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A few weeks back I saw someone type /explodedview /xray into an image prompt and get back a Flutter app pulled apart like a Haynes repair manual. My first thought was that I had missed a product launch. I had not. These are not official commands. No AI tool ships a /blueprint button, and there is no secret prompt language. They are visual shorthand. Words like exploded view, cutaway and isometric already name well-known drawing styles, and putting a slash in front simply makes them short, memorable and easy to stack. The model reads them as plain English. The slash is for you, not for it. ...

There is a new comment doing the rounds in review meetings, Slack threads and document margins. It is rarely written this bluntly, but it means the same thing every time. “This looks AI-generated.” Notice it is not “this is wrong”. It is not even “I disagree”. It is a comment about where the work came from, and somehow it lands harder than any technical objection. Once it is said, the document is no longer being read. It is being suspected. ...

Three weeks. That is all it took. In early September Meta launched Muse, a personal agent that runs on its own little Linux machine in the cloud. Then on 29 September, at DevDay, OpenAI announced Dots, “always-on agents” that also get their own cloud computer and keep working after you close the chat. I read both announcements on my phone, standing in the kitchen, and my first thought was not “wow”. My first thought was: finally, somebody to use all that plumbing. ...

This weekend I did something no sensible man should do on a Sunday. I went into my Sent folder. Not for anything important, only to find one old thread. Instead I ended up reading some of my old emails, the ones I had drafted with AI’s help. They were borderline cringe. Actually, remove the borderline. The worst ones were the charming ones You might expect AI cringe to look stiff and corporate. Mine was the opposite. It was trying very hard to be friendly and human. ...

You have used ChatGPT, Claude or Gemini. You know roughly how an LLM works. And this week your feed has been full of something called Jev, with people calling it 200 times faster than an LLM, “the end of chatbots”, or just “a fancy classifier”, depending on who you follow. So you searched. Welcome, sit down, I will try to be the explanation I was looking for last week. The short answer first. Jev is not a better LLM and it is not a replacement for one. It is a different kind of model that does one job, making structured decisions inside software, and does it very fast and very cheap. It cannot write a sentence. The most useful way to think about it is as the fast half of a system in which the LLM remains the slow, thoughtful half. ...

A recruiter, a candidate and a large language model join a Teams call. Nobody on the call is a person, and the call still starts eight minutes late. That is the joke. Here is the news. Companies have started putting AI in the first round of hiring. Not just the CV filter, which has been a machine for years, but the actual interview. An avatar with a name and a haircut asks you to tell it a story about a time you showed initiative, and something behind it scores your answer. Semafor and The Next Web have both covered the next turn of the screw, which is that candidates are now sending avatars of their own to sit those interviews, and recruiters are spending five minutes working out that the very polished person on screen is not, strictly speaking, a person. ...

I took the Gallup CliftonStrengths assessment recently. Thirty four themes, ranked one to thirty four, the whole thing. Now the sensible person opens the report, reads the top five, feels quietly wonderful about themselves, and gets on with their day. I did not do the sensible thing. I opened a chat instead and said: before I tell you anything, you rank all thirty four for me. In order. With reasoning for each. ...

Best conversation I had all month happened in the ten minutes after a school meeting, standing around, the way the good ones usually do. My son’s maths teacher was showing me what the class is working through at the moment, and the topic on the screen was vector coordinates. Points in space, described by numbers. Year 8 stuff. I could not help myself. I said, do you know this is more or less how the whole of modern AI works? Every word, every image, every sentence you have ever typed into ChatGPT gets turned into exactly this, a list of coordinates. Just not two of them or three. Thousands. Meaning becomes a direction in a space with more dimensions than you can picture, and similar things end up pointing the same way. Your Year 8 class is doing the toy version of the thing that ate the technology industry. ...

I sit on a school board, which means I am occasionally asked what children should be learning, and I have been giving an answer I no longer believe. The answer was: learn to code. Learn the technical thing, because the technical thing is where the leverage is. I have said this at dinner tables, to nephews, to parents who wanted reassurance, and I said it with real confidence because for twenty five years it was simply true. It was true for me. ...

The usual version of the abundance story goes like this. AI gets good. Companies build robots. The robots are expensive and narrow at first, so only the big players can afford them, and those players pull further ahead while everybody else has a difficult decade. Then, at some point, the robots start building robots. Curve goes vertical. Everything gets cheap. Prices fall, not because money became scarce but because supply became absurd, and we all sit around trying to work out what to do with ourselves. ...