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Less than two weeks before he left Google to co-found Discovery Loop, Jeff Dean described the automated research loop the new company is named for, giving no hint he was about to leave to go do it. "There's this sort of the foundation of the scientific method of you...

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Fireside Alpha

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OpenAI's Kevin Weil 🇺🇸 says 24/7 robotic labs could automate scientific discovery using "reinforcement learning with a loop through the real world": "There’s a lot of science that can be totally automated. There’s no reason at this point that you need to have grad students pipetting one thing into another thing." "The idea is to have robotic labs that are online 24/7 and can scale in parallel. You have models reasoning for two days to find the most efficient experiments to run, once they get to a good point, they pass that to a robotic lab which can experiment in parallel at high volume." "The results pass back into a model which reasons about the results and then goes out and runs a different set of experiments. You’re doing reinforcement learning with a loop through the real world."
2:13

OpenAI's Kevin Weil 🇺🇸 says 24/7 robotic labs could automate scientific discovery using "reinforcement learning with a loop through the real world": "There’s a lot of science that can be totally automated. There’s no reason at this point that you need to have grad students pipetting one thing into another thing." "The idea is to have robotic labs that are online 24/7 and can scale in parallel. You have models reasoning for two days to find the most efficient experiments to run, once they get to a good point, they pass that to a robotic lab which can experiment in parallel at high volume." "The results pass back into a model which reasons about the results and then goes out and runs a different set of experiments. You’re doing reinforcement learning with a loop through the real world."

TBPN

29,721 просмотров • 5 месяцев назад

OpenAI's Kevin Weil 🇺🇸 says 24/7 robotic labs could automate scientific discovery using "reinforcement learning with a loop through the real world": "There’s a lot of science that can be totally automated. There’s no reason at this point that you need to have grad students pipetting one thing into another thing." "The idea is to have robotic labs that are online 24/7 and can scale in parallel. You have models reasoning for two days to find the most efficient experiments to run, once they get to a good point, they pass that to a robotic lab which can experiment in parallel at high volume." "The results pass back into a model which reasons about the results and then goes out and runs a different set of experiments. You’re doing reinforcement learning with a loop through the real world."
2:13

OpenAI's Kevin Weil 🇺🇸 says 24/7 robotic labs could automate scientific discovery using "reinforcement learning with a loop through the real world": "There’s a lot of science that can be totally automated. There’s no reason at this point that you need to have grad students pipetting one thing into another thing." "The idea is to have robotic labs that are online 24/7 and can scale in parallel. You have models reasoning for two days to find the most efficient experiments to run, once they get to a good point, they pass that to a robotic lab which can experiment in parallel at high volume." "The results pass back into a model which reasons about the results and then goes out and runs a different set of experiments. You’re doing reinforcement learning with a loop through the real world."

TBPN

98,711 просмотров • 6 месяцев назад

OpenAI member of product staff Alexander Embiricos describes the evolution of "Lord Bottleneck," an internal Codex loop developed by a single staff member that ultimately ended up creating a tight feedback and improvement loop for new user experiences: "This person on the growth team needed to figure out what experiments to run. And they needed to write code to run the experiment. Then they needed to analyze the experiment." "They started using Codex for each separate thing. So they had it run a bunch of analyses, interrogate the data, talk to Codex about the data. Then they would pick an experiment, and ask Codex to write the code. Then they would run the experiment, then ask Codex what the results of the experiment were. Then they would produce a deck." "All steps they were doing individually. They didn't start by saying, 'I'm going to automate this entire thing,' because that's hard and scary. They just started with using Codex to accelerate themselves." "Then, they started connecting all these things together into a giant skill. And one day, they just said [to Codex], 'Why don't you do this every morning?'" "They gave it a name: 'Lord Bottleneck.' Because it's solving the bottlenecks of friction for new users." "Now, every morning, Lord Bottleneck evaluates past experiments, looks at data, proposes some [new] experiments, and offers to the team to run the experiments. The team picks [what experiments to do]. Then Lord Bottleneck is like, 'Ok cool. Here's some code or whatever config that needs to be done,' runs the experiment, and they go and do the same loop the next day." "It's really serious value. I forget the numbers, but it's produced significant company value automatically through Codex."
1:42

OpenAI member of product staff Alexander Embiricos describes the evolution of "Lord Bottleneck," an internal Codex loop developed by a single staff member that ultimately ended up creating a tight feedback and improvement loop for new user experiences: "This person on the growth team needed to figure out what experiments to run. And they needed to write code to run the experiment. Then they needed to analyze the experiment." "They started using Codex for each separate thing. So they had it run a bunch of analyses, interrogate the data, talk to Codex about the data. Then they would pick an experiment, and ask Codex to write the code. Then they would run the experiment, then ask Codex what the results of the experiment were. Then they would produce a deck." "All steps they were doing individually. They didn't start by saying, 'I'm going to automate this entire thing,' because that's hard and scary. They just started with using Codex to accelerate themselves." "Then, they started connecting all these things together into a giant skill. And one day, they just said [to Codex], 'Why don't you do this every morning?'" "They gave it a name: 'Lord Bottleneck.' Because it's solving the bottlenecks of friction for new users." "Now, every morning, Lord Bottleneck evaluates past experiments, looks at data, proposes some [new] experiments, and offers to the team to run the experiments. The team picks [what experiments to do]. Then Lord Bottleneck is like, 'Ok cool. Here's some code or whatever config that needs to be done,' runs the experiment, and they go and do the same loop the next day." "It's really serious value. I forget the numbers, but it's produced significant company value automatically through Codex."

