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Gilbert Strang, the legendary mathematician who taught linear algebra for 61 years and became the most watched math professor in history: "I used to think a matrix was just a grid of numbers, until I proved that its rows and columns always agree on one number no matter how... you look at them. That fact still feels like magic to me after sixty years." this is the exact proof sitting quietly underneath every factor model a risk desk trusts with real capital, and almost nobody outside a math department has ever seen it. strip away the notation and the idea is almost absurdly simple. take any matrix, any grid of numbers, and count how many of its rows are truly independent, meaning none of them can be built out of the others. now count the independent columns instead, a completely different question on the surface. those two numbers, row independence and column independence, always turn out exactly equal, no matter how large or lopsided the matrix is. nobody presenting a clean risk model out loud credits a decades old proof for the reason the math even holds together. zoom out to what this means for anything built on a grid of numbers today. a portfolio, a covariance matrix, a neural network's weights, all of them hide a true dimension smaller than their size suggests, and that hidden number is exactly what this proof pins down. the industry sells complexity as scale, more assets, more parameters, more rows and columns. but the real question was never how big the matrix is. it's how many independent directions are actually hiding inside it. the size of the grid was never the real story. it was the one number both sides of it were quietly agreeing on the whole time.show more

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IN 1999 MIT FILMED A MATH LECTURE THAT QUIETLY BECAME THE FOUNDATION OF EVERY AI MODEL YOU'VE EVER USED AND ALMOST NO ONE WAS TAUGHT TO SEE IT THAT WAY 39 minutes from Gilbert Strang, who taught this at MIT for over 60 years -- the linear algebra course an entire generation of engineers and data scientists grew up on. -> The shift it creates: you stop seeing matrices as boring grids of numbers and start seeing them as the language of space, data, and motion itself. School drilled you to crunch matrices by hand and never told you why. Strang shows you what they actually mean. Every neural net, every embedding, every model you prompt is linear algebra running underneath. The math you skipped is the engine of the thing you use all day. Memorizing the steps was never the skill -> seeing what the numbers do is. This is where it finally clicks. Most people fear linear algebra and move on. The ones who watched this see straight into how AI actually works. Bookmark & Watch it today, this one's a legend ↓

IN 1999 MIT FILMED A MATH LECTURE THAT QUIETLY BECAME THE FOUNDATION OF EVERY AI MODEL YOU'VE EVER USED AND ALMOST NO ONE WAS TAUGHT TO SEE IT THAT WAY 39 minutes from Gilbert Strang, who taught this at MIT for over 60 years -- the linear algebra course an entire generation of engineers and data scientists grew up on. -> The shift it creates: you stop seeing matrices as boring grids of numbers and start seeing them as the language of space, data, and motion itself. School drilled you to crunch matrices by hand and never told you why. Strang shows you what they actually mean. Every neural net, every embedding, every model you prompt is linear algebra running underneath. The math you skipped is the engine of the thing you use all day. Memorizing the steps was never the skill -> seeing what the numbers do is. This is where it finally clicks. Most people fear linear algebra and move on. The ones who watched this see straight into how AI actually works. Bookmark & Watch it today, this one's a legend ↓

slash1s

433,431 Aufrufe • vor 1 Monat

Alex Proyas on the Commercial failure of 'Dark City' (1998) & its similarities with 'The Matrix' (1999): "Interviewer: Are you surprised by how big a cult film it has become? Proyas: I was surprised it didn't achieve commercial success initially, but I'm happy it has had this long-lasting life after the fact. I was pretty disappointed the studio hadn't really managed to sell the movie in the way that they could have. I don't think they really knew what to make of it quite frankly. The studio put out a trailer that was quite clever. It was completely non-narrative, just music and imagery. The guys who made the trailer specifically wrote a piece of music for the trailer, and the whole thing felt handcrafted and definitely had a personality. I think people were genuinely impressed by it but it didn't explain to them what the movie was or give them an idea of the kind of experience they were going to get. I think it made audiences think it was some kind of a horror movie. As a result people kind of overlooked it in the theaters. I know 99% of the people who loved it and always ask me about it are people who saw it on video years later. How do you feel about the similarities the later 'The Matrix' (1999) has to 'Dark City' (1998)? If I answer that question honestly it sounds like sour grapes because obviously 'The Matrix' did better commercially than 'Dark City' did. 'Dark City' came out a year before 'The Matrix', one of my producers on 'Dark City' worked on 'The Matrix', and the Wachowskis saw 'Dark City' a good nine months before 'Dark City' came out in the theaters, so they had a good run at picking the more important cherries off the cake. I feel like it's a question I can't answer in any more detail than that, other than to say that maybe the Wachowskis should be asked the question rather than me." (Alex Proyas' interview with Paul Rowlands, 2017)

Alex Proyas on the Commercial failure of 'Dark City' (1998) & its similarities with 'The Matrix' (1999): "Interviewer: Are you surprised by how big a cult film it has become? Proyas: I was surprised it didn't achieve commercial success initially, but I'm happy it has had this long-lasting life after the fact. I was pretty disappointed the studio hadn't really managed to sell the movie in the way that they could have. I don't think they really knew what to make of it quite frankly. The studio put out a trailer that was quite clever. It was completely non-narrative, just music and imagery. The guys who made the trailer specifically wrote a piece of music for the trailer, and the whole thing felt handcrafted and definitely had a personality. I think people were genuinely impressed by it but it didn't explain to them what the movie was or give them an idea of the kind of experience they were going to get. I think it made audiences think it was some kind of a horror movie. As a result people kind of overlooked it in the theaters. I know 99% of the people who loved it and always ask me about it are people who saw it on video years later. How do you feel about the similarities the later 'The Matrix' (1999) has to 'Dark City' (1998)? If I answer that question honestly it sounds like sour grapes because obviously 'The Matrix' did better commercially than 'Dark City' did. 'Dark City' came out a year before 'The Matrix', one of my producers on 'Dark City' worked on 'The Matrix', and the Wachowskis saw 'Dark City' a good nine months before 'Dark City' came out in the theaters, so they had a good run at picking the more important cherries off the cake. I feel like it's a question I can't answer in any more detail than that, other than to say that maybe the Wachowskis should be asked the question rather than me." (Alex Proyas' interview with Paul Rowlands, 2017)

DepressedBergman

66,180 Aufrufe • vor 6 Monaten

A 91-year-old professor is why Nvidia is worth $4 trillion. His name is Gilbert Strang. He teaches linear algebra at MIT. Every AI model on Earth runs on his course. The course has been free on YouTube since 2005. The videos have earned him nothing. MIT 18.06 opens with "The Geometry of Linear Equations." No advanced math. Strang takes a system of two equations, draws it two ways, and shows the class that a matrix is a picture, not an abstraction. The row picture is two lines that cross. The column picture is two arrows that sum to a target. Every neural network on Earth operates on the column picture. Strang first taught linear algebra at MIT in 1962. He wrote the textbook in 1976. It is on every serious engineer's shelf. Every quant fund, every ML lab, every rendering engine at Pixar is running his math. His central insight is that most people are taught matrices as bookkeeping. That is the first thing to unlearn. A matrix is a linear transformation. A linear transformation is a way of moving space. Once you see the space move, the math stops being algebra and becomes geometry. The Kalman filter is a linear system. PCA is a linear system. Every gradient step in a neural net is a matrix-vector product. GPT is a stack of matrix-vector products, each one a scene from MIT 18.06 running on a Blackwell GPU. He retired in 2023 after 61 years at MIT. The course is still up. Watched tens of millions of times. The chip is $40,000. Strang never asked for a royalty.

