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𝗣𝗼𝗽𝘂𝗹𝗮𝗿 𝗼𝗽𝗶𝗻𝗶𝗼𝗻: "𝗪𝗲 𝗻𝗲𝗲𝗱 𝗺𝗼𝗿𝗲 𝗱𝗮𝘁𝗮." 𝗔𝗰𝘁𝘂𝗮𝗹 𝗿𝗲𝗮𝗹𝗶𝘁𝘆: 𝟭𝟬𝟬 𝗴𝗿𝗲𝗮𝘁 𝗱𝗲𝗺𝗼𝘀 > 𝟭𝟬,𝟬𝟬𝟬 𝗺𝗲𝗱𝗶𝗼𝗰𝗿𝗲 𝗼𝗻𝗲𝘀. 𝗧𝗵𝗲 𝗺𝘆𝘁𝗵: More demonstrations always mean better models. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹𝗶𝘁𝘆: 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... motions (not rushed or hesitant) - Consistent task execution strategy across demos - Clear success/failure outcomes with proper labeling - Representative of actual deployment conditions - Performed by skilled demonstrators who understand the task What kills demonstration quality: - Human fatigue after 50+ consecutive demos - Time pressure ("let's just get this data collected") - Inconsistent demonstrators with different techniques - Collecting in unrealistic lab conditions - No real-time feedback on demonstration quality Unlike computer vision where you can throw away bad images easily, 𝗿𝗼𝗯𝗼𝘁 𝗱𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗿𝗲 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲 𝗮𝗻𝗱 𝗵𝗮𝗿𝗱 𝘁𝗼 𝗿𝗲𝗽𝗹𝗮𝗰𝗲. Every single one needs to count. 𝗗𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗶𝘀 𝗮 𝗱𝗮𝘁𝗮 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆, 𝗻𝗼𝘁 𝗮 𝗰𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗮𝗳𝘁𝗲𝗿𝘁𝗵𝗼𝘂𝗴𝗵𝘁. What's your ratio of "data collected" vs "data actually used for training"?show more

Stephen James

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35,147 次观看 • 11 个月前 •via X (Twitter)

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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 → 98.5% average success > 86.6% zero-shot accuracy in active disturbance environments > 47.4 on VLABench → 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?

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 → 98.5% average success > 86.6% zero-shot accuracy in active disturbance environments > 47.4 on VLABench → 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 次观看 • 3 个月前

$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 $60 trillion per year in aggregate, and a lion's share of that compensation goes to physical labor - tasks that require navigating real spaces, manipulating real objects, and negotiating the infinite variability of the physical world. Human-to-robot transfer will be one of the most important infrastructures that will shape our society in the near future, and if it works, the economic impact will dwarf every technology transition that came before it in an exponential manner and lead to the creation of goods and services we can’t imagine today. Our mission is to lay the groundwork for us to transition into this future - the future of abundance. We are deeply grateful to our earliest believers, Paras Chopra and Lossfunk, who played a critical role in shaping our thinking.$

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 can’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

81,285 次观看 • 3 个月前

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. That’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 don’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.

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. That’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 don’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

67,789 次观看 • 1 年前

$The U.S. MUST win the AI race We’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 actors—who will naturally prioritize expert tasks aligned with their own interests—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. Here’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 DeepSeek’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. 🇺🇸$

The U.S. MUST win the AI race We’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 actors—who will naturally prioritize expert tasks aligned with their own interests—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. Here’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 DeepSeek’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. 🇺🇸

Ali Ansari

395,197 次观看 • 5 个月前

🚀 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 — 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 — data, forecasting, and infrastructure — but they all share the same vision: decentralization, community ownership, and sustainable value creation. Let’s take a deeper look 👇 🧠 Perceptron Network The world’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 isn’t experimental AI infrastructure — 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 — accelerating adoption, partnerships, and ecosystem growth. 🌌 intodotspace The first 10× 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: 10× leveraged predictions Ultra-fast execution on Solana Deep liquidity and composable market design The market’s confidence is already clear — 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 “truth machines.” intodotspace upgrades them into high-signal, high-efficiency forecasting layers — 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. 🌐 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. DeepNode’s core philosophy: “Own what you build — or someone else will.” This is more than infrastructure. It’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 — not just Big Tech. 🤝 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 don’t compete — 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. 🔥 The future of AI is open, composable, and community-owned — and it’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? 👇 Share your thoughts and join the conversation.

🚀 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 — 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 — data, forecasting, and infrastructure — but they all share the same vision: decentralization, community ownership, and sustainable value creation. Let’s take a deeper look 👇 🧠 Perceptron Network The world’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 isn’t experimental AI infrastructure — 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 — accelerating adoption, partnerships, and ecosystem growth. 🌌 intodotspace The first 10× 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: 10× leveraged predictions Ultra-fast execution on Solana Deep liquidity and composable market design The market’s confidence is already clear — 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 “truth machines.” intodotspace upgrades them into high-signal, high-efficiency forecasting layers — 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. 🌐 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. DeepNode’s core philosophy: “Own what you build — or someone else will.” This is more than infrastructure. It’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 — not just Big Tech. 🤝 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 don’t compete — 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. 🔥 The future of AI is open, composable, and community-owned — and it’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? 👇 Share your thoughts and join the conversation.

Hồng Ngọc | Ruby💎

12,837 次观看 • 5 个月前

Hell froze over: announcing FormKit for React. Secretly framework-agnostic since inception, today we’re open sourcing the most popular Vue form library…for 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 aren’t great at that. It’s table stakes for FormKit. 2. Single component. This matters more than you would think, but FormKit doesn’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 form’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 “FormKit” it immediately knows “oh, that’s part of the form’s data”. 3. No plumbing. FormKit doesn’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. FormKit’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. FormKit’s own inputs are not written using Vue or React — instead, FormKit has its own render schema — think of it like an AST for the DOM — and you can modify it on the fly. It’s not very human-friendly to write, but it turns out most models are already pretty well trained on FormKit’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 doesn’t just collect data, it also passes down configuration and plugins. Want one form to work a bit differently than another one? No problem — 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 wasn’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 — a completely framework-agnostic form framework that happens to unlock your coding agents. ➡️

Hell froze over: announcing FormKit for React. Secretly framework-agnostic since inception, today we’re open sourcing the most popular Vue form library…for 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 aren’t great at that. It’s table stakes for FormKit. 2. Single component. This matters more than you would think, but FormKit doesn’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 form’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 “FormKit” it immediately knows “oh, that’s part of the form’s data”. 3. No plumbing. FormKit doesn’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. FormKit’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. FormKit’s own inputs are not written using Vue or React — instead, FormKit has its own render schema — think of it like an AST for the DOM — and you can modify it on the fly. It’s not very human-friendly to write, but it turns out most models are already pretty well trained on FormKit’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 doesn’t just collect data, it also passes down configuration and plugins. Want one form to work a bit differently than another one? No problem — 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 wasn’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 — a completely framework-agnostic form framework that happens to unlock your coding agents. ➡️

Justin Schroeder

11,549 次观看 • 3 个月前

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

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

GREG ISENBERG

193,095 次观看 • 3 个月前

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, I’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 😆) - 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, I’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 Internet😄 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.

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, I’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 😆) - 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, I’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 Internet😄 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

179,129 次观看 • 2 年前

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 doesn’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 I’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, it’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 don’t fix it . You start from the first principles concept that I’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 it’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 you’re not an AI expert, you would likely already understand what I’m saying. If you are an AI expert, you will already have been discounting what I’m saying because it’s not in the current mindset that’s fashionable today. Yet the employees that I talk to at anthropic already understand what I’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 I’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.

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 doesn’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 I’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, it’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 don’t fix it . You start from the first principles concept that I’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 it’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 you’re not an AI expert, you would likely already understand what I’m saying. If you are an AI expert, you will already have been discounting what I’m saying because it’s not in the current mindset that’s fashionable today. Yet the employees that I talk to at anthropic already understand what I’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 I’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 次观看 • 8 个月前

Brett Adcock, Figure CEO joined our 8 hour(!) live stream where a bunch of us were bird dogging and discussing Figure’s own 8 hour livestream showing a F.03 bot doing a logistics task completely autonomously including shift changes between bots. Here’s my summary of Brett’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 didn’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 they’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 Figure’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 they’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 “Apple” 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 can’t do something a human demonstrates, then you’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 - that’s how complicated it is. Brett’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

