the reason it doesn't have consensus is it's stupid idea, doesn't work, and completely fails at technical consensus. polar opposite to segwit which did have technical AND ecosystem consensus (modulo a later stage attempt to abuse the activation process to veto what was agreed)

the bip is stupid, it doesn't work, and it doesn't have technical nor ecosystem consensus. forcing it anyway is how you end up on a minority contentious fork. but people done last year explaining things. fork off and find out👍

github.com/8144225309/SuperS… SuperScalar 0.2.0-rc1 is available now. Pseudo-Spilman k² sub-factories Stateless Musig2 Rewrite Mixed-arity static-near-root Trustless watchtowers Reorg correctness Crash recovery Cheat-engine adversarial campaign (CL1–CL7) Observability dashboard

529 Overloaded

Happy Birthday Hal Finney! 🧡 You started something, we keep it going on: "Running Bitcoin" 🧡

Can’t wait to see what horrific event this boat will be tied to in the future of On Cinema

Ark protocol running on hardware!! Coming soon to a wallet near you?

Biology already solved it better: cortical opponent columns one slow theta metronome (4-8 Hz) 3 simple equations. Nature’s anti-collapse clock gives true world understanding & conscious streaming. No hacks needed. Copy evolution: doi.org/10.5281/zenodo.18241…

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.

🆙 Project Update: At @BitcoinRoatan Kids' gym transformation hits new heights! Walls almost painted, floor next. Funds look solid, but upgrades rise with more support!

its a sign

We have officially swapped over 10 Th/s to @Parasite_wtf 🕷️ With the remainder on @AtlasPool_io solo mining ⛏️

whoopsie poopsie bloomberg.com/opinion/articl…

long time no update, add Infinite scroll component for now. i have plan to implement some map UI from evangelion but still dont have clue how it will be used on my project

This whole bitcoin thing might actually be scalable?

IT'S WORKING!!