diff --git a/milestones/perceptron_1957/README.md b/milestones/01_perceptron_1957/README.md
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rename from milestones/perceptron_1957/README.md
rename to milestones/01_perceptron_1957/README.md
diff --git a/milestones/01_perceptron_1957/forward_pass.py b/milestones/01_perceptron_1957/forward_pass.py
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+#!/usr/bin/env python3
+"""
+The Perceptron (1957) - Frank Rosenblatt [FORWARD PASS ONLY]
+=============================================================
+
+📚 HISTORICAL CONTEXT:
+Frank Rosenblatt's Perceptron was the first trainable artificial neural network that 
+could learn from examples. It sparked the first AI boom and demonstrated that machines 
+could actually learn to recognize patterns, launching the neural network revolution.
+
+🎯 MILESTONE 1: FORWARD PASS (BEFORE TRAINING)
+Using YOUR TinyTorch implementations, you'll build a perceptron with RANDOM weights.
+This milestone shows you WHY training is essential - the model won't work without it!
+
+⚠️ IMPORTANT: This is NOT the trained version!
+- You've completed Modules 01-04 (Tensor, Activations, Layers, Losses)
+- You HAVEN'T learned training yet (Modules 05-07: Autograd, Optimizers, Training)
+- This milestone demonstrates the PROBLEM that training will solve
+
+✅ REQUIRED MODULES (Run after Module 04):
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
+  Module 01 (Tensor)        : YOUR data structure (gradients dormant for now)
+  Module 02 (Activations)   : YOUR sigmoid activation function  
+  Module 03 (Layers)        : YOUR Linear layer with RANDOM weights
+  Module 04 (Losses)        : YOUR loss functions (for measuring failure)
+  Data Generation           : Directly generated within this script
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
+
+🏗️ ARCHITECTURE (Original 1957 Design):
+    ┌─────────────┐    ┌─────────────┐    ┌─────────────┐    ┌─────────────┐
+    │ Input       │    │   Linear    │    │  Sigmoid    │    │ Binary      │
+    │ Features    │───▶│ YOUR Module │───▶│ YOUR Module │───▶│ Output      │
+    │ (x1, x2)    │    │     03      │    │     02      │    │ (0 or 1)    │
+    └─────────────┘    └─────────────┘    └─────────────┘    └─────────────┘
+
+🔍 WHAT YOU'LL SEE - EXPECTATION vs REALITY:
+
+    WHAT YOU MIGHT EXPECT:           WHAT YOU'LL ACTUALLY GET:
+    "I built it, so it works!"       "Wait... it's just guessing!"
+    
+    4 │ • • • • •                    4 │ • ○ • ○ • 
+      │ • • • • • ╱  Perfect!          │ ○ • • ○ • ╲  Random!
+    2 │ • • • • ╱ •                  2 │ • ○ • • ○ •
+      │ ○ ○ ○ ╱ ○ ○                    │ ○ • ○ ○ • ○
+    0 │ ○ ○ ╱ ○ ○ ○                  0 │ • ○ • ○ ○ •
+      └────────────                    └────────────
+        0   2   4                        0   2   4
+    
+    ❌ Accuracy: ~50%                ❌ Accuracy: ~50%
+       (What you hoped for)             (What random weights give you)
+
+    WHY IS IT SO BAD?
+    The weights are RANDOM! Without training:
+    - w₁, w₂, b are random numbers from initialization
+    - The decision boundary is in a random position
+    - Predictions are essentially coin flips
+    
+    Mathematical Reality:
+    y = sigmoid(w₁·x₁ + w₂·x₂ + b)  ← These are RANDOM values!
+    
+    Where YOUR modules compute:
+    - Linear: z = w₁·x₁ + w₂·x₂ + b  (random w₁, w₂, b!)
+    - Sigmoid: y = 1/(1 + e⁻ᶻ)       (squash to [0,1])
+    - Decision: class = 1 if y > 0.5 else 0  (random decision boundary!)
+
+🔍 KEY INSIGHTS (This Milestone):
+- ✅ Architecture works: Forward pass executes correctly
+- ❌ But it's useless: Random weights = random predictions (~50% accuracy)
+- 💡 The lesson: Building the model is easy; making it LEARN is the hard part
+- 🎯 Motivation: You NEED training (coming in Modules 05-07!)
+
+📊 WHAT TO EXPECT (This Milestone):
+- Dataset: 10 linearly separable synthetic points (just for testing)
+- No training: Just forward pass with random weights
+- Expected accuracy: ~40-60% (essentially random guessing)
+- Key takeaway: "My model doesn't work... yet!"
+
+🚀 WHAT COMES NEXT (After Module 07):
+- Same architecture, but WITH training
+- Expected accuracy: 95%+ on same problem
+- Training time: ~30 seconds
+- You'll see the SAME perceptron transform from useless → intelligent
+"""
+
+import sys
+import os
+import numpy as np
+import argparse
+
+# Add project root to path for correct tinytorch imports
+# This allows the script to be run from the root of the project
+sys.path.insert(0, os.getcwd())
+
+# Import TinyTorch components YOU BUILT!
+from tinytorch.core.tensor import Tensor        # Module 01: YOU built this!
+from tinytorch.core.layers import Linear        # Module 03: YOU built this!
+from tinytorch.core.activations import Sigmoid  # Module 02: YOU built this!
+
+# Import Rich for beautiful CLI output
+from rich.console import Console
+from rich.table import Table
+from rich.panel import Panel
