This NEW NotebookLM + Claude Code integration from @Julian Goldie is totally awesome, and it just changed AI research and execution forever. By connecting these two tools via an MCP server, you can now use Claude to control your research, generate audio, and create infographics with simple terminal commands. No manual clicking. No switching tabs. Just pure automation power. In this video, Julian Goldie breaks down: 🔥 What the NotebookLM MCP server actually is ⚡ How to connect Claude Code directly to your research notebooks 🧠 How to generate audio overviews and deep research automatically 🚀 How to operate 100+ notebooks from a single chat terminal 💡 The step-by-step setup so anyone can start automating today Whether you’re new to NotebookLM or already building advanced AI workflows, this method from Julian Goldie will change how you work forever. If you are a serious business owner ready to scale, stop guessing and join the AI Profit Boardroom. You’ll get a clear 6-week roadmap to success, access to daily accountability groups, and 4 live coaching calls a week to implement these exact playbooks for your business. 🔗 GET INTO THE AI PROFIT BOARDROOM HERE — https://clinthermanlikes.com/aipb

Ever feel like you're not getting the most out of Claude Code? Here's a methodology that's transforming how developers work with AI assistants. What is it? The Ralph Wiggum Loop is an iterative approach based on Geoffrey Huntley's technique, named after the Simpsons character known for unexpected brilliance. The core insight: single-shot prompting rarely produces optimal results, but intelligent iteration creates magic. Get Started with the Official Repo 🔗 github.com/snarktank/ralph This is the autonomous AI agent loop that runs repeatedly until all PRD items are complete. Each iteration spawns a fresh instance with clean context, while memory persists via git history, progress.txt, and prd.json. How It Works Write a PRD — Define what "done" looks like Run the loop — Ralph executes your task iteratively Persist progress — Git + artifacts maintain state between iterations Repeat until complete — Fresh context each cycle, building on previous work Quick Setup bash# From your project root mkdir -p scripts/ralph cp /path/to/ralph/ralph.sh scripts/ralph/ cp /path/to/ralph/prompt.md scripts/ralph/ chmod +x scripts/ralph/ralph.sh The Philosophy Don't aim for perfect on first try — Let the loop refine the work Failures are informative — "Deterministically bad" means failures are predictable Prompt engineering is key — Success depends on writing good prompts Fresh context is reliability — Each iteration clears context, re-reads specs, plans, and codes Key Tips Set --max-iterations as your safety net (start with 20-30) Include what to do if stuck in your prompt Keep PRD items small enough to complete in one context window Let Ralph update AGENTS.md with learnings for future iterations Why It Works You're not babysitting Claude step-by-step anymore. Define success criteria upfront, then walk away. Solutions that survive multiple iterations are stress-tested far more thoroughly than single-shot generations. Check out the repo and try it on your next project 👇 🔗 github.com/snarktank/ralph