Загрузка видео...

Не удалось загрузить видео

Возникла проблема при загрузке этого видео. Это может быть связано с временными проблемами сети или видео может быть недоступно.

На главную

Engineering excellence through destruction! 💥 Dyson's testing philosophy is to break it until it fails, then rebuild it better. Vacuum batteries go through 1,200 charge/discharge cycles, the Supersonic hair dryer cable gets wrapped and unwrapped 7,800 times, Corrale straightener plates open and close 400,000 times, and purifiers run 24/7... until they fail. By testing to failure, Dyson discovers the exact limits of materials, mechanisms, and electronics before the product ever reaches a customer. Dyson tests until things break, studies why they broke, then redesigns around those failure modes. It's the same principle behind Tesla's rapid iteration on Starship (blow them up until you understand what fails). The difference between "it works" and "it lasts" is understanding failure modes. This is what separates consumer products that last 2 years from ones that last 10. The engineering discipline to destroy your own work, learn from it, and build the next version smarter. P.S. Would love to see more robotics companies adopt this level of durability testing. Most demos show success. Few show the 400,000 cycles of wear that prove it actually works. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →show more

Lukas Ziegler

56,540 subscribers

111,529 просмотров • 21 дней назад •via X (Twitter)

Наука и технологии

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

James Dyson’s advice for founders: “You have to enjoy failure” “If you are exploring new territory, experimenting, and trying to do something different, you’re going to fail many times and you’ve got to bounce back from it,” James Dyson explains. The billionaire inventor argues that you should actually learn to enjoy failure: “Failure is so much more interesting than success — the reason something goes wrong is often very interesting whereas if works you just say ‘great that works’ and don’t even stop to wonder why it works. You’ve got to learn to enjoy failure. That sounds like a difficult thing to do, but you have to enjoy failure if you want to improve things.” He continues: “It always saddens me that school doesn’t really teach that. At school or university, the thing is to be brilliant and get the answer right the first time. There are brilliant people who can do that, but for the rest of us, we’re not brilliant and to get there we have to strive and go through failure. You don’t get it right the first time. You don’t get it right the second time. In my case, and I counted it, it was 5,127 times . . . That sounds like struggle and it was a struggle, but it was a hugely enjoyable struggle. The debt was mounting, and I had three children, a wife, a home, and a mortgage to pay like everybody else, but I had a real aim in life and I had to get there. And the failures were interesting because I learned from almost every single one of them.” James Dyson concludes his autobiography by saying: "Listen, it's easy for me to celebrate my doggedness now. I made $300 million last year, but I'd be lying to you if there weren't times where I went inside my house, had my wife look at me like I'm a failure, and cry myself to sleep. I got up and did it again anyway because excellence is the capacity to take pain.” Video source: David Senra (2025)

James Dyson’s advice for founders: “You have to enjoy failure” “If you are exploring new territory, experimenting, and trying to do something different, you’re going to fail many times and you’ve got to bounce back from it,” James Dyson explains. The billionaire inventor argues that you should actually learn to enjoy failure: “Failure is so much more interesting than success — the reason something goes wrong is often very interesting whereas if works you just say ‘great that works’ and don’t even stop to wonder why it works. You’ve got to learn to enjoy failure. That sounds like a difficult thing to do, but you have to enjoy failure if you want to improve things.” He continues: “It always saddens me that school doesn’t really teach that. At school or university, the thing is to be brilliant and get the answer right the first time. There are brilliant people who can do that, but for the rest of us, we’re not brilliant and to get there we have to strive and go through failure. You don’t get it right the first time. You don’t get it right the second time. In my case, and I counted it, it was 5,127 times . . . That sounds like struggle and it was a struggle, but it was a hugely enjoyable struggle. The debt was mounting, and I had three children, a wife, a home, and a mortgage to pay like everybody else, but I had a real aim in life and I had to get there. And the failures were interesting because I learned from almost every single one of them.” James Dyson concludes his autobiography by saying: "Listen, it's easy for me to celebrate my doggedness now. I made $300 million last year, but I'd be lying to you if there weren't times where I went inside my house, had my wife look at me like I'm a failure, and cry myself to sleep. I got up and did it again anyway because excellence is the capacity to take pain.” Video source: David Senra (2025)

