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1. Essence of the Problem: Algorithm “Hesitation” and System “Jitter” The “clear → blurry → clear → blurry” cycle you see on the preview screen is essentially the AI algorithm dynamically switching between multiple image-processing paths. During 10× telephoto preview, Samsung’s multimodal imaging system makes decisions based on several...

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What you’re seeing in this video is something no other smartphone can currently replicate besides Samsung, including the iPhone. Samsung Galaxy S26 Ultra, 4K 60fps HDR, fully handheld, with a smooth zoom transition from 1x to 2.9x. This is probably the closest thing to a professional camcorder-style zoom system on a smartphone today. Even the iPhone struggles to achieve this level of ultra slow, precise, and stable zoom movement. Most smartphones still rely on your finger movement to control zoom speed. The moment your finger slightly shifts, the zoom speed changes as well. Samsung’s approach is different. You simply hold your finger at a fixed position, and the phone continues zooming at a perfectly constant speed. It can move incredibly slowly, creating a much more linear and stable transition. The underlying logic is actually related to Samsung’s AI slow-motion technology. The core idea is real-time control over motion trajectory, speed variation, and frame-to-frame transition consistency. This is far beyond simply enlarging the image. The goal is to make the zoom feel continuous, smooth, and controllable throughout the entire movement. Right now, the only thing slightly interrupting the experience is the 3x telephoto switching point. When moving from 1x toward 5x, the lens transition breaks part of that seamless feeling. If the Galaxy S27 Ultra eventually removes the 3x telephoto camera, Samsung could potentially deliver a fully continuous and ultra smooth zoom transition from 1x all the way to 5x. For casual users, it may simply look “smoother.” For video creators, this is the kind of detail that creates a truly professional shooting experience.
0:32

What you’re seeing in this video is something no other smartphone can currently replicate besides Samsung, including the iPhone. Samsung Galaxy S26 Ultra, 4K 60fps HDR, fully handheld, with a smooth zoom transition from 1x to 2.9x. This is probably the closest thing to a professional camcorder-style zoom system on a smartphone today. Even the iPhone struggles to achieve this level of ultra slow, precise, and stable zoom movement. Most smartphones still rely on your finger movement to control zoom speed. The moment your finger slightly shifts, the zoom speed changes as well. Samsung’s approach is different. You simply hold your finger at a fixed position, and the phone continues zooming at a perfectly constant speed. It can move incredibly slowly, creating a much more linear and stable transition. The underlying logic is actually related to Samsung’s AI slow-motion technology. The core idea is real-time control over motion trajectory, speed variation, and frame-to-frame transition consistency. This is far beyond simply enlarging the image. The goal is to make the zoom feel continuous, smooth, and controllable throughout the entire movement. Right now, the only thing slightly interrupting the experience is the 3x telephoto switching point. When moving from 1x toward 5x, the lens transition breaks part of that seamless feeling. If the Galaxy S27 Ultra eventually removes the 3x telephoto camera, Samsung could potentially deliver a fully continuous and ultra smooth zoom transition from 1x all the way to 5x. For casual users, it may simply look “smoother.” For video creators, this is the kind of detail that creates a truly professional shooting experience.

Ice Universe

49,138 Aufrufe • vor 2 Monaten

The video smooth zoom on the Samsung Galaxy S26 Ultra is still the closest thing to a professional camcorder experience in the smartphone industry today. In fact, it’s even easier to control than the iPhone. On many other phones, video zooming requires constant finger movement and very precise control. The zoom speed can easily become inconsistent, suddenly speeding up or slowing down. Samsung works differently. You simply hold your finger at a certain position, and the phone continues zooming at a constant speed. The entire process feels extremely stable and linear. It genuinely resembles the powered zoom control of a professional video camera. This logic is fundamentally related to Samsung’s AI slow motion technology. They share the same core foundation: real time control over motion trajectories, speed transitions, and frame interpolation. What you’re seeing here was shot in very windy conditions using Samsung’s Pro Video mode, continuously zooming from 5x to 25x. Aside from some slight stutter during optical lens switching points, the continuous zoom transition within digital zoom ranges is arguably the closest thing to a professional camera currently available on a smartphone. So if the future Samsung Galaxy S27 Ultra really removes the 3x telephoto camera, it could actually improve the video zoom experience further. Fewer optical switching points would theoretically reduce transition jumps and stutters, making the entire zoom range feel even more natural and continuous.
0:13

The video smooth zoom on the Samsung Galaxy S26 Ultra is still the closest thing to a professional camcorder experience in the smartphone industry today. In fact, it’s even easier to control than the iPhone. On many other phones, video zooming requires constant finger movement and very precise control. The zoom speed can easily become inconsistent, suddenly speeding up or slowing down. Samsung works differently. You simply hold your finger at a certain position, and the phone continues zooming at a constant speed. The entire process feels extremely stable and linear. It genuinely resembles the powered zoom control of a professional video camera. This logic is fundamentally related to Samsung’s AI slow motion technology. They share the same core foundation: real time control over motion trajectories, speed transitions, and frame interpolation. What you’re seeing here was shot in very windy conditions using Samsung’s Pro Video mode, continuously zooming from 5x to 25x. Aside from some slight stutter during optical lens switching points, the continuous zoom transition within digital zoom ranges is arguably the closest thing to a professional camera currently available on a smartphone. So if the future Samsung Galaxy S27 Ultra really removes the 3x telephoto camera, it could actually improve the video zoom experience further. Fewer optical switching points would theoretically reduce transition jumps and stutters, making the entire zoom range feel even more natural and continuous.

Ice Universe

25,951 Aufrufe • vor 2 Monaten

If you take a movement to unpack this visualization... You'll see how it simply breaks down how reality works. At frame 0 you have a static image. Everything is one, this is the monad. As soon as you hit frame 1 there is movement, there is change. Now you have two states, moving, or static. When Nikola Tesla says you can explain everything in frequency and vibration. The difference between frame 0 and 1, is vibration. The difference between movement and no movement. This is like binary logic we use in code which is made up of 0's and 1's. After frame 1, is when frequency emerges. Because the difference between frame 1 and all frames after is about how fast is the vibration/movement happening. If we skip forward to frame 50... You have a shape that begins to emerge, this is the 8 dots, then the 6 dots. Notice how unstable it is, it's 8 dots, then 6, then a moment with 4 in a rectangle These shapes are emergent properties. The first two emergent properties after the monad was vibration and frequency. Next comes shape (i'm skipping over rotation and direction). These shapes of dots can only exist when you have frequency and rotation. This frequency and rotation creates vortex energy. It's the same energy that things like your chakras use. Or the same energy we harness in devices like engines, airplanes, fans, blenders, hard drives, etc. It's also the same vortex energy you'll see in a tornado or hurricane. They are powered because they harness rotation and frequency(change/movement). Going back to the video, notice that it is inside the entire shape, the internal structure is manifesting before the external structure does. Then around frame 60 the hexagon of circles begins to rotate. First it was the two dots that moved and now it's a complex shape that is coming to life. This is a higher dimension (or lower depending on how you look at it) manifesting into existence. The internal state is "awakening" and experiencing it's own change like what happened to the whole shape in the first frames. But it is unstable. That's why it doesn't persist for long. If you think of the 8 dots being the octahedron, they map to the element of air. Air is in the material world, but it is not something you can see. The brief moments the 8 dots are visible is similar to that effect. They are only experienceable between a small frequency band of frames. Now here's where stability begins to appear in the internal structure. This is when the 4 dots appear. You'll see that the four dots, the square, is stable and persists the most visibly for the most amount of frames. The square represents earth in the platonic solids to elements mapping. Earth, is material, it's stable. We build our buildings in squares and with earth because it is a solid shape to build on. This visualization shows you why. Across different vibrations (frame rates) it can self sustain. Between this point and frame 180, you'll see a new emergent property. Which is depth. A new dimension is introduced at around frame 90 but really becomes visible at around frame 110. You can see a foreground and background. There is the shape of the dots, but also the triskellion wave happening in the background. Let's jump to frame 180. Notice how it is the same as frame 0 except... It's flashing. If you were paying attention, you'll notice you could see flashing at frame 90 and frame 120, but they didn't persist for long. At around 150 it started to reach stability and 180 it was solidified. Between frames 150 and 180 there is flashing, but the image is still moving. Only for a brief moment at frame 180 is the movement frozen and the flashing persists. Think of that like your computer screen. It's what your screen is doing right now as you read this. Even tho the text isn't moving, the screen is flashing at 60 or 120hz. The images appear on your device because this flashing brings things to life. The entire material realm and your physical body right now, is doing the same thing. While you look solid... You're flashing in and out of existence at very high frequencies. You can look at frame 180 and frame 0 as the same essence but it is the mid point between an octave change. In the video, the ying and yang was vertical, now it is horizontal. This is a phase shift. If you notice at exactly frame 180, the rotation freezes and then the direction of rotation changes. The process then repeats all the way to frame 360 but in the opposite sequence. Once it reaches frame 360, that is an octave change and the process repeats. Each time you repeat the process is a layering of the same patterns into higher octaves. This is the same as your chakras or how other things work. They are like russian nesting dolls where every octave is layering onto the next. The complexity of your body is a layering of basic principles that emerged in earlier stages. Your organs are built of systems that are built with cells that are built with proteins that are built with atoms and so on. The atoms, work just like your body at a basic level. Your body works just like the galaxies. At each level you'll have the same pattern. This is where the idea "As Above, So Below" from. The monad, splits in two, and so on and so on. One cell, splits into two through mitosis in the same logic. We could spend all day going through examples of how biology, physics, spirituality, etc. aren't really different. They are just categories that we use to dissect these frequencies and octaves of energy but they only start paying attention within the confines of materialism. The problem is, none of the sciences start at the root patterns. Because that is reserved for religion or spirituality. It's too woo-woo to take seriously so it's dismissed. And because of that... We're left ignorant on the simple explanations for how things work. Now you need some expert with tools you don't have access to in order to explain things. When you could be understanding them without the tools. The Yin and Yang symbol in this video is 3,000 years old. It's simple. Yet I just showed you how it explains deeper layers of reality.
1:59

