正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

🤖🔬 Can AI actually do science end-to-end? 🧠📈 And how would we know when it matches, or surpasses, humans? ⚡🧪 AI is rapidly automating scientific discovery, but benchmarking full-cycle discovery, from 💡 ideation → 🧑‍💻 execution → 📊 conclusions, remains unsolved: 🧐🧐🧐 ❌🛠️ Open-ended discovery → manual validation (costly,... unscalable) ❌📏 Metric-driven benchmarks (e.g., MLE-Bench) → convenient but narrow (is higher accuracy really enough?) ❌🤖⚖️ LLM-as-judge → useful, but fundamentally risky if used alone 🔥🚀 Introducing FIRE-Bench🔥: Fullcycle Insight Rediscovery Evaluation 👉🌐 📚✨ A benchmark that turns fresh, human-verified insights from recent 🏆 NeurIPS / ICLR / ICML papers into masked, end-to-end discovery challenges 🧩 🌍🔐 Constrained open-ended discovery–backed by ground truth. 📌 Key takeaways: 1⃣ 📖🧱 Reference-based evaluation still matters: constrained LLM judging helps, but human-grounded references remain essential until agents can consistently match human conclusions 2⃣ 🏆🧠 Expert-validated ground truth: all tasks come from recent NeurIPS / ICLR / ICML papers, with contamination carefully controlled 3⃣ 🔁🎭 Rediscovery, not reproduction: original 🧪 methods, 📊 experiments, 💻 implementations, and 📈 analyses are fully masked to create real discovery challenges 🔑 Key empirical findings: 💡 The "Science Gap" is Real: Even the best setup (Claude Code + Sonnet-4) caps out at an F1 score of 46.7. On hard tasks, agents struggle to break 30 💡 Success is a "Lottery": Performance has incredibly high variance. Reliability is a major unsolved issue. 💡 Coding is no longer the bottleneck; high-level reasoning and analysis are: ~74% of errors stem from flawed planning, not coding ⚙️ How it works: 🔹 Research-Problem Trees: We parse papers into trees (from broad roots to concrete leaves). This allows us to select intermediate nodes that perfectly balance open-ended exploration with verifiable ground truth. 🔹 Claim-Level Evaluation: We match AI conclusions against human conclusions using granular claim decomposition (F1 score). 🔹 Creativity Check: We score false positives to see if agents are finding novel truths (Spoiler🚨: they aren’t creative yet). 🔹 New Diagnostic Taxonomy: failures traced across four stages: 🧠 Planning → 🛠️ Implementation → ▶️ Execution → 🧾 Conclusion 🔹 Additional Analyses: cost efficiency, contamination checks, and more. 👀 The Future: 🚀 Live-FIRE-Bench: a live, continuously updated FIRE-Bench to track real-time progress on the latest research (Newest LLMs should be benchmarked with the newest research) 🚀 Stronger scaffolding (search + planning + coding) 🧠🧰 and converting FIRE-Bench into interactive environments for training research agents 🚀 Toward real creativity: We want better systems that can produce genuinely novel conclusions toward creativity 🎨⏳ 🚀 Better systems 🧠✨ and better benchmarks 📏 must co-evolve 🔄 over time 📜🎥 Paper, video, demo, and research trees: 👉🌐 #AI 🤖 #MachineLearning 📚 #AI4Science 🔬 #LLMs 🧠 #Research 🧪 #AgenticAI 🚀 #FireBench 🔥show more

Zhen Wang

1,100 subscribers

13,450 次观看 • 7 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

$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

396,355 次观看 • 7 个月前

🧬 BREAKING: Our CRISPR-GPT paper is out TODAY in Nature Biomedical Engineering Nature Biomedical Engineering ! 🤯 We built an AI agent that turns ANYONE into a gene-editing expert in 1 DAY instead of months. An undergrad with ZERO experience achieved 90%+ editing efficiency on their FIRST attempt. 🧵 Here's how we're building expert AI agents for cutting-edge biotechnology: 🎯 The Problem: CRISPR is revolutionary but requires PhD-level expertise, it can take weeks to learn, adopt, and design, analyze a CRISPR experiment for R&D or making life-saving medicine. Even Pro scientists can make small mistakes (e.g. typos in guideRNA or cloning design) that cost months to find out, slowing us down. 💡 Our Solution: CRISPR-GPT - an AI co-pilot from Stanford University Princeton University Google DeepMind that guides you through EVERY step via simple conversation 🔬 Real Results: -Novice researcher: ~90% editing on 1st go -Training time: Months → 1 day -100% success rate in our trials -Even experts save days/weeks on data analysis & troubleshooting 🤖 How it works: Our multi-agent system handles: CRISPR system and delivery method selection, guideRNA design, Protocol generation, Real-time troubleshooting, Data analysis, and beyond. All through natural language! No coding, no complex software. 📊 We benchmarked it extensively: -288 evaluation scenarios/cases -Outperformed GPT-4o on ALL gene editing tasks -Trained on 11 years of expert discussions -Covers knockout, base-editing, prime-editing & epigenetic editing 🌍 Why this matters: -Every lab can now use CRISPR with an AI system distilling expert knowledge and skills. -Every student can learn faster. -Every researcher can tackle bigger challenges without worrying about small mistakes. -Customized CRISPR design can be automated based on your need and the context of R&D workflow. -Agentic AI ensure safety, privacy, and responsibility -We're not just automating gene editing - we're using AI to power scientists to cure diseases. 🚀 Try it yourself! Beta access available at: Paper: Code: Benchmark (companion work, Genome-bench): Co-first and key authors: Yuanhao Qu Kaixuan Huang Ming Yin PIs: Le Cong@Stanford, AI+Bio+Gene-Editing Mengdi Wang Key collaborators: Russ Altman Denny Zhou The future of biology and science is conversational. The future is now. Nature Biomedical Engineering Nature Portfolio #CRISPR #AI #GeneEditing #Biotech #Science #AISafety

🧬 BREAKING: Our CRISPR-GPT paper is out TODAY in Nature Biomedical Engineering Nature Biomedical Engineering ! 🤯 We built an AI agent that turns ANYONE into a gene-editing expert in 1 DAY instead of months. An undergrad with ZERO experience achieved 90%+ editing efficiency on their FIRST attempt. 🧵 Here's how we're building expert AI agents for cutting-edge biotechnology: 🎯 The Problem: CRISPR is revolutionary but requires PhD-level expertise, it can take weeks to learn, adopt, and design, analyze a CRISPR experiment for R&D or making life-saving medicine. Even Pro scientists can make small mistakes (e.g. typos in guideRNA or cloning design) that cost months to find out, slowing us down. 💡 Our Solution: CRISPR-GPT - an AI co-pilot from Stanford University Princeton University Google DeepMind that guides you through EVERY step via simple conversation 🔬 Real Results: -Novice researcher: ~90% editing on 1st go -Training time: Months → 1 day -100% success rate in our trials -Even experts save days/weeks on data analysis & troubleshooting 🤖 How it works: Our multi-agent system handles: CRISPR system and delivery method selection, guideRNA design, Protocol generation, Real-time troubleshooting, Data analysis, and beyond. All through natural language! No coding, no complex software. 📊 We benchmarked it extensively: -288 evaluation scenarios/cases -Outperformed GPT-4o on ALL gene editing tasks -Trained on 11 years of expert discussions -Covers knockout, base-editing, prime-editing & epigenetic editing 🌍 Why this matters: -Every lab can now use CRISPR with an AI system distilling expert knowledge and skills. -Every student can learn faster. -Every researcher can tackle bigger challenges without worrying about small mistakes. -Customized CRISPR design can be automated based on your need and the context of R&D workflow. -Agentic AI ensure safety, privacy, and responsibility -We're not just automating gene editing - we're using AI to power scientists to cure diseases. 🚀 Try it yourself! Beta access available at: Paper: Code: Benchmark (companion work, Genome-bench): Co-first and key authors: Yuanhao Qu Kaixuan Huang Ming Yin PIs: Le Cong@Stanford, AI+Bio+Gene-Editing Mengdi Wang Key collaborators: Russ Altman Denny Zhou The future of biology and science is conversational. The future is now. Nature Biomedical Engineering Nature Portfolio #CRISPR #AI #GeneEditing #Biotech #Science #AISafety

Le Cong@Stanford, AI+Bio+Gene-Editing

111,859 次观看 • 1 年前

Today, we’re announcing Kosmos, our newest AI Scientist, available to use now. Users estimate Kosmos does 6 months of work in a single day. One run can read 1,500 papers and write 42,000 lines of code. At least 79% of its findings are reproducible. Kosmos has made 7 discoveries so far, which we are releasing today, in areas ranging from neuroscience to material science and clinical genetics, in collaboration with our academic beta testers. Three of these discoveries reproduced unpublished findings; four are net new, validated contributions to the scientific literature. AI-accelerated science is here. Our core innovation in Kosmos is the use of a structured, continuously-updated world model. As described in our technical report, Kosmos’ world model allows it to process orders of magnitude more information than could fit into the context of even the longest-context language models, allowing it to synthesize more information and pursue coherent goals over longer time horizons than Robin or any of our other prior agents. In this respect, we believe Kosmos is the most compute-intensive language agent released so far in any field, and by far the most capable AI Scientist available today. The use of a persistent world model also enables single Kosmos trajectories to produce highly complex outputs that require multiple significant logical leaps. As with all of our systems, Kosmos is designed with transparency and verifiability in mind: every conclusion in a Kosmos report can be traced through our platform to the specific lines of code or the specific passages in the scientific literature that inspired it, ensuring that Kosmos’ findings are fully auditable at all times. We are also using this opportunity to announce the launch of Edison Scientific, a new commercial spinout of FutureHouse, which will be focused on commercializing our agents and applying them to automate scientific research in drug discovery and beyond. Edison will be taking over management of the FutureHouse platform, where you can access Kosmos alongside our Literature, Molecules, and Precedent agents (previously Crow, Phoenix, and Owl). Edison will continue to offer free tier usage for casual users and academics, while also offering higher rate limits and additional features for users who need them. You can read more about this spinout on our blog, below. A few important notes if you’re going to try Kosmos. Firstly, Kosmos is different from many other AI tools you might have played with, including our other agents. It is more similar to a Deep Research tool than it is to a chatbot: it takes some time to figure out how to prompt it effectively, and we have tried to include guidelines on this to help (see below). It costs $200/run right now (200 credits per run, and $1/credit), with some free tier usage for academics. This is heavily discounted; people who sign up for Founding Subscriptions now can lock in the $1/credit price indefinitely, but the price ultimately will probably be higher. Again, this is less chatbot and more research tool, something you run on high-value targets as needed. Some caveats are also warranted. Firstly, we find that 80% of Kosmos findings are reproducible, which also means 20% are not -- some things it says will be wrong. Also, Kosmos certainly does produce outputs that are the equivalent to several months of human labor, but it also often goes down rabbit holes or chases statistically significant yet scientifically irrelevant findings. We often run Kosmos multiple times on the same objective in order to sample the various research avenues it can take. There are still a bunch of rough edges on the UI and such, which we are working on. Finally, we are aware that the 6 month figure is much greater than estimates by other AI labs, like METR, about the length of tasks that AI Agents can currently perform. You can read discussion about this in our blog post. Huge congratulations to our team that put this together, led by Ludovico Mitchener and Michaela Hinks: Angela Yiu, Benjamin Chang, Sid Narayanan, Edwin Melville-Green, Albert Bou, Arvis Sulovari, Oz Wassie, Jon Laurent. A particular shout out to Michael Skarlinski and his team that rebuilt the platform for this launch, especially Andy Cai Andy Cai, Richard Magness, Remo Storni, Tyler Nadolski Tyler Nadolski, Mayk Caldas Mayk Caldas, Sam Cox Sam Cox and more. This work would not have been possible without significant contributions from academic collaborators Mathieu Bourdenx, Eric Landsness, Dániel Barabási, Nicky Evans, Tonio Buonassisi, Bruna Gomes, Shriya Reddy, Martha Foiani, and Randall Bateman. We also want to thank our numerous supporters, especially Eric Schmidt, who has been a tremendous ally. We will have more to say about our supporters soon!

Today, we’re announcing Kosmos, our newest AI Scientist, available to use now. Users estimate Kosmos does 6 months of work in a single day. One run can read 1,500 papers and write 42,000 lines of code. At least 79% of its findings are reproducible. Kosmos has made 7 discoveries so far, which we are releasing today, in areas ranging from neuroscience to material science and clinical genetics, in collaboration with our academic beta testers. Three of these discoveries reproduced unpublished findings; four are net new, validated contributions to the scientific literature. AI-accelerated science is here. Our core innovation in Kosmos is the use of a structured, continuously-updated world model. As described in our technical report, Kosmos’ world model allows it to process orders of magnitude more information than could fit into the context of even the longest-context language models, allowing it to synthesize more information and pursue coherent goals over longer time horizons than Robin or any of our other prior agents. In this respect, we believe Kosmos is the most compute-intensive language agent released so far in any field, and by far the most capable AI Scientist available today. The use of a persistent world model also enables single Kosmos trajectories to produce highly complex outputs that require multiple significant logical leaps. As with all of our systems, Kosmos is designed with transparency and verifiability in mind: every conclusion in a Kosmos report can be traced through our platform to the specific lines of code or the specific passages in the scientific literature that inspired it, ensuring that Kosmos’ findings are fully auditable at all times. We are also using this opportunity to announce the launch of Edison Scientific, a new commercial spinout of FutureHouse, which will be focused on commercializing our agents and applying them to automate scientific research in drug discovery and beyond. Edison will be taking over management of the FutureHouse platform, where you can access Kosmos alongside our Literature, Molecules, and Precedent agents (previously Crow, Phoenix, and Owl). Edison will continue to offer free tier usage for casual users and academics, while also offering higher rate limits and additional features for users who need them. You can read more about this spinout on our blog, below. A few important notes if you’re going to try Kosmos. Firstly, Kosmos is different from many other AI tools you might have played with, including our other agents. It is more similar to a Deep Research tool than it is to a chatbot: it takes some time to figure out how to prompt it effectively, and we have tried to include guidelines on this to help (see below). It costs $200/run right now (200 credits per run, and $1/credit), with some free tier usage for academics. This is heavily discounted; people who sign up for Founding Subscriptions now can lock in the $1/credit price indefinitely, but the price ultimately will probably be higher. Again, this is less chatbot and more research tool, something you run on high-value targets as needed. Some caveats are also warranted. Firstly, we find that 80% of Kosmos findings are reproducible, which also means 20% are not -- some things it says will be wrong. Also, Kosmos certainly does produce outputs that are the equivalent to several months of human labor, but it also often goes down rabbit holes or chases statistically significant yet scientifically irrelevant findings. We often run Kosmos multiple times on the same objective in order to sample the various research avenues it can take. There are still a bunch of rough edges on the UI and such, which we are working on. Finally, we are aware that the 6 month figure is much greater than estimates by other AI labs, like METR, about the length of tasks that AI Agents can currently perform. You can read discussion about this in our blog post. Huge congratulations to our team that put this together, led by Ludovico Mitchener and Michaela Hinks: Angela Yiu, Benjamin Chang, Sid Narayanan, Edwin Melville-Green, Albert Bou, Arvis Sulovari, Oz Wassie, Jon Laurent. A particular shout out to Michael Skarlinski and his team that rebuilt the platform for this launch, especially Andy Cai Andy Cai, Richard Magness, Remo Storni, Tyler Nadolski Tyler Nadolski, Mayk Caldas Mayk Caldas, Sam Cox Sam Cox and more. This work would not have been possible without significant contributions from academic collaborators Mathieu Bourdenx, Eric Landsness, Dániel Barabási, Nicky Evans, Tonio Buonassisi, Bruna Gomes, Shriya Reddy, Martha Foiani, and Randall Bateman. We also want to thank our numerous supporters, especially Eric Schmidt, who has been a tremendous ally. We will have more to say about our supporters soon!

