diff --git a/book/resources.md b/book/resources.md
index 09ce99a8..03b505d7 100644
--- a/book/resources.md
+++ b/book/resources.md
@@ -1,8 +1,8 @@
 # 📚 Additional Learning Resources
 
-**Complement your TinyTorch journey with these carefully curated resources.**
+**Complement your TinyTorch journey with these carefully selected resources.**
 
-While TinyTorch teaches you to build ML systems from scratch, these resources provide broader context, alternative perspectives, and deeper dives into specific topics.
+While TinyTorch teaches you to build complete ML systems from scratch, these resources provide broader context, alternative perspectives, and production tools.
 
 ---
 
@@ -12,9 +12,6 @@ While TinyTorch teaches you to build ML systems from scratch, these resources pr
 - **[CS 329S: Machine Learning Systems Design](https://stanford-cs329s.github.io/)** (Stanford)  
   *Production ML systems, infrastructure, and deployment at scale*
 
-- **[Machine Learning Systems](https://mlsysbook.ai)** by Prof. Vijay Janapa Reddi (Harvard)  
-  *Comprehensive systems perspective on ML engineering and optimization*
-
 - **[CS 6.S965: TinyML and Efficient Deep Learning](https://hanlab.mit.edu/courses/2024-fall-65940)** (MIT)  
   *Edge computing, model compression, and efficient ML algorithms*
 
@@ -33,118 +30,88 @@ While TinyTorch teaches you to build ML systems from scratch, these resources pr
 ## 📖 **Recommended Books**
 
 ### **Systems & Engineering**
-- **"Designing Machine Learning Systems"** by Chip Huyen  
+- **[Machine Learning Systems](https://mlsysbook.ai)** by Prof. Vijay Janapa Reddi (Harvard)  
+  *Comprehensive systems perspective on ML engineering and optimization - the perfect companion to TinyTorch*
+
+- **[Designing Machine Learning Systems](https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/)** by Chip Huyen  
   *Production ML engineering, data pipelines, and system design*
 
-- **"Machine Learning Engineering"** by Andriy Burkov  
+- **[Machine Learning Engineering](https://www.mlebook.com/wiki/doku.php)** by Andriy Burkov  
   *End-to-end ML project lifecycle and best practices*
 
-- **"Reliable Machine Learning"** by Cathy Chen, Niall Richard Murphy  
-  *SRE principles applied to ML systems and production reliability*
-
-### **Deep Learning Implementation**
-- **"Deep Learning"** by Ian Goodfellow, Yoshua Bengio, Aaron Courville  
+### **Implementation & Theory**
+- **[Deep Learning](https://www.deeplearningbook.org/)** by Ian Goodfellow, Yoshua Bengio, Aaron Courville  
   *Mathematical foundations - the theory behind what you implement in TinyTorch*
 
-- **"Hands-On Machine Learning"** by Aurélien Géron  
-  *Practical implementations using established frameworks like TensorFlow/PyTorch*
+- **[Hands-On Machine Learning](https://www.oreilly.com/library/view/hands-on-machine-learning/9781098125967/)** by Aurélien Géron  
+  *Practical implementations using established frameworks*
 
 ---
 
-## 🛠️ **Framework Deep Dives**
+## 🛠️ **Alternative Implementations**
 
-### **PyTorch Internals**
+**Different approaches to building ML systems from scratch - see how others tackle the same challenge:**
+
+### **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.***
+
+- **[Tinygrad](https://github.com/geohot/tinygrad)** by George Hotz  
+  *Performance-focused educational framework. **Tinygrad optimizes for speed, TinyTorch optimizes for learning systems thinking.***
+
+- **[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.***
+
+---
+
+## 🏭 **Production Tools & Platforms**
+
+### **Framework Deep Dives**
 - **[PyTorch Internals](http://blog.ezyang.com/2019/05/pytorch-internals/)** by Edward Yang  
   *How PyTorch actually works under the hood - see what you built in TinyTorch at production scale*
 
 - **[PyTorch Documentation: Extending PyTorch](https://pytorch.org/docs/stable/notes/extending.html)**  
   *Custom operators and autograd functions - apply your TinyTorch knowledge*
 
-### **TensorFlow Architecture**
-- **[TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf)**  
-  *Original TensorFlow paper - understand different architectural choices*
+### **MLOps & Production**
+- **[Papers With Code](https://paperswithcode.com/)**  
+  *Research papers with implementation code - apply your skills to reproduce results*
 
-- **[XLA: Tensorflow Compiled](https://developers.google.com/machine-learning/xla)**  
-  *Compilation and optimization techniques for ML frameworks*
+- **[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*
 
 ---
 
-## 🔬 **Research Papers & Implementations**
+## 🌐 **Learning Communities**
 
-### **Foundational Papers**
-- **["Automatic Differentiation in Machine Learning: A Survey"](https://jmlr.org/papers/v18/17-468.html)**  
-  *Comprehensive overview of autograd techniques you implemented*
+### **Technical Discussion**
+- **[r/MachineLearning](https://www.reddit.com/r/MachineLearning/)**  
+  *Research discussions and paper releases*
 