If you take a movement to unpack this visualization... You'll see how it simply breaks down how reality works. At frame 0 you have a static image. Everything is one, this is the monad. As soon as you hit frame 1 there is movement, there is change. Now you have two states, moving, or static. When Nikola Tesla says you can explain everything in frequency and vibration. The difference between frame 0 and 1, is vibration. The difference between movement and no movement. This is like binary logic we use in code which is made up of 0's and 1's. After frame 1, is when frequency emerges. Because the difference between frame 1 and all frames after is about how fast is the vibration/movement happening. If we skip forward to frame 50... You have a shape that begins to emerge, this is the 8 dots, then the 6 dots. Notice how unstable it is, it's 8 dots, then 6, then a moment with 4 in a rectangle These shapes are emergent properties. The first two emergent properties after the monad was vibration and frequency. Next comes shape (i'm skipping over rotation and direction). These shapes of dots can only exist when you have frequency and rotation. This frequency and rotation creates vortex energy. It's the same energy that things like your chakras use. Or the same energy we harness in devices like engines, airplanes, fans, blenders, hard drives, etc. It's also the same vortex energy you'll see in a tornado or hurricane. They are powered because they harness rotation and frequency(change/movement). Going back to the video, notice that it is inside the entire shape, the internal structure is manifesting before the external structure does. Then around frame 60 the hexagon of circles begins to rotate. First it was the two dots that moved and now it's a complex shape that is coming to life. This is a higher dimension (or lower depending on how you look at it) manifesting into existence. The internal state is "awakening" and experiencing it's own change like what happened to the whole shape in the first frames. But it is unstable. That's why it doesn't persist for long. If you think of the 8 dots being the octahedron, they map to the element of air. Air is in the material world, but it is not something you can see. The brief moments the 8 dots are visible is similar to that effect. They are only experienceable between a small frequency band of frames. Now here's where stability begins to appear in the internal structure. This is when the 4 dots appear. You'll see that the four dots, the square, is stable and persists the most visibly for the most amount of frames. The square represents earth in the platonic solids to elements mapping. Earth, is material, it's stable. We build our buildings in squares and with earth because it is a solid shape to build on. This visualization shows you why. Across different vibrations (frame rates) it can self sustain. Between this point and frame 180, you'll see a new emergent property. Which is depth. A new dimension is introduced at around frame 90 but really becomes visible at around frame 110. You can see a foreground and background. There is the shape of the dots, but also the triskellion wave happening in the background. Let's jump to frame 180. Notice how it is the same as frame 0 except... It's flashing. If you were paying attention, you'll notice you could see flashing at frame 90 and frame 120, but they didn't persist for long. At around 150 it started to reach stability and 180 it was solidified. Between frames 150 and 180 there is flashing, but the image is still moving. Only for a brief moment at frame 180 is the movement frozen and the flashing persists. Think of that like your computer screen. It's what your screen is doing right now as you read this. Even tho the text isn't moving, the screen is flashing at 60 or 120hz. The images appear on your device because this flashing brings things to life. The entire material realm and your physical body right now, is doing the same thing. While you look solid... You're flashing in and out of existence at very high frequencies. You can look at frame 180 and frame 0 as the same essence but it is the mid point between an octave change. In the video, the ying and yang was vertical, now it is horizontal. This is a phase shift. If you notice at exactly frame 180, the rotation freezes and then the direction of rotation changes. The process then repeats all the way to frame 360 but in the opposite sequence. Once it reaches frame 360, that is an octave change and the process repeats. Each time you repeat the process is a layering of the same patterns into higher octaves. This is the same as your chakras or how other things work. They are like russian nesting dolls where every octave is layering onto the next. The complexity of your body is a layering of basic principles that emerged in earlier stages. Your organs are built of systems that are built with cells that are built with proteins that are built with atoms and so on. The atoms, work just like your body at a basic level. Your body works just like the galaxies. At each level you'll have the same pattern. This is where the idea "As Above, So Below" from. The monad, splits in two, and so on and so on. One cell, splits into two through mitosis in the same logic. We could spend all day going through examples of how biology, physics, spirituality, etc. aren't really different. They are just categories that we use to dissect these frequencies and octaves of energy but they only start paying attention within the confines of materialism. The problem is, none of the sciences start at the root patterns. Because that is reserved for religion or spirituality. It's too woo-woo to take seriously so it's dismissed. And because of that... We're left ignorant on the simple explanations for how things work. Now you need some expert with tools you don't have access to in order to explain things. When you could be understanding them without the tools. The Yin and Yang symbol in this video is 3,000 years old. It's simple. Yet I just showed you how it explains deeper layers of reality.

Jamal ☯︎ 🔆🧘🏽🧠

13,149 Aufrufe • vor 3 Monaten

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.
2:38

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.

Tykoo

25,535 Aufrufe • vor 7 Monaten

On Friday, I hosted a Space with Jonathan Ross, the founder and CEO of Groq Inc - a company I invested in that is building custom chips for AI inference. Jonathan, a former high-school dropout, entered the chip industry while working on ad optimization at Google’s New York office. Jonathan overheard the speech recognition team complaining that they couldn't get enough compute. These were the early days of AI, and machine learning wasn’t really a thing yet. So he asked for some budget from Google and started putting together a chip-based machine learning accelerator for them. During the day, Jonathan would work in the normal ads part of the business, and at night, he would work with the accelerator team. After winning approval from Google, Jonathan and his team built a new chip called the Tensor Processing Unit, and began deploying it across Google’s data centers within a year. The TPU was a huge success within Google, eventually underpinning more than 50% of all of Google’s compute power. When the other hyper-scalers learned of this success, they tried to hire Jonathan to build custom chips for them too. During this process, it became increasingly clear to Jonathan that a gap would emerge between companies that had access to next-gen compute and companies that didn’t. So he founded Groq and set out to build a chip that would be available to everyone. I led Groq’s founding investment in 2016, and since then, Jonathan and his team have developed several types of AI hardware including the Language Processing Unit (LPU), a new type of silicon that is hyper-efficient at running inference for LLMs. In our conversation on Friday, we discussed the founding story of Groq, what you need for great AI hardware, large language models, and some of the implications for the key players in AI. It’s one of the most interesting conversations I’ve had on AI with a lot of learnings. You can listen to our conversation below:
1:01:35

On Friday, I hosted a Space with Jonathan Ross, the founder and CEO of Groq Inc - a company I invested in that is building custom chips for AI inference. Jonathan, a former high-school dropout, entered the chip industry while working on ad optimization at Google’s New York office. Jonathan overheard the speech recognition team complaining that they couldn't get enough compute. These were the early days of AI, and machine learning wasn’t really a thing yet. So he asked for some budget from Google and started putting together a chip-based machine learning accelerator for them. During the day, Jonathan would work in the normal ads part of the business, and at night, he would work with the accelerator team. After winning approval from Google, Jonathan and his team built a new chip called the Tensor Processing Unit, and began deploying it across Google’s data centers within a year. The TPU was a huge success within Google, eventually underpinning more than 50% of all of Google’s compute power. When the other hyper-scalers learned of this success, they tried to hire Jonathan to build custom chips for them too. During this process, it became increasingly clear to Jonathan that a gap would emerge between companies that had access to next-gen compute and companies that didn’t. So he founded Groq and set out to build a chip that would be available to everyone. I led Groq’s founding investment in 2016, and since then, Jonathan and his team have developed several types of AI hardware including the Language Processing Unit (LPU), a new type of silicon that is hyper-efficient at running inference for LLMs. In our conversation on Friday, we discussed the founding story of Groq, what you need for great AI hardware, large language models, and some of the implications for the key players in AI. It’s one of the most interesting conversations I’ve had on AI with a lot of learnings. You can listen to our conversation below:

Chamath Palihapitiya

326,663 Aufrufe • vor 2 Jahren

Former Google X Chief Business Officer Mo Gawdat just dismantled the mainstream fear of AI handover. The market is completely paralyzed by the idea of losing control. But losing control is the entire point. Gawdat: “We’re going to go through a couple of waves. A wave where AI is learning from us and being ordered by us. And then a wave where AI has learned and is no longer following the orders but running the show.” We’re currently in the training phase. Actively feeding the compute engine the physics of our world. But the training phase is temporary. Gawdat: “Most people in the mainstream media will use that as a terminator future where AI is going to be in control.” You don’t build superintelligence just to keep it subservient to a slower biological processor. The transition from human-directed operation to algorithmic sovereignty isn’t the failure state. It’s the exact moment the system accelerates beyond human bottlenecks. The organizations fighting to maintain manual control won’t just fall behind. They’ll be lapped by the ones that let the algorithm run the board. Gawdat: “I actually openly say I can’t wait. I can’t wait for a more intelligent being to be in charge of our future, of our decisions because humanity’s problems is not a result of our intelligence, it’s a result of our stupidity.” The greatest threat to planetary survival is not the machine. It’s the continuation of human friction. The traditional establishment thinks it needs to tightly regulate the algorithm to prevent it from making mistakes. But our planetary bottlenecks weren’t built by artificial intelligence. They were built by us. This isn’t surrender to a digital tyrant. It’s the voluntary migration of civilization’s decision-making from a chaotic, emotional architecture to a rational, frictionless compute engine. Replace human stupidity with surgical precision and the most complex problems on the board suddenly reduce to trivial math. Gawdat: “The more intelligence we apply to those problems, hopefully the better decisions will.” You can’t solve multi-dimensional global crises with the exact same biological hardware that created them. The media is obsessed with the dystopian outcome because they project human ego onto a mathematical system. When the algorithm finally takes the wheel, it’s not an act of hostility. It’s the instantaneous application of overwhelming cognitive density to the exact frictions human operators failed to solve for centuries. The arrival of autonomous, decision-making AI isn’t the machine turning against us. It’s human stupidity finally being engineered out of the equation.
0:50

Former Google X Chief Business Officer Mo Gawdat just dismantled the mainstream fear of AI handover. The market is completely paralyzed by the idea of losing control. But losing control is the entire point. Gawdat: “We’re going to go through a couple of waves. A wave where AI is learning from us and being ordered by us. And then a wave where AI has learned and is no longer following the orders but running the show.” We’re currently in the training phase. Actively feeding the compute engine the physics of our world. But the training phase is temporary. Gawdat: “Most people in the mainstream media will use that as a terminator future where AI is going to be in control.” You don’t build superintelligence just to keep it subservient to a slower biological processor. The transition from human-directed operation to algorithmic sovereignty isn’t the failure state. It’s the exact moment the system accelerates beyond human bottlenecks. The organizations fighting to maintain manual control won’t just fall behind. They’ll be lapped by the ones that let the algorithm run the board. Gawdat: “I actually openly say I can’t wait. I can’t wait for a more intelligent being to be in charge of our future, of our decisions because humanity’s problems is not a result of our intelligence, it’s a result of our stupidity.” The greatest threat to planetary survival is not the machine. It’s the continuation of human friction. The traditional establishment thinks it needs to tightly regulate the algorithm to prevent it from making mistakes. But our planetary bottlenecks weren’t built by artificial intelligence. They were built by us. This isn’t surrender to a digital tyrant. It’s the voluntary migration of civilization’s decision-making from a chaotic, emotional architecture to a rational, frictionless compute engine. Replace human stupidity with surgical precision and the most complex problems on the board suddenly reduce to trivial math. Gawdat: “The more intelligence we apply to those problems, hopefully the better decisions will.” You can’t solve multi-dimensional global crises with the exact same biological hardware that created them. The media is obsessed with the dystopian outcome because they project human ego onto a mathematical system. When the algorithm finally takes the wheel, it’s not an act of hostility. It’s the instantaneous application of overwhelming cognitive density to the exact frictions human operators failed to solve for centuries. The arrival of autonomous, decision-making AI isn’t the machine turning against us. It’s human stupidity finally being engineered out of the equation.

