buxombox

[Charles Curran @charliebcurran]

I used AI to explain the Open Model Wars, with fruit.

[MA_Octo @MaOcto]

@sama "Homeboy"

[Elon Musk @elonmusk]

@sama We start flying them next year. Maybe you can come see them if your parole officer approves.

After stealing an open source AI charity, you then stole all of Apple’s phone technology! Wow.

What do you plan for an encore? That’s tough to beat.

[Elon Musk @elonmusk]

@cb_doge Scam Altman strikes again …

[ThePrimeagen @ThePrimeagen]

1 hour of hand programming

ngmi

[How To Prompt @HowToPrompt__]

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.

[buxombox @BuxomBox]

@Zephyr_hg NEWS

[Shaw @shawmakesmagic]

The quality of your vibecoded slop is horrible. I've seen it. Absolute dogshit.

Fortunately, there is a fix.

Use this prompt:

I want to clean up my codebase and improve code quality. This is a complex task, so we'll need 8 subagents. Make a sub agent for each of the following:
1. Deduplicate and consolidate all code, and implement DRY where it reduces complexity
2. Find all type definitions and consolidate any that should be shared
3. Use tools like knip to find all unused code and remove, ensuring that it's actually not referenced anywhere
4. Untangle any circular dependencies, using tools like madge
5. Remove any weak types, for example 'unknown' and 'any' (and the equivalent in other languages), research what the types should be, research in the codebase and related packages to make sure that the replacements are strong types and there are no type issues
6. Remove all try catch and equivalent defensive programming if it doesn't serve a specific role of handling unknown or unsanitized input or otherwise has a reason to be there, with clear error handling and no error hiding or fallback patterns
7. Find any deprecated, legacy or fallback code, remove, and make sure all code paths are clean, concise and as singular as possible
8. Find any AI slop, stubs, larp, unnecessary comments and remove. Any comments that describe in-motion work, replacements of previous work with new work, or otherwise are not helpful should be either removed or replaced with helpful comments for a new user trying to understand the codebase-- but if you do edit, be concise

I want each to do detailed research on their task, write a critical assessment of the current code and recommendations, and then implement all high confidence recommendations.

[toly 🇺🇸 @toly]

@hthieblot Weltrekordblitzschnellhochdurchsatzgebührenminimalisierendeparallelverarbeitungshochskalierbaredezentralisiertehochleistungsblockkettentransaktionsverarbeitungsplattform

[Noisy @noisyb0y1]

https://t.co/IOfam4j2sI

[Wes Bos @wesbos]

Claude Code leaked their source map, effectively giving you a look into the codebase.

I immediately went for the one thing that mattered: spinner verbs

There are 187

[buxombox @BuxomBox]

@jakeglmn @grok how does too much exercise affect?

[buxombox @BuxomBox]

@CryptomancerNL @Aleph__Zero @MarmosetClub $AZERO 546f20746865206d6f6f6e2121 !!!

[buxombox @BuxomBox]

@nft_onward @Moutai_Sol @wastedG_ 金液 LIQUID GOLD 🌕🌕🌕🌕

[buxombox @BuxomBox]

@lemonfloss $AZERO

[buxombox @BuxomBox]

@wapfarm https://t.co/KmsW7X4TJ1
https://t.co/iSrjttgEvg

[buxombox @BuxomBox]

@virtualbacon https://t.co/KmsW7X4TJ1
https://t.co/iSrjttgEvg

[buxombox @BuxomBox]

@jimbol14 @MantaNetwork https://t.co/KmsW7X4TJ1
https://t.co/iSrjttgEvg

[buxombox @BuxomBox]

@MantaNetwork @LayerZero_Labs @Stake_Stone https://t.co/KmsW7X4TJ1
https://t.co/iSrjttgEvg

[buxombox @BuxomBox]

@peer_pur @zerokn0wledge_ @blockchainrulez Some more $MANTA codes:
https://t.co/86BBzwMX4s https://t.co/iDt8IJATJQ https://t.co/TrgP1mdfDl https://t.co/5EVhPvamms https://t.co/Dw9gKzaPDk https://t.co/pydUImA0bj https://t.co/ohyNlSeDqY https://t.co/ghKSSoRkvE