
diff --git a/instructor/guides/educational_analysis_report.md b/instructor/guides/educational_analysis_report.md
deleted file mode 100644
index 6fc4493d..00000000
--- a/instructor/guides/educational_analysis_report.md
+++ /dev/null
@@ -1,136 +0,0 @@
-# TinyTorch Educational Content Analysis Report
-==================================================
-
-## 📊 Overall Statistics
-- Total modules analyzed: 8
-- Total lines of content: 7,057
-- Total cells: 89
-- Average scaffolding quality: 1.9/5.0
-
-## 📚 Module-by-Module Analysis
-
-### 00_setup
-- **Lines**: 300
-- **Cells**: 7
-- **Concepts**: 38
-- **TODOs**: 2
-- **Hints**: 2
-- **Tests**: 0
-- **Scaffolding Quality**: 2/5
-- **⚠️ Potential Overwhelm Points**:
-  - Cell 6: Long implementation without guidance (56 lines)
-  - Cell 6: High complexity without student scaffolding
-
-### 01_tensor
-- **Lines**: 1,232
-- **Cells**: 17
-- **Concepts**: 73
-- **TODOs**: 1
-- **Hints**: 1
-- **Tests**: 1
-- **Scaffolding Quality**: 2/5
-- **⚠️ Potential Overwhelm Points**:
-  - Cell 8: Long implementation without guidance (125 lines)
-  - Cell 8: High complexity without student scaffolding
-  - Cell 8: Sudden complexity jump from 1 to 4
-
-### 02_activations
-- **Lines**: 1,417
-- **Cells**: 17
-- **Concepts**: 90
-- **TODOs**: 4
-- **Hints**: 4
-- **Tests**: 1
-- **Scaffolding Quality**: 2/5
-- **⚠️ Potential Overwhelm Points**:
-  - Cell 2: Long implementation without guidance (86 lines)
-  - Cell 2: High complexity without student scaffolding
-  - Cell 2: Sudden complexity jump from 1 to 4
-
-### 03_layers
-- **Lines**: 1,162
-- **Cells**: 12
-- **Concepts**: 63
-- **TODOs**: 2
-- **Hints**: 2
-- **Tests**: 1
-- **Scaffolding Quality**: 2/5
-- **⚠️ Potential Overwhelm Points**:
-  - Cell 2: Long implementation without guidance (52 lines)
-  - Cell 2: High complexity without student scaffolding
-  - Cell 2: Sudden complexity jump from 1 to 4
-
-### 04_networks
-- **Lines**: 1,273
-- **Cells**: 13
-- **Concepts**: 65
-- **TODOs**: 2
-- **Hints**: 2
-- **Tests**: 1
-- **Scaffolding Quality**: 2/5
-- **⚠️ Potential Overwhelm Points**:
-  - Cell 2: Long implementation without guidance (58 lines)
-  - Cell 2: High complexity without student scaffolding
-  - Cell 2: Sudden complexity jump from 1 to 4
-
-### 05_cnn
-- **Lines**: 774
-- **Cells**: 12
-- **Concepts**: 72
-- **TODOs**: 3
-- **Hints**: 3
-- **Tests**: 1
-- **Scaffolding Quality**: 2/5
-- **⚠️ Potential Overwhelm Points**:
-  - Cell 2: Long implementation without guidance (55 lines)
-  - Cell 2: High complexity without student scaffolding
-  - Cell 2: Sudden complexity jump from 1 to 4
-
-### 06_dataloader
-- **Lines**: 899
-- **Cells**: 11
-- **Concepts**: 76
-- **TODOs**: 3
-- **Hints**: 3
-- **Tests**: 1
-- **Scaffolding Quality**: 2/5
-- **⚠️ Potential Overwhelm Points**:
-  - Cell 2: Long implementation without guidance (53 lines)
-  - Cell 2: High complexity without student scaffolding
-  - Cell 2: Sudden complexity jump from 1 to 4
-
-### 07_autograd
-- **Lines**: 0
-- **Cells**: 0
-- **Concepts**: 0
-- **TODOs**: 0
-- **Hints**: 0
-- **Tests**: 0
-- **Scaffolding Quality**: 1/5
-
-## 🎯 Educational Recommendations
-
-### 🚨 Modules Needing Better Scaffolding:
-- **00_setup**: Quality 2/5
-- **01_tensor**: Quality 2/5
-- **02_activations**: Quality 2/5
-- **03_layers**: Quality 2/5
-- **04_networks**: Quality 2/5
-- **05_cnn**: Quality 2/5
-- **06_dataloader**: Quality 2/5
-- **07_autograd**: Quality 1/5
-
-### 📈 Modules with High Complexity:
-- **00_setup**: 42.9% high-complexity cells
-- **01_tensor**: 35.3% high-complexity cells
-- **02_activations**: 76.5% high-complexity cells
-- **03_layers**: 83.3% high-complexity cells
-- **04_networks**: 84.6% high-complexity cells
-- **05_cnn**: 83.3% high-complexity cells
-- **06_dataloader**: 72.7% high-complexity cells
-
-### ✅ Recommended Best Practices:
-- **Ideal module length**: 200-400 lines (current range: 300-1417)
-- **Cell complexity**: Max 30% high-complexity cells
-- **Scaffolding ratio**: All implementation cells should have hints
-- **Progression**: Concept → Example → Implementation → Verification
\ No newline at end of file
diff --git a/instructor/guides/implementation_plan.md b/instructor/guides/implementation_plan.md
deleted file mode 100644
index b96a1c15..00000000
--- a/instructor/guides/implementation_plan.md
+++ /dev/null
@@ -1,287 +0,0 @@
-# Implementation Plan: Transforming TinyTorch Educational Experience
-
-## 🚨 Current State Summary
-
-**CRITICAL FINDINGS**: Our analysis reveals a student overwhelm crisis:
-- **Scaffolding Quality**: 1.9/5.0 (Target: 4.0+)
-- **High-Complexity Cells**: 70-80% (Target: <30%)
-- **Complexity Cliffs**: Every module jumps 1→4 suddenly
-- **Implementation Blocks**: 50-125 lines without guidance
-
-**IMPACT**: Students likely experience frustration, anxiety, and reduced learning effectiveness.
-
----
-
-## 🎯 Implementation Strategy: "Fix One, Learn, Scale"
-
-### Phase 1: Pilot Implementation (Week 1)
-**Goal**: Prove the scaffolding approach works with one module
-
-**Target Module**: `02_activations` 
-- **Why**: High complexity (77% complex cells), clear math concepts, manageable size
-- **Current Issues**: Math-heavy without scaffolding, sudden complexity jumps
-- **Success Metrics**: Reduce complexity from 77% to <30%, add scaffolding to 4/5 rating
-
-### Phase 2: Core Module Improvements (Weeks 2-3)
-**Goal**: Apply learnings to most critical modules
-
-**Target Modules**: `01_tensor`, `03_layers`, `04_networks`
-- **Priority Order**: Based on impact and complexity issues
-- **Approach**: Apply proven scaffolding patterns from pilot
-
-### Phase 3: System Integration (Week 4)
-**Goal**: Ensure coherent learning progression across modules
-
-**Focus**: Cross-module connections, integrated testing, overall flow
-
----
-
-## 🔧 Pilot Implementation: Activations Module Transformation
-
-### Current State Analysis
-```
-02_activations:
-- Lines: 1,417 (target: 300-500)
-- Cells: 17 (reasonable)
-- Scaffolding: 2/5 (poor)
-- High-complexity: 77% (terrible)
-- Main issue: Mathematical concepts without bridges
-```
-
-### Transformation Plan
-
-#### 1. **Apply "Rule of 3s"**
-- **Break down** 86-line implementation cells into 3 steps max
-- **Limit** to 3 new concepts per cell
-- **Create** 3-level complexity progression (not 1→4 jumps)
-
-#### 2. **Add Concept Bridges**
-```markdown
-## Understanding ReLU: From Light Switches to Neural Networks
-
-### 🔌 Familiar Analogy: Light Switch
-ReLU is like a light switch for neurons:
-- **Negative input**: Switch is OFF (output = 0)
-- **Positive input**: Switch is ON (output = input)
-- **At zero**: Right at the threshold
-
-### 🧮 Mathematical Definition
-ReLU(x) = max(0, x)
-- If x < 0, output 0
-- If x ≥ 0, output x
-
-### 💻 Code Implementation
-```python
-def relu(x):
-    return np.maximum(0, x)  # Element-wise max with 0
-```
-
-### 🧠 Why Neural Networks Need This
-- **Problem**: Without activation functions, neural networks are just linear
-- **Solution**: ReLU adds non-linearity, allowing complex patterns
-- **Real-world**: This is how ChatGPT learns to understand language!
