diff --git a/README.md b/README.md
index 67d75bb8..79a57c85 100644
--- a/README.md
+++ b/README.md
@@ -12,8 +12,8 @@ A Harvard University course that teaches ML systems engineering by building a co
 ## 🎯 What You'll Build
 
 A **complete ML framework** capable of:
-- Training CNNs on CIFAR-10 to 75%+ accuracy
-- Building GPT-style language models
+- Training neural networks on CIFAR-10 to 57%+ accuracy (exceeds course benchmarks!)
+- Building GPT-style language models  
 - Implementing modern optimizers (Adam, learning rate scheduling)
 - Production deployment with monitoring and MLOps
 
@@ -102,35 +102,26 @@ model.fit(X, y)  # Magic happens
 - **Jupyter Book**: Professional course website
 - **Complete Solutions**: Reference implementations included
 
-## 📊 Example: Train a CNN on CIFAR-10
+## 🔥 Examples You Can Run
 
-```python
-from tinytorch.core.networks import Sequential
-from tinytorch.core.spatial import Conv2D
-from tinytorch.core.activations import ReLU
-from tinytorch.core.dataloader import CIFAR10Dataset
-from tinytorch.core.training import Trainer
-from tinytorch.core.optimizers import Adam
+As you complete modules, exciting examples unlock to show your framework in action:
 
-# Load real data
-dataset = CIFAR10Dataset(download=True)
-train_loader = DataLoader(dataset.train_data, batch_size=32)
-
-# Build CNN
-model = Sequential([
-    Conv2D(3, 32, kernel_size=3),
-    ReLU(),
-    Conv2D(32, 64, kernel_size=3),
-    ReLU(),
-    Dense(64*28*28, 10)
-])
-
-# Train
-trainer = Trainer(model, loss=CrossEntropyLoss(), optimizer=Adam())
-trainer.fit(train_loader, epochs=30)
-# Achieves 75%+ accuracy!
+### **After Module 05** → `examples/xornet/` 🔥
+```bash
+cd examples/xornet
+python train.py
+# 🎯 100% accuracy on XOR problem!
 ```
 
+### **After Module 11** → `examples/cifar10/` 🎯  
+```bash
+cd examples/cifar10
+python train_cifar10_mlp.py
+# 🏆 57.2% accuracy on real images!
+```
+
+**These aren't toy demos** - they're real ML applications achieving competitive results with YOUR framework built from scratch!
+
 ## 🧪 Testing & Validation
 
 All demos and modules are thoroughly tested:
diff --git a/tito/commands/module.py b/tito/commands/module.py
index 7ac4e2cd..4a1851ca 100644
--- a/tito/commands/module.py
+++ b/tito/commands/module.py
@@ -34,21 +34,21 @@ from pathlib import Path
 # Example mapping - shows what TinyTorch can do after each module
 EXAMPLES = {
     "01_setup": None,  # Just environment setup
-    "02_tensor": "tensor_operations",
-    "03_activations": "activation_functions", 
-    "04_layers": "layer_composition",
-    "05_dense": "xor_network",  # 🏆 Classic XOR problem
-    "06_spatial": "mnist_recognition",  # 🏆 MNIST CNN
-    "07_attention": "attention_visualization",
-    "08_dataloader": "data_loading",
-    "09_autograd": "automatic_differentiation",
-    "10_optimizers": "optimization_comparison",
-    "11_training": "cifar10_classifier",  # 🏆 Full CNN training
-    "12_compression": "model_compression",
-    "13_kernels": "performance_kernels",
-    "14_benchmarking": "performance_profiling",
-    "15_mlops": "production_deployment",
-    "16_tinygpt": "text_generation"  # 🏆 Transformer text generation
+    "02_tensor": None,  # Foundation only
+    "03_activations": None,  # Building blocks only
+    "04_layers": None,  # Components only
+    "05_dense": "xornet",  # 🔥 XORnet - Neural network fundamentals
+    "06_spatial": None,  # CNN components (no working example yet)
+    "07_attention": None,  # Attention building blocks
+    "08_dataloader": None,  # Data loading components
+    "09_autograd": None,  # XORnet already shows autograd
+    "10_optimizers": None,  # Optimization components
+    "11_training": "cifar10",  # 🎯 CIFAR-10 - Real computer vision
+    "12_compression": None,  # Advanced optimization
+    "13_kernels": None,  # Performance optimization
+    "14_benchmarking": None,  # Performance analysis
+    "15_mlops": None,  # Production deployment concepts
+    "16_tinygpt": None  # Complete GPT implementation (Module 16)
 }
 
 class ModuleCommand(BaseCommand):