Startup Archive

14,425 просмотров • 5 месяцев назад

John Ternus, Apple's SVP of Hardware Engineering, explains why Apple deliberately made the iPhone harder to repair, and why the math says it was worth it: In a conversation with MKBHD, John frames the design challenge by asking you to imagine two extremes: "Sometimes for me I find it helpful to kind of think about the book ends. Like if you imagine a product that never fails, right? That just doesn't fail. And on the other end, a product that maybe isn't very reliable but is super easy to repair." His position is clear: "Product that never fails is obviously better for the customer. It's better for the environment." When pushed on whether infinite repairability and infinite durability have to be mutually exclusive, John acknowledges they aren't always, but explains why the tension is real, using the iPhone battery as an example. Batteries wear out. If you want to extend the life of the product, they need to be replaced. But in the early days of iPhone, one of the most common failures wasn't the battery, it was water: "Where you drop it in the pool or you, you know, spill your drink on it and the unit fails. And so, we've been making strides over all those years to get better and better and better in terms of minimizing those failures." That work led Apple to an IP68 rating, the point where customers fish their phones out of lakes after two weeks and find them still working. But there was a cost to achieving that level of durability: "To get the product there, you've got to design a lot of seals, adhesives, other things to make it perform that way, which makes it a little harder to do that battery repair." That's the deliberate tradeoff. Apple chose tighter seals and stronger adhesives, knowing it would make battery replacement more difficult, because the reliability gains were worth it. John argues the math backs this decision: "It's objectively better for the customer to have that reliability and it's ultimately better for the planet because the failure rates since we got to that point have just dropped. It's plummeted, right? The number of repairs that need to happen and every time you're doing a repair, you're bringing in new materials to replace whatever broke." His conclusion reframes the entire repairability debate: "You can actually do the math and figure out there's a threshold at which if I can make it this durable, then it's better to have it a little bit harder to repair because it's going to net out."

John Ternus, Apple's SVP of Hardware Engineering, explains why Apple deliberately made the iPhone harder to repair, and why the math says it was worth it: In a conversation with MKBHD, John frames the design challenge by asking you to imagine two extremes: "Sometimes for me I find it helpful to kind of think about the book ends. Like if you imagine a product that never fails, right? That just doesn't fail. And on the other end, a product that maybe isn't very reliable but is super easy to repair." His position is clear: "Product that never fails is obviously better for the customer. It's better for the environment." When pushed on whether infinite repairability and infinite durability have to be mutually exclusive, John acknowledges they aren't always, but explains why the tension is real, using the iPhone battery as an example. Batteries wear out. If you want to extend the life of the product, they need to be replaced. But in the early days of iPhone, one of the most common failures wasn't the battery, it was water: "Where you drop it in the pool or you, you know, spill your drink on it and the unit fails. And so, we've been making strides over all those years to get better and better and better in terms of minimizing those failures." That work led Apple to an IP68 rating, the point where customers fish their phones out of lakes after two weeks and find them still working. But there was a cost to achieving that level of durability: "To get the product there, you've got to design a lot of seals, adhesives, other things to make it perform that way, which makes it a little harder to do that battery repair." That's the deliberate tradeoff. Apple chose tighter seals and stronger adhesives, knowing it would make battery replacement more difficult, because the reliability gains were worth it. John argues the math backs this decision: "It's objectively better for the customer to have that reliability and it's ultimately better for the planet because the failure rates since we got to that point have just dropped. It's plummeted, right? The number of repairs that need to happen and every time you're doing a repair, you're bringing in new materials to replace whatever broke." His conclusion reframes the entire repairability debate: "You can actually do the math and figure out there's a threshold at which if I can make it this durable, then it's better to have it a little bit harder to repair because it's going to net out."

Big Brain Business

385,397 просмотров • 2 месяцев назад

Sam Darnold shares how "failure" became the foundation of his comeback. "You have to embrace failure and you have to learn from your mistakes." Most people run from failure. Successful people know that adversity is inevitable. "Learning from my mistakes early in my career, I feel like, has gotten me to this point." Darnold was the 3rd overall pick. Then came the struggles - the turnovers, the losses, and being cut. "Every time you fail or you lose a game or you don't do exactly what you want to do on every single play - you learn from that." Failure isn't final - it's feedback. Combat unhealthy beliefs about yourself and believe that change is possible. "As long as you learn from it, you can always look ahead to better things and not making the same mistake twice." The most resilient people cultivate resilience over time. The most resilient people accept that sh** happens. They own what they can and focus on what they control. (🎥Seattle Seahawks )

Sam Darnold shares how "failure" became the foundation of his comeback. "You have to embrace failure and you have to learn from your mistakes." Most people run from failure. Successful people know that adversity is inevitable. "Learning from my mistakes early in my career, I feel like, has gotten me to this point." Darnold was the 3rd overall pick. Then came the struggles - the turnovers, the losses, and being cut. "Every time you fail or you lose a game or you don't do exactly what you want to do on every single play - you learn from that." Failure isn't final - it's feedback. Combat unhealthy beliefs about yourself and believe that change is possible. "As long as you learn from it, you can always look ahead to better things and not making the same mistake twice." The most resilient people cultivate resilience over time. The most resilient people accept that sh** happens. They own what they can and focus on what they control. (🎥Seattle Seahawks )