-```
-
-#### 3. **Create Implementation Ladders**
-```python
-# ❌ Current: Complexity cliff
-class ReLU:
-    def __call__(self, x):
-        # TODO: Implement ReLU activation (86 lines)
-        raise NotImplementedError("Student implementation required")
-
-# ✅ New: Progressive ladder
-class ReLU:
-    def forward_single_value(self, x):
-        """
-        TODO: Implement ReLU for a single number
-        
-        APPROACH:
-        1. Check if x is positive or negative
-        2. Return x if positive, 0 if negative
-        
-        EXAMPLE:
-        Input: -2.5 → Output: 0
-        Input: 3.7 → Output: 3.7
-        """
-        pass  # 3-5 lines
-    
-    def forward_array(self, x):
-        """
-        TODO: Extend to work with arrays
-        
-        APPROACH:
-        1. Use your single_value logic as inspiration
-        2. Apply to each element in the array
-        3. Hint: np.maximum(0, x) does this automatically!
-        """
-        pass  # 5-8 lines
-    
-    def __call__(self, x):
-        """
-        TODO: Add tensor compatibility and error checking
-        
-        APPROACH:
-        1. Handle both numpy arrays and Tensor objects
-        2. Use your forward_array implementation
-        3. Return a Tensor object
-        """
-        pass  # 8-12 lines
-```
-
-#### 4. **Add Confidence Builders**
-```python
-def test_relu_confidence_builder():
-    """🎉 Confidence Builder: Can you create a ReLU?"""
-    relu = ReLU()
-    assert relu is not None, "🎉 Great! Your ReLU class exists!"
-    
-    print("🎊 SUCCESS! You've created your first activation function!")
-    print("🧠 This is the same building block used in:")
-    print("   • ChatGPT (GPT transformers)")
-    print("   • Image recognition (ResNet, VGG)")
-    print("   • Game AI (AlphaGo, OpenAI Five)")
-
-def test_relu_simple_case():
-    """🎯 Learning Test: Does your ReLU work on simple inputs?"""
-    relu = ReLU()
-    
-    # Test positive number
-    result_pos = relu.forward_single_value(5.0)
-    if result_pos == 5.0:
-        print("✅ Perfect! Positive inputs work correctly!")
-    
-    # Test negative number  
-    result_neg = relu.forward_single_value(-3.0)
-    if result_neg == 0.0:
-        print("✅ Excellent! Negative inputs are zeroed!")
-        print("🎉 You understand the core concept of ReLU!")
-```
-
-#### 5. **Create Educational Tests**
-```python
-def test_relu_with_learning():
-    """📚 Educational Test: Learn how ReLU affects neural networks"""
-    
-    print("\n🧠 Neural Network Learning Simulation:")
-    print("Imagine a neuron trying to recognize a cat in an image...")
-    
-    relu = ReLU()
-    
-    # Simulate neuron responses
-    cat_features = Tensor([0.8, -0.3, 0.6, -0.9, 0.4])  # Mixed positive/negative
-    
-    print(f"Raw neuron responses: {cat_features.data}")
-    
-    activated = relu(cat_features)
-    print(f"After ReLU activation: {activated.data}")
-    
-    print("\n💡 What happened?")
-    print("• Positive responses (0.8, 0.6, 0.4) → Strong cat features detected!")
-    print("• Negative responses (-0.3, -0.9) → No cat features, so ignore (→ 0)")
-    print("🎯 This is how neural networks focus on relevant features!")
-    
-    expected = np.array([0.8, 0.0, 0.6, 0.0, 0.4])
-    assert np.allclose(activated.data, expected), "ReLU should zero negative values"
-```
-
----
-
-## 📊 Success Metrics and Validation
-
-### Quantitative Targets (Pilot Module)
-- [ ] **Scaffolding Quality**: 2/5 → 4/5
-- [ ] **High-Complexity Cells**: 77% → <30%
-- [ ] **Average Cell Length**: <30 lines per implementation
-- [ ] **Concept Density**: ≤3 new concepts per cell
-- [ ] **Test Pass Rate**: 90%+ on confidence builders
-
-### Qualitative Validation
-- [ ] **Concept Understanding**: Can students explain ReLU in their own words?
-- [ ] **Implementation Success**: Do students complete implementations without excessive help?
-- [ ] **Confidence Level**: Do students feel prepared for the next module?
-- [ ] **Real-world Connection**: Do students understand how this relates to production ML?
-
-### Testing Process
-1. **Run analysis script** before and after improvements
-2. **Test inline functionality** to ensure nothing breaks
-3. **Measure completion time** for the module
-4. **Gather feedback** from test users (if available)
-
----
-
-## 🔄 Iteration and Scaling Process
-
-### Pilot Feedback Loop
-1. **Implement** scaffolding improvements in activations module
-2. **Test** with analysis script and manual review
-3. **Measure** against success metrics
-4. **Refine** approach based on learnings
-5. **Document** what works and what doesn't
-
-### Scaling Strategy
-1. **Template Creation**: Turn successful patterns into reusable templates
-2. **Priority Ranking**: Focus on modules with worst scaffolding scores
-3. **Parallel Development**: Apply learnings to multiple modules simultaneously
-4. **Cross-module Integration**: Ensure coherent learning progression
-
-### Quality Assurance
-- [ ] **Automated Analysis**: Run scaffolding analysis after each improvement
-- [ ] **Functionality Testing**: Ensure all inline tests still pass
-- [ ] **Integration Testing**: Verify modules work together
-- [ ] **Educational Review**: Check that improvements actually help learning
-
----
-
-## 🚀 Implementation Timeline
-
-### Week 1: Pilot (Activations Module)
-- **Day 1-2**: Analyze current activations module in detail
-- **Day 3-4**: Implement scaffolding improvements
-- **Day 5**: Test, measure, and document learnings
-
-### Week 2-3: Core Modules
-- **Week 2**: Apply to tensor and layers modules
-- **Week 3**: Apply to networks and CNN modules
-
-### Week 4: Integration and Polish
-- **Integration**: Ensure smooth progression across modules
-- **Testing**: Comprehensive system testing
-- **Documentation**: Update guidelines based on experience
-
----
-
-## 🎯 Key Success Factors
-
-### Technical
-- **Maintain Functionality**: All existing tests must still pass
-- **Preserve Learning Objectives**: Don't sacrifice depth for ease
-- **Ensure Scalability**: Patterns must work across all modules
-
-### Educational
-- **Build Confidence**: Students should feel successful early and often
-- **Maintain Challenge**: Still push students to grow
-- **Connect to Reality**: Always link to real ML systems
-
-### Practical
-- **Measure Progress**: Use quantitative metrics to track improvement
-- **Gather Feedback**: Listen to student experience (when possible)
-- **Iterate Quickly**: Small improvements are better than perfect plans
-
----
-
-## 💡 Expected Outcomes
-
-### Short-term (1 month)
-- **Reduced Student Overwhelm**: Lower complexity ratios across modules
-- **Improved Learning Progression**: Smoother difficulty curves
-- **Better Test Experience**: More educational, less intimidating tests
-- **Higher Completion Rates**: More students finishing modules
-
-### Long-term (End of course)
-- **Confident ML Engineers**: Students who understand systems deeply
-- **Better Learning Outcomes**: Higher comprehension and retention
-- **Positive Course Experience**: Students enjoy learning challenging material
-- **Industry Readiness**: Graduates prepared for real ML systems work
-
-This implementation plan provides a practical path from our current state (student overwhelm crisis) to our target state (confident, capable ML systems engineers) through systematic application of educational scaffolding principles. 
\ No newline at end of file
diff --git a/instructor/guides/scaffolding_analysis_and_recommendations.md b/instructor/guides/scaffolding_analysis_and_recommendations.md
deleted file mode 100644
index d16651fd..00000000
--- a/instructor/guides/scaffolding_analysis_and_recommendations.md
+++ /dev/null
@@ -1,309 +0,0 @@
-# TinyTorch Educational Scaffolding Analysis & Recommendations
-
-## 🚨 Critical Findings: Student Overwhelm Crisis
-
-Our analysis reveals serious pedagogical issues that could severely impact student learning experience:
-
-### 📊 Key Metrics (Current vs. Target)
-- **Average Scaffolding Quality**: 1.9/5.0 (Target: 4.0+)
-- **High-Complexity Cells**: 70-80% (Target: <30%)
-- **Modules with Sudden Complexity Jumps**: 7/8 (Target: 0)
-- **Long Implementations Without Guidance**: 50-125 lines (Target: <30 lines)
-
-### 🎯 Impact on Machine Learning Systems Learning
-This is particularly problematic for an **ML Systems course** where students need to:
-1. Build intuition about complex mathematical concepts
-2. Understand system-level interactions
-3. Connect theory to practical implementation
-4. Maintain motivation through challenging material
-
----
-
-## 🔍 Detailed Module Analysis
-
-### Current State Summary
-| Module | Lines | Cells | Scaffolding | High-Complexity | Main Issues |
-|--------|-------|-------|-------------|-----------------|-------------|
-| 00_setup | 300 | 7 | 2/5 | 43% | Long config without guidance |
-| 01_tensor | 1,232 | 17 | 2/5 | 35% | 125-line implementation block |
-| 02_activations | 1,417 | 17 | 2/5 | 77% | Math-heavy without scaffolding |
-| 03_layers | 1,162 | 12 | 2/5 | 83% | Linear algebra complexity jump |
-| 04_networks | 1,273 | 13 | 2/5 | 85% | Composition without building blocks |
-| 05_cnn | 774 | 12 | 2/5 | 83% | Spatial reasoning not developed |
-| 06_dataloader | 899 | 11 | 2/5 | 73% | Data engineering concepts rushed |
-| 07_autograd | 0 | 0 | 1/5 | N/A | Missing entirely |
-
-### 🚩 Pattern: The "Complexity Cliff"
-Every module follows the same problematic pattern:
-1. **Cell 1**: Simple concept introduction (Complexity: 1)
-2. **Cell 2**: **SUDDEN JUMP** to complex implementation (Complexity: 4-5)
-3. **Cells 3+**: High complexity maintained without scaffolding
-
-This creates a "complexity cliff" that students fall off rather than a "learning ladder" they can climb.
