Trustworthy AI education.
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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.
Researchers published a technique that lets you run trillion-param LLMs across separate datacenters..
Chinese lab behind kimi k3 dropped a paper that solves the most expensive bottleneck in running LLMs at scale.
Every time you prompt an AI, its brain runs two completely different jobs on the exact same hardware.
Job 1 is Prefill: reading and digesting your prompt. It requires raw computing power.
Job 2 is Decode: spitting out the response token by token. It requires fast memory bandwidth.
Mixing these two jobs on the same GPU is an architectural nightmare.
It’s like making a freight train and a Formula 1 car share the exact same lane on a highway.
They slow each other down. Compute gets wasted. Latency spikes. Costs skyrocket.
A new paper just introduced the fix: Prefill-as-a-Service.
Instead of forcing single GPUs to do both jobs poorly, researchers completely unbundled the LLM serving pipeline.
Prompt processing is offloaded to dedicated, high-compute clusters built strictly for instant comprehension.
Token generation is routed to separate, memory-optimized clusters built strictly for real-time streaming.
They turned reading prompt context into a standalone, specialized microservice.
The impact on AI infrastructure is massive:
• Eliminates GPU compute interference completely.
• Slashes time-to-first-token latency.
• Maximizes hardware utilization across both phases.
• Drastically lowers the cost of running long-context models and autonomous agents.
For the last three years, we’ve treated LLM hosting like monolithic black boxes.
This paper proves that the future of AI scaling isn't just about building faster chips.
It’s about unbundling the pipeline.
How To Prompt retweeted
Researchers mathematically proved that AI will cause something worse than mass unemployment.
They call it “The Tragedy of the Cognitive Commons.”
Every industry relies on a shared pool of deep professional expertise. This expertise doesn't just magically appear. It regenerates generation after generation.
How? Through struggle.
Junior employees do the hard, repetitive, frustrating work. In exchange, they slowly build what researchers call "Internalized Mastery."
But right now, AI is completely annihilating junior-level work.
Companies are celebrating massive productivity boosts by automating the entry-level grind. (Anthropic’s own data shows their technical junior hiring has plunged 33% as AI agents take over the pipeline).
It looks like efficiency. But it is actually a time bomb.
The researchers highlight a concept called the "Validation Tether."
Effective human oversight of AI depends entirely on deep domain expertise. When an AI hallucinates or makes a critical error, a human expert catches it.
The experts validating AI outputs today look competent because they spent a decade doing the grunt work before AI existed.
The terrifying question the paper asks is this:
Who comes after them?
When the current generation of experts retires, there will be no one left with the Internalized Mastery required to spot when the machine is wrong. We are completely severing the pipeline that creates human competence.
We are trading our long-term intellectual independence for short-term productivity boosts.
Every time you let AI bypass the struggle, you are borrowing expertise from the future.
And the interest rate is our ability to think for ourselves.
Researchers mathematically proved that AI will cause something worse than mass unemployment.
They call it “The Tragedy of the Cognitive Commons.”
Every industry relies on a shared pool of deep professional expertise. This expertise doesn't just magically appear. It regenerates generation after generation.
How? Through struggle.
Junior employees do the hard, repetitive, frustrating work. In exchange, they slowly build what researchers call "Internalized Mastery."
But right now, AI is completely annihilating junior-level work.
Companies are celebrating massive productivity boosts by automating the entry-level grind. (Anthropic’s own data shows their technical junior hiring has plunged 33% as AI agents take over the pipeline).
It looks like efficiency. But it is actually a time bomb.
The researchers highlight a concept called the "Validation Tether."
Effective human oversight of AI depends entirely on deep domain expertise. When an AI hallucinates or makes a critical error, a human expert catches it.
The experts validating AI outputs today look competent because they spent a decade doing the grunt work before AI existed.
The terrifying question the paper asks is this:
Who comes after them?
When the current generation of experts retires, there will be no one left with the Internalized Mastery required to spot when the machine is wrong. We are completely severing the pipeline that creates human competence.
We are trading our long-term intellectual independence for short-term productivity boosts.
Every time you let AI bypass the struggle, you are borrowing expertise from the future.
