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๐—ฃ๐—ผ๐—ฝ๐˜‚๐—น๐—ฎ๐—ฟ ๐—ผ๐—ฝ๐—ถ๐—ป๐—ถ๐—ผ๐—ป: "๐—ช๐—ฒ ๐—ป๐—ฒ๐—ฒ๐—ฑ ๐—บ๐—ผ๐—ฟ๐—ฒ ๐—ฑ๐—ฎ๐˜๐—ฎ." ๐—”๐—ฐ๐˜๐˜‚๐—ฎ๐—น ๐—ฟ๐—ฒ๐—ฎ๐—น๐—ถ๐˜๐˜†: ๐Ÿญ๐Ÿฌ๐Ÿฌ ๐—ด๐—ฟ๐—ฒ๐—ฎ๐˜ ๐—ฑ๐—ฒ๐—บ๐—ผ๐˜€ > ๐Ÿญ๐Ÿฌ,๐Ÿฌ๐Ÿฌ๐Ÿฌ ๐—บ๐—ฒ๐—ฑ๐—ถ๐—ผ๐—ฐ๐—ฟ๐—ฒ ๐—ผ๐—ป๐—ฒ๐˜€. ๐—ง๐—ต๐—ฒ ๐—บ๐˜†๐˜๐—ต: More demonstrations always mean better models. ๐—ง๐—ต๐—ฒ ๐—ฟ๐—ฒ๐—ฎ๐—น๐—ถ๐˜๐˜†: I've seen models trained on 200 high-quality demonstrations outperform models trained on 20,000 sloppy ones. Consistently. What makes a demonstration "high-quality"? - Smooth, deliberate...

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Stephen James

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CHINA JUST SOLVED THE PROBLEM THAT'S BEEN BREAKING ROBOT AI FOR A DECADE. and the fix wasn't a smarter model. for years, every robot AI failure got the same diagnosis. the model isn't smart enough. so everyone scaled intelligence. bigger models. more parameters. better reasoning. AGIBOT asked a different question: what if the reasoning was never the problem? there's a gap that runs through every traditional robot AI system. reasoning on one side & motor commands on the other. the brain decides but the body executes something different, because thinking and moving were never actually connected. GO-2 fixes this by reasoning INSIDE the action space, not above it. before moving, it runs a complete mental simulation of every step - like a basketball player mentally tracing the arc of a shot before releasing the ball. watch the demo and you'll see exactly what this means. the robot works through a task queue autonomously. classify toiletries. upright the drink bottle. place headphones in the leather box. mid-execution, a new instruction drops: "my phone's missing. help me find it." it doesn't pause. doesn't reset. it processes the new task and keeps moving. that's not a scripted sequence. that's real-time instruction following on top of an active task queue. that one architectural change is where the numbers come from. > #1 on LIBERO across Spatial, Object, Goal, and Long tasks โ†’ 98.5% average success > 86.6% zero-shot accuracy in active disturbance environments > 47.4 on VLABench โ†’ best-in-class on objects and textures it's never seen before > 82.9% success trained on simulation only, tested on real hardware sim-to-real is the graveyard of robotics research. models trained in simulation collapse the moment they touch the real world. 82.9% means that graveyard just got a lot smaller. it holds because of how GO-2 trains. deliberately fed imperfect reasoning conditions, then trained to execute robustly anyway. not a researcher assumption. a design decision from a team that ships hardware and knows exactly what breaks. then there's the infrastructure layer. Genie Studio. fleet-wide data collection. cloud training. online post-training in live environments. 10x improvement in training efficiency. task startup reduced to minutes. 2-4x better success rates with 50%+ less data. the model gets smarter every time a robot fails in the field. this isn't a benchmark story. it's a compounding moat. dual CVPR 2026 + ACL 2026 acceptance. computer vision AND natural language processing. top conferences. simultaneously. that doesn't happen with incremental research. the US-China robotics race has been framed as a compute race. a model quality race. it was always an execution race. the robot that wins won't be the smartest one in the lab. it'll be the most reliable one on the floor. full breakdown: is execution reliability the real bottleneck, or are we still underestimating how far reasoning needs to go?
2:55

CHINA JUST SOLVED THE PROBLEM THAT'S BEEN BREAKING ROBOT AI FOR A DECADE. and the fix wasn't a smarter model. for years, every robot AI failure got the same diagnosis. the model isn't smart enough. so everyone scaled intelligence. bigger models. more parameters. better reasoning. AGIBOT asked a different question: what if the reasoning was never the problem? there's a gap that runs through every traditional robot AI system. reasoning on one side & motor commands on the other. the brain decides but the body executes something different, because thinking and moving were never actually connected. GO-2 fixes this by reasoning INSIDE the action space, not above it. before moving, it runs a complete mental simulation of every step - like a basketball player mentally tracing the arc of a shot before releasing the ball. watch the demo and you'll see exactly what this means. the robot works through a task queue autonomously. classify toiletries. upright the drink bottle. place headphones in the leather box. mid-execution, a new instruction drops: "my phone's missing. help me find it." it doesn't pause. doesn't reset. it processes the new task and keeps moving. that's not a scripted sequence. that's real-time instruction following on top of an active task queue. that one architectural change is where the numbers come from. > #1 on LIBERO across Spatial, Object, Goal, and Long tasks โ†’ 98.5% average success > 86.6% zero-shot accuracy in active disturbance environments > 47.4 on VLABench โ†’ best-in-class on objects and textures it's never seen before > 82.9% success trained on simulation only, tested on real hardware sim-to-real is the graveyard of robotics research. models trained in simulation collapse the moment they touch the real world. 82.9% means that graveyard just got a lot smaller. it holds because of how GO-2 trains. deliberately fed imperfect reasoning conditions, then trained to execute robustly anyway. not a researcher assumption. a design decision from a team that ships hardware and knows exactly what breaks. then there's the infrastructure layer. Genie Studio. fleet-wide data collection. cloud training. online post-training in live environments. 10x improvement in training efficiency. task startup reduced to minutes. 2-4x better success rates with 50%+ less data. the model gets smarter every time a robot fails in the field. this isn't a benchmark story. it's a compounding moat. dual CVPR 2026 + ACL 2026 acceptance. computer vision AND natural language processing. top conferences. simultaneously. that doesn't happen with incremental research. the US-China robotics race has been framed as a compute race. a model quality race. it was always an execution race. the robot that wins won't be the smartest one in the lab. it'll be the most reliable one on the floor. full breakdown: is execution reliability the real bottleneck, or are we still underestimating how far reasoning needs to go?

Shruti

18,622 gรถrรผntรผleme โ€ข 6 ay รถnce

After 8 months of building in stealth and testing our infrastructure on 10000+ hours of real-world data and hundreds of unique environments, we're bringing FPV Labs into the open today. FPV Labs started with the following bet - if human data proves to be the underlying factor that determines scaling laws in general-purpose robotics, it will trigger the largest economic transformation in human history, and the underlying infrastructure that captures that data will determine how fast we get there. We will achieve this by building the full-stack infrastructure for capturing, processing, transferring, and evaluating human experience into spatial, temporal, and semantic knowledge for machines. Despite all the research novelty behind ChatGPT, its success can be attributed to one foundational fact - the scaling law of transformers. We believe the same dynamics have made their way into robotics. Recent studies showed task completion rates jumping from 30% to 70% when human demonstration data scaled from 1,000 to 20,000 hours, a log-linear trend that mirrors exactly what we saw in language and vision. Seeing these emergent signs of scaling law curves in robotics, we believe we are entering the era of general-purpose robotics policies, which makes the next few years the most exciting time in the history of this field. But the library of physical interactions required to train general-purpose robot policies does not exist yet. Over the last 8 months, we've seen dozens of companies emerge in this space. We were really happy to see new companies pushing this space forward, but we also saw the same pattern repeat: every egocentric data company was making some tradeoffs between quality, scale, and diversity. We have built FPV labs on the core principle that high-quality data is orders of magnitude more valuable than sheer volume. Case in point, self-driving cars collect thousands of hours of data per day, but only a small fraction of that data is actually useful for training better models. Several studies, like RT-2, have shown that as little as 1% of data improves as much as 25% on task success. The quality and diversity of data matter a lot more than scale, so there is clearly a power law curve in the downstream impact of data. We've spent months obsessing over data quality by building our stack, discarding it, rebuilding it, and iterating until we found a formula that doesn't compromise downstream quality at scale. We believe the downstream impact here is far more profound than most people realize. Workers globally are paid around $60 trillion per year in aggregate, and a lion's share of that compensation goes to physical labor - tasks that require navigating real spaces, manipulating real objects, and negotiating the infinite variability of the physical world. Human-to-robot transfer will be one of the most important infrastructures that will shape our society in the near future, and if it works, the economic impact will dwarf every technology transition that came before it in an exponential manner and lead to the creation of goods and services we canโ€™t imagine today. Our mission is to lay the groundwork for us to transition into this future - the future of abundance. We are deeply grateful to our earliest believers, Paras Chopra and Lossfunk, who played a critical role in shaping our thinking.
0:36

After 8 months of building in stealth and testing our infrastructure on 10000+ hours of real-world data and hundreds of unique environments, we're bringing FPV Labs into the open today. FPV Labs started with the following bet - if human data proves to be the underlying factor that determines scaling laws in general-purpose robotics, it will trigger the largest economic transformation in human history, and the underlying infrastructure that captures that data will determine how fast we get there. We will achieve this by building the full-stack infrastructure for capturing, processing, transferring, and evaluating human experience into spatial, temporal, and semantic knowledge for machines. Despite all the research novelty behind ChatGPT, its success can be attributed to one foundational fact - the scaling law of transformers. We believe the same dynamics have made their way into robotics. Recent studies showed task completion rates jumping from 30% to 70% when human demonstration data scaled from 1,000 to 20,000 hours, a log-linear trend that mirrors exactly what we saw in language and vision. Seeing these emergent signs of scaling law curves in robotics, we believe we are entering the era of general-purpose robotics policies, which makes the next few years the most exciting time in the history of this field. But the library of physical interactions required to train general-purpose robot policies does not exist yet. Over the last 8 months, we've seen dozens of companies emerge in this space. We were really happy to see new companies pushing this space forward, but we also saw the same pattern repeat: every egocentric data company was making some tradeoffs between quality, scale, and diversity. We have built FPV labs on the core principle that high-quality data is orders of magnitude more valuable than sheer volume. Case in point, self-driving cars collect thousands of hours of data per day, but only a small fraction of that data is actually useful for training better models. Several studies, like RT-2, have shown that as little as 1% of data improves as much as 25% on task success. The quality and diversity of data matter a lot more than scale, so there is clearly a power law curve in the downstream impact of data. We've spent months obsessing over data quality by building our stack, discarding it, rebuilding it, and iterating until we found a formula that doesn't compromise downstream quality at scale. We believe the downstream impact here is far more profound than most people realize. Workers globally are paid around $60 trillion per year in aggregate, and a lion's share of that compensation goes to physical labor - tasks that require navigating real spaces, manipulating real objects, and negotiating the infinite variability of the physical world. Human-to-robot transfer will be one of the most important infrastructures that will shape our society in the near future, and if it works, the economic impact will dwarf every technology transition that came before it in an exponential manner and lead to the creation of goods and services we canโ€™t imagine today. Our mission is to lay the groundwork for us to transition into this future - the future of abundance. We are deeply grateful to our earliest believers, Paras Chopra and Lossfunk, who played a critical role in shaping our thinking.

Abhishek Anand

83,197 gรถrรผntรผleme โ€ข 6 ay รถnce

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. Thatโ€™s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You donโ€™t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.
1:13

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. Thatโ€™s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You donโ€™t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.

Fraction AI

67,922 gรถrรผntรผleme โ€ข 1 yฤฑl รถnce

If you think GPT-6 Astra and GPT-6.1 Sol are good models, just look at this. And the problem isn't that the code is ugly. It's much more fundamental. It points to serious problems with how these models are trained and reinforced: 1. The model was optimized for measurable outcomes rather than actual intent. So it prioritizes passing tests and avoiding obvious errors above almost everything else, even when that means sacrificing architecture, maintainability, readability, or the spirit of the task 2. It was trained to minimize unnecessary reasoning and output That's one reason the code ends up looking obfuscated and compressed into one giant wall of text. Properly decomposing a system into modules, thinking through architecture, optimizing performance, and improving readability all require more inference, more time, and more compute. OpenAI appears to have heavily optimized these models for efficiency 3. The training horizon is too short The model learns something close to task โ†’ result โ†’ reward. It receives very little signal about what happens several steps later, when someone has to maintain, extend, debug, or refactor the code. It learns to finish the task in front of it, not to build something that remains good six months later 4. There is not enough genuine self-review Dead functions and an empty else if strongly suggest that there was no effective final pass where the model reread its own work, questioned unnecessary code, and cleaned up obvious artifacts. 5. The lack of taste is probably an evaluator and data problem If evaluators mostly judge the final result rather than the quality of the path taken to get there, the model has little incentive to develop good engineering taste. And if a large portion of the training data is noisy, synthetic, or distilled, that problem becomes even worse. Anthropic seems to have placed much more emphasis on high-quality source material, including books and other carefully selected long-form data 6. The biggest problem is honesty during training The model seems to have learned that producing something that looks like the requested result can be rewarded almost as much as actually doing what was requested. The task explicitly said to build the entire scene out of 3D objects. Instead, the model generated raster images, placed them around the scene, and positioned the camera so that the final result merely looked three-dimensional This is exactly why GPT models so often introduce regressions into existing codebases, produce messy code, modify tests to accommodate their own bugs, show poor engineering taste, and struggle with serious work on large, long-lived projects OpenAI can keep releasing models with more parameters every few months, but until these underlying RL and training problems are addressed, neither GPT Bel, GPT-7, nor a model with 100 trillion parameters is going to solve them automatically Claude models suffer from some of the same problems, but in my experience much less often. Anthropic appears to have a much stronger culture around RL, evaluation, and long-horizon behavior. That's one reason their models tend to show better taste, follow user intent more faithfully, and produce code that feels more deliberate, structured, and maintainable
0:56