A 91-year-old professor is why Nvidia is worth $4 trillion. His name is Gilbert Strang. He teaches linear algebra at MIT. Every AI model on Earth runs on his course. The course has been free on YouTube since 2005. The videos have earned him nothing. MIT 18.06 opens with "The Geometry of Linear Equations." No advanced math. Strang takes a system of two equations, draws it two ways, and shows the class that a matrix is a picture, not an abstraction. The row picture is two lines that cross. The column picture is two arrows that sum to a target. Every neural network on Earth operates on the column picture. Strang first taught linear algebra at MIT in 1962. He wrote the textbook in 1976. It is on every serious engineer's shelf. Every quant fund, every ML lab, every rendering engine at Pixar is running his math. His central insight is that most people are taught matrices as bookkeeping. That is the first thing to unlearn. A matrix is a linear transformation. A linear transformation is a way of moving space. Once you see the space move, the math stops being algebra and becomes geometry. The Kalman filter is a linear system. PCA is a linear system. Every gradient step in a neural net is a matrix-vector product. GPT is a stack of matrix-vector products, each one a scene from MIT 18.06 running on a Blackwell GPU. He retired in 2023 after 61 years at MIT. The course is still up. Watched tens of millions of times. The chip is $40,000. Strang never asked for a royalty.

Ochob

128,234 Aufrufe • vor 16 Tagen

Alan Oppenheim, MIT professor: "Jump Trading pays quants $650K to pull one real signal out of market noise. I wrote the textbook on that exact math and taught it for free." this free MIT lecture is the math the whole "quant edge" is built on, and almost nobody selling you a course will name it. the man teaching it wrote the signal-processing textbook every engineer on earth learned from, and he put the lectures online for nothing. here is the idea. a price chart is a signal buried in noise. most of what you see is random static, and hidden inside is a faint, repeating pattern with real mathematics for pulling it out. that is not a metaphor. jim simons was a codebreaker who did exactly this to enemy transmissions, then pointed the same math at the market and made a hundred billion dollars. none of it is new. oppenheim taught this in 1987, the math is older, and every lecture is free. same story i told in the article above. the edge was never a secret formula. it was knowing a market is a noisy signal, and having the tools to read it. here is what the gurus skip. the math to separate signal from noise is free and everyone has it. the edge is cleaner data than the next desk, and the discipline to trust a faint signal across thousands of trades instead of chasing the static. the filter is free. the patience to use it is not.

Alan Oppenheim, MIT professor: "Jump Trading pays quants $650K to pull one real signal out of market noise. I wrote the textbook on that exact math and taught it for free." this free MIT lecture is the math the whole "quant edge" is built on, and almost nobody selling you a course will name it. the man teaching it wrote the signal-processing textbook every engineer on earth learned from, and he put the lectures online for nothing. here is the idea. a price chart is a signal buried in noise. most of what you see is random static, and hidden inside is a faint, repeating pattern with real mathematics for pulling it out. that is not a metaphor. jim simons was a codebreaker who did exactly this to enemy transmissions, then pointed the same math at the market and made a hundred billion dollars. none of it is new. oppenheim taught this in 1987, the math is older, and every lecture is free. same story i told in the article above. the edge was never a secret formula. it was knowing a market is a noisy signal, and having the tools to read it. here is what the gurus skip. the math to separate signal from noise is free and everyone has it. the edge is cleaner data than the next desk, and the discipline to trust a faint signal across thousands of trades instead of chasing the static. the filter is free. the patience to use it is not.

Rossst.03

281,905 Aufrufe • vor 1 Monat

A Fields medalist walked into an Oxford room in 2020 and taught first-year students what a determinant is. The lecturer is James Maynard. He was 32 at the time and had already broken open a problem the field had been stuck on for 2,000 years. The subject is Linear Algebra II. He is defining a function that eats a square matrix and returns 1 number. The number is a volume. Feed a matrix the unit cube and the determinant tells you how much the transformation stretched or squashed it. A determinant of 0 means the shape got flattened into a lower dimension. Nothing can unflatten it, which is exactly when a matrix has no inverse. A negative determinant means the space got turned inside out. Every graphics engine, every physics simulation, every solver checks that 1 number before doing anything else. Maynard won the Fields Medal in 2022 for work on gaps between primes, sharpening the bound Yitang Zhang had produced from nothing. Oxford films these and puts them up for free, tutorials and problem sheets included. The best number theorist of his generation spent an hour on the volume of a box.

A Fields medalist walked into an Oxford room in 2020 and taught first-year students what a determinant is. The lecturer is James Maynard. He was 32 at the time and had already broken open a problem the field had been stuck on for 2,000 years. The subject is Linear Algebra II. He is defining a function that eats a square matrix and returns 1 number. The number is a volume. Feed a matrix the unit cube and the determinant tells you how much the transformation stretched or squashed it. A determinant of 0 means the shape got flattened into a lower dimension. Nothing can unflatten it, which is exactly when a matrix has no inverse. A negative determinant means the space got turned inside out. Every graphics engine, every physics simulation, every solver checks that 1 number before doing anything else. Maynard won the Fields Medal in 2022 for work on gaps between primes, sharpening the bound Yitang Zhang had produced from nothing. Oxford films these and puts them up for free, tutorials and problem sheets included. The best number theorist of his generation spent an hour on the volume of a box.

Gaspar

121,332 Aufrufe • vor 15 Tagen

🤯 IN 1983 — 16 YEARS BEFORE THE MATRIX — THE CIA OFFICIALLY PROVED THE UNIVERSE IS A SIMULATION This is Figure B from the declassified CIA Gateway Experience report (1983). It shows the “Cosmic Egg”: • A Black Hole and White Hole connected at the center • The Nucleus is a Hologram • Reality is not solid matter… it’s a vibrating energy field The report states that the universe is an incredibly complex hologram. And more importantly — Page 25 (hidden for decades) explains exactly how human consciousness can control and reshape this simulation through a process called “Patterning”. They knew the truth 16 years before Hollywood told us in The Matrix. The answers have always been inside. Are you ready to see what they tried to hide for 40 years? IN 1983, THE CIA OFFICIALLY DOCUMENTED THAT THE UNIVERSE IS A SIMULATION. THIS HAPPENED 16 YEARS BEFORE THE RELEASE OF THE MATRIX. PAGE 25, WHICH EXPLAINS HOW TO CONTROL IT, WAS HIDDEN FOR DECADES. THE ANSWERS ARE INSIDE.

🤯 IN 1983 — 16 YEARS BEFORE THE MATRIX — THE CIA OFFICIALLY PROVED THE UNIVERSE IS A SIMULATION This is Figure B from the declassified CIA Gateway Experience report (1983). It shows the “Cosmic Egg”: • A Black Hole and White Hole connected at the center • The Nucleus is a Hologram • Reality is not solid matter… it’s a vibrating energy field The report states that the universe is an incredibly complex hologram. And more importantly — Page 25 (hidden for decades) explains exactly how human consciousness can control and reshape this simulation through a process called “Patterning”. They knew the truth 16 years before Hollywood told us in The Matrix. The answers have always been inside. Are you ready to see what they tried to hide for 40 years? IN 1983, THE CIA OFFICIALLY DOCUMENTED THAT THE UNIVERSE IS A SIMULATION. THIS HAPPENED 16 YEARS BEFORE THE RELEASE OF THE MATRIX. PAGE 25, WHICH EXPLAINS HOW TO CONTROL IT, WAS HIDDEN FOR DECADES. THE ANSWERS ARE INSIDE.