-
----
-
-## 🎓 Educational Psychology Insights
-
-### Why This Matters for ML Systems Learning
-
-**Cognitive Load Theory**: Students have limited working memory. Our current approach:
-- ❌ **Overloads** cognitive capacity with sudden complexity
-- ❌ **Lacks** progressive skill building
-- ❌ **Missing** conceptual bridges between theory and implementation
-
-**Self-Efficacy Theory**: Student confidence affects learning. Our current approach:
-- ❌ **Intimidates** with large implementation blocks
-- ❌ **Frustrates** with insufficient guidance
-- ❌ **Discourages** with sudden difficulty spikes
-
-**Constructivist Learning**: Students build knowledge incrementally. Our current approach:
-- ❌ **Skips** foundational building blocks
-- ❌ **Jumps** to complex implementations too quickly
-- ❌ **Lacks** scaffolded practice opportunities
-
----
-
-## 🎯 Specific Scaffolding Recommendations
-
-### 1. **Implement the "Rule of 3s"**
-- **Max 3 new concepts per cell**
-- **Max 3 complexity levels per module** (1→2→3, not 1→4)
-- **Max 30 lines per implementation cell**
-
-### 2. **Create Progressive Implementation Ladders**
-
-Instead of:
-```python
-# Current: Sudden complexity cliff
-def forward(self, x):
-    # TODO: Implement entire forward pass (125 lines)
-    raise NotImplementedError("Student implementation required")
-```
-
-Use:
-```python
-# Step 1: Simple case (5-10 lines)
-def forward_single_example(self, x):
-    """
-    TODO: Implement forward pass for ONE example
-    
-    APPROACH:
-    1. Apply weights: result = x * self.weights
-    2. Add bias: result = result + self.bias
-    3. Return result
-    
-    EXAMPLE:
-    Input: [1, 2] → Expected: [weighted_sum + bias]
-    """
-    pass
-
-# Step 2: Batch processing (10-15 lines)  
-def forward_batch(self, x):
-    """
-    TODO: Extend to handle multiple examples
-    HINT: Use your forward_single_example as a starting point
-    """
-    pass
-
-# Step 3: Full implementation (15-20 lines)
-def forward(self, x):
-    """
-    TODO: Add error checking and optimization
-    HINT: Combine previous steps with shape validation
-    """
-    pass
-```
-
-### 3. **Implement "Concept Bridges"**
-
-Before each implementation, include:
-- **Visual analogy** (e.g., "Think of a layer like a filter...")
-- **Real-world connection** (e.g., "This is how ChatGPT processes words...")
-- **Mathematical intuition** (e.g., "Matrix multiplication is like...")
-- **System context** (e.g., "In a real ML pipeline, this step...")
-
-### 4. **Add "Confidence Builders"**
-
-Between complex sections:
-- **Quick wins** (simple exercises that always work)
-- **Progress celebrations** (visual confirmations)
-- **Checkpoint tests** (immediate feedback)
-- **Connection summaries** (how this fits the bigger picture)
-
----
-
-## 🔧 Implementation Strategy
-
-### Phase 1: Emergency Scaffolding (Week 1)
-**Target**: Reduce student overwhelm immediately
-
-1. **Break down the "Big 3" problem modules**:
-   - `02_activations`: Split math explanations into digestible chunks
-   - `03_layers`: Add linear algebra review before implementation
-   - `04_networks`: Build composition step-by-step
-
-2. **Add emergency scaffolding**:
-   - Insert "PAUSE" cells with reflection questions
-   - Add "HINT" sections to all TODO blocks
-   - Create "CHECKPOINT" tests for immediate feedback
-
-### Phase 2: Systematic Restructuring (Weeks 2-3)
-**Target**: Rebuild learning progression
-
-1. **Apply "Rule of 3s"** to all modules
-2. **Create implementation ladders** for complex functions
-3. **Add concept bridges** between theory and practice
-4. **Insert confidence builders** at regular intervals
-
-### Phase 3: Advanced Scaffolding (Week 4)
-**Target**: Optimize for ML Systems learning
-
-1. **Add system thinking prompts**:
-   - "How would this scale to 1M examples?"
-   - "What would break in production?"
-   - "How does PyTorch solve this differently?"
-
-2. **Create cross-module connections**:
-   - "Remember how tensors work? Now we're using them in layers..."
-   - "This builds on the activation functions you just learned..."
-
-3. **Add real-world context**:
-   - Industry examples
-   - Performance considerations
-   - Production trade-offs
-
----
-
-## 📏 Specific Length Guidelines
-
-### Per Module Targets
-- **Total lines**: 300-500 (current: 300-1,417)
-- **Cells**: 10-15 (current: 7-17)
-- **Implementation cells**: 15-25 lines max (current: 50-125)
-- **Concept cells**: 100-200 words (current: varies widely)
-
-### Per Cell Guidelines
-- **Concept introduction**: 1-2 new ideas max
-- **Implementation**: 1 function or method max
-- **Testing**: 3-5 test cases max
-- **Reflection**: 2-3 questions max
-
-### Complexity Progression
-- **Cells 1-3**: Complexity 1-2 (foundation)
-- **Cells 4-7**: Complexity 2-3 (building)
-- **Cells 8+**: Complexity 3-4 (integration)
-- **Never**: Complexity 5 (reserved for stretch goals)
-
----
-
-## 🧪 Testing Strategy Improvements
-
-### Current Test Issues
-- **Too intimidating**: Complex test suites scare students
-- **Poor feedback**: Cryptic error messages
-- **Missing progression**: No intermediate checkpoints
-
-### Recommended Test Structure
-
-1. **Confidence Tests** (always pass with minimal implementation):
-   ```python
-   def test_basic_creation():
-       """This should work with any reasonable implementation"""
-       t = Tensor([1, 2, 3])
-       assert t is not None  # Just check it exists!
-   ```
-
-2. **Learning Tests** (guide implementation):
-   ```python
-   def test_addition_step_by_step():
-       """Guides students through addition implementation"""
-       a, b = Tensor([1, 2]), Tensor([3, 4])
-       result = a + b
-       
-       # Clear, helpful assertions
-       assert result.data.tolist() == [4, 6], f"Expected [4, 6], got {result.data.tolist()}"
-       assert result.shape == (2,), f"Expected shape (2,), got {result.shape}"
-   ```
-
-3. **Challenge Tests** (stretch goals, clearly marked):
-   ```python
-   @pytest.mark.stretch_goal
-   def test_advanced_broadcasting():
-       """Optional: For students who want extra challenge"""
-       # More complex test here
-   ```
-
----
-
-## 🎯 Success Metrics
-
-### Short-term (2 weeks)
-- [ ] Scaffolding quality: 2.0 → 3.5+
-- [ ] High-complexity cells: 70% → 40%
-- [ ] Student completion rate: Track module completion
-- [ ] Time per module: Measure average completion time
-
-### Medium-term (1 month)
-- [ ] Scaffolding quality: 3.5 → 4.0+
-- [ ] High-complexity cells: 40% → 30%
-- [ ] Test anxiety: Survey student confidence
-- [ ] Learning effectiveness: Quiz comprehension
-
-### Long-term (End of course)
-- [ ] Student retention: Track course completion
-- [ ] Skill transfer: Assess project quality
-- [ ] Satisfaction: Course evaluation scores
-- [ ] Industry readiness: Portfolio assessment
-
----
-
-## 🚀 Next Steps
-
-### Immediate Actions (This Week)
-1. **Commit this analysis** to document current state
-2. **Choose 1-2 pilot modules** for emergency scaffolding
-3. **Test with small group** of students or colleagues
-4. **Gather feedback** on scaffolding improvements
-
-### Development Workflow
-1. **Pick one module** (recommend starting with `02_activations`)
-2. **Apply scaffolding principles** systematically
-3. **Test with inline execution** to verify functionality
-4. **Run pytest** to ensure compatibility
-5. **Measure complexity metrics** to track improvement
-6. **Iterate based on feedback**
-
-### Quality Assurance
-- [ ] Every TODO has specific guidance
-- [ ] Every complex concept has a bridge
-- [ ] Every implementation has checkpoints
-- [ ] Every module has confidence builders
-- [ ] Every test provides helpful feedback
-
----
-
-## 💡 Key Insights for ML Systems Education
-
-### What Makes This Different
-ML Systems courses require students to:
-1. **Build systems** (not just use them)
-2. **Understand trade-offs** (performance vs. simplicity)
-3. **Think at scale** (production considerations)
-4. **Connect theory to practice** (math to code to systems)
-
-### Scaffolding Must Address
-- **Mathematical intimidation**: Make math approachable
-- **System complexity**: Break down interactions
-- **Implementation gaps**: Bridge theory to code
-- **Production reality**: Connect to real-world systems
-
-### Success Looks Like
-Students who can:
-- **Explain** why ML systems work the way they do
-- **Implement** core components from scratch
-- **Optimize** for real-world constraints
-- **Debug** when things go wrong
-- **Design** systems for production use
-
-This scaffolding analysis provides the foundation for creating an educational experience that builds confident, capable ML systems engineers rather than overwhelmed students. 
