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Terence Tao thinks AI is already very good at using existing, well-understood math techniques to solve problems. An important question is how many open problems in math could be solved this way, without developing any new ideas. An extreme case of a proof like this is the four-color theorem,... show more

Dwarkesh Patel

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64,547 views • 4 months ago •via X (Twitter)

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Terence Tao: AI isn’t hype anymore in Math discovery. Terence Tao is one of the greatest living mathematicians, in his new lecture explains how AI and human professional mathematicians are now complementary. "There has been a really visible increase in capability. It is not pure hype by any means. To me, these advances show there is a complementary way to do mathematics. Humans traditionally work in small groups on hard problems for months, and we will keep doing that. But we can also now set AI to scale: sweep a thousand problems and pick up all the low-hanging fruit. Figure out all the ways to match problems to methods. If there are 20 different techniques, apply them all to 1,000 problems and see which ones can be solved by these methods. This is the capability that is present today." From 'Institute for Pure & Applied Mathematics (IPAM)' YT channel.

Terence Tao: AI isn’t hype anymore in Math discovery. Terence Tao is one of the greatest living mathematicians, in his new lecture explains how AI and human professional mathematicians are now complementary. "There has been a really visible increase in capability. It is not pure hype by any means. To me, these advances show there is a complementary way to do mathematics. Humans traditionally work in small groups on hard problems for months, and we will keep doing that. But we can also now set AI to scale: sweep a thousand problems and pick up all the low-hanging fruit. Figure out all the ways to match problems to methods. If there are 20 different techniques, apply them all to 1,000 problems and see which ones can be solved by these methods. This is the capability that is present today." From 'Institute for Pure & Applied Mathematics (IPAM)' YT channel.

Rohan Paul

173,987 views • 5 months ago

How does math research change when the cost of trying your first dumb idea goes to zero? Daniel Litt joins Greg Burnham and Anson Ho to discuss what today’s models can and can’t do in math, and how far they are from doing high-quality research. 0:00:00 What's the hardest math problem AI can solve today? 00:16:08 How helpful are today’s AI models for math research? 00:23:36 Junk papers, LLM-generated proofs, and the refereeing crisis 00:27:21 AI enables searching through problems at scale 00:33:49 When will AI be good enough to publish in top math journals? 00:42:15 What are the returns to intelligence? 00:59:50 Will AI solve Millennium problems? 01:11:54 Is math full of low-hanging fruit? 01:18:47 How Daniel has adapted his professional life to AI progress 01:25:28 What do AI math benchmarks actually measure? 01:33:05 Designing the Open Problems benchmark 01:56:35 Do mathematicians believe heuristic arguments about conjectures? 02:01:24 What if FrontierMath: Open Problems gets solved? 02:06:53 Is AI on the cusp of accelerating math progress?

How does math research change when the cost of trying your first dumb idea goes to zero? Daniel Litt joins Greg Burnham and Anson Ho to discuss what today’s models can and can’t do in math, and how far they are from doing high-quality research. 0:00:00 What's the hardest math problem AI can solve today? 00:16:08 How helpful are today’s AI models for math research? 00:23:36 Junk papers, LLM-generated proofs, and the refereeing crisis 00:27:21 AI enables searching through problems at scale 00:33:49 When will AI be good enough to publish in top math journals? 00:42:15 What are the returns to intelligence? 00:59:50 Will AI solve Millennium problems? 01:11:54 Is math full of low-hanging fruit? 01:18:47 How Daniel has adapted his professional life to AI progress 01:25:28 What do AI math benchmarks actually measure? 01:33:05 Designing the Open Problems benchmark 01:56:35 Do mathematicians believe heuristic arguments about conjectures? 02:01:24 What if FrontierMath: Open Problems gets solved? 02:06:53 Is AI on the cusp of accelerating math progress?