33,861 次观看 • 2 个月前

"I'm not sure that we need the dog whistle at this point. And maybe there are ways to re-create it." ~Nolan "There might be a day when Skywatcher doesn't need to exist." ~Nolan "If I had been running the whole show, nobody would even know what Skywatcher is right now." ~Nolan ~My comments in ( )~ Garry P. Nolan: "We've got a lot of data from multiple alleged sightings, both radar and other kinds of data. First it was about getting the raw-data files all put in one place because some of the data was collected by James (Fowler) before there was, officially, kind of a Skywatcher. "And so, getting that data, getting the instrument names that he used for those, then getting the technical manuals of what the settings might be and how much of that information is collected in the metadata when you're collecting the thing... All of this is just the organization that you need to do before you do anything else. "And then, getting from the companies how it is that they parse their raw data. Because some of these data files are put into...I wouldn't call them encrypted, but they're stacked into a certain kind of file structure - and I've seen the file structure - which is, you can think of it as a giant spreadsheet with headings and numbers for each of the columns and time on the row axis. "And so, you know, we've started looking at some of the data and put it into, let's say, 3D tracking. And it's clear that there are some things about the data that we needed to go back to the vendor who makes the instrument and say, 'Why is this and this and this happening, you know, every few dozen milliseconds?'" (I wonder if some of what they saw in their data, and labelled as anomalous, has maybe turned out to be a sensor artifact?) Nolan: "And so, you know, just getting an answer from these companies, often, when you don't even own the instrument, they're like, 'Well, why should we give you the information about how our data is constructed? How do we know that you're not a competitor?' Right? I mean, and so these are the kinds of things that we then contact somebody who has a behind-the-scenes access to this so that we can, again, it's all of these little steps. "And I'm sure there's somebody who's gonna tweet, 'Well, why don't you just put all the raw data out on the internet?' For exactly the same reason you don't put the raw data from ancient DNA sequencing. Because people will make mistakes about it. And so, if I'm going to be involved, I'm not gonna make any mistakes like that. So I'm sorry if people want stuff early. "I think you know, perhaps, if... Well, if I had been running the whole show, nobody would even know what Skywatcher is right now. We'd just be collecting the data in a fully-stealthed mode. And...but, you know, it's...there's reasons, good reasons, why they wanted some publicity. And, you know, but I don't always get my way." Vinnie - 𝐕𝐢𝐧𝐧𝐢𝐞 𝐀𝐝𝐚𝐦𝐬 𝕏: "Would you say that the data is exciting?" Nolan: "Oh, there's some interesting stuff in there. I mean, frankly, perhaps some of the better data that we have is just a couple of pictures from the ground of the helicopter with something about, you know, 200 feet in front of it. It's a clear blue sky and there's an object right in front of the helicopter. And the people in the helicopter said at the time that they couldn't see anything, even though we could see it from the ground. "But meanwhile, all of their instruments are going haywire. So, there was an effect. So why couldn't they see it? Maybe it was just out of view? Who knows? So it wasn't a lens flare, and it certainly wasn't a seagull. Mick (both laugh)." Vinnie: "Not in the desert anyway." Nolan: "I can't help myself." Vinnie: "I'm all for it. I'm sure Mick would, too. Hopefully. You know, James Fowler, we know he's left and moved on working with a new company. You know, all the best to him. Am I right in saying some of the technology being utilized by Skywatcher was proprietary to him, specifically? Maybe the dog whistle even? Is that still going to be able to be used by Skywatcher? How's that going to look going forward?" Nolan: "Umm, I'm not sure that we need the dog whistle at this point. And maybe there are ways to recreate it. I'm not party to the discussions around that. And so, we'll see where that goes." (That sounds like Fowler is NOT going to allow Skywatcher to use the dog whistle. That's a big disappointment. I mean, if this is really NHI and the dog whistle works 100% of the time, as claimed, then the whole world deserves to know about it.) Nolan: "I mean, James is not like, gone and forgotten. I mean, I could Signal chat him right now. And so he's there to help us. But, you know, my take on things is, you know, James has a life to live and a family to feed, and maybe his focus isn't entirely on UAP. He certainly has an interest in it. And maybe he has, you know, a company to build, and an opportunity that, actually, we all see now in terms of detecting drones. And, you know, if he wants to run a company like that then running around with a bunch of UAPologists might not be to that benefit. "And there might be a day when Skywatcher doesn't need to exist. The whole idea of Skywatcher is to show that something like this can be done, and it can be done in a serious way." (jakebarber claimed that they could BRING DOWN a craft. If that's true, it would change the world. What happened with that? Barber also said, "Who is operating [UAP]? How are they being operated? Where are they coming from? We should be able to answer those questions, probably entirely, in the next 12 months." Time is running out. Does he still stand behind that? Full Barber post with video clip: ) ~ Nolan: "I mean, I would...I, frankly, hope that if UAPDA - Disclosure Act is passed, because then the information that can be allowed to be out can be let out, and the stuff that needs to be kept secret stays secret. Again, I'm not, I would never advocate for a data dump." (I would 100% advocate for a data dump, minus details on anything that can be used as a weapon. Nobody, including the USG or private contractors owns this information. If it's gonna be a slow drip, for decades, then I fully support an Edward Snowden-type of leaker.) Nolan: "And so, you know, call it controlled disclosure, what have you. It needs to be done the proper way. And, you know, if any of the claims are true, there are reasons why you want to be methodical about it." (Who gets to decide what "the proper way" looks like? It seems like we're having more gatekeeping on top of the original gatekeeping. Not good.)

"I'm not sure that we need the dog whistle at this point. And maybe there are ways to re-create it." ~Nolan "There might be a day when Skywatcher doesn't need to exist." ~Nolan "If I had been running the whole show, nobody would even know what Skywatcher is right now." ~Nolan ~My comments in ( )~ Garry P. Nolan: "We've got a lot of data from multiple alleged sightings, both radar and other kinds of data. First it was about getting the raw-data files all put in one place because some of the data was collected by James (Fowler) before there was, officially, kind of a Skywatcher. "And so, getting that data, getting the instrument names that he used for those, then getting the technical manuals of what the settings might be and how much of that information is collected in the metadata when you're collecting the thing... All of this is just the organization that you need to do before you do anything else. "And then, getting from the companies how it is that they parse their raw data. Because some of these data files are put into...I wouldn't call them encrypted, but they're stacked into a certain kind of file structure - and I've seen the file structure - which is, you can think of it as a giant spreadsheet with headings and numbers for each of the columns and time on the row axis. "And so, you know, we've started looking at some of the data and put it into, let's say, 3D tracking. And it's clear that there are some things about the data that we needed to go back to the vendor who makes the instrument and say, 'Why is this and this and this happening, you know, every few dozen milliseconds?'" (I wonder if some of what they saw in their data, and labelled as anomalous, has maybe turned out to be a sensor artifact?) Nolan: "And so, you know, just getting an answer from these companies, often, when you don't even own the instrument, they're like, 'Well, why should we give you the information about how our data is constructed? How do we know that you're not a competitor?' Right? I mean, and so these are the kinds of things that we then contact somebody who has a behind-the-scenes access to this so that we can, again, it's all of these little steps. "And I'm sure there's somebody who's gonna tweet, 'Well, why don't you just put all the raw data out on the internet?' For exactly the same reason you don't put the raw data from ancient DNA sequencing. Because people will make mistakes about it. And so, if I'm going to be involved, I'm not gonna make any mistakes like that. So I'm sorry if people want stuff early. "I think you know, perhaps, if... Well, if I had been running the whole show, nobody would even know what Skywatcher is right now. We'd just be collecting the data in a fully-stealthed mode. And...but, you know, it's...there's reasons, good reasons, why they wanted some publicity. And, you know, but I don't always get my way." Vinnie - 𝐕𝐢𝐧𝐧𝐢𝐞 𝐀𝐝𝐚𝐦𝐬 𝕏: "Would you say that the data is exciting?" Nolan: "Oh, there's some interesting stuff in there. I mean, frankly, perhaps some of the better data that we have is just a couple of pictures from the ground of the helicopter with something about, you know, 200 feet in front of it. It's a clear blue sky and there's an object right in front of the helicopter. And the people in the helicopter said at the time that they couldn't see anything, even though we could see it from the ground. "But meanwhile, all of their instruments are going haywire. So, there was an effect. So why couldn't they see it? Maybe it was just out of view? Who knows? So it wasn't a lens flare, and it certainly wasn't a seagull. Mick (both laugh)." Vinnie: "Not in the desert anyway." Nolan: "I can't help myself." Vinnie: "I'm all for it. I'm sure Mick would, too. Hopefully. You know, James Fowler, we know he's left and moved on working with a new company. You know, all the best to him. Am I right in saying some of the technology being utilized by Skywatcher was proprietary to him, specifically? Maybe the dog whistle even? Is that still going to be able to be used by Skywatcher? How's that going to look going forward?" Nolan: "Umm, I'm not sure that we need the dog whistle at this point. And maybe there are ways to recreate it. I'm not party to the discussions around that. And so, we'll see where that goes." (That sounds like Fowler is NOT going to allow Skywatcher to use the dog whistle. That's a big disappointment. I mean, if this is really NHI and the dog whistle works 100% of the time, as claimed, then the whole world deserves to know about it.) Nolan: "I mean, James is not like, gone and forgotten. I mean, I could Signal chat him right now. And so he's there to help us. But, you know, my take on things is, you know, James has a life to live and a family to feed, and maybe his focus isn't entirely on UAP. He certainly has an interest in it. And maybe he has, you know, a company to build, and an opportunity that, actually, we all see now in terms of detecting drones. And, you know, if he wants to run a company like that then running around with a bunch of UAPologists might not be to that benefit. "And there might be a day when Skywatcher doesn't need to exist. The whole idea of Skywatcher is to show that something like this can be done, and it can be done in a serious way." (jakebarber claimed that they could BRING DOWN a craft. If that's true, it would change the world. What happened with that? Barber also said, "Who is operating [UAP]? How are they being operated? Where are they coming from? We should be able to answer those questions, probably entirely, in the next 12 months." Time is running out. Does he still stand behind that? Full Barber post with video clip: ) ~ Nolan: "I mean, I would...I, frankly, hope that if UAPDA - Disclosure Act is passed, because then the information that can be allowed to be out can be let out, and the stuff that needs to be kept secret stays secret. Again, I'm not, I would never advocate for a data dump." (I would 100% advocate for a data dump, minus details on anything that can be used as a weapon. Nobody, including the USG or private contractors owns this information. If it's gonna be a slow drip, for decades, then I fully support an Edward Snowden-type of leaker.) Nolan: "And so, you know, call it controlled disclosure, what have you. It needs to be done the proper way. And, you know, if any of the claims are true, there are reasons why you want to be methodical about it." (Who gets to decide what "the proper way" looks like? It seems like we're having more gatekeeping on top of the original gatekeeping. Not good.)

Joe Murgia

32,437 次观看 • 11 个月前

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

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 次观看 • 5 个月前

$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 aren’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 it’s kinda boring, Claude is your hippie friend who loves to get vulnerable but takes the whole “express yourself” 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 aren’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. I’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: “The future is here, it’s just not evenly distributed yet.” You can get a glimpse of the future by looking at how a small percentage of writers are already using AI. 7. I’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: “What very important truth do few people agree with you on?” 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: “The new workflow for me is I think with AI and work with my colleagues.” 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 won’t care how it was made. They’ll just care that it’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 aren’t as good as the world’s best editors, but they’re a fraction of the cost, they’ll instantly give you 80th percentile feedback, and they work 24/7. As a novelist recently said to me: “Paying an editor to review my novel costs me $7,000 and a 4-6 week turnaround time, whereas Claude costs me $1.25 and gets me results a few minutes later.” The edits definitely aren’t as good, but there’s a virtue to speed (and this guy isn’t a chump writer). 18. The way AI-skeptics hate on LLMs while using old models is like driving a ‘92 Honda while hating on a self-driving Tesla. 19. AI-generated fiction makes people very upset. A friend insists it’s like having sex with a robot. Doesn’t matter how good it is. It ain’t human-generated, and there’s something uniquely repulsive about that. I’ve shared the full conversation below. It’s a solo-episode of me riffing on what I’ve learned about AI for ~90 minutes. If you’d rather watch it on YouTube or listen on Apple or Spotify, I’ve shared the links in the reply tweets. And if you have any questions, I’ll be extra active in the replies for this episode.$

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 aren’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 it’s kinda boring, Claude is your hippie friend who loves to get vulnerable but takes the whole “express yourself” 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 aren’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. I’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: “The future is here, it’s just not evenly distributed yet.” You can get a glimpse of the future by looking at how a small percentage of writers are already using AI. 7. I’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: “What very important truth do few people agree with you on?” 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: “The new workflow for me is I think with AI and work with my colleagues.” 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 won’t care how it was made. They’ll just care that it’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 aren’t as good as the world’s best editors, but they’re a fraction of the cost, they’ll instantly give you 80th percentile feedback, and they work 24/7. As a novelist recently said to me: “Paying 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.” The edits definitely aren’t as good, but there’s a virtue to speed (and this guy isn’t a chump writer). 18. The way AI-skeptics hate on LLMs while using old models is like driving a ‘92 Honda while hating on a self-driving Tesla. 19. AI-generated fiction makes people very upset. A friend insists it’s like having sex with a robot. Doesn’t matter how good it is. It ain’t human-generated, and there’s something uniquely repulsive about that. I’ve shared the full conversation below. It’s a solo-episode of me riffing on what I’ve learned about AI for ~90 minutes. If you’d rather watch it on YouTube or listen on Apple or Spotify, I’ve shared the links in the reply tweets. And if you have any questions, I’ll be extra active in the replies for this episode.

