Everyday AI
How to Spot AI Hallucinations (2026)
Why fluent and wrong look identical, the five tells of an invented answer, and the 30-second habit that catches most mistakes before they cost you.
Field notes
Short, honest, practitioner-written answers to the questions people actually ask about owning, bounding, and trusting AI — drawn from the same systems behind the books. No hype, no oracle promises. Where a number is real, it's real.
Why fluent and wrong look identical, the five tells of an invented answer, and the 30-second habit that catches most mistakes before they cost you.
How voice-cloning and deepfake scams actually work — from the fake family emergency to the $25M deepfake call — and the one defense that beats almost all of them.
The two real risks — wrong answers and what you reveal — what to never paste, and how to ask so it's wrong less often.
What "local AI" really means, what a normal computer can run, and how to keep a private model from becoming a liability you host.
Where AI saves real hours, the five ways it burns you, and the simple leash that keeps the upside without the risk.
Why you can't tell from the outside — and what reading a model's internal belief actually catches. Above chance, not an oracle.
How an agent pays on its own over Lightning — and how to bound the wallet so a fooled agent still can't drain you.
Why a better score isn't a better system — and the discipline that turns an eval number into trust.
What hallucination really is, why fluent and wrong look identical, and the architecture that keeps it from reaching your users.
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