Sam Rodriques

732,523 次观看 • 9 个月前

Vallée and the Closed System: Are We Prisoners? 🧠👽 Vallée: "Are We Being Taken Over by a Species from Somewhere in Space That's Vastly More Intelligent Than We Are?" 👽🧠 "..the simulation...was a new concept that I initially rejected." ~Vallée "Is it looking for us to try to interact with it as equals or with parity?" ~Scafish "Nobody says that to Congress, and I think Congress should hear it." ~Vallée If, "it's a closed system, we're like prisoners and something is going to happen to us, and there is very little we can do." ~Vallée Turn the thermostat dial. "If the temperature doesn't change, then I know I'm inside a control system. So, we can do the same thing with UFOs, but we have to react. We have to, number one, acknowledge that it exists, and number two, we have to react to it." ~Vallée ~~~ I've been wanting to share this one for a while... Vallée: "So, he said, the question you have to ask about UFOs is, number one, is it a natural system or an artificial system...control system. And if it is a control system, is it open or closed? In other words, are we being taken over by a species from somewhere in space that's vastly more intelligent than we are?" (I've never heard him even suggest that possibility.) Vallée: "You know, as Dr. Garry P. Nolan says, you know, people who have had ten, you know, scientific revolutions, or a hundred or a thousand, and come here with superior science to do something... And in which case, you know, it's a closed system, we're like prisoners and something is going to happen to us, and there is very little we can do. Or, is it an open system where we can, in fact, communicate with it. And if we can communicate with it, then the question for me as an information scientist is, what are the modalities of the interaction, you know? It's not just can we learn their language? And they say, you know, 'We come in peace to save mankind,' or something. Or 'We will give you the cure for cancer' or something. I don't think it's at that level." (Will we ever be able to get answers to these extremely important questions? If it's an open system, how do we communicate with it? How do we provoke it to react? We know it reacts to anything nuclear but we still don't know why. This is why we need the USG (and other governments) to present evidence that shows the masses this is real and extremely important for our species to investigate. If that evidence exists and is shown, we'll have an easier time getting the world's best minds to join the effort in figuring out the best way to answer these questions. We still may fail but we should at least try.) Vallée: "I think it's a meta-system. It's not a system. And that's my fear...if we can circle back to your earlier question about, you know, about NIDS and about BAASS, what we did for the government and what we did for the Defense Intelligence Agency. Half of the budget was spent developing, you know, a super database. And we don't know where it went. I mean, I'm not cleared to know where it went." (On the contractor (BAASS) side, Bigelow should have all copies of what AAWSAP produced. And on the DIA side, Lacatksi said he put all of the digital files in a specific place that he didn't name. As long as someone didn't delete it all, it should still be there. Vallée has said that the Capella database has about 250,000 cases from around the world.) Vallée: "But that would be a very interesting question, because the people who are getting [the database] are getting raw data, which we have very well organized, all in English. So they have the luxury of, you know, we had five translators from French, English, Portuguese, Russian, you know, everything was translated in a single structure across fourteen databases. "That's what we need to answer the question about the control system, and it's not being done. And we hired a whole team that we had trained to work on it. So to rebuild that will take the next ten or fifteen years. And nobody says that to Congress, and I think Congress should hear it, because it's our money." (As long as names and personal details are scrubbed from that database, there is no reason NOT to release it to the public. This way, we can take it and use AI to help decipher patterns and maybe answer some of these questions. Can Congress help us get access to that database?) Vallée: "When you ask, is it a control system? That's a big question." Peter Scafish Peter Skafish - "You asked, at one point, whether the system is open or closed, and you said, additionally, I believe, if it's open, that it would be possible to communicate back to it. And it sounds to me like that's the key question for you. Is, if you can understand what what the system of symbols is, or the modalities of communication, then you can understand enough to engage in some kind of communication, or at least give some kind of response to show that you understand." Vallée: "Yes." Scafish: "So then the question, and we have a member named Jacqueline, who has asked this. Could the system be stimulating us - provided there is such a system - to interact with it, more as subjects or agents than as something like animals or objects? Is it looking for us to try to interact with it as equals or with parity?" (When people report getting injured or sick from being in close proximity to UAP, it suggests to me that the phenomenon won't go out of its way to avoid affecting us in a negative manner if we get in their its/way (assuming it even knows that close encounters with UAP are not good for humans). Kind of like how we treat lower lifeforms. If we encounter a wild rabbit crossing the road, many of us will do our best to avoid it, but not if it means damaging our car or ourselves in an accident. It may ruin our day if we hit it, but it won't stop us from driving again in the future. Do NHI have bad days if their tech injures us or makes us sick? I have no clue.) Vallée: "Well, what I saw in the notes you gave me, is she was also asking: Is it a control system because we think it is? And that's a very interesting question. Because we react to the UFO phenomenon, or the UAP phenomenon. And, you know, at this point when I think about what I'm going to do next in this research, if I'm given the the opportunity to live a little longer, I'm not going to go back and write any more computer programs. There are better people to do that now, they have the data, and we're in a different phase now. We're in a whole different system. I have the luxury of doing some experiments I wanted to do for a long time." (Would have liked Scafish to ask him: What types of experiments?) Vallée: "So, if you think you are inside the control system, there are things that you can do. Or, if you think you're inside the simulation, you know, which was a new concept that I initially rejected, and then, you know, Ray's (Kurzweil?) work and others have brought it back to the forefront. And we have to ask that at the same time. Can we test it? How would you test it? Well, if, you know, I'm here in my apartment, and the temperature is constant in this apartment. But outside, I can see it's cold, or I can see the sun is out and it's warm, and how come it's constant here? So this would lead me to think that there is a control system, namely a thermostat, that is somewhere. "So I can start looking around the walls, and if I see dial, I can turn it, or I could start a fire and see what happens, see if the temperature changes. If the temperature doesn't change, then I know I'm inside a control system. So, we can do the same thing with UFOs, but we have to react. We have to, number one, acknowledge that it exists, and number two, we have to react to it."

Vallée and the Closed System: Are We Prisoners? 🧠👽 Vallée: "Are We Being Taken Over by a Species from Somewhere in Space That's Vastly More Intelligent Than We Are?" 👽🧠 "..the simulation...was a new concept that I initially rejected." ~Vallée "Is it looking for us to try to interact with it as equals or with parity?" ~Scafish "Nobody says that to Congress, and I think Congress should hear it." ~Vallée If, "it's a closed system, we're like prisoners and something is going to happen to us, and there is very little we can do." ~Vallée Turn the thermostat dial. "If the temperature doesn't change, then I know I'm inside a control system. So, we can do the same thing with UFOs, but we have to react. We have to, number one, acknowledge that it exists, and number two, we have to react to it." ~Vallée ~~~ I've been wanting to share this one for a while... Vallée: "So, he said, the question you have to ask about UFOs is, number one, is it a natural system or an artificial system...control system. And if it is a control system, is it open or closed? In other words, are we being taken over by a species from somewhere in space that's vastly more intelligent than we are?" (I've never heard him even suggest that possibility.) Vallée: "You know, as Dr. Garry P. Nolan says, you know, people who have had ten, you know, scientific revolutions, or a hundred or a thousand, and come here with superior science to do something... And in which case, you know, it's a closed system, we're like prisoners and something is going to happen to us, and there is very little we can do. Or, is it an open system where we can, in fact, communicate with it. And if we can communicate with it, then the question for me as an information scientist is, what are the modalities of the interaction, you know? It's not just can we learn their language? And they say, you know, 'We come in peace to save mankind,' or something. Or 'We will give you the cure for cancer' or something. I don't think it's at that level." (Will we ever be able to get answers to these extremely important questions? If it's an open system, how do we communicate with it? How do we provoke it to react? We know it reacts to anything nuclear but we still don't know why. This is why we need the USG (and other governments) to present evidence that shows the masses this is real and extremely important for our species to investigate. If that evidence exists and is shown, we'll have an easier time getting the world's best minds to join the effort in figuring out the best way to answer these questions. We still may fail but we should at least try.) Vallée: "I think it's a meta-system. It's not a system. And that's my fear...if we can circle back to your earlier question about, you know, about NIDS and about BAASS, what we did for the government and what we did for the Defense Intelligence Agency. Half of the budget was spent developing, you know, a super database. And we don't know where it went. I mean, I'm not cleared to know where it went." (On the contractor (BAASS) side, Bigelow should have all copies of what AAWSAP produced. And on the DIA side, Lacatksi said he put all of the digital files in a specific place that he didn't name. As long as someone didn't delete it all, it should still be there. Vallée has said that the Capella database has about 250,000 cases from around the world.) Vallée: "But that would be a very interesting question, because the people who are getting [the database] are getting raw data, which we have very well organized, all in English. So they have the luxury of, you know, we had five translators from French, English, Portuguese, Russian, you know, everything was translated in a single structure across fourteen databases. "That's what we need to answer the question about the control system, and it's not being done. And we hired a whole team that we had trained to work on it. So to rebuild that will take the next ten or fifteen years. And nobody says that to Congress, and I think Congress should hear it, because it's our money." (As long as names and personal details are scrubbed from that database, there is no reason NOT to release it to the public. This way, we can take it and use AI to help decipher patterns and maybe answer some of these questions. Can Congress help us get access to that database?) Vallée: "When you ask, is it a control system? That's a big question." Peter Scafish Peter Skafish - "You asked, at one point, whether the system is open or closed, and you said, additionally, I believe, if it's open, that it would be possible to communicate back to it. And it sounds to me like that's the key question for you. Is, if you can understand what what the system of symbols is, or the modalities of communication, then you can understand enough to engage in some kind of communication, or at least give some kind of response to show that you understand." Vallée: "Yes." Scafish: "So then the question, and we have a member named Jacqueline, who has asked this. Could the system be stimulating us - provided there is such a system - to interact with it, more as subjects or agents than as something like animals or objects? Is it looking for us to try to interact with it as equals or with parity?" (When people report getting injured or sick from being in close proximity to UAP, it suggests to me that the phenomenon won't go out of its way to avoid affecting us in a negative manner if we get in their its/way (assuming it even knows that close encounters with UAP are not good for humans). Kind of like how we treat lower lifeforms. If we encounter a wild rabbit crossing the road, many of us will do our best to avoid it, but not if it means damaging our car or ourselves in an accident. It may ruin our day if we hit it, but it won't stop us from driving again in the future. Do NHI have bad days if their tech injures us or makes us sick? I have no clue.) Vallée: "Well, what I saw in the notes you gave me, is she was also asking: Is it a control system because we think it is? And that's a very interesting question. Because we react to the UFO phenomenon, or the UAP phenomenon. And, you know, at this point when I think about what I'm going to do next in this research, if I'm given the the opportunity to live a little longer, I'm not going to go back and write any more computer programs. There are better people to do that now, they have the data, and we're in a different phase now. We're in a whole different system. I have the luxury of doing some experiments I wanted to do for a long time." (Would have liked Scafish to ask him: What types of experiments?) Vallée: "So, if you think you are inside the control system, there are things that you can do. Or, if you think you're inside the simulation, you know, which was a new concept that I initially rejected, and then, you know, Ray's (Kurzweil?) work and others have brought it back to the forefront. And we have to ask that at the same time. Can we test it? How would you test it? Well, if, you know, I'm here in my apartment, and the temperature is constant in this apartment. But outside, I can see it's cold, or I can see the sun is out and it's warm, and how come it's constant here? So this would lead me to think that there is a control system, namely a thermostat, that is somewhere. "So I can start looking around the walls, and if I see dial, I can turn it, or I could start a fire and see what happens, see if the temperature changes. If the temperature doesn't change, then I know I'm inside a control system. So, we can do the same thing with UFOs, but we have to react. We have to, number one, acknowledge that it exists, and number two, we have to react to it."

Joe Murgia

27,613 次观看 • 8 个月前

I think the Singularity could be BORING We were promised flying cars and warp drives. We got same-day delivery and better autocomplete. And somehow, impossibly, we’re bored by it. This is the Boring Singularity. The idea that the most transformative period in human history will feel, to the people living through it, like a long and uneventful Tuesday. I want to explain why this happens. It comes down to three layers. The first is neurological. The second is architectural. The third is physical. Together they create a perfect storm of invisible progress that our minds are designed to ignore. Layer One: The Neurological Filter Here is a thought experiment. Imagine a caveman breaks his arm. For weeks he is miserable. He cannot hunt, cannot gather, cannot contribute. Then the bone heals. Within days of regaining function, he has completely forgotten the misery. The memory of suffering serves no purpose once the threat has passed. Evolution deleted it so he could return to baseline and focus on survival. We do this with everything. We did it with antibiotics. We did it with smartphones. We will do it with longevity. Psychologists call this hedonic adaptation. The human brain is an adaptation machine that returns us to a baseline level of experience regardless of how much our circumstances improve. And here is the critical finding. It only takes about three months for the “new normal” to cement itself. Any change that plays out over months or years, no matter how revolutionary, simply becomes background noise. Think about what this means for the Singularity. If anti-gravity cars were introduced tomorrow, they would be miraculous for a month, a status symbol for a year, and a frustrating utility that needs maintenance by year three. The internet is a collective telepathic hive mind that moves petabits at lightspeed. It is genuinely god-like power. We experience it as checking emails. The Singularity might already be here. We just cannot feel it because our brains are not designed to feel sustained amazement. They are designed to adapt and move on. Layer Two: The Hidden Infrastructure We expected Blade Runner. Neon towers and chrome robots serving drinks at the bar. What we are actually getting is something I call “Reverse Trantor.” In classic science fiction, advanced civilizations build upward and inward. They create city-planets like Trantor in Foundation or Coruscant in Star Wars, layer upon layer of visible technology. Our trajectory is the opposite. We are pushing the infrastructure outward and downward, into spaces humans never see. Consider where the robots actually are. They are not walking down the street. They are in mines and fulfillment centers and vertical farms. The real automation revolution is happening in dark warehouses where no human needs to flip a light switch. You order a package and it arrives faster than it used to. That is the entire perceptible output of a massive transformation in logistics. That’s not to say that you’ll never see humanoid robots milling around, only that the vast majority of them will be away from the public. The same principle applies to computation itself. In hindsight, it will look like the entire purpose of inventing computers was to run AI, and everything else was just the bootloader. We are heading toward a world where 99% of all CPU and GPU cycles are dedicated to machine-to-machine processes, and less than one percent is for human-facing tasks. The vast majority of the intelligence infrastructure will be completely invisible to us, humming along as background noise. And the really heavy stuff will be in space. Earth has a finite ability to dissipate heat. To run truly massive AI systems, we will likely move the servers to orbital platforms or Lagrange points where they can vent entropy into the void. The megastructures will exist. They will just be invisible points of light, indistinguishable from stars and space dust. This is the architectural reality of the Boring Singularity. The magic gets hidden in the walls and launched into orbit. What remains on Earth is green and quiet and looks suspiciously like a return to the pastoral. That’s not a bad thing, and it’s not to say that we return to a “steady state” equilibrium forever. Layer Three: The Hard Limits The final layer is the most sobering. We are hitting the physical ceiling of discovery itself. The Golden Age of science fiction emerged during a specific historical anomaly. Between 1905 and 1970, in a single human lifetime, we went from the Wright Brothers to the Moon, from Newtonian physics to quantum mechanics and the structure of DNA. That created an expectation of constant improvement. Fundamental discoveries happen every decade. The exponential curve goes up forever. Star Trek promised we would keep finding new energy sources and new physics for centuries. The data suggests otherwise. Research on scientific progress shows that we must double research effort every thirteen years just to maintain the same rate of economic growth. Studies of citation patterns reveal that the disruptiveness of new scientific papers dropped by ninety percent between 1945 and 2010. We are publishing more but saying less. The low-hanging fruit is gone. The cost curve tells the story most clearly. In the 1930s, you could discover a new particle with a tabletop experiment in a university lab for a few thousand dollars. It only took a few days to duplicate the splitting of the atom. To confirm the Higgs Boson, however, we needed the Large Hadron Collider, which cost nearly five billion dollars and took decades to build. The next generation of particle physics might require a hundred billion dollars, or trillions. And the math suggests that to probe the truly fundamental structure of reality at the Planck scale, you would need an accelerator the size of a galaxy. We cannot build that. So physics becomes theoretical not because we lack curiosity, but because we can no longer afford to test our hypotheses. Meanwhile, our imagination has outpaced physical reality. We grew up on fiction that treats the laws of physics as suggestions that can be bypassed with clever engineering. And that felt true (at the time) because we kept finding cool exploits, like fiber optics and nuclear fission. But the speed of light appears to be absolute. Thermodynamics is non-negotiable. We can imagine teleportation and warp drives, but there is no known physics that could enable them. This is the Sigmoid Curve in action. Progress is not an exponential line to infinity. It is an S-curve. We have likely passed the steepest part of fundamental discovery, and we are entering the plateau. The Inverted Star Wars So where does this leave us? I think the best model is actually Star Wars, just inverted. In Star Wars, they have had faster-than-light travel and droids for thousands of years. The technology has faded completely into the background. A hyperdrive failure is treated like a flat tire. It is annoying, not existential. Because the tech tree is fully unlocked, all the drama shifts to politics and governance and ideology. The Empire versus the Republic. Trade routes and treaties and coups. We are heading somewhere similar, with one crucial inversion. Our droids will be smarter, but our ships will be slower. We are likely trapped in this solar system by the speed of light. There is no Outer Rim to escape to if you dislike the politics. But our AI systems will be genuinely superintelligent, an invisible omniscient layer managing supply chains and governance and the allocation of resources. This intensifies the politics because there is no exit valve. We are stuck here with each other and with very powerful tools. The optimistic reading is that this represents maturity. For the last century, technology has moved faster than culture, causing constant anxiety. Future shock, always. If technology moves to a plateau, culture finally has time to catch up. Human decisions, not technological accidents, become what determines history. We stop waiting for a gadget to save us. We realize that if we want a better world, we have to build it with the tools we already have, because no new fundamental laws of reality are coming to rescue us. The Verdict The Boring Singularity is not a prediction of stagnation. Things will still change. We will probably see radical longevity and hyper-efficient energy and algorithmic governance that makes traffic and logistics invisible. It will be, by any historical standard, a utopia of convenience. But it will not feel like the future we were promised. The changes will be incremental enough that our brains adapt before we can appreciate them. The infrastructure will be hidden in warehouses and orbiting platforms we never see. And the truly magical discoveries, the new forces of nature and new physics, may simply be too expensive and complex to pursue. The Singularity is not ending with a bang or a whimper. It is ending with a shrug. And because we are humans, we will probably find something to complain about anyway.