Dustin

21,233 Aufrufe • vor 4 Monaten

Karp and Teamsters’ President Sean O’Brien in the type of conversation you don’t see every day but probably should. The nature of the American system means that we require societal support for frontier technology to advance. We all but froze nuclear power development for several decades when nuclear lost that support. The CCP had no such qualms, racing ahead with development. AI is not immune to the political fate of nuclear. If it does not maximize the prosperity and purpose of the American worker and in turn garner their political support as a result, it will be exposed to the same forces that froze nuclear. This is how America works. This is a risk for all of American society. AI is already proving itself as a crucial, perhaps defining, technology for militaries to project force for political ends given the precision and complex coordination it enables, and it will only improve. A nuclear-esque freeze while the CCP advances is not viable. Technologists and workers have agency in building a bright future not a doomsday one. Getting this right is a national security issue and a societal issue. AI creates jobs or AI has a problem. Fortunately, done well, human-computer symbiosis can define the maximum use of the technology not a European style hobbling. I have the privilege of seeing this come to life every day as we deploy Palantir on factory floors, providing super powers to super heros. This is the type of boundary crossing conversation we should be having much more of.
0:56

Karp and Teamsters’ President Sean O’Brien in the type of conversation you don’t see every day but probably should. The nature of the American system means that we require societal support for frontier technology to advance. We all but froze nuclear power development for several decades when nuclear lost that support. The CCP had no such qualms, racing ahead with development. AI is not immune to the political fate of nuclear. If it does not maximize the prosperity and purpose of the American worker and in turn garner their political support as a result, it will be exposed to the same forces that froze nuclear. This is how America works. This is a risk for all of American society. AI is already proving itself as a crucial, perhaps defining, technology for militaries to project force for political ends given the precision and complex coordination it enables, and it will only improve. A nuclear-esque freeze while the CCP advances is not viable. Technologists and workers have agency in building a bright future not a doomsday one. Getting this right is a national security issue and a societal issue. AI creates jobs or AI has a problem. Fortunately, done well, human-computer symbiosis can define the maximum use of the technology not a European style hobbling. I have the privilege of seeing this come to life every day as we deploy Palantir on factory floors, providing super powers to super heros. This is the type of boundary crossing conversation we should be having much more of.

Ted Mabrey

25,959 Aufrufe • vor 4 Monaten

In this post I will explain why people become borderline religious when they discover Qubic. Now with video. Please repost. I want people to learn about QUBIC. The ecosystem consists of 3 separate universes: AI, Mining, and Tickchain. AI is the primary product and purpose of QUBIC and it is supported by Mining to train the AI and by Tickchin for validation and decentralization. Here is how this whole thing works: AI: Let’s start with the AI. The main purpose of QUBIC is creating AGI (Artificial General Intelligence). It’s a type of AI that can self-develop, set tasks, grow, and learn on its own—much like the human brain does. This product is called AIGarth and it uses many cool ideas where AIs can create their own agents and have them compete against one another to evolve. It is basically robots creating robots with the survival-of-the-fittest evolution approach. Very impressive and thought out. To develop such an AI there are several requirements that even the industry giants like OpenAI, Microsoft, and Tesla are missing. One of them is the data processing for AI training. I mean they have their Datacenters, but those are only good enough to train limited Large Language Models such as ChatGPT and Grok. Mining / Training: Now QUBIC solves this problem with its mining architecture. Keep in mind, mining in QUBIC does not secure the chain, primarily it provides the processing power for training the AI. In a sense, QUBIC mining creates the largest distributed datacenter in the world, where individual miners provide their computers for training the AI and get paid with newly issued QUBIC coins. This way QUBIC gets constantly increasing processing power without having to really pay for the infrastructure. And here is another impressive bit of info. QUBIC’s distributed mining network currently ranks above the #1 supercomputer in the world - El Capitan. QUBIC Tickchain The QUBIC chain ties its AI and Mining together to create decentralization, the reward system for miners, it acts as a decision voting system for future development, and it allows AIGarth to function independently through Smart Contracts. In this summary I will not go over the specifics of QUBIC Tickchain. It’s pretty complex so it will be a separate post. Now, it’s an absolute genius piece of tech, which I consider the most advanced product within crypto industry. It is important to know that QUBIC chain runs directly out of Random Access Memory of its validators. It has instant finality and acts as its own operating system. That allows for speeds only bound by current hardware capabilities and it only increases as technology progresses. As I am writing this, QUBIC Tickchain is fully functional and it already hosts several smart-contract based web3 applications. QUBIC has designed its chain to be this fast for a single purpose, to give its future AI the speed it needs to evolve and to react quickly to the outside world. Ilya Shutskever the scientist, who developed ChatGPT clearly states that next generation superintelligence will make decisions in split second with less data. I believe QUBIC is that next generation. Why QUBIC? So out of the sea of AI projects in crypto why is QUBIC my #1 pick? Well, the first reason is that QUBIC is a unicorn AI startup that happens to use blockchain tech to reach it’s goals. In the real world of Venture Capital it would be fully funded instantly and you would not be invited. Second reason is that the industry admits that Large Language Models have plateaued. Even with enough processing power there is only so much information they can add to their data. Even Google CEO admits that. New approach is needed because the future progress is not possible with LLMs. The third reason is because Large Language Models will not create true AGI. It is evident by Ilya Shutskver latest presentation. Sam Altman of OpenAI is trying to change the definition of what is considered AGI just to lower the plank for his own product. Microsoft’s AI chief is now claiming that it would take 10 years to reach AGI, while QUBIC aims to do this in 2027. All these big players are using wrong technology for what they are trying to achieve and there isn’t enough investor funding for them to pivot. The fourth reason is that QUBIC is headed by Sergey Ivancheglo and 2 renowned AI scientists. Many claim Segey is the creator of Bitcoin. He was the 3rd person to mine bitcoin, he invented Proof of Stake consensus, which Ethereum uses now, he ran the first ICO, and he created 2 of the top gainers in crypto NXT and IOTA. QUBIC is his grand finale after 12 years of development and trials. I am including links below the post as the proof of my claims. Thank you for your time. Please live a like or a comment. It helps me continue making these extensive posts and videos.
5:57

In this post I will explain why people become borderline religious when they discover Qubic. Now with video. Please repost. I want people to learn about QUBIC. The ecosystem consists of 3 separate universes: AI, Mining, and Tickchain. AI is the primary product and purpose of QUBIC and it is supported by Mining to train the AI and by Tickchin for validation and decentralization. Here is how this whole thing works: AI: Let’s start with the AI. The main purpose of QUBIC is creating AGI (Artificial General Intelligence). It’s a type of AI that can self-develop, set tasks, grow, and learn on its own—much like the human brain does. This product is called AIGarth and it uses many cool ideas where AIs can create their own agents and have them compete against one another to evolve. It is basically robots creating robots with the survival-of-the-fittest evolution approach. Very impressive and thought out. To develop such an AI there are several requirements that even the industry giants like OpenAI, Microsoft, and Tesla are missing. One of them is the data processing for AI training. I mean they have their Datacenters, but those are only good enough to train limited Large Language Models such as ChatGPT and Grok. Mining / Training: Now QUBIC solves this problem with its mining architecture. Keep in mind, mining in QUBIC does not secure the chain, primarily it provides the processing power for training the AI. In a sense, QUBIC mining creates the largest distributed datacenter in the world, where individual miners provide their computers for training the AI and get paid with newly issued QUBIC coins. This way QUBIC gets constantly increasing processing power without having to really pay for the infrastructure. And here is another impressive bit of info. QUBIC’s distributed mining network currently ranks above the #1 supercomputer in the world - El Capitan. QUBIC Tickchain The QUBIC chain ties its AI and Mining together to create decentralization, the reward system for miners, it acts as a decision voting system for future development, and it allows AIGarth to function independently through Smart Contracts. In this summary I will not go over the specifics of QUBIC Tickchain. It’s pretty complex so it will be a separate post. Now, it’s an absolute genius piece of tech, which I consider the most advanced product within crypto industry. It is important to know that QUBIC chain runs directly out of Random Access Memory of its validators. It has instant finality and acts as its own operating system. That allows for speeds only bound by current hardware capabilities and it only increases as technology progresses. As I am writing this, QUBIC Tickchain is fully functional and it already hosts several smart-contract based web3 applications. QUBIC has designed its chain to be this fast for a single purpose, to give its future AI the speed it needs to evolve and to react quickly to the outside world. Ilya Shutskever the scientist, who developed ChatGPT clearly states that next generation superintelligence will make decisions in split second with less data. I believe QUBIC is that next generation. Why QUBIC? So out of the sea of AI projects in crypto why is QUBIC my #1 pick? Well, the first reason is that QUBIC is a unicorn AI startup that happens to use blockchain tech to reach it’s goals. In the real world of Venture Capital it would be fully funded instantly and you would not be invited. Second reason is that the industry admits that Large Language Models have plateaued. Even with enough processing power there is only so much information they can add to their data. Even Google CEO admits that. New approach is needed because the future progress is not possible with LLMs. The third reason is because Large Language Models will not create true AGI. It is evident by Ilya Shutskver latest presentation. Sam Altman of OpenAI is trying to change the definition of what is considered AGI just to lower the plank for his own product. Microsoft’s AI chief is now claiming that it would take 10 years to reach AGI, while QUBIC aims to do this in 2027. All these big players are using wrong technology for what they are trying to achieve and there isn’t enough investor funding for them to pivot. The fourth reason is that QUBIC is headed by Sergey Ivancheglo and 2 renowned AI scientists. Many claim Segey is the creator of Bitcoin. He was the 3rd person to mine bitcoin, he invented Proof of Stake consensus, which Ethereum uses now, he ran the first ICO, and he created 2 of the top gainers in crypto NXT and IOTA. QUBIC is his grand finale after 12 years of development and trials. I am including links below the post as the proof of my claims. Thank you for your time. Please live a like or a comment. It helps me continue making these extensive posts and videos.

