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“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,...

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Molly O’Shea

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Tudor Achim (Tudor Achim) is the co-founder and CEO of Harmonic, a startup working to solve one of AI’s hardest problems: mathematical reasoning. In our conversation, we explore: • Why Tudor believes math is the fundamental toolkit to understand the world • How Aristotle works and the applications beyond pure mathematics • The reinforcement learning process that lets Harmonic generate synthetic training data and solve problems humans have never attempted • Why Tudor believes AI could surpass human mathematicians on specific tasks within 2–3 years • Why the future of mathematics looks more like GitHub than academic journals Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Guru_HQ: The AI source of truth for work: - Rippling: Stop wasting time on admin tasks, build your startup faster: Timestamps (00:00) Intro (06:28) The mathematical foundations of music (and why Tudor keeps them separate) (12:52) Defining intelligence (22:55) The two breakthroughs that made mathematical AI possible in 2023 (35:25) An overview of Aristotle: the world’s first always-correct mathematical agent (45:34) Math in AI now and what’s next (56:02) History’s alternating rhythm of thinking and measuring (1:00:18) Final meditations
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Tudor Achim (Tudor Achim) is the co-founder and CEO of Harmonic, a startup working to solve one of AI’s hardest problems: mathematical reasoning. In our conversation, we explore: • Why Tudor believes math is the fundamental toolkit to understand the world • How Aristotle works and the applications beyond pure mathematics • The reinforcement learning process that lets Harmonic generate synthetic training data and solve problems humans have never attempted • Why Tudor believes AI could surpass human mathematicians on specific tasks within 2–3 years • Why the future of mathematics looks more like GitHub than academic journals Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Guru_HQ: The AI source of truth for work: - Rippling: Stop wasting time on admin tasks, build your startup faster: Timestamps (00:00) Intro (06:28) The mathematical foundations of music (and why Tudor keeps them separate) (12:52) Defining intelligence (22:55) The two breakthroughs that made mathematical AI possible in 2023 (35:25) An overview of Aristotle: the world’s first always-correct mathematical agent (45:34) Math in AI now and what’s next (56:02) History’s alternating rhythm of thinking and measuring (1:00:18) Final meditations

Mario Gabriele 🦊

17,899 Aufrufe • vor 3 Monaten

🧠David Friedberg explains the great hope of Artificial Superintelligence: Massive leverage on time to solve thousand-year problems "I don't think that humans are limited in our ability to solve problems." "I think we're limited in terms of time, and what digital intelligence gives us is leverage on time so that we can now tackle ever more complex tasks." "This idea of superintelligence is that it could give us immense leverage in solving that complex problem that we otherwise may be challenged to solve over decades, or hundreds of years, or think about one day solving a problem that would take humans thousands of years to solve." "So if I as a human said, 'I would like to harness fusion power similar to what the sun uses to make energy, and I wanna do that on Earth,' today, we're in, call it year 40 or 50 of a research cycle of humans trying to solve that particularly complex problem." "It's a scientific problem, it's a discovery problem, it's an engineering problem." "Functionally, leveraging to complexity is where this becomes super compelling for humans." "And it's why I think we can and should be highly optimistic about living very long lives, and traveling anywhere we want, and having abundance in food, and having abundance in resources, and having abundance in recouping our time to do the things we want to do instead of the things that we have to do today."
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🧠David Friedberg explains the great hope of Artificial Superintelligence: Massive leverage on time to solve thousand-year problems "I don't think that humans are limited in our ability to solve problems." "I think we're limited in terms of time, and what digital intelligence gives us is leverage on time so that we can now tackle ever more complex tasks." "This idea of superintelligence is that it could give us immense leverage in solving that complex problem that we otherwise may be challenged to solve over decades, or hundreds of years, or think about one day solving a problem that would take humans thousands of years to solve." "So if I as a human said, 'I would like to harness fusion power similar to what the sun uses to make energy, and I wanna do that on Earth,' today, we're in, call it year 40 or 50 of a research cycle of humans trying to solve that particularly complex problem." "It's a scientific problem, it's a discovery problem, it's an engineering problem." "Functionally, leveraging to complexity is where this becomes super compelling for humans." "And it's why I think we can and should be highly optimistic about living very long lives, and traveling anywhere we want, and having abundance in food, and having abundance in resources, and having abundance in recouping our time to do the things we want to do instead of the things that we have to do today."

