Multimodal

Context, Compute, Capital, Culture: 13 Lectures From Stanford's CS153

Context, Compute, Capital, Culture: 13 Lectures From Stanford's CS153

Enrichment is the bottleneck to nuclear. Nuclear is the bottleneck to electricity. Electricity is the bottleneck to AI. That slide came from the 7th of 13 sessions in Stanford's CS153: Frontier Systems. A course about frontier AI spent its midpoint on uranium. I worked through all 13 in September 2026, from Anjney Midha's opening map of the stack to guests from ElevenLabs, NVIDIA, OpenAI and Microsoft. This recap walks the course in order and reads it through the four lenses Midha gave the class: context, compute, capital and culture. It ends on the number I'll carry forward: Google depreciates its compute hardware over six years, and NVIDIA ships a new architecture every year.

Stanford CME 296, Condensed: How A Teddy Bear Becomes An Image

Stanford CME 296, Condensed: How A Teddy Bear Becomes An Image

Every lecture in Stanford's CME 296 generates the same picture: a teddy bear reading a book. Noise on the first slide, a finished image on the last. I watched all 8 lectures from Afshine and Shervine Amidi, just over 14 hours of video. The claim underneath them: diffusion, score matching, and flow matching are three coordinate systems on one object. This recap walks the course in order and argues that FID is a regression test the field keeps publishing as a result. One problem won't yield to a better loss, provenance you can strip with a screenshot.