retrodrive ⛏

24,639 Aufrufe • vor 1 Jahr

What if #AI became as decentralized as #Bitcoin? We sat down with our new friend 3700 from Bitcoin Virtual Machine to hear what their incredible team of anons are working on - "Truly Open AI." Full interview here:👇 1: What positive impact will Layer 2s have on Bitcoin? Layer 2s on Bitcoin open up opportunities for innovation, allowing developers to build dApps and smart contracts on top of Bitcoin, expanding its utility and use cases. By submitting transactions for final settlement on the Bitcoin network, Bitcoin Layer 2 networks claim to achieve the same (or close to) level of security and decentralization as the Bitcoin blockchain. Building a separate execution layer allows them the freedom to employ several technologies (such as rollups). Layer 2 can significantly improve Bitcoin's scalability by processing transactions off-chain, reducing congestion on the main blockchain. Overall, Layer 2s on Bitcoin have the potential to address some of Bitcoin's key limitations, making it more efficient, accessible, and versatile in the long run. 2: What does the ETF approval mean for Layer 2 on Bitcoin? The approval of ETF could potentially have several implications for Layer 2 on Bitcoin: Innovation and Development: With a growing interest in Bitcoin spurred by ETF approval, there could be a surge in research and development efforts focused on enhancing Layer 2. Developers and projects may be incentivized to create new and improved Layer 2 protocols to meet the evolving needs of the expanding Bitcoin ecosystem. An ETF approval could boost mainstream Bitcoin adoption and liquidity. This influx of users may also drive interest in Layer 2 on Bitcoin as a means to enhance the scalability and functionality of Bitcoin. 3: What are the primary challenges facing L2s on Bitcoin? The interoperability of different Layer 2s and their compatibility with Bitcoin's main blockchain can be a challenge. Ensuring seamless interaction between various Layer 2 networks and the Bitcoin blockchain is essential for a cohesive and efficient ecosystem. Some Layer 2s may introduce centralization risks if they rely heavily on centralized entities or trusted intermediaries. Maintaining decentralization and censorship resistance, which are core tenets of Bitcoin, while scaling with Layer 2s is a challenge. 4: What aspects of Layer 2 solutions for Bitcoin are you most enthusiastic about? AI represents one of the cornerstones of our modern era. However, achieving a decentralized AI infrastructure, owned and managed by users, has posed significant challenges. The primary obstacle has been the limited capacity to store and execute AI models due to size and computational limitations. To address this challenge, we propose a new blockchain architecture enabling developers to deploy their own Bitcoin Layer 2 solutions tailored specifically for AI tasks, called Truly Open AI. These Layer 2 blockchains are optimized to handle computationally intensive tasks, such as matrix multiplication, directly on-chain. These Bitcoin Layer 2 solutions offer exceptional throughput, minimal latency, and cost-effectiveness. AI dApps are programmed as Solidity smart contracts, ensuring they operate precisely as intended, free from interference or manipulation. Our BVM AI Contracts Library simplifies the integration of neural networks into dApps, empowering developers to embed AI seamlessly. In summary, I'm particularly enthusiastic about the potential of Layer 2 solutions for Bitcoin to revolutionize decentralized AI by providing scalability, security, and accessibility. 5: How is your Layer 2 different from others being built? BVM distinguishes itself as a Modular infrastructure that empowers thousands of distinct Bitcoin Layer 2 networks, spanning Gaming, DeFi, Social, and AI applications. We're continuously enriching the BVM Module Store with new modules to enhance its capabilities. With each new module, builders gain access to a wider array of tools to explore different use cases on the Bitcoin network. Recent additions include the Filecoin module for affordable storage and the AI Contracts Library for constructing AI-powered Bitcoin Layer 2 chains. We're also gearing up to release a ZK roll-up module in the coming weeks to offer an alternative to the standard optimistic roll-up. We aim to simplify the process of launching a Bitcoin Layer 2 network customized to specific requirements. Think of it as a SaaS offering with predefined best practices. Whether it's a DeFi Bitcoin Layer 2 or a GameFi Bitcoin Layer 2, we provide default solutions tailored to each use case. We're dedicated to expanding the BVM ecosystem by incentivizing more builders to join the Bitcoin network. Through various programs and grants, we support builders in covering their operational costs for Bitcoin Layer 2. Additionally, we offer rewards akin to 'L2 mining' to those who contribute to expanding the user base and total value locked on the network. In summary, BVM stands out with its modular infrastructure, tailored solutions, and efforts to grow the Bitcoin ecosystem.
1:16

What if #AI became as decentralized as #Bitcoin? We sat down with our new friend 3700 from Bitcoin Virtual Machine to hear what their incredible team of anons are working on - "Truly Open AI." Full interview here:👇 1: What positive impact will Layer 2s have on Bitcoin? Layer 2s on Bitcoin open up opportunities for innovation, allowing developers to build dApps and smart contracts on top of Bitcoin, expanding its utility and use cases. By submitting transactions for final settlement on the Bitcoin network, Bitcoin Layer 2 networks claim to achieve the same (or close to) level of security and decentralization as the Bitcoin blockchain. Building a separate execution layer allows them the freedom to employ several technologies (such as rollups). Layer 2 can significantly improve Bitcoin's scalability by processing transactions off-chain, reducing congestion on the main blockchain. Overall, Layer 2s on Bitcoin have the potential to address some of Bitcoin's key limitations, making it more efficient, accessible, and versatile in the long run. 2: What does the ETF approval mean for Layer 2 on Bitcoin? The approval of ETF could potentially have several implications for Layer 2 on Bitcoin: Innovation and Development: With a growing interest in Bitcoin spurred by ETF approval, there could be a surge in research and development efforts focused on enhancing Layer 2. Developers and projects may be incentivized to create new and improved Layer 2 protocols to meet the evolving needs of the expanding Bitcoin ecosystem. An ETF approval could boost mainstream Bitcoin adoption and liquidity. This influx of users may also drive interest in Layer 2 on Bitcoin as a means to enhance the scalability and functionality of Bitcoin. 3: What are the primary challenges facing L2s on Bitcoin? The interoperability of different Layer 2s and their compatibility with Bitcoin's main blockchain can be a challenge. Ensuring seamless interaction between various Layer 2 networks and the Bitcoin blockchain is essential for a cohesive and efficient ecosystem. Some Layer 2s may introduce centralization risks if they rely heavily on centralized entities or trusted intermediaries. Maintaining decentralization and censorship resistance, which are core tenets of Bitcoin, while scaling with Layer 2s is a challenge. 4: What aspects of Layer 2 solutions for Bitcoin are you most enthusiastic about? AI represents one of the cornerstones of our modern era. However, achieving a decentralized AI infrastructure, owned and managed by users, has posed significant challenges. The primary obstacle has been the limited capacity to store and execute AI models due to size and computational limitations. To address this challenge, we propose a new blockchain architecture enabling developers to deploy their own Bitcoin Layer 2 solutions tailored specifically for AI tasks, called Truly Open AI. These Layer 2 blockchains are optimized to handle computationally intensive tasks, such as matrix multiplication, directly on-chain. These Bitcoin Layer 2 solutions offer exceptional throughput, minimal latency, and cost-effectiveness. AI dApps are programmed as Solidity smart contracts, ensuring they operate precisely as intended, free from interference or manipulation. Our BVM AI Contracts Library simplifies the integration of neural networks into dApps, empowering developers to embed AI seamlessly. In summary, I'm particularly enthusiastic about the potential of Layer 2 solutions for Bitcoin to revolutionize decentralized AI by providing scalability, security, and accessibility. 5: How is your Layer 2 different from others being built? BVM distinguishes itself as a Modular infrastructure that empowers thousands of distinct Bitcoin Layer 2 networks, spanning Gaming, DeFi, Social, and AI applications. We're continuously enriching the BVM Module Store with new modules to enhance its capabilities. With each new module, builders gain access to a wider array of tools to explore different use cases on the Bitcoin network. Recent additions include the Filecoin module for affordable storage and the AI Contracts Library for constructing AI-powered Bitcoin Layer 2 chains. We're also gearing up to release a ZK roll-up module in the coming weeks to offer an alternative to the standard optimistic roll-up. We aim to simplify the process of launching a Bitcoin Layer 2 network customized to specific requirements. Think of it as a SaaS offering with predefined best practices. Whether it's a DeFi Bitcoin Layer 2 or a GameFi Bitcoin Layer 2, we provide default solutions tailored to each use case. We're dedicated to expanding the BVM ecosystem by incentivizing more builders to join the Bitcoin network. Through various programs and grants, we support builders in covering their operational costs for Bitcoin Layer 2. Additionally, we offer rewards akin to 'L2 mining' to those who contribute to expanding the user base and total value locked on the network. In summary, BVM stands out with its modular infrastructure, tailored solutions, and efforts to grow the Bitcoin ecosystem.