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Gov. Spencer Cox: "I think it's important that we, with eyes wide open, understand what's happening in our country today...It is an attack on all of us. It is an attack on the American experiment. It is an attack on our ideals. This cuts to the very foundation of who we are, who we have been, and who we could be...Political violence is different than any other kind of violence. The very act that Charlie championed of expression, freedom of expression that is defined in our founding documents. Having his life taken in that very act makes it more difficult for people to feel like they can share their ideas, that they can speak freely. We will never be able to solve all the other problems, including the violence problems, if we can't have a clash of ideas safely and securely — especially those ideas with which you disagree."
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Gov. Spencer Cox: "I think it's important that we, with eyes wide open, understand what's happening in our country today...It is an attack on all of us. It is an attack on the American experiment. It is an attack on our ideals. This cuts to the very foundation of who we are, who we have been, and who we could be...Political violence is different than any other kind of violence. The very act that Charlie championed of expression, freedom of expression that is defined in our founding documents. Having his life taken in that very act makes it more difficult for people to feel like they can share their ideas, that they can speak freely. We will never be able to solve all the other problems, including the violence problems, if we can't have a clash of ideas safely and securely — especially those ideas with which you disagree."

The Bulwark

233,061 Aufrufe • vor 10 Monaten

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.”
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TBPN

39,589 Aufrufe • vor 4 Monaten

The controversy is global warming. Now, I'm a physicist, but I'm not the right kind of physicist. In regard to global warming, I'm just a layman. And the rational thing for a layman to do is to take seriously the prevailing scientific theory. And according to that theory, it's already too late to avoid a disaster, because if it's true that our best option at the moment is to prevent CO2 emissions with something like the Kyoto Protocol, with its constraints on economic activity and its enormous cost of hundreds of billions of dollars or whatever it is, then that is already a disaster by any reasonable measure. And the actions that are advocated are not even purported to solve the problem, merely to postpone it by a little. So it's already too late to avoid it, and it probably has been too late to avoid it ever since before anyone realized the danger. It was probably already too late in the 1970s when the best available scientific theory was telling us that industrial emissions were about to precipitate a new ice age in which billions would die. Now, the lesson of that seems clear to me, and I don't know why it isn't informing public debate. It is that we can't always know. When we know of an impending disaster and how to solve it at a cost less than the cost of the disaster itself, then there's not going to be much argument, really. But no precautions and no precautionary principle can avoid problems that we do not yet foresee. Hence, we need a stance of problem fixing, not just problem avoidance. It's true that an ounce of prevention equals a pound of cure, but that's only if we know what to prevent. If you've been punched on the nose, then the science of medicine does not consist of teaching you how to avoid punches. If medical science stopped seeking cures and concentrated on prevention only, then it would achieve very little of either. The world is buzzing at the moment with plans to force reductions in gas emissions at all costs. It ought to be buzzing with plans to reduce the temperature and with plans to live at the higher temperature, and not at all costs, but efficiently and cheaply. Some such plans exist, things like swarms of mirrors in space to deflect the sunlight away and encouraging aquatic organisms to eat more carbon dioxide. At the moment, these things are fringe research. They're not central to the human effort to face this problem or problems in general. And with problems that we are not aware of yet, the ability to put right, not the sheer good luck of avoiding indefinitely, is our only hope, not just of solving problems, but of survival. So, take two stone tablets and carve on them, on one of them, carve, problems are soluble. And on the other one, carve, problems are inevitable. David Deutsch
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The controversy is global warming. Now, I'm a physicist, but I'm not the right kind of physicist. In regard to global warming, I'm just a layman. And the rational thing for a layman to do is to take seriously the prevailing scientific theory. And according to that theory, it's already too late to avoid a disaster, because if it's true that our best option at the moment is to prevent CO2 emissions with something like the Kyoto Protocol, with its constraints on economic activity and its enormous cost of hundreds of billions of dollars or whatever it is, then that is already a disaster by any reasonable measure. And the actions that are advocated are not even purported to solve the problem, merely to postpone it by a little. So it's already too late to avoid it, and it probably has been too late to avoid it ever since before anyone realized the danger. It was probably already too late in the 1970s when the best available scientific theory was telling us that industrial emissions were about to precipitate a new ice age in which billions would die. Now, the lesson of that seems clear to me, and I don't know why it isn't informing public debate. It is that we can't always know. When we know of an impending disaster and how to solve it at a cost less than the cost of the disaster itself, then there's not going to be much argument, really. But no precautions and no precautionary principle can avoid problems that we do not yet foresee. Hence, we need a stance of problem fixing, not just problem avoidance. It's true that an ounce of prevention equals a pound of cure, but that's only if we know what to prevent. If you've been punched on the nose, then the science of medicine does not consist of teaching you how to avoid punches. If medical science stopped seeking cures and concentrated on prevention only, then it would achieve very little of either. The world is buzzing at the moment with plans to force reductions in gas emissions at all costs. It ought to be buzzing with plans to reduce the temperature and with plans to live at the higher temperature, and not at all costs, but efficiently and cheaply. Some such plans exist, things like swarms of mirrors in space to deflect the sunlight away and encouraging aquatic organisms to eat more carbon dioxide. At the moment, these things are fringe research. They're not central to the human effort to face this problem or problems in general. And with problems that we are not aware of yet, the ability to put right, not the sheer good luck of avoiding indefinitely, is our only hope, not just of solving problems, but of survival. So, take two stone tablets and carve on them, on one of them, carve, problems are soluble. And on the other one, carve, problems are inevitable. David Deutsch