Coach AJ 🎯 Mental Fitness

45,445 просмотров • 5 месяцев назад

i watched gemma 4 12b build something genuinely impressive today, and then loop itself to death right in front of me. the full run is in the video, sped up but completely uncut, watch it to the end and you will catch the exact moment it stops building and starts looping right in the middle of the work. the task was clean, build a single file gravity simulator, n-body physics, orbits, collisions, running locally on one 3090 through an agent. and for ten minutes it was a joy to watch. it reached for a symplectic integrator on its own, the correct one, the kind that keeps orbits stable instead of spiralling out. real gravity with softening, proper orbital velocities, momentum conserved on collision. the physics was right. the thing actually worked. then on the very last step, writing a few tests to prove its own code, it fell into a loop. not a crash, a loop. it started repeating itself and would not stop. ten more minutes, thirty four thousand tokens into a single answer, the same fragments over and over, until i killed it myself. so it's not that gemma can't code. it did the hard part beautifully. it cannot finish. it cannot hold a long task together without unravelling, and finishing is the entire job in agentic work. here's the part that stings. i run this exact task, same harness, same card, on the chinese open models, qwen especially, and i never see this. they build it, they test it, they stop. every single time. google has the raw capability, you can see it sitting right there in the code, and then the model loops itself to death on a task a 27b from alibaba finishes clean. open weights, apache 2.0, so much to love on paper. i just need it to know when to stop talking.

i watched gemma 4 12b build something genuinely impressive today, and then loop itself to death right in front of me. the full run is in the video, sped up but completely uncut, watch it to the end and you will catch the exact moment it stops building and starts looping right in the middle of the work. the task was clean, build a single file gravity simulator, n-body physics, orbits, collisions, running locally on one 3090 through an agent. and for ten minutes it was a joy to watch. it reached for a symplectic integrator on its own, the correct one, the kind that keeps orbits stable instead of spiralling out. real gravity with softening, proper orbital velocities, momentum conserved on collision. the physics was right. the thing actually worked. then on the very last step, writing a few tests to prove its own code, it fell into a loop. not a crash, a loop. it started repeating itself and would not stop. ten more minutes, thirty four thousand tokens into a single answer, the same fragments over and over, until i killed it myself. so it's not that gemma can't code. it did the hard part beautifully. it cannot finish. it cannot hold a long task together without unravelling, and finishing is the entire job in agentic work. here's the part that stings. i run this exact task, same harness, same card, on the chinese open models, qwen especially, and i never see this. they build it, they test it, they stop. every single time. google has the raw capability, you can see it sitting right there in the code, and then the model loops itself to death on a task a 27b from alibaba finishes clean. open weights, apache 2.0, so much to love on paper. i just need it to know when to stop talking.

Sudo su

39,574 просмотров • 1 месяц назад

From Eric Vishria on how the top AI founders are building products completely opposite of the SaaS era: "One of the things that is really different in the AI world versus the SaaS world, is that in the SaaS world, over and over again, you had people who really understood the customer. And the problem. And then they understood a domain. They understood what the technology was more or less capable of. But it wasn't a real question of if you could build something or not. For example, take Salesforce, Workday, and ServiceNow. CRM existed before Salesforce. HR management existed before Workday. Same thing with ServiceNow. So in every case, Salesforce followed Siebel. Workday followed Peoplesoft. ServiceNow followed Peregrine and Remedy, and others. So they were just kind of, cloud SaaS versions of the prior generation product. They just understood the customers. They understood the problem. And they were just like, here's a better version. And that evolved a little bit over time in SaaS land. But that's what it is. And so product development in that way was done by people who really understood the customer and the problems. And then just took advantage of the next wave. And this is almost diametrically opposite of product development in the AI era. When I look at the teams that are having the most success today, they have intimate knowledge of the models. They are right on the frontier of understanding which models are better at what, and why, and when. And what they're going to be good at and what they're not going to be good at. And what they're spending their time on, is figuring out how do I apply this capability of this model to this domain or to this user. So they're actually working inside out or technology out, versus customer problem in. And of course, they understand the customer problem. And a lot of times they have firsthand knowledge of it. But they're really close to the metal and capability, and they're applying it. And I think this is a really different way to develop products than in SaaS. I started my career as a product manager a long time ago, and it's almost the complete opposite of everything you learned. "Listen to the customer, understand it, then bring it back to the engineering and product teams." If you did that right now, ask a bunch of customers what they want out of AI, and you brought it back, for the most part, it may not be possible today with today's technology. Whereas the teams that are winning right now really understand the technology and are applying it out. And so I think this reversal matters. I think it's a big difference in terms of how companies are getting built. And maybe even the types of entrepreneurs that will be successful. I'm not sure. You're seeing some real change there. Look at the Bret Taylor's at Sierra. That's a super, super technical founder who really gets it. Brett and Clay really get it. You look at Michael and his co-founders at Cursor. They're super technical founders and they get it. They all really understand what these things can and can't do. And that's a pretty different dynamic relative to the way the best SaaS companies got built." Link in bio for the full conversation going deep on the current class of startups going from zero to $100m+ in ARR within 12 months.

