Mostly a ton of JavaScript. As for the concepts explained visually, ICYMI there is a small footnote at the end of the article:

> To generate the 50D word embeddings we used the GloVe 6B 50D pre-trained model and converted to Word2Vec format. To generate the 2D representation of word embeddings we used the BERT large language model and reduced dimensionality using UMAP. The self-attention values and the probability scores in the beam search section are conceptual.

Distill was a new take at publishing research/ideas in deep learning in a visual way: https://distill.pub/

I love their articles and while it was hard to sustain, the quality of the ones in their are pretty good.

They provide some tips and templates on how to develop such visual storytelling articles.

Hey, I'm one of the graphics journalists/authors on the piece

This is not very helpful, but the answers to most of your questions is "it depends"

Our team is made up of reporters, designers, and graphics journalists, but the specific makeup of the team on a given project or who else gets drawn into it varies a lot depending on the topic/scope of the story

For our stories, always lot of React and headache-inducing CSS transition stuff, but the tools/libraries beyond that depend a lot on the needs of the project

- On some stories, there's a lot of blender/threejs work, like this one on quantum computing: https://ig.ft.com/quantum-computing/

- For others, like this one, there's a lot of mapping/data work: https://ig.ft.com/ukraine-war-food-insecurity/

Some stories take a couple of weeks, some take a couple of months and feature fairly large codebases with 500+ commits

Agree. Ironically, I turned to ChatGPT to ask how one would design a webpage such as that!