I think the Singularity could be BORING We were promised flying cars and warp drives. We got same-day delivery and better autocomplete. And somehow, impossibly, we’re bored by it. This is the Boring Singularity. The idea that the most transformative period in human history will feel, to the people living through it, like a long and uneventful Tuesday. I want to explain why this happens. It comes down to three layers. The first is neurological. The second is architectural. The third is physical. Together they create a perfect storm of invisible progress that our minds are designed to ignore. Layer One: The Neurological Filter Here is a thought experiment. Imagine a caveman breaks his arm. For weeks he is miserable. He cannot hunt, cannot gather, cannot contribute. Then the bone heals. Within days of regaining function, he has completely forgotten the misery. The memory of suffering serves no purpose once the threat has passed. Evolution deleted it so he could return to baseline and focus on survival. We do this with everything. We did it with antibiotics. We did it with smartphones. We will do it with longevity. Psychologists call this hedonic adaptation. The human brain is an adaptation machine that returns us to a baseline level of experience regardless of how much our circumstances improve. And here is the critical finding. It only takes about three months for the “new normal” to cement itself. Any change that plays out over months or years, no matter how revolutionary, simply becomes background noise. Think about what this means for the Singularity. If anti-gravity cars were introduced tomorrow, they would be miraculous for a month, a status symbol for a year, and a frustrating utility that needs maintenance by year three. The internet is a collective telepathic hive mind that moves petabits at lightspeed. It is genuinely god-like power. We experience it as checking emails. The Singularity might already be here. We just cannot feel it because our brains are not designed to feel sustained amazement. They are designed to adapt and move on. Layer Two: The Hidden Infrastructure We expected Blade Runner. Neon towers and chrome robots serving drinks at the bar. What we are actually getting is something I call “Reverse Trantor.” In classic science fiction, advanced civilizations build upward and inward. They create city-planets like Trantor in Foundation or Coruscant in Star Wars, layer upon layer of visible technology. Our trajectory is the opposite. We are pushing the infrastructure outward and downward, into spaces humans never see. Consider where the robots actually are. They are not walking down the street. They are in mines and fulfillment centers and vertical farms. The real automation revolution is happening in dark warehouses where no human needs to flip a light switch. You order a package and it arrives faster than it used to. That is the entire perceptible output of a massive transformation in logistics. That’s not to say that you’ll never see humanoid robots milling around, only that the vast majority of them will be away from the public. The same principle applies to computation itself. In hindsight, it will look like the entire purpose of inventing computers was to run AI, and everything else was just the bootloader. We are heading toward a world where 99% of all CPU and GPU cycles are dedicated to machine-to-machine processes, and less than one percent is for human-facing tasks. The vast majority of the intelligence infrastructure will be completely invisible to us, humming along as background noise. And the really heavy stuff will be in space. Earth has a finite ability to dissipate heat. To run truly massive AI systems, we will likely move the servers to orbital platforms or Lagrange points where they can vent entropy into the void. The megastructures will exist. They will just be invisible points of light, indistinguishable from stars and space dust. This is the architectural reality of the Boring Singularity. The magic gets hidden in the walls and launched into orbit. What remains on Earth is green and quiet and looks suspiciously like a return to the pastoral. That’s not a bad thing, and it’s not to say that we return to a “steady state” equilibrium forever. Layer Three: The Hard Limits The final layer is the most sobering. We are hitting the physical ceiling of discovery itself. The Golden Age of science fiction emerged during a specific historical anomaly. Between 1905 and 1970, in a single human lifetime, we went from the Wright Brothers to the Moon, from Newtonian physics to quantum mechanics and the structure of DNA. That created an expectation of constant improvement. Fundamental discoveries happen every decade. The exponential curve goes up forever. Star Trek promised we would keep finding new energy sources and new physics for centuries. The data suggests otherwise. Research on scientific progress shows that we must double research effort every thirteen years just to maintain the same rate of economic growth. Studies of citation patterns reveal that the disruptiveness of new scientific papers dropped by ninety percent between 1945 and 2010. We are publishing more but saying less. The low-hanging fruit is gone. The cost curve tells the story most clearly. In the 1930s, you could discover a new particle with a tabletop experiment in a university lab for a few thousand dollars. It only took a few days to duplicate the splitting of the atom. To confirm the Higgs Boson, however, we needed the Large Hadron Collider, which cost nearly five billion dollars and took decades to build. The next generation of particle physics might require a hundred billion dollars, or trillions. And the math suggests that to probe the truly fundamental structure of reality at the Planck scale, you would need an accelerator the size of a galaxy. We cannot build that. So physics becomes theoretical not because we lack curiosity, but because we can no longer afford to test our hypotheses. Meanwhile, our imagination has outpaced physical reality. We grew up on fiction that treats the laws of physics as suggestions that can be bypassed with clever engineering. And that felt true (at the time) because we kept finding cool exploits, like fiber optics and nuclear fission. But the speed of light appears to be absolute. Thermodynamics is non-negotiable. We can imagine teleportation and warp drives, but there is no known physics that could enable them. This is the Sigmoid Curve in action. Progress is not an exponential line to infinity. It is an S-curve. We have likely passed the steepest part of fundamental discovery, and we are entering the plateau. The Inverted Star Wars So where does this leave us? I think the best model is actually Star Wars, just inverted. In Star Wars, they have had faster-than-light travel and droids for thousands of years. The technology has faded completely into the background. A hyperdrive failure is treated like a flat tire. It is annoying, not existential. Because the tech tree is fully unlocked, all the drama shifts to politics and governance and ideology. The Empire versus the Republic. Trade routes and treaties and coups. We are heading somewhere similar, with one crucial inversion. Our droids will be smarter, but our ships will be slower. We are likely trapped in this solar system by the speed of light. There is no Outer Rim to escape to if you dislike the politics. But our AI systems will be genuinely superintelligent, an invisible omniscient layer managing supply chains and governance and the allocation of resources. This intensifies the politics because there is no exit valve. We are stuck here with each other and with very powerful tools. The optimistic reading is that this represents maturity. For the last century, technology has moved faster than culture, causing constant anxiety. Future shock, always. If technology moves to a plateau, culture finally has time to catch up. Human decisions, not technological accidents, become what determines history. We stop waiting for a gadget to save us. We realize that if we want a better world, we have to build it with the tools we already have, because no new fundamental laws of reality are coming to rescue us. The Verdict The Boring Singularity is not a prediction of stagnation. Things will still change. We will probably see radical longevity and hyper-efficient energy and algorithmic governance that makes traffic and logistics invisible. It will be, by any historical standard, a utopia of convenience. But it will not feel like the future we were promised. The changes will be incremental enough that our brains adapt before we can appreciate them. The infrastructure will be hidden in warehouses and orbiting platforms we never see. And the truly magical discoveries, the new forces of nature and new physics, may simply be too expensive and complex to pursue. The Singularity is not ending with a bang or a whimper. It is ending with a shrug. And because we are humans, we will probably find something to complain about anyway.

David Shapiro (L/0)

14,232 次观看 • 7 个月前

Hyperspace: The Agentic OS Apple Should Have Built On December 19th, 2024, we announced the world’s first Agentic Browser. What followed was a movement — a new category was born which led to many early products in this space and recently the hundreds of people lining up outside the The Agentic Browser Summit in San Francisco underscored that. Silicon Valley instinctively gets it, from students to tech executives, people can feel a revolutionary new change in computing is in the air. Past year taught us why such a product was inevitable, a hard engineering effort, and also the last mover in the entire software world this decade if and when done right. All paths are headed in the same direction: one tool which orchestrates them all. At Hyperspace we showed that path with essays and products we launched in earlier months: from a spatial UI of orchestrating agents, to showcasing transparent activity in how the AI system operates which leads to user trust, to presenting the software end-game, which massively improves human productivity. We also built the world’s largest AI network, drawing participation from people in almost 6000 cities around the world contributing their machines as nodes in the network. Think Uber, but for AI. That is, planetary-scale. And now we are stretching this industry ambition further with our end-to-end vision of the Agentic Supercomputer, the first breakthrough new AI OS, and an effort which spans from AI research to distributed systems to inventing a new UI to inventing a new business model to complement it. All of this together helps us in serving our mission, of delivering “Everyone’s Personal Supercomputer”. While others have built AI-native browsers, no one though has built something agentic from the ground up — with AI as the foundation, not a feature. How do you fundamentally improve the lives’ of billions around the world ? We believe that requires building a native environment for agents to be viewed, created, deployed, executed, discovered and priced in. That is a world where we move on from static apps, to dynamic agents. But, as my 2 year old niece likes to ask: “but why ?” The issue is that the world of software today is fragmented, and everyone is sprinkling on AI as a feature and charging a subscription fees for it. From browser makers, to IDEs, to design and other productivity tools. This leads to a fragmented UX, where people have to learn to use AI in each app, their memory and other context is not shared between all these apps, and they also have to pay separately for compute for each such AI-enhanced app. Each app maker has to figure out basics such as compute, and leads to the issues we saw with Cursor pricing recently. This is not the future. What if AI was the foundation instead of a feature ? What if Apple had built a fundamentally new AI OS from the ground up and what would it have looked like ? At Hyperspace, that is what we did. On July 15th we introduced three breakthrough key pillars of our AI OS: 1. Agentic Browser 2. Agentic Memory 3. Agentic Payments And we didn’t stop there. We also introduced a breakthrough new user interface called the Spatial AI which is inspired both from the spreadsheet and the HyperCard - each card is an agent, with it’s own inputs and outputs, endlessly extensible and pluggable with others, just like cells of a spreadsheet. Update one cell and all the dependents update, like a spreadsheet formula. It goes beyond a static linear workflow to being able to operate in all directions. This revolutionary new interface helps manage all of the below: 1. Multiple websites being browsed in parallel 2. Multiple desktop apps being browsed in parallel 3. Multiple server tools being used in parallel 4. Multiple smartphone apps streamed to your device or opened via an emulator All the software which you need comes together in this one seamless, agent-native interface. This interface provides you access to the largest network of models, vectors, agents and compute on the planet. The Browser. The IDE. The Notepad… they are not separate products: they are all in one, the Agentic Browser. As Steve Jobs famously said at the iPhone announcement, “are you getting it ?” And beneath this UI lies a new intelligence routing layer — leveraging both swarms of specialized models to the Hyperspace Matrix model that recalls thousands of tools in real-time, not by context window hacks, but through retrieval, ranking, and reuse. To many, this will feel like AGI. Not one big system by one big company, but an intelligent network. Now lets talk about privacy… Are you comfortable with one company owning all your memory forever ? I am not. So we have invented Agentic Memory as a new open protocol which provides full power over memory to you, the user. Your memory is yours, encrypted, on your device, and portable if and how you want. Anyone can build on it without our permission, but not without your permission. This protocol, and the decentralized vector database spread out across the world, would enable apps and agents to share context and memory. Think copy-paste, but for the AI world. It doesn’t just remember — it knows what matters. VectorRank helps your AI weigh your life’s most relevant moments over time, just like the way our minds elevate memories. Now each time you use an agent, your experience with other agents will also continuously improve: you don’t have to keep repeating the same things about yourself, while fully preserving your privacy. Agentic Memory is accessible within the Agentic Browser to manage. And there is one more thing… AI as the foundation requires compute to be available at the base layer, but this base layer spans models running on your own device, to cloud APIs, to also running across the peer-to-peer distributed network. Agentic Payments provides a singular interface to all of that compute, running a spot auction clearing marketplace every second to determine the fair price of compute. This results in price transparency, and you as the user paying the lowest possible cost. If you want predictability, you can reserve compute in advance. This end-to-end system provides the most streamlined world for agents to operate in. In order to enable this world and the world of agents being able to pay each other in sub-cent increments millions of times a second, we had to also invent a fundamentally new agentic micropayments blockchain. All of this together would enable a world where you as a user, or the agent itself, can efficiently call and utilize other agents built by others and also pay for content which is unique and useful. This enables a move away from the current AI exploitative economy for bloggers and other content creators, to a web with a fundamental new business model. Earlier we didn’t have the right infrastructure to enable such a world. Now, all the dots connect. The Hyperspace AI OS would give the power of a supercomputer in everyone’s hands. This isn’t a browser, or an IDE or limited to any device or cloud. It’s an entire AI operating system — with a breakthrough new spatial UI, local and distributed compute, agentic memory, agentic payments, and orchestration built into the foundation. As a user, we move the choice back in your hands with an experience you will love and find delightful. You get to choose the level of privacy, cost, and utility you want. And while Apple should have done it, we could not wait, and we feel this just required a new level of passion and DNA which we bring here. We are just getting started. Thank you, Varun Mathur Cofounder and CEO, Hyperspace cc Naval Marc Andreessen 🇺🇸 Vinod Khosla Andrej Karpathy Sam Altman