David Perell

257,480 次观看 • 1 年前

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 it’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 you’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 ball’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 you’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 doesn’t mean much then what it actually means is that you don’t understand what I’m saying as what I’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. I’d recommend an amazing video by DST studios called “Lowlight cameras can’t defeat stealth” 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 wasn’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 isn’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 won’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, I’m not saying you should just use web cams to do this, I’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 wouldn’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 don’t care about the military uses of this technology, if all it could do is precisely track stealth fighters then I wouldn’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 hasn’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 people’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 wasn’t. What I’m saying here isn’t that this is a Radon transform or gaussian splat or whatever, I’m saying that this is able to get new information that wasn’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 won’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. I’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 can’t really explain in text so if you have any questions such as why this hasn’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

50,687 次观看 • 11 个月前

Matthew Gallagher Built a $401M Company in Year One with 2 People. And the tool behind it? Claude Code. This year he's on track for $1.8B. Sam Altman predicted this. It's happening now. The problem? It costs money. API credits stack up. Monthly bills keep growing. Every prompt eats your budget. Every project drains your wallet faster. Until now. Two methods. 99% cheaper. One is completely free. Forever. $0. Not a trial. This video breaks down both step by step. ↓ Let me put this in perspective. $100-$500. That's monthly. That's what you spend. That's $6,000/year on API credits. Just to use a tool you haven't shipped anything with. The $401M guy? Spending $0. Same capability. Shipping weekly. Different cost structure. Different results. Different life. I'm about to hand you his cost structure for free. ↓ Open source vs closed source. Pay attention. Closed source: Claude. GPT-4. Pay per token. Meter always running. Open source: Qwen. Llama. Mistral. Free to download. Free to run. Free forever. No meter. No tokens. No bill. Here's what nobody tells you: 80% of coding tasks? Open source handles them. More than handles them. Writes clean code. Debugs errors. Generates boilerplate. Handles routine work perfectly. You're paying premium prices for tasks that don't need premium intelligence. That's hiring a brain surgeon to put on a bandaid. Smart play: Free models for the 80%. Paid credits for the 20%. That's what the $401M guy does. That's what this video teaches you. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 1: Ollama. Local. Free. Forever. Download it. Pull a model. Point Claude Code at it. Done. No internet needed. No API keys required. No monthly subscription. No token counting ever. No bill. Today. Tomorrow. Ever. Your data never leaves your computer. Complete privacy. Complete freedom. Claude Code thinks it's talking to the cloud. It's talking to your laptop. For $0. The video walks through every step: Every config file. Every variable. Every command. Every click. If you can follow a recipe, you can do this. People who set this up 3 months ago? Saved $300-$1,500 since then. Workflow didn't change one bit. ↓ Hardware you need: 16GB RAM: 7B models run smooth. 32GB RAM: 32B models run comfortable. 64GB + GPU: biggest models available. No GPU? Still works. Just slower. Few extra seconds. That's it. Your $1,500 laptop is sitting there running Chrome and Spotify. Put it to work saving you $200/month instead. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 2: Open Router. Free Cloud. No Hardware. Weak machine? Don't want local setup? This method is for you. Free AI models in the cloud. No download. No hardware. Configure Claude Code to route through Open Router. The config: Base URL: Open Router API. API key: free Open Router key. Default Sonnet: free. Default Opus: free. Default Haiku: free. Small fast model: free. Subagent model: free. Free. Free. Free. Free. Free across the board. Same interface. Same commands. Same workflow. Zero cost. Copy the config from the video. Paste it. Save $200/month. Starting today. Right now. ↓ When to use which: Ollama (local): Best for privacy. Best for offline work. Best for unlimited usage. Best if you have decent hardware. Open Router (cloud): Best for weak machines. Best for instant setup. Best for trying different models. Best if you don't want to manage anything. Both methods: Best for 80% of your daily work. Still use paid Claude for: Complex architecture. Multi-file refactoring. Deep reasoning tasks. The 20% that actually needs it. $20/month instead of $200/month. Same output. 90% less cost. ↓ The math that should make you angry. You (current): $200-$500/month. $2,400-$6,000/year. $7,200-$18,000 over 3 years. You (after this video): $20-$50/month. $240-$600/year. $720-$1,800 over 3 years. Savings over 3 years: $6,480-$16,200. That's a used car. That's seed money. That's 6 months of rent. All from one 25-minute video. All from 15 minutes of configuration. Highest ROI 25 minutes you'll spend this year. ↓ The limitations. I won't lie to you. Open source is not Opus. Not as smart on complex reasoning. Not as good at long-context tasks. Makes more mistakes on nuanced problems. But they are: Free. Capable. Getting better monthly. Good enough for 80% of daily work. Smart cost management isn't being cheap. It's being strategic. Expensive tool when it matters. Free tool when it doesn't. ↓ The one-person billion-dollar company is coming. $401M in year one proved it's possible. The building blocks: AI that codes: Claude Code. Way to run it free: this video. Distribution: the internet. Customers: everyone. Only missing ingredient? Someone who builds. Not reads about building. Not saves posts about building. Not bookmarks videos about building. Builds. Tools are free. Knowledge is free. Opportunity is screaming. You're still "thinking about it." ↓ Your action plan: Tonight: Watch the video. Tomorrow morning: Set up Ollama or Open Router. Tomorrow afternoon: Build something. Anything. This week: Build a second thing. Faster. This month: Charge someone for it. One video. One setup. One weekend. $0 cost. Unlimited potential. Or keep paying $200/month for something you could get free. Keep consuming instead of building. Keep planning instead of shipping. Matthew Gallagher didn't plan a $401M company. He built it. Full video attached. Every method. Every config. Every tradeoff. 25 minutes. Your move. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses.

Matthew Gallagher Built a $401M Company in Year One with 2 People. And the tool behind it? Claude Code. This year he's on track for $1.8B. Sam Altman predicted this. It's happening now. The problem? It costs money. API credits stack up. Monthly bills keep growing. Every prompt eats your budget. Every project drains your wallet faster. Until now. Two methods. 99% cheaper. One is completely free. Forever. $0. Not a trial. This video breaks down both step by step. ↓ Let me put this in perspective. $100-$500. That's monthly. That's what you spend. That's $6,000/year on API credits. Just to use a tool you haven't shipped anything with. The $401M guy? Spending $0. Same capability. Shipping weekly. Different cost structure. Different results. Different life. I'm about to hand you his cost structure for free. ↓ Open source vs closed source. Pay attention. Closed source: Claude. GPT-4. Pay per token. Meter always running. Open source: Qwen. Llama. Mistral. Free to download. Free to run. Free forever. No meter. No tokens. No bill. Here's what nobody tells you: 80% of coding tasks? Open source handles them. More than handles them. Writes clean code. Debugs errors. Generates boilerplate. Handles routine work perfectly. You're paying premium prices for tasks that don't need premium intelligence. That's hiring a brain surgeon to put on a bandaid. Smart play: Free models for the 80%. Paid credits for the 20%. That's what the $401M guy does. That's what this video teaches you. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 1: Ollama. Local. Free. Forever. Download it. Pull a model. Point Claude Code at it. Done. No internet needed. No API keys required. No monthly subscription. No token counting ever. No bill. Today. Tomorrow. Ever. Your data never leaves your computer. Complete privacy. Complete freedom. Claude Code thinks it's talking to the cloud. It's talking to your laptop. For $0. The video walks through every step: Every config file. Every variable. Every command. Every click. If you can follow a recipe, you can do this. People who set this up 3 months ago? Saved $300-$1,500 since then. Workflow didn't change one bit. ↓ Hardware you need: 16GB RAM: 7B models run smooth. 32GB RAM: 32B models run comfortable. 64GB + GPU: biggest models available. No GPU? Still works. Just slower. Few extra seconds. That's it. Your $1,500 laptop is sitting there running Chrome and Spotify. Put it to work saving you $200/month instead. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 2: Open Router. Free Cloud. No Hardware. Weak machine? Don't want local setup? This method is for you. Free AI models in the cloud. No download. No hardware. Configure Claude Code to route through Open Router. The config: Base URL: Open Router API. API key: free Open Router key. Default Sonnet: free. Default Opus: free. Default Haiku: free. Small fast model: free. Subagent model: free. Free. Free. Free. Free. Free across the board. Same interface. Same commands. Same workflow. Zero cost. Copy the config from the video. Paste it. Save $200/month. Starting today. Right now. ↓ When to use which: Ollama (local): Best for privacy. Best for offline work. Best for unlimited usage. Best if you have decent hardware. Open Router (cloud): Best for weak machines. Best for instant setup. Best for trying different models. Best if you don't want to manage anything. Both methods: Best for 80% of your daily work. Still use paid Claude for: Complex architecture. Multi-file refactoring. Deep reasoning tasks. The 20% that actually needs it. $20/month instead of $200/month. Same output. 90% less cost. ↓ The math that should make you angry. You (current): $200-$500/month. $2,400-$6,000/year. $7,200-$18,000 over 3 years. You (after this video): $20-$50/month. $240-$600/year. $720-$1,800 over 3 years. Savings over 3 years: $6,480-$16,200. That's a used car. That's seed money. That's 6 months of rent. All from one 25-minute video. All from 15 minutes of configuration. Highest ROI 25 minutes you'll spend this year. ↓ The limitations. I won't lie to you. Open source is not Opus. Not as smart on complex reasoning. Not as good at long-context tasks. Makes more mistakes on nuanced problems. But they are: Free. Capable. Getting better monthly. Good enough for 80% of daily work. Smart cost management isn't being cheap. It's being strategic. Expensive tool when it matters. Free tool when it doesn't. ↓ The one-person billion-dollar company is coming. $401M in year one proved it's possible. The building blocks: AI that codes: Claude Code. Way to run it free: this video. Distribution: the internet. Customers: everyone. Only missing ingredient? Someone who builds. Not reads about building. Not saves posts about building. Not bookmarks videos about building. Builds. Tools are free. Knowledge is free. Opportunity is screaming. You're still "thinking about it." ↓ Your action plan: Tonight: Watch the video. Tomorrow morning: Set up Ollama or Open Router. Tomorrow afternoon: Build something. Anything. This week: Build a second thing. Faster. This month: Charge someone for it. One video. One setup. One weekend. $0 cost. Unlimited potential. Or keep paying $200/month for something you could get free. Keep consuming instead of building. Keep planning instead of shipping. Matthew Gallagher didn't plan a $401M company. He built it. Full video attached. Every method. Every config. Every tradeoff. 25 minutes. Your move. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses.