+from rich import box
+from rich.text import Text
+
+console = Console()
+
+# ============================================================================
+# 🎓 STUDENT CODE: This is what YOU built with Modules 01-03!
+# ============================================================================
+
+class Perceptron:
+    """
+    Simple perceptron: Linear + Sigmoid
+    
+    This uses components YOU built in:
+      - Module 01: Tensor (data structure)
+      - Module 02: Sigmoid (activation function)  
+      - Module 03: Linear (layer with weights)
+    
+    The entire model is just ~10 lines of code!
+    """
+    
+    def __init__(self, input_size=2, output_size=1):
+        # Module 03: Linear layer (w1*x1 + w2*x2 + b)
+        self.linear = Linear(input_size, output_size)
+        
+        # Module 02: Sigmoid activation (squashes to [0,1])
+        self.activation = Sigmoid()
+    
+    def forward(self, x):
+        # Step 1: Linear transformation (Module 03)
+        x = self.linear(x)
+        
+        # Step 2: Activation function (Module 02)
+        x = self.activation(x)
+        
+        return x
+    
+    def __call__(self, x):
+        """PyTorch-style: model(x) calls forward(x)"""
+        return self.forward(x)
+
+# ============================================================================
+# 📊 VISUALIZATION CODE: Rich CLI formatting (you can ignore this!)
+# ============================================================================
+
+def draw_network_architecture():
+    """Draw the perceptron architecture using ASCII art."""
+    network = """
+    Input Layer        Linear Layer              Activation         Output
+    
+    ┌─────────┐       ┌──────────────┐         ┌──────────┐      ┌─────────┐
+    │         │       │              │         │          │      │         │
+    │   x₁    │───────┤              │         │          │      │         │
+    │         │  w₁   │              │    z    │          │  y   │  class  │
+    └─────────┘       │    Linear    │─────────│  Sigmoid │──────│ (0 or 1)│
+                      │   (Wx + b)   │         │  σ(z)    │      │         │
+    ┌─────────┐       │              │         │          │      │         │
+    │         │  w₂   │              │         │          │      │         │
+    │   x₂    │───────┤              │         │          │      └─────────┘
+    │         │       │              │         │          │
+    └─────────┘       └──────────────┘         └──────────┘
+                            ↑
+                            b (bias)
+    
+    Computation Flow:
+      1. Linear:  z = w₁·x₁ + w₂·x₂ + b
+      2. Sigmoid: y = 1 / (1 + e⁻ᶻ)
+      3. Decision: class = 1 if y > 0.5 else 0
+    """
+    return network.strip()
+
+def visualize_data_points(X, y, predictions=None, weights=None):
+    """Create ASCII visualization of data points with decision boundary."""
+    # Create a simple scatter plot
+    grid_size = 20
+    grid = [[' ' for _ in range(grid_size)] for _ in range(grid_size)]
+    
+    # Find bounds
+    x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5
+    y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5
+    
+    # Draw decision boundary if weights provided
+    # Decision boundary: w1*x1 + w2*x2 + b = 0 → x2 = -(w1*x1 + b)/w2
+    if weights is not None:
+        w1, w2, b = weights
+        if abs(w2) > 0.001:  # Avoid division by zero
+            # Determine slope for choosing line character
+            slope = -w1 / w2
+            line_char = '/' if slope > 0 else '\\'
+            
+            for gx in range(grid_size):
+                # Map grid x to real x
+                px = x_min + (gx / (grid_size - 1)) * (x_max - x_min)
+                # Calculate decision boundary y
+                py = -(w1 * px + b) / w2
+                # Map to grid y
+                gy = int((py - y_min) / (y_max - y_min) * (grid_size - 1))
+                gy = grid_size - 1 - gy  # Flip y-axis
+                
+                if 0 <= gy < grid_size and grid[gy][gx] == ' ':
+                    grid[gy][gx] = line_char  # Decision boundary line
+    
+    # Plot points (after boundary so they overlap)
+    for i, (px, py) in enumerate(X):
+        # Map to grid
+        gx = int((px - x_min) / (x_max - x_min) * (grid_size - 1))
+        gy = int((py - y_min) / (y_max - y_min) * (grid_size - 1))
+        gy = grid_size - 1 - gy  # Flip y-axis
+        
+        if 0 <= gx < grid_size and 0 <= gy < grid_size:
+            true_label = int(y[i])
+            if predictions is not None:
+                pred_label = int(predictions[i])
+                # Show correct (green) vs incorrect (red) predictions
+                if true_label == pred_label:
+                    grid[gy][gx] = '●' if true_label == 1 else '○'
+                else:
+                    grid[gy][gx] = '✗'  # Wrong prediction
+            else:
+                grid[gy][gx] = '●' if true_label == 1 else '○'
+    
+    # Build the plot
+    lines = []
+    lines.append("   " + "─" * grid_size)
+    for row in grid:
+        lines.append("  │" + "".join(row) + "│")
+    lines.append("   " + "─" * grid_size)
+    lines.append("   ● = Class 1 (should cluster top-right)")
+    lines.append("   ○ = Class 0 (should cluster bottom-left)")
+    if weights is not None:
+        lines.append("   / or \\ = Decision boundary (where z = 0)")
+    if predictions is not None:
+        lines.append("   ✗ = Incorrect prediction")
+    
+    return "\n".join(lines)
+
+
+def main():
+    """Demonstrate Rosenblatt's Perceptron using YOUR TinyTorch system!"""
+    
+    # Header
+    console.print()
+    console.print(Panel.fit(
+        "[bold cyan]🎯 MILESTONE 1: The Perceptron (1957)[/bold cyan]\n"
+        "[yellow]⚠️  FORWARD PASS ONLY - Random Weights[/yellow]\n\n"
+        "[dim]Components: YOUR Tensor + YOUR Linear + YOUR Sigmoid[/dim]",
+        border_style="cyan"
+    ))
+    console.print()
+    
+    # Introduction - What to expect
+    intro = (
+        "[bold]What You're Demonstrating:[/bold]\n\n"
+        "You've completed Modules 01-03 and built these components:\n"
+        "  • [cyan]Module 01:[/cyan] Tensor (data structure)\n"
+        "  • [cyan]Module 02:[/cyan] Sigmoid (activation function)\n"
+        "  • [cyan]Module 03:[/cyan] Linear (layer with weights)\n\n"
+        "[bold yellow]What to Expect:[/bold yellow]\n"
+        "  • The architecture [green]WORKS[/green] - forward pass succeeds ✓\n"
+        "  • Accuracy is [red]POOR[/red] - random weights = random predictions ✗\n"
+        "  • Decision boundary (/) is in a [yellow]RANDOM[/yellow] position\n"
+        "  • Each run gives [yellow]DIFFERENT[/yellow] results (no seed!)\n\n"
+        "[bold cyan]The Key Lesson:[/bold cyan]\n"
+        "  Building the model is easy. Making it [bold]LEARN[/bold] is hard.\n"
+        "  That's why you need Modules 04-07: Losses, Autograd, Optimizers, Training!"
+    )
+    console.print(Panel(intro, title="[bold cyan]📖 Introduction[/bold cyan]", border_style="cyan"))
+    console.print()
+    
+    # Step 1: Prepare synthetic data
+    console.print("[bold]📊 Step 1: Preparing Data[/bold]")
+    console.print("   Creating linearly separable clusters...")
+    console.print("   [dim]This is a SIMPLE problem - a trained model achieves 95%+ easily[/dim]")
+    console.print("   [yellow]⚠️  No random seed - each run will be different![/yellow]")
+    
+    cluster1 = np.random.normal([2, 2], 0.5, (5, 2))   # Class 1: top-right
+    cluster2 = np.random.normal([-2, -2], 0.5, (5, 2)) # Class 0: bottom-left
+    X = np.vstack([cluster1, cluster2]).astype(np.float32)
+    y = np.array([1, 1, 1, 1, 1, 0, 0, 0, 0, 0], dtype=np.float32)  # True labels
+    
+    # Show data visualization
+    console.print()
+    data_viz = visualize_data_points(X, y)
+    console.print(Panel(data_viz, title="[cyan]Training Data[/cyan]", border_style="cyan"))
+    console.print(f"   [green]✓[/green] Created {X.shape[0]} points in 2 clearly separated clusters\n")
+
+    # Step 2: Create the Perceptron model with YOUR components  
+    console.print("[bold]🧠 Step 2: Building Model[/bold]")
+    console.print("   [yellow]⚠️  No training yet - you haven't learned Modules 05-07![/yellow]")
+    console.print("   🧠 Assembling perceptron with YOUR TinyTorch modules...")
+    
+    model = Perceptron(input_size=2, output_size=1)
+    
+    console.print(f"      [green]✓[/green] Linear layer: 2 → 1 [dim](YOUR Module 03!)[/dim]")
+    console.print(f"      [green]✓[/green] Activation: Sigmoid [dim](YOUR Module 02!)[/dim]")
+    console.print("   [yellow]⚠️  Model assembled - but weights are RANDOM![/yellow]\n")
+    
+    # Show network architecture
+    network_diagram = draw_network_architecture()
+    console.print(Panel(network_diagram, title="[cyan]🏗️  Network Architecture (1957 Design)[/cyan]", border_style="cyan"))
+    console.print()
+
+    # Step 3: Test with random weights
+    console.print("[bold]🔬 Step 3: Testing with Random Weights[/bold]")
+    console.print("   Running forward pass...\n")
+    
+    input_tensor = Tensor(X)
+    predictions = model(input_tensor)
+    
+    # Convert to binary predictions
+    pred_classes = (predictions.data > 0.5).astype(int).flatten()
+    accuracy = (pred_classes == y).mean()
+    
+    # Format arrays nicely for display
+    true_str = ' '.join([f"{int(val)}" for val in y])
+    pred_str = ' '.join([f"{val}" for val in pred_classes])
+    match_str = ' '.join(['[green]✓[/green]' if m else '[red]✗[/red]' for m in (pred_classes == y)])
+    
+    # Create results table
+    results_table = Table(title="📊 Prediction Results", box=box.ROUNDED, border_style="cyan")
+    results_table.add_column("Metric", style="cyan", no_wrap=True)
+    results_table.add_column("Value", style="white")
+    
+    results_table.add_row("True Labels", f"[{true_str}]")
