BREAKING: The plane believed to be carrying a number of ISIS-linked women and children arrived at Melbourne Airport this afternoon ab.co/4dJxbmp

I am deeply concerned about what we are hearing about an Iran “deal,” being pushed by some voices in the administration. President Trump’s decision to strike Iran was the most consequential decision of his second term. He was right to do so, and we achieved extraordinary military results—including destroying all of their missiles & drones and sinking their entire navy. If the result of all that is to be an Iranian regime—still run by Islamists who chant “death to America”—now receiving billions of dollars, being able to enrich uranium & develop nuclear weapons, and having effective control over the Strait of Hormuz, then that outcome would be a disastrous mistake. The details are still coming out—and I pray the early reports are wrong—but the fact that Biden’s Rob Malley is praising the deal is not encouraging. President Trump believes in peace through strength, and his strong leadership has already made America much safer. He should continue to hold the line, defend America & enforce the red lines he has repeatedly drawn.

Muslims in Brooklyn attacked Jewish children in the street. This is what globalizing the intifada looks like. This is what electing an Islamic jihadi to run the city looks like. Pro-Palestine protesters should be treated as terrorists. Enough.

For the first time in years, particle physics has a new anomaly, or rather, the return of a previous one that just won't go away. What could it be? I have a brief summary

Yann LeCun was right the entire time. And generative AI might be a dead end. For the last three years, the entire industry has been obsessed with building bigger LLMs. Trillions of parameters. Billions in compute. The theory was simple: if you make the model big enough, it will eventually understand how the world works. Yann LeCun said that was stupid. He argued that generative AI is fundamentally inefficient. When an AI predicts the next word, or generates the next pixel, it wastes massive amounts of compute on surface-level details. It memorizes patterns instead of learning the actual physics of reality. He proposed a different path: JEPA (Joint-Embedding Predictive Architecture). Instead of forcing the AI to paint the world pixel by pixel, JEPA forces it to predict abstract concepts. It predicts what happens next in a compressed "thought space." But for years, JEPA had a fatal flaw. It suffered from "representation collapse." Because the AI was allowed to simplify reality, it would cheat. It would simplify everything so much that a dog, a car, and a human all looked identical. It learned nothing. To fix it, engineers had to use insanely complex hacks, frozen encoders, and massive compute overheads. Until today. Researchers just dropped a paper called "LeWorldModel" (LeWM). They completely solved the collapse problem. They replaced the complex engineering hacks with a single, elegant mathematical regularizer. It forces the AI's internal "thoughts" into a perfect Gaussian distribution. The AI can no longer cheat. It is forced to understand the physical structure of reality to make its predictions. The results completely rewrite the economics of AI. LeWM didn't need a massive, centralized supercomputer. It has just 15 million parameters. It trains on a single, standard GPU in a few hours. Yet it plans 48x faster than massive foundation world models. It intrinsically understands physics. It instantly detects impossible events. We spent billions trying to force massive server farms to memorize the internet. Now, a tiny model running locally on a single graphics card is actually learning how the real world works.