Himanshu Kumar

13,573 次观看 • 3 个月前

I pay Claude $20 a month. Most $TAO holders do too. There is a stack you can build in 15 minutes that fixes that completely. It runs on Bittensor. It costs $10. You do not write a single line of code. Here is how every AI chat product actually works under the hood. Three layers. Always three. The model. The brain. GPT, Claude, DeepSeek, Kimi, GLM. The inference layer. The GPU that runs the model when you hit send. The interface. The chat box you actually look at. ChatGPT and Claude bundle all three and hand you the result. You cannot change the model. You cannot change the inference. The interface is non-negotiable. Every prompt you type goes to a server run by a private company whose terms of service can quietly change next month. The anti-ChatGPT move is to pick each layer yourself. This is where $TAO comes in. Chutes is Subnet 64 on Bittensor. It is the inference layer. Open source models like DeepSeek, Kimi, GLM, and Llama get served by a global network of miner-operated GPUs. Validators score the output quality. The best inference wins the emissions. You hit send. A miner somewhere runs your prompt. You get the answer back. The TAO you hold is in part paying for the GPU you just used. The basic stack is one URL. chutes. ai/chat No account. No API key. No setup. Switch models mid-conversation. Web search built in. Image generation. File uploads. Free. The advanced stack is Chutes plus TypingMind. One-time license. No recurring fee. Plugins, agents, custom personas, a prompt library you build over months. Full model switching between Chutes, OpenAI, and Anthropic from the same window. Total cost: $10 a month to Chutes for inference. That $10 buys you $50 in actual usage. But here is the signal most people missed inside this story. Chutes ran a free tier until February. Then they killed it. Then they raised the minimum to $10 in May. Most people saw that as bad news. It is the opposite. Free things on the internet do not last. Real products do. Chutes is becoming a real product. A subnet that generates actual revenue from actual users paying actual money for actual AI inference. That is what $43 million in Q1 network revenue looks like at the individual subnet level. And there is one more thing ChatGPT and Claude cannot offer that Chutes already has. Trusted Execution Environments. Your prompt gets encrypted on your device, shipped to a confidential compute GPU, and the lock only breaks inside the chip. The miner running the model physically cannot read your prompt. ChatGPT cannot promise that. Claude cannot promise that. Bittensor already built it. You are holding a network where the subnets are generating real revenue, shipping real privacy infrastructure, and replacing $20 a month centralised subscriptions with $10 a month decentralised inference. The people who use the product always understand the investment better than the people who only watch the price.

I pay Claude $20 a month. Most $TAO holders do too. There is a stack you can build in 15 minutes that fixes that completely. It runs on Bittensor. It costs $10. You do not write a single line of code. Here is how every AI chat product actually works under the hood. Three layers. Always three. The model. The brain. GPT, Claude, DeepSeek, Kimi, GLM. The inference layer. The GPU that runs the model when you hit send. The interface. The chat box you actually look at. ChatGPT and Claude bundle all three and hand you the result. You cannot change the model. You cannot change the inference. The interface is non-negotiable. Every prompt you type goes to a server run by a private company whose terms of service can quietly change next month. The anti-ChatGPT move is to pick each layer yourself. This is where $TAO comes in. Chutes is Subnet 64 on Bittensor. It is the inference layer. Open source models like DeepSeek, Kimi, GLM, and Llama get served by a global network of miner-operated GPUs. Validators score the output quality. The best inference wins the emissions. You hit send. A miner somewhere runs your prompt. You get the answer back. The TAO you hold is in part paying for the GPU you just used. The basic stack is one URL. chutes. ai/chat No account. No API key. No setup. Switch models mid-conversation. Web search built in. Image generation. File uploads. Free. The advanced stack is Chutes plus TypingMind. One-time license. No recurring fee. Plugins, agents, custom personas, a prompt library you build over months. Full model switching between Chutes, OpenAI, and Anthropic from the same window. Total cost: $10 a month to Chutes for inference. That $10 buys you $50 in actual usage. But here is the signal most people missed inside this story. Chutes ran a free tier until February. Then they killed it. Then they raised the minimum to $10 in May. Most people saw that as bad news. It is the opposite. Free things on the internet do not last. Real products do. Chutes is becoming a real product. A subnet that generates actual revenue from actual users paying actual money for actual AI inference. That is what $43 million in Q1 network revenue looks like at the individual subnet level. And there is one more thing ChatGPT and Claude cannot offer that Chutes already has. Trusted Execution Environments. Your prompt gets encrypted on your device, shipped to a confidential compute GPU, and the lock only breaks inside the chip. The miner running the model physically cannot read your prompt. ChatGPT cannot promise that. Claude cannot promise that. Bittensor already built it. You are holding a network where the subnets are generating real revenue, shipping real privacy infrastructure, and replacing $20 a month centralised subscriptions with $10 a month decentralised inference. The people who use the product always understand the investment better than the people who only watch the price.

2xnmore

27,019 次观看 • 2 个月前

🔥 Knapp and I, "have seen high-quality everything [UAP] that you'd ever want to see. It's amazing." ~Corbell 🔥 (Great way to end the year. Unless you think Corbell and Knapp are lying or have been fooled by nothing but fake photos and videos, the good stuff DOES exist. Damn. Wasn't expecting this, but it's very important to hear. You'll have to decide what this means for you. Happy New Year!) ~~~ Jeremy Kenyon Lockyer Corbell: "George and I are reporters and people send us stuff. He and I can both attest: We have seen high-fidelity, of things that like, are so egregiously bizarre, you guys (Knapp shaking his head up and down in agreement). I'll just admit it. We have been exposed to every type of footage you can imagine. Every type. Lots of it. And this has happened for a while, and I don't know what to do. I mean, as a journalist, we are able to be exposed to these things, and they're...you know, it's shocking. "There's a lot of footage from this aggregate program called IMMACULATE CONSTELLATION, and not even within that program. That program was just siphoning off. Here's the deal: Our sensor platforms - from space, to air, to sea, to undersea - pick up, all the time, highly-anomalous things that are unambiguously UAP. That's fact. I can attest to that as a human being sitting in front of you, that George and I have been exposed to it. I'm not gonna go any further today, but that is fact, we have been. "So, what happens is, you have our government saying, all of this reporting is coming in and it's going up on these classified servers, but like, what do we do with it, what do we do with it? So they've developed one of many programs that used to be called Immaculate Constellation, where they will pull the best of the best, a lot of it using AI. And they will pull these into an archive and have them assessed in secret groups. So that's what Immaculate Constellation actually was, was an aggregate program to take all of that footage, bring it, and nothing facing internally to the U.S. So if we're doing any exploitation (reverse engineering efforts. ~Joe), if it's anything with us? Nothing. But to other countries, and just passive and active picking up UAP, that's what Immaculate Constellation did. And now it's under a different name. But it does the same thing, and it's one of many programs. "So, I can attest, and so can George, that that footage of high-quality from all-domain is something that exists, it's unambiguous. Now the question is: Who do you show it to? Who gets to see it? What actions can they take? I don't know. I don't know. I'm not in charge here. Is there an adult in the room?" George Knapp: "Jeremy, DoD spokesperson says there is no record of anything called Immaculate Constellation within the Department of Defense. I guess it can't be real then, right?" Corbell: "You know, I think there will be clarity on that, because I think there's humans behind whistleblowing, and I think one day we're going to learn more about that. I hope that that helps. Like, when David Grusch came forward, and he said, 'Look, I dug into all these black projects, and I had 40 witnesses.' By the way, it's not the people that you know of and this sort of thing. This is like, deep within our intelligence agencies. David Grusch DID bring 40 people to the IC IG. He did do that. He told it like it is. Yeah, it exists. It exists. It's the most frustrating thing in the world, once you know for sure that high-quality data exists and that people that should see it haven't seen it, and you're kind of stuck. You don't know what to do about it. That's how I feel.: Knapp: "But maybe a technicality could be it exists or existed, but not within the Pentagon." Corbell: "Oh, yeah, all those games like, Immaculate Constellation was not a DoD USAP. So that's like, why you see Susan Gough like, technically, she's telling the truth. So, Immaculate Constellation was a USAP (Unacknowledged Special Access Program ~Joe) under a different heading. So imagine this: Imagine even the Department of Energy has its own USAPs. The White House has its own USAPs. So you can technically say it's not under DoD. They might have actuated some things for that operation, which was an operation, Immaculate Constellation, but they can get away with these technicalities. But there are USAPs in a lot of different departments, as you can imagine." Joe Murgia - Joe Murgia: "I am not letting you guys get away with not commenting again on what you've seen, image-wise. I mean, have you guys seen craft, structured craft, up close (Corbell and Knapp both shaking their heads up and down)?" Corbell: "Yep." Murgia: "Flying saucer-type craft?" Corbell: "Yep." Murgia: "Triangles?" Corbell: "Yeah." Murgia: "What else can you say about that?" Corbell: "The weirdest sh*t I've ever seen in my life, man. As journalists, we get a lot of weird stuff, and George and I, we'll look at it and assess it, and it's legitimate, and we've seen it. And, yeah, I don't know what else to say, George, unless you want to say something." Knapp: "We've seen stuff that we're not supposed to see. Put it that way." Murgia: "From government sensors?" Corbell: "Oh yeah." Knapp: "Yeah." Corbell: "Yeah. Look, as journalists, we don't know at first, right? Like, we'll get a bunch of information. We'll be exposed to a bunch of information. It's a very tricky place for us to be. First, are people f*cking with us? You know, is this a lie, is this real? We had that debate about the Jellyfish. Like, we had to, like, dig in and find out, which base, where was it, who's seen it? Multiple sources that have seen it at that base. It takes time. We're in that position now. We've been exposed to a lot of stuff and everything you just said, Joe, yeah. I mean, I could tell you as a friend, straight up, George could affirm this, with his own eyes, too. What that means, I don't know, and I don't know where to go from there. But we have seen high-quality everything that you'd ever want to see. It's amazing."