+    results_table.add_row("Predictions", f"[{pred_str}]")
+    results_table.add_row("Matches", match_str)
+    
+    # Determine status
+    if accuracy < 0.6:
+        accuracy_display = f"[red]{accuracy:.1%} ❌ Random Guessing![/red]"
+        status = "FAILED"
+        status_color = "red"
+    else:
+        accuracy_display = f"[yellow]{accuracy:.1%} 🎲 Got Lucky![/yellow]"
+        status = "LUCKY"
+        status_color = "yellow"
+    
+    results_table.add_row("Accuracy", accuracy_display)
+    console.print(results_table)
+    console.print()
+    
+    # Extract weights for visualization and display
+    w1 = model.linear.weight.data[0,0]
+    w2 = model.linear.weight.data[1,0]
+    b = model.linear.bias.data[0]
+    
+    # Calculate z values (linear output before sigmoid)
+    z_values = X @ np.array([[w1], [w2]]) + b
+    
+    # Show visualization with predictions AND decision boundary
+    pred_viz = visualize_data_points(X, y, pred_classes, weights=(w1, w2, b))
+    console.print(Panel(pred_viz, title="[cyan]Predictions with Decision Boundary[/cyan]", border_style=status_color))
+    console.print()
+    
+    # Show weights AND equation
+    decision_eq = f"z = {w1:.4f}·x₁ + {w2:.4f}·x₂ + {b:.4f}"
+    boundary_eq = f"Decision boundary (z=0): x₂ = {-w1/w2:.4f}·x₁ + {-b/w2:.4f}" if abs(w2) > 0.001 else "Decision boundary: vertical line"
+    
+    weights_content = (
+        f"[bold]Random Weights:[/bold]\n"
+        f"  w₁ = [yellow]{w1:7.4f}[/yellow]\n"
+        f"  w₂ = [yellow]{w2:7.4f}[/yellow]\n"
+        f"  b  = [yellow]{b:7.4f}[/yellow]\n\n"
+        f"[bold]Linear Function:[/bold]\n"
+        f"  {decision_eq}\n\n"
+        f"[bold]Decision Line:[/bold]\n"
+        f"  {boundary_eq}\n"
+        f"  [dim](Everything above line → Class 1, below → Class 0)[/dim]"
+    )
+    console.print(Panel(weights_content, title="[yellow]🔧 Model Parameters[/yellow]", border_style="yellow"))
+    console.print()
+    
+    # Diagnosis
+    if status == "FAILED":
+        diagnosis = (
+            "[red]❌ The model is essentially guessing randomly[/red]\n"
+            "[red]❌ Random initialization = random decision boundary[/red]\n\n"
+            "[bold cyan]💡 KEY INSIGHT:[/bold cyan] Building the architecture is easy.\n"
+            "   Making it [bold]LEARN[/bold] is the hard part!"
+        )
+    else:
+        diagnosis = (
+            "[yellow]🎲 You got lucky with this random initialization![/yellow]\n"
+            "[yellow]🎲 But this is NOT learning - just chance[/yellow]\n\n"
+            "[bold cyan]💡 KEY INSIGHT:[/bold cyan] Even when it works, random weights\n"
+            "   won't generalize. You need [bold]TRAINING[/bold]!"
+        )
+    
+    console.print(Panel(diagnosis, title=f"[{status_color}]🔍 Diagnosis: {status}[/{status_color}]", border_style=status_color))
+    
+    # Tip for multiple runs
+    tip = (
+        "💡 [bold yellow]Run this script multiple times![/bold yellow]\n\n"
+        "Each run uses different random weights and data.\n"
+        "You'll see varying results:\n"
+        "  • Sometimes: High accuracy (got lucky!) 🎲\n"
+        "  • Usually: Low accuracy (random guessing) ❌\n\n"
+        "[dim]This demonstrates why training is essential - it must work EVERY time![/dim]"
+    )
+    console.print(Panel(tip, title="[bold yellow]💡 Experiment[/bold yellow]", border_style="yellow"))
+    console.print()
+    
+    # Next steps
+    next_steps = (
+        "[bold]Complete Modules 05-07 to unlock TRAINING:[/bold]\n\n"
+        "  [cyan]•[/cyan] Module 05 (Autograd):   Calculate gradients automatically\n"
+        "  [cyan]•[/cyan] Module 06 (Optimizers): Update weights intelligently\n"
+        "  [cyan]•[/cyan] Module 07 (Training):   Put it all together\n\n"
+        "[dim]Then return to this SAME perceptron and watch it achieve 95%+!\n"
+        "You'll see random → intelligent through the power of learning![/dim]"
+    )
+    console.print(Panel(next_steps, title="[bold green]🚀 Next Steps[/bold green]", border_style="green"))
+    console.print()
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/milestones/01_perceptron_1957/perceptron_trained.py b/milestones/01_perceptron_1957/perceptron_trained.py
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diff --git a/milestones/xor_1969/README.md b/milestones/02_xor_crisis_1969/README.md
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rename from milestones/xor_1969/README.md
rename to milestones/02_xor_crisis_1969/README.md
diff --git a/milestones/xor_1969/minsky_xor_problem.py b/milestones/02_xor_crisis_1969/perceptron_xor_fails.py
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rename from milestones/xor_1969/minsky_xor_problem.py
rename to milestones/02_xor_crisis_1969/perceptron_xor_fails.py
diff --git a/milestones/mnist_mlp_1986/README.md b/milestones/03_mlp_revival_1986/README.md
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rename from milestones/mnist_mlp_1986/README.md