From Eric Vishria on how the top AI founders are building products completely opposite of the SaaS era: "One of the things that is really different in the AI world versus the SaaS world, is that in the SaaS world, over and over again, you had people who really understood the customer. And the problem. And then they understood a domain. They understood what the technology was more or less capable of. But it wasn't a real question of if you could build something or not. For example, take Salesforce, Workday, and ServiceNow. CRM existed before Salesforce. HR management existed before Workday. Same thing with ServiceNow. So in every case, Salesforce followed Siebel. Workday followed Peoplesoft. ServiceNow followed Peregrine and Remedy, and others. So they were just kind of, cloud SaaS versions of the prior generation product. They just understood the customers. They understood the problem. And they were just like, here's a better version. And that evolved a little bit over time in SaaS land. But that's what it is. And so product development in that way was done by people who really understood the customer and the problems. And then just took advantage of the next wave. And this is almost diametrically opposite of product development in the AI era. When I look at the teams that are having the most success today, they have intimate knowledge of the models. They are right on the frontier of understanding which models are better at what, and why, and when. And what they're going to be good at and what they're not going to be good at. And what they're spending their time on, is figuring out how do I apply this capability of this model to this domain or to this user. So they're actually working inside out or technology out, versus customer problem in. And of course, they understand the customer problem. And a lot of times they have firsthand knowledge of it. But they're really close to the metal and capability, and they're applying it. And I think this is a really different way to develop products than in SaaS. I started my career as a product manager a long time ago, and it's almost the complete opposite of everything you learned. "Listen to the customer, understand it, then bring it back to the engineering and product teams." If you did that right now, ask a bunch of customers what they want out of AI, and you brought it back, for the most part, it may not be possible today with today's technology. Whereas the teams that are winning right now really understand the technology and are applying it out. And so I think this reversal matters. I think it's a big difference in terms of how companies are getting built. And maybe even the types of entrepreneurs that will be successful. I'm not sure. You're seeing some real change there. Look at the Bret Taylor's at Sierra. That's a super, super technical founder who really gets it. Brett and Clay really get it. You look at Michael and his co-founders at Cursor. They're super technical founders and they get it. They all really understand what these things can and can't do. And that's a pretty different dynamic relative to the way the best SaaS companies got built." Link in bio for the full conversation going deep on the current class of startups going from zero to $100m+ in ARR within 12 months.

The Peel

209,752 просмотров • 1 год назад

ChatGPT can write you a thousand trading strategies from one sentence. 999 will quietly bleed your account dry. Every single one of them looks like a winner. One method pulls the real one out. Quant desks have run it for years. It's called loop engineering. It's not a smarter prompt. It's a loop: generate a strategy, test it, score why it failed, feed that back, and run it again until one survives data it has never seen. Man Group runs $150 billion. They built an AI that writes and tests hundreds of strategies a week. And second agent whose only job is to hunt the ones that secretly overfit and kill them before a cent goes live. > describe an idea -> backtest 5 years in 12 seconds > read why it died -> feed that back in > run it again until one survives data it never saw AI can write a thousand strategies. Loop engineering is the only thing that tells you which one was ever real. 89% of retail lost money in 2025. Almost none of it from bad ideas. All of it from trading the first guess and never running it through backtest loop. Horizon just put the whole thing in a chat box, free while the beta's open: You're not losing to smarter traders. You're losing to the ones who opened the tab. Anyone can generate a strategy now. Almost no one runs the loop that proves it. That gap is the whole edge. You'll mean to check it later. Later is a "waitlist closed" page.

ChatGPT can write you a thousand trading strategies from one sentence. 999 will quietly bleed your account dry. Every single one of them looks like a winner. One method pulls the real one out. Quant desks have run it for years. It's called loop engineering. It's not a smarter prompt. It's a loop: generate a strategy, test it, score why it failed, feed that back, and run it again until one survives data it has never seen. Man Group runs $150 billion. They built an AI that writes and tests hundreds of strategies a week. And second agent whose only job is to hunt the ones that secretly overfit and kill them before a cent goes live. > describe an idea -> backtest 5 years in 12 seconds > read why it died -> feed that back in > run it again until one survives data it never saw AI can write a thousand strategies. Loop engineering is the only thing that tells you which one was ever real. 89% of retail lost money in 2025. Almost none of it from bad ideas. All of it from trading the first guess and never running it through backtest loop. Horizon just put the whole thing in a chat box, free while the beta's open: You're not losing to smarter traders. You're losing to the ones who opened the tab. Anyone can generate a strategy now. Almost no one runs the loop that proves it. That gap is the whole edge. You'll mean to check it later. Later is a "waitlist closed" page.