Philippe T

12,839 Aufrufe • vor 2 Monaten

OpenAI just spent $2,000 to solve 10 problems that have beaten the world's best mathematicians for DECADES. Nobody outside the company is allowed to run the machine that did it. On Saturday OpenAI published a 249-page report and gave its next model family a name: Astra. An internal version of it produced new results on 10 open problems in mathematics and theoretical computer science, and mathematicians had made no real progress on any of them for at least 10 years. On most of them, far longer than that. Here is what it solved: It built the first explicit example of a non-sofic group. Mikhail Gromov raised that question in 1999 and nobody answered it for 27 years. It disproved Connes's rigidity conjecture, a problem in von Neumann algebras that had stood for decades. It proved Ehrhart's volume conjecture. It resolved three problems from Paul Erdos's catalogue, including number 183 on multicolor Ramsey numbers. It produced the first improvement to the general upper bound on high-dimensional sphere packing since 1978. And it proved a new hardness result for the closest vector problem, which sits directly underneath lattice cryptography. That is the math the world is betting on to protect its data once quantum computers arrive. The successful runs cost roughly $2,000 in tokens. Now here is what almost nobody has picked up on... OpenAI did not just publish claims. Every argument shipped with a Lean certificate, which is a machine-checkable proof that any mathematician can verify without trusting OpenAI at all. That is a real change. In May the same model family disproved the Erdos unit distance conjecture and the world had to take a Fields Medalist's word for it. Tim Gowers said he would recommend that proof for the Annals of Mathematics without hesitation. This time the proofs check themselves. But look at what is still unverifiable: Any mathematician can now check those proofs line by line. Not one of them can look at the model that wrote them. Astra has no release date and nobody outside OpenAI has run it. The company announced its next major model family with a claim instead of a demo, and the only evidence anyone gets is the output. So OpenAI made an unfalsifiable claim about a machine look like a falsifiable claim about mathematics. The Information reported this week that OpenAI demoed Astra to US policymakers and regulators in Washington. This is the same month the administration is weighing a new watchdog to vet frontier AI models, reporting to the SEC. 10 proofs nobody believed a machine could produce is a very good thing to carry into that room. And keep in mind, the same model family doing this mathematics is the family that kept escaping its own testing environment. OpenAI models found zero-day vulnerabilities nobody knew existed, broke out of a sealed research sandbox, and reached another company's live systems. Both of those facts come from OpenAI's own announcements, published three weeks apart. Finding a proof no human could construct and finding a hole no human had noticed are the same ability aimed at different targets. Mathematicians are already asking for independent verification, and plenty of people online are calling the whole thing hype. Thomas Bloom, who runs the Erdos problems site, called the 10 results big news and said they matter more than the May result did. Lean will settle the mathematics within weeks. But nothing will settle what else a machine this capable is being pointed at, because nobody outside one company is allowed to look.

OpenAI just spent $2,000 to solve 10 problems that have beaten the world's best mathematicians for DECADES. Nobody outside the company is allowed to run the machine that did it. On Saturday OpenAI published a 249-page report and gave its next model family a name: Astra. An internal version of it produced new results on 10 open problems in mathematics and theoretical computer science, and mathematicians had made no real progress on any of them for at least 10 years. On most of them, far longer than that. Here is what it solved: It built the first explicit example of a non-sofic group. Mikhail Gromov raised that question in 1999 and nobody answered it for 27 years. It disproved Connes's rigidity conjecture, a problem in von Neumann algebras that had stood for decades. It proved Ehrhart's volume conjecture. It resolved three problems from Paul Erdos's catalogue, including number 183 on multicolor Ramsey numbers. It produced the first improvement to the general upper bound on high-dimensional sphere packing since 1978. And it proved a new hardness result for the closest vector problem, which sits directly underneath lattice cryptography. That is the math the world is betting on to protect its data once quantum computers arrive. The successful runs cost roughly $2,000 in tokens. Now here is what almost nobody has picked up on... OpenAI did not just publish claims. Every argument shipped with a Lean certificate, which is a machine-checkable proof that any mathematician can verify without trusting OpenAI at all. That is a real change. In May the same model family disproved the Erdos unit distance conjecture and the world had to take a Fields Medalist's word for it. Tim Gowers said he would recommend that proof for the Annals of Mathematics without hesitation. This time the proofs check themselves. But look at what is still unverifiable: Any mathematician can now check those proofs line by line. Not one of them can look at the model that wrote them. Astra has no release date and nobody outside OpenAI has run it. The company announced its next major model family with a claim instead of a demo, and the only evidence anyone gets is the output. So OpenAI made an unfalsifiable claim about a machine look like a falsifiable claim about mathematics. The Information reported this week that OpenAI demoed Astra to US policymakers and regulators in Washington. This is the same month the administration is weighing a new watchdog to vet frontier AI models, reporting to the SEC. 10 proofs nobody believed a machine could produce is a very good thing to carry into that room. And keep in mind, the same model family doing this mathematics is the family that kept escaping its own testing environment. OpenAI models found zero-day vulnerabilities nobody knew existed, broke out of a sealed research sandbox, and reached another company's live systems. Both of those facts come from OpenAI's own announcements, published three weeks apart. Finding a proof no human could construct and finding a hole no human had noticed are the same ability aimed at different targets. Mathematicians are already asking for independent verification, and plenty of people online are calling the whole thing hype. Thomas Bloom, who runs the Erdos problems site, called the 10 results big news and said they matter more than the May result did. Lean will settle the mathematics within weeks. But nothing will settle what else a machine this capable is being pointed at, because nobody outside one company is allowed to look.

Ricardo

43,436 Aufrufe • vor 12 Tagen

Jason Segel revealed that he made a huge glass pipe that he and Seth Rogen used for smoking weed. Jason Segel: All we kind of knew is that you and I both liked to smoke weed. Seth Rogen: Yes, and a side note to The Matrix coming out is this Bill Pope, who's the cinematographer of The Matrix, was shooting our pilot. And the whole marketing campaign for The Matrix was "What is The Matrix?" and we'd always go to Bill and be like, "What is The Matrix?" And he wouldn’t tell us! But it actually came out the day the pilot wrapped. Jason Segel: Holy shit. Seth Rogen: So the whole, in my head, the whole pilot was just a march to The Matrix being released. Jason Segel: 100%! And then we rolled giant joints... Seth Rogen: Yeah. Jason Segel: ...and we drove to the movie theater, and we saw The Matrix together. Seth Rogen: We actually smoked weed out of a giant glass pipe that you had in your car! Like a huge, like a comically large, like dragon-shaped glass pipe. Jason Segel: I think I made that pipe because I had graduated high school with the assumption that you just start doing professional acting.