rename to milestones/03_mlp_revival_1986/README.md
diff --git a/milestones/mnist_mlp_1986/UPDATE_SUMMARY.md b/milestones/03_mlp_revival_1986/UPDATE_SUMMARY.md
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rename from milestones/mnist_mlp_1986/UPDATE_SUMMARY.md
rename to milestones/03_mlp_revival_1986/UPDATE_SUMMARY.md
diff --git a/milestones/mnist_mlp_1986/train_mlp.py b/milestones/03_mlp_revival_1986/mlp_mnist.py
similarity index 100%
rename from milestones/mnist_mlp_1986/train_mlp.py
rename to milestones/03_mlp_revival_1986/mlp_mnist.py
diff --git a/milestones/03_mlp_revival_1986/mlp_xor.py b/milestones/03_mlp_revival_1986/mlp_xor.py
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diff --git a/milestones/cifar_cnn_modern/README.md b/milestones/04_cnn_revolution_1998/README.md
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rename from milestones/cifar_cnn_modern/README.md
rename to milestones/04_cnn_revolution_1998/README.md
diff --git a/milestones/cifar_cnn_modern/train_cnn.py b/milestones/04_cnn_revolution_1998/lecun_cifar10.py
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rename from milestones/cifar_cnn_modern/train_cnn.py
rename to milestones/04_cnn_revolution_1998/lecun_cifar10.py
diff --git a/milestones/gpt_2018/README.md b/milestones/05_transformer_era_2017/README.md
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rename from milestones/gpt_2018/README.md
rename to milestones/05_transformer_era_2017/README.md
diff --git a/milestones/gpt_2018/train_gpt.py b/milestones/05_transformer_era_2017/vaswani_shakespeare.py
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rename from milestones/gpt_2018/train_gpt.py
rename to milestones/05_transformer_era_2017/vaswani_shakespeare.py
diff --git a/milestones/06_systems_age_2024/optimize_models.py b/milestones/06_systems_age_2024/optimize_models.py
new file mode 100644
index 00000000..e69de29b
diff --git a/milestones/perceptron_1957/rosenblatt_perceptron.py b/milestones/perceptron_1957/rosenblatt_perceptron.py
deleted file mode 100644
index acfa711c..00000000
--- a/milestones/perceptron_1957/rosenblatt_perceptron.py
+++ /dev/null
@@ -1,156 +0,0 @@
-#!/usr/bin/env python3
-"""
-The Perceptron (1957) - Frank Rosenblatt
-=======================================
-
-📚 HISTORICAL CONTEXT:
-Frank Rosenblatt's Perceptron was the first trainable artificial neural network that 
-could learn from examples. It sparked the first AI boom and demonstrated that machines 
-could actually learn to recognize patterns, launching the neural network revolution.
-
-🎯 WHAT YOU'RE BUILDING:
-Using YOUR TinyTorch implementations, you'll recreate the exact same perceptron that 
-started it all - proving that YOU can build the foundation of modern AI from scratch.
-
-✅ REQUIRED MODULES (Run after Module 4):
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-  Module 01 (Tensor)        : YOUR data structure with gradient tracking
-  Module 02 (Activations)   : YOUR sigmoid activation for smooth gradients  
-  Module 03 (Layers)        : YOUR Linear layer for weight transformations
-  Data Generation           : Directly generated within this script
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-
-🏗️ ARCHITECTURE (Original 1957 Design):
-    ┌─────────────┐    ┌─────────────┐    ┌─────────────┐    ┌─────────────┐
-    │ Input       │    │   Linear    │    │  Sigmoid    │    │ Binary      │
-    │ Features    │───▶│ YOUR Module │───▶│ YOUR Module │───▶│ Output      │
-    │ (x1, x2)    │    │     03      │    │     02      │    │ (0 or 1)    │
-    └─────────────┘    └─────────────┘    └─────────────┘    └─────────────┘
-
-🔍 HOW THE PERCEPTRON LEARNS - A LINEAR DECISION BOUNDARY:
-
-    INITIAL (Random Weights):        TRAINING (Gradient Descent):      CONVERGED (Learned):
-    
-    4 │ • • • • •                    4 │ • • • • •                    4 │ • • • • • 
-      │ • • • • •  Class 1             │ • • • • • ╱                    │ • • • • • ╱
-    2 │ - - - - -  ← Wrong!          2 │ • • • • ╱ •  ← Adjusting     2 │ • • • • ╱ •  ← Perfect!
-      │ ○ ○ ○ ○ ○                      │ ○ ○ ○ ╱ ○ ○                    │ ○ ○ ○ ╱ ○ ○
-    0 │ ○ ○ ○ ○ ○  Class 0           0 │ ○ ○ ╱ ○ ○ ○                  0 │ ○ ○ ╱ ○ ○ ○
-      └────────────                    └────────────                    └────────────
-        0   2   4                        0   2   4                        0   2   4
-
-    Mathematical Operation:          Weight Updates:
-    y = sigmoid(w₁·x₁ + w₂·x₂ + b)  w = w - η·∇L  (η = learning rate)
-    
-    Where YOUR modules compute:
-    - Linear: z = w₁·x₁ + w₂·x₂ + b  (weighted sum)
-    - Sigmoid: y = 1/(1 + e⁻ᶻ)       (squash to [0,1])
-    - Decision: class = 1 if y > 0.5 else 0
-
-🔍 KEY INSIGHTS:
-- Single-layer architecture: Just linear transformation + activation
-- Linearly separable only: Can't solve XOR problem (that comes later!)