\ No newline at end of file
diff --git a/instructor/guides/test_anxiety_analysis.md b/instructor/guides/test_anxiety_analysis.md
deleted file mode 100644
index ad07ed3a..00000000
--- a/instructor/guides/test_anxiety_analysis.md
+++ /dev/null
@@ -1,344 +0,0 @@
-# Test Anxiety Analysis: Making Tests Student-Friendly
-
-## 🚨 Current Test Anxiety Sources
-
-Based on analysis of test files across modules, several factors contribute to student intimidation and test anxiety:
-
-### 1. **Overwhelming Test Volume**
-- **Tensor module**: 337 lines, 33 tests across 5 classes
-- **Activations module**: 332 lines, ~25 tests across 6 classes
-- **Intimidation factor**: Students see massive test files and panic
-
-### 2. **Complex Test Structure**
-- Multiple test classes with technical names (`TestTensorCreation`, `TestArithmeticOperations`)
-- Advanced testing patterns (fixtures, parametrization, edge cases)
-- Professional-level test organization that overwhelms beginners
-
-### 3. **Cryptic Error Messages**
-```python
-# Current: Confusing for students
-assert t.dtype == np.int32  # Integer list defaults to int32
-# Error: AssertionError: assert dtype('int64') == <class 'numpy.int32'>
-
-# Current: Technical jargon
-assert np.allclose(y.data, expected), f"Expected {expected}, got {y.data}"
-```
-
-### 4. **All-or-Nothing Testing**
-- Tests either pass completely or fail completely
-- No partial credit or progress indicators
-- Students can't see incremental progress
-
-### 5. **Missing Educational Context**
-- Tests focus on correctness, not learning
-- No explanations of WHY tests matter
-- No connection to real ML applications
-
-### 6. **Advanced Features Before Basics**
-- Tests for stretch goals (reshape, transpose) mixed with core functionality
-- Students see "SKIPPED" tests and feel incomplete
-- No clear progression from basic to advanced
-
----
-
-## 🎯 Student-Friendly Testing Strategy
-
-### Core Principle: **Tests Should Teach, Not Just Verify**
-
-### 1. **Progressive Test Revelation**
-
-Instead of showing all tests at once, reveal them progressively:
-
-```python
-# Level 1: Confidence Builders (Always shown)
-class TestBasicFunctionality:
-    """These tests check that your basic implementation works!"""
-    
-    def test_tensor_exists(self):
-        """Can you create a tensor? (This should always work!)"""
-        t = Tensor([1, 2, 3])
-        assert t is not None, "Great! Your Tensor class exists!"
-    
-    def test_tensor_has_data(self):
-        """Does your tensor store data?"""
-        t = Tensor([1, 2, 3])
-        assert hasattr(t, 'data'), "Perfect! Your tensor stores data!"
-
-# Level 2: Core Learning (Shown after Level 1 passes)
-class TestCoreOperations:
-    """These tests check your main implementations."""
-    
-    def test_addition_simple(self):
-        """Can you add two simple tensors?"""
-        a = Tensor([1, 2])
-        b = Tensor([3, 4])
-        result = a + b
-        
-        # Student-friendly assertion
-        expected = [4, 6]
-        actual = result.data.tolist()
-        assert actual == expected, f"""
-        🎯 Addition Test:
-        Input: {a.data.tolist()} + {b.data.tolist()}
-        Expected: {expected}
-        Your result: {actual}
-        
-        💡 Hint: Addition should combine corresponding elements
-        """
-
-# Level 3: Advanced (Only shown when ready)
-class TestAdvancedFeatures:
-    """Challenge yourself with these advanced features!"""
-    # More complex tests here
-```
-
-### 2. **Educational Test Messages**
-
-Transform cryptic assertions into learning opportunities:
-
-```python
-# Before: Intimidating
-assert t.dtype == np.int32
-
-# After: Educational
-def test_data_types_learning(self):
-    """Understanding tensor data types"""
-    t = Tensor([1, 2, 3])
-    
-    print(f"📚 Learning moment: Your tensor has dtype {t.dtype}")
-    print(f"💡 NumPy typically uses int64, but ML frameworks prefer int32/float32")
-    print(f"🎯 This is about memory efficiency in real ML systems!")
-    
-    # Flexible assertion with learning
-    acceptable_types = [np.int32, np.int64]
-    assert t.dtype in acceptable_types, f"""
-    🔍 Data Type Check:
-    Your tensor type: {t.dtype}
-    Acceptable types: {acceptable_types}
-    
-    💭 Why this matters: In production ML, data types affect:
-    - Memory usage (int32 uses half the memory of int64)
-    - GPU compatibility (many GPUs prefer 32-bit)
-    - Training speed (smaller types = faster computation)
-    """
-```
-
-### 3. **Confidence Building Test Structure**
-
-```python
-class TestConfidenceBuilders:
-    """These tests are designed to make you feel successful! 🎉"""
-    
-    def test_you_can_create_tensors(self):
-        """Step 1: Can you create any tensor at all?"""
-        # This should work with even minimal implementation
-        t = Tensor(5)
-        assert True, "🎉 Success! You created a tensor!"
-    
-    def test_your_tensor_has_shape(self):
-        """Step 2: Does your tensor know its shape?"""
-        t = Tensor([1, 2, 3])
-        assert hasattr(t, 'shape'), "🎉 Great! Your tensor has a shape property!"
-    
-    def test_basic_math_works(self):
-        """Step 3: Can you do basic math?"""
-        a = Tensor([1])
-        b = Tensor([2])
-        try:
-            result = a + b
-            assert True, "🎉 Amazing! Your tensor can do addition!"
-        except:
-            assert False, "💡 Hint: Make sure your + operator returns a new Tensor"
-
-class TestLearningProgressChecks:
-    """These tests help you learn step by step 📚"""
-    
-    def test_addition_with_guidance(self):
-        """Learn how tensor addition works"""
-        print("\n📚 Learning: Tensor Addition")
-        print("In ML, we add tensors element-wise:")
-        print("[1, 2] + [3, 4] = [1+3, 2+4] = [4, 6]")
-        
-        a = Tensor([1, 2])
-        b = Tensor([3, 4])
-        result = a + b
-        
-        expected = [4, 6]
-        actual = result.data.tolist()
-        
-        if actual == expected:
-            print("🎉 Perfect! You understand tensor addition!")
-        else:
-            print(f"🤔 Let's debug together:")
-            print(f"   Expected: {expected}")
-            print(f"   You got: {actual}")
-            print(f"💡 Check: Are you adding corresponding elements?")
-        
-        assert actual == expected
-
-class TestRealWorldConnections:
-    """See how your code connects to real ML! 🚀"""
-    
-    def test_like_pytorch(self):
-        """Your tensor works like PyTorch!"""
-        print("\n🚀 Real World Connection:")
-        print("In PyTorch, you'd write: torch.tensor([1, 2]) + torch.tensor([3, 4])")
-        print("You just implemented the same thing!")
-        
-        a = Tensor([1, 2])
-        b = Tensor([3, 4])
-        result = a + b
-        
-        print(f"Your result: {result.data.tolist()}")
-        print("🎉 This is exactly how real ML frameworks work!")
-        
-        assert result.data.tolist() == [4, 6]
-```
-
-### 4. **Graduated Testing System**
-
-```python
-# tests/level_1_confidence.py
-"""Level 1: Build Confidence (Everyone should pass these!)"""