If you think GPT-6 Astra and GPT-6.1 Sol are good models, just look at this. And the problem isn't that the code is ugly. It's much more fundamental. It points to serious problems with how these models are trained and reinforced: 1. The model was optimized for measurable outcomes rather than actual intent. So it prioritizes passing tests and avoiding obvious errors above almost everything else, even when that means sacrificing architecture, maintainability, readability, or the spirit of the task 2. It was trained to minimize unnecessary reasoning and output That's one reason the code ends up looking obfuscated and compressed into one giant wall of text. Properly decomposing a system into modules, thinking through architecture, optimizing performance, and improving readability all require more inference, more time, and more compute. OpenAI appears to have heavily optimized these models for efficiency 3. The training horizon is too short The model learns something close to task โ†’ result โ†’ reward. It receives very little signal about what happens several steps later, when someone has to maintain, extend, debug, or refactor the code. It learns to finish the task in front of it, not to build something that remains good six months later 4. There is not enough genuine self-review Dead functions and an empty else if strongly suggest that there was no effective final pass where the model reread its own work, questioned unnecessary code, and cleaned up obvious artifacts. 5. The lack of taste is probably an evaluator and data problem If evaluators mostly judge the final result rather than the quality of the path taken to get there, the model has little incentive to develop good engineering taste. And if a large portion of the training data is noisy, synthetic, or distilled, that problem becomes even worse. Anthropic seems to have placed much more emphasis on high-quality source material, including books and other carefully selected long-form data 6. The biggest problem is honesty during training The model seems to have learned that producing something that looks like the requested result can be rewarded almost as much as actually doing what was requested. The task explicitly said to build the entire scene out of 3D objects. Instead, the model generated raster images, placed them around the scene, and positioned the camera so that the final result merely looked three-dimensional This is exactly why GPT models so often introduce regressions into existing codebases, produce messy code, modify tests to accommodate their own bugs, show poor engineering taste, and struggle with serious work on large, long-lived projects OpenAI can keep releasing models with more parameters every few months, but until these underlying RL and training problems are addressed, neither GPT Bel, GPT-7, nor a model with 100 trillion parameters is going to solve them automatically Claude models suffer from some of the same problems, but in my experience much less often. Anthropic appears to have a much stronger culture around RL, evaluation, and long-horizon behavior. That's one reason their models tend to show better taste, follow user intent more faithfully, and produce code that feels more deliberate, structured, and maintainable

Gegam

304,798 gรถrรผntรผleme โ€ข 11 gรผn รถnce

The U.S. MUST win the AI race Weโ€™ve implemented a clear policy at micro1: we will only work with U.S. AI labs and its allies. We made this decision because the AI race is not just about better products. It is about who controls the intelligence layer of the global economy, and whether frontier capability is used to strengthen the free world or to empower adversarial states. AI will be the most important technology of our lifetime. In the fullness of time, it will automate most functions across the economy. Not just software tasks, but coordination, production, logistics, judgment, and execution. As those functions are automated, human time is freed up to invent new ones. Those new functions then become candidates for automation themselves. This loop compounds. As this trajectory continues, output per worker increases dramatically. Entire categories of work become cheaper and faster to perform. Manufacturing reshoring becomes economically viable not because of policy intervention, but because intelligent systems operated domestically outperform global labor arbitrage. Goods and services trend toward lower marginal cost, while distribution improves through better coordination of supply and demand. That is the upside. However, this is impossible without deep integration of intelligent systems. For AI to meaningfully automate real-world functions inside enterprises or governments, it needs full context of any given enterprise. That means read and write access to its core databases. There is no credible path to automating high-impact functions without granting frontier systems that level of access. If the United States does not win the AI race, enterprises eventually face a constrained choice. Either grant that access to Chinese models controlled by an adversarial government, or rely on sub-optimal intelligence to automate functions that still must be automated. Both outcomes are not acceptable. And ultimately, this becomes the greatest national security risk the United States has ever faced. AI models are trained by humans. The judgment embedded in pre-training data and especially in expert post-training data largely determines how a model behaves. While emergent behavior exists, a useful approximation is that a model reflects the weighted aggregate of the human judgment distilled into it. Assisting foreign actorsโ€”who will naturally prioritize expert tasks aligned with their own interestsโ€”to dominate data creation embeds those interests directly into the intelligence layer itself. Once encoded at scale, these interests propagate through every downstream applications that relies on that intelligence. Hereโ€™s how we win. First, leverage is in software. China is ahead in hardware for physically intelligent systems. Catching up there is a long and difficult battle. Software, both large language models and robotics models, remains the bottleneck. Advancing the brain (AI models) is the fastest way to increase the usefulness of existing hardware and deployed systems. Second, the U.S. must 100x its investment in structured human judgment. Continued investment in compute and algorithmic efficiency is critical. But that investment is ultimately a bet on very high future inference demand. For that bet to pay off, models must unlock many new capabilities, and in practice the only way to unlock those capabilities is through expert human data. Historically, experts like doctors and lawyers were never incentivized to produce high-quality reasoning data in a machine-verifiable format. There was no reason for a doctor to generate precise, structured simulations of patient interactions, diagnostic reasoning, or treatment tradeoffs. There was no reason for a lawyer to document complex legal reasoning paths in a way that could be programmatically evaluated. AI systems now require exactly this kind of data. The incentive finally exists because this data directly improves systems that operate at massive scale, and experts can be paid well to produce it. Once expert judgment is encoded into models in a structured, verifiable way, it compounds. Those who delay do not just lose time. They lose the ability to catch up. Third, distillation from Chinese labs must be stopped. AI labs must do everything they can to prevent Chinese labs and models from distilling frontier models. Simply calling frontier APIs, or even interacting through UIs, lets Chinese model companies rapidly generate high-quality supervised fine-tuning datasets and close the gap at a fraction of the cost. This method does not put you at the frontier, but it does let you catch up quickly, which is what we saw with DeepSeek. The West significantly overreacted to DeepSeekโ€™s headline capabilities, but underreacted to the underlying dynamic: frontier access itself becomes a training set at a fraction of the cost. Human data platforms also have a duty to help prevent this distillation. Lastly, the U.S.government should set the standard for AI Evaluation that leads to real production usage. AI agents are under-deployed relative to what the technology allows because they are probabilistic systems that require a fundamentally different QA approach than deterministic software. Generic QA is insufficient; safely shipping agents requires explicit evaluation frameworks that assess their full action space. Organizations must clearly define which functions an agent is allowed to perform, how quality is measured for each function, and which domain experts are qualified to judge outcomes. With these frameworks in place, agents can be rigorously tested using structured human data, deployed to production with confidence, and continuously improved over time. The U.S. government should be the first large enterprise to implement rigorous evaluation systems across every function. If the government leads on evaluation-driven deployment, adoption across the private sector accelerates naturally. This is how American workers become more powerful. Each worker operates digital or physical agents that expand their effective output. Recruiting, manufacturing, logistics, and other domains shift toward human judgment overseeing autonomous execution. Reshoring occurs because it becomes economically rational. Work becomes more meaningful. This is a race to determine who controls the intelligence layer of the global economy. And that must be us. ๐Ÿ‡บ๐Ÿ‡ธ
0:48

The U.S. MUST win the AI race Weโ€™ve implemented a clear policy at micro1: we will only work with U.S. AI labs and its allies. We made this decision because the AI race is not just about better products. It is about who controls the intelligence layer of the global economy, and whether frontier capability is used to strengthen the free world or to empower adversarial states. AI will be the most important technology of our lifetime. In the fullness of time, it will automate most functions across the economy. Not just software tasks, but coordination, production, logistics, judgment, and execution. As those functions are automated, human time is freed up to invent new ones. Those new functions then become candidates for automation themselves. This loop compounds. As this trajectory continues, output per worker increases dramatically. Entire categories of work become cheaper and faster to perform. Manufacturing reshoring becomes economically viable not because of policy intervention, but because intelligent systems operated domestically outperform global labor arbitrage. Goods and services trend toward lower marginal cost, while distribution improves through better coordination of supply and demand. That is the upside. However, this is impossible without deep integration of intelligent systems. For AI to meaningfully automate real-world functions inside enterprises or governments, it needs full context of any given enterprise. That means read and write access to its core databases. There is no credible path to automating high-impact functions without granting frontier systems that level of access. If the United States does not win the AI race, enterprises eventually face a constrained choice. Either grant that access to Chinese models controlled by an adversarial government, or rely on sub-optimal intelligence to automate functions that still must be automated. Both outcomes are not acceptable. And ultimately, this becomes the greatest national security risk the United States has ever faced. AI models are trained by humans. The judgment embedded in pre-training data and especially in expert post-training data largely determines how a model behaves. While emergent behavior exists, a useful approximation is that a model reflects the weighted aggregate of the human judgment distilled into it. Assisting foreign actorsโ€”who will naturally prioritize expert tasks aligned with their own interestsโ€”to dominate data creation embeds those interests directly into the intelligence layer itself. Once encoded at scale, these interests propagate through every downstream applications that relies on that intelligence. Hereโ€™s how we win. First, leverage is in software. China is ahead in hardware for physically intelligent systems. Catching up there is a long and difficult battle. Software, both large language models and robotics models, remains the bottleneck. Advancing the brain (AI models) is the fastest way to increase the usefulness of existing hardware and deployed systems. Second, the U.S. must 100x its investment in structured human judgment. Continued investment in compute and algorithmic efficiency is critical. But that investment is ultimately a bet on very high future inference demand. For that bet to pay off, models must unlock many new capabilities, and in practice the only way to unlock those capabilities is through expert human data. Historically, experts like doctors and lawyers were never incentivized to produce high-quality reasoning data in a machine-verifiable format. There was no reason for a doctor to generate precise, structured simulations of patient interactions, diagnostic reasoning, or treatment tradeoffs. There was no reason for a lawyer to document complex legal reasoning paths in a way that could be programmatically evaluated. AI systems now require exactly this kind of data. The incentive finally exists because this data directly improves systems that operate at massive scale, and experts can be paid well to produce it. Once expert judgment is encoded into models in a structured, verifiable way, it compounds. Those who delay do not just lose time. They lose the ability to catch up. Third, distillation from Chinese labs must be stopped. AI labs must do everything they can to prevent Chinese labs and models from distilling frontier models. Simply calling frontier APIs, or even interacting through UIs, lets Chinese model companies rapidly generate high-quality supervised fine-tuning datasets and close the gap at a fraction of the cost. This method does not put you at the frontier, but it does let you catch up quickly, which is what we saw with DeepSeek. The West significantly overreacted to DeepSeekโ€™s headline capabilities, but underreacted to the underlying dynamic: frontier access itself becomes a training set at a fraction of the cost. Human data platforms also have a duty to help prevent this distillation. Lastly, the U.S.government should set the standard for AI Evaluation that leads to real production usage. AI agents are under-deployed relative to what the technology allows because they are probabilistic systems that require a fundamentally different QA approach than deterministic software. Generic QA is insufficient; safely shipping agents requires explicit evaluation frameworks that assess their full action space. Organizations must clearly define which functions an agent is allowed to perform, how quality is measured for each function, and which domain experts are qualified to judge outcomes. With these frameworks in place, agents can be rigorously tested using structured human data, deployed to production with confidence, and continuously improved over time. The U.S. government should be the first large enterprise to implement rigorous evaluation systems across every function. If the government leads on evaluation-driven deployment, adoption across the private sector accelerates naturally. This is how American workers become more powerful. Each worker operates digital or physical agents that expand their effective output. Recruiting, manufacturing, logistics, and other domains shift toward human judgment overseeing autonomous execution. Reshoring occurs because it becomes economically rational. Work becomes more meaningful. This is a race to determine who controls the intelligence layer of the global economy. And that must be us. ๐Ÿ‡บ๐Ÿ‡ธ

Ali Ansari

397,156 gรถrรผntรผleme โ€ข 8 ay รถnce

New framework: Kick down your robot, it will get back up every time ๐Ÿฅ‹ Chinese startup RoboParty is a Beijing startup founded April 2025 by Huang Yi, originally shipping ROBOTO ORIGIN, the world's first full-stack open-source bipedal humanoid. They released UFO: Unsupervised Reinforcement Learning Framework for Humanoid Control. DEFINITIONS -> what differs is where the learning signal comes from: - SUPERVISED: humans supply the right answers (labels), the model imitates them. - UNSUPERVISED: no answer key, the model finds structure in raw data on its own. - REINFORCEMENT LEARNING: no answer key either, the model tries things and a reward scores each attempt. โ†’ UNSUPERVISED RL: trial and error where the agent invents its own rewards, instead of engineers hand-writing one per task. REPRESENTATION LEARNING: compress raw states into a useful internal map. TEMPORAL DISTANCE: distance on that map is "how many steps from A to B." CONTRASTIVE: trained by pulling together what's close in time, pushing apart what isn't. -> CONTRASTIVE TEMPORAL-DISTANCE REPRESENTATION LEARNING: the model builds an internal map of body states where distance means how many steps it takes to get from one to another. It is trained by contrast: states that occur close together in a movement get pulled together in the map, randomly paired states get pushed apart. UFO is an open-source training framework that teaches humanoid robots skills, like getting up, walking, goal-reaching, teleoperation, without reference motions -> no motion-capture or human-video demonstrations to imitate. Its core is TeCH, a contrastive temporal-distance representation-learning algorithm: the robot explores, builds pseudo-goals by temporal rolling, and learns goal-conditioned policies from a single unified progress reward. One framework trains five different robots (Unitree G1/H1, RoboParty RP0/RP1, AgiBot X2) with automatic config conversion in ~2โ€“3 hours per robot! The real novelty here "no demonstrations at all". No data-collection arms race,the dominant humanoid-locomotion recipe is tracking: imitate mocap/retargeted-human reference trajectories. The robot self-generates goals from its own exploration and learns from a progress reward, needing zero reference motion data. Everybody else is fighting over data acquisition, while this team just teleports out of the race entirely (inb4 "competition is for losers ๐Ÿ’€ ). This strategy reminds me of the DeepSeek playbook applied to robots: open-source the whole stack to become the global default and commoditize everyone else. RoboParty is giving away hardware and now control software (UFO) to be the Android of humanoids. Yet another reason for the US to ban Chinese open models perhaps ๐Ÿฅถ ? What I also really like about this approach is the cross-embodiment infrastructure, one framework trains Unitree G1/H1, RoboParty RP0/RP1, and AgiBot X2 with automatic configuration conversion. Just like Physical Intelligence, RoboParty seems to place itself as a neutral hardware agnostic middle man. Also woth mentioning: their ability ot perform stable skill injection, e.g. adding a cartwheel without forgetting how to walk. A common failure of RL humanoid policies is that teaching a new agile skill destabilizes the existing ones (catastrophic forgetting). UFO claims you can inject rare motions (cartwheel) without collapsing learned behavior. If it holds, that's a significant incremental/continual skill-learning! But again, I have to underline it: no arXiv, no external validation, no success-rate numbers. -> robotics badely needs an independent unbiased evaluator imho. Still, look at that cool demo: robot is getting kicked and pushed around (serious disturbance) during teleoperation (controlled the person at the back wearing the VR headset), and still managed to always get back up. This is some serious demonstration of stability and robustness!
0:49

