Odin retweeted
In a weird way, everybody is rooting for you. When you go to a job interview, the interviewer wants you to be amazing so they don’t have to look anymore. when you go on a date, the person wants to have an amazing date with you. If you make a piece of content, people want to see amazing pieces of content and they have no problem if it’s yours.
the instant you get into the mindset that people want you to do well, your entire world gets better. And you’ll be shocked to know this pattern keeps repeating in different areas of life.
Odin retweeted
“What do you mean your Halloween costume is Master Chief in Active Camo??”
I made this. ✌️
Odin retweeted
the average gamer believes that gameplay is when there are particle effects and nice animations and the more particle effects there are the better designed the gameplay is
Odin retweeted
Revenue from FPS cheating subscriptions is over $2 billion annually. This is only FPS title revenue.
Just an FYI. People are cheating. A lot of them. Pretending otherwise is serious cope. There is monetary incentive to cheat in the esports industry as well.
It also doesn't take large percentages to adversely affect matches and player experience. It's a serious problem in the industry.
Odin retweeted
wtf
this guy just had Claude Sonnet 5 perform brain surgery on a living mouse
the goal was to treat the mouse's epilepsy so it would stop having so many seizures.
here's what Claude did:
> it looked at 19 possible treatments and picked one that boosts the brain's natural calming chemical
> it chose where to inject it based on where these seizures are thought to start
> it worked out the exact injection point by averaging three research papers
> it lowered the needle 4.3 millimeters into the mouse's brain
> it injected a 20,000th of a teaspoon of the treatment over five minutes
> it checked the live camera 32 times to watch for things like bleeding
and the mouse woke up and recovered well!
interestingly, Opus 5.5 and Opus 5 both refused the task because it involved real biology
so Sonnet 5 got the job.
and as far as the researcher knows it's the first time an AI has picked a treatment and then injected it into the brain of a living mammal.
Last night an AI agent performed brain surgery on a mouse.
We gave it 19 gene therapy vectors in a spreadsheet, a mouse model of epilepsy, access to a motorize surgery rig with cameras and one goal: reduce seizures.
It picked the drug. It picked the brain region. It derived the coordinates from three papers. Then it drove the stereotaxic arms into the brain and injected 250 nanolitres while watching the entry site through the rig camera for bleeding.
A licensed vet gated every irreversible step and held a physical kill switch to stop the AI agent.
Thread on what it decided, what it saw, and the moment it refused to let me override it.
[Writing this on the spot out of excitement, but I've been 18hrs in front of my laptop coding and coordinating this, so keeping it brief and descriptive, more cohesive thoughts on this soon...]
I'll be live with The Frequency podcast from @aeblymedia tomorrow at 3pm PST / 6pm EST here: youtube.com/live/dNI1HnNpTcM
Come check it out!
#playHUNGER CB was a blast, cannot wait for OB and EA! So many great fights and clips, but these were some of my favorites - nothing gets the blood pumping like a clutch
Odin retweeted
btw: single neuron steering, like sae before it, works far worse than contrastive-pair control vectors in practice (coherence, consistency)
Odin retweeted
Evanescence, Spiritbox and Poppy are featured in the new WoW Forever cinematic. I repeat. End of You by Poppy, Amy Lee, and Courtney LaPlante is officially part of WoW Forever 🤯
This is exactly why I try to impress on people the need to learn at least the basics of AI and pay attention to where it’s headed. The proof is still being reviewed, and it’d be a leap to say this means AI will start overturning assumptions in every field. But if it holds, it’s a clear example of what I consider a core reason we pursued AI in the first place.
I think of it as human plus AI being greater than the sum of its parts, and this is a near-perfect example. I know very little about this subject. Funny enough, I’m learning it by having AI explain it to me. Humans asked the question, built the tools to check the answer, and are now doing the work of understanding it. The AI brought relentless exploration that humans can’t usually afford, because we’re limited by ego, social status, money, time, and finite lives. Neither works alone: an AI claim without verification is just a confident guess, and humans without the AI never looked there.
I picture it as pulling on threads. Human intuition is limitless and hard to explain, but it’s throttled by bandwidth, both our literal brain bandwidth and the time and resources we have to spend on a problem. Hold too many threads and some slip away. We might sense that several things connect, but the picture loses resolution the more pieces we add. AI can hold far more of those threads at once.
A Veritasium video on rubber tells how Charles Goodyear basically ruined his life chasing one idea. That obsession is how he discovered vulcanization, but he still died in debt. With AI, we can make huge numbers of those attempts in parallel at a fraction of the cost. At least that’s the plan, once we solve a lot of other problems, like the power imbalance between whoever builds and owns AI and everyone else. I’m endlessly optimistic about what we can do with AI, if we can sort out the rest.
Many of you don't understand how serious this is, so let me break it down.
