Freaky herk

AGI Late2027/2028

[Freaky herk @jokinglp]

@Impenitant @Sargon_of_Akkad rich people who can afford a healthy lifestyle and good healthcare tend to live longer? damn

[Freaky herk @jokinglp]

@scaling01 Token cost: 2 kidneys and a liver

[Freaky herk @jokinglp]

@mark_k im not worried the chance that all labs will agree is basically 0

[Freaky herk @jokinglp]

@YousefPLE dont spread fitna

[Freaky herk @jokinglp]

@scaling01 "If such systems existed, we expect that we would slow down or temporarily pause, if other developers at or near the frontier also did so in a verifiable manner." alright pack it up bro

[Freaky herk @jokinglp]

@abysskive it all comes down to abandoning your morales simply because you find someone physically attractive

[Freaky herk @jokinglp]

@notevenrealbtw he reposted 😂😂😂😂

[Freaky herk @jokinglp]

@scaling01 next week

[Freaky herk @jokinglp]

@scaling01 Models like these are the reason why sonnet isn't behind a 100 dollar paywall

[Freaky herk @jokinglp]

@scaling01 For the price it's really good

[Freaky herk @jokinglp]

@scaling01 Tbh it's really taking a long time but I doubt it

[Freaky herk @jokinglp]

@ravikiran_dev7 👍

[Freaky herk @jokinglp]

@scaling01 the only reason theyre not luxury yet completely is because open source hasnt catched up lets hope chinese AI doesnt slow down wich will allow closed AI to oversell their stuff..

[Freaky herk @jokinglp]

@icuselle you want saudi government to pay for plane fuel accomodation ration guides and infrastructure out of thin air?

[Champagne Joshi @JoshWalkos]

Look how friendly they are.

[Mayor Zohran Kwame Mamdani @NYCMayor]

Today marks Nakba Day, an annual day of remembrance to commemorate the expulsion of more than 700,000 Palestinians between 1947 and 1949 during the creation of the State of Israel and the year that followed.

Inea is a New Yorker and a Nakba survivor. She shared her story with us — one of home, tradition and memory over generations.

[Alexander Whedon @alex_whedon]

Introducing SubQ - a major breakthrough in LLM intelligence.

It is the first model built on a fully sub-quadratic sparse-attention architecture (SSA),

And the first frontier model with a 12 million token context window which is:

- 52x faster than FlashAttention at 1MM tokens
- Less than 5% the cost of Opus

Transformer-based LLMs waste compute by processing every possible relationship between words (standard attention).

Only a small fraction actually matter.

@subquadratic finds and focuses only on the ones that do.

That's nearly 1,000x less compute and a new way for LLMs to scale.

[Freaky herk @jokinglp]

@alex_whedon Whats the catch

[Freaky herk @jokinglp]

@nicdunz what $$ compared to $ bro what are those metric is it twice as expensive as medium? doubt it

[How To AI @HowToAI_]

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.