How To Use NotebookLM With Any Agent - 7 Crazy Ways

Описание: How To Use NotebookLM as a second brain for AI agents. This Google NotebookLM tutorial walks through 7 real NotebookLM use cases and shows what is NotebookLM truly capable of when paired with AI coding tools.


Community with All Resources 📦: http://ailabspro.io/
Video code: V41

In this NotebookLM video, we tested Google NotebookLM from every angle to see how it fits into AI-powered development workflows. This is not just another NotebookLM tutorial. We put the tool through 7 real use cases, from research and debugging to full codebase onboarding, all using the NLM CLI.
Here's how to use NotebookLM as a development companion: we set it up as a second brain for Claude Code, letting it store architectural decisions and project documentation in a single notebook. Instead of bloating context with raw file dumps, Claude queries NotebookLM Gemini-powered RAG to get synthesized, relevant answers. This Gemini NotebookLM integration means your agent works with clean, structured knowledge instead of noisy web results.
We also explored how to use NotebookLM for studying codebases you didn't write. Using Repomix, we packed an entire repo into a token-efficient format, uploaded it as a source, and used NotebookLM Antigravity to generate interactive mind maps and visual breakdowns of the code. The Antigravity NotebookLM visualizations, including the NotebookLM infographic outputs, gave both agents and humans a clear picture of how everything connects, without crawling through files manually.
Other NotebookLM use cases we covered include building a dedicated debugging knowledge base from official docs, community forums, and GitHub repos, creating a cross-tool context builder that any AI tools or agents can query, and running a grounded security audit using 61 curated sources from OWASP and CVE databases.
Whether you're into deep learning projects, building digital products, or just keeping up with AI news, this approach to artificial intelligence assisted development changes how agents interact with information. Instead of relying on broad web searches and machine learning pattern matching across scattered files, your agent gets grounded answers from a controlled, organized knowledge base built on Google AI Studio's ecosystem.
Everything shown in this video, including the claude.md files, prompts, and templates, is available in AI Labs Pro. Link in the description.

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