-
-# tests/level_2_core.py  
-"""Level 2: Core Learning (Main learning objectives)"""
-
-# tests/level_3_integration.py
-"""Level 3: Integration (Connecting concepts)"""
-
-# tests/level_4_stretch.py
-"""Level 4: Stretch Goals (For ambitious students)"""
-```
-
-### 5. **Visual Progress Indicators**
-
-```python
-def run_student_friendly_tests():
-    """Run tests with visual progress and encouragement"""
-    
-    print("🎯 TinyTorch Learning Progress")
-    print("=" * 40)
-    
-    # Level 1: Confidence
-    print("\n📍 Level 1: Building Confidence")
-    level_1_passed = run_confidence_tests()
-    print(f"✅ Confidence Level: {level_1_passed}/3 tests passed")
-    
-    if level_1_passed >= 2:
-        print("🎉 Great start! Moving to core learning...")
-        
-        # Level 2: Core Learning
-        print("\n📍 Level 2: Core Learning")
-        level_2_passed = run_core_tests()
-        print(f"✅ Core Learning: {level_2_passed}/5 tests passed")
-        
-        if level_2_passed >= 4:
-            print("🚀 Excellent! You're ready for integration...")
-            
-            # Level 3: Integration
-            print("\n📍 Level 3: Integration")
-            level_3_passed = run_integration_tests()
-            print(f"✅ Integration: {level_3_passed}/3 tests passed")
-    
-    print("\n🎊 Overall Progress:")
-    print(f"📊 You've mastered {total_passed}/{total_tests} concepts!")
-    print("💪 Keep going - you're building real ML systems!")
-```
-
----
-
-## 🛠️ Implementation Recommendations
-
-### Immediate Changes (This Week)
-
-1. **Split Test Files by Difficulty**:
-   ```
-   tests/
-   ├── test_01_confidence.py      # Always pass with minimal effort
-   ├── test_02_core.py           # Main learning objectives
-   ├── test_03_integration.py    # Connecting concepts
-   └── test_04_stretch.py        # Advanced/optional
-   ```
-
-2. **Add Educational Context to Every Test**:
-   - Why this test matters for ML
-   - How it connects to real frameworks
-   - What students learn from passing it
-
-3. **Create Student-Friendly Error Messages**:
-   - Clear explanation of what went wrong
-   - Specific hints for fixing the issue
-   - Connection to learning objectives
-
-### Medium-term Changes (2-3 Weeks)
-
-1. **Interactive Test Runner**:
-   ```bash
-   python run_learning_tests.py --module tensor --level 1
-   # Shows progress, gives hints, celebrates successes
-   ```
-
-2. **Visual Test Reports**:
-   - Progress bars for each module
-   - Skill trees showing unlocked abilities
-   - Connections between modules
-
-3. **Adaptive Testing**:
-   - Tests adjust difficulty based on student progress
-   - Extra hints for struggling students
-   - Bonus challenges for advanced students
-
-### Long-term Vision (1 Month)
-
-1. **Gamified Learning**:
-   - "Unlock" advanced tests by passing basics
-   - Achievement badges for different skills
-   - Leaderboards (optional, anonymous)
-
-2. **Intelligent Feedback**:
-   - AI-powered hints based on common mistakes
-   - Personalized learning paths
-   - Automated code review with suggestions
-
----
-
-## 📊 Success Metrics for Test Anxiety Reduction
-
-### Quantitative Measures
-- **Test completion rate**: % of students who run all tests
-- **Time to first success**: How quickly students get their first passing test
-- **Help-seeking behavior**: Reduced questions about "why tests fail"
-- **Module completion rate**: % who finish each module
-
-### Qualitative Measures
-- **Student confidence surveys**: Before/after each module
-- **Feedback on test experience**: "Tests helped me learn" vs "Tests were scary"
-- **Learning effectiveness**: Do students understand concepts better?
-
-### Target Improvements
-- **Confidence building**: 90%+ students pass Level 1 tests
-- **Learning progression**: 80%+ students reach Level 3
-- **Anxiety reduction**: <20% report test anxiety
-- **Educational value**: 85%+ say "tests helped me learn"
-
----
-
-## 🎯 Key Principles for Student-Friendly Testing
-
-### 1. **Tests Should Celebrate Progress**
-Every test should make students feel accomplished when they pass it.
-
-### 2. **Failure Should Teach**
-When tests fail, students should learn something specific about how to improve.
-
-### 3. **Progression Should Be Visible**
-Students should see their skills building across tests and modules.
-
-### 4. **Context Should Be Clear**
-Every test should connect to real ML applications and learning objectives.
-
-### 5. **Confidence Should Build**
-Early tests should be designed for success, building confidence for harder challenges.
-
-This approach transforms testing from a source of anxiety into a powerful learning tool that guides students through the complex journey of building ML systems from scratch. 
\ No newline at end of file
diff --git a/paper/references.bib b/paper/references.bib
index 2b2110b0..d7dfadcd 100644
--- a/paper/references.bib
+++ b/paper/references.bib
@@ -77,9 +77,16 @@
   title = {Mind in Society},
 }
 
-@book{bruner1960process,
-  author = {Bruner, Jerome S.},
-  title = {The Process of Education},
+@article{bruner1960process,
+  author = {Frolli, A and Cerciello, F and Ciotola, S and Ricci, MC and Esposito, C and Sica, LS},
+  title = {Narrative Approach and Mentalization.},
+  volume = {13},
+  number = {12},
+  issn = {2076-328X},
+  essn = {2076-328X},
+  doi = {10.3390/bs13120994},
+  journal = {Behavioral sciences (Basel, Switzerland)},
+  source = {PubMed},
   year = {1960},
   publisher = {Harvard University Press},
   address = {Cambridge, MA},
@@ -349,9 +356,16 @@
 }
 
 @article{perkins1992transfer,
-  author = {Perkins, D. N. and Salomon, Gavriel},
-  title = {Transfer of Learning},
-  journal = {International Encyclopedia of Education},
+  author = {Burstein, R and Henry, NJ and Collison, ML and Marczak, LB and Sligar, A and Watson, S and Marquez, N and Abbasalizad-Farhangi, M and Abbasi, M and Abd-Allah, F and Abdoli, A and Abdollahi, M and Abdollahpour, I and Abdulkader, RS and Abrigo, MRM and Acharya, D and Adebayo, OM and Adekanmbi, V and Adham, D and Afshari, M and Aghaali, M and Ahmadi, K and Ahmadi, M and Ahmadpour, E and Ahmed, R and Akal, CG and Akinyemi, JO and Alahdab, F and Alam, N and Alamene, GM and Alene, KA and Alijanzadeh, M and Alinia, C and Alipour, V and Aljunid, SM and Almalki, MJ and Al-Mekhlafi, HM and Altirkawi, K and Alvis-Guzman, N and Amegah, AK and Amini, S and Amit, AML and Anbari, Z and Androudi, S and Anjomshoa, M and Ansari, F and Antonio, CAT and Arabloo, J and Arefi, Z and Aremu, O and Armoon, B and Arora, A and Artaman, A and Asadi, A and Asadi-Aliabadi, M and Ashraf-Ganjouei, A and Assadi, R and Ataeinia, B and Atre, SR and Quintanilla, BPA and Ayanore, MA and Azari, S and Babaee, E and Babazadeh, A and Badawi, A and Bagheri, S and Bagherzadeh, M and Baheiraei, N and Balouchi, A and Barac, A and Bassat, Q and Baune, BT and Bayati, M and Bedi, N and Beghi, E and Behzadifar, M and Behzadifar, M and Belay, YB and Bell, B and Bell, ML and Berbada, DA and Bernstein, RS and Bhattacharjee, NV and Bhattarai, S and Bhutta, ZA and Bijani, A and Bohlouli, S and Breitborde, NJK and Britton, G and Browne, AJ and Nagaraja, SB and Busse, R and Butt, ZA and Car, J and Cárdenas, R and Castañeda-Orjuela, CA and Cerin, E and Chanie, WF and Chatterjee, P and Chu, DT and Cooper, C and Costa, VM and Dalal, K and Dandona, L and Dandona, R and Daoud, F and Daryani, A and Das Gupta, R and Davis, I and Davis Weaver, N and Davitoiu, DV and De Neve, JW and Demeke, FM and Demoz, GT and Deribe, K and Desai, R and Deshpande, A and Desyibelew, HD and Dey, S and Dharmaratne, SD and Dhimal, M and Diaz, D and Doshmangir, L and Duraes, AR and Dwyer-Lindgren, L and Earl, L and Ebrahimi, R and Ebrahimpour, S and Effiong, A and Eftekhari, A and Ehsani-Chimeh, E and El Sayed, I and El Sayed Zaki, M and El Tantawi, M and El-Khatib, Z and Emamian, MH and Enany, S and Eskandarieh, S and Eyawo, O and Ezalarab, M and Faramarzi, M and Fareed, M and Faridnia, R and Faro, A and Fazaeli, AA and Fazlzadeh, M and Fentahun, N and Fereshtehnejad, SM and Fernandes, JC and Filip, I and Fischer, F and Foigt, NA and Foroutan, M and Francis, JM and Fukumoto, T and Fullman, N and Gallus, S and Gebre, DG and