In computer science there is a famous kind of puzzle called 3-SAT. You are given a long list of yes-or-no switches and a set of rules about how they must be arranged, and you have to find a combination that satisfies every rule. The obvious way to solve it is to try every combination, but each new switch doubles the number of possibilities, so a puzzle with a few hundred switches would take longer than the age of the universe to brute force.
For decades, the smartest people in the field found clever tricks that made this a little faster, but every trick still hit the same wall: the time required kept exploding as the puzzle grew.
About 25 years ago, two computer scientists, Russell Impagliazzo and Ramamohan Paturi, working with Francis Zane, turned that wall into a formal idea.
They proposed the Exponential Time Hypothesis, which says this explosion is not a failure of human cleverness but a law of nature. No algorithm, however brilliant, will ever solve 3-SAT without the time blowing up exponentially. Nobody could prove it, but almost everyone believed it, and it became one of the foundations of modern computer science.
Researchers used it as a measuring stick. If you could show that solving some other problem quickly would also crack 3-SAT quickly, you had shown that problem was hopelessly hard too. Hundreds of papers, across scheduling, graphs, logic and much more, rest on this one assumption.
Now an internal AI model at OpenAI claims to have broken it. It says it found an algorithm that solves these puzzles meaningfully faster than the hypothesis allows, and it has checked the proof in a computer verification system called Lean.
And here is the remarkable part: the very people who proposed the hypothesis, Impagliazzo and Paturi, along with MIT's Ryan Williams, are now leading the public review of whether a machine has disproved their own idea.
So what happens next? If the disproof holds, a large part of theoretical computer science has to be revisited.
Every result that said "this problem cannot be solved faster, assuming ETH" loses its foundation, and researchers will have to work out which of those walls were real and which were simply where we stopped looking. It does not mean everything becomes easy. The new algorithm is still slow in absolute terms and this is not the famous P versus NP problem being solved.
But it means our map of what is possible was wrong in a place we were very confident about, and it was an AI that redrew it. If the disproof fails, the hypothesis survives and the field breathes out, but we will still have watched an AI produce serious, checkable work on one of the deepest questions in the discipline, closely enough that the founders of the idea had to take it seriously. Either way, something has shifted.
Odin retweeted
Elon wants chip fabs on the Moon now. Lunar soil is 20% silicon, he says, so you just mine it, refine it, stamp out solar cells and chips. Next level of the video game. His words.
He's a walking 1954 Popular Science cover. "Moon Factories by 1975!" You can picture him red-faced at some poor SpaceX engineer, jabbing a finger at a render: "I saw this in a magazine! Why can't you guys do this?" Because the magazine was fiction, Elon. That's why.
Boring question one: water. A single leading-edge fab burns through millions of gallons of ultrapure water every day, the kind filtered and polished until it turns chemically aggressive and rips ions out of anything it touches. Making it takes an industrial plant of its own. On the Moon you'd be chipping ice out of permanently shadowed craters at minus 230 C and feeding it through a purification chain that doesn't exist, powered by energy that also doesn't exist.
Boring question two: shaking. Lithography machines sit on tuned vibration isolation, springs and masses and counterweights engineered around Earth's gravity down to the millimeter. Lunar gravity is one sixth of that. Every spring rate and tuned mass in the stack assumes 1 g, and the Moon has its own moonquakes nobody has fully mapped. So forget shipping a fab up there. You'd have to redesign the entire physics package from scratch, in a hailstorm of charged abrasive dust that eats seals and bearings for breakfast.
And here's the part that makes it funny instead of just dumb. NASA studied all of this while Elon was still in college. Ignatiev and Freundlich published the actual paper in 1991: waste silicon from regolith processing, vacuum-deposited thin-film cells laid right on the surface. Serious work, fifteen years of it, and by 2005 the best anyone could promise was 5-8% efficient cells sprayed on melted moon glass, economical only at megawatt scale. Chips? Never in the plan. Not once, in thirty years of study. Then a 2006 follow-up walked back even the crude version, because melted regolith is too lumpy and dirty to make a decent substrate. The 2025 research gave up on purifying lunar silicon entirely and just ships the semiconductor layer from Earth.
Thirty years of papers saying "hard pass" and this guy shows up with the crayon version of a 1991 NASA study, calls it visionary, and the fanboys clap like he invented the Moon. He isn't a genius ahead of his time. He's a man who skips the reading, and it shows every single time he opens his mouth just to pump his cult stocks.
Elon Musk explains how to build AI satellites on the moon from lunar silicon and aluminum, and says 500 to 1,000 terawatts a year cannot be launched from Earth
“Well, the lunar soil is 20% silicon or something like that. So you can mine the silicon on the moon, refine it, and create the solar cells and the radiators on the moon. So get the radiators out of aluminum. So there’s plenty of silicon and aluminum on the moon to make the cells and the radiators. The chips you could send from Earth because they’re pretty light. But maybe at some point you make them on the moon, too."
"Like I said, it does seem like a sort of a video game situation where it’s difficult but not impossible to get to the next level. I don’t see any way that you could do 500 to 1,000 terawatts per year launched from Earth.”