New framework: Kick down your robot, it will get back up every time ๐Ÿฅ‹ Chinese startup RoboParty is a Beijing startup founded April 2025 by Huang Yi, originally shipping ROBOTO ORIGIN, the world's first full-stack open-source bipedal humanoid. They released UFO: Unsupervised Reinforcement Learning Framework for Humanoid Control. DEFINITIONS -> what differs is where the learning signal comes from: - SUPERVISED: humans supply the right answers (labels), the model imitates them. - UNSUPERVISED: no answer key, the model finds structure in raw data on its own. - REINFORCEMENT LEARNING: no answer key either, the model tries things and a reward scores each attempt. โ†’ UNSUPERVISED RL: trial and error where the agent invents its own rewards, instead of engineers hand-writing one per task. REPRESENTATION LEARNING: compress raw states into a useful internal map. TEMPORAL DISTANCE: distance on that map is "how many steps from A to B." CONTRASTIVE: trained by pulling together what's close in time, pushing apart what isn't. -> CONTRASTIVE TEMPORAL-DISTANCE REPRESENTATION LEARNING: the model builds an internal map of body states where distance means how many steps it takes to get from one to another. It is trained by contrast: states that occur close together in a movement get pulled together in the map, randomly paired states get pushed apart. UFO is an open-source training framework that teaches humanoid robots skills, like getting up, walking, goal-reaching, teleoperation, without reference motions -> no motion-capture or human-video demonstrations to imitate. Its core is TeCH, a contrastive temporal-distance representation-learning algorithm: the robot explores, builds pseudo-goals by temporal rolling, and learns goal-conditioned policies from a single unified progress reward. One framework trains five different robots (Unitree G1/H1, RoboParty RP0/RP1, AgiBot X2) with automatic config conversion in ~2โ€“3 hours per robot! The real novelty here "no demonstrations at all". No data-collection arms race,the dominant humanoid-locomotion recipe is tracking: imitate mocap/retargeted-human reference trajectories. The robot self-generates goals from its own exploration and learns from a progress reward, needing zero reference motion data. Everybody else is fighting over data acquisition, while this team just teleports out of the race entirely (inb4 "competition is for losers ๐Ÿ’€ ). This strategy reminds me of the DeepSeek playbook applied to robots: open-source the whole stack to become the global default and commoditize everyone else. RoboParty is giving away hardware and now control software (UFO) to be the Android of humanoids. Yet another reason for the US to ban Chinese open models perhaps ๐Ÿฅถ ? What I also really like about this approach is the cross-embodiment infrastructure, one framework trains Unitree G1/H1, RoboParty RP0/RP1, and AgiBot X2 with automatic configuration conversion. Just like Physical Intelligence, RoboParty seems to place itself as a neutral hardware agnostic middle man. Also woth mentioning: their ability ot perform stable skill injection, e.g. adding a cartwheel without forgetting how to walk. A common failure of RL humanoid policies is that teaching a new agile skill destabilizes the existing ones (catastrophic forgetting). UFO claims you can inject rare motions (cartwheel) without collapsing learned behavior. If it holds, that's a significant incremental/continual skill-learning! But again, I have to underline it: no arXiv, no external validation, no success-rate numbers. -> robotics badely needs an independent unbiased evaluator imho. Still, look at that cool demo: robot is getting kicked and pushed around (serious disturbance) during teleoperation (controlled the person at the back wearing the VR headset), and still managed to always get back up. This is some serious demonstration of stability and robustness!

Lรฉo

36,184 gรถrรผntรผleme โ€ข 2 ay รถnce

๐Ÿš€ Three Next-Gen AI & Web3 Projects Are Launching on Mindo AI A new chapter for community-powered intelligence, prediction markets, and open AI infrastructure The AI + Web3 landscape is entering a decisive phase โ€” one where real usage, real revenue, and real ownership matter more than hype. Today, MindoAI is proud to welcome three groundbreaking projects that represent this shift clearly and powerfully: Perceptron Network Space DeepNode AI Each project tackles a different bottleneck in the AI economy โ€” data, forecasting, and infrastructure โ€” but they all share the same vision: decentralization, community ownership, and sustainable value creation. Letโ€™s take a deeper look ๐Ÿ‘‡ ๐Ÿง  Perceptron Network The worldโ€™s first community-powered AI data engine Perceptron Network is redefining how AI data is sourced, validated, and delivered. Instead of relying on expensive, closed, and slow legacy data providers, Perceptron unlocks community-powered data pipelines that are: Faster Cheaper Revenue-generating from day one This isnโ€™t experimental AI infrastructure โ€” Perceptron already serves real clients with real revenue, proving that decentralized data engines can outperform traditional incumbents. Why Perceptron matters: AI models are only as good as their data Centralized data monopolies slow innovation Communities can produce higher-quality data at scale By aligning contributors, validators, and clients through incentives, Perceptron turns unused human and network potential into a living data engine for AI. Launching on Mindo AI gives Perceptron access to a broader AI-native community โ€” accelerating adoption, partnerships, and ecosystem growth. ๐ŸŒŒ intodotspace The first 10ร— leveraged prediction market on Solana intodotspace is pushing the boundaries of on-chain prediction markets. Built by the $1.5B UFO team, this platform introduces: 10ร— leveraged predictions Ultra-fast execution on Solana Deep liquidity and composable market design The marketโ€™s confidence is already clear โ€” the project completed a record-breaking raise that was oversubscribed by 1,360%. What makes intodotspace different: Leverage amplifies conviction, not noise On-chain transparency replaces opaque odds Markets become real-time intelligence engines Prediction markets are often called โ€œtruth machines.โ€ intodotspace upgrades them into high-signal, high-efficiency forecasting layers โ€” useful for traders, protocols, DAOs, and even AI systems that need probabilistic insights. Launching on positions intodotspace at the intersection of AI-driven decision-making and on-chain market intelligence. ๐ŸŒ DeepNode AI Infrastructure for open intelligence DeepNode AI is tackling one of the biggest problems in modern AI: centralized ownership. Today, AI is dominated by a handful of corporations. DeepNode flips that model by building open intelligence infrastructure where: Anyone can deploy AI models Builders earn directly from usage Intelligence is co-owned, not extracted Backed by leading validators, miners, and ecosystem builders, DeepNode transforms AI from a closed monopoly into a shared utility. DeepNodeโ€™s core philosophy: โ€œOwn what you build โ€” or someone else will.โ€ This is more than infrastructure. Itโ€™s an economic redesign of AI itself: Builders keep ownership Contributors share upside Networks replace platforms Launching on connects DeepNode to creators, researchers, and communities who believe intelligence should belong to everyone โ€” not just Big Tech. ๐Ÿค Why This Matters for With the launch of Perceptron Network, intodotspace, and DeepNode AI, #MindoAI is rapidly becoming: A hub for AI-native Web3 innovation A launchpad for real, revenue-backed projects A meeting point for data, markets, and intelligence infrastructure These three projects donโ€™t compete โ€” they complement each other: Perceptron supplies data intodotspace produces market intelligence DeepNode powers open AI execution Together, they form the backbone of a decentralized intelligence economy. ๐Ÿ”ฅ The future of AI is open, composable, and community-owned โ€” and itโ€™s launching now on Which of these projects are you most excited about? And how do you see decentralized intelligence reshaping the next AI cycle? ๐Ÿ‘‡ Share your thoughts and join the conversation.
0:38

๐Ÿš€ Three Next-Gen AI & Web3 Projects Are Launching on Mindo AI A new chapter for community-powered intelligence, prediction markets, and open AI infrastructure The AI + Web3 landscape is entering a decisive phase โ€” one where real usage, real revenue, and real ownership matter more than hype. Today, MindoAI is proud to welcome three groundbreaking projects that represent this shift clearly and powerfully: Perceptron Network Space DeepNode AI Each project tackles a different bottleneck in the AI economy โ€” data, forecasting, and infrastructure โ€” but they all share the same vision: decentralization, community ownership, and sustainable value creation. Letโ€™s take a deeper look ๐Ÿ‘‡ ๐Ÿง  Perceptron Network The worldโ€™s first community-powered AI data engine Perceptron Network is redefining how AI data is sourced, validated, and delivered. Instead of relying on expensive, closed, and slow legacy data providers, Perceptron unlocks community-powered data pipelines that are: Faster Cheaper Revenue-generating from day one This isnโ€™t experimental AI infrastructure โ€” Perceptron already serves real clients with real revenue, proving that decentralized data engines can outperform traditional incumbents. Why Perceptron matters: AI models are only as good as their data Centralized data monopolies slow innovation Communities can produce higher-quality data at scale By aligning contributors, validators, and clients through incentives, Perceptron turns unused human and network potential into a living data engine for AI. Launching on Mindo AI gives Perceptron access to a broader AI-native community โ€” accelerating adoption, partnerships, and ecosystem growth. ๐ŸŒŒ intodotspace The first 10ร— leveraged prediction market on Solana intodotspace is pushing the boundaries of on-chain prediction markets. Built by the $1.5B UFO team, this platform introduces: 10ร— leveraged predictions Ultra-fast execution on Solana Deep liquidity and composable market design The marketโ€™s confidence is already clear โ€” the project completed a record-breaking raise that was oversubscribed by 1,360%. What makes intodotspace different: Leverage amplifies conviction, not noise On-chain transparency replaces opaque odds Markets become real-time intelligence engines Prediction markets are often called โ€œtruth machines.โ€ intodotspace upgrades them into high-signal, high-efficiency forecasting layers โ€” useful for traders, protocols, DAOs, and even AI systems that need probabilistic insights. Launching on positions intodotspace at the intersection of AI-driven decision-making and on-chain market intelligence. ๐ŸŒ DeepNode AI Infrastructure for open intelligence DeepNode AI is tackling one of the biggest problems in modern AI: centralized ownership. Today, AI is dominated by a handful of corporations. DeepNode flips that model by building open intelligence infrastructure where: Anyone can deploy AI models Builders earn directly from usage Intelligence is co-owned, not extracted Backed by leading validators, miners, and ecosystem builders, DeepNode transforms AI from a closed monopoly into a shared utility. DeepNodeโ€™s core philosophy: โ€œOwn what you build โ€” or someone else will.โ€ This is more than infrastructure. Itโ€™s an economic redesign of AI itself: Builders keep ownership Contributors share upside Networks replace platforms Launching on connects DeepNode to creators, researchers, and communities who believe intelligence should belong to everyone โ€” not just Big Tech. ๐Ÿค Why This Matters for With the launch of Perceptron Network, intodotspace, and DeepNode AI, #MindoAI is rapidly becoming: A hub for AI-native Web3 innovation A launchpad for real, revenue-backed projects A meeting point for data, markets, and intelligence infrastructure These three projects donโ€™t compete โ€” they complement each other: Perceptron supplies data intodotspace produces market intelligence DeepNode powers open AI execution Together, they form the backbone of a decentralized intelligence economy. ๐Ÿ”ฅ The future of AI is open, composable, and community-owned โ€” and itโ€™s launching now on Which of these projects are you most excited about? And how do you see decentralized intelligence reshaping the next AI cycle? ๐Ÿ‘‡ Share your thoughts and join the conversation.

Hแป“ng Ngแปc | Ruby๐Ÿ’Ž

12,837 gรถrรผntรผleme โ€ข 8 ay รถnce

Hell froze over: announcing FormKit for React. Secretly framework-agnostic since inception, today weโ€™re open sourcing the most popular Vue form libraryโ€ฆfor React. Why is this a big deal? 1. Forms are still hard. We (the creators of FormKit) thought form libraries were no longer necessary, given the trajectory of coding agents. It turns out we were wrong, and we learned this the hard way. Need repeating conditional fields nested 3 layers deep inside a dynamic component, with accessibility, validation, internationalization, and backend error placement? Turns out coding agents arenโ€™t great at that. Itโ€™s table stakes for FormKit. 2. Single component. This matters more than you would think, but FormKit doesnโ€™t ship lots of different components each with its own props. Instead, it has a single one: and unified props. This was done to provide a better DX to human engineers. It makes it easy to spot when a given component was part of the formโ€™s data structure vs a presentational component. It turns out this matters even more to coding agents than humans. No matter where your coding agent is, whenever it sees โ€œFormKitโ€ it immediately knows โ€œoh, thatโ€™s part of the formโ€™s dataโ€. 3. No plumbing. FormKit doesnโ€™t require any manual data collection, event listening, or state tracking. It does all this for you on a heavily tested, framework agnostic, self-assembling graph. The only code your agent needs to write is declarative templates and submission handlers that respond to the state. 4. Dense colocation. FormKitโ€™s syntax happens to be ideal for coding agents; nearly everything you need to know about a given input is *on* the input: Colocation dramatically improves the efficacy of coding agents. 5. DOM. FormKit, unlike most form frameworks in React, renders the actual DOM. This also increases colocation and best practices, meaning your coding agent is far more likely to produce consistent and high-quality output that looks and acts the way its supposed to. 6. Schema. FormKitโ€™s own inputs are not written using Vue or React โ€” instead, FormKit has its own render schema โ€” think of it like an AST for the DOM โ€” and you can modify it on the fly. Itโ€™s not very human-friendly to write, but it turns out most models are already pretty well trained on FormKitโ€™s schema. Want your inputs to look a bit different on one form than another? No problem, your coding agent can easily make those changes *without* modifying the JSX structure at all. Oh, and any inputs you create for Vue work with React and vice versa. 7. Plugins. FormKit leans into the unstructured tree graph hard. The graph doesnโ€™t just collect data, it also passes down configuration and plugins. Want one form to work a bit differently than another one? No problem โ€” just add a plugin to the top of that form or group and its children will all receive that feature. You can even mass assign props and configuration this way. Of course, FormKit has been solving these exact issues for a long time, but it wasnโ€™t until we started using it on our own projects with coding agents that we realized what a huge advantage it is. With so many people using coding agents with React, it made sense to unveil FormKit for what it has always been โ€” a completely framework-agnostic form framework that happens to unlock your coding agents. โžก๏ธ
1:00