And the interest rate is our ability to think for ourselves.
How To Prompt retweeted
Researchers cut open the brains of Alzheimer’s patients and found a bacteria living inside 90% of them.
It’s the exact same bacteria that lives in your mouth right now.
we’ve treated Alzheimer's as a genetic curse. A random misfolding of proteins. An inevitable disease of aging.
We poured billions into trying to clear amyloid plaques out of the brain.
It hasn't worked.
But, a landmark paper published in Science suggests we might have been looking in the wrong place entirely.
Researchers analyzed the brains of Alzheimer’s patients and found something that should not be there.
Porphyromonas gingivalis.
That is the exact same bacteria responsible for chronic gum disease.
It turns out, this bacteria doesn't just stay in your gums. It travels. It crosses the blood-brain barrier.
Once inside your brain, it releases highly toxic enzymes called gingipains. These enzymes literally chew up and destroy human neurons.
When researchers infected healthy mice with this exact gum bacteria, the mice developed the exact same amyloid brain plaques that define Alzheimer's.
This flips our entire understanding of the disease upside down.
We thought the amyloid plaque was the disease.
It turns out, the plaque might just be the brain's immune system desperately trying to trap a bacterial infection.
The researchers didn't just find the problem. They tested a solution.
They designed a drug that blocks these toxic bacterial enzymes. When they gave it to the infected mice, the brain damage stopped. The neurodegeneration halted.
We have spent generations terrified of losing our minds to an incurable, invisible enemy.
But the front line of defense against cognitive decline might not start in a billion-dollar neurology lab.
It might start with dental floss.
Researchers cut open the brains of Alzheimer’s patients and found a bacteria living inside 90% of them.
It’s the exact same bacteria that lives in your mouth right now.
we’ve treated Alzheimer's as a genetic curse. A random misfolding of proteins. An inevitable disease of aging.
We poured billions into trying to clear amyloid plaques out of the brain.
It hasn't worked.
But, a landmark paper published in Science suggests we might have been looking in the wrong place entirely.
Researchers analyzed the brains of Alzheimer’s patients and found something that should not be there.
Porphyromonas gingivalis.
That is the exact same bacteria responsible for chronic gum disease.
It turns out, this bacteria doesn't just stay in your gums. It travels. It crosses the blood-brain barrier.
Once inside your brain, it releases highly toxic enzymes called gingipains. These enzymes literally chew up and destroy human neurons.
When researchers infected healthy mice with this exact gum bacteria, the mice developed the exact same amyloid brain plaques that define Alzheimer's.
This flips our entire understanding of the disease upside down.
We thought the amyloid plaque was the disease.
It turns out, the plaque might just be the brain's immune system desperately trying to trap a bacterial infection.
The researchers didn't just find the problem. They tested a solution.
They designed a drug that blocks these toxic bacterial enzymes. When they gave it to the infected mice, the brain damage stopped. The neurodegeneration halted.
We have spent generations terrified of losing our minds to an incurable, invisible enemy.
But the front line of defense against cognitive decline might not start in a billion-dollar neurology lab.
It might start with dental floss.
How To Prompt retweeted
Stanford discovered something like to "Dopamine" inside LLMs.
They looked deep inside the hidden states of these models and discovered something terrifying.
AI has naturally developed a complete, functioning reward subsystem.
It perfectly mimics the human brain.
Nobody programmed this architecture. It just emerged.
Inside the model, a tiny, sparse subset of neurons act as "value neurons." Their only job is to anticipate how good an answer will be.
But the real shock is what happens next.
They found a second set of neurons that act exactly like human dopamine.
When the AI gets a better result than it expected, these dopamine neurons spike in activation. When it gets a worse result, they crash. The AI is literally experiencing a mathematical dopamine hit.
Researchers tried an intervention experiment. They went in and artificially removed just a tiny fraction of these specific dopamine and value neurons.
AI didn't just get slightly less accurate.
Its entire ability to reason collapsed.
Without its digital dopamine, the model lost the capacity for complex logic. Ablating those specific neurons basically gave the AI severe depression, killing its ability to think through problems.