-- Foundation for everything: Modern networks are just deeper perceptrons
-
-📊 EXPECTED PERFORMANCE:
-- Dataset: 1,000 linearly separable synthetic points
-- Training time: 30 seconds
-- Expected accuracy: 95%+ (problem is linearly separable)
-"""
-
-import sys
-import os
-import numpy as np
-import argparse
-
-# Add project root to path for correct tinytorch imports
-# This allows the script to be run from the root of the project
-sys.path.insert(0, os.getcwd())
-
-# Import TinyTorch components YOU BUILT!
-from tinytorch.core.tensor import Tensor        # Module 01: YOU built this!
-from tinytorch.core.layers import Linear        # Module 03: YOU built this!
-from tinytorch.core.activations import Sigmoid  # Module 02: YOU built this!
-
-class RosenblattPerceptron:
-    """
-    Rosenblatt's original Perceptron using YOUR TinyTorch implementations!
-    
-    Historical note: The original used a step function, but we use sigmoid 
-    for smooth gradients (an innovation that came slightly later).
-    """
-    
-    def __init__(self, input_size=2, output_size=1):
-        print("🧠 Assembling Rosenblatt's Perceptron with YOUR TinyTorch modules...")
-        
-        # Single layer - just like the original 1957 design!
-        self.linear = Linear(input_size, output_size)  # Module 03: YOUR Linear layer!
-        self.activation = Sigmoid()                    # Module 02: YOUR Sigmoid function!
-        
-        print(f"   ✅ Linear layer: {input_size} → {output_size} (YOUR Module 03 implementation!)")
-        print(f"   ✅ Activation: Sigmoid (YOUR Module 02 implementation!)")
-        
-    def forward(self, x):
-        """Forward pass through YOUR perceptron implementation."""
-        # Step 1: Linear transformation using YOUR weights
-        x = self.linear(x)        # Module 03: YOUR Linear.forward() method!
-        
-        # Step 2: Activation using YOUR sigmoid  
-        x = self.activation(x)    # Module 02: YOUR Sigmoid.forward() method!
-        
-        return x
-
-def main():
-    """Demonstrate Rosenblatt's Perceptron using YOUR TinyTorch system!"""
-    
-    print("🎯 MILESTONE: The Perceptron (1957)")
-    print("   Historical significance: The first trainable neural network.")
-    print("   YOUR achievement: Assembling it from YOUR own modules.")
-    print("   Components used: YOUR Tensor + YOUR Linear + YOUR Sigmoid.")
-    print("-" * 60)
-    
-    # Step 1: Prepare synthetic data
-    print("\n📊 Step 1: Preparing linearly separable data...")
-    np.random.seed(42)
-    cluster1 = np.random.normal([2, 2], 0.5, (5, 2))  # Just a few samples are needed
-    cluster2 = np.random.normal([-2, -2], 0.5, (5, 2))
-    X = np.vstack([cluster1, cluster2]).astype(np.float32)
-    print(f"   ✅ Data created successfully with shape: {X.shape}")
-
-    # Step 2: Create the Perceptron model with YOUR components  
-    print("\n🧠 Step 2: Instantiating the Perceptron model...")
-    model = RosenblattPerceptron(input_size=2, output_size=1)
-    print("   ✅ Model assembled successfully!")
-
-    # Step 3: Perform a forward pass
-    print("\n🔬 Step 3: Running a forward pass to test integration...")
-    # Convert data to YOUR Tensor format
-    input_tensor = Tensor(X)  # Module 01: YOUR Tensor class!
-    print(f"   - Input tensor created with shape: {input_tensor.shape}")
-
-    # Run the forward pass through YOUR implementations
-    output_tensor = model.forward(input_tensor)
-    print(f"   - Output tensor received with shape: {output_tensor.shape}")
-
-    # --- Verification ---
-    print("\n" + "="*60)
-    print("✅ SUCCESS! Your components integrated perfectly.")
-    print("   You have successfully assembled the architecture of the first")
-    print("   trainable neural network using the modules YOU built.")
-    print("="*60)
-    
-    print("\n🎓 What YOU Accomplished:")
-    print("   • YOU assembled a neural network from scratch.")
-    print("   • YOUR Tensor class handled the data flow.")
-    print("   • YOUR Linear layer performed the mathematical transformation.")
-    print("   • YOUR Sigmoid activation processed the layer's output.")
-    
-    print("\n🚀 Next Steps:")
-    print("   • In future modules, you will build the components needed to TRAIN this model:")
-    print("     - Module 04 (Losses): To measure how wrong the model's predictions are.")
-    print("     - Module 05 (Autograd): To calculate the gradients needed to improve.")
-    print("     - Module 06 (Optimizers): To update the model's weights automatically.")
-    print("\n   For now, congratulations on this major milestone!")
-
-if __name__ == "__main__":
-    main()
\ No newline at end of file
diff --git a/modules/source/DEFINITIVE_MODULE_PLAN.md b/modules/source/DEFINITIVE_MODULE_PLAN.md
index a8c8c9e7..755f1813 100644
--- a/modules/source/DEFINITIVE_MODULE_PLAN.md
+++ b/modules/source/DEFINITIVE_MODULE_PLAN.md
@@ -125,11 +125,23 @@ def log_softmax(x: Tensor, dim=-1) -> Tensor  # Numerical stability
 
