2467 hand-picked resources, updated every week. Search it, filter it, or just browse a collection and see what catches your eye. Want today’s headlines instead? Read the free AI news feed.
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Answers come only from resources in this hub, with the sources listed underneath.
A Wharton working paper by Steven D Shaw and Gideon Nave, written 11 January 2026 and posted to SSRN on 2 February 2026. It proposes "Tri-System Theory" — adding a "System 3" (thinking done outside your head by a machine) to Kahneman's fast/slow account — and names "cognitive surrender": taking an AI's answer with barely a glance. Across three preregistered experiments (1,372 people, 9,593 trials) the researchers secretly varied whether the AI was right. People consulted it on more than half of questions; accuracy rose about 25 percentage points when the AI was right and fell about 15 when it was wrong, and confidence went up either way — even after errors. Time pressure, cash incentives and feedback all moved baseline scores but never removed the pattern.
Why I recommend it: Free to read and free to download the full 58-page PDF — no account needed. Two honest flags. It is a working paper: posted by the authors, and the SSRN version has not been through journal peer review, so treat the numbers as a strong first result rather than settled fact. And the copyright line says all rights reserved — read and cite it, do not republish the text. The finding worth carrying around is the one about confidence: people felt surer of themselves after the AI led them wrong. That is exactly why the checks on our AI Basics page are worth doing out loud.
Head-to-head model comparisons voted on by the public: you see two anonymous answers to the same prompt and pick the better one, and the rankings come from those votes. Free.
From the site: Chat, compare, vote for the world's best AI models. Join the community shaping the public leaderboard for LLMs, image, and code models through real-world evaluation.
Why I recommend it: The closest thing to a fair fight between models on ordinary prompts, instead of marketing claims. Votes are taste as much as accuracy, so read it as popularity with a purpose.
Eliezer Yudkowsky's collected essays on reasoning, bias and AI risk, free to read online in full.
From the site: Between 2006 and 2009, senior MIRI researcher Eliezer Yudkowsky wrote several hundred essays for the blogs Overcoming Bias and Less Wrong, collectively called
Why I recommend it: Long, opinionated and free. Read it for the thinking habits, not as settled fact.
Robin Hanson's long-running blog on why we believe and do what we do, and what our descendants might do — free to read.
From the site: This is a blog on why we believe and do what we do, why we pretend otherwise, how we might do better, and what our descendants might do, if they don't all die. Click to read Overcoming Bias, by Robin Hanson, a Substack publication with tens of thousands of subscribers.
Why I recommend it: Deliberately contrarian. Useful for pressure-testing your own assumptions.
The central free hub for effective altruism — essays, career guidance and research on how to do the most good with your time and money.
From the site: Effective altruism is a philosophy and a movement that asks the question: how can we do the most good with our time, money, and resources?
Why I recommend it: useful career thinking here, and a movement with real critics — read both.
Eliezer Yudkowsky's free novel-length story teaching scientific reasoning, cognitive bias and decision-making through fiction; widely read as an entry point to rationality writing.
Why I recommend it: An unusual entry, but it is free and it teaches how to test your own reasoning better than most textbooks.
Finding the Optimal Human-AI Relationship — practice worksheet
A two-page printable worksheet from Pilyoung Kim, Ph.D. for setting deliberate terms with AI: comparing warm, sycophantic, and machine-like responses, setting your own tone dials, turning them into a reusable custom-instructions prompt, deciding what goes to AI versus a person, and guarding your judgment against flattery.
Why I recommend it: Print it and actually fill it in. Section 5 — writing your own view down before you ask AI — is the single habit that keeps these tools from quietly making your decisions for you.
How to Decide, Delegate, and Build Systems That Remember. A practical book for knowledge workers who want to use AI as a real operating layer.
Why I recommend it: I am always looking for resources that treat AI as an operating layer rather than a toy. This book is a practical frame for deciding what to delegate and what to keep human.
