Show Notes
Notebook LM has quickly become my go-to for researching new topics. In this video I break down how I’ve been using it to learn faster, reverse engineer ideas, and turn dense content into bite-sized, practical workflows you can reuse.
Core features and UX
- Centered assistant with a node-based, mind-map style flow
- You add sources, run queries in the center with the assistant, and see content generation in the right pane.
- The assistant is somewhat ephemeral—results can disappear unless you save them.
- Right-hand pane for content generation
- Deep-dive conversations, customized prompts, and a place to store notes, briefings, timelines, FAQ, and more.
- Mind map view
- New and powerful for visualizing topics, key technologies, and the relationships between sources.
- Source discovery and discovery UX
- Core discovery features let you pull in relevant sources without manually hunting for every link.
How to pull sources quickly
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Create new → Discover sources
- Enter a topic and pick a mode (e.g., “I’m feeling curious”) to surface relevant material.
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Advanced setup for targeted research
- Add a specific link (e.g., a YouTube video) and the assistant will pull in the related notes and references.
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Custom prompts and tone
- You can tailor the assistant’s conversational style (default, longer, shorter) and even preload prompts to steer depth and tone.
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Example prompt customization
- A sample prompt I use to force a concise, bullet-focused output (you can adapt the flavor to taste and topic):
- Structure your writing. Think and respond like a consultant. Segment your answer into categories. Adopt a conversational tone. Use simple language. Incorporate real life examples.
- A sample prompt I use to force a concise, bullet-focused output (you can adapt the flavor to taste and topic):
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Quick note: you can refresh chat history to start fresh with your new prompts, which helps you compare how the same topic responds under different instructions.
Practical learning flows you can build with Notebook LM
- Learn a new framework fast (example: FastAPI)
- Input a short learning goal, then generate a conversational podcast of bite-sized, technical content (e.g., 30 minutes).
- Use mind map to identify core topics: web framework basics, routing, docs, deployment basics.
- Click into nodes to see how sources summarize each topic and what’s being said about key technologies.
- Bite-sized, high-ROI study sessions
- Instead of a long-form video, you can ingest a source and run quick briefs, then expand on the parts you find most useful.
- Quick comparisons and context
- Ask the assistant to discuss what the sources say about topics like time-series databases (e.g., how TimeScaleDB fits into the stack) and then drill into the details.
- Real-time resource curation
- Paste a topic or doc, have Notebook LM generate a curated set of references with quick takeaways, then open and skim the sources to verify.
A deeper dive: building a private API-like workflow with Notebook LM (and the caveats)
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Objective: explore ways to interface with a service that lacks a public API, using Notebook LM as the research and planning engine.
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The pragmatic roadmap
- The idea is to map the problem, scope approaches, and generate concrete next steps and code stubs you can borrow.
- The assistant can help you think through decision points and lay out a plan that’s easier to implement safely.
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Approaches and tradeoffs
- Approach A: XHR/GraphQL-based interactions
- Approach B: Headless browser automation (Playwright or Puppeteer)
- The tool will lay out a pros/cons table for each approach to help you decide which path to prototype.
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Guardrails and ethics
- The approach includes a guardrails check (to avoid crossing terms of service, sensitive data leakage, etc.). This matters when you’re considering unofficial or private endpoints.
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Outcome you can reuse
- A keyword-driven prompt that frames the topic (e.g., “unofficial API wrapping aka reverse engineering a private web API with headless browser automation”) so Notebook LM can quickly surface the right resources.
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Practical note
- This is useful for quickly framing and researching what “unofficial API wrapping” could look like, but proceed with caution and respect for provider terms.
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Sample prompt excerpt for topic framing (illustrative)
- Structure your writing. Think and respond like a consultant. Segment your answer into categories. Adopt a conversational tone. Use simple language. Incorporate real life examples.
- Then prompt the assistant to produce a concise pros/cons table for XHR/GraphQL vs Playwright/Puppeteer, followed by concrete next steps and example stubs you can adapt.
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What you can learn from this workflow
- You can generate a compact, well-structured research plan, identify the right sources quickly, and validate the approach against guardrails before you code.
- You’ll also get a clean set of keywords and a high-signal topic summary you can share with teammates or keep in a study doc.
Real-world takeaways
- Research is a first-class part of development
- The video emphasizes investing time in research up front to avoid long trial-and-error sprints later.
- Use bite-sized learning to build confidence
- Break down complex topics (like FastAPI or time-series databases) into digestible chunks with targeted sources.
- Customize prompts to control depth and style
- Your prompts drastically influence what you get back. A well-tuned prompt saves time and yields more actionable outputs.
- Validate with sources
- Always check the references the assistant provides and skim the sources to confirm alignment with your goals.
- Treat advanced workflows with care
- When exploring unofficial APIs or reverse engineering concepts, be mindful of terms of service and legal/ethical boundaries. Use guardrails to keep projects on the right side of policy.
Quick takeaways you can apply today
- Start with Discover Sources for any new topic, then flesh out a mind map to visualize relationships.
- Create a tailored prompt to match your learning style (short, long, or technical).
- Convert long-form learning videos into bite-sized podcasts or briefs to speed up comprehension.
- Use the “unofficial API” framing as a way to plan research, not as a blueprint for rushed implementation.
- Always verify with the original sources before acting on any insights.