Deutsch Explains

43,872 Aufrufe • vor 1 Jahr

The DeepSeek-R1 paper is a gem! Highly encourage everyone to read it. It's clear that LLM reasoning capabilities can be learned in different ways. RL, if applied correctly and at scale, can lead to some really powerful and interesting scaling and emergent properties. There is more to RL than meets the eye! Here is my breakdown of the paper along with a few tests: The multi-state training might not make sense initially but they provide clues on optimizations that we can continue to tap into. Data quality is still very important for enhancing the usability of the LLM. Unlike other reasoning LLMs, DeepSeek-R1's training recipe and weights are open so we can build on top of it. This opens up exciting research opportunities. About the attached clip: the previous preview model wasn't able to solve this task. DeepSeek-R1 can solve this and many other tasks that o1 can solve. It's a very good model for coding and math.
1:57

The DeepSeek-R1 paper is a gem! Highly encourage everyone to read it. It's clear that LLM reasoning capabilities can be learned in different ways. RL, if applied correctly and at scale, can lead to some really powerful and interesting scaling and emergent properties. There is more to RL than meets the eye! Here is my breakdown of the paper along with a few tests: The multi-state training might not make sense initially but they provide clues on optimizations that we can continue to tap into. Data quality is still very important for enhancing the usability of the LLM. Unlike other reasoning LLMs, DeepSeek-R1's training recipe and weights are open so we can build on top of it. This opens up exciting research opportunities. About the attached clip: the previous preview model wasn't able to solve this task. DeepSeek-R1 can solve this and many other tasks that o1 can solve. It's a very good model for coding and math.

elvis

140,692 Aufrufe • vor 1 Jahr

This is one of my favorite movie endings... It has a powerful lesson: "At some point, everything is going to go south on you...and you're gonna say 'this is it, this is how I end.' Now you can either accept that, or you can get to work. That's all it is, you just begin. You do the math. You solve one problem. Then you solve the next one. And the next. And if you solve enough problems, you get to come home." This is the beautiful thing about life: No matter where you are today—no matter how deep in the darkness—you are always just one good decision away from being in a better place tomorrow. You can always focus on that one decision—that one problem—in front of you. Don't worry about the hundreds or thousands of decisions that you still have to make to get to where you want to be—just focus on the next decision. Just begin. Solve one problem. Then solve the next one. And the next. "Life is really simple, but we insist on making it complicated." - Confucius
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This is one of my favorite movie endings... It has a powerful lesson: "At some point, everything is going to go south on you...and you're gonna say 'this is it, this is how I end.' Now you can either accept that, or you can get to work. That's all it is, you just begin. You do the math. You solve one problem. Then you solve the next one. And the next. And if you solve enough problems, you get to come home." This is the beautiful thing about life: No matter where you are today—no matter how deep in the darkness—you are always just one good decision away from being in a better place tomorrow. You can always focus on that one decision—that one problem—in front of you. Don't worry about the hundreds or thousands of decisions that you still have to make to get to where you want to be—just focus on the next decision. Just begin. Solve one problem. Then solve the next one. And the next. "Life is really simple, but we insist on making it complicated." - Confucius

