fingogh

how has @perlexity_ai been faring in most recent runs

@mreiffy One “bad” prompt with no cache and you’re on the bench till resets

Opus 4.7 has become unusable. Literally death by compute.

The world is a crap place sometimes. Just search for discourse around “spirit customers”… people who chose not to spend extra money for unneeded perks get bashed by “premium” travelers crammed in @Delta I doubt will be “overrun” to ruin your experience

🚨 Breaking: you can now turn your unused spirit flight $ into @SpiritAirlines equity 1:1.

@grok what is the read on brkb earnings positive or negative for the shares

@elonmusk Thermodynamics enters the room. Respectfully disagrees. Not for the lack of vision, just physics and energy absorption.

True

@legen_eth But the equity holders would technically be wiped? Government buyout will keep the equities going? @grok

So spirit is bankrupt but why are their stock still trading?

@WallStreetMav You’re aware he’s been in office since Jan?

Socialist NYC mayor Mamdani is begging for a bailout from the state of New York. The state has a budget deficit also: $2.2 billion in 2026 and $10.4 billion in 2027 "We cannot close this deficit with savings alone. We need new revenue, and we need a structural reset in our relationship with the state. That is the only way to meet our legal obligation to pass a balanced budget."

anyone else’s @claudeai becoming less and less useful? what are these new limits 🤦🏽‍♂️

@om_patel5 Performance of the updated models keep degrading…menial tasks are now spooling behind the “skill selection, planning and all the other good stuff they throw at you instead of “loading”

SOMEONE ACTUALLY MEASURED HOW MUCH DUMBER CLAUDE GOT. THE ANSWER IS 67%. the data shows Opus 4.6 is thinking 67% less than it used to. anthropic said nothing until the numbers went public. then suddenly Boris Cherny (creator of Claude Code) shows up on the GitHub issue. users are calling it "AI shrinkflation" (same price, less intelligence) we already know from the leaked source code that they have an internal switch that keeps the models working to their full extent for anthropic employees. in the last week Claude went from WOW to being a more restricted and expensive version of ChatGPT. people are saying Anthropic is deliberately downgrading Opus to save compute for training Mythos, their next model.

@ClownWorld It’s a buffet.

@dweekly @grok what changes to regulations, reporting or policing are the primary factors

It's not your imagination. SF is getting a lot better.

@icanvardar Even the post is chat gpt generated 🤦🏽‍♂️

this was posted 5 hours ago when this happened nearly a week ago linkedin never lets us down

@the_dadchef Good luck shipping it back to Korea with these oil prices

Never buying a Samsung again. Second time this has happened.

@heynavtoor Just posted this 8months ago? breaking

🚨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.

@pmarca That would be quite the disregard to foreign policy and current internal affairs. In short, disconnect with reality.

I have never been so bullish on the United States of America.

@business @markets @WSJ

$MSFT has the widest price-to-target gap in the Mag7 and it's the most complicated one. Price: $373. Consensus target: $587. Implied upside: 57%. 52-week high: $555. Stock is at 67% of its high. But analyst targets are being CUT, not raised. Avg PT down $37.9 YTD across 22 actions. The $587 consensus includes targets set before the stock broke down. The dislocation is closing from the top, not the bottom. Floor: $392. Ceiling: $730. That spread tells you how little consensus there actually is.

@pdicarlotrader Lost my sox on this one…damn you red bx

$MSFT monthly bx is still red. Not buying yet. That said long term institutional support is holding up well. 80% of the time price bounces off this level. This is a strong long term entry zone. ✅ When the monthly flips I will be ready to move.

@heyshrutimishra Appreciate the detail. This is nuts

Anthropic leaked 512,000 lines of Claude Code source code yesterday. What happened in the next 12 hours is absolutely wild. 4 AM. Anthropic pushes an update to npm. Inside the package: their entire codebase. A 60 MB debugging file accidentally bundled in. 23 minutes later, researcher Chaofan Shou spots it. Downloads the zip. Posts it on X. Within 6 hours: 3 million views. By the time Anthropic’s team woke up, the code was forked 41,000+ times across GitHub. Anthropic started firing DMCA takedowns. Too late. A Korean developer named Sigrid Jin woke up to his phone exploding. He’s Claude Code’s biggest power user. WSJ reported he burned through 25 billion tokens last year. He read the leaked code. Rewrote the entire thing in Python in 8 hours. His repo hit 30,000 stars faster than any GitHub project in history. Then he rewrote it again in Rust. That version now has 49,000 stars. Someone mirrored it to a decentralized platform with one message: “will never be taken down.” The code is permanent. Anthropic cannot get it back. Here’s the part I can’t stop thinking about: Anthropic built something called “Undercover Mode.” Its only job: prevent Claude from accidentally leaking internal secrets. They shipped an entire anti-leak system in their own product. Then leaked their own source code in a .map file. Irony is beautiful