Six new people this week, which doubles the size of this place. Welcome Zan, Paulette, Bhanu, Nagamani, Dhanush and Mohan. Rather than a generic welcome, here's the one thing worth knowing about what this community is for. Almost all AI training teaches you to get output. Very little teaches you to tell whether the output is any good. That gap is where people actually get burned - not because AI is wrong sometimes, but because it's wrong in exactly the same confident tone it uses when it's right. There's no tell. You can't spot it by reading; only by checking. So that's what the free AI Literacy course in the Classroom is built around. About an hour, no coding, no maths. Half of it is prompting; the more useful half is evaluation - spotting fabricated facts, knowing what never to paste into a chatbot, and breaking work into steps you can actually verify. If you're new, one question, and a one-line answer is fine: what's the last thing AI got confidently wrong for you? Could be a made-up statistic, a citation that didn't exist, code that looked right and wasn't, or advice that fell apart when you checked it. I'm asking because the answers decide what I build next. The pinned intro post is still there if you'd rather do a proper introduction.

Most AI advice tells you to "just use it." Nobody tells you where to start, how to tell if the answer is right, or where to stop. That gap is the whole reason this course exists. It's now live in the Classroom, free, and takes about an hour end to end. What it covers across 11 short lectures: — How AI actually works, in plain language (no maths, no coding) — Anatomy of a good prompt, and how context and examples change the output — Why evaluation matters most — how to check whether an answer is actually correct — Spotting hallucinations and confident-but-invented answers — Data privacy: what you should never paste into a chatbot — Responsible use, transparency, and who decides what on a human + AI team — Breaking one big task into steps, with a worked demo at the end Who it's for: anyone doing everyday office work who wants a method instead of guesswork. No technical background needed. What it's not: no coding, no maths, and no predictions about whether AI is coming for your job. Head to the Classroom tab to start. One tip from the first lecture — keep two tabs open while you watch: one for the video, one to practise in as you go. The people who do that finish with something they can actually use on Monday. If you go through it, tell me in the comments which lecture was most useful and which one fell flat. That feedback shapes what gets built next — that's the whole point of this place.