Epoch AI

178,703 views • 6 months ago

AI has solved 50 Erdős problems in the last year. But on a wider sweep of problems, the models’ success rate is only about 1-2%: labs have just been publishing the wins. This isn’t because AI isn’t useful for mathematicians. Terence Tao thinks the models are currently at the level of a trustworthy coworker. But while they’ve got a strong ability to apply standard math techniques to problems, often more reliably than humans, Terence thinks they currently aren’t great at iterating on partial successes - their understanding of the mathematical object does not advance from session to session. I swear I wasn’t trying to get him to talk about continual learning.

AI has solved 50 Erdős problems in the last year. But on a wider sweep of problems, the models’ success rate is only about 1-2%: labs have just been publishing the wins. This isn’t because AI isn’t useful for mathematicians. Terence Tao thinks the models are currently at the level of a trustworthy coworker. But while they’ve got a strong ability to apply standard math techniques to problems, often more reliably than humans, Terence thinks they currently aren’t great at iterating on partial successes - their understanding of the mathematical object does not advance from session to session. I swear I wasn’t trying to get him to talk about continual learning.

Dwarkesh Patel

146,244 views • 4 months ago

Terence Tao summarized how AI is massively accelerating math career and math research. "In math, you previously had to basically go through years and years of education to be a math PhD before you could contribute to the frontier of math research. But now it's quite possible at the high school level or whatever, that you could get involved in a math project and actually make a real contribution because of all these AI tools and lean and everything else." From Dwarkesh Patel podcast (link to full video in comment)

Terence Tao summarized how AI is massively accelerating math career and math research. "In math, you previously had to basically go through years and years of education to be a math PhD before you could contribute to the frontier of math research. But now it's quite possible at the high school level or whatever, that you could get involved in a math project and actually make a real contribution because of all these AI tools and lean and everything else." From Dwarkesh Patel podcast (link to full video in comment)

Rohan Paul

61,705 views • 1 month ago

Axiom Math founder Carina Hong on why math is the path to general superintelligence: “Math is the sandbox for reality. You very quickly see verifiable rewards because in math, there’s an absolute right or wrong.” “And especially when you have Lean, you can check the proof or solution step by step. You’ll be able to apply reinforcement learning in a much more efficient way.” “We have currently scaled from winning a Putnam perfect score to solving a batch of research problems that professional mathematicians find really challenging. And we also see this transfer to code verification.”

Axiom Math founder Carina Hong on why math is the path to general superintelligence: “Math is the sandbox for reality. You very quickly see verifiable rewards because in math, there’s an absolute right or wrong.” “And especially when you have Lean, you can check the proof or solution step by step. You’ll be able to apply reinforcement learning in a much more efficient way.” “We have currently scaled from winning a Putnam perfect score to solving a batch of research problems that professional mathematicians find really challenging. And we also see this transfer to code verification.”

TBPN

39,589 views • 4 months ago

The best MOTW in Precure are the one that just fight by making questions like is a exam Mostly because is funny seeing how this girls lose to something like the power of MATH

The best MOTW in Precure are the one that just fight by making questions like is a exam Mostly because is funny seeing how this girls lose to something like the power of MATH

*Why this Ayabe so mad 😭😭😭

164,677 views • 2 months ago

The way math is presented can be really misleading about the actual nature of math. In theory, someone could have discovered quantum information theory decades before quantum mechanics even existed. It's just about combining linear algebra with probability. But nobody would have known it was worth combining these fields - or how, exactly, to combine them - before QM. Math papers and textbooks go rigourously step-by-step, starting with axioms and definitions, then deducing theorems. It's great for making sure proofs can be verified and ensuring the subject is based on sound logic. But for learners, it hides the really important thing: where did the axioms come from? Why these definitions and not others? The thing that's missing is the sense that there were concrete problems that these ideas helped solve.

The way math is presented can be really misleading about the actual nature of math. In theory, someone could have discovered quantum information theory decades before quantum mechanics even existed. It's just about combining linear algebra with probability. But nobody would have known it was worth combining these fields - or how, exactly, to combine them - before QM. Math papers and textbooks go rigourously step-by-step, starting with axioms and definitions, then deducing theorems. It's great for making sure proofs can be verified and ensuring the subject is based on sound logic. But for learners, it hides the really important thing: where did the axioms come from? Why these definitions and not others? The thing that's missing is the sense that there were concrete problems that these ideas helped solve.