🔥 Knapp and I, "have seen high-quality everything [UAP] that you'd ever want to see. It's amazing." ~Corbell 🔥 (Great way to end the year. Unless you think Corbell and Knapp are lying or have been fooled by nothing but fake photos and videos, the good stuff DOES exist. Damn. Wasn't expecting this, but it's very important to hear. You'll have to decide what this means for you. Happy New Year!) ~~~ Jeremy Kenyon Lockyer Corbell: "George and I are reporters and people send us stuff. He and I can both attest: We have seen high-fidelity, of things that like, are so egregiously bizarre, you guys (Knapp shaking his head up and down in agreement). I'll just admit it. We have been exposed to every type of footage you can imagine. Every type. Lots of it. And this has happened for a while, and I don't know what to do. I mean, as a journalist, we are able to be exposed to these things, and they're...you know, it's shocking. "There's a lot of footage from this aggregate program called IMMACULATE CONSTELLATION, and not even within that program. That program was just siphoning off. Here's the deal: Our sensor platforms - from space, to air, to sea, to undersea - pick up, all the time, highly-anomalous things that are unambiguously UAP. That's fact. I can attest to that as a human being sitting in front of you, that George and I have been exposed to it. I'm not gonna go any further today, but that is fact, we have been. "So, what happens is, you have our government saying, all of this reporting is coming in and it's going up on these classified servers, but like, what do we do with it, what do we do with it? So they've developed one of many programs that used to be called Immaculate Constellation, where they will pull the best of the best, a lot of it using AI. And they will pull these into an archive and have them assessed in secret groups. So that's what Immaculate Constellation actually was, was an aggregate program to take all of that footage, bring it, and nothing facing internally to the U.S. So if we're doing any exploitation (reverse engineering efforts. ~Joe), if it's anything with us? Nothing. But to other countries, and just passive and active picking up UAP, that's what Immaculate Constellation did. And now it's under a different name. But it does the same thing, and it's one of many programs. "So, I can attest, and so can George, that that footage of high-quality from all-domain is something that exists, it's unambiguous. Now the question is: Who do you show it to? Who gets to see it? What actions can they take? I don't know. I don't know. I'm not in charge here. Is there an adult in the room?" George Knapp: "Jeremy, DoD spokesperson says there is no record of anything called Immaculate Constellation within the Department of Defense. I guess it can't be real then, right?" Corbell: "You know, I think there will be clarity on that, because I think there's humans behind whistleblowing, and I think one day we're going to learn more about that. I hope that that helps. Like, when David Grusch came forward, and he said, 'Look, I dug into all these black projects, and I had 40 witnesses.' By the way, it's not the people that you know of and this sort of thing. This is like, deep within our intelligence agencies. David Grusch DID bring 40 people to the IC IG. He did do that. He told it like it is. Yeah, it exists. It exists. It's the most frustrating thing in the world, once you know for sure that high-quality data exists and that people that should see it haven't seen it, and you're kind of stuck. You don't know what to do about it. That's how I feel.: Knapp: "But maybe a technicality could be it exists or existed, but not within the Pentagon." Corbell: "Oh, yeah, all those games like, Immaculate Constellation was not a DoD USAP. So that's like, why you see Susan Gough like, technically, she's telling the truth. So, Immaculate Constellation was a USAP (Unacknowledged Special Access Program ~Joe) under a different heading. So imagine this: Imagine even the Department of Energy has its own USAPs. The White House has its own USAPs. So you can technically say it's not under DoD. They might have actuated some things for that operation, which was an operation, Immaculate Constellation, but they can get away with these technicalities. But there are USAPs in a lot of different departments, as you can imagine." Joe Murgia - Joe Murgia: "I am not letting you guys get away with not commenting again on what you've seen, image-wise. I mean, have you guys seen craft, structured craft, up close (Corbell and Knapp both shaking their heads up and down)?" Corbell: "Yep." Murgia: "Flying saucer-type craft?" Corbell: "Yep." Murgia: "Triangles?" Corbell: "Yeah." Murgia: "What else can you say about that?" Corbell: "The weirdest sht I've ever seen in my life, man. As journalists, we get a lot of weird stuff, and George and I, we'll look at it and assess it, and it's legitimate, and we've seen it. And, yeah, I don't know what else to say, George, unless you want to say something." Knapp: "We've seen stuff that we're not supposed to see. Put it that way." Murgia: "From government sensors?" Corbell: "Oh yeah." Knapp: "Yeah." Corbell: "Yeah. Look, as journalists, we don't know at first, right? Like, we'll get a bunch of information. We'll be exposed to a bunch of information. It's a very tricky place for us to be. First, are people fcking with us? You know, is this a lie, is this real? We had that debate about the Jellyfish. Like, we had to, like, dig in and find out, which base, where was it, who's seen it? Multiple sources that have seen it at that base. It takes time. We're in that position now. We've been exposed to a lot of stuff and everything you just said, Joe, yeah. I mean, I could tell you as a friend, straight up, George could affirm this, with his own eyes, too. What that means, I don't know, and I don't know where to go from there. But we have seen high-quality everything that you'd ever want to see. It's amazing."

Joe Murgia

140,668 次观看 • 1 年前

Why Opus 4.6 Is The Final Boss Of Algorithmic Trading (Full Bot Build) the day of the human trader is officially over and most people are still staring at charts like it is 1995. wall street is terrified because the barrier to entry just got deleted by a piece of software that can outthink a stanford graduate in seconds. they want you to believe that you need a multi million dollar education to compete with the big banks. they want you to stay stuck in the cycle of emotional trading and leverage because that is how they pay for their hamptons houses. but there is a specific reason why every retail trader is about to become obsolete unless they pivot right now. i am going to show you exactly why your current strategy is a mathematical death trap and how a single jump in technology just changed the game forever every time you sit down at your computer to draw lines on a chart you are entering a gunfight with a toothpick. the institutions have been using high frequency algorithms for decades while you are trying to guess which way the candle is going to move based on a feeling in your gut. it is not a fair fight and it was never intended to be. last year we were looking at models that could barely handle basic logic but now the intelligence has scaled to a point where the machines are finding edges we did not even know existed. there is a ghost in the machine that is pulling out strategies with sharp ratios so high they look like typos. if you do not understand how to harness this power you are essentially donating your capital to the people who already have too much of it i spent hundreds of thousands of dollars on developers because i was too scared to learn how to code myself. i thought that being the idea guy was enough and that i could just hire people from upwork to build my dreams. i got rinsed for years paying for apps and bots that did not work because i did not have my hands on the wheel. it took losing a massive amount of money through liquidations and over trading to realize that nobody was coming to save me. i had to become the person who could build the systems or i was going to be another statistic in the graveyard of traders who thought they were smarter than the math. once i finally sat down and forced myself to understand the syntax everything shifted and the world became a giant playground of data the truth is that code is the great equalizer because it does not care where you came from or what school you went to. i got held back in seventh grade and my teacher told me i would not make it around here. that kind of talk is meant to keep you in your place but the computer does not have a bias. if you can write the logic the system will execute it exactly as told regardless of your background. we are living in a time where a kid in a basement can build a system that rivals a hedge fund because the big tech companies are subsidizing our intelligence. they are spending hundreds of billions of dollars on infrastructure and we are the ones who get to reap the rewards of their competition most people fail in this game because they fall in love with a single idea and refuse to let it go even when it is burning their account to the ground. they spend months or years trying to make one indicator work when the data clearly shows it is trash. you have to drop the ego and realize that your intuition is probably your biggest liability. the secret to winning is iterating to success by testing a hundred ideas until you find the one that actually sticks. i call it the rbi system which stands for research backtest and implement. if you skip any of these steps you are just gambling with extra steps and the house always wins in the end research is where most traders get lazy because they just want a magic bot that prints money while they sleep. they go to youtube and find some guy promising a ninety percent win rate with a rsi crossover. that is not research that is falling for marketing fluff designed to sell you a dream. real research happens when you dive into white papers and study what the quants are actually doing on wall street. you look for market inefficiencies like liquidation clusters and cross exchange discrepancies that are hidden in plain sight. by the time you finish this process you should have a list of ideas that are grounded in reality instead of wishful thinking backtesting is the filter that saves you from losing your life savings on a bad hunch. most people use tools that repaint or give them false confidence because the data is not being handled correctly. if you are using a basic charting platform to see if your strategy works you are likely seeing a version of history that does not exist. you need to use raw python libraries like backtesting py to see the cold hard truth of how your logic would have performed. when you see a drawdown of thirty percent on paper you realize that using ten times leverage would have deleted your account five times over. the math does not lie and it is the only thing that can protect you from your own greed the most dangerous drug in the world is leverage because it makes you feel like a genius right before it makes you a pauper. i have watched two billion dollars get liquidated in a single day because people thought they could predict the bottom with fifty times leverage. the exchanges can see exactly where your liquidation price is and they have every incentive to push the price there to hunt your liquidity. you are playing in a casino where the house can see your cards and they are actively trying to take them from you. the only way to win is to stop playing their game and start using limit orders to save on the fees that are slowly bleeding you dry it is funny how much money people will spend on food and entertainment but they will hesitate to invest in their own education. they will spend a thousand dollars on a weekend out but will not put that same money into learning a skill that could provide for them for the rest of their lives. money is just a tool of exchange and it always replenishes if you are providing value to the world. if you spend your capital on knowledge you are buying back your time and your freedom. i decided to live my life on youtube and build in public because i wanted to show people that a regular guy could do this. now i have fully automated systems trading for me while i sleep and i never have to worry about getting licked by a sudden market move again chasing the greats like jim simons is not about the money it is about the mastery of the system. he ran up a net worth of over thirty billion dollars by doing exactly what we are talking about here. he did not stare at charts all day and hope for the best he built models that exploited the mathematical laws of the market. he was a scientist first and a trader second and that is the mindset you need to adopt. if you are not approaching this quantitatively you are just a gambler who happens to be sitting at a computer. the goal is to become a quant researcher who happens to have robots executing their findings the transition from hand trader to automated builder is the most liberating thing you can do for your mental health. you go from waking up in a cold sweat checking your phone to waking up and checking your logs to see how the system performed. even if the day was red you have data that tells you why and you can use that to make the system better tomorrow. it is a process of constant improvement and refinement that never really ends. you are building a legacy of code that will continue to work for you as long as the electricity is running. i am not afraid to die on a treadmill because i know that i will outwork anyone who is just looking for a shortcut if you are still on the fence about whether or not you can do this just remember that i was exactly where you are. i was losing money and feeling like the market was rigged against me because it actually was. i had to decide that i was going to change my environment and take control of my own destiny. you have the same opportunity right now to pivot and start building your own automated future. the models are getting better every single day and the barrier to entry is lower than it has ever been in human history. you just have to decide to lock in and do the work for a thousand days until you become undeniable there is no better feeling than finding a strategy that has a sharp ratio over ten and knowing that you built it with your own two hands. it is a moment of pure clarity where you realize that you are no longer a victim of the market. you are the architect of your own financial reality and the possibilities are literally endless. i am going to keep sharing everything i find because i believe that we can take on wall street together. as long as i am breathing i will be stepping on the gas and pushing the boundaries of what is possible with code. welcome to the family and let's get after it because the machines are already running and they are not waiting for anyone

