New guy

TCG fans hating on Crypto bros for ruining the hobby by doing it for the money. Meanwhile there’s a dude, not even from crypto, making a walkway out of Prismatic Evolutions ETBs.

@nonpareil_0 @PokemonRestockr Or multiple browsers at all. This is not Walmart.

@nonpareil_0 @PokemonRestockr You shouldnt use multiple devices on the same network or it causes issues. Phone on cellular, one pc on wifi. Don't double up

This is a new technique. @pok_re92689

Every company’s AI workflow rn be like 😭💀

ascending credit card debt

🚨 DOT-COM 2.0 IS ALREADY HERE A $2 trillion AI economy built on the same dollars being passed in a circle I'm not being dramatic. The accounting trick is right there in the filings The scariest part? It's all 100% legal Here's how it works: A tech giant gives an AI startup billions in "investment." The contract forces that startup to spend the exact same money renting servers from… the tech giant. The tech giant then books that server usage as brand new "cloud revenue." Translation: they're paying themselves with their own money and calling it a sale. Look at Microsoft and OpenAI. Microsoft "invested" $13 billion in OpenAI. Most of it never left Microsoft - it was cloud credits that could only be spent on Microsoft servers. OpenAI used those credits to train its models. Microsoft turned around and recorded that exact spend as new cloud revenue That's why OpenAI's annual cloud bill is now $60 BILLION For a company doing only $25 BILLION in actual revenue It's not a customer. It's a recycled funding loop Anthropic runs the exact same script: $2.66 billion paid to AWS in 9 months - basically 100% of everything Anthropic earned. And it gets worse Every time these AI startups raise at a higher valuation, the tech giants mark up their equity and book the paper gain as PROFIT. Q1 2026: ➮ Alphabet reported $62.6B in profit. $28.7B of it (nearly half) was just a paper markup on Anthropic. ➮ Amazon reported $30.3B in profit. $16.8B of it was the same Anthropic paper gain. While Amazon was reporting record profits, its actual free cash flow collapsed 95% to just $1.2 billion Because they had to spend $44.2 BILLION in REAL money building data centers Real cash going out. Paper "profits" coming in Now here's where it gets dangerous: ➮ Microsoft has 49% of its $627 billion future backlog tied to OpenAI alone ➮ Oracle has 54% of its $553 billion pipeline depending on OpenAI alone Trillions of dollars of "demand" resting on one or two unprofitable startups If this all sounds familiar, it should This is 2001 all over again Back then, Global Crossing and Qwest swapped identical fiber-optic capacity with each other just to book fake sales Qwest had to erase $1.4 billion in fake income Global Crossing went bankrupt The only difference between then and now? The dot-com swaps were illegal Today's AI loop is fully legal under current accounting rules That's not a comfort. That's a warning Legal doesn't mean safe. It just means nobody can stop it before it blows up And here's the part most people don't realize: Every 401k, every index fund, every retirement account in America is being forced to buy more of these tech stocks every month. The loop inflates the stock prices The funds chase the prices The chase inflates them further Until the day the music stops and there's no real cash underneath. Don't worry though - my system flags the exact moment the market shifts from caution to DANGER. You'll be warned before it hits, like always. All you need to NOT miss my next call is to keep NOTIFS ON

Short squeeze of the century is coming. (i don’t own the stock)

Me and all my friends on Twitter are bidding for @GameStop memorabilia on @eBay, we have these wallstreet bastards on the ropes. You wouldn't understand, Sharon. #GameStop #GME $GME #eBay

$GME RYAN COHEN SELLING ITEMS ON EBAY TO PAY FOR EBAY 🌊🌊🌊

Wow they REALLY dont want this deal going through.

これはやばい。 AIエージェントが144体収録された「丸ごと1社のAIカンパニー」がオープンソース公開されました。名前は The Agency。GitHubのスター数9万超。 ・エンジニア／デザイナー／マーケ／営業／財務など12部門の専門エージェント ・Claude Code、Cursor、GitHub Copilot、Gemini CLI、Windsurfなど主要コーディングAIにスクリプト2本で導入 ・MITライセンスで完全無料 など、コーディングAIを「専門家チーム」に変えられます👇

Lol 😂😂

You realize what this means!? Stripe just gave you a new way to create generational wealth. They invented an entirely new marketplace. Agents will be spending millions, and eventually billions of dollars for individuals and corporations. The marketpalce is empty right now. IT IS YOURS FOR THE TAKING. Build a service as a software ( YES SAAS V2 ), make it so that agents can use and spend on your platform. WIN WIN WIN. WAAAAAAKE UPPPPP.

NO WAY THIS IS REAL.. Claude can do CAD now. someone connected claude directly to blender.. you just describe what you want and it generates complex 3d geometries from scratch this is what 200k 3d designers were getting paid to do

The co-founder of OpenAI just built an entire AI training engine in 200 lines of code. No dependencies. No libraries. No frameworks. Pure Python. And he says he cannot make it any shorter. Andrej Karpathy — former Director of AI at Tesla, founding member of OpenAI, one of the most respected AI researchers alive — published microgpt on February 12, 2026. It is 200 lines. It trains and runs a GPT model completely from scratch. Here is what those 200 lines actually contain. A full dataset loader. A tokenizer. An autograd engine that computes gradients. A GPT-2 architecture neural network. The Adam optimizer. A complete training loop. A complete inference loop. Everything needed to build, train, and run a large language model — in a file you could print on two pages of paper. This is the culmination of a decade-long obsession. Karpathy previously built micrograd, makemore, and nanoGPT — each one a step toward stripping AI down to its mathematical skeleton. microgpt is the final answer. The irreducible core. He wrote: "This script is the culmination of multiple projects and a decade-long obsession to simplify LLMs to their bare essentials. I cannot simplify this any further." Here is why this matters beyond the elegance.Every AI course in the world teaches through abstraction. You use PyTorch. You import transformers. You call functions you do not understand. You build things without knowing how they work. Karpathy's entire career has been a war against that approach. He believes the only way to truly understand intelligence — artificial or otherwise — is to build it from nothing .200 lines. No dependencies. From nothing. For anyone who has ever wanted to understand what a large language model actually is — not what it does, but what it is — this file is the answer. Free. Open source. On GitHub right now. gist.github.com/karpathy/8627f…

Persistent memory is the Achilles heel of AI. Engramme’s Large Memory Models (LMMs) empower every app with persistent memory. Google solved search. OpenAI solved language. Engramme solved memory. Join beta: engramme.com/signup

🚨 The AI industry just wasted 3 years. Trillions spent. Billions burned. All on the wrong idea. Yann LeCun said it from day one. Nobody listened. Until now. 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.