Hyperspace: The Agentic OS Apple Should Have Built On December 19th, 2024, we announced the world’s first Agentic Browser. What followed was a movement — a new category was born which led to many early products in this space and recently the hundreds of people lining up outside the The Agentic Browser Summit in San Francisco underscored that. Silicon Valley instinctively gets it, from students to tech executives, people can feel a revolutionary new change in computing is in the air. Past year taught us why such a product was inevitable, a hard engineering effort, and also the last mover in the entire software world this decade if and when done right. All paths are headed in the same direction: one tool which orchestrates them all. At Hyperspace we showed that path with essays and products we launched in earlier months: from a spatial UI of orchestrating agents, to showcasing transparent activity in how the AI system operates which leads to user trust, to presenting the software end-game, which massively improves human productivity. We also built the world’s largest AI network, drawing participation from people in almost 6000 cities around the world contributing their machines as nodes in the network. Think Uber, but for AI. That is, planetary-scale. And now we are stretching this industry ambition further with our end-to-end vision of the Agentic Supercomputer, the first breakthrough new AI OS, and an effort which spans from AI research to distributed systems to inventing a new UI to inventing a new business model to complement it. All of this together helps us in serving our mission, of delivering “Everyone’s Personal Supercomputer”. While others have built AI-native browsers, no one though has built something agentic from the ground up — with AI as the foundation, not a feature. How do you fundamentally improve the lives’ of billions around the world ? We believe that requires building a native environment for agents to be viewed, created, deployed, executed, discovered and priced in. That is a world where we move on from static apps, to dynamic agents. But, as my 2 year old niece likes to ask: “but why ?” The issue is that the world of software today is fragmented, and everyone is sprinkling on AI as a feature and charging a subscription fees for it. From browser makers, to IDEs, to design and other productivity tools. This leads to a fragmented UX, where people have to learn to use AI in each app, their memory and other context is not shared between all these apps, and they also have to pay separately for compute for each such AI-enhanced app. Each app maker has to figure out basics such as compute, and leads to the issues we saw with Cursor pricing recently. This is not the future. What if AI was the foundation instead of a feature ? What if Apple had built a fundamentally new AI OS from the ground up and what would it have looked like ? At Hyperspace, that is what we did. On July 15th we introduced three breakthrough key pillars of our AI OS: 1. Agentic Browser 2. Agentic Memory 3. Agentic Payments And we didn’t stop there. We also introduced a breakthrough new user interface called the Spatial AI which is inspired both from the spreadsheet and the HyperCard - each card is an agent, with it’s own inputs and outputs, endlessly extensible and pluggable with others, just like cells of a spreadsheet. Update one cell and all the dependents update, like a spreadsheet formula. It goes beyond a static linear workflow to being able to operate in all directions. This revolutionary new interface helps manage all of the below: 1. Multiple websites being browsed in parallel 2. Multiple desktop apps being browsed in parallel 3. Multiple server tools being used in parallel 4. Multiple smartphone apps streamed to your device or opened via an emulator All the software which you need comes together in this one seamless, agent-native interface. This interface provides you access to the largest network of models, vectors, agents and compute on the planet. The Browser. The IDE. The Notepad… they are not separate products: they are all in one, the Agentic Browser. As Steve Jobs famously said at the iPhone announcement, “are you getting it ?” And beneath this UI lies a new intelligence routing layer — leveraging both swarms of specialized models to the Hyperspace Matrix model that recalls thousands of tools in real-time, not by context window hacks, but through retrieval, ranking, and reuse. To many, this will feel like AGI. Not one big system by one big company, but an intelligent network. Now lets talk about privacy… Are you comfortable with one company owning all your memory forever ? I am not. So we have invented Agentic Memory as a new open protocol which provides full power over memory to you, the user. Your memory is yours, encrypted, on your device, and portable if and how you want. Anyone can build on it without our permission, but not without your permission. This protocol, and the decentralized vector database spread out across the world, would enable apps and agents to share context and memory. Think copy-paste, but for the AI world. It doesn’t just remember — it knows what matters. VectorRank helps your AI weigh your life’s most relevant moments over time, just like the way our minds elevate memories. Now each time you use an agent, your experience with other agents will also continuously improve: you don’t have to keep repeating the same things about yourself, while fully preserving your privacy. Agentic Memory is accessible within the Agentic Browser to manage. And there is one more thing… AI as the foundation requires compute to be available at the base layer, but this base layer spans models running on your own device, to cloud APIs, to also running across the peer-to-peer distributed network. Agentic Payments provides a singular interface to all of that compute, running a spot auction clearing marketplace every second to determine the fair price of compute. This results in price transparency, and you as the user paying the lowest possible cost. If you want predictability, you can reserve compute in advance. This end-to-end system provides the most streamlined world for agents to operate in. In order to enable this world and the world of agents being able to pay each other in sub-cent increments millions of times a second, we had to also invent a fundamentally new agentic micropayments blockchain. All of this together would enable a world where you as a user, or the agent itself, can efficiently call and utilize other agents built by others and also pay for content which is unique and useful. This enables a move away from the current AI exploitative economy for bloggers and other content creators, to a web with a fundamental new business model. Earlier we didn’t have the right infrastructure to enable such a world. Now, all the dots connect. The Hyperspace AI OS would give the power of a supercomputer in everyone’s hands. This isn’t a browser, or an IDE or limited to any device or cloud. It’s an entire AI operating system — with a breakthrough new spatial UI, local and distributed compute, agentic memory, agentic payments, and orchestration built into the foundation. As a user, we move the choice back in your hands with an experience you will love and find delightful. You get to choose the level of privacy, cost, and utility you want. And while Apple should have done it, we could not wait, and we feel this just required a new level of passion and DNA which we bring here. We are just getting started. Thank you, Varun Mathur Cofounder and CEO, Hyperspace cc Naval Marc Andreessen 🇺🇸 Vinod Khosla Andrej Karpathy Sam Altman

Varun

169,177 次观看 • 1 年前

The $AEGIS DApp portal is now open to all: 🛡️ At Aegis, we believe in empowering the blockchain full of security, transparency and innovation. The Aegis Dapp has been under development for several months prior to the launch of $AEGIS and with that we have been able to build what we believe has the potential to change how users go about their day to day security. We are thrilled to share our progress and truly exciting news with you all. 🎯 First things first, at Aegis, we want to make it clear that the value of what we seek to bring to security across the blockchain, comes from our big vision, our strong team, and our commitment to long-term goals. ℹ️ Let’s kick this off with some information that is constantly happening, which is behind the scenes. Our full team is dedicated to the opportunity that lays ahead of us with becoming the leading voice/name for security, grasping every aspect with innovation, hard work, passion and commitment to see this sector grow. Everyone is aware of how important security is, a heartwarming mention to Messari for including us on how they see this sector growing rapidly and pushing a 10 Billion evaluation. We take that recognition with full responsibility and gratitude as we've been working hard on some really powerful stuff that could change the game for our industry. If you read the title and report itself, I’m sure that’ll give you some insight to what’s coming, and to the vast extent of what you can expect Aegis to be working towards. —> 🤝 This comes from teaming up with others within this sector and coming up with new tech to projects driven by our community, within the pipeline you can be confident that what we are building will push the cryptocurrency industry as a whole into a better future, the magnitude to what Aegis brings will not stop until we can confidently say, “Negative security reports across the blockchain are at an all time low, thousands of users are satisfied that Aegis is protecting them and their assets.” We're sticking to our vision no matter what the market does or whatever else comes our way. We plan to build what we set out to and we will see to it that our ecosystem is met. We've been working on some pretty amazing products that will be available within our Dapp, let’s go over what we offer: * AI Audits * Live Monitoring * Penetration Testing * Bug Bounties * Live Watchdog * Token analytics for everyday users, developers, teams, auditors, institutions, investors. ⬇️ Let’s break it down for you in some simple steps: AI AUDITS: We have trained our LLM models as AI AGENTS, these consist of 3 people ( AI AGENTS ) for the audits that are performed. - Audit - Reviewer - Judge Each one analyzes with a different personality, let’s check what personalities our AI AGENTS consist of: 3 different perspective auditors. 1 - Fine-tuned model x amount reads the code and generates the audit. ✅ 2 - Model x amount reviews the code and fact checks thoroughly. ✅ 3 - Model x amount ranks the code based on the severity outcome. ✅ ⌚️ Live Monitoring/Watchdog: The Live Monitoring/Watchdog system is designed to provide real-time surveillance of smart contracts, ensuring the detection and prevention of any potentially harmful transactions or malicious activities. Through the utilization of an AI Agent model, the system is trained to proactively identify and thwart suspicious behavior, thereby safeguarding the integrity of the smart contracts. Also, a paid sophisticated threat detection model is available for more intricate protocols and Dapps, offering an advanced level of protection against potential threats. This proactive approach is crucial in mitigating the risk of exploitation and ensuring the security of the smart contract ecosystem. 🖊️ Pen Testing: Our platform offers Pen Testing services to developers, providing a controlled environment for whitehat hackers to simulate attacks and identify vulnerabilities in smart contracts and protocols. In addition to human whitehat hackers, our AI Agents function as Red and Blue teams, actively engaging in simulated attacks to stress-test protocols and identify potential weaknesses. This comprehensive approach allows developers to proactively identify and address security issues, ultimately enhancing the robustness and resilience of their projects. 🕷️ Bug Bounties: Our Bug Bounty listing platform provides developers with the opportunity to list their protocols and offer bounties to white hat hackers for identifying vulnerabilities. By aggregating millions of bounties from various platforms and utilizing AI tools, we streamline the testing process, reducing up to 80% of the workload typically associated with security testing. This allows developers to efficiently identify and address potential vulnerabilities in their protocols, ultimately enhancing the overall security and resilience of their projects. 🪙 And lot more token analytics features for regular users, this will give you the opportunity to explore our Dapp for yourself and have some fun diving into the security platform of the future! I’m sure you’re excited to try it all out yourself, which is why we have some exciting news to bring to the #Guardians of the blockchain! But just before you continue the read and see the beans have been spilled, we have to take this opportunity to share with you that this large step to becoming a security leader is but only 20% of what we have revealed. This will be at the core of what Aegis stands for and hopes to achieve. The focus here is upon our Dapp, and in time we will slowly bring forward information/updates regarding segments of what makes Aegis a force to be reckoned with. Now that you’re fired up and excited to all of the announcements to come, let’s get to the news you’ve been waiting for! 🎉 We’re spilling the good news, and are happy to say we are now set for public release! The team at Aegis are overwhelmed with the development, support from teams, community, partners and more on what we believe to be an institutional-grade product. But the fun doesn’t stop there, this marks the start of what we aim to become, as it will take time and cycles to become better and better. Constant advancements will be set in place to attain the goal of achieving blockchain security. A statement from our CEO- Brian Hunt: “I can confirm from the security conferences I attended with Centralized security firms Peckshield, Hacken, Certik, BlockSec presentations, they are trying to achieve something similar and it will take them years. Decentralized AI for Security!” This initial drop of our dapp will be to get users signed up to gain access, in which we’ll whitelist users to get the ball rolling. 📣 To end this segment, let’s get the party started with the long awaited Aegis Ai Security Dapp and sign up now!

The $AEGIS DApp portal is now open to all: 🛡️ At Aegis, we believe in empowering the blockchain full of security, transparency and innovation. The Aegis Dapp has been under development for several months prior to the launch of $AEGIS and with that we have been able to build what we believe has the potential to change how users go about their day to day security. We are thrilled to share our progress and truly exciting news with you all. 🎯 First things first, at Aegis, we want to make it clear that the value of what we seek to bring to security across the blockchain, comes from our big vision, our strong team, and our commitment to long-term goals. ℹ️ Let’s kick this off with some information that is constantly happening, which is behind the scenes. Our full team is dedicated to the opportunity that lays ahead of us with becoming the leading voice/name for security, grasping every aspect with innovation, hard work, passion and commitment to see this sector grow. Everyone is aware of how important security is, a heartwarming mention to Messari for including us on how they see this sector growing rapidly and pushing a 10 Billion evaluation. We take that recognition with full responsibility and gratitude as we've been working hard on some really powerful stuff that could change the game for our industry. If you read the title and report itself, I’m sure that’ll give you some insight to what’s coming, and to the vast extent of what you can expect Aegis to be working towards. —> 🤝 This comes from teaming up with others within this sector and coming up with new tech to projects driven by our community, within the pipeline you can be confident that what we are building will push the cryptocurrency industry as a whole into a better future, the magnitude to what Aegis brings will not stop until we can confidently say, “Negative security reports across the blockchain are at an all time low, thousands of users are satisfied that Aegis is protecting them and their assets.” We're sticking to our vision no matter what the market does or whatever else comes our way. We plan to build what we set out to and we will see to it that our ecosystem is met. We've been working on some pretty amazing products that will be available within our Dapp, let’s go over what we offer: * AI Audits * Live Monitoring * Penetration Testing * Bug Bounties * Live Watchdog * Token analytics for everyday users, developers, teams, auditors, institutions, investors. ⬇️ Let’s break it down for you in some simple steps: AI AUDITS: We have trained our LLM models as AI AGENTS, these consist of 3 people ( AI AGENTS ) for the audits that are performed. - Audit - Reviewer - Judge Each one analyzes with a different personality, let’s check what personalities our AI AGENTS consist of: 3 different perspective auditors. 1 - Fine-tuned model x amount reads the code and generates the audit. ✅ 2 - Model x amount reviews the code and fact checks thoroughly. ✅ 3 - Model x amount ranks the code based on the severity outcome. ✅ ⌚️ Live Monitoring/Watchdog: The Live Monitoring/Watchdog system is designed to provide real-time surveillance of smart contracts, ensuring the detection and prevention of any potentially harmful transactions or malicious activities. Through the utilization of an AI Agent model, the system is trained to proactively identify and thwart suspicious behavior, thereby safeguarding the integrity of the smart contracts. Also, a paid sophisticated threat detection model is available for more intricate protocols and Dapps, offering an advanced level of protection against potential threats. This proactive approach is crucial in mitigating the risk of exploitation and ensuring the security of the smart contract ecosystem. 🖊️ Pen Testing: Our platform offers Pen Testing services to developers, providing a controlled environment for whitehat hackers to simulate attacks and identify vulnerabilities in smart contracts and protocols. In addition to human whitehat hackers, our AI Agents function as Red and Blue teams, actively engaging in simulated attacks to stress-test protocols and identify potential weaknesses. This comprehensive approach allows developers to proactively identify and address security issues, ultimately enhancing the robustness and resilience of their projects. 🕷️ Bug Bounties: Our Bug Bounty listing platform provides developers with the opportunity to list their protocols and offer bounties to white hat hackers for identifying vulnerabilities. By aggregating millions of bounties from various platforms and utilizing AI tools, we streamline the testing process, reducing up to 80% of the workload typically associated with security testing. This allows developers to efficiently identify and address potential vulnerabilities in their protocols, ultimately enhancing the overall security and resilience of their projects. 🪙 And lot more token analytics features for regular users, this will give you the opportunity to explore our Dapp for yourself and have some fun diving into the security platform of the future! I’m sure you’re excited to try it all out yourself, which is why we have some exciting news to bring to the #Guardians of the blockchain! But just before you continue the read and see the beans have been spilled, we have to take this opportunity to share with you that this large step to becoming a security leader is but only 20% of what we have revealed. This will be at the core of what Aegis stands for and hopes to achieve. The focus here is upon our Dapp, and in time we will slowly bring forward information/updates regarding segments of what makes Aegis a force to be reckoned with. Now that you’re fired up and excited to all of the announcements to come, let’s get to the news you’ve been waiting for! 🎉 We’re spilling the good news, and are happy to say we are now set for public release! The team at Aegis are overwhelmed with the development, support from teams, community, partners and more on what we believe to be an institutional-grade product. But the fun doesn’t stop there, this marks the start of what we aim to become, as it will take time and cycles to become better and better. Constant advancements will be set in place to attain the goal of achieving blockchain security. A statement from our CEO- Brian Hunt: “I can confirm from the security conferences I attended with Centralized security firms Peckshield, Hacken, Certik, BlockSec presentations, they are trying to achieve something similar and it will take them years. Decentralized AI for Security!” This initial drop of our dapp will be to get users signed up to gain access, in which we’ll whitelist users to get the ball rolling. 📣 To end this segment, let’s get the party started with the long awaited Aegis Ai Security Dapp and sign up now!