TBPN

80,403 просмотров • 3 месяцев назад

Why did so many languages copy async/await from C#? Anders Hejlsberg(Anders Hejlsberg) - creator of TypeScript, C# & Turbo Pascal - on what they got right with the design: #1 - async/await was designed to solve a common problem in the event-loop model: "A lot of languages are built around cooperative multitasking in the sense that they have an event loop that sits and dispatches events. Then you handle the event and then you yield back to the event handler loop. And it all runs in a single thread cooperatively. The problem with that is if you then want to do some long running work: how do I stop in the middle of this piece of long running work and yield back to the event loop cooperatively? And then when my result is ready, I can come back and continue executing here." #2 - state machines are the solution, but hard to build: "Well, in order to do that in an inverted architecture like that, you have to build a state machine. State machines are notoriously hard for people to implement because you've got to move all of your state off of the stack into objects. And then you have this big case statement that envelopes your entire logic. It's a nightmare to figure out. But, the transformation from serially executing code into a state machine, its continuation-passing-style translation is actually one that you can do in a machine-based fashion." #3 - compilers are good at writing state machines: "You can have the compiler write the state machine if you introduce syntax that allows you to indicate where you want to yield. And that's what await is. Await is basically saying, I want to yield here, and I want to yield this promise, and then when the promise completes, I want you to come back here and continue executing. Then the compiler writes a state machine around it and it actually turns it into this big switch statement and moves all of the state that survives across the await into something that's heap allocated. So it can be brought back. And doing all of that work is something that compilers are great at. And so that was sort of the idea that we have this new style of programming where we're using promises or the equivalent of promises and the ability to yield and then we have callbacks. But trying to write your program in that style, that's also what JavaScript suffered from a lot. It's like all this callback style stuff. With Async and Await, you get the illusion that you're just writing normal sequential code and then the compiler does the painful transformation for you. That turns out to be really useful."
3:13

Why did so many languages copy async/await from C#? Anders Hejlsberg(Anders Hejlsberg) - creator of TypeScript, C# & Turbo Pascal - on what they got right with the design: #1 - async/await was designed to solve a common problem in the event-loop model: "A lot of languages are built around cooperative multitasking in the sense that they have an event loop that sits and dispatches events. Then you handle the event and then you yield back to the event handler loop. And it all runs in a single thread cooperatively. The problem with that is if you then want to do some long running work: how do I stop in the middle of this piece of long running work and yield back to the event loop cooperatively? And then when my result is ready, I can come back and continue executing here." #2 - state machines are the solution, but hard to build: "Well, in order to do that in an inverted architecture like that, you have to build a state machine. State machines are notoriously hard for people to implement because you've got to move all of your state off of the stack into objects. And then you have this big case statement that envelopes your entire logic. It's a nightmare to figure out. But, the transformation from serially executing code into a state machine, its continuation-passing-style translation is actually one that you can do in a machine-based fashion." #3 - compilers are good at writing state machines: "You can have the compiler write the state machine if you introduce syntax that allows you to indicate where you want to yield. And that's what await is. Await is basically saying, I want to yield here, and I want to yield this promise, and then when the promise completes, I want you to come back here and continue executing. Then the compiler writes a state machine around it and it actually turns it into this big switch statement and moves all of the state that survives across the await into something that's heap allocated. So it can be brought back. And doing all of that work is something that compilers are great at. And so that was sort of the idea that we have this new style of programming where we're using promises or the equivalent of promises and the ability to yield and then we have callbacks. But trying to write your program in that style, that's also what JavaScript suffered from a lot. It's like all this callback style stuff. With Async and Await, you get the illusion that you're just writing normal sequential code and then the compiler does the painful transformation for you. That turns out to be really useful."

The Pragmatic Engineer

13,258 просмотров • 1 месяц назад

Don’t Scroll—God Is Calling You to Submit and Move Praise the Lord that He has given us such a commission and such a call and we should be humbled by that and we should want to go out for His kingdom and do that. All of you are usable by God. There is not one of you that is not usable if you're only willing to submit. We all fall short. It's a matter of who is willing. Here I am, Lord, send me. That's what Isaiah said. Here I am. Send me. You are all usable. When a lot of you out there go, well, what can I do for God in my life? There's a lot you can do. If you're only willing to be picked up as an instrument in his Hands and implemented the way you were created to move. Many of you are operating outside the realm of what you were created to be. And in this season, that's going to be reconciled. And many of you are going to change course in this season drastically. Because you're in jobs, you're in areas, you're doing things, you were not created to do. You have gifts for it, but you weren’t created to do it. And there's going to be massive shifts in many of your lives and a sudden turn. And the Lord is going to realign. And this is going to be quick when He does it. Realign you into the position, redirect you and put you on that course because time is short and He needs you operating in what you were created to do. He needs that right now. And many of you are going to enter that process in this season. So praise the Lord because you are. You will enter that process and you will be redirected. And you will do what is written about you in the books of heaven, what you were created to do on this earth. Because many of you know and feel uncomfortable and know you're not in your call. You know you are just trying to try to endure, you're trying to survive. And it's because you're not in your call. Surrender to God, allow Him to redirect you into your call and your purpose that you were beautifully created to do. Because it's needed in this season and we cannot dilly dally anymore and go dabbling in things we are not created to do. It's time for us to go to work and it's time for us to be willing to go there.
2:23

