How human brains and AI models use analogies to bridge structured data, and why the future of reading belongs to hyper-personalized, custom-assembled books.
How the shift from writing letters to directing AI language models makes human taste, discretion, and authenticity the ultimate competitive edge.
An essay exploring the contrast between the intensive accumulation of structured, source-grounded knowledge and the retreat to storytelling and personality in the age of AI.
Not a shorter version — what each chapter actually explains. Distill core mental models, frameworks, and transformative insights from books instantly.
When you want to learn something, the internet hands you an endless stream of fragments. A good book is compressed intellectual capital. Instead of treating knowledge like static content, iChatbook treats it like something alive.
How to have a real conversation with any book using AI — ask questions, explore ideas, and get answers grounded in the actual text.
How AI-powered book chat compares to traditional reading — and why the two work better together than either does alone.
Reading a book is usually seen as a linear process. But that is not how we actually learn. To master a topic, you need a path through the landscape of ideas.
Most people who read non-fiction books don't actually learn anything. They just get better at recognizing ideas they've already seen. The real test of an idea is application.
There's a common misconception about how we acquire knowledge. We imagine it as a one-way street: information goes in, and understanding follows.
Yes — here is how to turn any PDF or book into an interactive AI conversation, and what to expect from the experience.
Cross-book synthesis, source-grounded answers, and the difference between asking a chatbot and asking a book.
One of the first ideas people have about AI and books is: what if you could chat with a book? But the more interesting idea appears one step later: what if you could chat with many books?
Not all AI book summarizers are equal. What to look for, what to avoid, and why source-grounded summaries beat generic ones.
Explore why large language models often appear more intelligent than they are, the impact of "averaging" internet knowledge, and why the real value lies in the curated information you connect to them.