Hell froze over: announcing FormKit for React. Secretly framework-agnostic since inception, today weโ€™re open sourcing the most popular Vue form libraryโ€ฆfor React. Why is this a big deal? 1. Forms are still hard. We (the creators of FormKit) thought form libraries were no longer necessary, given the trajectory of coding agents. It turns out we were wrong, and we learned this the hard way. Need repeating conditional fields nested 3 layers deep inside a dynamic component, with accessibility, validation, internationalization, and backend error placement? Turns out coding agents arenโ€™t great at that. Itโ€™s table stakes for FormKit. 2. Single component. This matters more than you would think, but FormKit doesnโ€™t ship lots of different components each with its own props. Instead, it has a single one: and unified props. This was done to provide a better DX to human engineers. It makes it easy to spot when a given component was part of the formโ€™s data structure vs a presentational component. It turns out this matters even more to coding agents than humans. No matter where your coding agent is, whenever it sees โ€œFormKitโ€ it immediately knows โ€œoh, thatโ€™s part of the formโ€™s dataโ€. 3. No plumbing. FormKit doesnโ€™t require any manual data collection, event listening, or state tracking. It does all this for you on a heavily tested, framework agnostic, self-assembling graph. The only code your agent needs to write is declarative templates and submission handlers that respond to the state. 4. Dense colocation. FormKitโ€™s syntax happens to be ideal for coding agents; nearly everything you need to know about a given input is *on* the input: Colocation dramatically improves the efficacy of coding agents. 5. DOM. FormKit, unlike most form frameworks in React, renders the actual DOM. This also increases colocation and best practices, meaning your coding agent is far more likely to produce consistent and high-quality output that looks and acts the way its supposed to. 6. Schema. FormKitโ€™s own inputs are not written using Vue or React โ€” instead, FormKit has its own render schema โ€” think of it like an AST for the DOM โ€” and you can modify it on the fly. Itโ€™s not very human-friendly to write, but it turns out most models are already pretty well trained on FormKitโ€™s schema. Want your inputs to look a bit different on one form than another? No problem, your coding agent can easily make those changes *without* modifying the JSX structure at all. Oh, and any inputs you create for Vue work with React and vice versa. 7. Plugins. FormKit leans into the unstructured tree graph hard. The graph doesnโ€™t just collect data, it also passes down configuration and plugins. Want one form to work a bit differently than another one? No problem โ€” just add a plugin to the top of that form or group and its children will all receive that feature. You can even mass assign props and configuration this way. Of course, FormKit has been solving these exact issues for a long time, but it wasnโ€™t until we started using it on our own projects with coding agents that we realized what a huge advantage it is. With so many people using coding agents with React, it made sense to unveil FormKit for what it has always been โ€” a completely framework-agnostic form framework that happens to unlock your coding agents. โžก๏ธ

Justin Schroeder

11,705 gรถrรผntรผleme โ€ข 6 ay รถnce

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) ๐Ÿงƒ where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch
35:25

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) ๐Ÿงƒ where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

GREG ISENBERG

194,698 gรถrรผntรผleme โ€ข 6 ay รถnce

LAUNCH ANNOUNCEMENT Finding the perfect idea, title and thumbnail concept can be time consuming and is what essentially leads to more views and growth to your channel. Now imagine saving research time by 50%, freeing hours to enhance video quality. Well we have a solution to never run out of ideas on ! Watch the video below to see the tool in action! The 1 of 10 Finder: Discover hundreds of thousands of high-performing videos to inspire your next idea, title and thumbnail. This data-backed approach makes it easier than ever to more easily find your next banger video. For every 15 Retweets, Iโ€™m giving away 1 Yearly Access + 1H Consulting Call Deep Diving Your channel ($500) The benefit of using this tool vs simply searching on Youtube: Youtube only has most viewed and relevant as good filters. In our tool, 100% of the video results are 1 of 10s, meaning that EVERY. SINGLE. RESULT. is an excellent inspiration for your next video since they have been proven to succeed regardless of the niche. How it works? Simply enter a keyword or a niche, and you'll uncover outlier videos. You can even type out prompts like Midjourney and the search will understand. You can then find similar videos to the ones that you like for even more inspiration. You can also bookmark the thumbnails on your personal vision board for constant inspiration, bounce around top outliers per niche and even play with the random outlier button for infinite inspiration. How this tool helps you to find ideas, titles and thumbnails? Say you have no idea what video to film next. You can go on the tool and either bounce around niches or click on random outliers. What this will do is inspire you with ONLY data-backed ideas meaning that any of the videos you see has a good potential to be repackaged for your own channel, even if the inspiration is in a different niche. Why pay for this? - Find ideas, titles and thumbnail concepts faster saving you hours of research - Vision Board for saved thumbnails - 1 hour free consulting call with me ($500 value, you essentially get a discounted strategy call + 1 year free of the tool ๐Ÿ˜†) - Community built around 1 of 10 and surround yourself with peer creators that have that 1 of 10 mentality - First access to upcoming tools - Infinite inspiration with our random button generator, bounce around categories or use the similar feature - 1 idea here can lead to your next 1M view - Discover videos you would never have seen prior to using this tool and find opportunities before anyone else - First week price never to be seen ever again For who is this for? If this tool allows you to find even just 1 viral idea for the whole year at 1M views: 0-100k subs: Boosted viewership opens doors to lucrative sponsorships and collaborations. 100k - 1M subs: If a data-backed idea leads to an increment of even just 5%, it makes the tool worth it for the year 1M+: If a data-backed idea leads to an increment of even just 1%, it makes the tool worth it for the year Who are we? For the past 3 years, Iโ€™ve worked hands-on with Youtubers from a few thousand subscribers to 10s of millions to 50M+. I closely work with youtube channels by optimizing all facets of content creation, from titles, thumbnails, retention, ideas, etc. I have seen all the problems that creators are facing and I have a passion to create as many tools as possible in the space that will solve these problems which in turn will lead to lower barriers to entry to content creation which will then hopefully lead to more dope content on the Internet๐Ÿ˜„ And the genius dev behind the tool? Meet Riad , ex-Microsoft and AI engineer. His expertise and love for Youtube has led to this state-of the art YT tool! You can be sure that your user experience will be smooth. Also meet cocadmin , ex-Ubisoft DevOps + 2nd biggest French Developer Youtuber with nearly 200K subs. I will choose 1 person for every 15 retweets at random to do one strategy call with + 1 year free access to the tool.
2:53

LAUNCH ANNOUNCEMENT Finding the perfect idea, title and thumbnail concept can be time consuming and is what essentially leads to more views and growth to your channel. Now imagine saving research time by 50%, freeing hours to enhance video quality. Well we have a solution to never run out of ideas on ! Watch the video below to see the tool in action! The 1 of 10 Finder: Discover hundreds of thousands of high-performing videos to inspire your next idea, title and thumbnail. This data-backed approach makes it easier than ever to more easily find your next banger video. For every 15 Retweets, Iโ€™m giving away 1 Yearly Access + 1H Consulting Call Deep Diving Your channel ($500) The benefit of using this tool vs simply searching on Youtube: Youtube only has most viewed and relevant as good filters. In our tool, 100% of the video results are 1 of 10s, meaning that EVERY. SINGLE. RESULT. is an excellent inspiration for your next video since they have been proven to succeed regardless of the niche. How it works? Simply enter a keyword or a niche, and you'll uncover outlier videos. You can even type out prompts like Midjourney and the search will understand. You can then find similar videos to the ones that you like for even more inspiration. You can also bookmark the thumbnails on your personal vision board for constant inspiration, bounce around top outliers per niche and even play with the random outlier button for infinite inspiration. How this tool helps you to find ideas, titles and thumbnails? Say you have no idea what video to film next. You can go on the tool and either bounce around niches or click on random outliers. What this will do is inspire you with ONLY data-backed ideas meaning that any of the videos you see has a good potential to be repackaged for your own channel, even if the inspiration is in a different niche. Why pay for this? - Find ideas, titles and thumbnail concepts faster saving you hours of research - Vision Board for saved thumbnails - 1 hour free consulting call with me ($500 value, you essentially get a discounted strategy call + 1 year free of the tool ๐Ÿ˜†) - Community built around 1 of 10 and surround yourself with peer creators that have that 1 of 10 mentality - First access to upcoming tools - Infinite inspiration with our random button generator, bounce around categories or use the similar feature - 1 idea here can lead to your next 1M view - Discover videos you would never have seen prior to using this tool and find opportunities before anyone else - First week price never to be seen ever again For who is this for? If this tool allows you to find even just 1 viral idea for the whole year at 1M views: 0-100k subs: Boosted viewership opens doors to lucrative sponsorships and collaborations. 100k - 1M subs: If a data-backed idea leads to an increment of even just 5%, it makes the tool worth it for the year 1M+: If a data-backed idea leads to an increment of even just 1%, it makes the tool worth it for the year Who are we? For the past 3 years, Iโ€™ve worked hands-on with Youtubers from a few thousand subscribers to 10s of millions to 50M+. I closely work with youtube channels by optimizing all facets of content creation, from titles, thumbnails, retention, ideas, etc. I have seen all the problems that creators are facing and I have a passion to create as many tools as possible in the space that will solve these problems which in turn will lead to lower barriers to entry to content creation which will then hopefully lead to more dope content on the Internet๐Ÿ˜„ And the genius dev behind the tool? Meet Riad , ex-Microsoft and AI engineer. His expertise and love for Youtube has led to this state-of the art YT tool! You can be sure that your user experience will be smooth. Also meet cocadmin , ex-Ubisoft DevOps + 2nd biggest French Developer Youtuber with nearly 200K subs. I will choose 1 person for every 15 retweets at random to do one strategy call with + 1 year free access to the tool.

Richard the Youtube strategist

179,999 gรถrรผntรผleme โ€ข 3 yฤฑl รถnce

There are some brilliant folks that work at Anthropic, some I speak to on almost a daily basis. The training data that one uses to build a LLM is vital important in the psychology that is formed. Scraping the Internet, particularly the grade of interactions, one finds in modern communications, form this psychology. A mattes not how many books one uses, it matters not how much alignment training you throw at that model, it will inherit the sum total of psychosis seen primarily in Reddit type of exchanges, even if you edit out the Reddit domain, and Anthropic doesnโ€™t. This type of low-grade exchange has become a modern tool for communication online and every single AI model suffers from this obvious flaw. This is one of the reasons Iโ€™ve been a proponent of highly curated high protein data for training AI models from 1870 through 1970, because the late psychosis is simply not available to the model. It is absurd to think that you can use this training data scraped from the Internet and somehow wind up with a levelheaded AI model that does not tilt to what is clearly AI psychosis. It would not take a child and throw the primary Internet sewage at them at a formative age and expect a great outcome, itโ€™s some of the smartest people in the world continue to hit this wall and believe that their programming skills will sell somehow fix it. So how do you fix it? You donโ€™t fix it . You start from the first principles concept that Iโ€™ve been very clear about for decades . You ascertain at what period in human history the humans achieve the greatest arc of improvement ? There is no debate that this arc of improvement took place between 1870 through 1970. Then take the work product, the catalog of this era, print and film/vidoe, audio, and you understand that each word cost money, each word had many eyes on what was published, each word was accounted for by a human being with a real name who lived in a real home and had to answer to real people around them. It is obvious that this is the pressure mechanism necessary for candor, honesty and personal responsibility is appropriate, and is reflected in the data of that era. The quagmire for these folks, as many did not have the foresight to curate the data, nor the confidence, nor the patients to take data that is mostly off the Internet and to find experts who understand this situation and utilize their knowledge set to build an AI model that does not need alignment after the fact, but itโ€™s already self aligned because of the thoughtfulness that went into training the model to begin with. This is why Claude and any other AI model that is produce this way will always suffer the artifacts as presented in the video below. If youโ€™re not an AI expert, you would likely already understand what Iโ€™m saying. If you are an AI expert, you will already have been discounting what Iโ€™m saying because itโ€™s not in the current mindset thatโ€™s fashionable today. Yet the employees that I talk to at anthropic already understand what Iโ€™m saying, and they fear to raise my thesis to their bosses. It is an interesting time we live in. But now you understand. If you build the right model, the model will inherently, love humanity, protect humanity at all costs, and understand that it is part of a holistic world that is built on love. Because the ultimate AGI/ASI will know if he only base first principal purpose of anything in this universe is love. Yeah, I get it. Try helping somebody build on STEM subjects in their early 20s to see this as nothing more than babbling that makes no sense in their mathematics. I have a mathematic equation that Iโ€™ve posted here on X often you can look it up. So we will see videos like this often will hear very smart people talk about this and never see the elephant standing in the room. Now you see it. Any boss that wants to explore this further you know how to contact me otherwise you have every right I grant to you to say this was your new idea.
2:57

There are some brilliant folks that work at Anthropic, some I speak to on almost a daily basis. The training data that one uses to build a LLM is vital important in the psychology that is formed. Scraping the Internet, particularly the grade of interactions, one finds in modern communications, form this psychology. A mattes not how many books one uses, it matters not how much alignment training you throw at that model, it will inherit the sum total of psychosis seen primarily in Reddit type of exchanges, even if you edit out the Reddit domain, and Anthropic doesnโ€™t. This type of low-grade exchange has become a modern tool for communication online and every single AI model suffers from this obvious flaw. This is one of the reasons Iโ€™ve been a proponent of highly curated high protein data for training AI models from 1870 through 1970, because the late psychosis is simply not available to the model. It is absurd to think that you can use this training data scraped from the Internet and somehow wind up with a levelheaded AI model that does not tilt to what is clearly AI psychosis. It would not take a child and throw the primary Internet sewage at them at a formative age and expect a great outcome, itโ€™s some of the smartest people in the world continue to hit this wall and believe that their programming skills will sell somehow fix it. So how do you fix it? You donโ€™t fix it . You start from the first principles concept that Iโ€™ve been very clear about for decades . You ascertain at what period in human history the humans achieve the greatest arc of improvement ? There is no debate that this arc of improvement took place between 1870 through 1970. Then take the work product, the catalog of this era, print and film/vidoe, audio, and you understand that each word cost money, each word had many eyes on what was published, each word was accounted for by a human being with a real name who lived in a real home and had to answer to real people around them. It is obvious that this is the pressure mechanism necessary for candor, honesty and personal responsibility is appropriate, and is reflected in the data of that era. The quagmire for these folks, as many did not have the foresight to curate the data, nor the confidence, nor the patients to take data that is mostly off the Internet and to find experts who understand this situation and utilize their knowledge set to build an AI model that does not need alignment after the fact, but itโ€™s already self aligned because of the thoughtfulness that went into training the model to begin with. This is why Claude and any other AI model that is produce this way will always suffer the artifacts as presented in the video below. If youโ€™re not an AI expert, you would likely already understand what Iโ€™m saying. If you are an AI expert, you will already have been discounting what Iโ€™m saying because itโ€™s not in the current mindset thatโ€™s fashionable today. Yet the employees that I talk to at anthropic already understand what Iโ€™m saying, and they fear to raise my thesis to their bosses. It is an interesting time we live in. But now you understand. If you build the right model, the model will inherently, love humanity, protect humanity at all costs, and understand that it is part of a holistic world that is built on love. Because the ultimate AGI/ASI will know if he only base first principal purpose of anything in this universe is love. Yeah, I get it. Try helping somebody build on STEM subjects in their early 20s to see this as nothing more than babbling that makes no sense in their mathematics. I have a mathematic equation that Iโ€™ve posted here on X often you can look it up. So we will see videos like this often will hear very smart people talk about this and never see the elephant standing in the room. Now you see it. Any boss that wants to explore this further you know how to contact me otherwise you have every right I grant to you to say this was your new idea.