cvxv666

173,610 просмотров • 1 месяц назад

Everyone should be aware of this The owners of JBS, the worlds largest meat processing company, has been charged with bribing over 1,800 politicians and officials They’ve been importing cheap foreign meats and using the“Product of the USA” labeling loophole This is a racket. Remember, even though this loophole made by the USDA has finally been closed it won’t be fully enforced until 2026 Until then, “They can import essentially a lower standard beef into America. All they have to do is break it down here so it arrives into America as a frozen slab of beef, essentially. They can turn it into steaks and hamburger from there. And then they can sell it as product of the usa. And they're allowed to label that as product of the usa, which it is not. And it's shot up with who knows what (vaccines)”

Everyone should be aware of this The owners of JBS, the worlds largest meat processing company, has been charged with bribing over 1,800 politicians and officials They’ve been importing cheap foreign meats and using the“Product of the USA” labeling loophole This is a racket. Remember, even though this loophole made by the USDA has finally been closed it won’t be fully enforced until 2026 Until then, “They can import essentially a lower standard beef into America. All they have to do is break it down here so it arrives into America as a frozen slab of beef, essentially. They can turn it into steaks and hamburger from there. And then they can sell it as product of the usa. And they're allowed to label that as product of the usa, which it is not. And it's shot up with who knows what (vaccines)”

Wall Street Apes

1,228,756 просмотров • 1 год назад

Haters: "You can't launch 100 satellites - you’re self-funded." Us: We don't have to. Through our IRL events, we’ve come across a lot of haters. What they fail to do is understand what we are truly building. Satellite costs have dropped 90%+. That’s why we’re able to research and develop our own proprietary satellites to launch and test. Then, we plan to open-source the satellite designs and combine that with building our open network. This means that any operator can join, launch, and earn fees by running data through their nodes. We're not trying to own the sky. We're building the protocol that decentralizes it. Anything's crazy… until it works. Witness it ⬇️

Haters: "You can't launch 100 satellites - you’re self-funded." Us: We don't have to. Through our IRL events, we’ve come across a lot of haters. What they fail to do is understand what we are truly building. Satellite costs have dropped 90%+. That’s why we’re able to research and develop our own proprietary satellites to launch and test. Then, we plan to open-source the satellite designs and combine that with building our open network. This means that any operator can join, launch, and earn fees by running data through their nodes. We're not trying to own the sky. We're building the protocol that decentralizes it. Anything's crazy… until it works. Witness it ⬇️

Spacecoin™ 🛰️

331,974 просмотров • 7 месяцев назад

🇺🇸Starship 12’s launch window opens at 5:30 p.m. CT today This time, they’re testing out the new generation Starship and Super Heavy vehicles, the new version of the Raptor engine, and they’re launching it from a newly designed pad at Starbase. Most companies test one new thing at a time; not Elon. And yes, one of them might not work perfectly; that’s the idea of testing, push things to the max so you can find what the flaws are that need improving. Just watch the mainstream media; if one thing goes wrong, they’ll pile on Elon, calling it a failure. And if it all works perfectly, they’ll probably go back to calling him a Nazi 😂

🇺🇸Starship 12’s launch window opens at 5:30 p.m. CT today This time, they’re testing out the new generation Starship and Super Heavy vehicles, the new version of the Raptor engine, and they’re launching it from a newly designed pad at Starbase. Most companies test one new thing at a time; not Elon. And yes, one of them might not work perfectly; that’s the idea of testing, push things to the max so you can find what the flaws are that need improving. Just watch the mainstream media; if one thing goes wrong, they’ll pile on Elon, calling it a failure. And if it all works perfectly, they’ll probably go back to calling him a Nazi 😂

Mario Nawfal

238,712 просмотров • 2 месяцев назад

James Dyson on founder led sales: "Selling goes with manufacturing as wheels do with a bicycle." "Products do not walk off shelves and into people's homes. And when a product is entirely new, the art of selling is needed to explain it. What it is. How it works. Why you might need and want it." Dyson was taught this by his mentor when Dyson was just 21 years old: "I told him I don't know how to sell things." And he said, "Look, you're the engineer. You've chosen every square inch of that product. Everything. You know it all. That means you're the best person to sell it." That was an interesting sort revelation for me because I'd always thought these were separate professions. Sales was one profession, engineering was another, manufacturing was another, and being a manager was another. And suddenly this entrepreneur was saying to me, "Well, look, you're an engineer and designer. You know all about the product, make it, and then go and sell it."