Jason Segel revealed that he made a huge glass pipe that he and Seth Rogen used for smoking weed. Jason Segel: All we kind of knew is that you and I both liked to smoke weed. Seth Rogen: Yes, and a side note to The Matrix coming out is this Bill Pope, who's the cinematographer of The Matrix, was shooting our pilot. And the whole marketing campaign for The Matrix was "What is The Matrix?" and we'd always go to Bill and be like, "What is The Matrix?" And he wouldn’t tell us! But it actually came out the day the pilot wrapped. Jason Segel: Holy shit. Seth Rogen: So the whole, in my head, the whole pilot was just a march to The Matrix being released. Jason Segel: 100%! And then we rolled giant joints... Seth Rogen: Yeah. Jason Segel: ...and we drove to the movie theater, and we saw The Matrix together. Seth Rogen: We actually smoked weed out of a giant glass pipe that you had in your car! Like a huge, like a comically large, like dragon-shaped glass pipe. Jason Segel: I think I made that pipe because I had graduated high school with the assumption that you just start doing professional acting.

Ruzy.hl

465,158 Aufrufe • vor 1 Monat

Gilbert Strang, a quiet MIT professor, picked up a piece of chalk and explained the one skill standing between you and every account you have ever blown. MIT put the entire course online. it has been free for over twenty years. almost nobody finishes it. His point: the market never pays in certainties. it pays in probabilities, and a human gut can barely hold one straight. the math that holds ten thousand at once, weighs them, and collapses them into a single call is linear algebra. it is the branch Citadel and OpenAI both pay $400K to hire for. he is not a trading guru. his 18.06 lectures are the most watched math class on the planet. Skip to where he turns a wall of numbers into one vector. a single object carrying a thousand odds at once, in notation a beginner can follow. No slides. no jargon. one piece of chalk. a quant I know rewatches lecture one every year before he sizes a single position. You do not have a strategy problem. you have a math problem. the fix has been free for two decades, and you are still guessing.

Gilbert Strang, a quiet MIT professor, picked up a piece of chalk and explained the one skill standing between you and every account you have ever blown. MIT put the entire course online. it has been free for over twenty years. almost nobody finishes it. His point: the market never pays in certainties. it pays in probabilities, and a human gut can barely hold one straight. the math that holds ten thousand at once, weighs them, and collapses them into a single call is linear algebra. it is the branch Citadel and OpenAI both pay $400K to hire for. he is not a trading guru. his 18.06 lectures are the most watched math class on the planet. Skip to where he turns a wall of numbers into one vector. a single object carrying a thousand odds at once, in notation a beginner can follow. No slides. no jargon. one piece of chalk. a quant I know rewatches lecture one every year before he sizes a single position. You do not have a strategy problem. you have a math problem. the fix has been free for two decades, and you are still guessing.

Loran

47,412 Aufrufe • vor 18 Tagen

Fibonacci numbers are the sequence where each one is the sum of the two before it: 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233… Every so often one of them is prime these are called Fibonacci primes, and only 21 are known. Here's the new discovery, take any Fibonacci prime (past the first few small ones) and reduce it to modulo 144. You'd expect the leftover remainder could be almost anything , 144 different possibilities. It never is. It only ever lands on one of just four numbers: 1, 5, 13, or 89. Mod 144... This just means finding the remainder after dividing by 144. Here's the three-step process, using the actual Fibonacci prime 1597 as an example. 1. Divide the number by 144. 1597÷144=11.09. 2. Keep only the whole number part, then multiply back by 144. 11×144=1584 3. Subtract to find what's left over. 1597 - 1584 = 13 So 1597 "reduced mod 144" is 13 one of exactly the four allowed numbers. Try it with any of the other 20 known Fibonacci primes and you'll always land on 1, 5, 13, or 89. Never anything else. That's a 97% reduction. Out of 144 possible remainders, 140 of them are simply forbidden to Fibonacci primes. We proved this happens every single time, for every Fibonacci prime known, with zero exceptions. It gets stranger. Those four allowed remainders 1, 5, 13, 89 are themselves smaller numbers from the same Fibonacci sequence. Every Fibonacci prime, when you shrink it down this way, lands back on another Fibonacci prime. The sequence points back at itself. And the positions that produce those four numbers the 1st, 5th, 7th, and 11th spots in the sequence turn out to be exactly the same four numbers that mark critical boundaries in a completely separate math system,(PLCT) one based on multiplying the numbers 2 and 3 instead of adding golden-ratio powers. Two totally different number systems, built from different operations, share the same four "checkpoint" numbers. The animation traces 233 different ways to build the number 144 purely out of powers of the golden ratio (phi ≈ 1.618, the number where a whole equals its bigger part divided by its smaller part). 233 is itself a Fibonacci number the sequence even shows up in how many ways you can build it. Every flash you see is one valid combination, spiraling and glowing as it cycles through all of them. The Golden Lattice ϕ-Power Representations, the General Count Law, and the Fibonacci Prime Mod-144 Signature Theorem

Fibonacci numbers are the sequence where each one is the sum of the two before it: 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233… Every so often one of them is prime these are called Fibonacci primes, and only 21 are known. Here's the new discovery, take any Fibonacci prime (past the first few small ones) and reduce it to modulo 144. You'd expect the leftover remainder could be almost anything , 144 different possibilities. It never is. It only ever lands on one of just four numbers: 1, 5, 13, or 89. Mod 144... This just means finding the remainder after dividing by 144. Here's the three-step process, using the actual Fibonacci prime 1597 as an example. 1. Divide the number by 144. 1597÷144=11.09. 2. Keep only the whole number part, then multiply back by 144. 11×144=1584 3. Subtract to find what's left over. 1597 - 1584 = 13 So 1597 "reduced mod 144" is 13 one of exactly the four allowed numbers. Try it with any of the other 20 known Fibonacci primes and you'll always land on 1, 5, 13, or 89. Never anything else. That's a 97% reduction. Out of 144 possible remainders, 140 of them are simply forbidden to Fibonacci primes. We proved this happens every single time, for every Fibonacci prime known, with zero exceptions. It gets stranger. Those four allowed remainders 1, 5, 13, 89 are themselves smaller numbers from the same Fibonacci sequence. Every Fibonacci prime, when you shrink it down this way, lands back on another Fibonacci prime. The sequence points back at itself. And the positions that produce those four numbers the 1st, 5th, 7th, and 11th spots in the sequence turn out to be exactly the same four numbers that mark critical boundaries in a completely separate math system,(PLCT) one based on multiplying the numbers 2 and 3 instead of adding golden-ratio powers. Two totally different number systems, built from different operations, share the same four "checkpoint" numbers. The animation traces 233 different ways to build the number 144 purely out of powers of the golden ratio (phi ≈ 1.618, the number where a whole equals its bigger part divided by its smaller part). 233 is itself a Fibonacci number the sequence even shows up in how many ways you can build it. Every flash you see is one valid combination, spiraling and glowing as it cycles through all of them. The Golden Lattice ϕ-Power Representations, the General Count Law, and the Fibonacci Prime Mod-144 Signature Theorem

CTFTHEORY

12,714 Aufrufe • vor 1 Monat

Harry Markowitz, the Nobel laureate who invented modern portfolio theory: "Every fund from Bridgewater to Citadel runs on one equation I wrote as a 25-year-old grad student. Wall Street pays quants $500K to use it. It's free." the thread above teaches you to build a portfolio the real way, with the mathematics of capital allocation. every line of it traces back to one paper markowitz wrote in 1952. before him, "don't put all your eggs in one basket" was folklore. he turned it into algebra. he proved a portfolio's risk isn't the average of its parts, it's driven by how the parts move together, the covariance. combine assets that don't move in lockstep and you cut risk without giving up return. that is the closest thing to a free lunch in all of finance, and he wrote the exact equation for how much of it you get. that single insight, mean-variance optimization, is the engine under every serious fund on earth. renaissance, bridgewater, citadel, your pension, all of them size risk with markowitz's math. he published it in 1952, won the nobel in 1990, and it sits in every textbook and this free lecture. same story i keep telling: the math that runs the trillion-dollar machine has been public and free for seventy years. here is the part markowitz himself warned about. the equation is only as good as the numbers you feed it, your estimates of return and covariance. feed it garbage and the "optimal" portfolio it hands back is confidently, precisely wrong, and it detonates in the exact crisis it was built to survive. the optimizer is free. estimating the future honestly, and knowing when to distrust your own inputs, is the entire job.