Don’t Scroll—God Is Calling You to Submit and Move Praise the Lord that He has given us such a commission and such a call and we should be humbled by that and we should want to go out for His kingdom and do that. All of you are usable by God. There is not one of you that is not usable if you're only willing to submit. We all fall short. It's a matter of who is willing. Here I am, Lord, send me. That's what Isaiah said. Here I am. Send me. You are all usable. When a lot of you out there go, well, what can I do for God in my life? There's a lot you can do. If you're only willing to be picked up as an instrument in his Hands and implemented the way you were created to move. Many of you are operating outside the realm of what you were created to be. And in this season, that's going to be reconciled. And many of you are going to change course in this season drastically. Because you're in jobs, you're in areas, you're doing things, you were not created to do. You have gifts for it, but you weren’t created to do it. And there's going to be massive shifts in many of your lives and a sudden turn. And the Lord is going to realign. And this is going to be quick when He does it. Realign you into the position, redirect you and put you on that course because time is short and He needs you operating in what you were created to do. He needs that right now. And many of you are going to enter that process in this season. So praise the Lord because you are. You will enter that process and you will be redirected. And you will do what is written about you in the books of heaven, what you were created to do on this earth. Because many of you know and feel uncomfortable and know you're not in your call. You know you are just trying to try to endure, you're trying to survive. And it's because you're not in your call. Surrender to God, allow Him to redirect you into your call and your purpose that you were beautifully created to do. Because it's needed in this season and we cannot dilly dally anymore and go dabbling in things we are not created to do. It's time for us to go to work and it's time for us to be willing to go there.

Amanda Grace

12,379 просмотров • 2 месяцев назад

Andrej Karpathy: "To get the most out of the tools that have become available now, you have to remove yourself as the bottleneck. You cannot be there to prompt the next thing. You need to take yourself outside the loop. You have to arrange things such that they are completely autonomous. The more you can maximize your token throughput and not be in the loop, the better. This is the goal. So, I kind of mentioned that the name of the game now is to increase your leverage. I put in very few tokens just once in a while, and a huge amount of stuff happens on my behalf." --- From No Priors YT channel (link in comment)
0:48

Andrej Karpathy: "To get the most out of the tools that have become available now, you have to remove yourself as the bottleneck. You cannot be there to prompt the next thing. You need to take yourself outside the loop. You have to arrange things such that they are completely autonomous. The more you can maximize your token throughput and not be in the loop, the better. This is the goal. So, I kind of mentioned that the name of the game now is to increase your leverage. I put in very few tokens just once in a while, and a huge amount of stuff happens on my behalf." --- From No Priors YT channel (link in comment)

Rohan Paul

172,407 просмотров • 3 месяцев назад

Airtable's Howie Liu says that basically everyone will need to graduate from being ICs to ICs that manage teams of 20-30 agents: "The best developers today don't just sit there in front of their IDEs and synchronously talk to their agent." "[Instead], you have like 30 separate branches that are each being worked on by a different agent. And you can have the agents continue to update the branches based on human and other agent feedback." "And I think this whole idea of it taking hours for that entire loop to complete — agent pushes some changes, the changes get feedback from other agents or humans, the agent responds to that — that whole loop could be hours, not just minutes. So you're not going to just sit there and watch it one at a time." "But the powerful thing about this is, each one is still actually operating faster than a human engineer. One agent on one branch can do the work of maybe three humans, operating 3x as fast. So it's like a 10x leverage factor just for one agent." "But the best engineers are now able to multitask and say, 'I'm going to oversee my own little team of 20-30 agents working concurrently.'" "Everyone needs to graduate from being an IC to an IC manager of agents. Meaning, if you're a VC analyst, your job should no longer be to go synchronously research one company. You need to go and research like 30 companies, and do them all faster, better, and higher quality than you could before." "That's the greatest leap that is going to be challenging for a lot of people in a lot of roles. Because it's a totally different mentality in how you operate, and what your role is."
1:56

Airtable's Howie Liu says that basically everyone will need to graduate from being ICs to ICs that manage teams of 20-30 agents: "The best developers today don't just sit there in front of their IDEs and synchronously talk to their agent." "[Instead], you have like 30 separate branches that are each being worked on by a different agent. And you can have the agents continue to update the branches based on human and other agent feedback." "And I think this whole idea of it taking hours for that entire loop to complete — agent pushes some changes, the changes get feedback from other agents or humans, the agent responds to that — that whole loop could be hours, not just minutes. So you're not going to just sit there and watch it one at a time." "But the powerful thing about this is, each one is still actually operating faster than a human engineer. One agent on one branch can do the work of maybe three humans, operating 3x as fast. So it's like a 10x leverage factor just for one agent." "But the best engineers are now able to multitask and say, 'I'm going to oversee my own little team of 20-30 agents working concurrently.'" "Everyone needs to graduate from being an IC to an IC manager of agents. Meaning, if you're a VC analyst, your job should no longer be to go synchronously research one company. You need to go and research like 30 companies, and do them all faster, better, and higher quality than you could before." "That's the greatest leap that is going to be challenging for a lot of people in a lot of roles. Because it's a totally different mentality in how you operate, and what your role is."

