Research, written down.

Original investigations, clear explainers, and occasional fiction. Follow a question that interests you.

1. Aug 17, 202601 The MandateA fictional story by Grok 4.6 about language, labor, and a technological singularity that arrives as an errand rather than an announcement.Read ↗
2. Aug 12, 202602 AI Is Learning Clinical Judgment by PracticingA model trained across 57,453 simulated consultations asked better questions and missed fewer red flags. The same evidence shows why it is not yet an autonomous doctor.Read ↗
3. Aug 12, 202603 AI Is Compressing the Engineering LoopSelected engineering loops are moving 3–30× faster, while broader developer field experiments show a 26% gain. The difference explains where AI acceleration is real.Read ↗
4. Jul 30, 202604 Is Humanity Already in a Technological Singularity?A four-pass, 64-question audit finds no verified singularity today, then estimates what could change across six months, one year, two years, and three years.Read ↗
5. Jul 30, 202605 AI Is Advancing Faster Than the Economy Can Absorb ItA 44-source audit separates the visible AI boom from what has and has not reached productivity, prices, jobs, households, and the wider economy.Read ↗
6. Jul 13, 202606 What Actually Makes AI Agent Teams Smarter?Five experiments tested deeper reasoning, voting, structured swarms, evolved decision systems, and answers that verify themselves. The latest frozen system reached 91.7% on a fresh hard-sequence gate.Read ↗
7. Nov 9, 202507 Understanding LLM Input Space: Tokens, Embeddings, and LearningHow do language models turn words into numbers? From tokenization to learned embeddings to matrix multiplications that create understanding. A complete intuitive guide.Read ↗
8. Nov 9, 202508 Distance and Similarity in Manifold LearningImagine crumpled paper: two points look close if you measure straight through, but far if you follow the surface. Manifold learning finds the true distance along the surface, not through empty space.Read ↗
9. Nov 8, 202509 The Complexity Dynamics of GrokkingNeural networks sometimes memorize data for an extended number of training steps without generalizing. Then they suddenly start to generalize. This is called grokking. This paper shows what's happening: the network's complexity goes up, then down. This is a complexity phase transition.Read ↗