Dwarkesh Patel

58,236 views • 1 month ago

Jony Ive: “Ideas are always fragile” “Ideas—by definition—are always fragile. If they were resolved, they wouldn’t be ideas. They would be products that were ready to ship.” The legendary Apple designer continues: “I’ve come to learn that you have to make an extraordinary effort not to focus on the problems, which are implicated with any new idea. These problems are known. They’re quantifiable and understood. But you have to focus on the actual idea, which is partial, tentative, and unproven.” All new ideas have problems, and if you don’t suspend your disbelief and trust that you will find a solution, you will lose faith in your idea. “That is why criticism and focusing on the problems can be so damaging—particularly in the absence of a constructive idea. Remember, opinions are not ideas. Opinions are not as important as ideas. Opinions are just opinions.” Source: California College of the Arts

Jony Ive: “Ideas are always fragile” “Ideas—by definition—are always fragile. If they were resolved, they wouldn’t be ideas. They would be products that were ready to ship.” The legendary Apple designer continues: “I’ve come to learn that you have to make an extraordinary effort not to focus on the problems, which are implicated with any new idea. These problems are known. They’re quantifiable and understood. But you have to focus on the actual idea, which is partial, tentative, and unproven.” All new ideas have problems, and if you don’t suspend your disbelief and trust that you will find a solution, you will lose faith in your idea. “That is why criticism and focusing on the problems can be so damaging—particularly in the absence of a constructive idea. Remember, opinions are not ideas. Opinions are not as important as ideas. Opinions are just opinions.” Source: California College of the Arts

Startup Archive

112,432 views • 2 years ago

"What is P vs NP and how does it relate to human knowledge?" Avi Wigderson (Turing Award & Abel Prize winner): "In a very basic way, all the problems we want to solve are really NP problems. A subset of it is the problems we can solve. So the question of P versus NP is whether we can solve all the problems we want to solve, whether we can know everything we want to know. This is basically about the limits of our knowledge. You can think there is no limit to what we may want to know but I claim that essentially in any human endeavor, what people are searching for have the property that when a solution is found, it is easily recognized. NP are all problems that we can honestly say we really want to solve and P are those that we can solve. Whether they are equal is just the very fact that a solution can be easily recognized would imply that it can be efficiently found. That means that we can know everything we ever want to know, it's a fundamental question about human knowledge."

"What is P vs NP and how does it relate to human knowledge?" Avi Wigderson (Turing Award & Abel Prize winner): "In a very basic way, all the problems we want to solve are really NP problems. A subset of it is the problems we can solve. So the question of P versus NP is whether we can solve all the problems we want to solve, whether we can know everything we want to know. This is basically about the limits of our knowledge. You can think there is no limit to what we may want to know but I claim that essentially in any human endeavor, what people are searching for have the property that when a solution is found, it is easily recognized. NP are all problems that we can honestly say we really want to solve and P are those that we can solve. Whether they are equal is just the very fact that a solution can be easily recognized would imply that it can be efficiently found. That means that we can know everything we ever want to know, it's a fundamental question about human knowledge."

Ryan Peterman

20,069 views • 1 month ago

.Adam Marblestone explains the value of all these efforts to have AI automate formal math reasoning: "I think of it like when everybody had to write assembly code. The amount of cool startups that got created was a lot less. Fewer people could do it. Progress was more grinding, slow, and lonely. I think there are actually lots of people who could have interesting ideas. Can you have outsider string theorists because the math is just done for them by the computer? Does that lead to more innovation in string theory? Maybe yes."

.Adam Marblestone explains the value of all these efforts to have AI automate formal math reasoning: "I think of it like when everybody had to write assembly code. The amount of cool startups that got created was a lot less. Fewer people could do it. Progress was more grinding, slow, and lonely. I think there are actually lots of people who could have interesting ideas. Can you have outsider string theorists because the math is just done for them by the computer? Does that lead to more innovation in string theory? Maybe yes."