Sahil Bloom

213,822 Aufrufe • vor 1 Jahr

#WATCH | Mumbai, Maharashtra: On attending the Yashraj Bharati Samman (YBS) 2026 Gratitude Ceremony, Indian-American economist Abhijit Banerjee says, "...What is development? I want to start with the same question, which is, what makes development? I think there are two very standard views you hear all the time, and there's, of course, some truth to them. One of them is that we need to have resources, and essentially, if we have resources, we can solve the problems... The other is that we need self-reliance, and that people need to help themselves and solve their problems... The real problem in education is not the lack of technology, that's not the lack of sophisticated material, it's not the lack of infrastructure. It was the simple fact that teachers don't teach the child; they teach the curriculum. By promoting the opposite, by promoting the idea that you should teach the child and not the curriculum, it's been shown that all over the world, education can improve very fast..." (03.05)
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#WATCH | Mumbai, Maharashtra: On attending the Yashraj Bharati Samman (YBS) 2026 Gratitude Ceremony, Indian-American economist Abhijit Banerjee says, "...What is development? I want to start with the same question, which is, what makes development? I think there are two very standard views you hear all the time, and there's, of course, some truth to them. One of them is that we need to have resources, and essentially, if we have resources, we can solve the problems... The other is that we need self-reliance, and that people need to help themselves and solve their problems... The real problem in education is not the lack of technology, that's not the lack of sophisticated material, it's not the lack of infrastructure. It was the simple fact that teachers don't teach the child; they teach the curriculum. By promoting the opposite, by promoting the idea that you should teach the child and not the curriculum, it's been shown that all over the world, education can improve very fast..." (03.05)

ANI

11,963 Aufrufe • vor 2 Monaten

Elon Musk Elon Musk on Neuralink: “So, when I emphasized the purpose of Neuralink, like what do we, what's our goal, our goal is to solve important spine and brain problems with a seamlessly implanted device. So, you wanna have a device that you can basically put in your head and feel and look totally normal, but it solves some important problem in your brain or spine. And the reality is that almost everyone, over time, will develop brain and spine problems. These range from minor to very severe, but if you live long enough, everyone's gonna basically have some kind of neurological disorder, from memory loss to brain damage. But the thing that's important to appreciate is that an implantable device can actually solve these problems. I think a lot of people don't quite realise that, but all of your senses, your sight, hearing, feeling, pain, these are all electrical signals sent by neurons to your brain.”
0:55

Elon Musk Elon Musk on Neuralink: “So, when I emphasized the purpose of Neuralink, like what do we, what's our goal, our goal is to solve important spine and brain problems with a seamlessly implanted device. So, you wanna have a device that you can basically put in your head and feel and look totally normal, but it solves some important problem in your brain or spine. And the reality is that almost everyone, over time, will develop brain and spine problems. These range from minor to very severe, but if you live long enough, everyone's gonna basically have some kind of neurological disorder, from memory loss to brain damage. But the thing that's important to appreciate is that an implantable device can actually solve these problems. I think a lot of people don't quite realise that, but all of your senses, your sight, hearing, feeling, pain, these are all electrical signals sent by neurons to your brain.”

