🔧 Fix MLOps over-emphasis and repetitive differentiation statements

✂️ Reduced MLOps Focus:
- Renamed 'MLOps & Production' → 'Development Tools'
- Removed redundant 'MLOps Community' link
- Focuses on practical development tools instead

🎯 Made Framework Differentiations Distinct:
- Micrograd: 'shows you the math, TinyTorch shows you the systems'
- Tinygrad: 'optimizes for speed, TinyTorch optimizes for learning'
- NNFS: 'focuses on algorithms, TinyTorch focuses on complete systems engineering'

💡 Benefits:
- Each differentiation now highlights specific strengths vs repetitive vehicle analogy
- Less MLOps emphasis (appears in course already)
- More concise and memorable comparisons

Result: Cleaner resource organization with unique, specific differentiations
that avoid repetition and over-emphasis on any single topic.

book/resources.md

@@ -54,13 +54,13 @@ While TinyTorch teaches you to build complete ML systems from scratch, these res
 
 ### **Minimal Frameworks**
 - **[Micrograd](https://github.com/karpathy/micrograd)** by Andrej Karpathy  
-  *Minimal autograd engine in 100 lines. **Micrograd teaches engine parts, TinyTorch teaches you to design the whole vehicle and drive it.***
+  *Minimal autograd engine in 100 lines. **Micrograd shows you the math, TinyTorch shows you the systems.***
 
 - **[Tinygrad](https://github.com/geohot/tinygrad)** by George Hotz  
-  *Performance-focused educational framework. **Tinygrad optimizes for speed, TinyTorch optimizes for learning systems thinking.***
+  *Performance-focused educational framework. **Tinygrad optimizes for speed, TinyTorch optimizes for learning.***
 
 - **[Neural Networks from Scratch](https://nnfs.io/)** by Harrison Kinsley  
-  *Math-heavy implementation approach. **NNFS teaches you the engine parts, TinyTorch teaches you to design the whole vehicle and drive it.***
+  *Math-heavy implementation approach. **NNFS focuses on algorithms, TinyTorch focuses on complete systems engineering.***
 
 ---
 
@@ -73,13 +73,10 @@ While TinyTorch teaches you to build complete ML systems from scratch, these res
 - **[PyTorch Documentation: Extending PyTorch](https://pytorch.org/docs/stable/notes/extending.html)**  
   *Custom operators and autograd functions - apply your TinyTorch knowledge*
 
-### **MLOps & Production**
+### **Development Tools**
 - **[Papers With Code](https://paperswithcode.com/)**  
   *Research papers with implementation code - apply your skills to reproduce results*
 
-- **[MLOps Community](https://mlops.community/)**  
-  *Production ML engineering discussions and best practices*
-
 - **[Weights & Biases](https://wandb.ai/)**  
   *Experiment tracking and model management - scale your TinyTorch training*