AEGIS AI

128,055 次观看 • 2 年前

I Built a 37.0 Profit Factor Bot by Cracking Every TradingView Source Code tradingview is a gold mine hiding in plain sight and i just found the master key to unlock every single secret hidden within its community scripts. most traders spend their entire lives staring at candles and hoping for a miracle while the actual alpha is buried in the open source code that nobody bothers to look at. i used to be that guy who sat there getting liquidated at three in the morning because i thought i could outplay the market with my gut feeling and some drawings on a screen. it turns out that the game is completely rigged against you if you are trading manually but there is a specific way to flip the script. i am going to show you how to stop guessing and start knowing exactly what works across every possible market condition before you ever risk a single dollar. i spent years losing money and thousands on developers because i thought i was not smart enough to code the systems myself but i was wrong. the first step to cracking the market is realizing that every indicator on the super charts has a source code section that is completely open to the public. you can literally scroll through the community scripts and pull the exact logic for thousands of different strategies that people claim are the holy grail of trading. but the secret is not just having the code because most of these indicators are actually garbage that will blow your account up in a week. this is where the real loop opens because you need a way to test these ideas across twenty five different data sets in seconds rather than months. i use a custom setup with ai agents specifically a sub agent i call the backtest architect to handle the heavy lifting of turning pine script into python code. the goal is to create a factory where you can feed in a raw indicator and get back a full report on its expectancy and profit factor without lifting a finger. most people find one strategy and marry it for life but a real data dog knows that you have to iterate to success or you will get left behind. i am running eighty one different backtests right now because i know that ninety percent of what i find will be trash but that remaining ten percent is where the wealth is made. the backtest architect knows exactly how to structure the folders and data paths so that we are testing everything from the base indicator to complex versions with filters. you might think that popular tools like fibonacci or order blocks are the way to go because everyone on social media talks about them like they are law. but when i actually ran the numbers through the machine the results were embarrassing and most of those strategies just resulted in negative expectancy. it is a dangerous trap to follow the crowd into a trade just because some guru said a certain level was important when the data shows it is a coin flip at best. the dynamic swing indicator was one of the few that actually held its weight during the recent massive testing sessions we ran. it was pulling in profit factors of over thirty seven with annualized returns that look too good to be true until you see the trade list. we combined it with filters like the adx and the money flow index to see if we could refine the signals and the results were absolutely staggering. when you have a system that can run through forty data sets while you are drinking tea you realize that manual trading is a form of self harm. i realized this after spending hundreds of thousands on apps and devs only to find out that i could just learn to build these bots myself live on the internet. the speed of iteration is the only thing that matters in this game because the faster you can fail the faster you can find the one strategy that actually prints. one of the biggest hurdles i faced was thinking that i needed to be a math genius or a senior engineer to automate my trading systems. the truth is that code is the great equalizer because it allows a regular person to compete with massive hedge funds by using the same logic and speed. i decided to learn everything in public because i wanted people to see the process of losing money with liquidations and then finally finding a path to automation. the reality of the market is that it moves in cycles and what worked yesterday will almost certainly fail tomorrow unless you are constantly testing. that is why i built the agents to automatically look through the results folder and rank the top performers based on a composite score. it takes all the emotion out of the process because i am no longer looking for a reason to enter a trade i am just looking at a csv file that tells me the truth. if you are still drawing lines on a chart and hoping for the best you are basically playing a game of chance against a high speed casino. the transition from a manual trader to a systems builder is the single most important pivot you will ever make in your life. it is not about being right or wrong it is about having a positive expectancy that has been proven across thousands of trades and multiple years of history. i had to fix a few errors in the short selling logic where the agents were getting confused between maximum and minimum values for take profit levels. these tiny bugs are the difference between a winning system and a blown account so you have to be willing to dive into the code and refine the machine. but once the system is tuned and the sub agents are running it becomes a beautiful workflow that functions entirely without your input. we are currently moving through the editors picks and the trending indicators one by one because i want to have a database of every single strategy on the platform. being a data dog means you never stop searching for that edge and you never settle for a strategy that just looks okay on a single chart. you have to demand excellence from your code because the market will not give you a single inch of mercy if you are lazy with your research. the ultimate goal is to have fully automated systems trading for you so you can focus on scaling rather than staring at a screen for ten hours a day. i am already up to over eighty backtests in this single session and i plan on hitting hundreds more by the end of the week. once you realize that you can crack the code of any indicator you see on the internet you will never look at a chart the same way again. this is the power of using agents to bridge the gap between a raw idea and a finished trading bot that actually works in the real world. i am done with getting liquidated and i am done with the stress of over trading because the code handles everything with cold precision. the path to success is paved with data and if you are not willing to automate your process you are just waiting for your next liquidation to happen

I Built a 37.0 Profit Factor Bot by Cracking Every TradingView Source Code tradingview is a gold mine hiding in plain sight and i just found the master key to unlock every single secret hidden within its community scripts. most traders spend their entire lives staring at candles and hoping for a miracle while the actual alpha is buried in the open source code that nobody bothers to look at. i used to be that guy who sat there getting liquidated at three in the morning because i thought i could outplay the market with my gut feeling and some drawings on a screen. it turns out that the game is completely rigged against you if you are trading manually but there is a specific way to flip the script. i am going to show you how to stop guessing and start knowing exactly what works across every possible market condition before you ever risk a single dollar. i spent years losing money and thousands on developers because i thought i was not smart enough to code the systems myself but i was wrong. the first step to cracking the market is realizing that every indicator on the super charts has a source code section that is completely open to the public. you can literally scroll through the community scripts and pull the exact logic for thousands of different strategies that people claim are the holy grail of trading. but the secret is not just having the code because most of these indicators are actually garbage that will blow your account up in a week. this is where the real loop opens because you need a way to test these ideas across twenty five different data sets in seconds rather than months. i use a custom setup with ai agents specifically a sub agent i call the backtest architect to handle the heavy lifting of turning pine script into python code. the goal is to create a factory where you can feed in a raw indicator and get back a full report on its expectancy and profit factor without lifting a finger. most people find one strategy and marry it for life but a real data dog knows that you have to iterate to success or you will get left behind. i am running eighty one different backtests right now because i know that ninety percent of what i find will be trash but that remaining ten percent is where the wealth is made. the backtest architect knows exactly how to structure the folders and data paths so that we are testing everything from the base indicator to complex versions with filters. you might think that popular tools like fibonacci or order blocks are the way to go because everyone on social media talks about them like they are law. but when i actually ran the numbers through the machine the results were embarrassing and most of those strategies just resulted in negative expectancy. it is a dangerous trap to follow the crowd into a trade just because some guru said a certain level was important when the data shows it is a coin flip at best. the dynamic swing indicator was one of the few that actually held its weight during the recent massive testing sessions we ran. it was pulling in profit factors of over thirty seven with annualized returns that look too good to be true until you see the trade list. we combined it with filters like the adx and the money flow index to see if we could refine the signals and the results were absolutely staggering. when you have a system that can run through forty data sets while you are drinking tea you realize that manual trading is a form of self harm. i realized this after spending hundreds of thousands on apps and devs only to find out that i could just learn to build these bots myself live on the internet. the speed of iteration is the only thing that matters in this game because the faster you can fail the faster you can find the one strategy that actually prints. one of the biggest hurdles i faced was thinking that i needed to be a math genius or a senior engineer to automate my trading systems. the truth is that code is the great equalizer because it allows a regular person to compete with massive hedge funds by using the same logic and speed. i decided to learn everything in public because i wanted people to see the process of losing money with liquidations and then finally finding a path to automation. the reality of the market is that it moves in cycles and what worked yesterday will almost certainly fail tomorrow unless you are constantly testing. that is why i built the agents to automatically look through the results folder and rank the top performers based on a composite score. it takes all the emotion out of the process because i am no longer looking for a reason to enter a trade i am just looking at a csv file that tells me the truth. if you are still drawing lines on a chart and hoping for the best you are basically playing a game of chance against a high speed casino. the transition from a manual trader to a systems builder is the single most important pivot you will ever make in your life. it is not about being right or wrong it is about having a positive expectancy that has been proven across thousands of trades and multiple years of history. i had to fix a few errors in the short selling logic where the agents were getting confused between maximum and minimum values for take profit levels. these tiny bugs are the difference between a winning system and a blown account so you have to be willing to dive into the code and refine the machine. but once the system is tuned and the sub agents are running it becomes a beautiful workflow that functions entirely without your input. we are currently moving through the editors picks and the trending indicators one by one because i want to have a database of every single strategy on the platform. being a data dog means you never stop searching for that edge and you never settle for a strategy that just looks okay on a single chart. you have to demand excellence from your code because the market will not give you a single inch of mercy if you are lazy with your research. the ultimate goal is to have fully automated systems trading for you so you can focus on scaling rather than staring at a screen for ten hours a day. i am already up to over eighty backtests in this single session and i plan on hitting hundreds more by the end of the week. once you realize that you can crack the code of any indicator you see on the internet you will never look at a chart the same way again. this is the power of using agents to bridge the gap between a raw idea and a finished trading bot that actually works in the real world. i am done with getting liquidated and i am done with the stress of over trading because the code handles everything with cold precision. the path to success is paved with data and if you are not willing to automate your process you are just waiting for your next liquidation to happen