Dwarkesh Patel

30,813 views • 6 months ago

Terence Tao: "Previously, you needed a PhD to contribute to math research. Now a high school student can." Dwarkesh asks the world's most famous mathematician: what's your advice for someone considering a career in math, especially in light of AI progress? Tao is honest about uncertainty: "We live in a time of change. A particularly unpredictable era. Things that we've taken for granted for centuries may not hold anymore. The way we do everything... not just mathematics... will change." He admits his preference: "In many ways, I would prefer a much more boring, quiet era where things are much the same as they were 10 or 20 years ago. But one just has to embrace this. There's going to be a lot of change. The things you study... some of them may become obsolete or revolutionized. But some things will be retained." On new opportunities: "Previously, you had to go through years and years of education and get a math PhD before you could contribute to the frontier of math research. But now it's quite possible at the high school level that you could get involved in a math project and actually make a real contribution... because of all these AI tools and Lean and everything else." His advice: "There will be a lot of non-traditional opportunities to learn. You need a very adaptable mindset. There'll be worth pursuing things just for curiosity and for playing around. Still go through traditional education and learn math and science the old-fashioned way for a while... credentials will still be important. But you should also be open to very, very different ways of doing science. Some of which don't exist yet." He concludes: "It's a scary time. But also very exciting."

Terence Tao: "Previously, you needed a PhD to contribute to math research. Now a high school student can." Dwarkesh asks the world's most famous mathematician: what's your advice for someone considering a career in math, especially in light of AI progress? Tao is honest about uncertainty: "We live in a time of change. A particularly unpredictable era. Things that we've taken for granted for centuries may not hold anymore. The way we do everything... not just mathematics... will change." He admits his preference: "In many ways, I would prefer a much more boring, quiet era where things are much the same as they were 10 or 20 years ago. But one just has to embrace this. There's going to be a lot of change. The things you study... some of them may become obsolete or revolutionized. But some things will be retained." On new opportunities: "Previously, you had to go through years and years of education and get a math PhD before you could contribute to the frontier of math research. But now it's quite possible at the high school level that you could get involved in a math project and actually make a real contribution... because of all these AI tools and Lean and everything else." His advice: "There will be a lot of non-traditional opportunities to learn. You need a very adaptable mindset. There'll be worth pursuing things just for curiosity and for playing around. Still go through traditional education and learn math and science the old-fashioned way for a while... credentials will still be important. But you should also be open to very, very different ways of doing science. Some of which don't exist yet." He concludes: "It's a scary time. But also very exciting."

Jaynit

77,410 views • 2 months ago

“Harmonic is building Mathematical Superintelligence (MSI)” With $295M+ in total funding at a recent $1.45B post-money valuation, Harmonic's mission is to solve math problems that have remained unsolved for centuries, unlocking progress across physics, engineering.. & maybe even time travel? Co-founded by Vlad Tenev (Vlad Tenev) CEO of Robinhood, & Harmonic CEO Tudor Achim (Tudor Achim), the company has raised from leading investors including Ribbit, Sequoia, Kleiner Perkins, Index, Paradigm, DST Global, & more.. Funding history & lead investors: - Series A (Sept 2024): $75M led by steve beaker - Series B (July 2025): $100M led by Kleiner Perkins - Series C (Nov 2025): $120M at a $1.45B post-money valuation led by Ribbit Capital "Harmonic’s flagship Aristotle model recently achieved gold-medal level performance at the International Mathematical Olympiad, considered the most prestigious mathematical competition in the world, and is now available to the public. Unlike other models, Aristotle makes use of formal verification using Lean4 to ensure accuracy and eliminate hallucinations. In the first few weeks since its API beta launch, Aristotle has already been used by mathematicians and researchers to accelerate progress and create novel discoveries." . . . "Harmonic is building what we call mathematical super intelligence, and it's an artificial intelligence that can solve math problems better than any human mathematician. The company's been around for a couple of years. The North Star was, can we actually solve really, really important math problems like the Riemann Hypothesis or Hodge Conjecture? There's this group of math problems that have been open for hundreds of years that are called the Millennium Prize problems, and they're considered very big, difficult, and actually valuable. So that was kind of the North Star, and the reason we wanted to do that was if we could solve those problems, everything downstream of math, like theoretical physics becomes unlocked. So then you can imagine solving really hard physics problems. And actually, if you can solve that, then there's all kinds of exciting engineering developments, like depending on how that theory looks, you can imagine things like faster than light travel and it gets really crazy."

