During my experiment to actively learn from audiobooks, I have started using AI to transform my pile of scribbled notes into a lasting knowledge graph. Now, I have discovered something counterintuitive: the best way to use AI for learning isn’t to let it summarize content for you, but to make it your co-worker in transforming your own imperfect recall into structured knowledge. Instead of asking AI to understand the book, I ask it to help me understand what I’ve understood—and the results are transforming my relationship with audio learning.

A week ago, I started an experiment. Instead of falling into my usual pattern of buying both the audiobook and the ebook version of “Broken Money” by Lyn Alden, I decided to challenge myself: Could I extract maximum knowledge from just the audiobook? Could I build a comprehensive knowledge graph without ever opening the Kindle version?

The stakes feel higher than usual because this isn’t just about saving money or time. It’s about fundamentally changing how I approach learning from audiobooks. And honestly, I’m not sure it’s going to work.

If you’re like most audiobook listeners, this scenario probably sounds familiar: You finish an excellent book, feeling inspired and full of new insights. A week later, someone asks you what the book was about, and you struggle to recall more than a few vague concepts. A month later, you can barely remember the main thesis.

Flashcards combined with the Spaced Repetition System is a solid way to learn and remember the main ideas of what you read. With this approach you break down your book into small, digestible pieces of knowledge and create flashcards to trigger active recall. The Spaced Repetition Method makes sure that you test your memory at strategically timed intervals with shorter intervals for harder to remember content and longer intervals for easier content.

But there’s a challenge: when you break a book into individual pieces, you might lose sight of how everything connects. Depending on the type of book and your learning goals, you may need to preserve not just individual facts, but also the structure of arguments and how different concepts relate to each other. This can be achieved by adding cards that focus specifically on the book’s organization, or by including hints on regular cards that remind you where each piece of information fits in the author’s overall argument.

You’ve probably done it countless times without realizing it. When preparing for a crucial exam, you instinctively break down a textbook chapter by chapter, then section by section, distilling complex ideas into concise summaries that capture the essence of hundreds of pages in just a few key points. This natural learning pattern has a name: Nested Summarization.

This isn’t just another study hack—it’s the way our minds naturally organize and retain complex information. By working from the bottom up through a book’s hierarchical structure, you’re not just memorizing facts; you’re building a mental framework that transforms how you understand and remember knowledge.

Whether you’re a student cramming for finals, a professional mastering new skills, or a lifelong learner diving into complex subjects, Nested Summarization will change your approach to learning. It’s time to give this intuitive technique the recognition and systematic approach it deserves.

The world of knowledge management has been revolutionized by tools that promise to transform how we connect ideas. Apps like Obsidian, Roam Research, and Notion have introduced millions to the concept of bidirectional linking, creating beautiful knowledge graphs that look like neural networks of pure thought. But beneath the visual appeal lies a fundamental question: do these connected notes actually help us learn better, or are we being seduced by complexity that doesn’t serve our cognitive needs?

The Zettelkasten method promises to revolutionize how we think, learn, and connect ideas. Yet for every success story, there are countless abandoned note-taking systems gathering digital dust. The problem isn’t with the core concept—it’s with how we’ve made it unnecessarily complicated.

Students scribble in margins while taking notes. Management consultants create elaborate slide decks to explain complex business strategies. Scientists design colorful posters to present their research at conferences. Yet when it comes to reading books, most of us simply… read.

This disconnect reveals a fundamental problem: books are purely text-based, but our brains are wired for visual information. If you’ve ever finished a book only to forget most of it within weeks, you’re experiencing the limitation of text-only learning.

The solution is to transform your reading with visual techniques that work with your brain’s natural strengths:
Words + images = stronger memory.

Identify and extract a book’s keywords to transform surface-level reading into deep, lasting comprehension that you can apply anywhere.

Today, I want to share a practical example of creating externalized knowledge—specifically a mind map—from my recent reading of Max Bennett’s “A Brief History of Intelligence: Why the Evolution of the Brain Holds the Key to the Future of AI.”

Have you ever finished a book that absolutely blew your mind, only to find yourself struggling to explain its core message just a few weeks later?
In this article, I’ll show you that investing just a small amount of effort after finishing a book will dramatically improve your retention. I’ll introduce a technique for processing and applying what you read that automatically builds your external knowledge structure. This system grows more valuable with every book you read and process.

Artificial intelligence has emerged as a powerful tool for enhancing the learning experience. From personalized tutoring to intelligent content curation, AI technologies are transforming how we acquire and retain knowledge. But as these tools become increasingly sophisticated, learners face an important question: how can we leverage AI to maximize learning outcomes without compromising the cognitive benefits that come from effort and struggle?
The goal isn’t to make learning effortless but to direct cognitive effort toward the activities with the highest learning return – we should use AI as a complement, not a replacement.