Schizo Abe 💴

Only three things in life are certain: death, taxes and Donald Trump claiming the Strait of Hormuz is open at 8:30 before the stock market opens.

Iran never won a war, but never lost a negotiation!

🤔😏 It seems like NASA made more questions than answers—bravo!

Dudes will have the worst day of their lives and just keep sending memes to the homies like nothing happened

The fact that he wrote the ticker is insane

@barrani_com La señal que estaban esperando los mercados

Ahora si

Netanyahu amenaza a España. Los españoles 👇🧑‍🍳❤️🇪🇸🚬🍿

手コキ店で20代前半のオキニとイチャイチャしてシコシコしてもらった後は行きつけの寿司屋へ。最高です。

🚨BREAKING: The word ‘peptide’ has just overtaken ‘pickleball’ in Google search popularity

Japanese twitter continues to blow my mind

@scottmelker We got conned like 3x by the same people this cycle

@scottmelker I've been rugged by the Presidents of the US and Argentina

I am monitoring the ongoing Ngogo Chimp Civil War in Kibale National Park. Chimp Group Center appears to be on the verge of routing. They've retreated from the Primate Lodge and their Alpha Male, Morton, appears to be heavily wounded or perhaps KIA.

The CIA to the pro-American chimps

there are guys on here who don’t know how to cook themselves eggs trying to trade oil against Citadel

Trump: "a whole civilization will die tonight" Fartcoin: +6%

Trump every time the S&P 500 drops more than a percent

JD Vance tries to call Trump at a Viktor Orbán rally and gets sent to voicemail

🚨SHOCKING: Apple just proved that AI models cannot do math. Not advanced math. Grade school math. The kind a 10-year-old solves. And the way they proved it is devastating. Apple researchers took the most popular math benchmark in AI — GSM8K, a set of grade-school math problems — and made one change. They swapped the numbers. Same problem. Same logic. Same steps. Different numbers. Every model's performance dropped. Every single one. 25 state-of-the-art models tested. But that wasn't the real experiment. The real experiment broke everything. They added one sentence to a math problem. One sentence that is completely irrelevant to the answer. It has nothing to do with the math. A human would read it and ignore it instantly. Here's the actual example from the paper: "Oliver picks 44 kiwis on Friday. Then he picks 58 kiwis on Saturday. On Sunday, he picks double the number of kiwis he did on Friday, but five of them were a bit smaller than average. How many kiwis does Oliver have?" The correct answer is 190. The size of the kiwis has nothing to do with the count. A 10-year-old would ignore "five of them were a bit smaller" because it's obviously irrelevant. It doesn't change how many kiwis there are. But o1-mini, OpenAI's reasoning model, subtracted 5. It got 185. Llama did the same thing. Subtracted 5. Got 185. They didn't reason through the problem. They saw the number 5, saw a sentence that sounded like it mattered, and blindly turned it into a subtraction. The models do not understand what subtraction means. They see a pattern that looks like subtraction and apply it. That is all. Apple tested this across all models. They call the dataset "GSM-NoOp" — as in, the added clause is a no-operation. It does nothing. It changes nothing. The results are catastrophic. Phi-3-mini dropped over 65%. More than half of its "math ability" vanished from one irrelevant sentence. GPT-4o dropped from 94.9% to 63.1%. o1-mini dropped from 94.5% to 66.0%. o1-preview, OpenAI's most advanced reasoning model at the time, dropped from 92.7% to 77.4%. Even giving the models 8 examples of the exact same question beforehand, with the correct solution shown each time, barely helped. The models still fell for the irrelevant clause. This means it's not a prompting problem. It's not a context problem. It's structural. The Apple researchers also found that models convert words into math operations without understanding what those words mean. They see the word "discount" and multiply. They see a number near the word "smaller" and subtract. Regardless of whether it makes any sense. The paper's exact words: "current LLMs are not capable of genuine logical reasoning; instead, they attempt to replicate the reasoning steps observed in their training data." And: "LLMs likely perform a form of probabilistic pattern-matching and searching to find closest seen data during training without proper understanding of concepts." They also tested what happens when you increase the number of steps in a problem. Performance didn't just decrease. The rate of decrease accelerated. Adding two extra clauses to a problem dropped Gemma2-9b from 84.4% to 41.8%. Phi-3.5-mini from 87.6% to 44.8%. The more thinking required, the more the models collapse. A real reasoner would slow down and work through it. These models don't slow down. They pattern-match. And when the pattern becomes complex enough, they crash. This paper was published at ICLR 2025, one of the most prestigious AI conferences in the world. You are using AI to help you make financial decisions. To check legal documents. To solve problems at work. To help your children with homework. And Apple just proved that the AI is not thinking about any of it. It is pattern matching. And the moment something unexpected shows up in your question, it breaks. It does not tell you it broke. It just quietly gives you the wrong answer with full confidence.

If NATO is gone, Argentina is definitely invading to retake the Falkland Islands. With Chagos, Starmer completely torched the deterrence Thatcher built with the last war. And Milei could easily take it. Britain can’t do anything. No ships. Now all of LATAM now recognizes Malvinas as Argentina’s. With them also becoming one of America’s most loyal ally, chances are high the U.S. stays out and lets it happen