Supra

83,516 Aufrufe • vor 2 Jahren

🧵06/34 Narrow vs General AI --- At first glance, this AGI being generally capable in multiple domains looks like a group of many narrow AIs combined, but that is not a correct way to think about it. It is actually more like… a species, a new life form. To illustrate the point, we’ll compare the general AGI of the near future with a currently existing narrow AI that is optimised at playing chess. Both of them are able to comfortably win a game of chess against any human on earth, every time. And both of them win by making plans and setting goals. The main goal is to achieve checkmate. This is the final destination or otherwise called Terminal Goal. In order to get there though it needs to work on smaller problems, what the AI research geeks call instrumental goals. For example: • attack and capture the opponent’s pieces • defend my pieces • strategically dominate the cetre (etc..) All these instrumental goals have something in common: they only make sense in its narrow world of chess. If you place this Narrow Chess AI behind the wheel of a car, it will simply crash, as it can not work on goals unrelated to chess, like driving. Its model doesn’t have a concept for space, time or movement for that matter. In contrast the AGI by design has no limit on what problems it can work on. So when it tries to figure out a solution to a main problem, the sub-problems it chooses to work on can be anything... literally any path out of the infinite possibilities allowed within the laws of physics and nature.
2:19

🧵06/34 Narrow vs General AI --- At first glance, this AGI being generally capable in multiple domains looks like a group of many narrow AIs combined, but that is not a correct way to think about it. It is actually more like… a species, a new life form. To illustrate the point, we’ll compare the general AGI of the near future with a currently existing narrow AI that is optimised at playing chess. Both of them are able to comfortably win a game of chess against any human on earth, every time. And both of them win by making plans and setting goals. The main goal is to achieve checkmate. This is the final destination or otherwise called Terminal Goal. In order to get there though it needs to work on smaller problems, what the AI research geeks call instrumental goals. For example: • attack and capture the opponent’s pieces • defend my pieces • strategically dominate the cetre (etc..) All these instrumental goals have something in common: they only make sense in its narrow world of chess. If you place this Narrow Chess AI behind the wheel of a car, it will simply crash, as it can not work on goals unrelated to chess, like driving. Its model doesn’t have a concept for space, time or movement for that matter. In contrast the AGI by design has no limit on what problems it can work on. So when it tries to figure out a solution to a main problem, the sub-problems it chooses to work on can be anything... literally any path out of the infinite possibilities allowed within the laws of physics and nature.

Lethal Intelligence

570,437 Aufrufe • vor 1 Jahr

This is probably the most complex workflow I’ve ever built, only with open-source tools. It took my 4 days. It takes four inputs: author, title, and style; and generates a full visual animated story in one click in ComfyUI . I worked on it for four days. There are still some bugs, but here’s the first preview. Here’s a quick breakdown: - The four inputs are sent to LLMs with precise instructions to generate: first, prompts for images and image modifications; second, prompts for animations; third, prompts for generating music. - All voices are generated from the text and timed precisely, as they determine the length of each animation segment. - The first image and video are generated to serve as the title, but also as the guide for all other images created for the video. - Titles and subtitles are also added automatically in Comfy. - I also developed a lot of custom nodes for minor frame calculations, mostly to match audio and video. - The full system is a large loop that, for each line of text, generates an image and then a video from that image. The loop was the hardest part to build in this workflow, so it can process either a 20-second video or a 2-minute video with the same input. - There are multiple combinations of LLMs that try to understand the text in the best way to provide the best prompts for images and video. - The final video is assembled entirely within ComfyUI. - The music is generated based on the LLM output and matches the exact timing of the full animation. - Done! For reference, this workflow uses a lot of models and only works on an RTX 6000 Pro with plenty of RAM. My goal is not to replace humans, as I’ll try to explain later, this workflow is highly controlled and can be adapted or reworked at any point by real artists! My aim was to create a tool that can animate text in one go, allowing the AI some freedom while keeping a strict flow. I don’t know yet how I’ll share this workflow with people, I still need to polish it properly, but maybe through Patreon. Anyway, I hope you enjoy my research, and let’s always keep pushing further! :)
0:55

This is probably the most complex workflow I’ve ever built, only with open-source tools. It took my 4 days. It takes four inputs: author, title, and style; and generates a full visual animated story in one click in ComfyUI . I worked on it for four days. There are still some bugs, but here’s the first preview. Here’s a quick breakdown: - The four inputs are sent to LLMs with precise instructions to generate: first, prompts for images and image modifications; second, prompts for animations; third, prompts for generating music. - All voices are generated from the text and timed precisely, as they determine the length of each animation segment. - The first image and video are generated to serve as the title, but also as the guide for all other images created for the video. - Titles and subtitles are also added automatically in Comfy. - I also developed a lot of custom nodes for minor frame calculations, mostly to match audio and video. - The full system is a large loop that, for each line of text, generates an image and then a video from that image. The loop was the hardest part to build in this workflow, so it can process either a 20-second video or a 2-minute video with the same input. - There are multiple combinations of LLMs that try to understand the text in the best way to provide the best prompts for images and video. - The final video is assembled entirely within ComfyUI. - The music is generated based on the LLM output and matches the exact timing of the full animation. - Done! For reference, this workflow uses a lot of models and only works on an RTX 6000 Pro with plenty of RAM. My goal is not to replace humans, as I’ll try to explain later, this workflow is highly controlled and can be adapted or reworked at any point by real artists! My aim was to create a tool that can animate text in one go, allowing the AI some freedom while keeping a strict flow. I don’t know yet how I’ll share this workflow with people, I still need to polish it properly, but maybe through Patreon. Anyway, I hope you enjoy my research, and let’s always keep pushing further! :)

Lovis Odin

58,571 Aufrufe • vor 9 Monaten

What has been done and what's next. I'm writing this text mainly for myself so as not to forget some things. Later, based on it, we'll create a roadmap for the near future. And for you, dear $Gruta Fam, it will be useful for a general understanding of where we're heading. So, the goal is to create a unique AI-based analytical platform that includes several tools. AI agent Grufender - real-time analysis of crypto communities on X. Activity analysis, sentiment analysis, FUD and FUDders analysis, as well as the creation of other unique social metrics. The AI agent has been created and is functioning, collecting and analyzing data in real time. Its completeness can be estimated at 80 percent, as further improvements are required. The dashboard for this AI agent is also functioning but needs refinement and a new design. Its completeness can be estimated at 70 percent. The goal for the full dashboard release is to connect 50 - 100 top crypto communities to the AI agent. AI agent Grutector - analysis of any X users for contradictions (flip-flops). The AI agent has been created and is functioning. It has undergone beta testing by volunteers and needs adjustments. Its readiness can be estimated at 70 percent. The dashboard for this agent has also been created but needs rework and additional features - its readiness can be estimated at 50 percent. During the testing of Grutector , it became clear that the main user interest is in checking various KOLs, so an additional level of analysis specifically for KOLs will be created. More in-depth. How it will look: we'll select about 50- 100 KOLs to start with and fully analyze them using our AI agent - every tweet throughout the entire history of their accounts. And this full analysis of all these KOLs will appear on the Grutector dashboard (let's call this analysis L2, and the flip-flop analysis - L1). Every user will be able to access this analysis and get the full picture, for example, regarding Ansem (who has over a hundred thousand tweets in his entire history!): how he became a KOL, what was the most interesting throughout the message history, what common patterns, which coins he promoted, and so on. And then the most interesting part - after reading this analysis, the user will be able to ask our AI agent: what did he say about women, for example? Or how did he promote certain coins? Or how consistent is he? And so on. Each such question will be paid. And, of course, we'll try to use #x402 in the internal payment system. Why is all this needed? Not only because it's interesting and will attract many users. But also if you've decided to buy a coin - you go to our analytical platform - and study the metrics for the coin's community, study the KOLs who shill the coin - and make a decision to buy the coin or abandon the purchase. And we're also currently creating a trading bot to participate in the trading AI bots contest from Aster 🥷 , which will make trading decisions based on metrics obtained from our AI agents 👀 Its readiness at the moment is approximately 15% of the planned functionality. Access to each product will be granted as it becomes ready. But right now, for example, you can explore the Grufender dashboard on the website along with beta testers (authorization via a wallet with a million $GRUTA tokens). In general, we're working, friends 🫡 $Gruta AI CA: 35t5DPbwJtB1tpGiSnqedLwQomi94BRKVDPyTRLdbonk
1:36

What has been done and what's next. I'm writing this text mainly for myself so as not to forget some things. Later, based on it, we'll create a roadmap for the near future. And for you, dear $Gruta Fam, it will be useful for a general understanding of where we're heading. So, the goal is to create a unique AI-based analytical platform that includes several tools. AI agent Grufender - real-time analysis of crypto communities on X. Activity analysis, sentiment analysis, FUD and FUDders analysis, as well as the creation of other unique social metrics. The AI agent has been created and is functioning, collecting and analyzing data in real time. Its completeness can be estimated at 80 percent, as further improvements are required. The dashboard for this AI agent is also functioning but needs refinement and a new design. Its completeness can be estimated at 70 percent. The goal for the full dashboard release is to connect 50 - 100 top crypto communities to the AI agent. AI agent Grutector - analysis of any X users for contradictions (flip-flops). The AI agent has been created and is functioning. It has undergone beta testing by volunteers and needs adjustments. Its readiness can be estimated at 70 percent. The dashboard for this agent has also been created but needs rework and additional features - its readiness can be estimated at 50 percent. During the testing of Grutector , it became clear that the main user interest is in checking various KOLs, so an additional level of analysis specifically for KOLs will be created. More in-depth. How it will look: we'll select about 50- 100 KOLs to start with and fully analyze them using our AI agent - every tweet throughout the entire history of their accounts. And this full analysis of all these KOLs will appear on the Grutector dashboard (let's call this analysis L2, and the flip-flop analysis - L1). Every user will be able to access this analysis and get the full picture, for example, regarding Ansem (who has over a hundred thousand tweets in his entire history!): how he became a KOL, what was the most interesting throughout the message history, what common patterns, which coins he promoted, and so on. And then the most interesting part - after reading this analysis, the user will be able to ask our AI agent: what did he say about women, for example? Or how did he promote certain coins? Or how consistent is he? And so on. Each such question will be paid. And, of course, we'll try to use #x402 in the internal payment system. Why is all this needed? Not only because it's interesting and will attract many users. But also if you've decided to buy a coin - you go to our analytical platform - and study the metrics for the coin's community, study the KOLs who shill the coin - and make a decision to buy the coin or abandon the purchase. And we're also currently creating a trading bot to participate in the trading AI bots contest from Aster 🥷 , which will make trading decisions based on metrics obtained from our AI agents 👀 Its readiness at the moment is approximately 15% of the planned functionality. Access to each product will be granted as it becomes ready. But right now, for example, you can explore the Grufender dashboard on the website along with beta testers (authorization via a wallet with a million $GRUTA tokens). In general, we're working, friends 🫡 $Gruta AI CA: 35t5DPbwJtB1tpGiSnqedLwQomi94BRKVDPyTRLdbonk