“Harmonic is building Mathematical Superintelligence (MSI)” With $295M+ in total funding at a recent $1.45B post-money valuation, Harmonic's mission is to solve math problems that have remained unsolved for centuries, unlocking progress across physics, engineering.. & maybe even time travel? Co-founded by Vlad Tenev (Vlad Tenev) CEO of Robinhood, & Harmonic CEO Tudor Achim (Tudor Achim), the company has raised from leading investors including Ribbit, Sequoia, Kleiner Perkins, Index, Paradigm, DST Global, & more.. Funding history & lead investors: - Series A (Sept 2024): $75M led by steve beaker - Series B (July 2025): $100M led by Kleiner Perkins - Series C (Nov 2025): $120M at a $1.45B post-money valuation led by Ribbit Capital "Harmonic’s flagship Aristotle model recently achieved gold-medal level performance at the International Mathematical Olympiad, considered the most prestigious mathematical competition in the world, and is now available to the public. Unlike other models, Aristotle makes use of formal verification using Lean4 to ensure accuracy and eliminate hallucinations. In the first few weeks since its API beta launch, Aristotle has already been used by mathematicians and researchers to accelerate progress and create novel discoveries." . . . "Harmonic is building what we call mathematical super intelligence, and it's an artificial intelligence that can solve math problems better than any human mathematician. The company's been around for a couple of years. The North Star was, can we actually solve really, really important math problems like the Riemann Hypothesis or Hodge Conjecture? There's this group of math problems that have been open for hundreds of years that are called the Millennium Prize problems, and they're considered very big, difficult, and actually valuable. So that was kind of the North Star, and the reason we wanted to do that was if we could solve those problems, everything downstream of math, like theoretical physics becomes unlocked. So then you can imagine solving really hard physics problems. And actually, if you can solve that, then there's all kinds of exciting engineering developments, like depending on how that theory looks, you can imagine things like faster than light travel and it gets really crazy."

Molly O’Shea

51,281 views • 6 months ago

Marginal Revolution co-creator Alex Tabarrok says that AI replacing all jobs (which he doesn't think will happen) is a rich man's problem. "People are worried AI is going to do all the jobs. But this means we are going to be fabulously wealthy." "Even without any jobs, just being fabulously wealthy — we'll figure things out. This is the sort of problem you want to have." "This is not like a natural disaster, which destroys wealth. This is a tsunami which creates wealth." "Yes, it could be a tsunami in the sense that it's going to be very dramatic. But it's going to be very dramatic in the sense of like, Santa Claus coming and leaving us stuff under the Christmas tree. That's drama we can handle." "It won't be without problems. But problems where the pie gets bigger are problems we can solve." "It's problems when the pie gets smaller, when we are forced into a zero-sum society of one person versus another, that's when society breaks down." "But not when the pie is getting bigger. We'll figure out ways to make sure everyone gets a decent slice. We can solve the problem of dividing it up with everybody being happy. I'm much less worried about that."

Marginal Revolution co-creator Alex Tabarrok says that AI replacing all jobs (which he doesn't think will happen) is a rich man's problem. "People are worried AI is going to do all the jobs. But this means we are going to be fabulously wealthy." "Even without any jobs, just being fabulously wealthy — we'll figure things out. This is the sort of problem you want to have." "This is not like a natural disaster, which destroys wealth. This is a tsunami which creates wealth." "Yes, it could be a tsunami in the sense that it's going to be very dramatic. But it's going to be very dramatic in the sense of like, Santa Claus coming and leaving us stuff under the Christmas tree. That's drama we can handle." "It won't be without problems. But problems where the pie gets bigger are problems we can solve." "It's problems when the pie gets smaller, when we are forced into a zero-sum society of one person versus another, that's when society breaks down." "But not when the pie is getting bigger. We'll figure out ways to make sure everyone gets a decent slice. We can solve the problem of dividing it up with everybody being happy. I'm much less worried about that."

