We’re about to spend $300,000,000,000 rebuilding Iran after spending $80,000,000,000 destroying it, while telling Americans on Medicaid to take a hike.
America First.
Terence Tao could have built a career without collaboration, but that’s not the way he likes to work. He views working with other researchers as a primary way to discover new ideas — take what you know, pair it with what I know, and see what happens.
https://t.co/alhsq5Mm8b
TRUMP: “I’m not concerned about the latest inflation numbers that came out this morning. I love it. I love the inflation.”
At this point I’m convinced Trump is intentionally trying to destroy the United States.
OMFG!
Trump literally just celebrated a massive increase in our trade deficit.
He doesn’t even know what a “widening of our trade deficit” means. He thinks it means that we are exporting more than we’re importing, which would be good, but it means the exact opposite.
Trump prevented everyone from celebrating near the game. He made everyone show up hours early. Women could not take in a bag. He inconvenienced the entire city. And then he fell asleep.
Donald had a temper tantrum on national television and walked out of an interview simply because Kristen Welker presented him with a basic fact.
Note to other journalists: now is the time to pile on. He won't be able to handle it.
Marco Rubio: “I have never seen Trump fall asleep.”
White House: “He’s blinking.”
Fox News: “He’s resting his eyes.”
Normal People: “THAT’S CALLED SLEEPING, YOU FUCKING PARASITES OF THE PRESIDENTIAL COLON SOCIETY.”
Pussy ass bitch always walks out of interviews when he’s called out on his lies. Then he reverts to name calling. The good part is watching the lumbering fat fuck struggle to get out of his chair. Have I ever told you guys how much I hate this motherfucker?
Earth’s climate system is undergoing a major atmospheric reset.
After several years of La Niña dominance, the tropical Pacific Ocean is rapidly shifting toward a strong El Niño, with some forecasts suggesting it could develop into a powerful “super” El Niño by late 2026 or early 2027.
A massive reservoir of subsurface heat is building and surging eastward across the equator, propelled by a strong downwelling Kelvin wave. Researchers say the speed and scale of this oceanic heat pulse mirror the early stages of some of the most intense El Niño events on record. Just months ago, weak La Niña conditions still lingered; now, bursts of westerly winds are helping release vast amounts of stored warmth.
El Niño and La Niña represent the warm and cool phases of the ENSO (El Niño-Southern Oscillation) cycle. During La Niña, strong trade winds push warm water westward, allowing cooler water to upwell in the east. When those winds weaken or reverse, the pent-up heat surges back eastward, injecting enormous energy into the global atmosphere, often likened to opening a pressure-release valve.
Current climate models indicate a high probability of El Niño developing between May and July 2026 (around 80–82% chance), with near-certain persistence through the Northern Hemisphere winter. There is now a roughly 1-in-3 chance it could reach “super” or very strong status (with sea surface temperature anomalies ≥ +2°C).
If a strong El Niño materializes, it could significantly influence global weather: disrupting jet streams, altering monsoon patterns, intensifying floods in some regions and droughts in others, suppressing Atlantic hurricane activity, affecting fisheries, and contributing to higher global temperatures.
Scientists stress that while the exact strength remains uncertain, preparedness for these wide-ranging impacts is essential.
[NOAA Climate Prediction Center ENSO updates (May 2026)]
Terence Tao says the math behind today’s LLMs is actually simple. Training and running them mostly uses linear algebra, matrix multiplication, and a bit of calculus, material an undergraduate can handle. We understand how to build and operate these models.
The real mystery is why they work so well on some tasks and fail on others, and why we cannot predict that in advance. We lack good rules for forecasting performance across tasks, so progress is largely empirical.
A key reason is the nature of real-world data. Pure noise is well understood, perfectly structured data is well understood, but natural text sits in between, partly structured and partly random. Mathematics for that middle regime is thin, similar to how physics struggles at meso-scales between atoms and continua.
Because of this gap, we can describe the mechanisms but cannot yet explain capability jumps or give reliable task-level predictions. That mismatch, simple machinery versus hard-to-predict behavior, is the core puzzle.
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Video from 'Dr Brian Keating' YT Channel (Link in comment)
The sickest thing about gas prices going from $2.84 to $4.99 in just two months is that Donald Trump is mentioned over 38,000 times in the Epstein Files.