-- **["Adam: A Method for Stochastic Optimization"](https://arxiv.org/abs/1412.6980)**  
-  *The optimizer paper - see the math behind your implementation*
-
-- **["Attention Is All You Need"](https://arxiv.org/abs/1706.03762)**  
-  *The transformer paper that revolutionized ML - builds on your attention module*
-
-### **Systems & Optimization**
-- **["TensorFlow: A System for Large-Scale Machine Learning"](https://www.usenix.org/conference/osdi16/technical-sessions/presentation/abadi)**  
-  *Systems design decisions for distributed ML frameworks*
-
-- **["PyTorch: An Imperative Style, High-Performance Deep Learning Library"](https://papers.nips.cc/paper/2019/hash/bdbca288fee7f92f2bfa9f7012727740-Abstract.html)**  
-  *Design philosophy behind PyTorch's eager execution model*
-
----
-
-## 💻 **Practical Implementation Guides**
-
-### **From Scratch Implementations**
-- **[Neural Networks from Scratch](https://nnfs.io/)** by Harrison Kinsley  
-  *Another from-scratch approach with Python - different style from TinyTorch*
-
-- **[Micrograd](https://github.com/karpathy/micrograd)** by Andrej Karpathy  
-  *Minimal autograd engine in 100 lines - see a different take on what you built*
-
-- **[Tinygrad](https://github.com/geohot/tinygrad)** by George Hotz  
-  *Another educational ML framework - compare approaches and implementations*
-
-### **Advanced Topics**
-- **[Quantization and Training of Neural Networks](https://arxiv.org/abs/1712.05877)**  
-  *Extends your compression module with cutting-edge techniques*
-
-- **[Mixed Precision Training](https://arxiv.org/abs/1710.03740)**  
-  *Optimization techniques for faster training and inference*
-
----
-
-## 🌐 **Online Communities & Blogs**
-
-### **Technical Blogs**
 - **[The Gradient](https://thegradient.pub/)**  
   *Deep technical articles on ML research and systems*
 
 - **[Distill.pub](https://distill.pub/)**  
   *Interactive explanations of ML concepts with beautiful visualizations*
 
-- **[Papers With Code](https://paperswithcode.com/)**  
-  *Research papers with implementation code - apply your skills to reproduce results*
-
-### **Discussion Forums**
-- **[r/MachineLearning](https://www.reddit.com/r/MachineLearning/)**  
-  *Research discussions and paper releases*
-
-- **[MLOps Community](https://mlops.community/)**  
-  *Production ML engineering discussions and best practices*
-
 ---
 
 ## 🎯 **Next Steps After TinyTorch**
 
 ### **Apply Your Skills**
-1. **Reproduce a Research Paper**: Use your TinyTorch foundation to implement papers from scratch
+1. **Reproduce Research**: Use your TinyTorch foundation to implement papers from scratch
 2. **Contribute to Open Source**: PyTorch, TensorFlow, JAX - you now understand the internals
-3. **Build Production Systems**: Apply MLOps principles from your final modules to real projects
-4. **Optimize for Edge**: Use compression and kernel techniques for mobile/embedded deployment
+3. **Build Production Systems**: Apply MLOps principles from your final modules
+4. **Optimize for Edge**: Use compression and kernel techniques for deployment
 
 ### **Advanced Specializations**
 - **Distributed Training**: Scale your framework knowledge to multi-GPU systems
-- **Compiler Design**: Build domain-specific languages for ML (like JAX or Triton)
-- **Hardware Acceleration**: Custom CUDA kernels and specialized processors
-- **Research**: Novel architectures and training techniques
+- **Compiler Design**: Build domain-specific languages for ML (JAX, Triton style)
+- **Hardware Acceleration**: Custom kernels and specialized processors
+- **Systems Research**: Novel architectures and training techniques
 
 ---
 
@@ -152,14 +119,14 @@ While TinyTorch teaches you to build ML systems from scratch, these resources pr
 
 ```{admonition} 🎯 Strategic Learning Path
 :class: tip
-**Parallel Learning**: Use these resources alongside TinyTorch modules for deeper context
+**Parallel Learning**: Use these alongside TinyTorch modules for broader context
 
-**Post-TinyTorch**: After completing the framework, dive into production systems and advanced topics
+**Post-TinyTorch**: After completing the framework, dive into production systems
 
-**Project-Based**: Apply concepts from multiple resources to build real projects
+**Compare & Contrast**: Study alternative implementations to understand design trade-offs
 ```
 
-**Remember**: You now have the implementation foundation that most ML engineers lack. These resources will help you apply that knowledge to broader systems and cutting-edge research.
+**Remember**: You now have the implementation foundation that most ML engineers lack. These resources help you apply that knowledge to broader systems and production environments.
 
 ---