TBPN

162,811 views • 2 months ago

Stanford mathematician Ravi Vakil, president of the American Mathematical Society, expects AI’s impact on mathematics to come as a phase change, not a slow climb. Every major shift in math has caught experts off guard, he says. This one will be no different, except that all our predictions will be even more wrong. -- Timestamps – 00:00:00 – Playing games with imperfect information against AI 00:02:35 – When AI will learn to be truly creative 00:03:48 – AI’s impact will be even more unpredictable than the internet 00:08:02 – What an “AlphaGo moment” would look like for math 00:10:35 – How AI will actually be useful in mathematical research 00:12:20 – Writing “wow”-level math problems for AI 00:15:06 – On a 0-10 scale, AI will change math 8 + 3i 00:16:17 – Is math the next chess?

Stanford mathematician Ravi Vakil, president of the American Mathematical Society, expects AI’s impact on mathematics to come as a phase change, not a slow climb. Every major shift in math has caught experts off guard, he says. This one will be no different, except that all our predictions will be even more wrong. -- Timestamps – 00:00:00 – Playing games with imperfect information against AI 00:02:35 – When AI will learn to be truly creative 00:03:48 – AI’s impact will be even more unpredictable than the internet 00:08:02 – What an “AlphaGo moment” would look like for math 00:10:35 – How AI will actually be useful in mathematical research 00:12:20 – Writing “wow”-level math problems for AI 00:15:06 – On a 0-10 scale, AI will change math 8 + 3i 00:16:17 – Is math the next chess?

Epoch AI

12,920 views • 9 months ago

A Canadian math education association put “2 + 2 = 4” on a white supremacy slide. In 2022, the Ontario Mathematics Coordinators Association ran a professional development webinar for math teachers across Ontario school boards. The slide was titled “White Supremacy in Math Education.” Under “covert white supremacy” it listed: standardized testing, “I don’t see colour in my math class,” and “Of course math is neutral because 2 + 2 = 4.” Not a fringe blog. Not a parody account. A professional association shaping how math teachers are trained. When the slide went viral in 2023, the OMCA released an official statement confirming that 2 + 2 = 4 is mathematically true — but argued the equation can be “weaponized to uphold bigotry and white supremacy ideology” when used to claim math is politically neutral. Their defense of the slide was the slide. Someone once wrote about a party that demanded citizens believe two and two made five. In 2022, a professional math association wrote a slide that said believing math is neutral is white supremacy. One was fiction. The other was professional development.

A Canadian math education association put “2 + 2 = 4” on a white supremacy slide. In 2022, the Ontario Mathematics Coordinators Association ran a professional development webinar for math teachers across Ontario school boards. The slide was titled “White Supremacy in Math Education.” Under “covert white supremacy” it listed: standardized testing, “I don’t see colour in my math class,” and “Of course math is neutral because 2 + 2 = 4.” Not a fringe blog. Not a parody account. A professional association shaping how math teachers are trained. When the slide went viral in 2023, the OMCA released an official statement confirming that 2 + 2 = 4 is mathematically true — but argued the equation can be “weaponized to uphold bigotry and white supremacy ideology” when used to claim math is politically neutral. Their defense of the slide was the slide. Someone once wrote about a party that demanded citizens believe two and two made five. In 2022, a professional math association wrote a slide that said believing math is neutral is white supremacy. One was fiction. The other was professional development.