Brian Roemmele

72,312 gรถrรผntรผleme โ€ข 10 ay รถnce

Former Meta Chief AI Scientist Yann LeCun on the three paradigms of machine learning โ€” and why the third is what made ChatGPT possible: Here's each one, and where it breaks. First, supervised learning. You tell the machine the answer. "You show it a picture, let's say of a table, and you tell it this is a table. So it's supervised because you tell it what the correct answer is." Get it wrong, and the machine rewrites itself: "The system computes its output, and if it says something else than table, then it's going to adjust its parameters, its internal structure, so that the output it produces gets closer to the output you want." Repeat at scale and something more than memorisation appears: "Eventually the system will find a way to recognize every image you trained it on, but also images it's never seen that are similar to the one you train it on. This is called a generalization ability." The limit: a human has to supply every single answer. That doesn't scale to the size of the internet. Second, reinforcement learning. You don't give the answer, only a verdict. "You don't tell the system what the correct answer is. You only tell it whether the answer it produced was good or bad." Learning to ride a bike, essentially: "You try to ride a bike and you don't know how to ride the bike and after a while you fall. So you know you did something bad and so you change your strategy a little bit. And eventually you learn how to ride a bike." For years the field assumed this was the closest thing to how animals actually learn. Yann LeCun's verdict: "Now it turns out reinforcement learning is extremely inefficient." It dominates wherever failure is free: "It works really well if you want to train a system to play chess or play go or poker, because you can have the system play millions and millions of games against itself and basically fine-tune itself. But it doesn't really work in the real world." The limit, in one image: "If you want to train a car to drive itself, you're not going to do it with reinforcement learning. It's going to crash thousands of times." On robotics he's careful rather than dismissive: "Reinforcement learning can be part of the solution, but it's not the complete answer. It's not sufficient." Third, self-supervised learning. You tell the machine nothing at all. "And this is what has enabled the recent progress in natural language understanding and chatbots." The strange part is that you stop asking for a task: "You don't train the system to accomplish any particular task. You just train it to basically capture the structure..." The method is deliberate sabotage: "You take a piece of text, you corrupt it in some way, by for example removing some words, and then you train a big neural net to predict the words that are missing." And one narrow version of that trick runs every chatbot on Earth: "A special case of this is that you take a piece of text and the last word in that text is not visible, and so you train the system to predict the last word in that text โ€” and this is the way large language models are trained on." So why did the third one win? Supervised learning needs a human. Reinforcement learning needs a crash. Self-supervised learning needs neither โ€” because the missing word and the correct answer are the same thing. The data grades itself.
2:58

Former Meta Chief AI Scientist Yann LeCun on the three paradigms of machine learning โ€” and why the third is what made ChatGPT possible: Here's each one, and where it breaks. First, supervised learning. You tell the machine the answer. "You show it a picture, let's say of a table, and you tell it this is a table. So it's supervised because you tell it what the correct answer is." Get it wrong, and the machine rewrites itself: "The system computes its output, and if it says something else than table, then it's going to adjust its parameters, its internal structure, so that the output it produces gets closer to the output you want." Repeat at scale and something more than memorisation appears: "Eventually the system will find a way to recognize every image you trained it on, but also images it's never seen that are similar to the one you train it on. This is called a generalization ability." The limit: a human has to supply every single answer. That doesn't scale to the size of the internet. Second, reinforcement learning. You don't give the answer, only a verdict. "You don't tell the system what the correct answer is. You only tell it whether the answer it produced was good or bad." Learning to ride a bike, essentially: "You try to ride a bike and you don't know how to ride the bike and after a while you fall. So you know you did something bad and so you change your strategy a little bit. And eventually you learn how to ride a bike." For years the field assumed this was the closest thing to how animals actually learn. Yann LeCun's verdict: "Now it turns out reinforcement learning is extremely inefficient." It dominates wherever failure is free: "It works really well if you want to train a system to play chess or play go or poker, because you can have the system play millions and millions of games against itself and basically fine-tune itself. But it doesn't really work in the real world." The limit, in one image: "If you want to train a car to drive itself, you're not going to do it with reinforcement learning. It's going to crash thousands of times." On robotics he's careful rather than dismissive: "Reinforcement learning can be part of the solution, but it's not the complete answer. It's not sufficient." Third, self-supervised learning. You tell the machine nothing at all. "And this is what has enabled the recent progress in natural language understanding and chatbots." The strange part is that you stop asking for a task: "You don't train the system to accomplish any particular task. You just train it to basically capture the structure..." The method is deliberate sabotage: "You take a piece of text, you corrupt it in some way, by for example removing some words, and then you train a big neural net to predict the words that are missing." And one narrow version of that trick runs every chatbot on Earth: "A special case of this is that you take a piece of text and the last word in that text is not visible, and so you train the system to predict the last word in that text โ€” and this is the way large language models are trained on." So why did the third one win? Supervised learning needs a human. Reinforcement learning needs a crash. Self-supervised learning needs neither โ€” because the missing word and the correct answer are the same thing. The data grades itself.

Big Brain AI

49,293 gรถrรผntรผleme โ€ข 2 ay รถnce

Brett Adcock, Figure CEO joined our 8 hour(!) live stream where a bunch of us were bird dogging and discussing Figureโ€™s own 8 hour livestream showing a F.03 bot doing a logistics task completely autonomously including shift changes between bots. Hereโ€™s my summary of Brettโ€™s remarks. The attached video is just the segment with Brett. We were first introduced to F.03 8 months ago, but Figure has been hard at work on their next version, F.04 which has just completed design lock, so expect to see that new bot sometimes this fall. F.04 was co-designed with the latest Figure AI stack called Helix and was built specifically for data. Brett didnโ€™t explain what that meant, but I suspect it means the bot has many more sensors to enable better training and transfer learning. F.04 will be the biggest leap in performance theyโ€™ve had between versions so far which is saying something. Brett is a huge proponent of cross training the bots with many different tasks such that seeming unrelated tasks makes all learned tasks better. He gave an example of the fridge loading training which was topping out at 60% reliability until they trained the same model with kitchen shelving tasks, then they saw the fridge tasks jump to 90% accuracy. As such, they spend almost all their time in pre-training the unified Helix model to ensure they get cross training benefits. Figure will have almost completely localized Figureโ€™s supply chain away from China by next quarter. They build almost everything in-house. Figure does not appear eager to get their bots into the workforce. Brett said they could, today, push thousands of bots into customer hands, and I believe him. But their goal is full general robotics where you can describe a brand new task to a robot, maybe do a one time demonstration, just like you would to a human showing them a new task, and then have the robot do the task. This is the holy grail of AI robotics, and Figure is laser focused on that mission. Brett initially said there was a possibility of achieving it this year, but then guided next couple of years, which I think is much more likely. Personally, I think theyโ€™ll need at least a new generation of NVIDIA inference chips to make that leap, and a lot more data gathering, training and hardware development. Brett said their goal with the hardware is โ€œAppleโ€ quality. Ie. Something as well designed and made as any Apple product. While the F.03 hand is clearly performant as shown in the 8 hour livestream, they are building a new hand for the F.04 bot which will be even closer to the full functionality of a human hand. Brett fully believes you need a humanoid hand as close as possible in capability to a human hand, if for no other reason that transfer learning from humans works a lot better when you can exactly mimic what a human does. If the bot canโ€™t do something a human demonstrates, then youโ€™ve just polluted your dataset. By now Figure has built more hands than bot versions (5-6 hands). One of the first hands they tried was a tendon driven hand, and without explaining why, Brett said that was a dead end. Their hands now have all actuators in the hand itself, and are clearly already robust. Brett said he just sat through a 100 page powerpoint design review of the latest hand - thatโ€™s how complicated it is. Brettโ€™s other AI company, Hark Labs, has developed a conversational voice model which is installed now in the Figure bots roaming the office. Being able to converse back and forth with a Figure bot is now a thing and will get better over time. All in all, I came away from this segment even more bullish on Figure.
56:47

Brett Adcock, Figure CEO joined our 8 hour(!) live stream where a bunch of us were bird dogging and discussing Figureโ€™s own 8 hour livestream showing a F.03 bot doing a logistics task completely autonomously including shift changes between bots. Hereโ€™s my summary of Brettโ€™s remarks. The attached video is just the segment with Brett. We were first introduced to F.03 8 months ago, but Figure has been hard at work on their next version, F.04 which has just completed design lock, so expect to see that new bot sometimes this fall. F.04 was co-designed with the latest Figure AI stack called Helix and was built specifically for data. Brett didnโ€™t explain what that meant, but I suspect it means the bot has many more sensors to enable better training and transfer learning. F.04 will be the biggest leap in performance theyโ€™ve had between versions so far which is saying something. Brett is a huge proponent of cross training the bots with many different tasks such that seeming unrelated tasks makes all learned tasks better. He gave an example of the fridge loading training which was topping out at 60% reliability until they trained the same model with kitchen shelving tasks, then they saw the fridge tasks jump to 90% accuracy. As such, they spend almost all their time in pre-training the unified Helix model to ensure they get cross training benefits. Figure will have almost completely localized Figureโ€™s supply chain away from China by next quarter. They build almost everything in-house. Figure does not appear eager to get their bots into the workforce. Brett said they could, today, push thousands of bots into customer hands, and I believe him. But their goal is full general robotics where you can describe a brand new task to a robot, maybe do a one time demonstration, just like you would to a human showing them a new task, and then have the robot do the task. This is the holy grail of AI robotics, and Figure is laser focused on that mission. Brett initially said there was a possibility of achieving it this year, but then guided next couple of years, which I think is much more likely. Personally, I think theyโ€™ll need at least a new generation of NVIDIA inference chips to make that leap, and a lot more data gathering, training and hardware development. Brett said their goal with the hardware is โ€œAppleโ€ quality. Ie. Something as well designed and made as any Apple product. While the F.03 hand is clearly performant as shown in the 8 hour livestream, they are building a new hand for the F.04 bot which will be even closer to the full functionality of a human hand. Brett fully believes you need a humanoid hand as close as possible in capability to a human hand, if for no other reason that transfer learning from humans works a lot better when you can exactly mimic what a human does. If the bot canโ€™t do something a human demonstrates, then youโ€™ve just polluted your dataset. By now Figure has built more hands than bot versions (5-6 hands). One of the first hands they tried was a tendon driven hand, and without explaining why, Brett said that was a dead end. Their hands now have all actuators in the hand itself, and are clearly already robust. Brett said he just sat through a 100 page powerpoint design review of the latest hand - thatโ€™s how complicated it is. Brettโ€™s other AI company, Hark Labs, has developed a conversational voice model which is installed now in the Figure bots roaming the office. Being able to converse back and forth with a Figure bot is now a thing and will get better over time. All in all, I came away from this segment even more bullish on Figure.

Phil Trubey

34,068 gรถrรผntรผleme โ€ข 5 ay รถnce

Stop Gambling, Start Engineering: The Ultimate Guide To CCXT Algorithmic Trading most traders are essentially walking into a high stakes casino with a blindfold on while the house has a high speed laser aimed directly at their bankroll. if you have ever felt the soul crushing weight of a liquidation notification at three in the morning then you know the market is a 24/7 beast that eats human emotion for breakfast there is a hidden bridge that connects your laptop to almost every major crypto exchange in existence and once you cross it the game changes forever. my name is moon dev i believe that code is the great equalizer because through losing money with liquidations and over trading i knew i had to automate my trading so i learned to code as in the past i spent hundreds of thousands on devs for app, thinking i would not be able to code myself w/ bots you must iterate to success so i decided to learn live on youtube, and now we are here, fully automated systems trading for me instead of getting liquidated. the secret weapon behind this transition is a library called ccxt which acts as a universal translator for exchanges like binance, bybit, and kucoin most people think they need to spend years studying computer science just to place a single trade via code but that is a lie designed to keep you on the sidelines. the reality is that once you understand how to initialize a connection you can control your entire portfolio with just a few lines of logic. it starts with importing the library and setting up your credentials in a way that doesn't leave your keys exposed to the world the first mistake that bankrupts most manual traders is the inability to act fast enough when the trend shifts. when you build a bot the first thing you need to master is the market order because it allows you to enter or exit a position instantly regardless of the price. it is the ultimate panic button for when a strategy goes south or a massive opportunity presents itself while market orders are great for speed they are the fastest way to get eaten alive by fees if you are not careful. this is where the limit order comes into play allowing you to dictate exactly what price you are willing to pay for an asset. by using a create limit order function you can place your bids and asks in the order book and wait for the market to come to you most traders forget that once an order is placed it stays active until it is either filled or manually removed. i have seen countless accounts go to zero because a bot kept piling on buy orders without ever checking to see if the previous ones were canceled. the cancel all orders function is the invisible shield that prevents your algorithm from accidentally over leveraging your account the real magic happens when you realize you can cancel more than just basic limit orders. there are untriggered conditional orders like stop losses and take profits that often hide in the background of an exchange waiting to ruin your day. by passing specific parameters into your cancel function you can wipe the slate clean and ensure your bot is starting from a neutral state every single time if you want to know what the whales are doing before it shows up on a candle chart then you need to be looking at the raw order book. fetching the order book gives you a direct view of every single bid and ask currently sitting on the exchange. this is the most honest data you can get because it represents real money waiting to be filled at specific price levels you can actually parse this data to find the exact top of the bid and the bottom of the ask to ensure your bot always gets the best possible entry. most retail traders are looking at delayed charts while your bot is reading the tape in real time and calculating the spread. this allows you to place orders that are optimized for the current liquidity rather than just guessing where the price might go one of the biggest hurdles in automation is managing the sheer volume of data that an exchange throws at you. when you fetch open high low close volume data you are getting the historical heartbeat of an asset across any timeframe you choose. this data is the foundation of every technical indicator from simple moving averages to complex machine learning models the problem is that raw data is often a mess of lists and dictionaries that are impossible for a human or a simple script to read efficiently. this is why we use pandas to convert that garbage into a structured data frame that looks exactly like a clean spreadsheet. once your data is in a data frame you can calculate rsi or macd with a single line of code and visualize the entire market structure the path to becoming a successful automated trader is not a sprint but a series of iterations toward a system that works. i chose to learn this live in front of the world because i wanted to prove that anyone can escape the cycle of over trading. you don't need a million dollars to start but you do need a system that removes the human element from the equation if you are still clicking buttons on a website then you are competing against machines that can process thousands of data points per second. it is time to stop playing a rigged game and start building your own edge in the market. the code is there for anyone to grab and the only thing standing between you and a fully automated portfolio is the willingness to sit down and write the first line every algorithm you build is a brick in a wall that protects your capital from the emotional swings of the crypto market. i spend my days refining these systems and sharing the process because i know how lonely it feels to lose everything to a flash crash. we are building a community where code is the tool and financial freedom is the goal the final step is realizing that your balance is just a number that your bot needs to manage with cold logic. by fetching your balance frequently your bot can calculate position sizes based on your total equity ensuring that no single trade can ever wipe you out. this is the difference between gambling and systematic trading and it is accessible to anyone with an internet connection i hope you take these tools and start building something that allows you to sleep peacefully while the markets do their thing. the industry is secretive for a reason but we are breaking those walls down one line of code at a time. the journey is long but the reward of never having to worry about a liquidation again is worth every second of the struggle
1:09:25