Beanie👾

1,182,454 Aufrufe • vor 9 Monaten

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.
1:19

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

562,582 Aufrufe • vor 2 Monaten

Tomorrow, we publish a new The Edge Podcast with Robinhood GM of Crypto, Johann Kerbrat. This clip might be the clearest articulation I’ve ever heard on why Ethereum's roadmap is working, and why Robinhood chose to build its own L2 using Arbitrum. + Ethereum offers security, with proven decentralization + New L1s = centralized databases + EVM liquidity is essential for tokenized stocks & RWAs + Arbitrum’s tech stack wins on engineering merit No edits. No spin. Just a major U.S. fintech explaining why they’re building the future of finance on Ethereum. Real validation for those of us who are long term Ethereum/ETH investors. Here is the clip and transcript for Johann answering why Robinhood chose to build its own Ethereum L2: "You see a lot of companies right now building their own L1. And for us, we felt excited about this idea where we can control anything that we want to build and we don't have to deal with any other chain to talk to or anything like that. But at the same time, creating the security of a real proper decentralized chain is extremely difficult, as you know. And we basically get that for free with Ethereum. The network has been going on for a very long time now and the security is stable and it's decentralized. And I think that's really the key two points that we wanted to have. When you look at some of these new L1s that are being created, it's not really decentralized and it's not really secure. So at the end of the day, you're basically having like a... fancy database that is probably a bit slower to use than an actual database. And so we didn't really see the value in that. With Ethereum, we get the security by default. The second thing that we get by having an L2 is that you get all this liquidity that is already part of all the EVM compatible chains. And that was also a very important decision factor for us. If we really want to bring the stock market onto the chain, we need to have this liquidity. It's not going to be possible if it's in a closed loop or in a closed chain that nobody can access and you need to have like 20 different hoops before you can bridge to that chain. So for us, that was kind of the two elements that we really wanted to focus on and that's why we decided on building on Ethereum. And then we decided on Arbitrum mostly because we love the technology. There's a few things that we are excited about. For example, Stylus. It's a way that we can basically use different language into the chain and we can build on top of that. We also like the way that they prioritize transactions that is, we think, a better way than some of the competition. And so basically we'll use the Arbitrum stack and we'll create our own L2, which will make us also compatible with all the Arbitrum chains. And so we think it's kind of the best of all the worlds. But for us, the idea is like we will start with public stock, and then we think that we can tokenize anything really, not just stocks. It can be private stock, it can be art, it can be real estate, whatever you want to think of. And so we really wanted to have a platform that is kind of easy to build on and we can keep adding on it. At the same time, we know that regulation are going to be hard on some of these products, especially when it comes to financial products. You always have different regulators. You have different rules depending on the region. If you're in the EU versus the US versus LATAM. And so we felt like we wanted to have our own layer that we could customize for these needs. And then anyone that is building on the chain will also be able to benefit from what we are building, versus just putting everything in a contract that will be just good for the people that are using the Robinhood contracts. But so at this point, we announced the chain in June. We are in private test right now, and then we'll do more announcements in the future." Subscribe for the full episode tomorrow! ► Newsletter: ► Spotify: ► Apple: ► Youtube: ► Pods:
3:27

