35 plain-English deep dives

Behind the Algorithm

You didn't choose most of what you saw today. A feed picked it, a model ranked it, a chatbot phrased it. I take those systems apart in plain English, so you can tell when the machine is helping you and when it's steering you.

Why AI sounds sure when it's wrong. Who decides your For You page. What your chats reveal. How a 3-second voice clip becomes a scam call.

35explainers, no jargon
5reading tracks
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Four things AI does that most people never notice

Guess before you open each one. Every answer links to the full explainer.

?Does a chatbot check its facts before it answers you?
No. It predicts the most likely next word, so a confident tone is a by-product of fluent writing, not a sign it verified anything. Why AI sounds sure when it's wrong →
?Will you and a friend get the same answer to the same prompt?
Often not. Memory, past chats, location and account settings all change what the model sees before it replies. What AI tools track per user →
?Can a browser extension read what you type into ChatGPT?
Yes, if it has permission to read page content, and many popular extensions ask for exactly that. How AI chat leaks happen →
?Does joining a call with an AI note-taker mean you agreed to be recorded?
In many tools, joining is treated as consent, and the full transcript is usually stored, not only the summary. The hidden risks of AI note-takers →
Pick a rabbit hole

Five questions this series answers

Choose the question that bugs you most. Each track runs from the basics to the deeper cuts.

🔍 How I write these

I read the vendor's own docs and privacy policies, check settings in the apps themselves, and link the primary sources (FTC, OECD, NIST) inside each post. When something isn't published, I say so instead of guessing. Some posts include affiliate links, which support the site at no extra cost to you (disclosure).

🧮 From theory to numbers

Two free tools I built that run in your browser and store nothing.

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