Gebrehiwot, TT and Gebremeskel, GG and Gessner, BD and Geta, B and Gething, PW and Ghadimi, R and Ghadiri, K and Ghajarzadeh, M and Ghashghaee, A and Gill, PS and Gill, TK and Golding, N and Gomes, NGM and Gona, PN and Gopalani, SV and Gorini, G and Goulart, BNG and Graetz, N and Greaves, F and Green, MS and Guo, Y and Haj-Mirzaian, A and Haj-Mirzaian, A and Hall, BJ and Hamidi, S and Haririan, H and Haro, JM and Hasankhani, M and Hasanpoor, E and Hasanzadeh, A and Hassankhani, H and Hassen, HY and Hegazy, MI and Hendrie, D and Heydarpour, F and Hird, TR and Hoang, CL and Hollerich, G and Rad, EH and Hoseini-Ghahfarokhi, M and Hossain, N and Hosseini, M and Hosseinzadeh, M and Hostiuc, M and Hostiuc, S and Househ, M and Hsairi, M and Ilesanmi, OS and Imani-Nasab, MH and Iqbal, U and Irvani, SSN and Islam, N and Islam, SMS and Jürisson, M and Balalami, NJ and Jalali, A and Javidnia, J and Jayatilleke, AU and Jenabi, E and Ji, JS and Jobanputra, YB and Johnson, K and Jonas, JB and Shushtari, ZJ and Jozwiak, JJ and Kabir, A and Kahsay, A and Kalani, H and Kalhor, R and Karami, M and Karki, S and Kasaeian, A and Kassebaum, NJ and Keiyoro, PN and Kemp, GR and Khabiri, R and Khader, YS and Khafaie, MA and Khan, EA and Khan, J and Khan, MS and Khang, YH and Khatab, K and Khater, A and Khater, MM and Khatony, A and Khazaei, M and Khazaei, S and Khazaei-Pool, M and Khubchandani, J and Kianipour, N and Kim, YJ and Kimokoti, RW and Kinyoki, DK and Kisa, A and Kisa, S and Kolola, T and Kosen, S and Koul, PA and Koyanagi, A and Kraemer, MUG and Krishan, K and Krohn, KJ and Kugbey, N and Kumar, GA and Kumar, M and Kumar, P and Kuupiel, D and Lacey, B and Lad, SD and Lami, FH and Larsson, AO and Lee, PH and Leili, M and Levine, AJ and Li, S and Lim, LL and Listl, S and Longbottom, J and Lopez, JCF and Lorkowski, S and Magdeldin, S and Abd El Razek, HM and Abd El Razek, MM and Majeed, A and Maleki, A and Malekzadeh, R and Malta, DC and Mamun, AA and Manafi, N and Manda, AL and Mansourian, M and Martins-Melo, FR and Masaka, A and Massenburg, BB and Maulik, PK and Mayala, BK and Mazidi, M and McKee, M and Mehrotra, R and Mehta, KM and Meles, GG and Mendoza, W and Menezes, RG and Meretoja, A and Meretoja, TJ and Mestrovic, T and Miller, TR and Miller-Petrie, MK and Mills, EJ and Milne, GJ and Mini, GK and Mir, SM and Mirjalali, H and Mirrakhimov, EM and Mohamadi, E and Mohammad, DK and Darwesh, AM and Mezerji, NMG and Mohammed, AS and Mohammed, S and Mokdad, AH and Molokhia, M and Monasta, L and Moodley, Y and Moosazadeh, M and Moradi, G and Moradi, M and Moradi, Y and Moradi-Lakeh, M and Moradinazar, M and Moraga, P and Morawska, L and Mosapour, A and Mousavi, SM and Mueller, UO and Muluneh, AG and Mustafa, G and Nabavizadeh, B and Naderi, M and Nagarajan, AJ and Nahvijou, A and Najafi, F and Nangia, V and Ndwandwe, DE and Neamati, N and Negoi, I and Negoi, RI and Ngunjiri, JW and Thi Nguyen, HL and Nguyen, LH and Nguyen, SH and Nielsen, KR and Ningrum, DNA and Nirayo, YL and Nixon, MR and Nnaji, CA and Nojomi, M and Noroozi, M and Nosratnejad, S and Noubiap, JJ and Motlagh, SN and Ofori-Asenso, R and Ogbo, FA and Oladimeji, KE and Olagunju, AT and Olfatifar, M and Olum, S and Olusanya, BO and Oluwasanu, MM and Onwujekwe, OE and Oren, E and Ortega-Altamirano, DDV and Ortiz, A and Osarenotor, O and Osei, FB and Osgood-Zimmerman, AE and Otstavnov, SS and Owolabi, MO and , P, A M and Pagheh, AS and Pakhale, S and Panda-Jonas, S and Pandey, A and Park, EK and Parsian, H and Pashaei, T and Patel, SK and Pepito, VCF and Pereira, A and Perkins, S and Pickering, BV and Pilgrim, T and Pirestani, M and Piroozi, B and Pirsaheb, M and Plana-Ripoll, O and Pourjafar, H and Puri, P and Qorbani, M and Quintana, H and Rabiee, M and Rabiee, N and Radfar, A and Rafiei, A and Rahim, F and Rahimi, Z and Rahimi-Movaghar, V and Rahimzadeh, S and Rajati, F and Raju, SB and Ramezankhani, A and Ranabhat, CL and Rasella, D and Rashedi, V and Rawal, L and Reiner, RC, Jr and Renzaho, AMN and Rezaei, S and Rezapour, A and Riahi, SM and Ribeiro, AI and Roever, L and Roro, EM and Roser, M and Roshandel, G and Roshani, D and Rostami, A and Rubagotti, E and Rubino, S and Sabour, S and Sadat, N and Sadeghi, E and Saeedi, R and Safari, Y and Safari-Faramani, R and Safdarian, M and Sahebkar, A and Salahshoor, MR and Salam, N and Salamati, P and Salehi, F and Zahabi, SS and Salimi, Y and Salimzadeh, H and Salomon, JA and Sambala, EZ and Samy, AM and Santric Milicevic, MM and Jose, BPS and Saraswathy, SYI and Sarmiento-Suárez, R and Sartorius, B and Sathian, B and Saxena, S and Sbarra, AN and Schaeffer, LE and Schwebel, DC and Sepanlou, SG and Seyedmousavi, S and Shaahmadi, F and Shaikh, MA and Shams-Beyranvand, M and Shamshirian, A and Shamsizadeh, M and Sharafi, K and Sharif, M and Sharif-Alhoseini, M and Sharifi, H and Sharma, J and Sharma, R and Sheikh, A and Shields, C and Shigematsu, M and Shiri, R and Shiue, I and Shuval, K and Siddiqi, TJ and Silva, JP and Singh, JA and Sinha, DN and Sisay, MM and Sisay, S and Sliwa, K and Smith, DL and Somayaji, R and Soofi, M and Soriano, JB and Sreeramareddy, CT and Sudaryanto, A and Sufiyan, MB and Sykes, BL and Sylaja, PN and Tabarés-Seisdedos, R and Tabb, KM and Tabuchi, T and Taveira, N and Temsah, MH and Terkawi, AS and Tessema, ZT and Thankappan, KR and Thirunavukkarasu, S and To, QG and Tovani-Palone, MR and Tran, BX and Tran, KB and Ullah, I and Usman, MS and Uthman, OA and Vahedian-Azimi, A and Valdez, PR and van Boven, JFM and Vasankari, TJ and Vasseghian, Y and Veisani, Y and Venketasubramanian, N and Violante, FS and Vladimirov, SK and Vlassov, V and Vos, T and Vu, GT and Vujcic, IS and Waheed, Y and Wakefield, J and Wang, H and Wang, Y and Wang, YP and Ward, JL and Weintraub, RG and Weldegwergs, KG and Weldesamuel, GT and Westerman, R and Wiysonge, CS and Wondafrash, DZ and Woyczynski, L and Wu, AM and Xu, G and Yadegar, A and Yamada, T and Yazdi-Feyzabadi, V and Yilgwan, CS and Yip, P and Yonemoto, N and Lebni, JY and Younis, MZ and Yousefifard, M and Yousof, HSA and Yu, C and Yusefzadeh, H and Zabeh, E and Moghadam, TZ and Bin Zaman, S and Zamani, M and Zandian, H and Zangeneh, A and Zerfu, TA and Zhang, Y and Ziapour, A and Zodpey, S and Murray, CJL and Hay, SI},
+  title = {Mapping 123 million neonatal, infant and child deaths between 2000 and 2017.},
+  volume = {574},
+  number = {7778},
+  pages = {353--358},
+  issn = {0028-0836},
+  essn = {1476-4687},
+  doi = {10.1038/s41586-019-1545-0},
+  journal = {Nature},
+  source = {PubMed},
   year = {1992},
   edition = {2nd},
   editor = {Husen, Torsten and Postlethwaite, T. Neville},
@@ -591,20 +605,26 @@
 }
 
 @article{paas1992training,
-  author = {Paas, Fred G. W. C.},
-  title = {Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach},
-  journal = {Journal of Educational Psychology},
-  volume = {84},
   number = {4},
-  pages = {429--434},
-  year = {1992},
   doi = {10.1037/0022-0663.84.4.429},
+  pages = {429--434},
+  source = {Crossref},
+  volume = {84},
+  author = {Paas, Fred G. W. C.},
+  date = {1992-12},
+  url = {https://doi.org/10.1037/0022-0663.84.4.429},
+  issn = {1939-2176,0022-0663},
+  journal = {Journal of Educational Psychology},
+  publisher = {American Psychological Association (APA)},
+  title = {Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach.},
 }
 
-@phdthesis{sorva2012visual,
+@book{sorva2012visual,
   author = {Sorva, Juha},
-  title = {Visual program simulation in introductory programming education},
+  title = {Visual program simulation in introductory programming education ; Visuaalinen ohjelmasimulaatio ohjelmoinnin alkeisopetuksessa.},
   year = {2012},
+  url = {https://aaltodoc.aalto.fi/handle/123456789/3534},
+  source = {DBLP},
   school = {Aalto University},
   type = {Doctoral dissertation},
   address = {Espoo, Finland},
diff --git a/scripts/cleanup_repo_history.sh b/scripts/cleanup_repo_history.sh
new file mode 100755
index 00000000..62535fba
--- /dev/null
+++ b/scripts/cleanup_repo_history.sh
@@ -0,0 +1,119 @@
+#!/bin/bash
+# Repository History Cleanup Script
+# Removes large files from Git history using BFG Repo-Cleaner
+#
+# WARNING: This rewrites Git history. Make sure you have a backup!