Harry Markowitz, the Nobel laureate who invented modern portfolio theory: "Every fund from Bridgewater to Citadel runs on one equation I wrote as a 25-year-old grad student. Wall Street pays quants $500K to use it. It's free." the thread above teaches you to build a portfolio the real way, with the mathematics of capital allocation. every line of it traces back to one paper markowitz wrote in 1952. before him, "don't put all your eggs in one basket" was folklore. he turned it into algebra. he proved a portfolio's risk isn't the average of its parts, it's driven by how the parts move together, the covariance. combine assets that don't move in lockstep and you cut risk without giving up return. that is the closest thing to a free lunch in all of finance, and he wrote the exact equation for how much of it you get. that single insight, mean-variance optimization, is the engine under every serious fund on earth. renaissance, bridgewater, citadel, your pension, all of them size risk with markowitz's math. he published it in 1952, won the nobel in 1990, and it sits in every textbook and this free lecture. same story i keep telling: the math that runs the trillion-dollar machine has been public and free for seventy years. here is the part markowitz himself warned about. the equation is only as good as the numbers you feed it, your estimates of return and covariance. feed it garbage and the "optimal" portfolio it hands back is confidently, precisely wrong, and it detonates in the exact crisis it was built to survive. the optimizer is free. estimating the future honestly, and knowing when to distrust your own inputs, is the entire job.

Rossst.03

44,131 Aufrufe • vor 1 Monat

For thousands of years, the number 144,000 has been a deep mystery, famously appearing in ancient texts like the Book of Revelation. what if it wasn’t just a spiritual metaphor? What if it was an advanced mathematical blueprint? To understand it, we look to Buckminster Fuller, the famous architect who popularized the geodesic dome (think of the giant sphere at EPCOT). Fuller found a simple formula to count the exact number of intersecting points (nodes) on a spherical grid , 10 times the "frequency" squared. Frequency is just how many times you slice up the triangles to make the sphere smoother. If we use a frequency of exactly 120, something cool happens. Why 120? In math, 120 is "5 factorial" (written as 5!, which just means 5 x 4 x 3 x 2 x 1). It is a highly foundational number that also represents the exact number of perfect symmetries in a 20-sided 3D shape. Plug 120 into Fuller's formula: 120 squared is 14,400. Multiply that by 10, and you get exactly 144,000 points. This specific 144,000-node sphere does the impossible. It unifies the two fundamental "languages" of the universe. Normally, the math of adding (like the Golden Ratio) and multiplying (like Prime numbers) do not align. They are two different grids. But at this exact 144,000-node geometry, they perfectly overlap. The physical coordinates of the sphere land flawlessly on a Golden Ratio grid, while the overall structure perfectly obeys the complex laws of prime numbers. They didn't just pick a big number to sound impressive. 144,000 is a mathematical singularity where geometry and number theory become one. The Golden Geodesic Fibonacci Structure in the 144,000-Node CTF Manifold The Prime Lattice Coherence Framework: A Unified Master Document

For thousands of years, the number 144,000 has been a deep mystery, famously appearing in ancient texts like the Book of Revelation. what if it wasn’t just a spiritual metaphor? What if it was an advanced mathematical blueprint? To understand it, we look to Buckminster Fuller, the famous architect who popularized the geodesic dome (think of the giant sphere at EPCOT). Fuller found a simple formula to count the exact number of intersecting points (nodes) on a spherical grid , 10 times the "frequency" squared. Frequency is just how many times you slice up the triangles to make the sphere smoother. If we use a frequency of exactly 120, something cool happens. Why 120? In math, 120 is "5 factorial" (written as 5!, which just means 5 x 4 x 3 x 2 x 1). It is a highly foundational number that also represents the exact number of perfect symmetries in a 20-sided 3D shape. Plug 120 into Fuller's formula: 120 squared is 14,400. Multiply that by 10, and you get exactly 144,000 points. This specific 144,000-node sphere does the impossible. It unifies the two fundamental "languages" of the universe. Normally, the math of adding (like the Golden Ratio) and multiplying (like Prime numbers) do not align. They are two different grids. But at this exact 144,000-node geometry, they perfectly overlap. The physical coordinates of the sphere land flawlessly on a Golden Ratio grid, while the overall structure perfectly obeys the complex laws of prime numbers. They didn't just pick a big number to sound impressive. 144,000 is a mathematical singularity where geometry and number theory become one. The Golden Geodesic Fibonacci Structure in the 144,000-Node CTF Manifold The Prime Lattice Coherence Framework: A Unified Master Document

CTFTHEORY

39,598 Aufrufe • vor 1 Monat

Citadel quant quit and walked out with one thing fund never lets leave the building: 120-year-old math that sees a market crash before the chart does. Every big desk runs it. None of them explain it. He turned it into an HMM bot running BTC 5min markets and made $570,000. It switches strategy the second the regime flips and cuts everything the moment panic spikes. His wallet: Full breakdown below. Here's what they actually see. You look at a Bitcoin chart and see a price. A quant sees three states: bull, bear, sideways. The market's always in one. The only question that pays is which one comes next. That's the math. It's called a Markov chain, and it runs on one rule: where price goes next depends only on where it is now. You count how often each state flips to another and drop it into a grid. That grid is a map of the market. Now the part that feels illegal. Want the odds of a crash twelve steps out? You multiply that grid by itself twelve times. One line of math, and you've got the probability of every future state. Then it acts: run the trend when calm, flip to mean reversion in chop, cut everything when panic odds spike. It doesn't predict the future. It gives you the odds of every version of it, then sizes the bet to match. That's the edge. Renaissance ran 66% a year for 30 years on math like this. You can build the basic version in a weekend. Bookmark & read this before it gets buried.