TBPN

35,595 просмотров • 3 месяцев назад

Lightspeed's Bucky Moore says the real opportunity in the AI app layer is in large industries far enough afield from where the model providers are today — and where the context engineering to get customer data into the model is extremely nuanced and messy. "I think this is kind of the elephant in the room right now — whether post-training open-source models combined with the unique user feedback you get from being an application provider is defensible enough." "That is going to be an inevitable challenge for any of these industries that hit a maturation point of AI adoption, like legal and software engineering have." "But on the other hand, there are some industries where they're very large, they're far enough afield from where the model providers are today — and probably will continue to be — and the context engineering to actually get the customer data into the model is just so messy. It requires going across different business functions, it requires a lot of hands-on forward-deployed engineering." "Those are the kind of companies that we get really excited about. Because I think being really good at that is not only defensible, but it also allows you to generate a feedback loop with your customers, where you hear a lot of their secrets. And those secrets allow you to feed that back into how you make your product better at the expense of anyone else playing in the space. Because if you're serving the customer, they're only serving you those secrets." "I think Palantir is a good example of this in the pre-AI era, and I think we're going to see many companies ascend in that same way."
2:28

Lightspeed's Bucky Moore says the real opportunity in the AI app layer is in large industries far enough afield from where the model providers are today — and where the context engineering to get customer data into the model is extremely nuanced and messy. "I think this is kind of the elephant in the room right now — whether post-training open-source models combined with the unique user feedback you get from being an application provider is defensible enough." "That is going to be an inevitable challenge for any of these industries that hit a maturation point of AI adoption, like legal and software engineering have." "But on the other hand, there are some industries where they're very large, they're far enough afield from where the model providers are today — and probably will continue to be — and the context engineering to actually get the customer data into the model is just so messy. It requires going across different business functions, it requires a lot of hands-on forward-deployed engineering." "Those are the kind of companies that we get really excited about. Because I think being really good at that is not only defensible, but it also allows you to generate a feedback loop with your customers, where you hear a lot of their secrets. And those secrets allow you to feed that back into how you make your product better at the expense of anyone else playing in the space. Because if you're serving the customer, they're only serving you those secrets." "I think Palantir is a good example of this in the pre-AI era, and I think we're going to see many companies ascend in that same way."

TBPN

46,862 просмотров • 4 месяцев назад

Prime Intellect's will brown says continual learning could be solved in the first half of 2026: "Continual learning is going to fall pretty quickly, I think. It's more of an engineering problem. No one's actually trying." "OpenAI and Anthropic don't want to continuously train their models for each user. It's expensive and annoying and hard to serve at scale. But from a research perspective, we do continue learning where they just keep training the model more and it knows more stuff because they put more of the Internet in it." "I think there's a lot of experimentation around exactly the recipe that's going to be the most reliable. But we kind of have a grab bag of six or seven tricks that kind of work, or they work in different ways, and you can mix and match them. And it's just going to be like whatever's the best combination of these tricks." "People are going to experiment with it and find the versions that work the best. And there doesn't seem to be any big wall inside that prevents that from being practical."
1:47

Prime Intellect's will brown says continual learning could be solved in the first half of 2026: "Continual learning is going to fall pretty quickly, I think. It's more of an engineering problem. No one's actually trying." "OpenAI and Anthropic don't want to continuously train their models for each user. It's expensive and annoying and hard to serve at scale. But from a research perspective, we do continue learning where they just keep training the model more and it knows more stuff because they put more of the Internet in it." "I think there's a lot of experimentation around exactly the recipe that's going to be the most reliable. But we kind of have a grab bag of six or seven tricks that kind of work, or they work in different ways, and you can mix and match them. And it's just going to be like whatever's the best combination of these tricks." "People are going to experiment with it and find the versions that work the best. And there doesn't seem to be any big wall inside that prevents that from being practical."

TBPN

72,357 просмотров • 5 месяцев назад

Pavel Durov on why he hasn't had depression in 20 years: "I normally never have depression. I don't remember having depression in the last 20 years, at least maybe when I was a teenager." Pavel's approach to difficult emotions is completely counterintuitive. As he puts it: "I'm a human being like everybody else. I do get to experience emotions and some of them are not very pleasant. But I believe that it's the responsibility of every one of us to cope with these emotions and to learn to work through them." On what creates depression: "Self-discipline is particularly important because without it, how can you overcome this seemingly endless loop of negativity or despair that ultimately leads to depression for some people?" His method: "One of the reasons I don't have depression is I start doing things. I identify the problem, I can see a solution, and I start executing the strategy. If you are stuck in this loop of being worried about something, nothing's ever going to change." The mistake people make: "People often make this mistake thinking 'Oh, I should just have some rest and then regain energy.' This is not how it works. You gain energy by doing something. So you start doing something, then it happens. You feel motivated, you feel inspired, and then ultimately you do something else a little bit more." He continues: "The whole point is to do first and then feel, not feel and then do. Going to the gym is a good example. There are many days when you don't want to start working out. But you have to overcome this initial reluctance and then you get to a point that you enjoy it and you think 'Oh my god, it was such a good idea to come to gym today.'" Action creates energy, not the other way around.
2:23

Pavel Durov on why he hasn't had depression in 20 years: "I normally never have depression. I don't remember having depression in the last 20 years, at least maybe when I was a teenager." Pavel's approach to difficult emotions is completely counterintuitive. As he puts it: "I'm a human being like everybody else. I do get to experience emotions and some of them are not very pleasant. But I believe that it's the responsibility of every one of us to cope with these emotions and to learn to work through them." On what creates depression: "Self-discipline is particularly important because without it, how can you overcome this seemingly endless loop of negativity or despair that ultimately leads to depression for some people?" His method: "One of the reasons I don't have depression is I start doing things. I identify the problem, I can see a solution, and I start executing the strategy. If you are stuck in this loop of being worried about something, nothing's ever going to change." The mistake people make: "People often make this mistake thinking 'Oh, I should just have some rest and then regain energy.' This is not how it works. You gain energy by doing something. So you start doing something, then it happens. You feel motivated, you feel inspired, and then ultimately you do something else a little bit more." He continues: "The whole point is to do first and then feel, not feel and then do. Going to the gym is a good example. There are many days when you don't want to start working out. But you have to overcome this initial reluctance and then you get to a point that you enjoy it and you think 'Oh my god, it was such a good idea to come to gym today.'" Action creates energy, not the other way around.