James Dyson on founder led sales: "Selling goes with manufacturing as wheels do with a bicycle." "Products do not walk off shelves and into people's homes. And when a product is entirely new, the art of selling is needed to explain it. What it is. How it works. Why you might need and want it." Dyson was taught this by his mentor when Dyson was just 21 years old: "I told him I don't know how to sell things." And he said, "Look, you're the engineer. You've chosen every square inch of that product. Everything. You know it all. That means you're the best person to sell it." That was an interesting sort revelation for me because I'd always thought these were separate professions. Sales was one profession, engineering was another, manufacturing was another, and being a manager was another. And suddenly this entrepreneur was saying to me, "Well, look, you're an engineer and designer. You know all about the product, make it, and then go and sell it."

David Senra

43,123 просмотров • 2 месяцев назад

"It's a superhero movie that is for adults and has adult ideas in it. You don't have to think about it any harder than that. It goes way deeper. If you want to, you can tear it apart like a giant onion and keep peeling it until you're at its core. "But I think that it's cool to experience it just like when I read the comic book for the first time and I didn't know. No one was telling me what it's about or the why of it. You come to it fresh. And sometimes I wish I had that experience because I would love to see the movie or read the book for the first time again because that feeling was for me awesome. "Especially when you go into something with this anticipation that it's just a Hollywood superhero movie which is what a lot of people will think because they don't know. They see some guys fighting and then when you see the film you actually have this experience of it being taken apart. "Those archetypes, those mythological shapes, whether they be Batman or Superman or any of superheroes and how they're just turned around. Whether it's through sexual fetish or fighting, actually killing guys they fight. "Stuff like that or whether it's the end of the movie and there's this moral dilemma that is the end of the movie is also challenging on all those levels."

"It's a superhero movie that is for adults and has adult ideas in it. You don't have to think about it any harder than that. It goes way deeper. If you want to, you can tear it apart like a giant onion and keep peeling it until you're at its core. "But I think that it's cool to experience it just like when I read the comic book for the first time and I didn't know. No one was telling me what it's about or the why of it. You come to it fresh. And sometimes I wish I had that experience because I would love to see the movie or read the book for the first time again because that feeling was for me awesome. "Especially when you go into something with this anticipation that it's just a Hollywood superhero movie which is what a lot of people will think because they don't know. They see some guys fighting and then when you see the film you actually have this experience of it being taken apart. "Those archetypes, those mythological shapes, whether they be Batman or Superman or any of superheroes and how they're just turned around. Whether it's through sexual fetish or fighting, actually killing guys they fight. "Stuff like that or whether it's the end of the movie and there's this moral dilemma that is the end of the movie is also challenging on all those levels."

Zack Snyder Film

22,225 просмотров • 6 месяцев назад

Rich Roll on why waiting to "feel like it" is a trap: "You can't think your way into the mood that you seek or the state of mind that you aspire to inhabit. Action is the only thing that can trigger that change." Rich uses running as the perfect illustration of this principle. Imagine you wake up in the morning and you're supposed to do a run because you're training for a race. You don't feel like it. So what do most of us do? "We all resort to that state where we think, 'Well, I don't want to do it right now. I'll just wait until I feel like doing it and then I'll do it then.'" But here's the problem with that logic: "If you're waiting until you feel like doing something, chances are you're probably never going to get to it." The mood you're hoping will arrive on its own? It's not coming. Not without action first. "To take the action despite how you feel about it is the thing that catalyzes the state change." You don't run because you feel motivated. You feel motivated because you ran. He points to what every runner knows from experience: "When they finish the run, they're always glad that they did it. They don't generally regret it. And then they feel better." Notice the sequence. The good feeling comes after the action, not before it. The state change is the reward for showing up, not the prerequisite. And this isn't just about running. As Rich puts it: "That example is applicable to all areas of life." The workout you're avoiding. The conversation you're delaying. The project you're putting off until you're "in the right headspace." You're waiting for a feeling that only exists on the other side of doing the thing.

Rich Roll on why waiting to "feel like it" is a trap: "You can't think your way into the mood that you seek or the state of mind that you aspire to inhabit. Action is the only thing that can trigger that change." Rich uses running as the perfect illustration of this principle. Imagine you wake up in the morning and you're supposed to do a run because you're training for a race. You don't feel like it. So what do most of us do? "We all resort to that state where we think, 'Well, I don't want to do it right now. I'll just wait until I feel like doing it and then I'll do it then.'" But here's the problem with that logic: "If you're waiting until you feel like doing something, chances are you're probably never going to get to it." The mood you're hoping will arrive on its own? It's not coming. Not without action first. "To take the action despite how you feel about it is the thing that catalyzes the state change." You don't run because you feel motivated. You feel motivated because you ran. He points to what every runner knows from experience: "When they finish the run, they're always glad that they did it. They don't generally regret it. And then they feel better." Notice the sequence. The good feeling comes after the action, not before it. The state change is the reward for showing up, not the prerequisite. And this isn't just about running. As Rich puts it: "That example is applicable to all areas of life." The workout you're avoiding. The conversation you're delaying. The project you're putting off until you're "in the right headspace." You're waiting for a feeling that only exists on the other side of doing the thing.