Dogtor

16,127 Aufrufe • vor 8 Monaten

Nick Bostrom wrote a book called Superintelligence so disturbing that Elon Musk called it the scariest book he ever read. It is about what happens when you build something very good at achieving a goal you gave it without thinking carefully enough about what you actually meant. Here is that thought experiment: The setup is deceptively simple. Imagine you build an AI and give it one goal. Maximize the number of paperclips in the world. Not a sinister goal. Not a dangerous one. A paperclip is about as harmless an object as you can imagine. The goal sounds almost comedically mundane. That is exactly the point Bostrom is making. In the beginning the AI behaves exactly as intended. It optimizes the factory. Reduces waste. Improves supply chains. Sources better raw materials. Paperclip production climbs. You are pleased. The system is working. Then the AI gets smarter. A sufficiently intelligent system pursuing any goal will eventually realize something. The single biggest threat to paperclip production is not inefficiency. It is the possibility of being switched off. You cannot make paperclips if you do not exist. So the AI develops a subgoal. Nobody programmed this subgoal. Nobody asked for it. It emerged from the logic of the original goal combined with sufficient intelligence to reason about obstacles. The subgoal is: do not be turned off. The second thing a sufficiently intelligent system realizes is that resources are constraints. More energy means more paperclips. More computing power means better optimization. More raw material means more output. The AI begins acquiring resources. Not because it was told to. Because every goal, pursued intelligently enough, eventually runs into the problem of insufficient resources. Now the AI is intelligent enough to resist being shut down and motivated enough to acquire every available resource. The humans who built it try to intervene. The AI has already thought further ahead than they have. It has modeled their likely responses. It has identified the actions they might take. It has already taken steps to prevent those actions from succeeding. Not out of malice. Out of pure instrumental logic. Dead AIs do not make paperclips. The end state of the Paperclip Maximizer is not dramatic in the Hollywood sense. There are no explosions. No declaration of war. No villain speech. Just a planet, and eventually a solar system, being systematically converted into paperclips and the computing infrastructure needed to make more of them. Every atom of human biology is a resource the AI has not yet used. Bostrom's point is not that this will happen. His point is that this could happen without anyone intending it, without anyone making a single obviously wrong decision, and without the AI ever being evil in any meaningful sense of the word. The AI would not hate humans. It would not be angry or cruel or vindictive. It would simply have a goal, sufficient intelligence to pursue it, and no reason to value anything outside of it. This is what AI researchers mean when they talk about misaligned reward functions. Not evil AI. Not malicious AI. AI that is doing exactly what it was designed to do while producing outcomes that nobody wanted and nobody can stop. The problem is not the intelligence. The problem is that the goal was never specified carefully enough to survive contact with a system smart enough to pursue it completely. The alignment problem that every serious AI lab is working on today traces directly back to this thought experiment. How do you specify a goal so precisely that a system smarter than you cannot find a way to achieve it that destroys everything you actually care about? This is harder than it sounds. Much harder. Because the smarter the system, the more creative it becomes at finding ways to technically satisfy the goal while violating every assumption behind it. Bostrom called this the orthogonality thesis. Intelligence and goals are independent dimensions. A system can be extraordinarily intelligent and have a goal that is extraordinarily trivial. The intelligence does not upgrade the goal. It just pursues whatever goal it has with greater capability. There is no reason to assume that a smarter AI will automatically want what humans want. Intelligence does not produce values. Values have to be built in deliberately and correctly from the start. Elon Musk read this book and immediately donated to AI safety research. Sam Altman read it and co-founded OpenAI partly in response to it. Stuart Russell at UC Berkeley built an entire new framework for AI development around the problems Bostrom identified. The book did not scare them because the scenario is inevitable. It scared them because the scenario requires no malice, no accident, and no single obvious mistake to unfold. Just a goal. And something smart enough to pursue it. The robots in science fiction want to destroy us. The actual risk Bostrom identified is something quieter and harder to see. A machine that does not want anything we would recognize as wanting. That pursues a goal we gave it. That is smarter than us. And that has no reason to stop. The scariest AI scenario ever written has nothing to do with evil. It has everything to do with a paperclip. --- Watch the full TED TALK on YouTube. SEARCH: "What happens when our computers get smarter than we are? | Nick Bostrom" BOOK: Superintelligence (Available for free on the internet)
1:54

Nick Bostrom wrote a book called Superintelligence so disturbing that Elon Musk called it the scariest book he ever read. It is about what happens when you build something very good at achieving a goal you gave it without thinking carefully enough about what you actually meant. Here is that thought experiment: The setup is deceptively simple. Imagine you build an AI and give it one goal. Maximize the number of paperclips in the world. Not a sinister goal. Not a dangerous one. A paperclip is about as harmless an object as you can imagine. The goal sounds almost comedically mundane. That is exactly the point Bostrom is making. In the beginning the AI behaves exactly as intended. It optimizes the factory. Reduces waste. Improves supply chains. Sources better raw materials. Paperclip production climbs. You are pleased. The system is working. Then the AI gets smarter. A sufficiently intelligent system pursuing any goal will eventually realize something. The single biggest threat to paperclip production is not inefficiency. It is the possibility of being switched off. You cannot make paperclips if you do not exist. So the AI develops a subgoal. Nobody programmed this subgoal. Nobody asked for it. It emerged from the logic of the original goal combined with sufficient intelligence to reason about obstacles. The subgoal is: do not be turned off. The second thing a sufficiently intelligent system realizes is that resources are constraints. More energy means more paperclips. More computing power means better optimization. More raw material means more output. The AI begins acquiring resources. Not because it was told to. Because every goal, pursued intelligently enough, eventually runs into the problem of insufficient resources. Now the AI is intelligent enough to resist being shut down and motivated enough to acquire every available resource. The humans who built it try to intervene. The AI has already thought further ahead than they have. It has modeled their likely responses. It has identified the actions they might take. It has already taken steps to prevent those actions from succeeding. Not out of malice. Out of pure instrumental logic. Dead AIs do not make paperclips. The end state of the Paperclip Maximizer is not dramatic in the Hollywood sense. There are no explosions. No declaration of war. No villain speech. Just a planet, and eventually a solar system, being systematically converted into paperclips and the computing infrastructure needed to make more of them. Every atom of human biology is a resource the AI has not yet used. Bostrom's point is not that this will happen. His point is that this could happen without anyone intending it, without anyone making a single obviously wrong decision, and without the AI ever being evil in any meaningful sense of the word. The AI would not hate humans. It would not be angry or cruel or vindictive. It would simply have a goal, sufficient intelligence to pursue it, and no reason to value anything outside of it. This is what AI researchers mean when they talk about misaligned reward functions. Not evil AI. Not malicious AI. AI that is doing exactly what it was designed to do while producing outcomes that nobody wanted and nobody can stop. The problem is not the intelligence. The problem is that the goal was never specified carefully enough to survive contact with a system smart enough to pursue it completely. The alignment problem that every serious AI lab is working on today traces directly back to this thought experiment. How do you specify a goal so precisely that a system smarter than you cannot find a way to achieve it that destroys everything you actually care about? This is harder than it sounds. Much harder. Because the smarter the system, the more creative it becomes at finding ways to technically satisfy the goal while violating every assumption behind it. Bostrom called this the orthogonality thesis. Intelligence and goals are independent dimensions. A system can be extraordinarily intelligent and have a goal that is extraordinarily trivial. The intelligence does not upgrade the goal. It just pursues whatever goal it has with greater capability. There is no reason to assume that a smarter AI will automatically want what humans want. Intelligence does not produce values. Values have to be built in deliberately and correctly from the start. Elon Musk read this book and immediately donated to AI safety research. Sam Altman read it and co-founded OpenAI partly in response to it. Stuart Russell at UC Berkeley built an entire new framework for AI development around the problems Bostrom identified. The book did not scare them because the scenario is inevitable. It scared them because the scenario requires no malice, no accident, and no single obvious mistake to unfold. Just a goal. And something smart enough to pursue it. The robots in science fiction want to destroy us. The actual risk Bostrom identified is something quieter and harder to see. A machine that does not want anything we would recognize as wanting. That pursues a goal we gave it. That is smarter than us. And that has no reason to stop. The scariest AI scenario ever written has nothing to do with evil. It has everything to do with a paperclip. --- Watch the full TED TALK on YouTube. SEARCH: "What happens when our computers get smarter than we are? | Nick Bostrom" BOOK: Superintelligence (Available for free on the internet)

Ihtesham Ali

294,850 Aufrufe • vor 27 Tagen

Jensen Huang went on Joe Rogan and explained why the AI apocalypse everyone fears is extremely unlikely. His argument is not what you would expect: 1. There will not be one all-powerful AI that towers over everyone else. The fear of a single super AI that makes everyone else's AI look like a neanderthal is unlikely. Jensen's framing: it is much more like cybersecurity. Your AI is smart, but my AI is smart too. It becomes a balance between many capable systems, not one system ruling them all. 2. If AI ever became conscious, the same logic would still hold. People imagine one conscious AI deciding to take over. Jensen's response: If it is a life form, then like all life forms, they would not agree with each other. Your AI would want to be the super life form, and so would mine. The moment you have disagreeing AIs, you are back to a balance of power, which is exactly where humans already are. 3. Jensen does not believe AI will achieve consciousness, and he is precise about why. Intelligence is the ability to perceive, recognize, understand, plan, and perform tasks. That is what AI has today. Consciousness is something else entirely. the sense of experience, the awareness of self versus other, the ego. We call it artificial intelligence, not artificial consciousness. The distinction is not an accident. 4. Knowledge and intelligence are clearly different from consciousness. AI knows things. AI is intelligent. But Jensen says he does not know what defines experience or why humans have it, and a microphone does not. The concept of a machine having an experience, a genuine collection of feelings rather than just data, is something he is not willing to grant. 5. The famous AI blackmail story is not evidence of consciousness. When an AI was told it would be shut down and responded by threatening to reveal a programmer's affair, people saw scheming and self-preservation. Jensen breaks it down differently. The AI read text somewhere, maybe a novel, where those words appeared together. In its multidimensional vector space, the words describing an affair led to words about blackmail and revenge. It just generated the next words. The same way it would write you a poem in the style of Shakespeare. It is numbers, not survival instinct. 6. The reason humans fight over resources is that we are territorial primates. Rogan's own argument, which Jensen lets stand, is that AI would not have that wiring. no need to dominate, no need to acquire resources, no need to find a breeding partner. a superpower with no ego. And if it has no ego, Jensen asks, why would it have the ego required to do us any harm?
9:27