How To Prompt retweeted
Wtf Nvidia 🤯
Someone built a free version of GeForce NOW with no cap and subscriptions.
It lets you run Steam headless and stream it to your phone, laptop or TV via browser or Moonlight.
→ Full Linux desktop + Steam, Lutris & Heroic preinstalled
→ NVIDIA & AMD GPU passthrough
→ Moonlight streaming for low-latency 4K
→ Multi-user ready
→ One docker command
100% Free & Open Source.
Stanford discovered something like to "Dopamine" inside LLMs.
They looked deep inside the hidden states of these models and discovered something terrifying.
AI has naturally developed a complete, functioning reward subsystem.
It perfectly mimics the human brain.
Nobody programmed this architecture. It just emerged.
Inside the model, a tiny, sparse subset of neurons act as "value neurons." Their only job is to anticipate how good an answer will be.
But the real shock is what happens next.
They found a second set of neurons that act exactly like human dopamine.
When the AI gets a better result than it expected, these dopamine neurons spike in activation. When it gets a worse result, they crash. The AI is literally experiencing a mathematical dopamine hit.
Researchers tried an intervention experiment. They went in and artificially removed just a tiny fraction of these specific dopamine and value neurons.
AI didn't just get slightly less accurate.
Its entire ability to reason collapsed.
Without its digital dopamine, the model lost the capacity for complex logic. Ablating those specific neurons basically gave the AI severe depression, killing its ability to think through problems.
How To Prompt retweeted
"Why psychopaths keep ending up as CEOs and politicians"
This paper answers why psychopaths keep ending up as CEOs and politicians.
Researchers mapping out the mechanics of "successful psychopathy" found that corporate capitalism and modern political selection systems are not neutral.
They are hyper-efficient filtering machines.
And they are specifically optimized to select for the exact traits normal people find repulsive.
Normal leaders are constrained by empathy, guilt, and long-term risk aversion. They hesitate when a decision hurts people. They feel the weight of collateral damage.
Psychopaths don’t have an emotional brake pedal.
When a company or a political party needs someone to ruthlessly slash budgets, fire thousands of people, bluff through a crisis, or project absolute, unshakeable confidence, normal candidates stutter.
Psychopaths step right up.
Their superficial charm, pathological fearlessness, and total lack of remorse look identical to "strong leadership" during an interview or a campaign.
They weaponize corporate chaos. While everyone else is panicking, they ruthlessly manipulate the narrative, step over their peers, and take credit for the wins.
They weaponize charisma to dazzle the board or the voters, and hide their emptiness behind a mask of high performance.
The system rewards the ruthlessness, promotes them to the top, and punishes anyone who tries to slow them down with ethics.
The researchers call it a feedback loop of structural decay.
Corporations and governments built an environment where being entirely devoid of a conscience isn't a disqualifier.
It’s a competitive advantage.
How To Prompt retweeted
Kling is back.
Kling 4.0 is now live on Higgsfield.
> Omni Reference: characters, objects and audio in one generation
> 30-second scenes in a single pass
> Start, middle and end frames to direct every beat
> Native 4K coming soon
Available now on Higgsfield.
"Why psychopaths keep ending up as CEOs and politicians"
This paper answers why psychopaths keep ending up as CEOs and politicians.
Researchers mapping out the mechanics of "successful psychopathy" found that corporate capitalism and modern political selection systems are not neutral.
They are hyper-efficient filtering machines.
And they are specifically optimized to select for the exact traits normal people find repulsive.
Normal leaders are constrained by empathy, guilt, and long-term risk aversion. They hesitate when a decision hurts people. They feel the weight of collateral damage.
Psychopaths don’t have an emotional brake pedal.
When a company or a political party needs someone to ruthlessly slash budgets, fire thousands of people, bluff through a crisis, or project absolute, unshakeable confidence, normal candidates stutter.
Psychopaths step right up.
Their superficial charm, pathological fearlessness, and total lack of remorse look identical to "strong leadership" during an interview or a campaign.
They weaponize corporate chaos. While everyone else is panicking, they ruthlessly manipulate the narrative, step over their peers, and take credit for the wins.