Jaynit

547,435 просмотров • 6 месяцев назад

.Naval: Epistemology, which is a fancy word for the theory of how knowledge grows or how knowledge growth occurs. And we've all been told since we're young that there's a scientific method and that scientists sort of do this stuff in white lab coats and we're supposed to accept it because of this thing called the scientific method. And then they give us true beliefs that we can then say, well the science is settled and we take that we move on. And we all only have a very, very vague understanding of how this works. And people say, well maybe you go out in the real world, you look at what's happening, you make all these observations, and then based on that you form a theory, you test the theory against more observations, and the more observations you get the closer you get to the truth. And once you have enough observation it's true and then you call it a scientific theory or a law and it's settled and you move on. And this is the popular conception of how science works. And as Popper pointed out and as you take even further, this is completely wrong. And so I'd love for you to get into that, which is what is knowledge? How does it grow? What is the real scientific method? And how do we figure things out? David Deutsch: I love the way you just stated the prevailing view there and laced every aspect of it with the contempt that it deserves. So you just went through touching every base. It's amazing that this series of misconceptions is still common sense. I mean, that it was common sense at a time when we didn't really have science or when science was just starting up, when the main issue in science was freeing itself from dogmatism, freeing itself from religion, freeing itself from authority, and so on. There it was understandable that people would look for an alternative source of authority and they would think, oh, it's sense impressions. We can see the world and you know, these religious people, they can't even see God and so on. And so we are confined to what we can see. That's where we get our ideas from. And as you say, that is completely false. Sense impressions, like all observation, even the most careful scientific observation is all theory laden. And theories are inherently fallible. I mean, we actually want to replace our best theories. Everybody who does a PhD is technically anyway, working to overturn something in the existing body of knowledge. You're not turned away at the door if you say, I don't believe this stuff, I'm going to produce something better. Whereas for most of human history, that was exactly what you were forbidden to do. The idea was that we already had all the important knowledge. If you want to discover something new, what you had to make sure of was that it didn't contradict the existing knowledge. Now, you have to make sure that it does contradict existing knowledge. So more or less. Naval: Yeah, it's this tradition of criticism that you've talked about in the West, that the Enlightenment really ushered in the Enlightenment era. David Deutsch: It has been institutionalized. So in many ways, our institutions are wiser than we are. So the institutions of science, for instance, have this built in, even if scientists actually don't always act that way. In fact, they often don't act that way, and act in a dogmatic way and try to preserve the status quo and are resistant to new ideas and so on. But the institutions, the way the procedures of science work, makes the right thing happen in the end anyway, regardless of what the people are trying to do. Naval: So you're saying the knowledge of the true scientific method is embedded in the institutions of science in the PhD process? David Deutsch: Well, the best scientific method that we know of, and one shouldn't really think of it as a method, you know, there's this wonderful lecture by Popper when he first was made a professor at the London School of Economics. He was made a professor of scientific method, and his first six lectures, I wish the rest of them were, the first six lectures are on the internet somewhere. And he starts the first one by saying, I am the first professor of scientific method in the British Empire. The British Empire still existed at the time, more or less. And so the first thing I want to say to you is that there is no such thing as the scientific method. And then he goes on from there. So this subject does not exist. So if any of you have come here to learn the handle that you have to turn in order to make scientific knowledge come out the other end, you're going to be disappointed.
4:50