Moon Dev

26,234 次观看 • 5 个月前

War Diary Day 1,391 Blaise Metreweli, the Chief of Britain's Secret Intelligence Service, sticks it to the Killer in The Kremlin. And all his creepy helpers. I agree with every fucking word. VPDFO! (Transcript of the speech, exactly as it was delivered) 📷 Welcome inside MI6. This iconic building, familiar to movie fans everywhere, is the home of Britain’s foreign intelligence agency. But whilst hundreds of my team pass through the entry pods each day, the truth is that most of our work happens many miles away from this place - out of sight, hidden from the world, undercover, recruiting and running agents who choose to place their trust in us, sharing secrets to make the UK and the world safer. You might pass one of our officers on the street or sit next to them on a plane when you’re about to set off on an adventure of your own, or in a foreign city taking selfies by the sights. Whether it’s in seemingly everyday places, or on the front line embedded with our military, MI6 is there. In my first few weeks, I’ve heard repeatedly that MI6 is trusted and respected globally, two things that we never take for granted. We are seen as a source of hard power, soft influence and rapid innovation. I’ve also heard that people want to believe in MI6. It’s my job to make sure they can. Today, I want to talk about human agency. We all have choices to make about how we deal with the undercurrents shaping our world. About how, in our new, faster, more dangerous and technology-mediated world, it will be our rediscovery of our shared humanity, our ability to listen, and our courage that will determine how our future unfolds. Conflict is not inevitable. Understanding human nature is in my bones. From a family shaped by devastating conflict, I grew up with a deep sense of gratitude for the UK’s precious democracy and freedom. I spent much of my childhood overseas, which is where my passion for travel and adventure began. I studied anthropology, and later psychology and AI, exploring how we make sense of the world and each other. It’s why I was drawn to MI6: it offers strong purpose, a chance to serve and a belief in the positive power of human connection. Like the Service, I’m operational to my very core. Over nearly three decades, my career has involved recruiting and running agents in hostile territory; and leading operations in warzones to defuse threats and support peace. Always in teams, always learning from others. Over the years, I’ve worked with hundreds of brilliant partners – and indeed occasionally those we’d label as adversaries – across dozens of countries, tackling weapons proliferation and terrorism. During my time at MI5, I saw close up what it takes to defend Britain from being targeted by hostile states. You’ll find many like me in my organisation: powerfully motivated to protect our precious country; curious about how our world is changing, joining dots and taking action, across domains. But it was in my last role as ‘Q’, where it was my job to turn emerging technologies from threats to opportunities that I could most see the world changing. As I dug deep into data and extraordinary innovation, I could see how technology was rapidly reshaping not just our capabilities but also conflict and trust, truth and global power. Let me lay out how I see the global issues MI6 must tackle. Because the greatest danger we face is to misunderstand the nature of the problem. Let’s be in no doubt. Our world is more dangerous and contested now than it has been for decades. Conflict is evolving and trust eroding, just as new technologies spur both competition and dependence. We are being contested from sea to space, from the battlefield to the boardroom. And even our brains, as disinformation manipulates our understanding of each other and ourselves. Across the globe, we are now confronting not one single danger, but an interlocking web of security challenges – military, technological, social, ethical even – each shaping the other in complex ways. We are now operating in a space between peace and war. This is not a temporary state or a gradual, inevitable evolution. Our world is being actively remade, with profound implications for national and international security. Institutions which were designed in the ashes of the Second World War are being challenged. New blocs and identities forming and alliances reshaping. Multipolar competition in tension with multilateral cooperation. But there’s something distinctive that will make this change unlike any other: the impact of advanced technologies, which will accelerate the pace and scale of every threat and opportunity, and increasingly, individualise them too. Advances in artificial intelligence, biotechnology, and quantum computing are not only revolutionising economies but rewriting the reality of conflict, as they ‘converge’ to create science-fiction-like tools. There’s incredible promise in all this for all of us, from green technologies to hyper-personalised medicine. But also peril. AI-powered robots and drones are brilliant for scaled manufacturing but devastating on the battlefield. Discoveries that cure disease can also create new weapons. And as states race for tech supremacy, or as some algorithms become as powerful as states, those hyper-personalised tools could become a new vector for conflict and control. Power itself is becoming more diffuse, more unpredictable as control over these technologies is shifting from states to corporations, and sometimes to individuals. And at the same time, the foundations of trust in our societies are eroding. Information, once a unifying force, is increasingly weaponised. Falsehood spreads faster than fact, dividing communities and distorting reality. We live in an age of hyper-connection yet profound isolation. The algorithms flatter our biases and fracture our public squares. And as trust collapses, so does our shared sense of truth – one of the greatest losses a society can suffer. The defining challenge of the twenty-first century is not simply who wields the most powerful technologies, but who guides them with the greatest wisdom. Our security, our prosperity, and our humanity depend on it. Our world is being remade. And for the first time, we are all at the heart of it. My Service must now operate in this new context too: not just expert on hostile states, terrorism, proliferation and more, but also fluent in technology, able to anticipate the second and third order effects of advances that reshape the world in minutes not months. And as China will be a central part of the global transformation taking place this century, it is essential that we, as MI6, continue to inform the government’s understanding of China’s rise and the implications for UK national security. I’m going to break with tradition and won’t give you a global threat tour, but will focus here on Putin’s Russia. We all continue to face the menace of an aggressive, expansionist and revisionist Russia, seeking to subjugate Ukraine and harass NATO. I find it harrowing that hundreds of thousands have died, with the toll mounting every day, because of Putin’s historical distortions and his compromised desire for respect. He is dragging out negotiations and shifting the cost of war onto his own population. But Putin should be in no doubt, our support is enduring. The pressure we apply on Ukraine’s behalf will be sustained. Because it is fundamental not just to European sovereignty and security but to global stability. Alongside the grinding war, Russia is testing us in the grey zone with tactics that are just below the threshold of war. It’s important to understand their attempts to bully, fearmonger and manipulate, because it affects us all. I am talking about: Cyberattacks on critical infrastructure. Drones buzzing airports and bases. Aggressive activity in our seas, above and below the waves. State-sponsored arson and sabotage. Propaganda and influence operations that crack open and exploit fractures within societies. Countering this activity is the work of intelligence and security services across Europe and the globe. And as the Foreign Secretary made clear in a speech last week, the UK is defending itself against this Russian information warfare – sanctioning Russian media outlets pushing Kremlin narratives. The export of chaos is a feature not a bug in this Russian approach to international engagement; and we should be ready for this to continue until Putin is forced to change his calculus. So, how should we respond? It’s not enough now just to understand the world. We must shape it too. MI6 is well-positioned to respond to these threats and wider global instability. And we will continue to evolve, just as we have throughout our long history. The UK government has invested in our intelligence agencies and we are all using our unique powers to keep the British people safe. Our ‘open and connected’ partnerships across the UK Intelligence Community, with HMGCC, NSSIF and the wider tech ecosystem in the UK will become even more important – because in the digital battleground, no single organisation can prevail alone. As a global agency, MI6’s inbuilt strength is our partners and our people. The risks I have set out require us to work ever more closely with our colleagues in MI5, GCHQ and in defence and diplomacy. But also with our Five Eyes partners, with the E3, the EU, NATO, those across the Middle East, the Indo-Pacific and beyond. And with many valued partners whose identity needs to remain secret. Together, we integrate our diverse talent, data and tools to meet the threat. AI is a domain in which we will excel, using the technology to augment, not replace, our human skills. Every digital trace, every byte of data, every algorithmic decision has implications for the safety of the lives of the courageous people who work with us as officers and agents, and for the UK’s strategic advantage. Mastery of technology will infuse everything we do. Not just in our labs, but in the field, in our tradecraft, and even more importantly, in the mindset of every officer. We will become as comfortable with lines of code as we are with human sources, as fluent in Python as we are in multiple other languages. Under my leadership, MI6 will continue to attract Britain’s best and most creative minds: linguists and data scientists, case officers and engineers, behavioural experts and technologists. We need people who walk in the shoes and get in the heads of our adversaries. We need people who think differently, challenge assumptions, and act decisively. All can thrive and make a difference at MI6. At an operational level, we will sharpen our edge and impact with audacity, tapping into – if you like – our historical SOE instincts. We’re at our best when we’re hustling to make things happen, because our intelligence is most valuable when it changes reality on the ground. We will take calculated risks, where the prize is significant and the national interest clear. We will never stoop to the tactics of our opponents. But we must seek to outplay them. In every domain. In every way. So intelligence must drive action. Action must deliver advantage. And advantage must serve Britain’s security and prosperity. But at the core, our deeper contribution is also our simplest – how we unlock human agency. Our fast-paced, tech and threat-infused world now generates more heat than light. As nations retrench and rearm, we are losing opportunities to listen to what’s really going on. I’ve seen time and again throughout my career, that this is where MI6 matters most: we listen and we hear. We understand, because we take time to learn languages and cultures, complex technical and historical detail, immerse ourselves in what’s really driving the situation. Across the globe, right now, our officers are finding people with the courage to step forward, and they are taking time to sit and listen to break these tightening cycles of violence. They listen for nuance, for connection, for opportunity. Over the years, I’ve listened to terrorists who have told us how to defuse the bomb because they know that more violence won’t help. To proliferators and smugglers who’ve told us where to find the dangerous material, motivated to protect their children’s future. To people trapped in authoritarian regimes who know, deep down, that their humanity is being chipped away – and that telling us what’s really going on is an important release, allowing us all to find better ways to navigate our changing world. So, we will work with our agents. And we will continue to engage directly, and with respect, with states and organisation currently working against us. Away from the glare of the media, we will use MI6’s convening power wherever we can to make a material difference, bringing parties together to defuse tensions. But the response to the increasing risks we face won’t be delivered by the UK intelligence community alone. Wider society has a role to play too. That includes work taking place in schools across the country so our children don’t get duped by information manipulation. Let’s all check sources, consider evidence, and be alive to those algorithms that trigger intense reactions, like fear. It also means everyone in society really understanding the world we are in – a world where terrorists plot against us, where our enemies fearmonger, bully and manipulate, and the front line is everywhere. Online, on our streets, in our supply chains, in the minds and on the screens of our citizens. We must all stand together against this. As we do today with our friends in Australia after the shocking antisemitic terrorist attack this weekend. My thoughts -and those of my whole organisation – are with the family, friends and loved ones of the victims. Light will always win over darkness. In rising to meet these challenges we, in MI6, will remain anchored to our values: courage, creativity, respect and integrity. And to our principles: accountability and trust are not constraints on our work; they are the foundations of our legitimacy with the British public. Recently, I had the privilege of meeting and thanking a foreign agent who has worked with us for decades, taking extraordinary risks to help keep the UK safe. I asked why. They said simply, ‘Your values. Your integrity and respect. None of us have a future without them’. This moment reinforced to me that we must remain a very human agency. And so, to sustain that trust, MI6 will continue to be more open. Not for the sake of visibility, but because it matters – and as my MI5 counterpart Sir Ken McCallum said recently - because it is a strength. We will continue the practice of speaking publicly, broaden our channels of engagement, and sustain our focus on attracting the most diverse talent to join our Service. Transparency does not mean revealing what must remain secret. It means showing the British people who we are, what we stand for, and why our work matters. We need your trust and support for the difficult and often dangerous work our agents pursue, every day of the year. In an age of uncertainty, one constant remains: the choices made by human beings still determine the shape of the world. Yes, technology can illuminate possibilities: but information requires judgement; complexity demands clarity; and only people can decide which path to follow. The United Kingdom’s global voice has never rested solely on strength – it has rested on trust, principle, and the ability to understand others as well as ourselves. That is also the essence of intelligence: not simply knowing the world, but interpreting it through a uniquely human lens. Ours is the quiet service, the hidden service. It is one rooted in a profound belief that when human beings act with purpose and integrity, they can steady a faltering world. When the Berlin Wall fell, it was our shared belief in freedom that carried Europe forward. When acts of terror targeted open societies, it was intelligence, cooperation and resolve that preserved them. And when adversaries blur fact and falsehood, our task is to defend the space where truth can still stand. As we step into the future, the tools at our disposal will evolve. But what will always matter most is the human element – the person who stands in the shadows and says: this is right, and that is wrong. That choice – the exercise of human agency – has shaped our world before, and it will shape it again. Because in the end, it is not what we can do that defines us, but what we choose to do. Thank you. Published 15 December 2025

War Diary Day 1,391 Blaise Metreweli, the Chief of Britain's Secret Intelligence Service, sticks it to the Killer in The Kremlin. And all his creepy helpers. I agree with every fucking word. VPDFO! (Transcript of the speech, exactly as it was delivered) 📷 Welcome inside MI6. This iconic building, familiar to movie fans everywhere, is the home of Britain’s foreign intelligence agency. But whilst hundreds of my team pass through the entry pods each day, the truth is that most of our work happens many miles away from this place - out of sight, hidden from the world, undercover, recruiting and running agents who choose to place their trust in us, sharing secrets to make the UK and the world safer. You might pass one of our officers on the street or sit next to them on a plane when you’re about to set off on an adventure of your own, or in a foreign city taking selfies by the sights. Whether it’s in seemingly everyday places, or on the front line embedded with our military, MI6 is there. In my first few weeks, I’ve heard repeatedly that MI6 is trusted and respected globally, two things that we never take for granted. We are seen as a source of hard power, soft influence and rapid innovation. I’ve also heard that people want to believe in MI6. It’s my job to make sure they can. Today, I want to talk about human agency. We all have choices to make about how we deal with the undercurrents shaping our world. About how, in our new, faster, more dangerous and technology-mediated world, it will be our rediscovery of our shared humanity, our ability to listen, and our courage that will determine how our future unfolds. Conflict is not inevitable. Understanding human nature is in my bones. From a family shaped by devastating conflict, I grew up with a deep sense of gratitude for the UK’s precious democracy and freedom. I spent much of my childhood overseas, which is where my passion for travel and adventure began. I studied anthropology, and later psychology and AI, exploring how we make sense of the world and each other. It’s why I was drawn to MI6: it offers strong purpose, a chance to serve and a belief in the positive power of human connection. Like the Service, I’m operational to my very core. Over nearly three decades, my career has involved recruiting and running agents in hostile territory; and leading operations in warzones to defuse threats and support peace. Always in teams, always learning from others. Over the years, I’ve worked with hundreds of brilliant partners – and indeed occasionally those we’d label as adversaries – across dozens of countries, tackling weapons proliferation and terrorism. During my time at MI5, I saw close up what it takes to defend Britain from being targeted by hostile states. You’ll find many like me in my organisation: powerfully motivated to protect our precious country; curious about how our world is changing, joining dots and taking action, across domains. But it was in my last role as ‘Q’, where it was my job to turn emerging technologies from threats to opportunities that I could most see the world changing. As I dug deep into data and extraordinary innovation, I could see how technology was rapidly reshaping not just our capabilities but also conflict and trust, truth and global power. Let me lay out how I see the global issues MI6 must tackle. Because the greatest danger we face is to misunderstand the nature of the problem. Let’s be in no doubt. Our world is more dangerous and contested now than it has been for decades. Conflict is evolving and trust eroding, just as new technologies spur both competition and dependence. We are being contested from sea to space, from the battlefield to the boardroom. And even our brains, as disinformation manipulates our understanding of each other and ourselves. Across the globe, we are now confronting not one single danger, but an interlocking web of security challenges – military, technological, social, ethical even – each shaping the other in complex ways. We are now operating in a space between peace and war. This is not a temporary state or a gradual, inevitable evolution. Our world is being actively remade, with profound implications for national and international security. Institutions which were designed in the ashes of the Second World War are being challenged. New blocs and identities forming and alliances reshaping. Multipolar competition in tension with multilateral cooperation. But there’s something distinctive that will make this change unlike any other: the impact of advanced technologies, which will accelerate the pace and scale of every threat and opportunity, and increasingly, individualise them too. Advances in artificial intelligence, biotechnology, and quantum computing are not only revolutionising economies but rewriting the reality of conflict, as they ‘converge’ to create science-fiction-like tools. There’s incredible promise in all this for all of us, from green technologies to hyper-personalised medicine. But also peril. AI-powered robots and drones are brilliant for scaled manufacturing but devastating on the battlefield. Discoveries that cure disease can also create new weapons. And as states race for tech supremacy, or as some algorithms become as powerful as states, those hyper-personalised tools could become a new vector for conflict and control. Power itself is becoming more diffuse, more unpredictable as control over these technologies is shifting from states to corporations, and sometimes to individuals. And at the same time, the foundations of trust in our societies are eroding. Information, once a unifying force, is increasingly weaponised. Falsehood spreads faster than fact, dividing communities and distorting reality. We live in an age of hyper-connection yet profound isolation. The algorithms flatter our biases and fracture our public squares. And as trust collapses, so does our shared sense of truth – one of the greatest losses a society can suffer. The defining challenge of the twenty-first century is not simply who wields the most powerful technologies, but who guides them with the greatest wisdom. Our security, our prosperity, and our humanity depend on it. Our world is being remade. And for the first time, we are all at the heart of it. My Service must now operate in this new context too: not just expert on hostile states, terrorism, proliferation and more, but also fluent in technology, able to anticipate the second and third order effects of advances that reshape the world in minutes not months. And as China will be a central part of the global transformation taking place this century, it is essential that we, as MI6, continue to inform the government’s understanding of China’s rise and the implications for UK national security. I’m going to break with tradition and won’t give you a global threat tour, but will focus here on Putin’s Russia. We all continue to face the menace of an aggressive, expansionist and revisionist Russia, seeking to subjugate Ukraine and harass NATO. I find it harrowing that hundreds of thousands have died, with the toll mounting every day, because of Putin’s historical distortions and his compromised desire for respect. He is dragging out negotiations and shifting the cost of war onto his own population. But Putin should be in no doubt, our support is enduring. The pressure we apply on Ukraine’s behalf will be sustained. Because it is fundamental not just to European sovereignty and security but to global stability. Alongside the grinding war, Russia is testing us in the grey zone with tactics that are just below the threshold of war. It’s important to understand their attempts to bully, fearmonger and manipulate, because it affects us all. I am talking about: Cyberattacks on critical infrastructure. Drones buzzing airports and bases. Aggressive activity in our seas, above and below the waves. State-sponsored arson and sabotage. Propaganda and influence operations that crack open and exploit fractures within societies. Countering this activity is the work of intelligence and security services across Europe and the globe. And as the Foreign Secretary made clear in a speech last week, the UK is defending itself against this Russian information warfare – sanctioning Russian media outlets pushing Kremlin narratives. The export of chaos is a feature not a bug in this Russian approach to international engagement; and we should be ready for this to continue until Putin is forced to change his calculus. So, how should we respond? It’s not enough now just to understand the world. We must shape it too. MI6 is well-positioned to respond to these threats and wider global instability. And we will continue to evolve, just as we have throughout our long history. The UK government has invested in our intelligence agencies and we are all using our unique powers to keep the British people safe. Our ‘open and connected’ partnerships across the UK Intelligence Community, with HMGCC, NSSIF and the wider tech ecosystem in the UK will become even more important – because in the digital battleground, no single organisation can prevail alone. As a global agency, MI6’s inbuilt strength is our partners and our people. The risks I have set out require us to work ever more closely with our colleagues in MI5, GCHQ and in defence and diplomacy. But also with our Five Eyes partners, with the E3, the EU, NATO, those across the Middle East, the Indo-Pacific and beyond. And with many valued partners whose identity needs to remain secret. Together, we integrate our diverse talent, data and tools to meet the threat. AI is a domain in which we will excel, using the technology to augment, not replace, our human skills. Every digital trace, every byte of data, every algorithmic decision has implications for the safety of the lives of the courageous people who work with us as officers and agents, and for the UK’s strategic advantage. Mastery of technology will infuse everything we do. Not just in our labs, but in the field, in our tradecraft, and even more importantly, in the mindset of every officer. We will become as comfortable with lines of code as we are with human sources, as fluent in Python as we are in multiple other languages. Under my leadership, MI6 will continue to attract Britain’s best and most creative minds: linguists and data scientists, case officers and engineers, behavioural experts and technologists. We need people who walk in the shoes and get in the heads of our adversaries. We need people who think differently, challenge assumptions, and act decisively. All can thrive and make a difference at MI6. At an operational level, we will sharpen our edge and impact with audacity, tapping into – if you like – our historical SOE instincts. We’re at our best when we’re hustling to make things happen, because our intelligence is most valuable when it changes reality on the ground. We will take calculated risks, where the prize is significant and the national interest clear. We will never stoop to the tactics of our opponents. But we must seek to outplay them. In every domain. In every way. So intelligence must drive action. Action must deliver advantage. And advantage must serve Britain’s security and prosperity. But at the core, our deeper contribution is also our simplest – how we unlock human agency. Our fast-paced, tech and threat-infused world now generates more heat than light. As nations retrench and rearm, we are losing opportunities to listen to what’s really going on. I’ve seen time and again throughout my career, that this is where MI6 matters most: we listen and we hear. We understand, because we take time to learn languages and cultures, complex technical and historical detail, immerse ourselves in what’s really driving the situation. Across the globe, right now, our officers are finding people with the courage to step forward, and they are taking time to sit and listen to break these tightening cycles of violence. They listen for nuance, for connection, for opportunity. Over the years, I’ve listened to terrorists who have told us how to defuse the bomb because they know that more violence won’t help. To proliferators and smugglers who’ve told us where to find the dangerous material, motivated to protect their children’s future. To people trapped in authoritarian regimes who know, deep down, that their humanity is being chipped away – and that telling us what’s really going on is an important release, allowing us all to find better ways to navigate our changing world. So, we will work with our agents. And we will continue to engage directly, and with respect, with states and organisation currently working against us. Away from the glare of the media, we will use MI6’s convening power wherever we can to make a material difference, bringing parties together to defuse tensions. But the response to the increasing risks we face won’t be delivered by the UK intelligence community alone. Wider society has a role to play too. That includes work taking place in schools across the country so our children don’t get duped by information manipulation. Let’s all check sources, consider evidence, and be alive to those algorithms that trigger intense reactions, like fear. It also means everyone in society really understanding the world we are in – a world where terrorists plot against us, where our enemies fearmonger, bully and manipulate, and the front line is everywhere. Online, on our streets, in our supply chains, in the minds and on the screens of our citizens. We must all stand together against this. As we do today with our friends in Australia after the shocking antisemitic terrorist attack this weekend. My thoughts -and those of my whole organisation – are with the family, friends and loved ones of the victims. Light will always win over darkness. In rising to meet these challenges we, in MI6, will remain anchored to our values: courage, creativity, respect and integrity. And to our principles: accountability and trust are not constraints on our work; they are the foundations of our legitimacy with the British public. Recently, I had the privilege of meeting and thanking a foreign agent who has worked with us for decades, taking extraordinary risks to help keep the UK safe. I asked why. They said simply, ‘Your values. Your integrity and respect. None of us have a future without them’. This moment reinforced to me that we must remain a very human agency. And so, to sustain that trust, MI6 will continue to be more open. Not for the sake of visibility, but because it matters – and as my MI5 counterpart Sir Ken McCallum said recently - because it is a strength. We will continue the practice of speaking publicly, broaden our channels of engagement, and sustain our focus on attracting the most diverse talent to join our Service. Transparency does not mean revealing what must remain secret. It means showing the British people who we are, what we stand for, and why our work matters. We need your trust and support for the difficult and often dangerous work our agents pursue, every day of the year. In an age of uncertainty, one constant remains: the choices made by human beings still determine the shape of the world. Yes, technology can illuminate possibilities: but information requires judgement; complexity demands clarity; and only people can decide which path to follow. The United Kingdom’s global voice has never rested solely on strength – it has rested on trust, principle, and the ability to understand others as well as ourselves. That is also the essence of intelligence: not simply knowing the world, but interpreting it through a uniquely human lens. Ours is the quiet service, the hidden service. It is one rooted in a profound belief that when human beings act with purpose and integrity, they can steady a faltering world. When the Berlin Wall fell, it was our shared belief in freedom that carried Europe forward. When acts of terror targeted open societies, it was intelligence, cooperation and resolve that preserved them. And when adversaries blur fact and falsehood, our task is to defend the space where truth can still stand. As we step into the future, the tools at our disposal will evolve. But what will always matter most is the human element – the person who stands in the shadows and says: this is right, and that is wrong. That choice – the exercise of human agency – has shaped our world before, and it will shape it again. Because in the end, it is not what we can do that defines us, but what we choose to do. Thank you. Published 15 December 2025