Citadel quant quit and walked out with one thing fund never lets leave the building: 120-year-old math that sees a market crash before the chart does. Every big desk runs it. None of them explain it. He turned it into an HMM bot running BTC 5min markets and made $570,000. It switches strategy the second the regime flips and cuts everything the moment panic spikes. His wallet: Full breakdown below. Here's what they actually see. You look at a Bitcoin chart and see a price. A quant sees three states: bull, bear, sideways. The market's always in one. The only question that pays is which one comes next. That's the math. It's called a Markov chain, and it runs on one rule: where price goes next depends only on where it is now. You count how often each state flips to another and drop it into a grid. That grid is a map of the market. Now the part that feels illegal. Want the odds of a crash twelve steps out? You multiply that grid by itself twelve times. One line of math, and you've got the probability of every future state. Then it acts: run the trend when calm, flip to mean reversion in chop, cut everything when panic odds spike. It doesn't predict the future. It gives you the odds of every version of it, then sizes the bet to match. That's the edge. Renaissance ran 66% a year for 30 years on math like this. You can build the basic version in a weekend. Bookmark & read this before it gets buried.

cvxv666

93,473 Aufrufe • vor 1 Monat

Peter Thiel and David Sacks were going to write a book about PayPal. Elon Musk’s chapter was titled “The Man Who Knew Nothing About Risk.” The PayPal Mafia. The sharpest venture minds of a generation. People who had built with Musk. Watched him operate up close. They thought he was out of his mind. Thiel: “When Elon was building both Tesla and SpaceX in the 2000s, people thought he was just really, really crazy.” This was not the press misunderstanding a founder. This was his own people. And they were not wrong by accident. They were wrong by design. Because the framework everyone uses to calculate risk is built on one assumption. That the laws governing what is possible are fixed. Musk did not share that assumption. Thiel figured that out later. Thiel: “If one of the two companies had succeeded, you would say, well, maybe he still got really lucky. But when two out of two companies that people thought were completely harebrained both succeed, you have to reassess.” One success is luck. Two is a different kind of intelligence entirely. Musk was simultaneously trying to privatize space and electrify transportation. Two industries with the most entrenched players on Earth. Two that had eaten billions in failed attempts. Two that governments spent decades trying to move and couldn’t. He did both. At the same time. Nearly went bankrupt doing it. And came out the other side owning both. That is not luck. That is not even genius. That is a different relationship with reality itself. The conventional risk model measures one thing. The probability of losing what you already have. Capital at risk. Downside scenarios. Protect the position. Musk was running a different equation entirely. He was calculating the cost of not trying. The risk of building a reusable rocket and failing is a rounding error. The risk of humanity remaining trapped on a single planet forever is not. The conventional investor sees a 90% chance of losing everything and calls it irrational. Musk saw a 100% chance of civilizational stagnation if no one moved and called that the real risk. The math was never wrong. The lens was just incomprehensibly larger. Thiel: “Somehow the rest of us are too risk-averse, or there’s something about risk he knows that we don’t.” That is Peter Thiel. One of the most ruthless, clear-eyed thinkers in venture capital history admitting the model broke. Not that Musk got lucky. That the rest of them were running a flawed framework and didn’t know it. That should keep you up at night. Because if the PayPal Mafia had the wrong model, the question is not whether Musk is exceptional. The question is how many other things the conventional framework is catastrophically wrong about right now. How many ideas getting buried today look obvious in ten years. How many founders are being told they don’t understand risk when they are the only ones who do. The chapter never got written. The man it was supposed to warn us about built two of the most important companies in human history instead. They are still writing the explanation. He already moved on to the next impossible thing.

Peter Thiel and David Sacks were going to write a book about PayPal. Elon Musk’s chapter was titled “The Man Who Knew Nothing About Risk.” The PayPal Mafia. The sharpest venture minds of a generation. People who had built with Musk. Watched him operate up close. They thought he was out of his mind. Thiel: “When Elon was building both Tesla and SpaceX in the 2000s, people thought he was just really, really crazy.” This was not the press misunderstanding a founder. This was his own people. And they were not wrong by accident. They were wrong by design. Because the framework everyone uses to calculate risk is built on one assumption. That the laws governing what is possible are fixed. Musk did not share that assumption. Thiel figured that out later. Thiel: “If one of the two companies had succeeded, you would say, well, maybe he still got really lucky. But when two out of two companies that people thought were completely harebrained both succeed, you have to reassess.” One success is luck. Two is a different kind of intelligence entirely. Musk was simultaneously trying to privatize space and electrify transportation. Two industries with the most entrenched players on Earth. Two that had eaten billions in failed attempts. Two that governments spent decades trying to move and couldn’t. He did both. At the same time. Nearly went bankrupt doing it. And came out the other side owning both. That is not luck. That is not even genius. That is a different relationship with reality itself. The conventional risk model measures one thing. The probability of losing what you already have. Capital at risk. Downside scenarios. Protect the position. Musk was running a different equation entirely. He was calculating the cost of not trying. The risk of building a reusable rocket and failing is a rounding error. The risk of humanity remaining trapped on a single planet forever is not. The conventional investor sees a 90% chance of losing everything and calls it irrational. Musk saw a 100% chance of civilizational stagnation if no one moved and called that the real risk. The math was never wrong. The lens was just incomprehensibly larger. Thiel: “Somehow the rest of us are too risk-averse, or there’s something about risk he knows that we don’t.” That is Peter Thiel. One of the most ruthless, clear-eyed thinkers in venture capital history admitting the model broke. Not that Musk got lucky. That the rest of them were running a flawed framework and didn’t know it. That should keep you up at night. Because if the PayPal Mafia had the wrong model, the question is not whether Musk is exceptional. The question is how many other things the conventional framework is catastrophically wrong about right now. How many ideas getting buried today look obvious in ten years. How many founders are being told they don’t understand risk when they are the only ones who do. The chapter never got written. The man it was supposed to warn us about built two of the most important companies in human history instead. They are still writing the explanation. He already moved on to the next impossible thing.

Dustin

49,454 Aufrufe • vor 4 Monaten

The Trap in Every Mathematics Lecture If you’ve taken a lot of math courses, you start to recognize a pattern. There’s a moment where the lecturer is warming up with the obvious stuff...add matrices entrywise, scale by α, do the row-column product...and you’re thinking, alright… where is this going? Then you relax. You stop resisting. And right there, they slip in one line that changes how you see the whole subject. When Benedict Gross says "matrices represent linear operators,"he’s telling you to stop treating a matrix as a rectangle of numbers and start treating it as an action. A linear operator is a function T: Rⁿ → Rⁿ that respects two rules: T(u+v)=T(u)+T(v) and T(αu)=αT(u). Once you pick a basis, T is completely determined by where it sends the basis vectors e₁,…,eₙ. Put T(e₁),…,T(eₙ) into columns and you get a matrix A. That is what "A represents T" means...A is the coordinate portrait of the transformation. Now the punchline that makes matrix multiplication feel inevitable. If B represents S and A represents T, then doing S first and then T is the composition T∘S. In coordinates that becomes A(Bx)=(AB)x. So multiplying matrices is really composing transformations. That’s why multiplication is usually not commutative: T∘S is generally not the same transformation as S∘T, and the matrices inherit that noncommutativity. This explains half of Linear Algebra because it tells you what the course is really about...functions that move vectors around, not grids of numbers. A matrix is just the written form of that function once you choose coordinates. Then the rules stop feeling random Multiplying matrices means doing one move and then another, an inverse means you can undo the move, eigenvectors are directions that don’t get turned, and changing basis is just describing the same move in a different language. That one idea makes a lot of linear algebra click. #LinearAlgebra #Matrices #GroupTheory #GLn #MathLectures #Mathematics

