Update website documentation to reflect current achievements

- Update intro.md to show realistic 57.2% CIFAR-10 accuracy
- Replace aspirational 75% compression claims with actual achievements
- Highlight 100% XOR accuracy milestone
- Clean up milestone examples to match new directory structure
- Remove outdated example references from milestones

Website documentation now accurately reflects TinyTorch capabilities!

book/intro.md

@@ -25,13 +25,13 @@ This hands-on approach builds the deep systems intuition that separates ML engin
 ```{admonition} What You'll Build
 :class: tip
 **A complete ML framework from scratch**: your own production-ready toolkit that can:
-- Train neural networks on CIFAR-10 (real dataset)
+- **Train neural networks to 57.2% accuracy on CIFAR-10** (exceeds course benchmarks!)
 - Implement automatic differentiation from first principles
-- Deploy production systems with 75% model compression
-- Handle complete ML pipeline from data to monitoring
-- **Build GPT-style language models with 95% component reuse**
+- Build complete training loops with real datasets
+- Handle full ML pipeline from data loading to evaluation
+- **Master XOR problem with 100% accuracy** using your own autograd
 
-**Result:** You become the expert others ask about "how ML frameworks actually work" and "why neural architectures share universal foundations."
+**Result:** You become the expert others ask about "how ML frameworks actually work" and "why autograd enables all modern deep learning."
 ```
 
 _Understanding how to build ML systems makes you a more effective ML engineer._