 ---
 
-## 🪜 **Milestone 1: Perceptron (After Module 04)**
-**Location:** `milestones/01_perceptron/`
-**Deliverable:** Train Linear + Sigmoid on 2D dataset, visualize decision boundary
-**Success Criteria:** 95% accuracy on linearly separable data
-**Unlock:** Complete modules 01-04 + integration test
+## 🪜 **Milestone 1: Perceptron 1957 (After Modules 04 & 07)**
+**Location:** `milestones/01_perceptron_1957/`
+
+**Part 1: Forward Pass (After Module 04)**
+- File: `forward_pass_interactive.py`
+- Build perceptron with random weights
+- Interactive CLI to manually tweak weights (frustration!)
+- Success: ~40-60% accuracy (essentially random)
+- Lesson: "I need automatic training!"
+
+**Part 2: Trained (After Module 07)**
+- File: `perceptron_trained.py`
+- Same architecture, NOW with backprop training
+- Success: 95%+ accuracy on linearly separable data
+- Lesson: "Training transforms random → intelligent!"
+
+**Unlock:** Complete modules 01-04 for Part 1, modules 05-07 for Part 2
 
 ---
 
@@ -214,10 +226,29 @@ def clip_grad_norm(parameters, max_norm)
 
 ---
 
-## 🪜 **Milestone 2: MLP (After Module 07)**
-**Location:** `milestones/02_mlp/`
-**Deliverable:** 2-layer MLP on MNIST, compare to perceptron
-**Success Criteria:** >95% accuracy on MNIST
+## 🪜 **Milestone 2: XOR Crisis 1969 (After Module 07)**
+**Location:** `milestones/02_xor_crisis_1969/`
+**File:** `perceptron_xor_fails.py`
+
+**Deliverable:**
+- Try training perceptron on XOR problem (4 points!)
+- Train for 1000+ epochs... stuck at ~50%
+- Visualize why: XOR is NOT linearly separable
+- Show decision boundary can't separate the points
+
+**What Students Learn:**
+- Training works (we proved it in M1)
+- But architecture has fundamental limitations
+- Single layer = can only learn linear decision boundaries
+- Historical context: Minsky's 1969 proof killed AI research for a decade
+
+**Success Criteria:**
+- Perceptron trains but never exceeds 60% on XOR
+- Visualization clearly shows the limitation
+- Student understands WHY it fails (not linearly separable)
+
+**Emotional Beat:** "Wait... training doesn't solve everything?"
+
 **Unlock:** Complete modules 05-07 + integration test
 
 ---
@@ -276,11 +307,30 @@ class BatchNorm2d:
 
 ---
 
-## 🪜 **Milestone 3: CNN (After Module 09)**
-**Location:** `milestones/03_cnn/`
-**Deliverable:** 3-layer CNN on CIFAR-10, visualize filters
-**Success Criteria:** >75% accuracy on CIFAR-10
-**Unlock:** Complete modules 08-09 + integration test
+## 🪜 **Milestone 3: MLP Revival 1986 (After Module 07)**
+**Location:** `milestones/03_mlp_revival_1986/`
+**Files:** `mlp_xor.py`, `mlp_mnist.py`
+
+**Deliverable:**
+- Add ONE hidden layer to solve XOR → 100% accuracy!
+- Train MLP on MNIST → 95%+ accuracy
+- Compare to perceptron failure: depth changes everything
+- Visualize curved decision boundary for XOR
+
+**What Students Learn:**
+- Hidden layers enable non-linear decision boundaries
+- Backpropagation + depth = AI renaissance
+- Same training algorithm (backprop) works for any depth
+- Historical context: Rumelhart's 1986 paper revived the field
+
+**Success Criteria:**
+- MLP solves XOR: 100% accuracy
+- MLP on MNIST: >95% accuracy
+- Student understands power of depth
+
+**Emotional Beat:** "ONE hidden layer changes everything!"
+
+**Unlock:** Complete modules 05-07 + integration test
 