Why Opus 4.6 Is The Final Boss Of Algorithmic Trading (Full Bot Build) the day of the human trader is officially over and most people are still staring at charts like it is 1995. wall street is terrified because the barrier to entry just got deleted by a piece of software that can outthink a stanford graduate in seconds. they want you to believe that you need a multi million dollar education to compete with the big banks. they want you to stay stuck in the cycle of emotional trading and leverage because that is how they pay for their hamptons houses. but there is a specific reason why every retail trader is about to become obsolete unless they pivot right now. i am going to show you exactly why your current strategy is a mathematical death trap and how a single jump in technology just changed the game forever every time you sit down at your computer to draw lines on a chart you are entering a gunfight with a toothpick. the institutions have been using high frequency algorithms for decades while you are trying to guess which way the candle is going to move based on a feeling in your gut. it is not a fair fight and it was never intended to be. last year we were looking at models that could barely handle basic logic but now the intelligence has scaled to a point where the machines are finding edges we did not even know existed. there is a ghost in the machine that is pulling out strategies with sharp ratios so high they look like typos. if you do not understand how to harness this power you are essentially donating your capital to the people who already have too much of it i spent hundreds of thousands of dollars on developers because i was too scared to learn how to code myself. i thought that being the idea guy was enough and that i could just hire people from upwork to build my dreams. i got rinsed for years paying for apps and bots that did not work because i did not have my hands on the wheel. it took losing a massive amount of money through liquidations and over trading to realize that nobody was coming to save me. i had to become the person who could build the systems or i was going to be another statistic in the graveyard of traders who thought they were smarter than the math. once i finally sat down and forced myself to understand the syntax everything shifted and the world became a giant playground of data the truth is that code is the great equalizer because it does not care where you came from or what school you went to. i got held back in seventh grade and my teacher told me i would not make it around here. that kind of talk is meant to keep you in your place but the computer does not have a bias. if you can write the logic the system will execute it exactly as told regardless of your background. we are living in a time where a kid in a basement can build a system that rivals a hedge fund because the big tech companies are subsidizing our intelligence. they are spending hundreds of billions of dollars on infrastructure and we are the ones who get to reap the rewards of their competition most people fail in this game because they fall in love with a single idea and refuse to let it go even when it is burning their account to the ground. they spend months or years trying to make one indicator work when the data clearly shows it is trash. you have to drop the ego and realize that your intuition is probably your biggest liability. the secret to winning is iterating to success by testing a hundred ideas until you find the one that actually sticks. i call it the rbi system which stands for research backtest and implement. if you skip any of these steps you are just gambling with extra steps and the house always wins in the end research is where most traders get lazy because they just want a magic bot that prints money while they sleep. they go to youtube and find some guy promising a ninety percent win rate with a rsi crossover. that is not research that is falling for marketing fluff designed to sell you a dream. real research happens when you dive into white papers and study what the quants are actually doing on wall street. you look for market inefficiencies like liquidation clusters and cross exchange discrepancies that are hidden in plain sight. by the time you finish this process you should have a list of ideas that are grounded in reality instead of wishful thinking backtesting is the filter that saves you from losing your life savings on a bad hunch. most people use tools that repaint or give them false confidence because the data is not being handled correctly. if you are using a basic charting platform to see if your strategy works you are likely seeing a version of history that does not exist. you need to use raw python libraries like backtesting py to see the cold hard truth of how your logic would have performed. when you see a drawdown of thirty percent on paper you realize that using ten times leverage would have deleted your account five times over. the math does not lie and it is the only thing that can protect you from your own greed the most dangerous drug in the world is leverage because it makes you feel like a genius right before it makes you a pauper. i have watched two billion dollars get liquidated in a single day because people thought they could predict the bottom with fifty times leverage. the exchanges can see exactly where your liquidation price is and they have every incentive to push the price there to hunt your liquidity. you are playing in a casino where the house can see your cards and they are actively trying to take them from you. the only way to win is to stop playing their game and start using limit orders to save on the fees that are slowly bleeding you dry it is funny how much money people will spend on food and entertainment but they will hesitate to invest in their own education. they will spend a thousand dollars on a weekend out but will not put that same money into learning a skill that could provide for them for the rest of their lives. money is just a tool of exchange and it always replenishes if you are providing value to the world. if you spend your capital on knowledge you are buying back your time and your freedom. i decided to live my life on youtube and build in public because i wanted to show people that a regular guy could do this. now i have fully automated systems trading for me while i sleep and i never have to worry about getting licked by a sudden market move again chasing the greats like jim simons is not about the money it is about the mastery of the system. he ran up a net worth of over thirty billion dollars by doing exactly what we are talking about here. he did not stare at charts all day and hope for the best he built models that exploited the mathematical laws of the market. he was a scientist first and a trader second and that is the mindset you need to adopt. if you are not approaching this quantitatively you are just a gambler who happens to be sitting at a computer. the goal is to become a quant researcher who happens to have robots executing their findings the transition from hand trader to automated builder is the most liberating thing you can do for your mental health. you go from waking up in a cold sweat checking your phone to waking up and checking your logs to see how the system performed. even if the day was red you have data that tells you why and you can use that to make the system better tomorrow. it is a process of constant improvement and refinement that never really ends. you are building a legacy of code that will continue to work for you as long as the electricity is running. i am not afraid to die on a treadmill because i know that i will outwork anyone who is just looking for a shortcut if you are still on the fence about whether or not you can do this just remember that i was exactly where you are. i was losing money and feeling like the market was rigged against me because it actually was. i had to decide that i was going to change my environment and take control of my own destiny. you have the same opportunity right now to pivot and start building your own automated future. the models are getting better every single day and the barrier to entry is lower than it has ever been in human history. you just have to decide to lock in and do the work for a thousand days until you become undeniable there is no better feeling than finding a strategy that has a sharp ratio over ten and knowing that you built it with your own two hands. it is a moment of pure clarity where you realize that you are no longer a victim of the market. you are the architect of your own financial reality and the possibilities are literally endless. i am going to keep sharing everything i find because i believe that we can take on wall street together. as long as i am breathing i will be stepping on the gas and pushing the boundaries of what is possible with code. welcome to the family and let's get after it because the machines are already running and they are not waiting for anyone

