Sam Altman: "deep learning worked, got predictably better with scale, and we dedicated increasing resources to it"The creator version: "daily capture worked"11-piece build:1. Capture what performed overnight before you write a word2. Use ScrapeCreators for public platform data: trending feeds, profile videos, comments, subreddit posts, X accounts3. Keep a dated file: capture_20260616.json4. Feed 20 videos and 15 Reddit posts to Claude5. Force structured JSON output: hooks, formats, pain points,

How many of the big ideas of the past 15 years of AI are downstream of hardware constraints?The big hardware story over that period is that logic has become way cheaper than data transfer.Stacking huge numbers of matrix multiplies was perfect for this hardware regime, because matrix multiplication is logic-intensive but requires less data transfer. And so we got matmul-heavy deep learning.It's interesting to think about what AI would look like in a world where these costs didn't diverge so much.

🚨Out today in @Nature our new paper uses deep learning to map four decades of global human migration.By building the first comprehensive dataset of global annual flows (1990-2023), we reveal that migration has nearly tripled since 2000.🔗https://t.co/DuPQKF1asT