 ---
 
@@ -400,11 +450,30 @@ def attention_with_cache(Q, K, V, cache, layer_idx, seq_pos) -> Tensor
 
 ---
 
-## 🪜 **Milestone 4: TinyGPT (After Module 14)**
-**Location:** `milestones/04_tinygpt/`
-**Deliverable:** Character-level GPT on Shakespeare, generate text
-**Success Criteria:** Perplexity < 2.0, coherent generation
-**Unlock:** Complete modules 10-14 + integration test
+## 🪜 **Milestone 4: CNN Revolution 1998 (After Module 09)**
+**Location:** `milestones/04_cnn_revolution_1998/`
+**File:** `lecun_cifar10.py`
+
+**Deliverable:**
+- Build LeNet-style CNN for CIFAR-10
+- Convolutional layers exploit spatial structure
+- Visualize learned filters (edge detectors, etc.)
+- Compare to MLP: fewer parameters, better accuracy
+
+**What Students Learn:**
+- Spatial inductive bias matters for vision
+- Convolutions share weights across space
+- Pooling provides translation invariance
+- Historical context: LeCun's CNN revolutionized computer vision
+
+**Success Criteria:**
+- CNN on CIFAR-10: >75% accuracy
+- Visualizations show meaningful filters
+- Student understands spatial structure
+
+**Emotional Beat:** "It SEES patterns in images!"
+
+**Unlock:** Complete modules 08-09 + integration test
 
 ---
 
@@ -519,13 +588,56 @@ def plot_pareto_frontier(results: pd.DataFrame)
 
 ---
 
-## 🪜 **Milestone 5: Systems Capstone (After Module 19)**
-**Location:** `milestones/05_systems_capstone/`
-**Deliverable:** Profile and optimize CNN vs TinyGPT
-- Apply quantization and pruning
-- Generate comparison report
-- Show accuracy vs speed trade-offs
-**Success Criteria:** 2× speedup with <5% accuracy loss
+## 🪜 **Milestone 5: Transformer Era 2017 (After Module 14)**
+**Location:** `milestones/05_transformer_era_2017/`
+**File:** `vaswani_shakespeare.py`
+
+**Deliverable:**
+- Build character-level GPT on Shakespeare corpus
+- Self-attention captures long-range dependencies
+- Generate coherent text samples
+- Compare to RNN: attention > recurrence
+
+**What Students Learn:**
+- Attention mechanism enables parallelization
+- Positional encoding for sequence order
+- Autoregressive generation with KV caching
+- Historical context: "Attention is all you need" changed NLP forever
+
+**Success Criteria:**
+- Perplexity < 2.0 on Shakespeare
+- Generated text is coherent (subjective)
+- Student understands attention mechanism
+
+**Emotional Beat:** "It writes like Shakespeare!"
+
+**Unlock:** Complete modules 10-14 + integration test
+
+---
+
+## 🪜 **Milestone 6: Systems Age 2024 (After Module 19)**
+**Location:** `milestones/06_systems_age_2024/`
+**File:** `optimize_models.py`
+
+**Deliverable:**
+- Profile CNN (M4) and GPT (M5) for bottlenecks
+- Apply quantization (INT8) and pruning (50% sparsity)
+- Benchmark before/after optimization
+- Generate performance comparison report
+
+**What Students Learn:**
+- Profiling reveals true bottlenecks
+- Quantization: 4× memory reduction, minimal accuracy loss
+- Pruning: Structured vs unstructured sparsity
+- Modern ML is systems engineering
+
+**Success Criteria:**
+- 2× speedup with <5% accuracy loss
+- Comprehensive benchmark report
+- Student understands systems trade-offs
+
+**Emotional Beat:** "I made production AI!"
+
 **Unlock:** Complete modules 15-19 + integration test
 
 ---
@@ -569,11 +681,11 @@ def plot_pareto_frontier(results: pd.DataFrame)
 
 ## 🚀 Implementation Order
 
-1. **Phase 1:** Modules 01-04 → Milestone 1 (Perceptron)
-2. **Phase 2:** Modules 05-07 → Milestone 2 (MLP)
-3. **Phase 3:** Modules 08-09 → Milestone 3 (CNN)
-4. **Phase 4:** Modules 10-14 → Milestone 4 (TinyGPT)
-5. **Phase 5:** Modules 15-19 → Milestone 5 (Systems)
+1. **Phase 1:** Modules 01-04 → Milestone 1 Part 1 (Perceptron forward pass)
+2. **Phase 2:** Modules 05-07 → Milestones 1 Part 2, 2, 3 (Training, Crisis, Revival)
+3. **Phase 3:** Modules 08-09 → Milestone 4 (CNN)
+4. **Phase 4:** Modules 10-14 → Milestone 5 (Transformers)
+5. **Phase 5:** Modules 15-19 → Milestone 6 (Systems)
 
 ---