I’ve spent the better part of this weekend putting OpenAI’s latest offerings through their paces - both the newly released open-weight models and GPT-5 itself. Armed with a selection of coding challenges, mathematical problems, and the sort of esoteric research queries that usually separate the wheat from the chaff, I’ve been conducting what amounts to a weekend-long torture test of these systems.

The Greeks loved oracles. The average temple of Apollo, who among others was in charge of soothsaying and predictery, was adorned to the gills with gifts from grateful worshippers whose inscrutable questions got equally inscrutable answers from Apollo’s oracles. None of these were more famous than the Pythia, the young ladies high as a kite on volcanic fumes at Apollo’s temple in Delphi.

There’s a curious terminological schism that nobody talks about at what passes for dinner parties for the AI crowd.

When I read Andrej Karpathy’s endorsement of “context engineering” in a Twitter exchange with Shopify’s Tobi Lutke, I felt he tapped into something we all felt to some degree: tweet={"url":"https:\/\/twitter.com\/karpathy\/status\/1937902205765607626","author_name":"Andrej Karpathy","author_url":"https:\/\/twitter.com\/karpathy","html":"\u003Cblockquote class=\"twitter-tweet\" align=\"center\"\u003E\u003Cp lang=\"en\" dir=\"ltr\"\u003E+1 for

There’s a pervasive problem with semantics in artificial intelligence. It’s present at the creation – the term itself characterises the subject as a man-made simulacrum of something ‘natural’ the way we speak of artificial flavourings and artificial rubber.

If you spend any time on LinkedIn, it’s almost a certainty that you have come across a bevy of alleged ‘agentic AI architectures’. They all look something like this: All very neat, but the audience might be forgiven for asking what exactly is agentic about this, except for relabeling subprocesses in what is a run-of-the-mill RPA workflow as ‘agents’. And the audience is, this once, perfectly right.

DeepSeek has been grabbing headlines in AI circles lately, showing up everywhere from Discord servers full of ML enthusiasts to LinkedIn posts where “thought leaders” tag each other in endless threads.

The Department for Science, Innovation and Technology has just dropped its long-awaited AI Opportunities Action Plan, a 50-page vision of how the UK government plans to guide us into an AI-powered future.

When he first began his excavations at what is today Hisarlik in modern-day Türkiye, Heinrich Schliemann set out to find a single city – the city of Homer’s Iliad, a city many actually felt lay in the realms of fiction rather than any map he could lay his hands on. By the time excavations were over, Schliemann would find not one but nine cities, all built on top of each other. ¹ In that, he found something relatively common – cities

As I sit here at year’s end, I’m reminded of the ancient Swedish tradition of årsgång - the ritual winter walk taken on New Year’s Eve to divine the fortunes of the coming year.

The awesome thing about language is that, well, we all mostly speak it, to some extent or another. This gives us an immensely powerful tool to manipulate transformational tasks. For the purposes of this post, I consider a transformational task to be essentially anything that takes an input and is largel intended to return some version of the same thing. This is not a very precise definition, but it will have to do for now.