This is by far the most interesting thing I’ve consumed all week. h/t @kottke.org –> - YouTube

Seems great for exploring embeddings –> Embedding Atlas

Fun project –> I Counted All of the Yurts in Mongolia Using Machine Learning | Monroe Clinton

Useful report on news consumption –> https://www.niemanlab.org/2025/06/for-the-first-time-social-media-overtakes-tv-as-americans-top-news-source/

Just released a plugin for llm to pull in U.S. legislation as fragments, enabling you to do stuff like: llm -f bill:hr1-119 'Anything AI-related in here?' or llm -f bill:hr1-119:section-110101 'Is there language in here to prevent fraud?' –> GitHub - kevinschaul/llm-fragments-us-legislation: Load bills from Congress.gov as LLM fragments

The best llm CLI program now supports tool-calling, very excited about these possibilities –> Large Language Models can run tools in your terminal with LLM 0.26

Great investigation –> Police secretly monitored New Orleans with facial recognition cameras

How reliable are LLMs at extracting data from pdfs? Inspired by Simon Willison’s PyCon talk, I added extracting FEMA’s daily operation briefing to my LLM evals suite.

Just one model extracted the data from the pdf correctly: Gemini 2.5 Pro Preview. –> Extract FEMA incidents | LLM evals - Kevin Schaul

Just click –> https://neal.fun/internet-roadtrip/

Made a lil plugin for llm that only actually calls the llm if it’s a new prompt. Should save a little time and money, especially when running evals. –> GitHub - kevinschaul/llm-cache-plugin: Check whether you’ve already run this prompt before calling the LLM