.Naval: Epistemology, which is a fancy word for the theory of how knowledge grows or how knowledge growth occurs. And we've all been told since we're young that there's a scientific method and that scientists sort of do this stuff in white lab coats and we're supposed to accept it because of this thing called the scientific method. And then they give us true beliefs that we can then say, well the science is settled and we take that we move on. And we all only have a very, very vague understanding of how this works. And people say, well maybe you go out in the real world, you look at what's happening, you make all these observations, and then based on that you form a theory, you test the theory against more observations, and the more observations you get the closer you get to the truth. And once you have enough observation it's true and then you call it a scientific theory or a law and it's settled and you move on. And this is the popular conception of how science works. And as Popper pointed out and as you take even further, this is completely wrong. And so I'd love for you to get into that, which is what is knowledge? How does it grow? What is the real scientific method? And how do we figure things out? David Deutsch: I love the way you just stated the prevailing view there and laced every aspect of it with the contempt that it deserves. So you just went through touching every base. It's amazing that this series of misconceptions is still common sense. I mean, that it was common sense at a time when we didn't really have science or when science was just starting up, when the main issue in science was freeing itself from dogmatism, freeing itself from religion, freeing itself from authority, and so on. There it was understandable that people would look for an alternative source of authority and they would think, oh, it's sense impressions. We can see the world and you know, these religious people, they can't even see God and so on. And so we are confined to what we can see. That's where we get our ideas from. And as you say, that is completely false. Sense impressions, like all observation, even the most careful scientific observation is all theory laden. And theories are inherently fallible. I mean, we actually want to replace our best theories. Everybody who does a PhD is technically anyway, working to overturn something in the existing body of knowledge. You're not turned away at the door if you say, I don't believe this stuff, I'm going to produce something better. Whereas for most of human history, that was exactly what you were forbidden to do. The idea was that we already had all the important knowledge. If you want to discover something new, what you had to make sure of was that it didn't contradict the existing knowledge. Now, you have to make sure that it does contradict existing knowledge. So more or less. Naval: Yeah, it's this tradition of criticism that you've talked about in the West, that the Enlightenment really ushered in the Enlightenment era. David Deutsch: It has been institutionalized. So in many ways, our institutions are wiser than we are. So the institutions of science, for instance, have this built in, even if scientists actually don't always act that way. In fact, they often don't act that way, and act in a dogmatic way and try to preserve the status quo and are resistant to new ideas and so on. But the institutions, the way the procedures of science work, makes the right thing happen in the end anyway, regardless of what the people are trying to do. Naval: So you're saying the knowledge of the true scientific method is embedded in the institutions of science in the PhD process? David Deutsch: Well, the best scientific method that we know of, and one shouldn't really think of it as a method, you know, there's this wonderful lecture by Popper when he first was made a professor at the London School of Economics. He was made a professor of scientific method, and his first six lectures, I wish the rest of them were, the first six lectures are on the internet somewhere. And he starts the first one by saying, I am the first professor of scientific method in the British Empire. The British Empire still existed at the time, more or less. And so the first thing I want to say to you is that there is no such thing as the scientific method. And then he goes on from there. So this subject does not exist. So if any of you have come here to learn the handle that you have to turn in order to make scientific knowledge come out the other end, you're going to be disappointed.

Deutsch Explains

114,992 просмотров • 1 год назад

Ok so Sonic Adventure VR is really coming along now. It's feeling really good. I also was able to make it so that you can toggle VR loops when you loop as sonic so the camera actually follows the loop instead of staying locked horizontally. Thanks to a suggestion on Reddit to have the game world itself rotate instead of sonic and that actually feels pretty comfortable.
1:29

Ok so Sonic Adventure VR is really coming along now. It's feeling really good. I also was able to make it so that you can toggle VR loops when you loop as sonic so the camera actually follows the loop instead of staying locked horizontally. Thanks to a suggestion on Reddit to have the game world itself rotate instead of sonic and that actually feels pretty comfortable.

BMFVR

50,216 просмотров • 8 дней назад

Americans are coming to the ultimate realization. After the assassination of Charlie Kirk, we can no longer coexist with the Left “The more and more I see today, the more pissed off I get, and the more I see that there's no peaceful way to fix this. I've tried to think of any way that I could, and there's not. You guys created something that you don't understand. When you sit out here and celebrate the death of someone, the assassination of someone, that shows me that you are so far gone that we can't be peaceful with you anymore. And that breaks my heart. Because the thing you idiots never understood is the most dangerous thing is a man that just wanted to be left the f*ck alone. And you wouldn't leave us the f*ck alone. So we're at a point now that it's either time for all of you to sit down and shut up. Or you're going to experience something that you will never be able to fathom. The sh*t that's coming your way, it's not going to be what you think it is. This is not something that you can win. This will be one-sided and you don't have a prayer.”
1:31

Sensitive content

Americans are coming to the ultimate realization. After the assassination of Charlie Kirk, we can no longer coexist with the Left “The more and more I see today, the more pissed off I get, and the more I see that there's no peaceful way to fix this. I've tried to think of any way that I could, and there's not. You guys created something that you don't understand. When you sit out here and celebrate the death of someone, the assassination of someone, that shows me that you are so far gone that we can't be peaceful with you anymore. And that breaks my heart. Because the thing you idiots never understood is the most dangerous thing is a man that just wanted to be left the f*ck alone. And you wouldn't leave us the f*ck alone. So we're at a point now that it's either time for all of you to sit down and shut up. Or you're going to experience something that you will never be able to fathom. The sh*t that's coming your way, it's not going to be what you think it is. This is not something that you can win. This will be one-sided and you don't have a prayer.”

Wall Street Apes

92,288 просмотров • 10 месяцев назад

ELON: TO ACTUALLY START A COMPANY IS SIMPLER THAN YOU FEAR Elon is stripping away the mystique of the startup world, offering a blunt blueprint for anyone with an engineering mind and the drive to build. He is arguing that the barrier to entry is not as high as the "fear of failure" suggests. By focusing on a compelling prototype and leveraging the world-class venture capital ecosystem in America. Elon believes the next generation of builders can bypass the bureaucracy and start creating value immediately: “If you study engineering and you figure out how to design new things, then it is relatively easy to start a company. You just need to get a few like-minded people with you and then focus on creating a prototype, a compelling prototype as soon as possible. And then there is a strong venture capital industry in this country that will give you funding to take things to the next level. And that is all there is to it. And you might try it a few times, you may or may not succeed, but I think sometimes people fear starting a company too much. You know, what is the worst that could go wrong? You are not going to starve to death. You are not going to die of exposure. What is the worst that could go wrong?” Source: Naruto
0:44

