A GUY AT GOOGLE DEEPMIND MADE AN ISOMETRIC PIXEL-ART MAP OF NEW YORK CITY AND PUT IT ON THE OPEN WEB FOR FREE it's called isometric.nyc you open the tab and the city is just sitting there in classic SimCity 2000 isometric pixel art. you scroll. and it keeps going. and going. i zoomed in on midtown and i could read the H&M signage in times square. in red. as actual pixel-art letters on the side of a building. i could see the crystalline spire of the Bank of America Tower poking out of a clump of skyscrapers. individual rooftop HVAC units. tiny green roof gardens. the little driveway loops in front of the hotels. he estimates the map needs roughly 40,000 tiles. nothing is a placeholder. the guy who made it is Andy Coenen, a senior staff engineer at Google DeepMind. he is not a pixel artist. by his own admission he is "a former electronic musician." what he actually did is kind of insane: > pulled NYC's geometry from the Google Maps 3D tiles API > fine-tuned an open-source image model (Qwen-Image-Edit) on ~40 hand-paired examples of "satellite tile → pixel art tile" > spun up 50 parallel instances on rented GPUs and generated tens of thousands of tiles in a few hours > the fine-tune cost him 12 bucks his own stated mission for the project, verbatim, is one sentence: "what's possible now that was impossible before?" apparently the answer is "one engineer can pixel-art most of a metropolis for the price of a sandwich." and the wildest part to me is he didn't sell it. no signup. no paywall. no NFT. you open the URL and the city is yours to wander. the post landed at 1,325 points on Hacker News and topped bestofshowhn's 2026 list. we live in a timeline where a senior engineer at one of the largest AI labs on earth spent his nights pixel-arting Manhattan for fun and then gave it away. the internet is healing.

I've created a loop of AI loops

How to create simple trails in IlluGen! #realtimevfx

😹😹

N=R*fp*ne*fl*fi*fc*L

Colorful God Rays Demo and Source Code: codepen.io/sabosugi/full/OPb… #threejs #webgl #shader

2D Fire Shader #b3d #npr

A simple way to make a crystal surface shader in Godot 💎 jettelly.com/bundles/the-sha… #godot #gamedev

Finally got a working holographic film effect in Blender. It mimics diffraction grating with repeating specular highlights. #b3d

計算の都合上，電気力線短くしてみたら，生き物っぽくなって可愛い

a C compiler once appeared to disprove Fermat’s Last Theorem Fermat’s Last Theorem says no integers satisfy a³ b³ = c³. proven by Andrew Wiles in 1995 after 358 years. then John Regehr showed a loop compiled with gcc -O2 that printed: "Fermat's Last Theorem has been disproved." it was not math, it was undefined behavior.

Another godbolt posting: Proof of collatz conjecture by C compiler

He is the Ronaldo of Excel. Is there a tournament for this kind of thing?

Очередная иллюзия, от которой ломается мозг Если не поняли – переверните картинку )

I actually like material2 more than material3 designs

"Dead Internet Theory" is no longer just a theory

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.

эмммм неожиданно понял что я не персонаж из симс, а чел который им управляет, и я могу просто выделить себя как персонажа, сказать "иди вынеси мусор" или "сиди делай работу" и сделать это, втф

Anthropic pays engineers $750,000 a year to understand how LLMs work. Stanford just put a 2 hour lecture that covers 80% of it for FREE. Bookmark this. Give it 2 hours today. It might be the highest ROI thing you do this month:

RT @kosa12m: Best paper I've read so far this month: All elementary functions (sin, cos, tan, exp, log, powers, roots, hyperbolic functio…