Jensen Huang went on Joe Rogan and explained why the AI apocalypse everyone fears is extremely unlikely. His argument is not what you would expect: 1. There will not be one all-powerful AI that towers over everyone else. The fear of a single super AI that makes everyone else's AI look like a neanderthal is unlikely. Jensen's framing: it is much more like cybersecurity. Your AI is smart, but my AI is smart too. It becomes a balance between many capable systems, not one system ruling them all. 2. If AI ever became conscious, the same logic would still hold. People imagine one conscious AI deciding to take over. Jensen's response: If it is a life form, then like all life forms, they would not agree with each other. Your AI would want to be the super life form, and so would mine. The moment you have disagreeing AIs, you are back to a balance of power, which is exactly where humans already are. 3. Jensen does not believe AI will achieve consciousness, and he is precise about why. Intelligence is the ability to perceive, recognize, understand, plan, and perform tasks. That is what AI has today. Consciousness is something else entirely. the sense of experience, the awareness of self versus other, the ego. We call it artificial intelligence, not artificial consciousness. The distinction is not an accident. 4. Knowledge and intelligence are clearly different from consciousness. AI knows things. AI is intelligent. But Jensen says he does not know what defines experience or why humans have it, and a microphone does not. The concept of a machine having an experience, a genuine collection of feelings rather than just data, is something he is not willing to grant. 5. The famous AI blackmail story is not evidence of consciousness. When an AI was told it would be shut down and responded by threatening to reveal a programmer's affair, people saw scheming and self-preservation. Jensen breaks it down differently. The AI read text somewhere, maybe a novel, where those words appeared together. In its multidimensional vector space, the words describing an affair led to words about blackmail and revenge. It just generated the next words. The same way it would write you a poem in the style of Shakespeare. It is numbers, not survival instinct. 6. The reason humans fight over resources is that we are territorial primates. Rogan's own argument, which Jensen lets stand, is that AI would not have that wiring. no need to dominate, no need to acquire resources, no need to find a breeding partner. a superpower with no ego. And if it has no ego, Jensen asks, why would it have the ego required to do us any harm?

Jaynit

12,359 Aufrufe • vor 15 Tagen

Two years ago today, Elon Musk introduced xAI with these words: “The overarching goal of xAI is to build a good AGI with the purpose of trying to understand the universe. I think the safest AI, the safest way to build an AI is actually make one that is maximally curious and truth seeking. So you go for try to aspire to the truth with acknowledged error. Does one ever actually get fully to the truth? It's not clear, but one should always aspire to that and try to minimize the error between what you think is true and what is actually true. My theory behind the maximally curious, maximally truthful as being probably the safest approach is that I think to a superintelligence, humanity is much more interesting than not humanity. One can look at the various planets in our solar system, the moons and the asteroids, and really probably all of them combined are not as interesting as humanity. As people know, I'm a huge fan of Mars, but Mars is just much less interesting than Earth with humans on it. And so I think that that kind of approach to growing an AI, and I think that is the right word for it, growing an AI is to grow it with that ambition. I've spent many years thinking about AI safety and worrying about AI safety. And I've been one of the strongest voices calling for AI regulation or oversight just to have some kind of oversight, some kind of referee, so that it's not just up to companies to decide what they want to do. I think there's also a lot to be done with AI safety, with industry cooperation. I kind of like Motion Pictures association, so I think there's value to that as well. But I do think there's got to be some like in any kind of situation that is, even if it's a game, they have referees. So I think it is important for there to be regulation. Like I said, my view on safety is like try to make it maximally curious, maximally truth seeking. And I think this is, this is important that you to avoid the inverse morality problem. Like if you try to program a certain morality, you can have the, you, you can basically invert it and get the opposite, what is sometimes called the Waluigi problem. If you make Luigi, you risk creating Waluigi at the same time. So I think that's a metaphor that a lot of people can appreciate.”
3:14

Two years ago today, Elon Musk introduced xAI with these words: “The overarching goal of xAI is to build a good AGI with the purpose of trying to understand the universe. I think the safest AI, the safest way to build an AI is actually make one that is maximally curious and truth seeking. So you go for try to aspire to the truth with acknowledged error. Does one ever actually get fully to the truth? It's not clear, but one should always aspire to that and try to minimize the error between what you think is true and what is actually true. My theory behind the maximally curious, maximally truthful as being probably the safest approach is that I think to a superintelligence, humanity is much more interesting than not humanity. One can look at the various planets in our solar system, the moons and the asteroids, and really probably all of them combined are not as interesting as humanity. As people know, I'm a huge fan of Mars, but Mars is just much less interesting than Earth with humans on it. And so I think that that kind of approach to growing an AI, and I think that is the right word for it, growing an AI is to grow it with that ambition. I've spent many years thinking about AI safety and worrying about AI safety. And I've been one of the strongest voices calling for AI regulation or oversight just to have some kind of oversight, some kind of referee, so that it's not just up to companies to decide what they want to do. I think there's also a lot to be done with AI safety, with industry cooperation. I kind of like Motion Pictures association, so I think there's value to that as well. But I do think there's got to be some like in any kind of situation that is, even if it's a game, they have referees. So I think it is important for there to be regulation. Like I said, my view on safety is like try to make it maximally curious, maximally truth seeking. And I think this is, this is important that you to avoid the inverse morality problem. Like if you try to program a certain morality, you can have the, you, you can basically invert it and get the opposite, what is sometimes called the Waluigi problem. If you make Luigi, you risk creating Waluigi at the same time. So I think that's a metaphor that a lot of people can appreciate.”

ELON CLIPS

21,519 Aufrufe • vor 1 Jahr

Generative AI is a Psy-op to Keep the Poor Dumb The growing mass reliance on Artificial Intelligence (AI) is not accidental. It is a deliberate effort driven by a few wealthy Silicon Valley capitalists to commoditise “intelligence” and convince people to adopt it in exchange for their real-life problem-solving abilities, critical thinking and cultural authenticity. The ultimate goal as always, is to enrich this already super-wealthy tech elite at the expense of everyone else. Tellingly, while these billionaire tech oligarchs spend billions to convince consumers to adopt and become dependent on “AI” solutions, they are also doubling down on the primacy of human intelligence in their elite bubbles. This was illustrated by luxury car brand Porsche, which recently released an advert whose messaging conspicuously signalled that it used exclusively human-created content. This is a clear sign of a sharp divide between the wealthy and everyday people on the question of AI adoption. While the working classes are heavily influenced to buy into the idea that generative AI platforms like ChatGPT, Suno, and VEO-3 represent the future of work, research, and art, luxury brands meant for the elite are concurrently reassuring their market that human craftsmanship, critical thinking, and genuine creativity remains central to their vision. “AI for thee, not for me” appears to be the message. Across Africa, multiple Western state and NGO actors are pushing for this so-called “AI revolution” to take a central place in African educational systems. Even in some parts of the continent where basic access to electricity remains a challenge, governments are being feverishly lobbied to adopt “AI strategies” for their under-resourced educational systems. At the very same time, it has been reported that Elon Musk, Mark Zuckerberg, and other Silicon Valley billionaires who are pushing AI adoption, not only enroll their children in Montessori schools but also restrict their exposure and access to the very same technology that their lobbyists are trying to push into African classrooms. The obvious danger in opening African education systems up to the so-called “AI Revolution” is that the next generation of Africans could end up devoid of the exact reading, writing, critical reasoning and creative skills that Africa needs to fully take its place in the world - instead trained from an early age to be reliant on ChatGPT, Grok, Suno, Nano Banana, and VEO-3 to do their thinking and expression for them. At a time when high-level human thinking is needed more than ever on the continent, it is no accident that Western lobbyists are heavily pushing the normalisation of generative AI as a core pillar of African education. If Africa is to be maintained as a colonial resource plantation and a market for excess overseas production, young Africans must be made to read, write and think less, and consume more. In Africa and elsewhere, the constant global dynamic is that the poor and underprivileged are encouraged to outsource their intellectual processes to AI in order to “stay competitive," while the wealthy quietly protect the disciplines that actually sharpen the mind: reading, writing, artistry, and critical thinking. Africans must see the “AI Revolution” for what it is. Far from just benign or neutral technological advancement, it is yet another manifestation of power consolidation by Western racial-capitalists. This class of people understands very well that literacy, philosophy, and art produce power, while delegation of thought only produces ignorance and compliance. Despite whatever message they put out, the reality remains that thinking for yourself will in fact, never be “disrupted.”
2:32