Tomorrow, we publish a new The Edge Podcast with Robinhood GM of Crypto, Johann Kerbrat. This clip might be the clearest articulation I’ve ever heard on why Ethereum's roadmap is working, and why Robinhood chose to build its own L2 using Arbitrum. + Ethereum offers security, with proven decentralization + New L1s = centralized databases + EVM liquidity is essential for tokenized stocks & RWAs + Arbitrum’s tech stack wins on engineering merit No edits. No spin. Just a major U.S. fintech explaining why they’re building the future of finance on Ethereum. Real validation for those of us who are long term Ethereum/ETH investors. Here is the clip and transcript for Johann answering why Robinhood chose to build its own Ethereum L2: "You see a lot of companies right now building their own L1. And for us, we felt excited about this idea where we can control anything that we want to build and we don't have to deal with any other chain to talk to or anything like that. But at the same time, creating the security of a real proper decentralized chain is extremely difficult, as you know. And we basically get that for free with Ethereum. The network has been going on for a very long time now and the security is stable and it's decentralized. And I think that's really the key two points that we wanted to have. When you look at some of these new L1s that are being created, it's not really decentralized and it's not really secure. So at the end of the day, you're basically having like a... fancy database that is probably a bit slower to use than an actual database. And so we didn't really see the value in that. With Ethereum, we get the security by default. The second thing that we get by having an L2 is that you get all this liquidity that is already part of all the EVM compatible chains. And that was also a very important decision factor for us. If we really want to bring the stock market onto the chain, we need to have this liquidity. It's not going to be possible if it's in a closed loop or in a closed chain that nobody can access and you need to have like 20 different hoops before you can bridge to that chain. So for us, that was kind of the two elements that we really wanted to focus on and that's why we decided on building on Ethereum. And then we decided on Arbitrum mostly because we love the technology. There's a few things that we are excited about. For example, Stylus. It's a way that we can basically use different language into the chain and we can build on top of that. We also like the way that they prioritize transactions that is, we think, a better way than some of the competition. And so basically we'll use the Arbitrum stack and we'll create our own L2, which will make us also compatible with all the Arbitrum chains. And so we think it's kind of the best of all the worlds. But for us, the idea is like we will start with public stock, and then we think that we can tokenize anything really, not just stocks. It can be private stock, it can be art, it can be real estate, whatever you want to think of. And so we really wanted to have a platform that is kind of easy to build on and we can keep adding on it. At the same time, we know that regulation are going to be hard on some of these products, especially when it comes to financial products. You always have different regulators. You have different rules depending on the region. If you're in the EU versus the US versus LATAM. And so we felt like we wanted to have our own layer that we could customize for these needs. And then anyone that is building on the chain will also be able to benefit from what we are building, versus just putting everything in a contract that will be just good for the people that are using the Robinhood contracts. But so at this point, we announced the chain in June. We are in private test right now, and then we'll do more announcements in the future." Subscribe for the full episode tomorrow! ► Newsletter: ► Spotify: ► Apple: ► Youtube: ► Pods:

DeFi Dad ⟠ defidad.eth

62,600 Aufrufe • vor 7 Monaten

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.
0:45

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

173,896 Aufrufe • vor 5 Monaten

Naval Ravikant on the importance of hiring high-agency people Naval defines agency as: “People who just solve problems without even being asked to solve the problem—they identify the problem, they go solve it, they don’t even necessarily have to update you every step of the way, they’re not asking silly questions, and they’re just coming up with solutions.” He believes this is important because “building a startup is an infinite set of problems that are being thrown at you.” And there comes a day where you can’t even look at every problem your company is facing—let alone solve every one of them. He cites the Vinod Khosla aphorism: "The team you build is the company you build, not the plan you make.” And your ability to solve problems is based entirely on how many problem-solvers you have at your company. As Naval puts it: “If you have somebody who takes 10% of your time and management to solve problems, you can only have 10 of those people working with you. But if somebody takes 5%, you can have 20 of those people.” When building Airchat and AngelList, he thought of each team as a Navy Seal team: “Everyone is just really good at what they do. They know their job. They do it. They don’t complain. They’re not egotistical about it. And if they have to constantly be corrected, led around by the nose, you have to clean up after them, or you question their judgement, it’s not going to work out.”
1:19

Naval Ravikant on the importance of hiring high-agency people Naval defines agency as: “People who just solve problems without even being asked to solve the problem—they identify the problem, they go solve it, they don’t even necessarily have to update you every step of the way, they’re not asking silly questions, and they’re just coming up with solutions.” He believes this is important because “building a startup is an infinite set of problems that are being thrown at you.” And there comes a day where you can’t even look at every problem your company is facing—let alone solve every one of them. He cites the Vinod Khosla aphorism: "The team you build is the company you build, not the plan you make.” And your ability to solve problems is based entirely on how many problem-solvers you have at your company. As Naval puts it: “If you have somebody who takes 10% of your time and management to solve problems, you can only have 10 of those people working with you. But if somebody takes 5%, you can have 20 of those people.” When building Airchat and AngelList, he thought of each team as a Navy Seal team: “Everyone is just really good at what they do. They know their job. They do it. They don’t complain. They’re not egotistical about it. And if they have to constantly be corrected, led around by the nose, you have to clean up after them, or you question their judgement, it’s not going to work out.”