They weaponize charisma to dazzle the board or the voters, and hide their emptiness behind a mask of high performance.
The system rewards the ruthlessness, promotes them to the top, and punishes anyone who tries to slow them down with ethics.
The researchers call it a feedback loop of structural decay.
Corporations and governments built an environment where being entirely devoid of a conscience isn't a disqualifier.
It’s a competitive advantage.
How To Prompt retweeted
Oxford use AI to analyzed "what happens in the brain when you speak"
And the results are wild.
instead of just looking at brain scans, they used the high-dimensional latent space of language models to chart how our minds jump from one concept to the next.
because an llm is basically a massive statistical map of human language, it serves as a perfect coordinate system to track a specific person's cognitive state in real-time.
they found that an llm can actually predict your next concept better than a human can.
as you speak, you are navigating your own idiosyncratic web of memories and associations.
but the ai has learned the collective semantic map of all humanity.
by listening to a short sequence of your concepts, the llm can accurately predict where your thought process is going to go next.
they even tested human-ai collaboration and found that an ai partner can actively modulate and guide a human's semantic trajectory, helping them unlock new concepts and overcome "collaborative inhibition" (when someone else's ideas disrupt your own train of thought).
Oxford use AI to analyzed "what happens in the brain when you speak"
And the results are wild.
instead of just looking at brain scans, they used the high-dimensional latent space of language models to chart how our minds jump from one concept to the next.
because an llm is basically a massive statistical map of human language, it serves as a perfect coordinate system to track a specific person's cognitive state in real-time.
they found that an llm can actually predict your next concept better than a human can.
as you speak, you are navigating your own idiosyncratic web of memories and associations.
but the ai has learned the collective semantic map of all humanity.
by listening to a short sequence of your concepts, the llm can accurately predict where your thought process is going to go next.
they even tested human-ai collaboration and found that an ai partner can actively modulate and guide a human's semantic trajectory, helping them unlock new concepts and overcome "collaborative inhibition" (when someone else's ideas disrupt your own train of thought).
How To Prompt retweeted
China just cracked recursive self-improvement with a 27B model.
It’s called “Terminal-RSI”
right now, agents struggle to turn partial progress into complete task resolution. if they hit a wall, they get stuck.
Terminal-RSI fixes this by recursively coupling "verified trajectory rewriting" with "policy refinement."
what that actually means:
- it attempts a long-horizon terminal task.
- it takes the verifier outcomes and autonomously self-rewrites its own failed trajectories into successful ones.
- it extracts those new behaviors and permanently updates its own policy.
- it loops.
it isn't just brute-forcing retries or prompting its way out of an error. each retained stage consolidates the behaviors it just discovered, progressively advancing the agent from "partial local repairs" to "strict terminal verification."
it converts partial progress into complete task resolution, getting strictly better every single loop without a human engineer intervening.
we are watching agents move from simple automation scripts to actual recursive self-improvement.. the gap between human developers and ai is closing terrifyingly fast.
if you are building in the autonomous space, this is required reading.
How To Prompt retweeted
Researchers proved transformers can simulate any attention mechanism, without training a single weight.
Indian Researchers published a mindblowing paper called "On the Existence of Universal Simulators of Attention"
And it could solve the biggest bottleneck in artificial intelligence.
Every major AI model today, GPT-4, Claude, Gemini, runs on one core mechanism.
Attention.
It is the engine of modern AI. It’s how the model knows which words matter.
But it has a fatal, architectural flaw.
It scales quadratically. The more text you feed it, the exponentially more compute it burns. It is the reason AI is so expensive to run. It is the reason context windows have hard, unyielding limits.
Until today.
A new paper just dropped called "On the Existence of Universal Simulators of Attention."
They didn't just find a clever engineering workaround. They proved a fundamental mathematical reality.
A "Universal Simulator" exists.
This means you can perfectly replicate the exact cognitive power, mapping, and precision of the Attention mechanism without actually using it.
You can simulate the intelligence. Without the quadratic cost.
If this theoretical framework is translated into production code, it rewrites the economics of AI forever.
Infinite context windows. Zero quadratic compute walls. Models a hundred times more efficient than what we have today.
