Here's the thing about tech events like CES - they're overwhelming. While everyone else is struggling to piece together information from scattered sources, I've discovered a systematic way to extract exactly what matters using NotebookLM.
You know how it goes. A major keynote drops, and suddenly your feeds are flooded. There's the two-hour presentation you don't have time to watch, dozens of summary articles that all say the same thing, and endless discussions trying to make sense of it all. Most people either miss important details or waste hours trying to stay informed.
I've developed something better.
Instead of relying on basic summaries or trying to consume everything, I use NotebookLM to combine three specific types of content that give you the complete picture: the raw keynote transcript, unique analyst perspectives, and community discussions. Each plays a crucial role in understanding what really matters.
But here's what makes this approach different.
NotebookLM acts as your personal research assistant, helping you extract insights that you'd miss with traditional approaches. The key is how these three sources work together to give you a deeper understanding than any single perspective could provide.
In this guide, I show you exactly how this works. I've included a detailed video tutorial, but first, let's break down why this combination of sources is so powerful and how you can adapt it for any tech event you're trying to understand.
The power of three perspectives
Most people rely on a single source when trying to understand tech events - usually whatever summary article appears first in their feed. But here's what I've learned: the real insights come from combining three distinct perspectives that each serve a unique purpose.
Think about NVIDIA's recent CES keynote. You could read a summary, sure. But you'd miss the raw context from the actual presentation, the critical analysis that spots what wasn't said, and the collective wisdom of developers and enthusiasts picking apart every detail.
Here's why each piece matters:
1. The raw source: Your foundation
When you feed a keynote transcript into NotebookLM, you're not just getting timestamps and quotes. You're creating a base layer of knowledge that lets you verify claims, spot patterns, and catch details that summaries miss. This becomes your ground truth - especially important when everyone's racing to interpret what was said.
2. The analyst perspective: Critical context
I specifically look for articles with unique angles - not basic summaries. For example, while everyone was talking about NVIDIA's announcements, one analyst spotted something crucial: what wasn't announced. This second layer helps you understand market implications and read between the lines.
3. The community insight: Collective wisdom
This is where things get interesting. By feeding in structured community discussions (I'll show you exactly how in the video), you tap into the collective expertise of developers, industry insiders, and power users who spot implications that journalists miss. During CES, this revealed concerns about performance claims that weren't obvious from official sources.
Building your knowledge ecosystem
This is where NotebookLM really shines - it's not just about collecting these three sources, but creating a dynamic system that lets you explore and extract exactly what matters to you.
Here's what happens when you bring everything together:
Instead of jumping between tabs and trying to connect dots manually, you're building what I call a "knowledge ecosystem." Your keynote transcript, analyst insights, and community discussions start working together in ways that reveal patterns and connections you'd never spot otherwise.
Let me give you a real example from CES: When I fed these three sources into NotebookLM, I could instantly cross-reference community reactions against specific keynote claims, or see how analyst concerns matched up with developer discussions. This isn't just summarizing - it's synthesizing information in a way that gives you genuine understanding.
The real power move? Using NotebookLM's question-answering capabilities against this combined knowledge base.
Instead of accepting generic summaries, you can ask specific questions that matter to you:
"What do developers think about the new upscaling claims?"
"Which announcements had the biggest market impact?"
"What crucial details got buried in the keynote?"
This approach completely transforms how you process tech events. Instead of drowning in information or relying on surface-level summaries, you're building a personalized research system that helps you extract exactly what you need to know.
In the video guide, I show you exactly how to set this up step by step. You'll see how to structure your sources, craft effective queries, and build this system for any tech event you want to understand deeply.
Testing and evolving the process
Let's be real - this approach takes your tech event analysis from surface-level summaries to genuine understanding. But the real magic happens when you start experimenting with it.
While I've shown you my core framework - combining keynotes, analysis, and community insights - the way you use it should evolve based on your needs. Maybe you'll focus more on developer discussions for technical events, or analyst perspectives for market-focused announcements. The system adapts to what you want to learn.
Some ways I could see people customize this:
Product managers using it to gauge market reception
Developers focusing on technical implementation details
Investors correlating community sentiment with market moves
Content creators extracting unique narrative angles
Here's something interesting: While I developed this for tech events like CES, I've found it works for understanding any complex topic. Product launches, research papers, industry trends - anywhere you need to combine multiple perspectives for deeper understanding.
What's next?
The video guide will walk you through every step of this process. You'll see exactly how I:
Structure the three types of content
Set up NotebookLM for optimal analysis
Extract insights that matter to you
Customize the approach for different scenarios
Try it out with any recent tech event that interests you. Start with the three-source framework I've shown you, then experiment with your own variations. The goal isn't perfect execution - it's better understanding with less overwhelm.
Pro tip: Start small. Pick a recent announcement you care about and try the process. You might be surprised by what you discover when you combine these different perspectives.
Ready to see exactly how this works? The video guide breaks down every step. Let me know what you discover when you try it yourself.
If you like guides like this, consider becoming a paid member and joining our community. The price is currently $10/month, but if I’m being honest, that is probably going to increase in the coming weeks for new members (existing members are grandfathered in).
This is quickly turning into my full-time job, and there is a lot of value coming out of it!