Startup Archive

552,782 Aufrufe • vor 2 Jahren

Superintelligence is Near! Three innovations that prove it! Three recent innovations lead me to believe that we've just seen the invention of the next generation of "cognitive primitives" that will lead us directly to ASI. Do you remember LSTM? At the time, there were jokes that "brains are just LSTMs!" This feels like another "just before GPT moment" to me. HRM (hierarchical reasoning models), combined with IMO gold medal models, and the ASI-ARCH "AlphaGo for neural architecture" tells me that we're about to completely decouple machine intelligence from even data inputs. Also, my intuitive definition of ASI is "beyond the 100th percentile of human capability" meaning that "AGI can do anything humans can do, perhaps just faster" - BUT - I suspect that we're about to see machines that can solve problems that humans simply are not biologically capable of solving. It's not necessarily a matter of speed anymore. Sure, these things are faster, and perhaps the ASI level machines could teach us to solve problems that they can, but it will take us a long time to catch up, and in the meantime, they will have moved on. Mark my words, I suspect that is what we've just seen happen this past week. This is well and truly the beginning of the fast takeoff.
8:44

Superintelligence is Near! Three innovations that prove it! Three recent innovations lead me to believe that we've just seen the invention of the next generation of "cognitive primitives" that will lead us directly to ASI. Do you remember LSTM? At the time, there were jokes that "brains are just LSTMs!" This feels like another "just before GPT moment" to me. HRM (hierarchical reasoning models), combined with IMO gold medal models, and the ASI-ARCH "AlphaGo for neural architecture" tells me that we're about to completely decouple machine intelligence from even data inputs. Also, my intuitive definition of ASI is "beyond the 100th percentile of human capability" meaning that "AGI can do anything humans can do, perhaps just faster" - BUT - I suspect that we're about to see machines that can solve problems that humans simply are not biologically capable of solving. It's not necessarily a matter of speed anymore. Sure, these things are faster, and perhaps the ASI level machines could teach us to solve problems that they can, but it will take us a long time to catch up, and in the meantime, they will have moved on. Mark my words, I suspect that is what we've just seen happen this past week. This is well and truly the beginning of the fast takeoff.

David Shapiro ⏩

31,137 Aufrufe • vor 11 Monaten

Keith Rabois on the “one person, one problem" framework he learned from Peter Thiel "Peter Thiel used to insist at PayPal that every single person could only do exactly one thing. And we all rebelled. You feel like it's insulting to be asked to do just one thing. But Peter would enforce this pretty strictly. He'd basically say: 'I will not talk to you about anything else except for this one thing that I've assigned to you. I don't want to hear about how great you're doing in this other area. Just focus until you conquer this one problem.'... The insight behind this is that most people will solve problems that they understand how to solve. Roughly speaking, they will solve B+ problems instead of A+ problems. A+ problems are high-impact problems for your company but they're difficult--you don't wake up in the morning with a solution to them, so you tend to procrastinate... If you have a company that's always solving B+ problems, you'll never create the breakthrough idea because no one is spending 100% of their time banging their head against the wall every day until they solve it" Video source: Y Combinator (2014)
0:59

Keith Rabois on the “one person, one problem" framework he learned from Peter Thiel "Peter Thiel used to insist at PayPal that every single person could only do exactly one thing. And we all rebelled. You feel like it's insulting to be asked to do just one thing. But Peter would enforce this pretty strictly. He'd basically say: 'I will not talk to you about anything else except for this one thing that I've assigned to you. I don't want to hear about how great you're doing in this other area. Just focus until you conquer this one problem.'... The insight behind this is that most people will solve problems that they understand how to solve. Roughly speaking, they will solve B+ problems instead of A+ problems. A+ problems are high-impact problems for your company but they're difficult--you don't wake up in the morning with a solution to them, so you tend to procrastinate... If you have a company that's always solving B+ problems, you'll never create the breakthrough idea because no one is spending 100% of their time banging their head against the wall every day until they solve it" Video source: Y Combinator (2014)

Startup Archive

108,875 Aufrufe • vor 1 Jahr

“People in crypto generally have a long term outlook. The crypto market actually really wants DeSci What we are seeing some of our DAOs do is generate roadmaps or knowledge trees for entire fields. Lets map it all out. And start deciding where we put that funding. And if it works get more funding. The really beautiful thing here is its solving a problem for two sides. We can solve a problem for patients who I think are comically disenfranchised from value creation in biopharma. And then we solve problems for scientists. If we can make scientists just a little bit richer and participate in this then we’ve won” Beautiful bio/acc edit by a community member of a panel at ResearchHub SciCon last month w/ Rohan Dixit Clarice D. Aiello (she,her)
1:01