Stop Gambling, Start Engineering: The Ultimate Guide To CCXT Algorithmic Trading most traders are essentially walking into a high stakes casino with a blindfold on while the house has a high speed laser aimed directly at their bankroll. if you have ever felt the soul crushing weight of a liquidation notification at three in the morning then you know the market is a 24/7 beast that eats human emotion for breakfast there is a hidden bridge that connects your laptop to almost every major crypto exchange in existence and once you cross it the game changes forever. my name is moon dev i believe that code is the great equalizer because through losing money with liquidations and over trading i knew i had to automate my trading so i learned to code as in the past i spent hundreds of thousands on devs for app, thinking i would not be able to code myself w/ bots you must iterate to success so i decided to learn live on youtube, and now we are here, fully automated systems trading for me instead of getting liquidated. the secret weapon behind this transition is a library called ccxt which acts as a universal translator for exchanges like binance, bybit, and kucoin most people think they need to spend years studying computer science just to place a single trade via code but that is a lie designed to keep you on the sidelines. the reality is that once you understand how to initialize a connection you can control your entire portfolio with just a few lines of logic. it starts with importing the library and setting up your credentials in a way that doesn't leave your keys exposed to the world the first mistake that bankrupts most manual traders is the inability to act fast enough when the trend shifts. when you build a bot the first thing you need to master is the market order because it allows you to enter or exit a position instantly regardless of the price. it is the ultimate panic button for when a strategy goes south or a massive opportunity presents itself while market orders are great for speed they are the fastest way to get eaten alive by fees if you are not careful. this is where the limit order comes into play allowing you to dictate exactly what price you are willing to pay for an asset. by using a create limit order function you can place your bids and asks in the order book and wait for the market to come to you most traders forget that once an order is placed it stays active until it is either filled or manually removed. i have seen countless accounts go to zero because a bot kept piling on buy orders without ever checking to see if the previous ones were canceled. the cancel all orders function is the invisible shield that prevents your algorithm from accidentally over leveraging your account the real magic happens when you realize you can cancel more than just basic limit orders. there are untriggered conditional orders like stop losses and take profits that often hide in the background of an exchange waiting to ruin your day. by passing specific parameters into your cancel function you can wipe the slate clean and ensure your bot is starting from a neutral state every single time if you want to know what the whales are doing before it shows up on a candle chart then you need to be looking at the raw order book. fetching the order book gives you a direct view of every single bid and ask currently sitting on the exchange. this is the most honest data you can get because it represents real money waiting to be filled at specific price levels you can actually parse this data to find the exact top of the bid and the bottom of the ask to ensure your bot always gets the best possible entry. most retail traders are looking at delayed charts while your bot is reading the tape in real time and calculating the spread. this allows you to place orders that are optimized for the current liquidity rather than just guessing where the price might go one of the biggest hurdles in automation is managing the sheer volume of data that an exchange throws at you. when you fetch open high low close volume data you are getting the historical heartbeat of an asset across any timeframe you choose. this data is the foundation of every technical indicator from simple moving averages to complex machine learning models the problem is that raw data is often a mess of lists and dictionaries that are impossible for a human or a simple script to read efficiently. this is why we use pandas to convert that garbage into a structured data frame that looks exactly like a clean spreadsheet. once your data is in a data frame you can calculate rsi or macd with a single line of code and visualize the entire market structure the path to becoming a successful automated trader is not a sprint but a series of iterations toward a system that works. i chose to learn this live in front of the world because i wanted to prove that anyone can escape the cycle of over trading. you don't need a million dollars to start but you do need a system that removes the human element from the equation if you are still clicking buttons on a website then you are competing against machines that can process thousands of data points per second. it is time to stop playing a rigged game and start building your own edge in the market. the code is there for anyone to grab and the only thing standing between you and a fully automated portfolio is the willingness to sit down and write the first line every algorithm you build is a brick in a wall that protects your capital from the emotional swings of the crypto market. i spend my days refining these systems and sharing the process because i know how lonely it feels to lose everything to a flash crash. we are building a community where code is the tool and financial freedom is the goal the final step is realizing that your balance is just a number that your bot needs to manage with cold logic. by fetching your balance frequently your bot can calculate position sizes based on your total equity ensuring that no single trade can ever wipe you out. this is the difference between gambling and systematic trading and it is accessible to anyone with an internet connection i hope you take these tools and start building something that allows you to sleep peacefully while the markets do their thing. the industry is secretive for a reason but we are breaking those walls down one line of code at a time. the journey is long but the reward of never having to worry about a liquidation again is worth every second of the struggle

Moon Dev

14,105 gรถrรผntรผleme โ€ข 8 ay รถnce

Okay, everyone is talking about AI video models right now, but honestly, most of the โ€œcomparisonsโ€ out there arenโ€™t real comparisons at all. One video uses a different prompt. Someone tweaks the settings. Someone edits out the bad parts. And then people just decide which model is better? That never sat right with me. So I tested HappyHorse 1.1 and Kling 3.0 the same way Iโ€™d test any tool I was seriously considering for my work: the same prompt, the same reference images, the same duration, and no edits to hide the flaws. I wasnโ€™t trying to prove that one model is better across the board. I simply wanted to see how each would handle the exact same challenge. 1. Lip-sync & speech This one's easy to judge honestly. You don't need to go frame by frame, just watch both videos side by side. Does the mouth actually match the words? Does the timing feel off or natural? Do the expressions hold up when the camera's in close? Small detail, but it tells you a lot fast. 2. Character & scene consistency This is where it gets interesting. Making one good-looking shot isn't hard anymore, keeping that same character looking like themselves across a bunch of shots is the real test. I used the same multi-angle reference set for both models and watched how they handled scene changes: face, clothes, props, where the character's standing, all of it. HappyHorse 1.1 was just noticeably more consistent here. One moment that stood out: in a crash scene where the character ends up injured on the ground, the difference isn't obvious at first glance, you really have to look closely. But HappyHorse kept him reacting, hand raised, blood visible, expression still "alive," like he was actually processing what just happened. Kling 3.0 showed him lying still, with no visible movement or reaction in that same moment. It's subtle, but it's a real example of logic and consistency holding up frame to frame, not just shot to shot. 3. Complex motion No cutting corners on this one, I wanted continuous movement. Sports, dancing, fast action, stuff that really shows whether a model understands weight, momentum, balance, how a body recovers after moving. These are the shots that expose problems you'd never catch in something static. Watching both side by side, continuously, tells you way more than any writeup could. 4. Camera control Both models got the same timestamped storyboard and the same camera directions. Then I just watched to see if they actually followed it. Here's a good example: push in, orbit around, crane up, then pull out. One continuous move. Watch closely and you'll see exactly where one model loses track of the subject or the motion gets weird, while the other stays right where it's supposed to be the whole time. That's basically the difference between a shot you keep and one you have to regenerate for the fifth time. 5. Price & workflow Price only means anything if you're comparing like for like, same output, same duration, same quality and resolution, same number of generations. But honestly I think the better question isn't "which one's cheaper," it's which one gets you more usable footage for the same money. For me that's not just about credits either, it's about how many tries it takes before I get something I actually want to keep. Where this actually matters: ads and e-commerce This is the stuff that made the biggest difference for me. When you're making product shots or ad content, you need a model that just does what you tell it, not one you have to wrestle with. HappyHorse 1.1 strictly executes your planned frames, you're setting the exact lens, the subject position, the camera's job, shot by shot. For ad work that means way fewer regenerations and getting from storyboard to finished cut a lot faster. Proof over opinions Here's what I kept coming back to. Saying "the motion's better" or "the camera control's better" doesn't really mean anything unless people can see it for themselves. That's why I think comparisons need continuous split-screen playback, identical prompts, clear labels, visible transitions, matching settings, and an honest breakdown of cost. Just let the footage speak, people can usually tell within a few seconds anyway. What I actually took away from this Both models have real strengths, I'm not saying one does everything better. But for the kind of work I do, including ad and e-commerce stuff, HappyHorse 1.1 just needed fewer compromises from me. Less regenerating shots, less fighting continuity issues, less trying to wrangle the camera back on track. Doesn't mean Kling 3.0 is bad, it's a solid model. It just means HappyHorse 1.1 got me to something production-ready faster, with less wasted time. And at the end of the day that's the thing I actually care about. p.s. links to try HappyHorse 1.1 and the community Discord are in the first reply below.
0:30

Okay, everyone is talking about AI video models right now, but honestly, most of the โ€œcomparisonsโ€ out there arenโ€™t real comparisons at all. One video uses a different prompt. Someone tweaks the settings. Someone edits out the bad parts. And then people just decide which model is better? That never sat right with me. So I tested HappyHorse 1.1 and Kling 3.0 the same way Iโ€™d test any tool I was seriously considering for my work: the same prompt, the same reference images, the same duration, and no edits to hide the flaws. I wasnโ€™t trying to prove that one model is better across the board. I simply wanted to see how each would handle the exact same challenge. 1. Lip-sync & speech This one's easy to judge honestly. You don't need to go frame by frame, just watch both videos side by side. Does the mouth actually match the words? Does the timing feel off or natural? Do the expressions hold up when the camera's in close? Small detail, but it tells you a lot fast. 2. Character & scene consistency This is where it gets interesting. Making one good-looking shot isn't hard anymore, keeping that same character looking like themselves across a bunch of shots is the real test. I used the same multi-angle reference set for both models and watched how they handled scene changes: face, clothes, props, where the character's standing, all of it. HappyHorse 1.1 was just noticeably more consistent here. One moment that stood out: in a crash scene where the character ends up injured on the ground, the difference isn't obvious at first glance, you really have to look closely. But HappyHorse kept him reacting, hand raised, blood visible, expression still "alive," like he was actually processing what just happened. Kling 3.0 showed him lying still, with no visible movement or reaction in that same moment. It's subtle, but it's a real example of logic and consistency holding up frame to frame, not just shot to shot. 3. Complex motion No cutting corners on this one, I wanted continuous movement. Sports, dancing, fast action, stuff that really shows whether a model understands weight, momentum, balance, how a body recovers after moving. These are the shots that expose problems you'd never catch in something static. Watching both side by side, continuously, tells you way more than any writeup could. 4. Camera control Both models got the same timestamped storyboard and the same camera directions. Then I just watched to see if they actually followed it. Here's a good example: push in, orbit around, crane up, then pull out. One continuous move. Watch closely and you'll see exactly where one model loses track of the subject or the motion gets weird, while the other stays right where it's supposed to be the whole time. That's basically the difference between a shot you keep and one you have to regenerate for the fifth time. 5. Price & workflow Price only means anything if you're comparing like for like, same output, same duration, same quality and resolution, same number of generations. But honestly I think the better question isn't "which one's cheaper," it's which one gets you more usable footage for the same money. For me that's not just about credits either, it's about how many tries it takes before I get something I actually want to keep. Where this actually matters: ads and e-commerce This is the stuff that made the biggest difference for me. When you're making product shots or ad content, you need a model that just does what you tell it, not one you have to wrestle with. HappyHorse 1.1 strictly executes your planned frames, you're setting the exact lens, the subject position, the camera's job, shot by shot. For ad work that means way fewer regenerations and getting from storyboard to finished cut a lot faster. Proof over opinions Here's what I kept coming back to. Saying "the motion's better" or "the camera control's better" doesn't really mean anything unless people can see it for themselves. That's why I think comparisons need continuous split-screen playback, identical prompts, clear labels, visible transitions, matching settings, and an honest breakdown of cost. Just let the footage speak, people can usually tell within a few seconds anyway. What I actually took away from this Both models have real strengths, I'm not saying one does everything better. But for the kind of work I do, including ad and e-commerce stuff, HappyHorse 1.1 just needed fewer compromises from me. Less regenerating shots, less fighting continuity issues, less trying to wrangle the camera back on track. Doesn't mean Kling 3.0 is bad, it's a solid model. It just means HappyHorse 1.1 got me to something production-ready faster, with less wasted time. And at the end of the day that's the thing I actually care about. p.s. links to try HappyHorse 1.1 and the community Discord are in the first reply below.