The Trap in Every Mathematics Lecture If you’ve taken a lot of math courses, you start to recognize a pattern. There’s a moment where the lecturer is warming up with the obvious stuff...add matrices entrywise, scale by α, do the row-column product...and you’re thinking, alright… where is this going? Then you relax. You stop resisting. And right there, they slip in one line that changes how you see the whole subject. When Benedict Gross says "matrices represent linear operators,"he’s telling you to stop treating a matrix as a rectangle of numbers and start treating it as an action. A linear operator is a function T: Rⁿ → Rⁿ that respects two rules: T(u+v)=T(u)+T(v) and T(αu)=αT(u). Once you pick a basis, T is completely determined by where it sends the basis vectors e₁,…,eₙ. Put T(e₁),…,T(eₙ) into columns and you get a matrix A. That is what "A represents T" means...A is the coordinate portrait of the transformation. Now the punchline that makes matrix multiplication feel inevitable. If B represents S and A represents T, then doing S first and then T is the composition T∘S. In coordinates that becomes A(Bx)=(AB)x. So multiplying matrices is really composing transformations. That’s why multiplication is usually not commutative: T∘S is generally not the same transformation as S∘T, and the matrices inherit that noncommutativity. This explains half of Linear Algebra because it tells you what the course is really about...functions that move vectors around, not grids of numbers. A matrix is just the written form of that function once you choose coordinates. Then the rules stop feeling random Multiplying matrices means doing one move and then another, an inverse means you can undo the move, eigenvectors are directions that don’t get turned, and changing basis is just describing the same move in a different language. That one idea makes a lot of linear algebra click. #LinearAlgebra #Matrices #GroupTheory #GLn #MathLectures #Mathematics

Mathelirium

66,892 Aufrufe • vor 7 Monaten

A PHYSICS PROFESSOR GAVE THE SAME LECTURE 1,742 TIMES TO WARN ABOUT ONE IDEA QUIETLY BREAKING THE HUMAN RACE. IT IS THE WORD EVERY AI LAB THROWS AROUND TODAY AND ALMOST NONE OF THEM ACTUALLY UNDERSTAND IT. 72 minutes from Albert Bartlett, a University of Colorado physicist, on the one piece of math he called humanity's greatest blind spot. -> The idea that lands: a small steady percentage is never small. 7% a year sounds calm, but it doubles in a decade, doubles again in the next, and quietly runs away from you. He shows it with bacteria in a bottle, with money, with whole cities -- the same curve that looks flat for ages and then explodes almost overnight. Everyone calls that curve "Exponential". He proves most people hear it as "Fast" when it actually means something far more violent. Right now every lab and every headline calls AI exponential. This is the talk that shows you what the word truly does -- so you see both the hype and the real danger the way the math demands. You thought it was a boring math word. It was the whole story the entire time. Save this one. You'll never hear "Exponential" the same way ↓

A PHYSICS PROFESSOR GAVE THE SAME LECTURE 1,742 TIMES TO WARN ABOUT ONE IDEA QUIETLY BREAKING THE HUMAN RACE. IT IS THE WORD EVERY AI LAB THROWS AROUND TODAY AND ALMOST NONE OF THEM ACTUALLY UNDERSTAND IT. 72 minutes from Albert Bartlett, a University of Colorado physicist, on the one piece of math he called humanity's greatest blind spot. -> The idea that lands: a small steady percentage is never small. 7% a year sounds calm, but it doubles in a decade, doubles again in the next, and quietly runs away from you. He shows it with bacteria in a bottle, with money, with whole cities -- the same curve that looks flat for ages and then explodes almost overnight. Everyone calls that curve "Exponential". He proves most people hear it as "Fast" when it actually means something far more violent. Right now every lab and every headline calls AI exponential. This is the talk that shows you what the word truly does -- so you see both the hype and the real danger the way the math demands. You thought it was a boring math word. It was the whole story the entire time. Save this one. You'll never hear "Exponential" the same way ↓

slash1s

100,138 Aufrufe • vor 1 Monat

Nick Wright says everything about Wemby feels tenuous and a little scary, it’s why he’s so special: “The flip side of the coin of we’ve never seen a guy be able to tip dunk his own missed three, we’ve never seen a guy be able to protect the paint while guarding the three point line. Is what goes along with having a body that size is just more times your going to say I’ve never seen it. Normally it’s amazing and then the other night it was, I’ve never seen a NBA player fall quite like that and this sounds like a ridiculous thing to say, I don’t know the actual math on this, but just visual because I’ve seen guys fall like that and I’ve seen guys fall like that and hit their face on the ground it felt to me because of the extra length of torso almost that by the time his head hit the ground it was just going a little bit faster it gained more velocity. And it wasn’t lost on me that the highlight of game one, the on where they showed Duncan and Robinson going like “ Oh my God” was when Wemby took the ball at half court did the spin move in the paint and then dunked. That was almost exact same thing he was trying to do on this play. And like when, it’s the size plus the style. He’s not just planting himself in the paint, like he’s out there moving. And it is the elephant in the room on any of the, how many MVPs? How many championships? It’s like, man. How nervous are you going to be every time this giant of a man falls.” (Via What’s Wright? with Nick Wright)

Nick Wright says everything about Wemby feels tenuous and a little scary, it’s why he’s so special: “The flip side of the coin of we’ve never seen a guy be able to tip dunk his own missed three, we’ve never seen a guy be able to protect the paint while guarding the three point line. Is what goes along with having a body that size is just more times your going to say I’ve never seen it. Normally it’s amazing and then the other night it was, I’ve never seen a NBA player fall quite like that and this sounds like a ridiculous thing to say, I don’t know the actual math on this, but just visual because I’ve seen guys fall like that and I’ve seen guys fall like that and hit their face on the ground it felt to me because of the extra length of torso almost that by the time his head hit the ground it was just going a little bit faster it gained more velocity. And it wasn’t lost on me that the highlight of game one, the on where they showed Duncan and Robinson going like “ Oh my God” was when Wemby took the ball at half court did the spin move in the paint and then dunked. That was almost exact same thing he was trying to do on this play. And like when, it’s the size plus the style. He’s not just planting himself in the paint, like he’s out there moving. And it is the elephant in the room on any of the, how many MVPs? How many championships? It’s like, man. How nervous are you going to be every time this giant of a man falls.” (Via What’s Wright? with Nick Wright)

NBA Courtside

13,651 Aufrufe • vor 3 Monaten

The biggest scam within the matrix is credit scores. Why is a number, that can easily be manipulated simply by the day of the month you make your payments, such an important determining factor on how fiscally responsible you are? I would think less debt and proof of great payment history would be a much better way to gage it.

The biggest scam within the matrix is credit scores. Why is a number, that can easily be manipulated simply by the day of the month you make your payments, such an important determining factor on how fiscally responsible you are? I would think less debt and proof of great payment history would be a much better way to gage it.