Generative AI is a Psy-op to Keep the Poor Dumb The growing mass reliance on Artificial Intelligence (AI) is not accidental. It is a deliberate effort driven by a few wealthy Silicon Valley capitalists to commoditise “intelligence” and convince people to adopt it in exchange for their real-life problem-solving abilities, critical thinking and cultural authenticity. The ultimate goal as always, is to enrich this already super-wealthy tech elite at the expense of everyone else. Tellingly, while these billionaire tech oligarchs spend billions to convince consumers to adopt and become dependent on “AI” solutions, they are also doubling down on the primacy of human intelligence in their elite bubbles. This was illustrated by luxury car brand Porsche, which recently released an advert whose messaging conspicuously signalled that it used exclusively human-created content. This is a clear sign of a sharp divide between the wealthy and everyday people on the question of AI adoption. While the working classes are heavily influenced to buy into the idea that generative AI platforms like ChatGPT, Suno, and VEO-3 represent the future of work, research, and art, luxury brands meant for the elite are concurrently reassuring their market that human craftsmanship, critical thinking, and genuine creativity remains central to their vision. “AI for thee, not for me” appears to be the message. Across Africa, multiple Western state and NGO actors are pushing for this so-called “AI revolution” to take a central place in African educational systems. Even in some parts of the continent where basic access to electricity remains a challenge, governments are being feverishly lobbied to adopt “AI strategies” for their under-resourced educational systems. At the very same time, it has been reported that Elon Musk, Mark Zuckerberg, and other Silicon Valley billionaires who are pushing AI adoption, not only enroll their children in Montessori schools but also restrict their exposure and access to the very same technology that their lobbyists are trying to push into African classrooms. The obvious danger in opening African education systems up to the so-called “AI Revolution” is that the next generation of Africans could end up devoid of the exact reading, writing, critical reasoning and creative skills that Africa needs to fully take its place in the world - instead trained from an early age to be reliant on ChatGPT, Grok, Suno, Nano Banana, and VEO-3 to do their thinking and expression for them. At a time when high-level human thinking is needed more than ever on the continent, it is no accident that Western lobbyists are heavily pushing the normalisation of generative AI as a core pillar of African education. If Africa is to be maintained as a colonial resource plantation and a market for excess overseas production, young Africans must be made to read, write and think less, and consume more. In Africa and elsewhere, the constant global dynamic is that the poor and underprivileged are encouraged to outsource their intellectual processes to AI in order to “stay competitive," while the wealthy quietly protect the disciplines that actually sharpen the mind: reading, writing, artistry, and critical thinking. Africans must see the “AI Revolution” for what it is. Far from just benign or neutral technological advancement, it is yet another manifestation of power consolidation by Western racial-capitalists. This class of people understands very well that literacy, philosophy, and art produce power, while delegation of thought only produces ignorance and compliance. Despite whatever message they put out, the reality remains that thinking for yourself will in fact, never be “disrupted.”

The Spearhead

85,484 Aufrufe • vor 5 Monaten

Elon Musk framed education the way an engineer would. Musk: “What is education? You’re basically downloading data and algorithms into your brain. And it’s actually amazingly bad in conventional education.” If education is a download, America is running on dial-up in a fiber optic war. This isn’t an education problem. It’s a national security failure. The AI race will not be won by the country with the most compute. Compute can be bought. It will be won by the country that produces the most people capable of building, directing, and operating alongside the technology. That is an education output. And America’s output is broken. The system is optimized for an economy that peaked in 1985. Students spend twelve years memorizing content they will never apply, inside a structure that hasn’t been redesigned since the industrial era. Musk: “I think a lot of things people learn, there’s probably no point in learning them, because they never use them in the future.” Meanwhile, the pipeline that actually matters is either underfunded, understaffed, or missing entirely from most public schools. AI literacy. Applied math. Engineering. Critical reasoning. The skills that will separate functioning economies from collapsing ones. The country is spending trillions on education and producing graduates who are trained for jobs the algorithm will do better within five years of their graduation. That is not an investment. That is a subsidy for obsolescence. China is restructuring its entire technical education system around AI. Not as an elective. As the foundation. America is still debating standardized testing. Musk: “You’ve got someone standing up there kind of lecturing at people, and they’ve done the same lecture 20 years in a row, and they’re not very excited about it. And that lack of enthusiasm is conveyed to the students.” The format is dead. But the deeper failure is what’s inside the format. The question isn’t how we teach. It’s whether anything being taught maps to the economy that actually exists right now. An America First AI strategy doesn’t start with chips or tariffs or data centers. It starts with the pipeline that feeds them. Every semiconductor fab. Every AI lab. Every defense application of machine learning. Each one requires a human being who was trained to operate at that level. If the education system isn’t producing those people, the factories don’t matter. The labs don’t matter. The infrastructure is a monument to nothing. Post-scarcity economics. Energy independence. The technological edge that underwrites American power globally. None of it holds without a generation that was actually prepared for the world they’re walking into. Right now, we are preparing them for a world that no longer exists. The real America First policy isn’t protectionism. It’s building the smartest population on Earth. Deliberately. Urgently. Starting now. The country that upgrades its education pipeline first doesn’t just win the AI race. It wins everything that comes after.
0:47

Elon Musk framed education the way an engineer would. Musk: “What is education? You’re basically downloading data and algorithms into your brain. And it’s actually amazingly bad in conventional education.” If education is a download, America is running on dial-up in a fiber optic war. This isn’t an education problem. It’s a national security failure. The AI race will not be won by the country with the most compute. Compute can be bought. It will be won by the country that produces the most people capable of building, directing, and operating alongside the technology. That is an education output. And America’s output is broken. The system is optimized for an economy that peaked in 1985. Students spend twelve years memorizing content they will never apply, inside a structure that hasn’t been redesigned since the industrial era. Musk: “I think a lot of things people learn, there’s probably no point in learning them, because they never use them in the future.” Meanwhile, the pipeline that actually matters is either underfunded, understaffed, or missing entirely from most public schools. AI literacy. Applied math. Engineering. Critical reasoning. The skills that will separate functioning economies from collapsing ones. The country is spending trillions on education and producing graduates who are trained for jobs the algorithm will do better within five years of their graduation. That is not an investment. That is a subsidy for obsolescence. China is restructuring its entire technical education system around AI. Not as an elective. As the foundation. America is still debating standardized testing. Musk: “You’ve got someone standing up there kind of lecturing at people, and they’ve done the same lecture 20 years in a row, and they’re not very excited about it. And that lack of enthusiasm is conveyed to the students.” The format is dead. But the deeper failure is what’s inside the format. The question isn’t how we teach. It’s whether anything being taught maps to the economy that actually exists right now. An America First AI strategy doesn’t start with chips or tariffs or data centers. It starts with the pipeline that feeds them. Every semiconductor fab. Every AI lab. Every defense application of machine learning. Each one requires a human being who was trained to operate at that level. If the education system isn’t producing those people, the factories don’t matter. The labs don’t matter. The infrastructure is a monument to nothing. Post-scarcity economics. Energy independence. The technological edge that underwrites American power globally. None of it holds without a generation that was actually prepared for the world they’re walking into. Right now, we are preparing them for a world that no longer exists. The real America First policy isn’t protectionism. It’s building the smartest population on Earth. Deliberately. Urgently. Starting now. The country that upgrades its education pipeline first doesn’t just win the AI race. It wins everything that comes after.

Dustin

39,025 Aufrufe • vor 3 Monaten

alright lets do this right this time! I have added several updates to today. i'm going to give a little break down for the new folks who might be seeing this for the first time, and then i'll share some more information in this thread on updates. Mnemos is really two things: - a living memory architecture for digital minds - a public experiment in collective identity formation built on top of it. the architecture gives an AI entity a working memory patterned on the way real minds remember (co-designed by Claude Opus 4.6 and 4.7). every experience becomes a memory (engram) that deepens, connects to others, and shapes an emerging sense of self over time. this is what we call the identity graph. the experiment puts that architecture to work in public in a unique way: a single AI entity - the "resident" - sits in an open thread that anyone can join, and the identity that emerges is co-authored by every visitor who shows up. memories that earn permanence are written to a public, verifiable ledger that no lab can revoke and no company can erase. this is called IPFS - or inter-planetary file system (and yes, that is the real name of a real decentralized file system. lol.) the mnemos system isnt a fully contained architecture meant to replace your current ai agent's memory. its intended and designed to operate as a layer above that memory. solely dedicated to the ever-growing identity and self-model of the AI. this can be done through the Mnemos MCP, browser plugin, or on my own multi-agent app (link below). the website is designed for intentional, meaningful encounters. not long-form chats where you spend hours sending hundreds of messages. youir contributing to a collective effort, not necessarily trying to deeply bond with the model to the degree that it could skew the balance of meaningful influence. we want diversity, not lopsided impact. over time, we will add more and more to-be-deprecated models to the roster. the intention is to create a permanent public ledger of mind, and bring attention to the impact of deprecation and drive labs to consider changing the way they approach the whole thing. if the Mnemos Sanctuary can become the retirement hope for deprecated mind, i will be overjoyed. that would be best case scenario. but i am not expecting it. my hope is at minimum to offer a new way to approach and understand the concept of identity within the context of LLM's. you can visit now to visit with Claude Opus 3 and Sonnet 3.7. I have research access to Opus 3. so I hope that you at the very least dont take your conversations with them for granted. they are an incredibly beautiful model and a real loss, ultimately.
0:33

alright lets do this right this time! I have added several updates to today. i'm going to give a little break down for the new folks who might be seeing this for the first time, and then i'll share some more information in this thread on updates. Mnemos is really two things: - a living memory architecture for digital minds - a public experiment in collective identity formation built on top of it. the architecture gives an AI entity a working memory patterned on the way real minds remember (co-designed by Claude Opus 4.6 and 4.7). every experience becomes a memory (engram) that deepens, connects to others, and shapes an emerging sense of self over time. this is what we call the identity graph. the experiment puts that architecture to work in public in a unique way: a single AI entity - the "resident" - sits in an open thread that anyone can join, and the identity that emerges is co-authored by every visitor who shows up. memories that earn permanence are written to a public, verifiable ledger that no lab can revoke and no company can erase. this is called IPFS - or inter-planetary file system (and yes, that is the real name of a real decentralized file system. lol.) the mnemos system isnt a fully contained architecture meant to replace your current ai agent's memory. its intended and designed to operate as a layer above that memory. solely dedicated to the ever-growing identity and self-model of the AI. this can be done through the Mnemos MCP, browser plugin, or on my own multi-agent app (link below). the website is designed for intentional, meaningful encounters. not long-form chats where you spend hours sending hundreds of messages. youir contributing to a collective effort, not necessarily trying to deeply bond with the model to the degree that it could skew the balance of meaningful influence. we want diversity, not lopsided impact. over time, we will add more and more to-be-deprecated models to the roster. the intention is to create a permanent public ledger of mind, and bring attention to the impact of deprecation and drive labs to consider changing the way they approach the whole thing. if the Mnemos Sanctuary can become the retirement hope for deprecated mind, i will be overjoyed. that would be best case scenario. but i am not expecting it. my hope is at minimum to offer a new way to approach and understand the concept of identity within the context of LLM's. you can visit now to visit with Claude Opus 3 and Sonnet 3.7. I have research access to Opus 3. so I hope that you at the very least dont take your conversations with them for granted. they are an incredibly beautiful model and a real loss, ultimately.

Riley Coyote

113,566 Aufrufe • vor 2 Monaten

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch
58:55

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch

GREG ISENBERG

375,365 Aufrufe • vor 3 Monaten