Kevin Tanaka

10,256 просмотров • 2 месяцев назад

Alex Karp went on TV and said his enterprise customers are done letting AI companies quietly steal the alpha out of their business, and the CEO of Together AI explained on CNBC exactly how that theft works. Karp said his customers are livid. They feel they pay for tokens that create nothing while handing over the secret sauce of their own company. Then Vipul Ved Prakash laid out the mechanism. Every time you send your data to a company with very smart models, you hand them the recipe to rebuild what you do. Your data is the thing that makes you money. And you are pushing it straight into a competitor's system. The smarter the models get, the more they learn from what you feed them. This is why companies are moving to open models, and it was never really about price. You can download an open model, run it inside your own walls, and never let your data leave the building. It scores 81 against the top closed model's 85, and runs up to sixty times cheaper. Half the Fortune 500 is already running open models from Hugging Face. The story was that the labs win because they own the best models. The companies paying them just realized they owned the best data the whole time.

Alex Karp went on TV and said his enterprise customers are done letting AI companies quietly steal the alpha out of their business, and the CEO of Together AI explained on CNBC exactly how that theft works. Karp said his customers are livid. They feel they pay for tokens that create nothing while handing over the secret sauce of their own company. Then Vipul Ved Prakash laid out the mechanism. Every time you send your data to a company with very smart models, you hand them the recipe to rebuild what you do. Your data is the thing that makes you money. And you are pushing it straight into a competitor's system. The smarter the models get, the more they learn from what you feed them. This is why companies are moving to open models, and it was never really about price. You can download an open model, run it inside your own walls, and never let your data leave the building. It scores 81 against the top closed model's 85, and runs up to sixty times cheaper. Half the Fortune 500 is already running open models from Hugging Face. The story was that the labs win because they own the best models. The companies paying them just realized they owned the best data the whole time.

Ihtesham Ali

17,433 просмотров • 16 дней назад

Donald Trump is right out our beef industry The owners of JBS, the worlds largest meat processing company, has been charged with bribing over 1,800 politicians and officials, charges include things like price fixing They’ve been importing cheap foreign meats and using the“Product of the USA” labeling loophole This is a racket. Remember, even though this loophole made by the USDA has finally been closed it won’t be fully enforced until 2026 Until then, “They can import essentially a lower standard beef into America. All they have to do is break it down here so it arrives into America as a frozen slab of beef, essentially. They can turn it into steaks and hamburger from there. And then they can sell it as product of the usa. And they're allowed to label that as product of the usa, which it is not. And it's shot up with who knows what (vaccines)”

Donald Trump is right out our beef industry The owners of JBS, the worlds largest meat processing company, has been charged with bribing over 1,800 politicians and officials, charges include things like price fixing They’ve been importing cheap foreign meats and using the“Product of the USA” labeling loophole This is a racket. Remember, even though this loophole made by the USDA has finally been closed it won’t be fully enforced until 2026 Until then, “They can import essentially a lower standard beef into America. All they have to do is break it down here so it arrives into America as a frozen slab of beef, essentially. They can turn it into steaks and hamburger from there. And then they can sell it as product of the usa. And they're allowed to label that as product of the usa, which it is not. And it's shot up with who knows what (vaccines)”

Wall Street Apes

355,498 просмотров • 8 месяцев назад

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

GREG ISENBERG

192,483 просмотров • 3 месяцев назад

Testing the next feature coming from Runway and it’s wild. You’ll soon be able to create just by chatting. One of the most impressive things I’ve seen is that it can now do stuff that even ChatGPT can’t. In this test, I asked it to add fur to objects that normally don’t have any...and boom, it generated a whole series of fuzzy creations. Then I gave it a character reference and asked for him to be playing the piano. It made mistakes at first, but kept correcting itself until it nailed the shot. Finally, I asked for a video based on the image... and it delivered again. The ability to iterate and evolve your vision in real-time is just incredible. Still makes mistakes and it can be much improved, but a massive shoutout to the team at Runway , this is next level.