Jake

112,641 views • 4 months ago

$GME GameStop okay I might be missing something here… But can’t stop thinking about something Ryan Cohen said yesterday… Let me begin by first saying Ryan Cohen is VERY careful with his words. Listen to this part of the interview. Ryan is using deal math that gets to a post-merger ownership of 60% eBay / 40% GameStop for existing shareholders. But at Friday’s closing stock price of $26.53, that math was 70:30 (see below) I don’t think Ryan meant this point as a hypothetical. Meaning I think the dilution math will ACTUALLY be 60:40. Which brings me to key point #1: This would mean Ryan is implying $GME stock will be at the price needed for 60:40 by the time the deal is finalized. Well, for ownership split to be 60:40… According to my math (disclaimer: I’m bad at math) that price is ~$40. Ryan also said he’s ready to GO on the transaction NOW. Which brings me to key point #2: This would mean GameStop is sitting on news. And Ryan is confident that news will send the stock to *drumroll please*… AT LEAST $40. That’s how he’s going to buy eBay. 50% cash, 50% stock. It’s on the website. In Ryan we trust. Moass is tomorrow.

$GME GameStop okay I might be missing something here… But can’t stop thinking about something Ryan Cohen said yesterday… Let me begin by first saying Ryan Cohen is VERY careful with his words. Listen to this part of the interview. Ryan is using deal math that gets to a post-merger ownership of 60% eBay / 40% GameStop for existing shareholders. But at Friday’s closing stock price of $26.53, that math was 70:30 (see below) I don’t think Ryan meant this point as a hypothetical. Meaning I think the dilution math will ACTUALLY be 60:40. Which brings me to key point #1: This would mean Ryan is implying $GME stock will be at the price needed for 60:40 by the time the deal is finalized. Well, for ownership split to be 60:40… According to my math (disclaimer: I’m bad at math) that price is ~$40. Ryan also said he’s ready to GO on the transaction NOW. Which brings me to key point #2: This would mean GameStop is sitting on news. And Ryan is confident that news will send the stock to drumroll please… AT LEAST $40. That’s how he’s going to buy eBay. 50% cash, 50% stock. It’s on the website. In Ryan we trust. Moass is tomorrow.

GregisKitty

389,781 views • 2 months ago

The lack of "feature design" is why so many products over time feel hollow or messy. This isn't visual design. This isn't architectural design. I thought that a short video lecture of what feature design is and a real case study of applying it in Ghostty would be helpful. Feature design is the planning step behind how you're going to solve one or more user problems with a product feature: what that feature looks like, how it feels, and not just how its going to tactically solve these specific problems, but how that solution is going to interface with the edges of other features that currently exist or are planned to exist in the future.

The lack of "feature design" is why so many products over time feel hollow or messy. This isn't visual design. This isn't architectural design. I thought that a short video lecture of what feature design is and a real case study of applying it in Ghostty would be helpful. Feature design is the planning step behind how you're going to solve one or more user problems with a product feature: what that feature looks like, how it feels, and not just how its going to tactically solve these specific problems, but how that solution is going to interface with the edges of other features that currently exist or are planned to exist in the future.

Mitchell Hashimoto

156,102 views • 7 months ago

Terence Tao has an IQ above 200. Youngest gold medalist in Math Olympiad history. Fields Medal winner. The greatest living mathematician by nearly any measure. And he just said something most people aren’t ready for. Tao: “This whole era of AI is teaching us that our idea of what intelligence is, is not really accurate.” We spent centuries building civilization on one assumption. That intelligence was sacred. Irreducible. Uniquely ours. The one thing that made the entire human story make sense. Then AI started solving things we swore only we could. Chess. Language. Vision. Math. And every time, we reached for the same defense. That’s not real intelligence. It’s just tricks. Just pattern matching. Just an algorithm. Tao: “You look at how it’s done and it doesn’t feel like intelligence.” So we moved the line. Again. And again. And again. Because intelligence was supposed to feel like something. Something deep. Something we could point to and say… this is what separates us from everything else. But AI kept solving the problems. And that feeling never arrived. Tao: “We were looking for some elusive, intelligent way of thinking and we don’t see it in the tools that actually solve our goals.” Here’s what makes it worse. Large language models work by predicting the next word. One word at a time. No grand architecture. No deep understanding. Just probability. And it works. Tao: “Maybe that’s actually a lot of what humans do as well.” The greatest living mathematician just told you human thought might run on the same machinery. Not some transcendent spark. Pattern recognition. Prediction. One thought, one decision, one word at a time. We built religion around intelligence. Philosophy around it. An entire species identity around it. And a machine running probability just held up a mirror. We didn’t lose intelligence to AI. We just finally saw what it always was. What haunts us isn’t that machines learned to think. It’s that thinking was never what we needed it to be.