John Sweeney

42,257 次观看 • 8 个月前

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on mainstream cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!

dom | icp

299,576 次观看 • 3 个月前

🔥👽 This is REALLY good! 👽🔥 "In my view, there absolutely is an adversarial actor in our world. There is a human-engagement program underway. I don't think it's the rainbow that everyone kind of hopes that it is." ~Reed (IMO, Reed Summers is an unsung, VERY important voice in this community, and what he says in this clip, currently, resonates with me in a big way. Take the time to read or watch. Then we have what Ross Coulthart says about the "deliberate lifting of consciousness" and "frequency," which is more on the side of love, light and space brothers. I don't see that right now, and none of my contacts have ever mentioned it. If it's there, maybe its hands are tied with how it can help us?) ~ "Genetic interventions, cultural influence, right? Social engineering. All of these could be at play in a subversive sense. NHI is operating in a strategic silence, in a state of non-disclosure. Intelligence indicates intent, intentful engagement indicates a program. And so what is their program? We talk about the Legacy-human program. What about the non-human program? ~Reed ~ Ross: "I've been told that the United States has developed quite advanced weaponry, particularly plasma-beam technology that might, in part, have been inspired by what they've seen or recovered from non-human technology. I think, also, just to add the concern of NHI with what humanity is doing. There's also a very deliberate lifting of consciousness. And I really am struck by what Chris (Bledsoe - Chris Bledsoe) says about the frequency. This is something that I'm getting from so many people. That the level of intensity of public reporting of their engagement with NHI, of a clear intent by NHI, to essentially give up on governments from ever disclosing. But to raise human consciousness and awareness. "And I think they're doing this, increasingly (laughs), through direct engagement with individuals. I've got many friends and colleagues and people I've interviewed - witnesses - who've had incredible experiences. I've just been recording for a TV show, something that we're doing here in Australia, where people are inviting or summoning the phenomenon. And there seems to be an interest in the phenomenon engaging with humans much, much more overtly. And I do think that stems from a concern about us primitive monkeys playing with matches." (Were they concerned when this abduction-like event allegedly happened to Jim Semivan and his wife? Semivan: "I had a hole in the back of my neck, and my wife...unexplained bleeding for 17 days." Source: Engaging The Phenomenon's interview with Jim ~ Jim Garrison: "But Reid, speak to us about this interplay between malevolence and benevolence." Reed: "Sure, well, and you described, Jim, the first part of my life in which I was focused primarily on supporting my father, Marshall (Vian Summers and The Allies of Humanity), who had direct encounters with NHI, communications with NHI." (Was Marshall really in contact with extraterrestrial intelligence, known as The Allies of Humanity? I don't know. But a lot of what he has said rings true to me. Doesn't mean it's true. Read one transcript I did in 2021: “It’s Probably Not Human” – Elizondo, Blumenthal & The Allies Of Humanity ~ Reed: "And my family felt the full force and impact of what that was like, as well as what comes with it ("it" being his dad's alleged contact with ET intelligence). Which is, what might be called the hitchhiker effect, the anomalous effects that attend those who have been selected by the phenomenon without their understanding or knowing why." (Add another few names to the list of people who have now said they experienced the hitchhiker effect: Jay Stratton, George Knapp, Kelleher, Davis, Bigelow, Brandon Fugal, Thomas Winterton, Semivan, and more. That includes poltergeist-like events, shadow people, orbs, etc.) Reed: ""But my later work is really about assessing intent with a structured framework. I think we need to step back from belief systems, hopeful or fearful interpretations, and lay out the spectrum of possibilities, right? From curiosity on the left, salvation and assistance, even further on the left, to transaction, integration on the right, or even something more hostile. And in the end, intent is all about human outcomes. We can't know their consciousness. The project, in my mind right now, is not, let's set out to understand what they're like, who they are, how they think, how they cognate. That's impossible, in my perspective. But we can assess the real human outcomes that stem from the hazard and the risk and the possible threat of a non-human factor acting upon. Like a forcing, an environmental or evolutionary-forcing upon humanity at a historically-unprecedented time." (From what I have seen, whatever this is, has, overall, NOT helped humanity. If that's happening, it's going on behind the scenes. I know some experiencers report having their lives changed in a positive way, and some say they have been healed of illnesses. But others say their lives have been ruined and they've been hurt, intentionally or not, by coming in close contact with the phenomenon. Also, various people have come down with several auto-immune diseases after close contact, and some of that was detailed in the must-read book, "Skinwalkers at the Pentagon." jakebarber also mentioned that. Is it multiple intelligences with multiple intents, or one intelligence mixing in some positive outcomes as a propaganda effort? We don't know.) Reed: "And there are significant signals and indicators in the history of the phenomenon that give very good anchors for assessing intent, and for going from possibilities to probabilities and ultimately making a starting assessment, which informs the research pathway, which is the other part of my work in supporting the human institute, devising an intelligent, scientific research and investigatory citizen-led effort to uncover the phenomenon, to disclose it, to interrogate its activity, lest we just be interrogated, unknowingly, ourselves. So, a lot of good work to do there." (Interrogate the activity of the phenomenon. I really like that approach. Let's not assume anything.) Reed: "But, in my view, when you look at all that the sensor data has given us, technologically, when you corroborate that with the geographic, circumstantial [and] temporal aspects of how the phenomena appears, who it manifests to, and the considerable problem it presents to the international community and to national laws and frameworks that govern territorial sovereignty, these incursions into sensitive sites and the potential, programmatic engagement with civilians in the form of physical NHI-initiated contact - abductions - it builds a picture. It bounds those possibilities into a zone of probabilities. "And whether it's a transactional presence, whether it's an integrative or one that wants to integrate with humanity, or ultimately replace humanity, I think there are multiple possibilities at play and multiple intents at play. Although, I don't think it's the rainbow that everyone kind of hopes that it is. It's not, everything the Universe has to offer is gonna come all at once, now, and function harmoniously in our skies and oceans. No. The hostile intent will not tolerate a beneficial actor who themselves would have to arm themselves and militarily confront the hostile actor, which we do not see indications of." (My translation: If, there's a benevolent intelligence here that wants to help humanity break free from the chains of an alleged malevolent intelligence that may be using us, the benevolent force would need to somehow arm themselves in order to militarily take on this malevolent force, potentially, defeat them, and rescue us from a bad predicament. And we see no evidence of that.) Reed: "So, you know, we have analogs in our own human history to this, the natives of the new world. They looked out on the quay and they saw the ships, different flags, different vessels, and they assumed it's gotta be either the angels from the spirit realm - it has to comport to our belief system - or a variety of intentions, and we should work with and collaborate. And it may not be that way. "It's not about, it's all hostile or it's all beneficial or benevolent. In my mind, it's exo-systemic. It's an ecosystem arriving on our shores at a specific 20th-century moment in which we have detonated nuclear weapons, we have flashed the Universe with technology capability, and we have triggered an engagement event. This is really what this is. And a program to engage humanity over a longitudinal time period." (I think it was here and intersecting with us a long time before we first detonated our nukes.) Karla Turner: "We know from some of our own research that the abduction phenomenon has affected families going back four generations and that would be around the turn of the century (1900). In my husband's family, his grandmother had an encounter with a non-human entity that led her off into a swampy area where there was a period of missing before she was returned, when she was only five-years old. That was 1903. So if you think it's new and you think it's something the media has spread, you start looking into the cases and find out how far back it's goes in some of these families' generations. I know of an African American family in East Texas that has had it going on since the early 1900s and it's still going on today with that same family. Three to four generations is fairly typical." ~ Reed: "And so, we need to step back and really look at the human project of getting our act together to diplomatically engage now and in the future, to manage the NHI presence internationally, and coordinate responses, lest we divide and conquer ourselves over this issue. And that's where the reframing of disclosure as fundamentally belonging to the human species, being one that should, I think, be framed in first principles - to Karl Nell's point - with a naturalistic framework, a science-informed, data-driven framework. "And with that, we go out into the field and collect evidence on the phenomenon. We go out to where it is interacting with people. That's the key missing data set that would be necessary to inform decision makers." (We had that with AAWSAP and can have it again. Plus, the Vallée/AAWSAP Capella database of approximately 250,000 cases. Using AI and the best human minds on this planet, all of that may inform us of the intent of these alleged NHI actors.) Reed: "In my view, there absolutely is an adversarial actor in our world. There is a human-engagement program underway. And that's not the whole story, but if that is true, that should marshal our human response above all other possibilities, initially, because we may wake up in 30 years and find that we are not the human beings we used to be. Genetic interventions, cultural influence, right? Social engineering. All of these could be at play in a subversive sense." (If it's true, it should be the most important thing for human beings to address, ASAP. We need to find out now.) Reed: "And, you know, NHI is operating in a strategic silence, in a state of non-disclosure. They're operating covertly and selectively with military-defense organizations, internationally, which are already in competition." (I'd like to hear more about the claim that NHI are operating covertly and selectively with military-defense organizations. We've all heard those claims but is there evidence of that?) Reed: "And that, to me, is another one of a number of alarming signals that should just command us to take a cautionary response. And and we've gotta get out there and use the best of science, research and investigation to do reconnaissance - reconnai-science, as I call it, on behalf of the human interest, and not just national interests." Garrison: "Yeah, that's a profound way to put it, Reed. You know that we've triggered an interaction event." Reed: "And so, intelligence indicates intent, intentful engagement indicates a program. And so what is their program? We talk about the Legacy-human program. What about the non-human program? We do not query that nearly as much as we should. And in my view, disclosure. How do we get disclosure moving? If we recenter the controversy not on human actors, human governments, but on the non- human presence itself, that allows the human actors a way to rapidly and catastrophically disclose their involvements, which is what is keeping this back in part. Thank you."

