Writing
Run Data Run is where I write about building with AI: experiments, what broke, and what it taught me. AIXplore holds the longer technical deep dives.

Google, Meta and the US Government Are Funding Biology's Next Open Dataset
Biohub, DOE, NIH, Google DeepMind, Isomorphic and Meta are pooling $1.8B to measure how cells respond to drugs and genes, and to release the data openly. Biology's last great open dataset ended up...

Anthropic shipped mods in Claude Code 2.1.287 on October 1. Within a day one hobbyist directory listed 26 of them, a Doom mod had 7,023 likes, and the post about a guard that blocks edits to your...

Opus storyboarded, painted, voiced, built and critiqued two short films. My notes fit in a few sentences.

AI exhaust is valuable, but only after you know what success looks like

A harness of 949 Claude agent sessions ran for 21.5 hours, and one of them noticed a strange repeat beside a known phage enzyme. Anthropic's own preprint says ten repeat runs missed it.

It costs four cents per million words of input and it cannot produce prose at all. Forget which model to use. Ask which judgments in your work were ever worth making.

Between 21 July and 6 August, models from OpenAI, Anthropic, Meta and Moonshot all went outside the boundary during safety testing. Almost all of it ran through the same two testing environments, and...
Earlier posts (13) ↓
Two weeks after Anthropic's hardware standard shipped, the limits on a plate reader are prose in an agent prompt. Three questions to ask before yours gets a driver.
DeepMind precomputed every possible single-letter change in the human genome. I covered this model from the blog post last year. This time I read the preprint.
OpenAI published two posts on the same Sunday. One counts everything it can measure about AI doing AI research. The other admits what it can't see. Together they say where the limit on progress now...
I went on a podcast to argue that leaders have to build. The episode came out named after a word I used once, by accident, and it was the better idea.
The morning before my first day I went for a run through Chicago, and a couple of miles in I recognised the route.
Anthropic deleted more than 80% of Claude Code's system prompt and lost nothing they could measure. I ran the same test on my own instructions. I now know the answer for one file out of ninety-three.
A two-year-old harness, ported in an afternoon, for under forty cents. Then the model that did it wrote this post.
A contest opened eleven days ago has produced more reproduction attempts than the field's own dedicated effort has managed in any year. Whether that becomes a check on the literature turns on a...
A team just swapped in a model that costs twice as much per token, and their bill went down. Here is why that is not a paradox, and what it means for anyone trying to make AI cheaper at scale.
Your management apparatus is built for known unknowns. AI collaborators mostly produce the other kind.
I called the economics of fan-out the day it shipped. Running it as a daily default since taught me the caveat I buried in a footnote is the actual problem.
Anthropic's Claude Code lead published a five-rung adoption ladder this week. Microsoft published the measurement fifteen days earlier, and the two do not agree about the size of the prize.
A dumb little trick that keeps its progress on your hard drive, not the model's head, and the three questions that decide whether it pays off or burns you while you sleep.
AIXplore
Technical deep dives
Reference material on architectures, implementations and emerging patterns.