“People in crypto generally have a long term outlook. The crypto market actually really wants DeSci What we are seeing some of our DAOs do is generate roadmaps or knowledge trees for entire fields. Lets map it all out. And start deciding where we put that funding. And if it works get more funding. The really beautiful thing here is its solving a problem for two sides. We can solve a problem for patients who I think are comically disenfranchised from value creation in biopharma. And then we solve problems for scientists. If we can make scientists just a little bit richer and participate in this then we’ve won” Beautiful bio/acc edit by a community member of a panel at ResearchHub SciCon last month w/ Rohan Dixit Clarice D. Aiello (she,her)

Paul Kohlhaas bio/acc

23,214 Aufrufe • vor 1 Jahr

BREAKING: Shaun Maguire (Shaun Maguire) of Sequoia Returns We cover - AI vs Humans: Vlad Tenev's Mathematical Superintelligence Harmonic - Physics, UAPs, & the limits of human understanding X Exclusive: KGB psyops & information warfare Shaun explains Sequoia Capital's investment in Harmonic, the Math Superintelligence company co-founded by Robinhood ( $HOOD) CEO Vlad Tenev + Tudor Achim, & why math + reinforcement learning may unlock the next major breakthrough in artificial intelligence. Harmonic uses a differentiated strategy using Lean, formalized proofs, & RL to train AI systems that reason rigorously about math, outperforming far larger labs with a fraction of the spend.. & of course, humans. Highlights: (00:00) Shaun Maguire, Partner at Sequoia Capital (01:26) How Sequoia ended up investing in Harmonic (06:52) Is time travel actually possible? (08:39) Why the speed of light and physical laws are often misunderstood (11:43) Vacuum energy, virtual particles, & the Casimir effect (14:09) Why “laws of physics” are averages, not absolutes (15:57) Palmer Luckey on invisibility cloaks, UAPs, & aliens (18:46) X Exclusive: KGB psyops & Cold War tactics (20:47) The Pope Pius XII story (22:51) How framing & fake archives shaped propaganda in the 1940s (28:09) KGB playbook: division as strategy (31:34) Socialism: Venezuela vs NYC (34:57) The cost of speaking out (36:41) Investing in X, Elon Musk & the debate over free speech
38:49

BREAKING: Shaun Maguire (Shaun Maguire) of Sequoia Returns We cover - AI vs Humans: Vlad Tenev's Mathematical Superintelligence Harmonic - Physics, UAPs, & the limits of human understanding X Exclusive: KGB psyops & information warfare Shaun explains Sequoia Capital's investment in Harmonic, the Math Superintelligence company co-founded by Robinhood ( $HOOD) CEO Vlad Tenev + Tudor Achim, & why math + reinforcement learning may unlock the next major breakthrough in artificial intelligence. Harmonic uses a differentiated strategy using Lean, formalized proofs, & RL to train AI systems that reason rigorously about math, outperforming far larger labs with a fraction of the spend.. & of course, humans. Highlights: (00:00) Shaun Maguire, Partner at Sequoia Capital (01:26) How Sequoia ended up investing in Harmonic (06:52) Is time travel actually possible? (08:39) Why the speed of light and physical laws are often misunderstood (11:43) Vacuum energy, virtual particles, & the Casimir effect (14:09) Why “laws of physics” are averages, not absolutes (15:57) Palmer Luckey on invisibility cloaks, UAPs, & aliens (18:46) X Exclusive: KGB psyops & Cold War tactics (20:47) The Pope Pius XII story (22:51) How framing & fake archives shaped propaganda in the 1940s (28:09) KGB playbook: division as strategy (31:34) Socialism: Venezuela vs NYC (34:57) The cost of speaking out (36:41) Investing in X, Elon Musk & the debate over free speech

Molly O’Shea

37,145 Aufrufe • vor 5 Monaten