ELON: TO ACTUALLY START A COMPANY IS SIMPLER THAN YOU FEAR Elon is stripping away the mystique of the startup world, offering a blunt blueprint for anyone with an engineering mind and the drive to build. He is arguing that the barrier to entry is not as high as the "fear of failure" suggests. By focusing on a compelling prototype and leveraging the world-class venture capital ecosystem in America. Elon believes the next generation of builders can bypass the bureaucracy and start creating value immediately: “If you study engineering and you figure out how to design new things, then it is relatively easy to start a company. You just need to get a few like-minded people with you and then focus on creating a prototype, a compelling prototype as soon as possible. And then there is a strong venture capital industry in this country that will give you funding to take things to the next level. And that is all there is to it. And you might try it a few times, you may or may not succeed, but I think sometimes people fear starting a company too much. You know, what is the worst that could go wrong? You are not going to starve to death. You are not going to die of exposure. What is the worst that could go wrong?” Source: Naruto

Mario Nawfal

205,115 просмотров • 7 месяцев назад

Rogin: It looks very likely that we're about to restart bombing of Iran. And there are a lot of reports out there that this is about to happen. And what that means is that all of the problems, not just with the US economy, with the economy of every country in the world, are about to get worse. And it means that the Iran war is going to go much longer than otherwise anticipated. And that is a disaster not just for Americans, but for all people. And then if you think about the effects on energy and then manufacturing, and then rates will continue to go up, and then inflation, it's a it's a calamity for the world economy with no strategy whatsoever.
1:12

Rogin: It looks very likely that we're about to restart bombing of Iran. And there are a lot of reports out there that this is about to happen. And what that means is that all of the problems, not just with the US economy, with the economy of every country in the world, are about to get worse. And it means that the Iran war is going to go much longer than otherwise anticipated. And that is a disaster not just for Americans, but for all people. And then if you think about the effects on energy and then manufacturing, and then rates will continue to go up, and then inflation, it's a it's a calamity for the world economy with no strategy whatsoever.

Acyn

74,258 просмотров • 2 месяцев назад

Jeff Bezos on the bet you should take every single time, even if you lose 90% of the time. "You cannot invent and pioneer if you cannot accept failure. To invent you need to experiment, and if you know in advance that it's going to work, it is not an experiment." "They're inseparable twins, failure and invention. So you have to be willing to do that." "It's embarrassing to fail. But you have to say, no, that's not how this works." "If I said to you you have a 10% chance of a 100x return, you should take that bet every time. But you're still gonna be wrong nine out of ten times, and it's gonna feel bad nine out of ten times." "If you swing for the fences you hit more home runs, but you also strike out more. But with baseball that analogy doesn't go far enough, because with baseball, no matter how well you connect with the ball, you can only get four runs." "But in business, every once in a while you step up to the plate, you hit the ball so hard you get a thousand runs." "When you have that kind of asymmetric payoff, it encourages you to experiment more. It's the right business decision to experiment more."
1:44

Jeff Bezos on the bet you should take every single time, even if you lose 90% of the time. "You cannot invent and pioneer if you cannot accept failure. To invent you need to experiment, and if you know in advance that it's going to work, it is not an experiment." "They're inseparable twins, failure and invention. So you have to be willing to do that." "It's embarrassing to fail. But you have to say, no, that's not how this works." "If I said to you you have a 10% chance of a 100x return, you should take that bet every time. But you're still gonna be wrong nine out of ten times, and it's gonna feel bad nine out of ten times." "If you swing for the fences you hit more home runs, but you also strike out more. But with baseball that analogy doesn't go far enough, because with baseball, no matter how well you connect with the ball, you can only get four runs." "But in business, every once in a while you step up to the plate, you hit the ball so hard you get a thousand runs." "When you have that kind of asymmetric payoff, it encourages you to experiment more. It's the right business decision to experiment more."

Naruto

13,084 просмотров • 16 дней назад

HOW TO USE AI LOOPS TO RUN YOUR BUSINESS 24/7 A lot has been written about loop engineering for building products. Almost nothing about using loops to run the business itself. That's the bigger idea. A loop is when you give an agent a goal, a way to check its own work, and permission to keep trying until it hits that goal. Build. Verify. Repeat. Stop when the condition is met. Here's what it looks like in practice: 1/SEO loop You're position 30 for a term you want. The loop runs once a month, makes changes, checks where you rank, and keeps pushing until you're on page one. This is running in production right now on Inbox Zero. 2/Ads loop You're spending $100 a day and losing money. The loop tests creative, checks profitability, kills what fails, and keeps going until the account is in the black. 3/Eval loop Your AI feature is only 88% accurate. The loop keeps adjusting the prompt and swapping the model until it passes 90%. 4/LLM visibility loop People search in ChatGPT now, not just Google. Same loop, new scoreboard. Are we the answer or not? The whole thing hinges on one thing: a metric that comes back black and white. Where do I rank? Did it hit profitability? Did the evals pass? Give an agent that scoreboard and it runs for months. Loops used to run for 30 minutes. These run for a year. Take a step, sleep, wake up next month, take another one. You're basically hiring an agency that never sleeps, gets paid in tokens instead of invoices, and undoes its own mistakes when the number goes down. Full episode on The Startup Ideas Podcast (SIP) 🧃 watch
39:43

