It's a bit sad and confusing that LLMs ("Large Language Models") have little to do with language; It's just historical. They are highly general purpose technology for statistical modeling of token streams. A better name would be Autoregressive Transformers or something.
They don't care if the tokens happen to represent little text chunks. It could just as well be little image patches, audio chunks, action choices, molecules, or whatever. If you can reduce your problem to that of modeling token streams (for any arbitrary vocabulary of some set of discrete tokens), you can "throw an LLM at it".
Recent articles
- LLM 0.27, the annotated release notes: GPT-5 and improved tool calling - 11th August 2025
- Qwen3-4B-Thinking: "This is art - pelicans don't ride bikes!" - 10th August 2025
- My Lethal Trifecta talk at the Bay Area AI Security Meetup - 9th August 2025