Moon Dev

46,677 次观看 • 5 个月前

Made $530,000 with Ai Bot that started with $313. Didn't know how to code. Now this bots run 24/7 printing money while sleeping. I've made the exact step-by-step guide to build this Claude Code Polymarket trading bot. Prompts. Code. Risk settings. Paper trading checklist. Everything from zero to running bot. It's free. For 24 hours. After that I'm charging $499 for it. To grab it right now: 1. Comment "Claude Bot" 2. Like and Retweet this post 3. Follow me Himanshu Kumar ( I can't send DMs to non-followers ) I'm DMing everyone who Complete the 3 steps. I spent hundreds of thousands hiring developers because he was too scared to learn. Then learned Claude Code. Built algorithmic trading systems. $313 → $530,000. You have the same tools available right now. And you're using them to ask ChatGPT for Instagram captions. This attached video is a goldmine. Full live walkthrough. Claude Code building actual Polymarket trading bots. From zero. Every line of code. Every decision explained. Now let me break down why everything you're doing in trading is wrong and exactly how to fix it. Save this post. You'll hate yourself if you lose it. ↓ Let's start with why you keep losing money. You already know the answer. You just won't admit it. You overtrade. Every. Single. Day. You see a candle move. You feel something. You enter. No plan. No edge. No reason. Just feelings. Then it goes against you. You feel something else. Panic. Anger. Denial. You move your stop loss. Or you didn't set one at all. "It'll come back." It doesn't come back. So you take another trade. A revenge trade. Bigger size this time. Because you need to "make it back." That one fails too. Now you're emotional. Now you're tilted. Now you're using leverage you have no business touching. 40x. 50x. 100x. On a trade you entered because a candle looked "bullish" and some guy on Twitter said "send it." You get liquidated. Close the laptop. Punch something. Tell yourself you'll be "more disciplined" tomorrow. Tomorrow comes. Same cycle. Same result. Same liquidation. You've been doing this for months. Maybe years. And you still think the problem is your strategy. The problem isn't your strategy. The problem is you. Save this post right now. What I'm about to show you is the only way to remove yourself from the equation. Follow Himanshu Kumar so you don't miss any of this. ↓ Here's what's actually killing your account. It's not the market. The market doesn't care about you. It's not your indicators. RSI works fine. MACD works fine. They all "work." It's not your timeframe. It's not your broker. It's not the "manipulation." It's four things: 1. Emotions. You hold losers because hope feels better than loss. You cut winners because fear feels stronger than greed. You size up when angry. You skip trades when scared. Your emotional state determines your position size. That's insane. And you know it's insane. But you keep doing it. 2. Overtrading. You take 15 trades a day. Maybe 5 of them had actual setups. The other 10 were boredom. Boredom trades are the most expensive hobby in human history. 3. Leverage. You use 20x-50x on trades where you're not even sure about the direction. That's not trading. That's a casino with a nicer interface. 4. Fees. You're smashing market orders. Paying spread. Paying commission. On 15 trades a day. Your broker makes more money from your account than you do. Think about that. Your broker is profitable on your account. You're not. You're the product. Not the trader. These four things are why 90% of traders lose. Not bad luck. Not the market. You. Save this post and follow Himanshu Kumar because the solution is coming next. ↓ The solution is painfully obvious. Remove yourself from the equation. Not partially. Not "I'll be more disciplined." Not "I'll journal my trades." Not "I'll meditate before trading." Completely remove yourself. Build a bot. Let the bot trade. You go live your life. The bot doesn't feel emotions. The bot doesn't overtrade. The bot doesn't use reckless leverage. The bot doesn't smash market orders and bleed fees. The bot follows the rules. Every single time. Without exception. Without "just this once." Without "I have a feeling about this one." Rules in. Execution out. No human in the middle to mess everything up. That's algorithmic trading. And before your ego jumps in with "but I'm different, I have discipline" — No you don't. Your account balance proves you don't. If you had discipline, your account would be green. It's not. So you don't. Accept it. Automate it. Move on. This is the hardest truth in trading. Your discipline will always fail. A bot's won't. Save this post. Follow Himanshu Kumar for the exact bot setup that removes your emotions permanently. ↓ "But I don't know how to code." Neither did he. The guy in this video didn't know how to code for most of his life. Got held back in 7th grade. People counted him out early. Spent years building apps and SaaS businesses without writing a single line of code. Hired developers on Upwork instead. Spent hundreds of thousands of dollars paying other people to build what he could have built himself. Because he was scared to learn. That fear cost him years. And hundreds of thousands of dollars. Sound familiar? You're doing the same thing right now. Not with developers. But with your time. You're spending thousands of hours trading manually because you're scared to learn the thing that would make trading automatic. The fear of learning to code is costing you more than any bad trade ever did. Because every month you trade manually is a month of emotional decisions, overleveraged entries, and unnecessary losses that a bot would never make. And here's the thing that should really frustrate you: AI does the hard parts now. You don't need a computer science degree. You don't need to work at a hedge fund. You don't need to be "good at math." Claude Code writes the code for you. You just need to think clearly about trading ideas. That's it. If you can describe a strategy in English, Claude can build it in Python. "I don't know how to code" stopped being a valid excuse in 2024. It's 2026. You're 2 years late on that excuse. Find a new one. Or stop making excuses entirely. Save this post. Follow Himanshu Kumar because I'm showing you how people with zero coding experience are building profitable bots. ↓ The process that actually makes money. Three letters. R. B. I. Research. Backtest. Implement. That's it. That's the entire process. Every single day. Research: Find an idea. A pattern. A market inefficiency. Don't trade it yet. Don't even think about trading it yet. Just research it. Backtest: Test the idea against historical data. Does it work? Not "does it look good on one chart." Does it work across thousands of trades? Across different market conditions? Across in-sample AND out-of-sample data? If no, kill it. Find another idea. If yes, move to step 3. Implement: Build the bot. Deploy it. Paper trade first. Then live with small size. Scale only on evidence. Research. Backtest. Implement. Every day. No exceptions. You know what your current process is? Feel. Enter. Pray. F. E. P. Feel bullish. Enter a trade. Pray it works. That's not a process. That's gambling with a TradingView subscription. RBI is the only process that works. Save this post. Tattoo it on your forearm. Follow Himanshu Kumar for daily RBI breakdowns. ↓ What Claude Code actually does that your manual process can't. You can maybe test 3-5 strategy ideas per week. Manually adjusting parameters. Manually checking results. Manually writing code (badly). Claude Code tests 50-100 ideas per week. With parallel agents running simultaneously. Multiple strategies being built, tested, and validated at the same time. While you sleep. The guy in this video spends 4-8 hours a day building systems with Claude Code. Not trading. Building. Research. Backtest. Implement. Then iterate. Improve. Optimize. Every day the systems get better. Every day the edge compounds. Every day the bots get smarter. While you? You spend 4-8 hours a day staring at charts making the same mistakes you made last month. Same indicators. Same patterns. Same entries. Same losses. He's iterating forward. You're running in circles. Same 8 hours per day. Completely different outcomes. Because he's building systems. And you're feeding a casino. Stop feeding the casino. Start building the machine. Save this post and follow Himanshu Kumar for the Claude Code workflow that iterates strategies while you sleep. ↓ Jim Simons. That's the benchmark. You probably don't know who Jim Simons is. And that tells me everything about how seriously you take trading. Jim Simons. Mathematician. Founded Renaissance Technologies. Built a net worth of $31 billion. 100% from algorithmic trading. Not one single manual trade. Not one "gut feeling" entry. Not one RSI divergence. Not one "smart money concept." Algorithms. Bots. Systems. Data. $31 billion. His fund averaged 66% annual returns for over 30 years. While you're excited about making $200 on a trade that you'll give back tomorrow. The best trader in human history never placed a manual trade in his life. And you think your edge is staring at a 5-minute chart with bloodshot eyes at 2 AM? Your edge is building the system. Not being inside it. Jim Simons is the benchmark. Everything else is noise. Save this post. Follow Himanshu Kumar because I'm building toward the same goal and showing every step publicly. ↓ What you need to understand about patience. This is not get-rich-overnight. The guy in this video says it directly: "This channel is not for people looking to get rich overnight. It's not plug and play. There are no shortcuts. If you're impatient, this probably isn't for you." And that's exactly why most people will fail at this. Because you want results now. Today. This trade. You don't want to spend a week building a bot. You don't want to paper trade for 2 weeks. You don't want to test 50 ideas to find 1 that works. You want to copy someone's bot, run it live with your rent money, and be rich by Friday. That's why you'll be broke by Friday. The guy making $2.3M spent months iterating. Testing. Failing. Rebuilding. Testing again. He was patient when you would have quit. He was calm when you would have panicked. He was consistent when you would have given up. Patience isn't just a virtue in trading. It's the only virtue. Without it, everything else fails. Impatience is the most expensive personality trait in trading. Save this post. Follow Himanshu Kumar and learn to build systems with the patience that actually pays. ↓ The live streams where the real learning happens. The YouTube video is the trailer. The live streams are the movie. Real-time bot building. Real-time questions answered. Real code shown. Real mistakes made and fixed. Not polished highlight reels where everything works perfectly. Actual development. Where things break. Where strategies fail. Where code doesn't compile. Where the fix takes 2 hours. Because that's what real development looks like. And seeing the messy parts is more valuable than any polished tutorial. Because when your bot breaks at 3 AM, you need to know how to fix it. Not just how to celebrate when it works. The streams mix beginner and advanced. Start with how to automate trading. How to use AI for code generation. Then dive into the daily work. Claude Code. Parallel agents. Constant iteration. Live debugging. 4-8 hours of real algorithmic trading development. Live. Uncut. No filter. Most "trading education" shows you the wins. This shows you the work. Save this post. Follow Himanshu Kumar for the stream schedules and breakdowns. ↓ The belief that changes everything. Code is the greatest equalizer. Not money. Not connections. Not a degree. Not where you grew up. Not what school you went to. Code. Once you can build systems, you can build anything. For the rest of your life. A trading bot today. A SaaS product tomorrow. An automation business next month. A completely different life next year. The skill isn't "algorithmic trading." The skill is building systems. And that skill transfers to everything. The guy who can build a trading bot can also build a lead gen tool. Can also build a content pipeline. Can also build a SaaS product. Can also build literally anything that runs on logic and code. One skill. Infinite applications. And AI makes learning it 100x easier than it was 5 years ago. You don't need to be smart. You don't need talent. You need Claude Code and the willingness to sit down and build something instead of consuming content about building something. Building is the skill. Everything else is entertainment disguised as education. Save this post. Follow Himanshu Kumar because I'm showing you how to build, not just how to watch. ↓ If any of this applies to you, pay attention. If you've lost money from overtrading. If you've been liquidated. If you know trading is the vehicle but manual execution keeps crashing you. If you've tried "being more disciplined" and it never lasted more than a week. If you keep saying "next month I'll start automating." If you've spent more money on courses than you've made from trading. There is a better way. It's not a magic indicator. It's not a signal group. It's not a $997 mentorship from a guy who makes money teaching, not trading. It's building your own system. A system that trades without emotion. A system that follows rules without exception. A system that runs while you sleep. A system that compounds while you live your life. That's the answer. It's always been the answer. You've just been too scared to accept that the solution requires building something instead of buying something. ↓ What the next 30 days look like if you actually commit. Week 1: Watch the video. Learn Claude Code basics. Build your first simple strategy. Run your first backtest. Week 2: Iterate. Let Claude improve the strategy. Run Monte Carlo validation. Paper trade. Week 3: Go live with $50-100. Tiny positions. Watch every trade. Compare to paper results. Week 4: Scale based on evidence. Not based on excitement. Not based on one good day. Based on data. 30 days from now you either have a running bot that trades without your emotions destroying every position. Or you're exactly where you are right now. Reading another post. Making another promise. Breaking it by Tuesday. Same 30 days either way. Different actions. Different results. Different life. ↓ Full video tutorial attached. Live bot building with Claude Code. From zero to running Polymarket trading bot. Every line of code. Every decision explained. The video is free. Claude Code is available now. The market is open 24/7. The only thing standing between you and a profitable trading bot is the same thing that's been standing there for months. You. Get out of your own way. Follow Himanshu Kumar for daily AI trading bot breakdowns, live build sessions, and the full RBI process. Save this post. Watch the video. Build the bot. Or keep trading manually and keep losing. The choice has never been easier. And you've never been more stubborn about making the wrong one.