HOW TO USE AI LOOPS TO RUN YOUR BUSINESS 24/7 A lot has been written about loop engineering for building products. Almost nothing about using loops to run the business itself. That's the bigger idea. A loop is when you give an agent a goal, a way to check its own work, and permission to keep trying until it hits that goal. Build. Verify. Repeat. Stop when the condition is met. Here's what it looks like in practice: 1/SEO loop You're position 30 for a term you want. The loop runs once a month, makes changes, checks where you rank, and keeps pushing until you're on page one. This is running in production right now on Inbox Zero. 2/Ads loop You're spending $100 a day and losing money. The loop tests creative, checks profitability, kills what fails, and keeps going until the account is in the black. 3/Eval loop Your AI feature is only 88% accurate. The loop keeps adjusting the prompt and swapping the model until it passes 90%. 4/LLM visibility loop People search in ChatGPT now, not just Google. Same loop, new scoreboard. Are we the answer or not? The whole thing hinges on one thing: a metric that comes back black and white. Where do I rank? Did it hit profitability? Did the evals pass? Give an agent that scoreboard and it runs for months. Loops used to run for 30 minutes. These run for a year. Take a step, sleep, wake up next month, take another one. You're basically hiring an agency that never sleeps, gets paid in tokens instead of invoices, and undoes its own mistakes when the number goes down. Full episode on The Startup Ideas Podcast (SIP) 🧃 watch

GREG ISENBERG

82,335 просмотров • 24 дней назад

Anthropic co-founder Jack Clark says the AI frontier is about to cross a much stranger line than coding assistants. "My prediction is by the end of 2028, it's more likely than not that we have an AI system where you would be able to say to it, 'Make a better version of yourself.' And it just goes off and does that completely autonomously." The reason: models have moved from basic coding into "the production of science itself" and the "unglamorous but fundamental engineering work" behind large-scale R&D. "An AI system that can build itself [has] big implications, and we'd like to start thinking about it now." That is the actual agent threshold: not demos, not chat UX, but AI becoming part of the loop that improves AI.
1:02

Anthropic co-founder Jack Clark says the AI frontier is about to cross a much stranger line than coding assistants. "My prediction is by the end of 2028, it's more likely than not that we have an AI system where you would be able to say to it, 'Make a better version of yourself.' And it just goes off and does that completely autonomously." The reason: models have moved from basic coding into "the production of science itself" and the "unglamorous but fundamental engineering work" behind large-scale R&D. "An AI system that can build itself [has] big implications, and we'd like to start thinking about it now." That is the actual agent threshold: not demos, not chat UX, but AI becoming part of the loop that improves AI.

Karl Mehta

208,701 просмотров • 1 месяц назад

Demis Hassabis confirmed every frontier AI lab is working on recursive self-improvement and in the same sentence said the safety risk of removing humans from the loop entirely keeps him up at night. That combination should stop you. The CEO of Google DeepMind just confirmed that the thing most people treat as a theoretical future risk is already the active focus of every serious lab on earth right now. He explained why it works in coding and math. The feedback loop is fast. You can verify whether an answer is correct almost instantly. You can generate synthetic training data from it. The loop closes quickly and cleanly. Then he said where it breaks down. In biology, chemistry and physics. Any domain where verifying a hypothesis requires a physical experiment in the real world. The loop does not close in seconds. It closes in weeks or months. Geoffrey Hinton said in his Nobel lecture that recursive self-improvement is the development he fears most and that once started it may not be possible to stop. Hassabis is not pushing back on that. He is describing the guardrails labs are building around a process they are already running. Every lab has to think carefully about the safety of a process where no human is in the loop. He said that as a constraint they are navigating right now. The question they are sitting with is how much of it to let run without a human watching. (Watch the full interview on YouTube at Two Minute Papers channel)
2:00

Demis Hassabis confirmed every frontier AI lab is working on recursive self-improvement and in the same sentence said the safety risk of removing humans from the loop entirely keeps him up at night. That combination should stop you. The CEO of Google DeepMind just confirmed that the thing most people treat as a theoretical future risk is already the active focus of every serious lab on earth right now. He explained why it works in coding and math. The feedback loop is fast. You can verify whether an answer is correct almost instantly. You can generate synthetic training data from it. The loop closes quickly and cleanly. Then he said where it breaks down. In biology, chemistry and physics. Any domain where verifying a hypothesis requires a physical experiment in the real world. The loop does not close in seconds. It closes in weeks or months. Geoffrey Hinton said in his Nobel lecture that recursive self-improvement is the development he fears most and that once started it may not be possible to stop. Hassabis is not pushing back on that. He is describing the guardrails labs are building around a process they are already running. Every lab has to think carefully about the safety of a process where no human is in the loop. He said that as a constraint they are navigating right now. The question they are sitting with is how much of it to let run without a human watching. (Watch the full interview on YouTube at Two Minute Papers channel)

Ihtesham Ali

68,231 просмотров • 1 месяц назад

“Crypto is really kind of like a laboratory for lots of disciplines to experiment in.” Tim Roughgarden on what makes blockchains uniquely useful for research: “If you study financial markets and you have an idea to make Wall Street better, that’s going to be pretty hard to even experiment with.” “But if you have an idea for a new kind of decentralized finance and a DeFi protocol, go ahead and deploy it. No one’s stopping you.” “You can try out ideas in the wild that you would never be able to in a more traditional application domain.” MTS sophia dew
2:54

“Crypto is really kind of like a laboratory for lots of disciplines to experiment in.” Tim Roughgarden on what makes blockchains uniquely useful for research: “If you study financial markets and you have an idea to make Wall Street better, that’s going to be pretty hard to even experiment with.” “But if you have an idea for a new kind of decentralized finance and a DeFi protocol, go ahead and deploy it. No one’s stopping you.” “You can try out ideas in the wild that you would never be able to in a more traditional application domain.” MTS sophia dew

a16z crypto

37,934 просмотров • 13 дней назад