Terence Tao has an IQ above 200. Youngest gold medalist in Math Olympiad history. Fields Medal winner. The greatest living mathematician by nearly any measure. And he just said something most people aren’t ready for. Tao: “This whole era of AI is teaching us that our idea of what intelligence is, is not really accurate.” We spent centuries building civilization on one assumption. That intelligence was sacred. Irreducible. Uniquely ours. The one thing that made the entire human story make sense. Then AI started solving things we swore only we could. Chess. Language. Vision. Math. And every time, we reached for the same defense. That’s not real intelligence. It’s just tricks. Just pattern matching. Just an algorithm. Tao: “You look at how it’s done and it doesn’t feel like intelligence.” So we moved the line. Again. And again. And again. Because intelligence was supposed to feel like something. Something deep. Something we could point to and say… this is what separates us from everything else. But AI kept solving the problems. And that feeling never arrived. Tao: “We were looking for some elusive, intelligent way of thinking and we don’t see it in the tools that actually solve our goals.” Here’s what makes it worse. Large language models work by predicting the next word. One word at a time. No grand architecture. No deep understanding. Just probability. And it works. Tao: “Maybe that’s actually a lot of what humans do as well.” The greatest living mathematician just told you human thought might run on the same machinery. Not some transcendent spark. Pattern recognition. Prediction. One thought, one decision, one word at a time. We built religion around intelligence. Philosophy around it. An entire species identity around it. And a machine running probability just held up a mirror. We didn’t lose intelligence to AI. We just finally saw what it always was. What haunts us isn’t that machines learned to think. It’s that thinking was never what we needed it to be.

Dustin

563,940 views • 2 months ago

Why do we do math in school? When I asked this question as a kid, my teachers always told me, "You'll use it one day." That's almost always false. Most adults are never going to be in a sticky situation with two binomials, thinking, thank goodness I can FOIL my way out of this mess. Most of us get by just fine without ever solving for x again. And especially in an Al age, fewer and fewer people will "use math" in the narrow, practical sense. But the origins of math have never been primarily about utility. Math is formative. It trains the mind to love what is true, to recognize what is orderly, and to be drawn toward what is beautiful. It teaches us that the universe is not chaos, but something intelligible, something structured, something that can be known. And you can see that truth made visible in the world's most breathtaking churches and cathedrals: arches, vaults, domes, proportions, symmetry, harmony, light-geometry turned into glory. The mathematics that shapes a cathedral is not cold or sterile. It's the language of wonder, carved into stone. We don't teach math because everyone will use it. We teach math because it forms the kind of person who can see that reality has meaning.

Why do we do math in school? When I asked this question as a kid, my teachers always told me, "You'll use it one day." That's almost always false. Most adults are never going to be in a sticky situation with two binomials, thinking, thank goodness I can FOIL my way out of this mess. Most of us get by just fine without ever solving for x again. And especially in an Al age, fewer and fewer people will "use math" in the narrow, practical sense. But the origins of math have never been primarily about utility. Math is formative. It trains the mind to love what is true, to recognize what is orderly, and to be drawn toward what is beautiful. It teaches us that the universe is not chaos, but something intelligible, something structured, something that can be known. And you can see that truth made visible in the world's most breathtaking churches and cathedrals: arches, vaults, domes, proportions, symmetry, harmony, light-geometry turned into glory. The mathematics that shapes a cathedral is not cold or sterile. It's the language of wonder, carved into stone. We don't teach math because everyone will use it. We teach math because it forms the kind of person who can see that reality has meaning.

Jeremy Wayne Tate

95,173 views • 6 months ago