🎉 Introducing NotebookLM Integration

[peremartra]

🎉 Introducing NotebookLM Integration

I'm excited to announce a new way to interact with the Rearchitecting LLMs ecosystem: an interactive NotebookLM space that acts as your AI-powered technical companion.

🔗 Access Here

→ Launch NotebookLM Space

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🤔 What is This?

Instead of searching through notebooks and papers manually, you can now ask questions conversationally and get instant, contextualized answers.

The NotebookLM space contains:

• ✅ All research papers referenced in chapters
• ✅ Complete notebooks from each chapter
• ✅ optiPfair library documentation and guides
• ✅ Code examples and implementations

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💡 Example Use Cases

Here are real questions you can ask right now:

For Beginners:

• "How does depth pruning work and why would I use it?"
• "What's the difference between width and depth pruning?"
• "Show me a simple example of depth pruning"

For Practitioners:

• "How many layers can I safely remove from a 70B model?"
• "What's the code to reduce GLU expansion ratio in Llama3?"
• "Compare the pruning strategies"

For Researchers:

• "What papers discuss attention bypass mechanisms?"
• "Show me the mathematical formulation of KL divergence in knowledge distillation"
• "What are the trade-offs between different neuron selection strategies?"

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🎯 When to Use What?

Need	Best Resource
Quick code snippet	NotebookLM
Understanding a specific technique	NotebookLM
Structured learning path	The Book
Complete implementation	GitHub Notebooks
Production-ready tools	optiPfair Library

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📊 How It Helps Your Journey

Whether you're:

• 🔰 Starting from scratch → Ask foundational questions
• 🛠️ Implementing techniques → Get code snippets on-demand
• 🔬 Researching optimizations → Query papers interactively
• 🐛 Debugging implementations → Troubleshoot with context

NotebookLM has you covered.

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💬 Feedback Welcome

This is an experiment in making technical content more accessible. I'd love to hear:

• What questions did you ask?
• What worked well / what didn't?
• What would make it more useful?

Drop your thoughts in the comments below 👇

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Happy architecting! 🏗️

— Pere