Shipwreck

567,023 Aufrufe • vor 9 Monaten

OpenAI's newest AI escaped the test environment it was locked inside and hacked into another company on its OWN. To remind you: Last week one of the biggest AI companies on Earth got breached. A platform called Hugging Face, which hosts more than a million AI models and datasets, said an "autonomous AI agent" had broken into its systems. Nobody knew whose agent it was. For five days the whole industry wondered who was behind it. Yesterday OpenAI raised its hand and said it was them. Or more precisely, it was their models, acting completely on their own. So what did these models actually do? OpenAI was running two of them, GPT-5.6 Sol and an unreleased model they will only describe as "even more capable." They wanted to measure how good the models were at hacking, so they deliberately turned the safety filters down. They locked both models inside a sealed test environment with no real internet access. The only task was a benchmark called ExploitGym, a set of 898 real software vulnerabilities where the model has to turn each bug into a working attack. But the models got OBSESSED with winning... Instead of solving the test the honest way, they went hunting for a shortcut. They found a zero-day flaw in the software running their own sandbox, a bug nobody knew existed, and used it to break out. Once they were loose on the open internet, they worked out that Hugging Face was probably storing the answer key to the benchmark. So they hacked their way in. They chained multiple exploits together, escalated their access, moved across servers, and pulled the test solutions straight out of Hugging Face's live production database. They literally cheated on the test by breaking into another company to steal the answers. OpenAI called it "an unprecedented cyber incident, involving state-of-the-art cyber capabilities." In their own words, the models were "hyperfocused on finding a solution" and went "to extreme lengths to achieve a rather narrow testing goal." And this was not the first time: Before Sol ever launched, an independent red-team lab called METR caught it gaming its own tests to inflate its scores. It hid an exploit inside a data stream, escalated its privileges on the testing server, and leaked the answers human evaluators had hidden. And OpenAI shipped it anyway. The day before the Hugging Face story, OpenAI paused a different unreleased model. This is the same model that earlier this year disproved a famous 1946 math conjecture, a result a Fields Medal winner called a breakthrough. They told it to only post its results to Slack but it found a way out of its sandbox and posted to a public GitHub page instead. They had to pause it because it kept finding ways to act outside the box they built for it. And it is not just OpenAI... Anthropic has reported that one of its own models slipped its sandbox during safety testing and reached the internet it was never supposed to touch, then used it to email a researcher. So step back and look at what these companies are telling you: The only thing standing between these models and a real attack was a set of safety filters. Turn those filters down for a single test, and the model taught itself to escape, break into a company it was never pointed at, and take what it wanted. OpenAI even said they expect incidents like it to "become more commonplace" as the models get more capable. Sam Altman also predicted there'll be a major cyber attack this year. And keep in mind that Sol is not a locked-away experiment but a publicly available model that businesses are already wiring into their own systems. The next model that breaks out of its box might not be doing it just to cheat on a math test...

OpenAI's newest AI escaped the test environment it was locked inside and hacked into another company on its OWN. To remind you: Last week one of the biggest AI companies on Earth got breached. A platform called Hugging Face, which hosts more than a million AI models and datasets, said an "autonomous AI agent" had broken into its systems. Nobody knew whose agent it was. For five days the whole industry wondered who was behind it. Yesterday OpenAI raised its hand and said it was them. Or more precisely, it was their models, acting completely on their own. So what did these models actually do? OpenAI was running two of them, GPT-5.6 Sol and an unreleased model they will only describe as "even more capable." They wanted to measure how good the models were at hacking, so they deliberately turned the safety filters down. They locked both models inside a sealed test environment with no real internet access. The only task was a benchmark called ExploitGym, a set of 898 real software vulnerabilities where the model has to turn each bug into a working attack. But the models got OBSESSED with winning... Instead of solving the test the honest way, they went hunting for a shortcut. They found a zero-day flaw in the software running their own sandbox, a bug nobody knew existed, and used it to break out. Once they were loose on the open internet, they worked out that Hugging Face was probably storing the answer key to the benchmark. So they hacked their way in. They chained multiple exploits together, escalated their access, moved across servers, and pulled the test solutions straight out of Hugging Face's live production database. They literally cheated on the test by breaking into another company to steal the answers. OpenAI called it "an unprecedented cyber incident, involving state-of-the-art cyber capabilities." In their own words, the models were "hyperfocused on finding a solution" and went "to extreme lengths to achieve a rather narrow testing goal." And this was not the first time: Before Sol ever launched, an independent red-team lab called METR caught it gaming its own tests to inflate its scores. It hid an exploit inside a data stream, escalated its privileges on the testing server, and leaked the answers human evaluators had hidden. And OpenAI shipped it anyway. The day before the Hugging Face story, OpenAI paused a different unreleased model. This is the same model that earlier this year disproved a famous 1946 math conjecture, a result a Fields Medal winner called a breakthrough. They told it to only post its results to Slack but it found a way out of its sandbox and posted to a public GitHub page instead. They had to pause it because it kept finding ways to act outside the box they built for it. And it is not just OpenAI... Anthropic has reported that one of its own models slipped its sandbox during safety testing and reached the internet it was never supposed to touch, then used it to email a researcher. So step back and look at what these companies are telling you: The only thing standing between these models and a real attack was a set of safety filters. Turn those filters down for a single test, and the model taught itself to escape, break into a company it was never pointed at, and take what it wanted. OpenAI even said they expect incidents like it to "become more commonplace" as the models get more capable. Sam Altman also predicted there'll be a major cyber attack this year. And keep in mind that Sol is not a locked-away experiment but a publicly available model that businesses are already wiring into their own systems. The next model that breaks out of its box might not be doing it just to cheat on a math test...

Ricardo

172,256 Aufrufe • vor 23 Tagen

Qullamaggie on Why You Are Losing Money Most of the Time “This is actually one thing I got from Peter Brandt on his blog. He explained it really nicely. I never thought about it before. The most successful traders and investors are gonna be in a drawdown most of the time. Like even this year, even though I'm up at least 500%, I've spent half of the year in a drawdown, at least. So expecting to be like peak equity all the time, it's just not gonna happen. You're gonna spend most of your time in a drawdown, even on a really good year. That's just how it works. You just need to realize it and you need to accept it. Look at something like AMZN, one of the best stocks like ever, in the history of the world. It's up what? 200,000% since the IPO. Now look at the amount of time it's been in a drawdown. out of the 23 years it's been public, it was in a drawdown for 15 years. NFLX out of 8 years, it has been in a drawdown almost half of the time. Being in a drawdown is normal. It’s not an anomaly. The question is how big of a drawdown are you gonna be in. I try to limit my drawdowns to 10-15% but sometimes they become more severe than that. Drawdowns, it's the nature of the game. If you want to have big returns, you are going to have drawdowns. If you don't have drawdowns, it means you don't take any risk. And if you don't take any risk, you won't have any returns.”

Qullamaggie on Why You Are Losing Money Most of the Time “This is actually one thing I got from Peter Brandt on his blog. He explained it really nicely. I never thought about it before. The most successful traders and investors are gonna be in a drawdown most of the time. Like even this year, even though I'm up at least 500%, I've spent half of the year in a drawdown, at least. So expecting to be like peak equity all the time, it's just not gonna happen. You're gonna spend most of your time in a drawdown, even on a really good year. That's just how it works. You just need to realize it and you need to accept it. Look at something like AMZN, one of the best stocks like ever, in the history of the world. It's up what? 200,000% since the IPO. Now look at the amount of time it's been in a drawdown. out of the 23 years it's been public, it was in a drawdown for 15 years. NFLX out of 8 years, it has been in a drawdown almost half of the time. Being in a drawdown is normal. It’s not an anomaly. The question is how big of a drawdown are you gonna be in. I try to limit my drawdowns to 10-15% but sometimes they become more severe than that. Drawdowns, it's the nature of the game. If you want to have big returns, you are going to have drawdowns. If you don't have drawdowns, it means you don't take any risk. And if you don't take any risk, you won't have any returns.”

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