Made $530,000 with Ai Bot that started with $313. Didn't know how to code. Now this bots run 24/7 printing money while sleeping. I've made the exact step-by-step guide to build this Claude Code Polymarket trading bot. Prompts. Code. Risk settings. Paper trading checklist. Everything from zero to running bot. It's free. For 24 hours. After that I'm charging $499 for it. To grab it right now: 1. Comment "Claude Bot" 2. Like and Retweet this post 3. Follow me Himanshu Kumar ( I can't send DMs to non-followers ) I'm DMing everyone who Complete the 3 steps. I spent hundreds of thousands hiring developers because he was too scared to learn. Then learned Claude Code. Built algorithmic trading systems. $313 → $530,000. You have the same tools available right now. And you're using them to ask ChatGPT for Instagram captions. This attached video is a goldmine. Full live walkthrough. Claude Code building actual Polymarket trading bots. From zero. Every line of code. Every decision explained. Now let me break down why everything you're doing in trading is wrong and exactly how to fix it. Save this post. You'll hate yourself if you lose it. ↓ Let's start with why you keep losing money. You already know the answer. You just won't admit it. You overtrade. Every. Single. Day. You see a candle move. You feel something. You enter. No plan. No edge. No reason. Just feelings. Then it goes against you. You feel something else. Panic. Anger. Denial. You move your stop loss. Or you didn't set one at all. "It'll come back." It doesn't come back. So you take another trade. A revenge trade. Bigger size this time. Because you need to "make it back." That one fails too. Now you're emotional. Now you're tilted. Now you're using leverage you have no business touching. 40x. 50x. 100x. On a trade you entered because a candle looked "bullish" and some guy on Twitter said "send it." You get liquidated. Close the laptop. Punch something. Tell yourself you'll be "more disciplined" tomorrow. Tomorrow comes. Same cycle. Same result. Same liquidation. You've been doing this for months. Maybe years. And you still think the problem is your strategy. The problem isn't your strategy. The problem is you. Save this post right now. What I'm about to show you is the only way to remove yourself from the equation. Follow Himanshu Kumar so you don't miss any of this. ↓ Here's what's actually killing your account. It's not the market. The market doesn't care about you. It's not your indicators. RSI works fine. MACD works fine. They all "work." It's not your timeframe. It's not your broker. It's not the "manipulation." It's four things: 1. Emotions. You hold losers because hope feels better than loss. You cut winners because fear feels stronger than greed. You size up when angry. You skip trades when scared. Your emotional state determines your position size. That's insane. And you know it's insane. But you keep doing it. 2. Overtrading. You take 15 trades a day. Maybe 5 of them had actual setups. The other 10 were boredom. Boredom trades are the most expensive hobby in human history. 3. Leverage. You use 20x-50x on trades where you're not even sure about the direction. That's not trading. That's a casino with a nicer interface. 4. Fees. You're smashing market orders. Paying spread. Paying commission. On 15 trades a day. Your broker makes more money from your account than you do. Think about that. Your broker is profitable on your account. You're not. You're the product. Not the trader. These four things are why 90% of traders lose. Not bad luck. Not the market. You. Save this post and follow Himanshu Kumar because the solution is coming next. ↓ The solution is painfully obvious. Remove yourself from the equation. Not partially. Not "I'll be more disciplined." Not "I'll journal my trades." Not "I'll meditate before trading." Completely remove yourself. Build a bot. Let the bot trade. You go live your life. The bot doesn't feel emotions. The bot doesn't overtrade. The bot doesn't use reckless leverage. The bot doesn't smash market orders and bleed fees. The bot follows the rules. Every single time. Without exception. Without "just this once." Without "I have a feeling about this one." Rules in. Execution out. No human in the middle to mess everything up. That's algorithmic trading. And before your ego jumps in with "but I'm different, I have discipline" — No you don't. Your account balance proves you don't. If you had discipline, your account would be green. It's not. So you don't. Accept it. Automate it. Move on. This is the hardest truth in trading. Your discipline will always fail. A bot's won't. Save this post. Follow Himanshu Kumar for the exact bot setup that removes your emotions permanently. ↓ "But I don't know how to code." Neither did he. The guy in this video didn't know how to code for most of his life. Got held back in 7th grade. People counted him out early. Spent years building apps and SaaS businesses without writing a single line of code. Hired developers on Upwork instead. Spent hundreds of thousands of dollars paying other people to build what he could have built himself. Because he was scared to learn. That fear cost him years. And hundreds of thousands of dollars. Sound familiar? You're doing the same thing right now. Not with developers. But with your time. You're spending thousands of hours trading manually because you're scared to learn the thing that would make trading automatic. The fear of learning to code is costing you more than any bad trade ever did. Because every month you trade manually is a month of emotional decisions, overleveraged entries, and unnecessary losses that a bot would never make. And here's the thing that should really frustrate you: AI does the hard parts now. You don't need a computer science degree. You don't need to work at a hedge fund. You don't need to be "good at math." Claude Code writes the code for you. You just need to think clearly about trading ideas. That's it. If you can describe a strategy in English, Claude can build it in Python. "I don't know how to code" stopped being a valid excuse in 2024. It's 2026. You're 2 years late on that excuse. Find a new one. Or stop making excuses entirely. Save this post. Follow Himanshu Kumar because I'm showing you how people with zero coding experience are building profitable bots. ↓ The process that actually makes money. Three letters. R. B. I. Research. Backtest. Implement. That's it. That's the entire process. Every single day. Research: Find an idea. A pattern. A market inefficiency. Don't trade it yet. Don't even think about trading it yet. Just research it. Backtest: Test the idea against historical data. Does it work? Not "does it look good on one chart." Does it work across thousands of trades? Across different market conditions? Across in-sample AND out-of-sample data? If no, kill it. Find another idea. If yes, move to step 3. Implement: Build the bot. Deploy it. Paper trade first. Then live with small size. Scale only on evidence. Research. Backtest. Implement. Every day. No exceptions. You know what your current process is? Feel. Enter. Pray. F. E. P. Feel bullish. Enter a trade. Pray it works. That's not a process. That's gambling with a TradingView subscription. RBI is the only process that works. Save this post. Tattoo it on your forearm. Follow Himanshu Kumar for daily RBI breakdowns. ↓ What Claude Code actually does that your manual process can't. You can maybe test 3-5 strategy ideas per week. Manually adjusting parameters. Manually checking results. Manually writing code (badly). Claude Code tests 50-100 ideas per week. With parallel agents running simultaneously. Multiple strategies being built, tested, and validated at the same time. While you sleep. The guy in this video spends 4-8 hours a day building systems with Claude Code. Not trading. Building. Research. Backtest. Implement. Then iterate. Improve. Optimize. Every day the systems get better. Every day the edge compounds. Every day the bots get smarter. While you? You spend 4-8 hours a day staring at charts making the same mistakes you made last month. Same indicators. Same patterns. Same entries. Same losses. He's iterating forward. You're running in circles. Same 8 hours per day. Completely different outcomes. Because he's building systems. And you're feeding a casino. Stop feeding the casino. Start building the machine. Save this post and follow Himanshu Kumar for the Claude Code workflow that iterates strategies while you sleep. ↓ Jim Simons. That's the benchmark. You probably don't know who Jim Simons is. And that tells me everything about how seriously you take trading. Jim Simons. Mathematician. Founded Renaissance Technologies. Built a net worth of $31 billion. 100% from algorithmic trading. Not one single manual trade. Not one "gut feeling" entry. Not one RSI divergence. Not one "smart money concept." Algorithms. Bots. Systems. Data. $31 billion. His fund averaged 66% annual returns for over 30 years. While you're excited about making $200 on a trade that you'll give back tomorrow. The best trader in human history never placed a manual trade in his life. And you think your edge is staring at a 5-minute chart with bloodshot eyes at 2 AM? Your edge is building the system. Not being inside it. Jim Simons is the benchmark. Everything else is noise. Save this post. Follow Himanshu Kumar because I'm building toward the same goal and showing every step publicly. ↓ What you need to understand about patience. This is not get-rich-overnight. The guy in this video says it directly: "This channel is not for people looking to get rich overnight. It's not plug and play. There are no shortcuts. If you're impatient, this probably isn't for you." And that's exactly why most people will fail at this. Because you want results now. Today. This trade. You don't want to spend a week building a bot. You don't want to paper trade for 2 weeks. You don't want to test 50 ideas to find 1 that works. You want to copy someone's bot, run it live with your rent money, and be rich by Friday. That's why you'll be broke by Friday. The guy making $2.3M spent months iterating. Testing. Failing. Rebuilding. Testing again. He was patient when you would have quit. He was calm when you would have panicked. He was consistent when you would have given up. Patience isn't just a virtue in trading. It's the only virtue. Without it, everything else fails. Impatience is the most expensive personality trait in trading. Save this post. Follow Himanshu Kumar and learn to build systems with the patience that actually pays. ↓ The live streams where the real learning happens. The YouTube video is the trailer. The live streams are the movie. Real-time bot building. Real-time questions answered. Real code shown. Real mistakes made and fixed. Not polished highlight reels where everything works perfectly. Actual development. Where things break. Where strategies fail. Where code doesn't compile. Where the fix takes 2 hours. Because that's what real development looks like. And seeing the messy parts is more valuable than any polished tutorial. Because when your bot breaks at 3 AM, you need to know how to fix it. Not just how to celebrate when it works. The streams mix beginner and advanced. Start with how to automate trading. How to use AI for code generation. Then dive into the daily work. Claude Code. Parallel agents. Constant iteration. Live debugging. 4-8 hours of real algorithmic trading development. Live. Uncut. No filter. Most "trading education" shows you the wins. This shows you the work. Save this post. Follow Himanshu Kumar for the stream schedules and breakdowns. ↓ The belief that changes everything. Code is the greatest equalizer. Not money. Not connections. Not a degree. Not where you grew up. Not what school you went to. Code. Once you can build systems, you can build anything. For the rest of your life. A trading bot today. A SaaS product tomorrow. An automation business next month. A completely different life next year. The skill isn't "algorithmic trading." The skill is building systems. And that skill transfers to everything. The guy who can build a trading bot can also build a lead gen tool. Can also build a content pipeline. Can also build a SaaS product. Can also build literally anything that runs on logic and code. One skill. Infinite applications. And AI makes learning it 100x easier than it was 5 years ago. You don't need to be smart. You don't need talent. You need Claude Code and the willingness to sit down and build something instead of consuming content about building something. Building is the skill. Everything else is entertainment disguised as education. Save this post. Follow Himanshu Kumar because I'm showing you how to build, not just how to watch. ↓ If any of this applies to you, pay attention. If you've lost money from overtrading. If you've been liquidated. If you know trading is the vehicle but manual execution keeps crashing you. If you've tried "being more disciplined" and it never lasted more than a week. If you keep saying "next month I'll start automating." If you've spent more money on courses than you've made from trading. There is a better way. It's not a magic indicator. It's not a signal group. It's not a $997 mentorship from a guy who makes money teaching, not trading. It's building your own system. A system that trades without emotion. A system that follows rules without exception. A system that runs while you sleep. A system that compounds while you live your life. That's the answer. It's always been the answer. You've just been too scared to accept that the solution requires building something instead of buying something. ↓ What the next 30 days look like if you actually commit. Week 1: Watch the video. Learn Claude Code basics. Build your first simple strategy. Run your first backtest. Week 2: Iterate. Let Claude improve the strategy. Run Monte Carlo validation. Paper trade. Week 3: Go live with $50-100. Tiny positions. Watch every trade. Compare to paper results. Week 4: Scale based on evidence. Not based on excitement. Not based on one good day. Based on data. 30 days from now you either have a running bot that trades without your emotions destroying every position. Or you're exactly where you are right now. Reading another post. Making another promise. Breaking it by Tuesday. Same 30 days either way. Different actions. Different results. Different life. ↓ Full video tutorial attached. Live bot building with Claude Code. From zero to running Polymarket trading bot. Every line of code. Every decision explained. The video is free. Claude Code is available now. The market is open 24/7. The only thing standing between you and a profitable trading bot is the same thing that's been standing there for months. You. Get out of your own way. Follow Himanshu Kumar for daily AI trading bot breakdowns, live build sessions, and the full RBI process. Save this post. Watch the video. Build the bot. Or keep trading manually and keep losing. The choice has never been easier. And you've never been more stubborn about making the wrong one.

Himanshu Kumar

37,367 次观看 • 3 个月前