+
+set -e  # Exit on error
+
+REPO_DIR="/Users/VJ/GitHub/TinyTorch"
+BACKUP_DIR="${REPO_DIR}_backup_$(date +%Y%m%d_%H%M%S)"
+CLEAN_REPO_DIR="${REPO_DIR}_clean"
+
+# Colors for output
+RED='\033[0;31m'
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+NC='\033[0m' # No Color
+
+echo -e "${YELLOW}=== TinyTorch Repository History Cleanup ===${NC}\n"
+
+# Check if BFG is installed
+if ! command -v bfg &> /dev/null; then
+    echo -e "${RED}ERROR: BFG Repo-Cleaner is not installed.${NC}"
+    echo "Install with: brew install bfg"
+    echo "Or download from: https://rtyley.github.io/bfg-repo-cleaner/"
+    exit 1
+fi
+
+# Check if we're in the right directory
+if [ ! -d "$REPO_DIR/.git" ]; then
+    echo -e "${RED}ERROR: Not a Git repository: $REPO_DIR${NC}"
+    exit 1
+fi
+
+# Safety check: warn about uncommitted changes
+if [ -n "$(git -C "$REPO_DIR" status --porcelain)" ]; then
+    echo -e "${YELLOW}WARNING: You have uncommitted changes!${NC}"
+    echo "Please commit or stash them before proceeding."
+    read -p "Continue anyway? (y/N): " -n 1 -r
+    echo
+    if [[ ! $REPLY =~ ^[Yy]$ ]]; then
+        exit 1
+    fi
+fi
+
+# Create backup
+echo -e "${GREEN}Step 1: Creating backup...${NC}"
+git -C "$REPO_DIR" clone --mirror "$REPO_DIR" "$BACKUP_DIR"
+echo -e "${GREEN}✓ Backup created: $BACKUP_DIR${NC}\n"
+
+# Create mirror clone for BFG
+echo -e "${GREEN}Step 2: Creating mirror clone for BFG...${NC}"
+rm -rf "$CLEAN_REPO_DIR"
+git clone --mirror "$REPO_DIR" "$CLEAN_REPO_DIR"
+echo -e "${GREEN}✓ Mirror clone created${NC}\n"
+
+# Change to clean repo directory
+cd "$CLEAN_REPO_DIR"
+
+# Remove large files/folders
+echo -e "${GREEN}Step 3: Removing large files from history...${NC}"
+
+# Remove CIFAR-10 dataset files
+echo "  - Removing CIFAR-10 dataset files..."
+bfg --delete-folders cifar-10-batches-py 2>&1 | grep -v "^Using.*repo" || true
+
+# Remove virtual environment directories
+echo "  - Removing virtual environment directories..."
+bfg --delete-folders bin 2>&1 | grep -v "^Using.*repo" || true
+bfg --delete-folders lib 2>&1 | grep -v "^Using.*repo" || true
+bfg --delete-folders include 2>&1 | grep -v "^Using.*repo" || true
+bfg --delete-folders share 2>&1 | grep -v "^Using.*repo" || true
+
+# Remove large GIF files (optional - comment out if you want to keep them)
+echo "  - Removing large GIF files..."
+bfg --delete-files "*.gif" --no-blob-protection 2>&1 | grep -v "^Using.*repo" || true
+
+# Remove large PNG files (optional - comment out if you want to keep them)
+echo "  - Removing large PNG files..."
+bfg --delete-files "Gemini_Generated_Image_*.png" --no-blob-protection 2>&1 | grep -v "^Using.*repo" || true
+
+# Remove pyvenv.cfg
+echo "  - Removing pyvenv.cfg..."
+bfg --delete-files pyvenv.cfg 2>&1 | grep -v "^Using.*repo" || true
+
+echo -e "${GREEN}✓ Files removed${NC}\n"
+
+# Clean up Git
+echo -e "${GREEN}Step 4: Cleaning up Git repository...${NC}"
+git reflog expire --expire=now --all
+git gc --prune=now --aggressive
+echo -e "${GREEN}✓ Cleanup complete${NC}\n"
+
+# Show results
+echo -e "${GREEN}Step 5: Results${NC}"
+CLEAN_SIZE=$(du -sh . | cut -f1)
+echo "  Clean repository size: $CLEAN_SIZE"
+
+echo -e "\n${YELLOW}=== Next Steps ===${NC}"
+echo "1. Review the cleaned repository:"
+echo "   cd $CLEAN_REPO_DIR"
+echo "   git log --oneline -10"
+echo ""
+echo "2. If satisfied, replace original .git:"
+echo "   cd $REPO_DIR"
+echo "   mv .git .git.backup"
+echo "   cp -r $CLEAN_REPO_DIR $REPO_DIR/.git"
+echo ""
+echo "3. Verify:"
+echo "   cd $REPO_DIR"
+echo "   git status"
+echo ""
+echo "4. Force push to GitHub (WARNING: rewrites history):"
+echo "   git push origin --force --all"
+echo "   git push origin --force --tags"
+echo ""
+echo -e "${YELLOW}Backup location: $BACKUP_DIR${NC}"
+echo -e "${YELLOW}Clean repo location: $CLEAN_REPO_DIR${NC}"
+
diff --git a/tests/TEST_STRATEGY.md b/tests/TEST_STRATEGY.md
deleted file mode 100644
index 3784488f..00000000
--- a/tests/TEST_STRATEGY.md
+++ /dev/null
@@ -1,300 +0,0 @@
-# TinyTorch Test Strategy
-
-> **📚 For the complete testing plan**: See `docs/development/MASTER_TESTING_PLAN.md`  
-> **🚀 Quick Reference**: See `docs/development/TESTING_QUICK_REFERENCE.md`
-
-## 🎯 Testing Philosophy
-
-TinyTorch uses a **two-tier testing approach** that separates component validation from system integration:
-
-1. **Inline Tests** (in module source files) - Component validation
-2. **Integration Tests** (in `tests/` directory) - Inter-module integration
-
-This separation follows ML engineering best practices: validate components in isolation, then test how they work together.
-
----
-
-## 📋 Tier 1: Inline Tests (Component Validation)
-
-### **Location**: `modules/XX_modulename/*_dev.py`
-
-### **Purpose**:
-- Validate individual components work correctly
-- Test in isolation from other modules
-- Provide immediate feedback during development
-- Educate students about expected behavior
-
-### **What to Test**:
-✅ Individual class/function correctness
-✅ Mathematical operations (forward passes)
-✅ Shape transformations
-✅ Edge cases and error handling
-✅ Basic functionality
-
-### **Format**:
-```python
-def test_unit_componentname():
-    """🧪 Unit Test: Component Name
-    
-    **This is a unit test** - it tests [component] in isolation.
-    """
-    print("🔬 Unit Test: Component...")
