I’m Nicole van der Hoeven, and Doing It in Public is a book about learning in public: what it is, why it matters more than ever, and practical instructions for how to do it.
I started writing this book in 2024. In 2026, I’m rewriting it — because AI changed the question.
AI can now write books, generate essays, compose music, and produce competent knowledge work in seconds. The question of whether it can do your thinking for you is settled. This book is about whether you want it to — and what you lose if you let it.
The central argument: learning in public has never been more valuable than now, not despite AI, but because of it. When anyone can generate polished output instantly, the work that matters is the visible work: showing your reasoning, your process, your genuine confusion and hard-won understanding. That’s what this book is about.
I’m writing it in public to stay true to that argument.
Giving feedback
A vital part of this process is getting feedback at every step:
- Join my Discord server and head to the
#book-doing-it-in-publicchannel - Raise an issue on GitHub
What to give feedback on: the structure of a chapter, whether something was unclear, ideas you think I should consider, arguments you think I’m getting wrong. I’m open to everything related to the content.
Please don’t give feedback on spelling and grammar — that’s for later.
Getting updates
- Join my Discord server — I’ll post when chapters are ready for review
- Sign up for Thinking in Public, my newsletter
Outline
Note: chapters marked 🚧 are in progress. Chapters without a link aren’t written yet.
Introduction
The story that started everything: learning Dutch, badly, in public. What learning in public actually is — and why AI makes the question of why so much more urgent.
Chapter 1: The Robots Are Writing Poetry 🚧
AI can do your knowledge work now. Write books. Generate essays. Compose music. Pass exams. The question isn’t whether it can — that question is answered. This chapter asks: do you want it to? And what does it mean for learning when polished output is free?
Plus: why learning in public has always mattered — from Athenian oratory to open source — and why in the AI age, not showing your work has two new costs.
Chapter 2: Supplement, Not Supplant
The third way between boycott and surrender. Nicole’s own AI journey — from “no AI ever touches my notes” to orchestrating three agents simultaneously from a ski mountain — and what she learned about the difference between AI that helps you think and AI that thinks for you.
The test isn’t quantitative. It’s about joy.
Chapter 3: Mindset
The five pillars of learning in public, updated for 2026: make it observable, make it continuous, make it fun, do it in a group, make it intentional. Each one is different when AI is in the room.
Plus: the Manifesto for Learning in Public.
Chapter 4: The Observable Learner
Observability — the ability to understand what’s happening inside a complex system by examining its outputs — is a concept from software engineering. It’s also the best frame I’ve found for thinking about learning.
When AI is producing output alongside you, observability becomes a transparency layer for the collaboration. This chapter is about how to keep the visible record of your real thinking — not just your results.
Chapter 5: Show Your Work (Even With AI)
The practical guide. A framework of five tiers — from “start before you’re ready” to “build a community” — each one updated for what AI changes. Plus: the difference between taking notes and making notes, and why that distinction matters more now than ever.
Chapter 6: Building Your Stack
How to actually set this up. The four layers of a learning-in-public system that incorporates AI without losing your own thinking: the PKM layer, the AI layer, the observability layer, and the publishing layer. Nicole’s specific setup as a worked example — with the caveat that tools change and principles don’t.
Chapter 7: Pitfalls
What goes wrong. The original pitfalls of learning in public, updated with the ones AI specifically introduces: authenticity theater, the echo chamber, dependency, voice erosion, premature automation, and the PKM trap.
Chapter 8: When to Learn in Private
The necessary flip side. Some things are worth doing without an audience. Some thoughts are yours to keep. Some learning requires genuine friction. This chapter isn’t a retreat from the thesis — it’s the honest completion of it.
Chapter 9: For the Love of Learning
There is genuine, irreplaceable pleasure in understanding something new. In the specific click of an idea landing. In making something that came from your own curiosity, your own confusion, your own way of seeing. AI cannot take this from you. But you can give it away without noticing.
An invitation: start before you’re ready. Show your work. Keep the garage door up.