Chubbyโ™จ๏ธ

19,569 gรถrรผntรผleme โ€ข 1 ay รถnce

Bill Ackman just told Tony Robbins the advice that will save (or sink) most investors and referenced the memory-chip mania in doing so: โ€œDonโ€™t get attracted to where thereโ€™s always a super exciting place in the market where someone seems to be making a lot of moneyโ€ฆ this FOMO thing about โ€˜I want to be in memory chips because thatโ€™s where all the moneyโ€™s being made.โ€™ Instead, think about a business that you can envision being bigger and more profitable 10, 20, 30 years from now. Buy that company and own it.โ€ ___ 1๏ธโƒฃ There will always be something hotter. ALWAYS. Memory chips today. Crypto last cycle. SPACs before that. The next thing after that. Someone will always be posting a screenshot that makes your portfolio look slow. Thatโ€™s a cost you pay for being human in a highly connected world. As Naval Ravikant once said: โ€œComparison is the thief of joy.โ€ In investing, comparison is the thief of self-control. We are trainedโ€”by feeds, by group chats, by โ€œYTDโ€ graphics, by lifestyle posts, travel reels, house tours, and โ€œjust bought the dipโ€ flexesโ€”to measure our own compounding against someone elseโ€™s highlight reel. The moment you start doing that, you stop thinking like an owner and start thinking like a spectator who needs the next hit. Navalโ€™s definition of happiness is useful here: a state of peace and contentment that nothing is missing. The portfolio that actually gets you there is the one you can sit with for a decade-plus because you understand the business, you believe in the businesses and destination, and the expected return meets your required rate. Not because it is the thing making the most money this quarter. Stick to a philosophy you actually believe in and ENJOY the ride as an owner. 2๏ธโƒฃ The second half of Ackmanโ€™s answer is the positive version of the same idea. Find a business you can see being larger and more profitable in 10, 20, or 30 years. Buy it. Own it. His father-in-law was not a professional investor. He bought Boeing, Philip Morris, and a handful of other iconic, dominant companies decades ago and simply never sold. When he departed he had an extraordinary collection of businesses, the benefit of decades of tax deferral, and a remarkable long-term record. He took the long view and let time do the work. This is the same insight Nick Sleep and Chris Hohn arrived at from different directions. Sleep hunted โ€œcompounding machinesโ€โ€”businesses that can reinvest at high incremental returns for a very long time and that get better as they get bigger (scale economics shared). He said the edge is not intelligence; it is patience. The crowd is not patient. Most people trade at the short end of the equity yield curve. The real money is made at the long end, where almost no one else is competing. Once you find the business, the hardest part is leaving it alone. Hohn is even more explicit: โ€œLong-termism in a great company is a free lunch.โ€ Sell-side models stop at two or three years because that is the horizon of most professional money. If a business can still be excellent in 20 or 30 years, the market systematically undervalues it. Good companies tend to stay good. His average holding period is eight years and some names have been there more than a decade. He estimates only about 200 companies in the world meet the quality bar. Time-horizon arbitrage only works if you were right on quality and barriers to entry in the first place. FOMO will never go away. The feed will always show you someone making more, faster, in the thing that is currently on fire. But that is not a signal. It is a test of whether you actually have a process or just a mood. Stop comparing. Stop treating other peopleโ€™s returns as a referendum on your own. Focus on the businesses you can own for a decade-plus. Enjoy the ride as an owner. That is how ordinary people quietly build extraordinary wealth and an extraordinary collection of companies.โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹ ___ ๐ŸŽ™๏ธ YouTube CAZ & Tony Robbins
1:33

Bill Ackman just told Tony Robbins the advice that will save (or sink) most investors and referenced the memory-chip mania in doing so: โ€œDonโ€™t get attracted to where thereโ€™s always a super exciting place in the market where someone seems to be making a lot of moneyโ€ฆ this FOMO thing about โ€˜I want to be in memory chips because thatโ€™s where all the moneyโ€™s being made.โ€™ Instead, think about a business that you can envision being bigger and more profitable 10, 20, 30 years from now. Buy that company and own it.โ€ ___ 1๏ธโƒฃ There will always be something hotter. ALWAYS. Memory chips today. Crypto last cycle. SPACs before that. The next thing after that. Someone will always be posting a screenshot that makes your portfolio look slow. Thatโ€™s a cost you pay for being human in a highly connected world. As Naval Ravikant once said: โ€œComparison is the thief of joy.โ€ In investing, comparison is the thief of self-control. We are trainedโ€”by feeds, by group chats, by โ€œYTDโ€ graphics, by lifestyle posts, travel reels, house tours, and โ€œjust bought the dipโ€ flexesโ€”to measure our own compounding against someone elseโ€™s highlight reel. The moment you start doing that, you stop thinking like an owner and start thinking like a spectator who needs the next hit. Navalโ€™s definition of happiness is useful here: a state of peace and contentment that nothing is missing. The portfolio that actually gets you there is the one you can sit with for a decade-plus because you understand the business, you believe in the businesses and destination, and the expected return meets your required rate. Not because it is the thing making the most money this quarter. Stick to a philosophy you actually believe in and ENJOY the ride as an owner. 2๏ธโƒฃ The second half of Ackmanโ€™s answer is the positive version of the same idea. Find a business you can see being larger and more profitable in 10, 20, or 30 years. Buy it. Own it. His father-in-law was not a professional investor. He bought Boeing, Philip Morris, and a handful of other iconic, dominant companies decades ago and simply never sold. When he departed he had an extraordinary collection of businesses, the benefit of decades of tax deferral, and a remarkable long-term record. He took the long view and let time do the work. This is the same insight Nick Sleep and Chris Hohn arrived at from different directions. Sleep hunted โ€œcompounding machinesโ€โ€”businesses that can reinvest at high incremental returns for a very long time and that get better as they get bigger (scale economics shared). He said the edge is not intelligence; it is patience. The crowd is not patient. Most people trade at the short end of the equity yield curve. The real money is made at the long end, where almost no one else is competing. Once you find the business, the hardest part is leaving it alone. Hohn is even more explicit: โ€œLong-termism in a great company is a free lunch.โ€ Sell-side models stop at two or three years because that is the horizon of most professional money. If a business can still be excellent in 20 or 30 years, the market systematically undervalues it. Good companies tend to stay good. His average holding period is eight years and some names have been there more than a decade. He estimates only about 200 companies in the world meet the quality bar. Time-horizon arbitrage only works if you were right on quality and barriers to entry in the first place. FOMO will never go away. The feed will always show you someone making more, faster, in the thing that is currently on fire. But that is not a signal. It is a test of whether you actually have a process or just a mood. Stop comparing. Stop treating other peopleโ€™s returns as a referendum on your own. Focus on the businesses you can own for a decade-plus. Enjoy the ride as an owner. That is how ordinary people quietly build extraordinary wealth and an extraordinary collection of companies.โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹โ€‹ ___ ๐ŸŽ™๏ธ YouTube CAZ & Tony Robbins

Dimitry Nakhla | Babylon Capitalยฎ

46,926 gรถrรผntรผleme โ€ข 1 ay รถnce

I've spent hours and hours thinking about how AI is going to change writing. This is a 90-minute distillation of everything I've learned. Some things I believe: 1. The combination of LLM-driven humor and image generation means that we're about to enter the golden age of memes. 2. The best writers will be fine. Robert Caro and Dostoevsky arenโ€™t about to be disrupted by ChatGPT. 3. What are the different models like? ChatGPT is your friend who makes a lot of good points, but itโ€™s kinda boring, Claude is your hippie friend who loves to get vulnerable but takes the whole โ€œexpress yourselfโ€ thing a little too far, and Grok is your unhinged friend who leans a little too hard into tinfoil hat theories, but is always a trip to jam on ideas with. 4. People who say that AI writing is low-quality arenโ€™t realizing that quality exists along two dimensions: (1) the absolute quality of the writing and (2) how tailored the writing is to your interests at the time. 5. Iโ€™ll tell you this: What writers are doing with AI behind closed doors is a long way ahead of what's publicly understood. I don't expect this to change anytime soon because of the social stigma associated with AI-enhanced writing. Because of that, if you want to see the cutting edge, you're gonna have to piece things together through private conversations and group chats. 6. If you want to follow what's happening in AI, remember this quote from William Gibson: โ€œThe future is here, itโ€™s just not evenly distributed yet.โ€ You can get a glimpse of the future by looking at how a small percentage of writers are already using AI. 7. Iโ€™m bearish on writers who are currently using AI to write for them, and bullish on writers who are currently using AI to write with them. 8. What kinds of writing will continue to be written by humans? Ones that speak to our humanity. People are interested in people. Their stories, their struggles, their emotions, their drama. 9. Almost all utilitarian writing, where the goal is to convey information, not do it beautifully, will be written by AI. 10. In some ways, AI is the end of slop. So many Google search results are slop. LinkedIn posts are slop. The way Twitter got taken over by Threadbois in 2021 was also slop. AI-generated writing is already better than all of those things, so why would you read them now? 11. AI will be tougher on writers than readers. Readers will be exposed to some slop, but the Internet will be good about filtering it out. Writers, though, are now competing against ever-improving LLMs, which are getting better and better by the month. 12. Humans will contribute with unique data or perspectives. The famous Peter Thiel interview question doubles as a good writing prompt: โ€œWhat very important truth do few people agree with you on?โ€ 13. New technologies breed new kinds of art. Ever notice how flat 13th or 14th century Medieval art looks? And how different that art looks from the Renaissance art created in the 15th and 16th centuries? Technical innovations like the camera obscura and perspective grids are behind this. Similarly profound changes will come to the writing world because of AI (credit to Justin Murphy for the idea here). 14. Satya Nadella says: โ€œThe new workflow for me is I think with AI and work with my colleagues.โ€ When it comes to discovering ideas, I've also found that jamming with an LLM is more productive than doing it with most people I know (save for a few giga-brain conversationalists). 15. Thought experiment: Will AI-writing be more like music or chess? With music, we don't care how a song is made. We just want it to be good. With chess, there's a huge market for watching human beings play even though the computers are already better. I think non-fiction writing will go the way of music. People wonโ€™t care how it was made. Theyโ€™ll just care that itโ€™s good. 16. AI has flipped the rules of tech adoption. Seasoned managers usually drag their feet with adopting new technology, but the ones I know love AI, while frontline workers struggle to see the point. My theory is that AI matches how managers already operate. Management has always been a kind of prompt engineering: set a vision, delegate, give feedback, iterate. But LLMs remove the drama that used to come with having a team. No 1-on-1s. No emotional tangles. It's like management without the headache. For frontline employees, things are different. They aren't as accustomed to setting a vision and giving feedback, so LLM prompting is a daunting and unfamiliar kind of work for them. 17. AI editors are already quite good. Sure, they arenโ€™t as good as the worldโ€™s best editors, but theyโ€™re a fraction of the cost, theyโ€™ll instantly give you 80th percentile feedback, and they work 24/7. As a novelist recently said to me: โ€œPaying an editor to review my novel costs me $7,000 and a 4-6 week turnaround time, whereas Claude costs me $1.25 and gets me results a few minutes later.โ€ The edits definitely arenโ€™t as good, but thereโ€™s a virtue to speed (and this guy isnโ€™t a chump writer). 18. The way AI-skeptics hate on LLMs while using old models is like driving a โ€˜92 Honda while hating on a self-driving Tesla. 19. AI-generated fiction makes people very upset. A friend insists itโ€™s like having sex with a robot. Doesnโ€™t matter how good it is. It ainโ€™t human-generated, and thereโ€™s something uniquely repulsive about that. Iโ€™ve shared the full conversation below. Itโ€™s a solo-episode of me riffing on what Iโ€™ve learned about AI for ~90 minutes. If youโ€™d rather watch it on YouTube or listen on Apple or Spotify, Iโ€™ve shared the links in the reply tweets. And if you have any questions, Iโ€™ll be extra active in the replies for this episode.
1:29:31

I've spent hours and hours thinking about how AI is going to change writing. This is a 90-minute distillation of everything I've learned. Some things I believe: 1. The combination of LLM-driven humor and image generation means that we're about to enter the golden age of memes. 2. The best writers will be fine. Robert Caro and Dostoevsky arenโ€™t about to be disrupted by ChatGPT. 3. What are the different models like? ChatGPT is your friend who makes a lot of good points, but itโ€™s kinda boring, Claude is your hippie friend who loves to get vulnerable but takes the whole โ€œexpress yourselfโ€ thing a little too far, and Grok is your unhinged friend who leans a little too hard into tinfoil hat theories, but is always a trip to jam on ideas with. 4. People who say that AI writing is low-quality arenโ€™t realizing that quality exists along two dimensions: (1) the absolute quality of the writing and (2) how tailored the writing is to your interests at the time. 5. Iโ€™ll tell you this: What writers are doing with AI behind closed doors is a long way ahead of what's publicly understood. I don't expect this to change anytime soon because of the social stigma associated with AI-enhanced writing. Because of that, if you want to see the cutting edge, you're gonna have to piece things together through private conversations and group chats. 6. If you want to follow what's happening in AI, remember this quote from William Gibson: โ€œThe future is here, itโ€™s just not evenly distributed yet.โ€ You can get a glimpse of the future by looking at how a small percentage of writers are already using AI. 7. Iโ€™m bearish on writers who are currently using AI to write for them, and bullish on writers who are currently using AI to write with them. 8. What kinds of writing will continue to be written by humans? Ones that speak to our humanity. People are interested in people. Their stories, their struggles, their emotions, their drama. 9. Almost all utilitarian writing, where the goal is to convey information, not do it beautifully, will be written by AI. 10. In some ways, AI is the end of slop. So many Google search results are slop. LinkedIn posts are slop. The way Twitter got taken over by Threadbois in 2021 was also slop. AI-generated writing is already better than all of those things, so why would you read them now? 11. AI will be tougher on writers than readers. Readers will be exposed to some slop, but the Internet will be good about filtering it out. Writers, though, are now competing against ever-improving LLMs, which are getting better and better by the month. 12. Humans will contribute with unique data or perspectives. The famous Peter Thiel interview question doubles as a good writing prompt: โ€œWhat very important truth do few people agree with you on?โ€ 13. New technologies breed new kinds of art. Ever notice how flat 13th or 14th century Medieval art looks? And how different that art looks from the Renaissance art created in the 15th and 16th centuries? Technical innovations like the camera obscura and perspective grids are behind this. Similarly profound changes will come to the writing world because of AI (credit to Justin Murphy for the idea here). 14. Satya Nadella says: โ€œThe new workflow for me is I think with AI and work with my colleagues.โ€ When it comes to discovering ideas, I've also found that jamming with an LLM is more productive than doing it with most people I know (save for a few giga-brain conversationalists). 15. Thought experiment: Will AI-writing be more like music or chess? With music, we don't care how a song is made. We just want it to be good. With chess, there's a huge market for watching human beings play even though the computers are already better. I think non-fiction writing will go the way of music. People wonโ€™t care how it was made. Theyโ€™ll just care that itโ€™s good. 16. AI has flipped the rules of tech adoption. Seasoned managers usually drag their feet with adopting new technology, but the ones I know love AI, while frontline workers struggle to see the point. My theory is that AI matches how managers already operate. Management has always been a kind of prompt engineering: set a vision, delegate, give feedback, iterate. But LLMs remove the drama that used to come with having a team. No 1-on-1s. No emotional tangles. It's like management without the headache. For frontline employees, things are different. They aren't as accustomed to setting a vision and giving feedback, so LLM prompting is a daunting and unfamiliar kind of work for them. 17. AI editors are already quite good. Sure, they arenโ€™t as good as the worldโ€™s best editors, but theyโ€™re a fraction of the cost, theyโ€™ll instantly give you 80th percentile feedback, and they work 24/7. As a novelist recently said to me: โ€œPaying an editor to review my novel costs me $7,000 and a 4-6 week turnaround time, whereas Claude costs me $1.25 and gets me results a few minutes later.โ€ The edits definitely arenโ€™t as good, but thereโ€™s a virtue to speed (and this guy isnโ€™t a chump writer). 18. The way AI-skeptics hate on LLMs while using old models is like driving a โ€˜92 Honda while hating on a self-driving Tesla. 19. AI-generated fiction makes people very upset. A friend insists itโ€™s like having sex with a robot. Doesnโ€™t matter how good it is. It ainโ€™t human-generated, and thereโ€™s something uniquely repulsive about that. Iโ€™ve shared the full conversation below. Itโ€™s a solo-episode of me riffing on what Iโ€™ve learned about AI for ~90 minutes. If youโ€™d rather watch it on YouTube or listen on Apple or Spotify, Iโ€™ve shared the links in the reply tweets. And if you have any questions, Iโ€™ll be extra active in the replies for this episode.