-    
-    # Test implementation
-    assert condition, "✅ Component works"
-    
-    print("✅ Component test passed")
-    print("📈 Progress: Component ✓")
-```
-
-### **Execution**:
-```bash
-# Run inline tests only
-tito test 01_tensor --inline-only
-
-# Tests run when you execute the module file
-python modules/01_tensor/tensor_dev.py
-```
-
-### **Current Status** (Modules 01-15):
-- ✅ **Passing**: 11/15 modules (73%)
-  - 01_tensor, 03_layers, 04_losses, 05_autograd
-  - 07_training, 08_dataloader, 09_spatial, 10_tokenization
-  - 11_embeddings, 13_transformers, 14_profiling
-
-- ❌ **Failing**: 4/15 modules (27%)
-  - 02_activations - Script execution error
-  - 06_optimizers - Script execution error
-  - 12_attention - Assertion error
-  - 15_memoization - Missing matplotlib dependency
-
----
-
-## 📊 Tier 2: Integration Tests (System Validation)
-
-### **Location**: `tests/XX_modulename/test_*_integration.py`
-
-### **Purpose**:
-- Test how modules work together
-- Validate cross-module dependencies
-- Test realistic workflows
-- Ensure system-level correctness
-
-### **What to Test**:
-✅ Module interactions (e.g., Tensor → Autograd → Optimizer)
-✅ End-to-end workflows (e.g., training loop)
-✅ Data flow through pipeline
-✅ Real-world use cases
-✅ Progressive integration (modules 1-N)
-
-### **Test Types**:
-
-#### 1. **Progressive Integration**
-Tests that module N works with all previous modules (1 through N-1):
-```python
-# tests/05_autograd/test_progressive_integration.py
-def test_autograd_with_all_previous_modules():
-    # Use Tensor (01), Activations (02), Layers (03), Losses (04)
-    # Then test Autograd (05) with all of them
-```
-
-#### 2. **Feature Integration**
-Tests specific feature combinations:
-```python
-# tests/07_training/test_training_integration.py
-def test_complete_training_loop():
-    # Combine: Tensor + Layers + Losses + Autograd + Optimizers + Training
-```
-
-#### 3. **Benchmark Integration**  
-Tests realistic end-to-end scenarios:
-```python
-# tests/14_profiling/test_benchmarking_integration.py
-def test_profile_real_model():
-    # Profile actual transformer with real data
-```
-
-### **Execution**:
-```bash
-# Run integration tests only
-tito test 01_tensor --external-only
-
-# Run both inline and integration
-tito test 01_tensor
-
-# Run all tests
-tito test --all
-```
-
-### **Current Structure**:
-```
-tests/
-├── 01_tensor/           ✅ (4 test files)
-├── 02_activations/      ✅ (5 test files)
-├── ...
-├── 15_memoization/      ✅ (4 test files)
-├── 16_quantization/     ✅ (2 files - pending implementation)
-├── 17_compression/      ✅ (2 files - pending implementation)
-├── 18_acceleration/     ✅ (2 files - pending implementation)
-├── 19_benchmarking/     ✅ (2 files - pending implementation)
-├── 20_capstone/         ✅ (2 files - pending implementation)
-├── integration/         ✅ (27 cross-module tests)
-├── checkpoints/         ✅ (23 milestone tests)
-├── milestones/          ✅ (4 historical milestone tests)
-└── TEST_STRATEGY.md     ✅ (this document)
-```
-
----
-
-## 🔄 Testing Workflow
-
-### For Students:
-
-```bash
-# 1. Work on module
-cd modules/01_tensor
-vim tensor_dev.py
-
-# 2. Run inline tests (fast feedback)
-python tensor_dev.py
-# or
-tito test 01_tensor --inline-only
-
-# 3. Export to package
-tito export 01_tensor
-
-# 4. Run integration tests (full validation)
-tito test 01_tensor
-
-# 5. Run progressive tests (ensure nothing broke)
-pytest tests/integration/
-```
-
-### For Instructors:
-
-```bash
-# Comprehensive test suite
-tito test --comprehensive
-
-# Specific module deep dive
-tito test 05_autograd --detailed
-
-# All inline tests only (quick check)
-tito test --all --inline-only
-```
-
----
-
-## 📈 Test Coverage Matrix
-
-| Module | Inline Tests | Integration Tests | Status |
-|--------|-------------|-------------------|--------|
-| 01_tensor | ✅ Pass | ✅ Implemented | Complete |
-| 02_activations | ❌ Fail | ✅ Implemented | Needs Fix |
-| 03_layers | ✅ Pass | ✅ Implemented | Complete |
-| 04_losses | ✅ Pass | ✅ Implemented | Complete |
-| 05_autograd | ✅ Pass | ✅ Implemented | Complete |
-| 06_optimizers | ❌ Fail | ✅ Implemented | Needs Fix |
-| 07_training | ✅ Pass | ✅ Implemented | Complete |
-| 08_dataloader | ✅ Pass | ✅ Implemented | Complete |
-| 09_spatial | ✅ Pass | ✅ Implemented | Complete |
-| 10_tokenization | ✅ Pass | ✅ Implemented | Complete |
-| 11_embeddings | ✅ Pass | ✅ Implemented | Complete |
-| 12_attention | ❌ Fail | ✅ Implemented | Needs Fix |
-| 13_transformers | ✅ Pass | ✅ Implemented | Complete |
-| 14_profiling | ✅ Pass | ✅ Implemented | Complete |
-| 15_memoization | ❌ Fail | ✅ Implemented | Needs Fix |
-| 16_quantization | ⏳ N/A | 📝 Pending | Needs Implementation |
-| 17_compression | ⏳ N/A | 📝 Pending | Needs Implementation |
-| 18_acceleration | ⏳ N/A | 📝 Pending | Needs Implementation |
-| 19_benchmarking | ⏳ N/A | 📝 Pending | Needs Implementation |
-| 20_capstone | ⏳ N/A | 📝 Pending | Needs Implementation |
-
-**Overall**: 11/15 modules passing inline tests (73%), all modules have test infrastructure
-
----
-
-## 🚀 Best Practices
-
-### **DO**:
-✅ Write inline tests immediately after implementing a component
-✅ Test one thing per inline test function
-✅ Use descriptive test function names (`test_unit_sigmoid`, not `test1`)
-✅ Add integration tests when combining multiple modules
-✅ Run inline tests frequently during development
-✅ Run full test suite before committing
-
-### **DON'T**:
-❌ Mix inline and integration test concerns
-❌ Test implementation details in integration tests
-❌ Skip inline tests and jump to integration
-❌ Test mocked/fake components (use real ones)
-❌ Create dependencies between test files
-
----
-
-## 🔧 Common Patterns
-
-### **Pattern 1: Test Component in Isolation**
-```python
-# Inline test in 02_activations/activations_dev.py
-def test_unit_sigmoid():
-    sigmoid = Sigmoid()
-    x = Tensor(np.array([-1.0, 0.0, 1.0]))
-    result = sigmoid.forward(x)
-    assert np.allclose(result.data, [0.269, 0.5, 0.731], atol=0.01)
-```
-
-### **Pattern 2: Test Module Integration**
-```python
-# Integration test in tests/05_autograd/test_progressive_integration.py
-def test_autograd_with_layers():
-    # Uses real Tensor, real Layers, real Autograd
-    x = Tensor(np.array([[1.0, 2.0]]), requires_grad=True)
-    layer = Linear(2, 3)
-    output = layer.forward(x)
-    output.backward()
-    assert x.grad is not None
-```
-
-### **Pattern 3: Test Full Pipeline**
-```python
-# Integration test in tests/13_transformers/test_transformer_integration.py
-def test_complete_transformer_pipeline():
-    # Tokenization → Embedding → Attention → Transformer → Generation
-    tokenizer = CharTokenizer("Hello")
-    model = GPT(vocab_size=tokenizer.vocab_size)
-    output = model.forward(tokenizer.encode("Hi"))
-    assert output.shape == (1, len("Hi"), vocab_size)
-```
-
----
-
-## 📚 Additional Resources
-
-- **Test Module Template**: `tests/module_template/`
-- **Integration Test Examples**: `tests/integration/`
-- **Checkpoint Tests**: `tests/checkpoints/`
-- **Historical Milestones**: `tests/milestones/`
-- **TinyTorch Testing Guide**: `docs/development/testing-guide.md`
-
----
-
-## 🎓 For Educators
-
-This testing structure provides:
-1. **Immediate Feedback**: Inline tests give instant validation
-2. **Progressive Learning**: Students see components work before integration
-3. **Real Systems**: Integration tests use actual components, not mocks
-4. **Industry Practices**: Mirrors professional ML engineering workflows
-5. **Debugging Aid**: Clear separation helps identify where issues occur
-
-Students learn that **component correctness ≠ system correctness**, a crucial lesson for building reliable ML systems.
-
----
-
-**Last Updated**: 2025-11-10
-**Test Infrastructure**: Complete (20/20 modules have test directories)
-**Inline Test Coverage**: 73% passing (11/15 implemented modules)
-**Integration Test Coverage**: 100% infrastructure ready, 75% implemented (15/20 modules)
-