Trying out a new format this week. Instead of links, here’s some shorts. Too little for a post, too much for a link. This week we have Power variation in AI data centres Using Claude Code for Terraform Adopting new CLI tools Historical maps Power variation in AI data centres (Link requires registration and full article isn’t free, but there’s a massive preview which is all I have access to) ...
The Anthropic Cookbook is an excellent resource for learning how to write LLM code and it’s continually being updated, most recently with example code for agents. The Cookbook comes as a set of Jupyter notebooks. I prefer Marimo to Jupyter, mainly because it supports reactive cell reloading i.e. if you change an earlier notebook cell that a later cell depends on, the chain of dependencies is calculated and everything is re-run correctly which saves a lot of mental energy and stupid mistakes when working with notebooks. ...
HEPI, a UK higher ed thinktank, released the results from their 2025 survey of AI use by students. There’s a lot in the report, so here’s a few quick highlights. The big headline is that basically all students are using genAI: In 2025, we find that the student use of AI has surged in the last year, with almost all students (92%) now using AI in some form, up from 66% in 2024, and some 88% having used GenAI for assessments, up from 53% in 2024. The main uses of GenAI are explaining concepts, summarising articles and suggesting research ideas, but a significant number of students – 18% – have included AI-generated text directly in their work. ...
This week we have drones in Ukraine and SpaceX’s totally-not-a-cock rocket. The use of drones in Ukraine is advancing the state of the art very, very rapidly. First we had drones trailing fibre optic cables to avoid jamming, now there’s drones that navigate via camera and landmarks and, very shortly, autonomous kill decisions: IEEE Spectrum - Ukraine’s Killer Drones A very hot take on Starship, SpaceX’s big “go to Mars” rocket: Starship was doomed from the start. I’m not informed enough to assess the post’s validity, but the thesis is that Starship is just too heavy, and that making it lighter makes it too fragile in ways that just can’t be fixed. There’s updates as well - 1 and 2
Piotr Mazurek and Felix Gabriel have an amazing post up on LLM Inference Economics from First Principles, which I found on Bluesky. They go into a huge amount of detail about how inference works and how that affects processing speed. But I saw the graph above and thought “we can get energy from that”. And so I asked chatgpt-o3: “Looking at these graphs of throughput at different batch sizes on a 4xH100 80gb cluster, what ranges of power per token do they equate to?” ...
Notes that I wrote for work on how to get started with Claude Code, Anthropic’s agentic code tool. Anthropic’s own documentation is good, but I wanted a single page that I could share with people to help them get rapidly up to speed. Once past this stage, their Claude code best practices page is excellent. Installation You’ll need to setup an API key to use it. Put this in $ANTHROPIC_API_KEY then run the installer: ...
A very late set of weekly links! This time it’s heavy on AI and it’s impact on research and teaching. Plus cyborg cicadas. First up: A NYMag article on LLM use at Universities went viral (at least in my education and AI heavy feeds!) last week. So that’s not the link as you’ve probably already read it. Instead, here’s Benjamin Breen on how AI makes the Humanities more important and also wierder and Will the Humanities Survive Artificial Intelligence? from the New Yorker. ...
We have some actual numbers on tokens! In their commentary on the latest earnings report, Microsoft report: We processed over 100 trillion tokens this quarter, up 5X year-over-year – including a record 50 trillion tokens last month alone So we’ve got a baseline, and can estimate a growth rate. Assuming exponential growth and a three month token split of 19/31/50 we get 62% month on month growth, or, if it keeps going 100 T → 424 T → 1.8 P → 7.6 P, which is lower than the 5x yoy growth MS quote. ...
As Andy Massick explains well, if you are making a few requests per day to a simple non-reasoning text based AI model the carbon impact is, to use a technical term, bugger all 1. I probably ask ChatGPT and Claude around 8 questions a day on average. Over the course of a year of using them, this uses up the same energy as running a single space heater in my room for 2 hours in total. Not 2 hours per day, just a one-off use of a single space heater for 2 hours […] By being vegan, I have as much climate impact as not prompting ChatGPT 400,000 times each year ...
Finally a week with more than two links! Beatrice de Graaf is a Professor at Utrecht University. Her talk at Stanford on The Quest for a Third Rome describes using text mining techniques to analyse Putin’s speeches since 1999. The paper, written with her student Neils Drost, is open access: Putin and the Third Rome:: Imperial-Eschatological Motives as a Usable Past “In this corpus, we ask how Putin used the combination of history and Russian Orthodox theological, biblical tropes as a usable past, to create his powerbase, to understand how he built up to defending and legitimizing the invasion in Ukraine” A similar area was covered by Teitelbaum in his book War for Eternity about the influence of Alexsandr Dugin and mysticism on both Putin and Steve Bannon in the US. Does Putin believe it? Maybe. Is he using mysticism and religion for his own ends? For sure. ...