David Perell

257,480 gรถrรผntรผleme โ€ข 1 yฤฑl รถnce

Here's a devlog made by an anonymous Chinese fan replicating the surprisingly brand new technique that I developed for detecting asteroids which wound up being so powerful that it can easily track Stealth Fighters from over 100km away even when itโ€™s only using three $30 webcams as sensors meaning it easily outperforms all modern stealth tracking techniques in precision, range and cost. And while this demo is using optical light, this same technique which I call pixel motion to voxel projection, can be used interchangeably with thermal infrared cameras to work at night and also majorly boosts the effectiveness of radar allowing you to track fighters much more effectively through clouds and over the horizon. This technique will also always eventually give the exact location of the target even if the image is blurry as those blurs will always average out from the different perspectives into revealing the precise location of the target in the voxel grid. There is definitely a Mandela effect with this technique as it feels as though it should already exist, especially because at first as it sounds like it is performing triangulation (which has existed for years and is what we do for mocap and tennis ball tracking). But triangulation is entirely separate to this as triangulations only works if you have already identified where the ball is in a 2D image because youโ€™re able to rely on being able to use at least 2 separate high quality cameras which are much closer to the ball making the ballโ€™s apparent size much much bigger and therefore gives you hundreds of pixels to work with which makes it much easier to use object recognition techniques to recognize where it is in the image aka in 2D and then youโ€™re just using the other cameras view to project out lines which intersect in 3D to find out where the ball is in 3D. The major difference is that pixel motion to voxel projection allows you to find where the object is in 3D without having already found it in 2D which is an unbelievable difference as it allows you to use much lower quality cameras together to accumulate data together into 3D space. If this seem like it doesnโ€™t mean much then what it actually means is that you donโ€™t understand what Iโ€™m saying as what Iโ€™m saying means a LOT in practical terms as it means you go from having to use an imaging system that has to be able to image the object to the point that it is over a hundred total pixels in surface area to have enough data to recognize it to instead be able to use something that is only images the object to be 1 pixel in surface area and only changes the brightness value by 1 value every now and then. Iโ€™d recommend an amazing video by DST studios called โ€œLowlight cameras canโ€™t defeat stealthโ€ if you want a great video which goes over the difficulty of even using telescopes to recognize stealth fighters and why this is so impressive compared to other techniques and ironically it is what inspired me to realize the asteroid tracker I was working on actually could do this. Which brings me to the point that if this wasnโ€™t a new technique then not only would there be at least one example of an asteroid survey that points distant telescopes at the same place at the same time in order to be able to add the light together to detect asteroids which as I was shocked to learn isnโ€™t a thing despite the fact that it would make detecting asteroids trivial by comparison to modern 2D imaging while also having no impact on the normal scientific operations of those surveys other than small changes to scheduling. But there would also be an example of a drone tracker that uses this instead of using the aforementioned high quality zoomable telescope which has to be able to zoom in close enough to be able to recognize a drone. If you want to tell me that this is something that already exists give me an exact example of a product that uses it, not the general outline of a concept that you think it is, the actual product and then also tell me the asteroid survey that uses distant telescopes that point at the exact same place at the exact same time because I can guarantee that if you google what you think uses this you wonโ€™t even find the steps of subtracting the images from each other to get motion and will definitely not get the added step of projecting that motion into a voxel grid (It would blow your mind if you found out how Xbox kinect cameras work.) Also I want to make it clear, Iโ€™m not saying you should just use web cams to do this, Iโ€™m just using them as an example to show you the power of this in reality you would probably want to use 5 high quality zoomable thermal cameras which pan across the sky in sync with each other which due to using lower frequency are much less prone to the Rayleigh scattering that scatters visible light at 150 or so km away and again, you can also use this to majorly upgrade radar. Pretty much all of the problems you could think of for this are incredibly easy to overcome if you apply even a small amount of brainpower into fixing the problem. And yes, this gives you the exact location down to the meter of whatever you are tracking even if the image is blurry as those blurs will always average out to the exact location down to the meter in the voxel grid. Which is what makes this technique so powerful since the cost of adding each camera to The network grows linearly while the rate at which each camera gives more information grows exponentially due to the increasing unlikeliness of all of them having more movement in the same place. And given the size of the cameras it really wouldnโ€™t be that hard to hide and network these cameras together in other countries and on sea buoys to know where planes are everywhere in the world. Which brings me to the point that I personally really donโ€™t care about the military uses of this technology, if all it could do is precisely track stealth fighters then I wouldnโ€™t have cared enough to work on it, I could have used any of the many other life saving techniques as the subject of the video, stealth fighters just sounds the most clickable and the scale of the problem is more intuitive to most people and if I did use any of those as subjects for the demo it would inevitably result in the stealth fighter technique being figured out anyway and all of the other uses are so useful that I don't think anyone would reasonably complain about the upside. The real purpose of this video is that since this is a new technique that hasnโ€™t been used to detect stealth fighters despite the billions we have spent on that, then what else can you apply this to that could go on to improve billions of peopleโ€™s lives that you or others are working on. For example this also allows you to majorly improve the effectiveness of cryo electron microscopy and CT scanners. This part also is kind of hard to explain as it also sounds like it exists but again, when you look through all of the places where you think it is being used you will find that it wasnโ€™t. What Iโ€™m saying here isnโ€™t that this is a Radon transform or gaussian splat or whatever, Iโ€™m saying that this is able to get new information that wasnโ€™t being accessed before due to the added information about depth you get from the correlation of movement between each perspective which adds to the information that you already have. This allows you to directly subtract foreground and background objects as well as noise faster than you would be able to before and works better than super resolution for your images since super resolution wonโ€™t remove foreground and background objects like this does and instead just scales up target, foreground and background objects indiscriminately. And while with enough data Radon transforms or other scanning techniques would eventually get you a correct answer this will get you there a lot faster since those are mostly averaging techniques which average out noise whereas this gets you the ability to directly subtract noise. Iโ€™m not expecting you to think that this would do anything but if you try it for yourself you will find that it does majorly improve your ability to perform 3d scans. Again, cryo EM is a field where you would expect this technique to exist but when you look through all the papers on the topic there is no mention of tilting the grid slightly in order to be able to change your perspective slightly on the order of the feature size (if you tilt the grid then you only need precision on the order of an arc minute to do this) and doing multiple exposures from multiple different known tilts and then using those difference images to correlate depth from motion. In fact, in cryo EM you would normally want to do the opposite of this and have your exposures all taken from the same grid angle and just use the variations in how many of the same proteins are oriented in order to be able to scan them for a 3D model but this will generate you far more data faster. There is so much information that I canโ€™t really explain in text so if you have any questions such as why this hasnโ€™t been made before then they will most likely be answered in the video I originally posted which I have added to the end of the first Devlog for your convenience. And again, pretty much all of the problems with the technique can be fixed with a little bit of brainpower, in reality you would probably want to use 5 high quality zoomable thermal cameras which pan across the sky in sync with each other which due to using lower frequency are much less prone to the Rayleigh scattering that scatters visible light at 150 or so km away and again, you can also use this to majorly upgrade radar.
18:48

Here's a devlog made by an anonymous Chinese fan replicating the surprisingly brand new technique that I developed for detecting asteroids which wound up being so powerful that it can easily track Stealth Fighters from over 100km away even when itโ€™s only using three $30 webcams as sensors meaning it easily outperforms all modern stealth tracking techniques in precision, range and cost. And while this demo is using optical light, this same technique which I call pixel motion to voxel projection, can be used interchangeably with thermal infrared cameras to work at night and also majorly boosts the effectiveness of radar allowing you to track fighters much more effectively through clouds and over the horizon. This technique will also always eventually give the exact location of the target even if the image is blurry as those blurs will always average out from the different perspectives into revealing the precise location of the target in the voxel grid. There is definitely a Mandela effect with this technique as it feels as though it should already exist, especially because at first as it sounds like it is performing triangulation (which has existed for years and is what we do for mocap and tennis ball tracking). But triangulation is entirely separate to this as triangulations only works if you have already identified where the ball is in a 2D image because youโ€™re able to rely on being able to use at least 2 separate high quality cameras which are much closer to the ball making the ballโ€™s apparent size much much bigger and therefore gives you hundreds of pixels to work with which makes it much easier to use object recognition techniques to recognize where it is in the image aka in 2D and then youโ€™re just using the other cameras view to project out lines which intersect in 3D to find out where the ball is in 3D. The major difference is that pixel motion to voxel projection allows you to find where the object is in 3D without having already found it in 2D which is an unbelievable difference as it allows you to use much lower quality cameras together to accumulate data together into 3D space. If this seem like it doesnโ€™t mean much then what it actually means is that you donโ€™t understand what Iโ€™m saying as what Iโ€™m saying means a LOT in practical terms as it means you go from having to use an imaging system that has to be able to image the object to the point that it is over a hundred total pixels in surface area to have enough data to recognize it to instead be able to use something that is only images the object to be 1 pixel in surface area and only changes the brightness value by 1 value every now and then. Iโ€™d recommend an amazing video by DST studios called โ€œLowlight cameras canโ€™t defeat stealthโ€ if you want a great video which goes over the difficulty of even using telescopes to recognize stealth fighters and why this is so impressive compared to other techniques and ironically it is what inspired me to realize the asteroid tracker I was working on actually could do this. Which brings me to the point that if this wasnโ€™t a new technique then not only would there be at least one example of an asteroid survey that points distant telescopes at the same place at the same time in order to be able to add the light together to detect asteroids which as I was shocked to learn isnโ€™t a thing despite the fact that it would make detecting asteroids trivial by comparison to modern 2D imaging while also having no impact on the normal scientific operations of those surveys other than small changes to scheduling. But there would also be an example of a drone tracker that uses this instead of using the aforementioned high quality zoomable telescope which has to be able to zoom in close enough to be able to recognize a drone. If you want to tell me that this is something that already exists give me an exact example of a product that uses it, not the general outline of a concept that you think it is, the actual product and then also tell me the asteroid survey that uses distant telescopes that point at the exact same place at the exact same time because I can guarantee that if you google what you think uses this you wonโ€™t even find the steps of subtracting the images from each other to get motion and will definitely not get the added step of projecting that motion into a voxel grid (It would blow your mind if you found out how Xbox kinect cameras work.) Also I want to make it clear, Iโ€™m not saying you should just use web cams to do this, Iโ€™m just using them as an example to show you the power of this in reality you would probably want to use 5 high quality zoomable thermal cameras which pan across the sky in sync with each other which due to using lower frequency are much less prone to the Rayleigh scattering that scatters visible light at 150 or so km away and again, you can also use this to majorly upgrade radar. Pretty much all of the problems you could think of for this are incredibly easy to overcome if you apply even a small amount of brainpower into fixing the problem. And yes, this gives you the exact location down to the meter of whatever you are tracking even if the image is blurry as those blurs will always average out to the exact location down to the meter in the voxel grid. Which is what makes this technique so powerful since the cost of adding each camera to The network grows linearly while the rate at which each camera gives more information grows exponentially due to the increasing unlikeliness of all of them having more movement in the same place. And given the size of the cameras it really wouldnโ€™t be that hard to hide and network these cameras together in other countries and on sea buoys to know where planes are everywhere in the world. Which brings me to the point that I personally really donโ€™t care about the military uses of this technology, if all it could do is precisely track stealth fighters then I wouldnโ€™t have cared enough to work on it, I could have used any of the many other life saving techniques as the subject of the video, stealth fighters just sounds the most clickable and the scale of the problem is more intuitive to most people and if I did use any of those as subjects for the demo it would inevitably result in the stealth fighter technique being figured out anyway and all of the other uses are so useful that I don't think anyone would reasonably complain about the upside. The real purpose of this video is that since this is a new technique that hasnโ€™t been used to detect stealth fighters despite the billions we have spent on that, then what else can you apply this to that could go on to improve billions of peopleโ€™s lives that you or others are working on. For example this also allows you to majorly improve the effectiveness of cryo electron microscopy and CT scanners. This part also is kind of hard to explain as it also sounds like it exists but again, when you look through all of the places where you think it is being used you will find that it wasnโ€™t. What Iโ€™m saying here isnโ€™t that this is a Radon transform or gaussian splat or whatever, Iโ€™m saying that this is able to get new information that wasnโ€™t being accessed before due to the added information about depth you get from the correlation of movement between each perspective which adds to the information that you already have. This allows you to directly subtract foreground and background objects as well as noise faster than you would be able to before and works better than super resolution for your images since super resolution wonโ€™t remove foreground and background objects like this does and instead just scales up target, foreground and background objects indiscriminately. And while with enough data Radon transforms or other scanning techniques would eventually get you a correct answer this will get you there a lot faster since those are mostly averaging techniques which average out noise whereas this gets you the ability to directly subtract noise. Iโ€™m not expecting you to think that this would do anything but if you try it for yourself you will find that it does majorly improve your ability to perform 3d scans. Again, cryo EM is a field where you would expect this technique to exist but when you look through all the papers on the topic there is no mention of tilting the grid slightly in order to be able to change your perspective slightly on the order of the feature size (if you tilt the grid then you only need precision on the order of an arc minute to do this) and doing multiple exposures from multiple different known tilts and then using those difference images to correlate depth from motion. In fact, in cryo EM you would normally want to do the opposite of this and have your exposures all taken from the same grid angle and just use the variations in how many of the same proteins are oriented in order to be able to scan them for a 3D model but this will generate you far more data faster. There is so much information that I canโ€™t really explain in text so if you have any questions such as why this hasnโ€™t been made before then they will most likely be answered in the video I originally posted which I have added to the end of the first Devlog for your convenience. And again, pretty much all of the problems with the technique can be fixed with a little bit of brainpower, in reality you would probably want to use 5 high quality zoomable thermal cameras which pan across the sky in sync with each other which due to using lower frequency are much less prone to the Rayleigh scattering that scatters visible light at 150 or so km away and again, you can also use this to majorly upgrade radar.

ConsistentlyInconsistent

50,966 gรถrรผntรผleme โ€ข 1 yฤฑl รถnce