Testing the next feature coming from Runway and it’s wild. You’ll soon be able to create just by chatting. One of the most impressive things I’ve seen is that it can now do stuff that even ChatGPT can’t. In this test, I asked it to add fur to objects that normally don’t have any...and boom, it generated a whole series of fuzzy creations. Then I gave it a character reference and asked for him to be playing the piano. It made mistakes at first, but kept correcting itself until it nailed the shot. Finally, I asked for a video based on the image... and it delivered again. The ability to iterate and evolve your vision in real-time is just incredible. Still makes mistakes and it can be much improved, but a massive shoutout to the team at Runway , this is next level.

@alexgnewmedia

21,183 просмотров • 1 год назад

This is what open robotics actually looks like! 🔓 I was in Bordeaux, sitting inside Pollen Robotics, and had a chance to play around with their humanoid robot Reachy 2. It’s expressive, modular, and can even bring you coffee, and of course runs Python or Rust. I was talking with Reachy in 3 different languages, tele-operating it and manipulating kitchen stuff, and make it even mimic my gestures. The best part is that there are no locked APIs, or hidden stuff, just an open platform that you can build upon. The connection with Hugging Face makes it super easy to start, as the open source approach allows everyone to contribute, experiment and add their own things. I already ordered his little brother Reachy Mini, let's see what a robotics rookie can do with that robot! 👀 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

This is what open robotics actually looks like! 🔓 I was in Bordeaux, sitting inside Pollen Robotics, and had a chance to play around with their humanoid robot Reachy 2. It’s expressive, modular, and can even bring you coffee, and of course runs Python or Rust. I was talking with Reachy in 3 different languages, tele-operating it and manipulating kitchen stuff, and make it even mimic my gestures. The best part is that there are no locked APIs, or hidden stuff, just an open platform that you can build upon. The connection with Hugging Face makes it super easy to start, as the open source approach allows everyone to contribute, experiment and add their own things. I already ordered his little brother Reachy Mini, let's see what a robotics rookie can do with that robot! 👀 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

19,433 просмотров • 6 месяцев назад

Wall Street burns billions trying to predict Bitcoin. A 28-year-old self-taught coder in Warsaw made $377,000 by not even trying. He'd lost money on three trading bots before this one. Each looked perfect on paper, then started losing money the moment he ran it for real market. So he built bot that doesn't trust itself. His wallet: The truth is simple: you can't predict the next five minutes of Bitcoin. It's a coin flip. Anyone selling you a "prediction" is selling you nothing. So he stopped predicting. The bot hunts the moments the crowd is wrong instead. Here's the part that makes the money, and it's the opposite of what everyone builds. Any strategy can be made to look amazing on past data. On a 5-minute chart, most of them are just lucky, not real - and they stop working fast. So the bot treats every strategy it finds as fake until it proves otherwise. Each one has to pass a hard test: > test it on old data → test it on data it's never seen → try to break it on purpose → cut it down to the one thing that matters → run it forward → keep it only if it still works Last round, 10 of its 12 "winning" strategies turned out to be fake. It kept the 2 that actually worked and dropped the rest. And it never stops - building, testing, and dumping strategies around the clock. What worked yesterday can stop working today, so the second one starts losing, it gets cut before it costs you a thing. The result: $433,000 across 2,955 trades All his old bots tried to be right. This one just tries to catch itself being wrong - and that's why it's still alive. Bookmark this article below - it's the breakdown that explains why your last bot died. It's pruning and trading right now. Copy its wallet and skip to the edges that survived:

Wall Street burns billions trying to predict Bitcoin. A 28-year-old self-taught coder in Warsaw made $377,000 by not even trying. He'd lost money on three trading bots before this one. Each looked perfect on paper, then started losing money the moment he ran it for real market. So he built bot that doesn't trust itself. His wallet: The truth is simple: you can't predict the next five minutes of Bitcoin. It's a coin flip. Anyone selling you a "prediction" is selling you nothing. So he stopped predicting. The bot hunts the moments the crowd is wrong instead. Here's the part that makes the money, and it's the opposite of what everyone builds. Any strategy can be made to look amazing on past data. On a 5-minute chart, most of them are just lucky, not real - and they stop working fast. So the bot treats every strategy it finds as fake until it proves otherwise. Each one has to pass a hard test: > test it on old data → test it on data it's never seen → try to break it on purpose → cut it down to the one thing that matters → run it forward → keep it only if it still works Last round, 10 of its 12 "winning" strategies turned out to be fake. It kept the 2 that actually worked and dropped the rest. And it never stops - building, testing, and dumping strategies around the clock. What worked yesterday can stop working today, so the second one starts losing, it gets cut before it costs you a thing. The result: $433,000 across 2,955 trades All his old bots tried to be right. This one just tries to catch itself being wrong - and that's why it's still alive. Bookmark this article below - it's the breakdown that explains why your last bot died. It's pruning and trading right now. Copy its wallet and skip to the edges that survived:

cvxv666

24,302 просмотров • 21 дней 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

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 • 6 месяцев назад