🔥👽 This is REALLY good! 👽🔥 "In my view, there absolutely is an adversarial actor in our world. There is a human-engagement program underway. I don't think it's the rainbow that everyone kind of hopes that it is." ~Reed (IMO, Reed Summers is an unsung, VERY important voice in this community, and what he says in this clip, currently, resonates with me in a big way. Take the time to read or watch. Then we have what Ross Coulthart says about the "deliberate lifting of consciousness" and "frequency," which is more on the side of love, light and space brothers. I don't see that right now, and none of my contacts have ever mentioned it. If it's there, maybe its hands are tied with how it can help us?) ~ "Genetic interventions, cultural influence, right? Social engineering. All of these could be at play in a subversive sense. NHI is operating in a strategic silence, in a state of non-disclosure. Intelligence indicates intent, intentful engagement indicates a program. And so what is their program? We talk about the Legacy-human program. What about the non-human program? ~Reed ~ Ross: "I've been told that the United States has developed quite advanced weaponry, particularly plasma-beam technology that might, in part, have been inspired by what they've seen or recovered from non-human technology. I think, also, just to add the concern of NHI with what humanity is doing. There's also a very deliberate lifting of consciousness. And I really am struck by what Chris (Bledsoe - Chris Bledsoe) says about the frequency. This is something that I'm getting from so many people. That the level of intensity of public reporting of their engagement with NHI, of a clear intent by NHI, to essentially give up on governments from ever disclosing. But to raise human consciousness and awareness. "And I think they're doing this, increasingly (laughs), through direct engagement with individuals. I've got many friends and colleagues and people I've interviewed - witnesses - who've had incredible experiences. I've just been recording for a TV show, something that we're doing here in Australia, where people are inviting or summoning the phenomenon. And there seems to be an interest in the phenomenon engaging with humans much, much more overtly. And I do think that stems from a concern about us primitive monkeys playing with matches." (Were they concerned when this abduction-like event allegedly happened to Jim Semivan and his wife? Semivan: "I had a hole in the back of my neck, and my wife...unexplained bleeding for 17 days." Source: Engaging The Phenomenon's interview with Jim ~ Jim Garrison: "But Reid, speak to us about this interplay between malevolence and benevolence." Reed: "Sure, well, and you described, Jim, the first part of my life in which I was focused primarily on supporting my father, Marshall (Vian Summers and The Allies of Humanity), who had direct encounters with NHI, communications with NHI." (Was Marshall really in contact with extraterrestrial intelligence, known as The Allies of Humanity? I don't know. But a lot of what he has said rings true to me. Doesn't mean it's true. Read one transcript I did in 2021: “It’s Probably Not Human” – Elizondo, Blumenthal & The Allies Of Humanity ~ Reed: "And my family felt the full force and impact of what that was like, as well as what comes with it ("it" being his dad's alleged contact with ET intelligence). Which is, what might be called the hitchhiker effect, the anomalous effects that attend those who have been selected by the phenomenon without their understanding or knowing why." (Add another few names to the list of people who have now said they experienced the hitchhiker effect: Jay Stratton, George Knapp, Kelleher, Davis, Bigelow, Brandon Fugal, Thomas Winterton, Semivan, and more. That includes poltergeist-like events, shadow people, orbs, etc.) Reed: ""But my later work is really about assessing intent with a structured framework. I think we need to step back from belief systems, hopeful or fearful interpretations, and lay out the spectrum of possibilities, right? From curiosity on the left, salvation and assistance, even further on the left, to transaction, integration on the right, or even something more hostile. And in the end, intent is all about human outcomes. We can't know their consciousness. The project, in my mind right now, is not, let's set out to understand what they're like, who they are, how they think, how they cognate. That's impossible, in my perspective. But we can assess the real human outcomes that stem from the hazard and the risk and the possible threat of a non-human factor acting upon. Like a forcing, an environmental or evolutionary-forcing upon humanity at a historically-unprecedented time." (From what I have seen, whatever this is, has, overall, NOT helped humanity. If that's happening, it's going on behind the scenes. I know some experiencers report having their lives changed in a positive way, and some say they have been healed of illnesses. But others say their lives have been ruined and they've been hurt, intentionally or not, by coming in close contact with the phenomenon. Also, various people have come down with several auto-immune diseases after close contact, and some of that was detailed in the must-read book, "Skinwalkers at the Pentagon." jakebarber also mentioned that. Is it multiple intelligences with multiple intents, or one intelligence mixing in some positive outcomes as a propaganda effort? We don't know.) Reed: "And there are significant signals and indicators in the history of the phenomenon that give very good anchors for assessing intent, and for going from possibilities to probabilities and ultimately making a starting assessment, which informs the research pathway, which is the other part of my work in supporting the human institute, devising an intelligent, scientific research and investigatory citizen-led effort to uncover the phenomenon, to disclose it, to interrogate its activity, lest we just be interrogated, unknowingly, ourselves. So, a lot of good work to do there." (Interrogate the activity of the phenomenon. I really like that approach. Let's not assume anything.) Reed: "But, in my view, when you look at all that the sensor data has given us, technologically, when you corroborate that with the geographic, circumstantial [and] temporal aspects of how the phenomena appears, who it manifests to, and the considerable problem it presents to the international community and to national laws and frameworks that govern territorial sovereignty, these incursions into sensitive sites and the potential, programmatic engagement with civilians in the form of physical NHI-initiated contact - abductions - it builds a picture. It bounds those possibilities into a zone of probabilities. "And whether it's a transactional presence, whether it's an integrative or one that wants to integrate with humanity, or ultimately replace humanity, I think there are multiple possibilities at play and multiple intents at play. Although, I don't think it's the rainbow that everyone kind of hopes that it is. It's not, everything the Universe has to offer is gonna come all at once, now, and function harmoniously in our skies and oceans. No. The hostile intent will not tolerate a beneficial actor who themselves would have to arm themselves and militarily confront the hostile actor, which we do not see indications of." (My translation: If, there's a benevolent intelligence here that wants to help humanity break free from the chains of an alleged malevolent intelligence that may be using us, the benevolent force would need to somehow arm themselves in order to militarily take on this malevolent force, potentially, defeat them, and rescue us from a bad predicament. And we see no evidence of that.) Reed: "So, you know, we have analogs in our own human history to this, the natives of the new world. They looked out on the quay and they saw the ships, different flags, different vessels, and they assumed it's gotta be either the angels from the spirit realm - it has to comport to our belief system - or a variety of intentions, and we should work with and collaborate. And it may not be that way. "It's not about, it's all hostile or it's all beneficial or benevolent. In my mind, it's exo-systemic. It's an ecosystem arriving on our shores at a specific 20th-century moment in which we have detonated nuclear weapons, we have flashed the Universe with technology capability, and we have triggered an engagement event. This is really what this is. And a program to engage humanity over a longitudinal time period." (I think it was here and intersecting with us a long time before we first detonated our nukes.) Karla Turner: "We know from some of our own research that the abduction phenomenon has affected families going back four generations and that would be around the turn of the century (1900). In my husband's family, his grandmother had an encounter with a non-human entity that led her off into a swampy area where there was a period of missing before she was returned, when she was only five-years old. That was 1903. So if you think it's new and you think it's something the media has spread, you start looking into the cases and find out how far back it's goes in some of these families' generations. I know of an African American family in East Texas that has had it going on since the early 1900s and it's still going on today with that same family. Three to four generations is fairly typical." ~ Reed: "And so, we need to step back and really look at the human project of getting our act together to diplomatically engage now and in the future, to manage the NHI presence internationally, and coordinate responses, lest we divide and conquer ourselves over this issue. And that's where the reframing of disclosure as fundamentally belonging to the human species, being one that should, I think, be framed in first principles - to Karl Nell's point - with a naturalistic framework, a science-informed, data-driven framework. "And with that, we go out into the field and collect evidence on the phenomenon. We go out to where it is interacting with people. That's the key missing data set that would be necessary to inform decision makers." (We had that with AAWSAP and can have it again. Plus, the Vallée/AAWSAP Capella database of approximately 250,000 cases. Using AI and the best human minds on this planet, all of that may inform us of the intent of these alleged NHI actors.) Reed: "In my view, there absolutely is an adversarial actor in our world. There is a human-engagement program underway. And that's not the whole story, but if that is true, that should marshal our human response above all other possibilities, initially, because we may wake up in 30 years and find that we are not the human beings we used to be. Genetic interventions, cultural influence, right? Social engineering. All of these could be at play in a subversive sense." (If it's true, it should be the most important thing for human beings to address, ASAP. We need to find out now.) Reed: "And, you know, NHI is operating in a strategic silence, in a state of non-disclosure. They're operating covertly and selectively with military-defense organizations, internationally, which are already in competition." (I'd like to hear more about the claim that NHI are operating covertly and selectively with military-defense organizations. We've all heard those claims but is there evidence of that?) Reed: "And that, to me, is another one of a number of alarming signals that should just command us to take a cautionary response. And and we've gotta get out there and use the best of science, research and investigation to do reconnaissance - reconnai-science, as I call it, on behalf of the human interest, and not just national interests." Garrison: "Yeah, that's a profound way to put it, Reed. You know that we've triggered an interaction event." Reed: "And so, intelligence indicates intent, intentful engagement indicates a program. And so what is their program? We talk about the Legacy-human program. What about the non-human program? We do not query that nearly as much as we should. And in my view, disclosure. How do we get disclosure moving? If we recenter the controversy not on human actors, human governments, but on the non- human presence itself, that allows the human actors a way to rapidly and catastrophically disclose their involvements, which is what is keeping this back in part. Thank you."

Joe Murgia

65,947 次观看 • 10 个月前

If you watch this ~50 minute screen recording closely (yeah, I know, it's long; there are also some times when my computer was very slow and laggy, just skip past that part. And at one point I had to run and get my 9-month-old a new bottle and left it on a boring screen, sorry!), I believe you can see real signs of the kind of runaway, recursive AI self-improvement that people have been warning of for a while (Mr. Kurzweil most notably and prophetically). Why do I say that? What's different now? Well, there's a reason my set of agent coding tooling is called the Flywheel. These tools all mutually self-reinforce each other. And they all flow directly into my ntm tool (short for "named_tmux_manager"), which acts as a sort of integration point and nerve center for the tools (this is becoming more true by the minute as I'm now seriously working on ntm). Now, ntm was something I started making to automate some aspects of my workflow, but it was the kind of thing where, until it was perfect, it sort of just slowed me down. So I didn't actually use it even though I kept working on it and trying to improve it, and suggested to users that they try it in my tutorials. Well anyway, I finally got around to "dogfooding" ntm last night, and now it's going to get very dramatically better at an alarming rate. Some of that is from applying my "idea wizard" prompt to generate more useful features and building that stuff out and addressing obvious pain points I encountered during my newfound usage of the tool. But a lot comes from my realization that, once again, ntm's true utility is not as a tool for ME, but for an agent. That is, ntm lets one instance of Claude Code or Codex act as, well, me, do the things that I had been doing manually. Do I wish I had started using ntm earlier? No, for two big reasons: 1) Doing it manually helped me build up my intuition massively, which directly led me down the path of creating useful prompt strategies and workflows; these often began as ad-hoc prompts that I realized could be generalized and made more versatile/universal. Lesson: don't prematurely automate until you have an intimate, intuitive feel for your "core value-add loop." Otherwise you'll have a fully automated system quickly that efficiently and automatically does a stupid or otherwise sub-optimal thing. 2) My eyes have been opened to the beauty and power of Skills. I'm not talking about your garden-variety skills that are just a simple markdown file. I'm talking about true tour-de-force directories of perfectly structured and organized files that are filled with good information, insights, workflows, etc., but presented in a way that is highly optimized for consumption by AI agents, with extreme attention paid to things like perfect progressive disclosure, token density, agent-ergonomics, agent-intuitiveness, etc. And also Skills that go way beyond markdown files, with full integration into Claude Code where it makes sense via hooks, sub-agents, and even Python scripts. These kinds of skills are a qualitative difference in expressive power and usefulness and a total game changer. They are also effectively composable, creating almost an algebra of skills that let you use them together in powerful ways. I'm working on a subscription service website and CLI tool now to share what I've learned here most effectively, stay tuned for that in the coming days. Anyway, I now know what to make and how to make it. So, getting back to that screen recording, what does it show that makes me claim recursive self-improvement is here? If you keep your eye on the upper left tmux pane, that's the "controller" agent. It is using ntm to control all the other panes which are also running Claude Code (but ntm fully supports other agent types like Codex and Gemini-CLI, and it's trivially easy to mix and match them if you wanted to have, say, 8 CCs and 6 Codexes for writing the code and 3 Gemini-CLIs for reviewing code.) Now, there's nothing that crazy about this much so far. But where it starts to get very cool is that as the session continues and we encounter real-world problems, things like my ridiculously overloaded computer that keeps hanging for long periods, Claude Code instances that crash and get into a frozen, unresponsive state, it can learn from that. And you can see it using my skill writing skill to refine its ntm vibe coding skill in real time. And then take that skill and refine it to be more intuitive for itself. Or use my cass tool skill to search all the session histories to look for problems that came up and strategize how to solve them. The most useful part was when, towards the end of the session, I told it to reflect on all the things we had done and problems we encountered. One way it can usefully leverage those reflections is by improving its ntm vibe coding skill to make it cover more edge cases and exigencies. But the other, more fundamental, way is for it to conceive of and design the optimal new features and functionality for ntm itself so that the tool embodies those lessons in a first-class way. This offloads cognition from its brain onto its tooling, just like how a person can lean on spellcheck or a calculator. It codifies correct, effective reasoning at the tool level, where it's more reliable and robust and repeatable. And btw, did you notice what code base it was working on the whole time? It was none other than ntm itself! So as it worked on its own tool, it had reflections and ideas about how to further improve the tool. Now, it could have just as easily gotten those insights and ideas while using ntm to work on a different project, but the fact that it was working on itself is almost gloriously meta and recursive. So by the end, after learning from tending to a big group of agent workers (btw, I have previously emphasized doing everything in a really distributed/decentralized way, where each fungible agent gets identical marching orders that tell it to use my bv tool to find the optimal bead to work on. This does work very well, but occasionally results in some contention and overlap from thundering herd, or at least wastes time/tokens/communication in avoiding that before the agents waste time duplicating work. But in this new ntm-oriented workflow, I was able to have the controller agent in the upper left use bv itself and then optimally parcel out the instructions to each agent so that we could know for sure that there's no overlap), I ended up with a ton of new beads for new features, which I had it optimize and polish a few times. Now I can swap to a new Claude Max account and have the swarm implement all those new features! It should only take a couple passes like the one shown in the screen recording to get everything implemented. Then we can rinse and repeat, having the agent read through the full session histories of each agent and its experience from its own session in sending ntm commands and seeing how they worked out in practice, to come up with the next batch of changes to both its ntm vibe coding skill AND to the ntm tool itself. Do you see how rapidly this turns into Skynet? My mistake earlier was in focusing on making myself a "faster horse" as Henry Ford used to joke about customers wanting before he showed them what they should really want (a Model T). That is, something that would make my experience nicer while doing this agent swarm based development workflow. But the obvious lesson is that you should make all your tooling agent-first because the agents are just better at this stuff. You can still watch, and of course I did add a ridiculous number of very nice human-centric features to ntm that you'll be seeing in the next day or two, but those are really kind of "for fun" to make us humans feel better about the process. All the real value-add is happening "by agents, for agents." PS: Towards the end, you can see me switch to my Mac and tell Claude to improve the skill that I made earlier today for taking the mkv screen recording files from OBS Studio and muxing them into MP4 files for sharing, while downloading songs from YouTube to serve as the background music. I made it so it can also grab the thumbnails and generate little song credit cards that show up in the lower right corner. This worked perfectly the first time! I'll include some screenshots in a response post showing how that worked, but it was awesome to witness. Skills are POWERFUL. I'll also post a link to this video on YouTube if you prefer to watch it there.

If you watch this ~50 minute screen recording closely (yeah, I know, it's long; there are also some times when my computer was very slow and laggy, just skip past that part. And at one point I had to run and get my 9-month-old a new bottle and left it on a boring screen, sorry!), I believe you can see real signs of the kind of runaway, recursive AI self-improvement that people have been warning of for a while (Mr. Kurzweil most notably and prophetically). Why do I say that? What's different now? Well, there's a reason my set of agent coding tooling is called the Flywheel. These tools all mutually self-reinforce each other. And they all flow directly into my ntm tool (short for "named_tmux_manager"), which acts as a sort of integration point and nerve center for the tools (this is becoming more true by the minute as I'm now seriously working on ntm). Now, ntm was something I started making to automate some aspects of my workflow, but it was the kind of thing where, until it was perfect, it sort of just slowed me down. So I didn't actually use it even though I kept working on it and trying to improve it, and suggested to users that they try it in my tutorials. Well anyway, I finally got around to "dogfooding" ntm last night, and now it's going to get very dramatically better at an alarming rate. Some of that is from applying my "idea wizard" prompt to generate more useful features and building that stuff out and addressing obvious pain points I encountered during my newfound usage of the tool. But a lot comes from my realization that, once again, ntm's true utility is not as a tool for ME, but for an agent. That is, ntm lets one instance of Claude Code or Codex act as, well, me, do the things that I had been doing manually. Do I wish I had started using ntm earlier? No, for two big reasons: 1) Doing it manually helped me build up my intuition massively, which directly led me down the path of creating useful prompt strategies and workflows; these often began as ad-hoc prompts that I realized could be generalized and made more versatile/universal. Lesson: don't prematurely automate until you have an intimate, intuitive feel for your "core value-add loop." Otherwise you'll have a fully automated system quickly that efficiently and automatically does a stupid or otherwise sub-optimal thing. 2) My eyes have been opened to the beauty and power of Skills. I'm not talking about your garden-variety skills that are just a simple markdown file. I'm talking about true tour-de-force directories of perfectly structured and organized files that are filled with good information, insights, workflows, etc., but presented in a way that is highly optimized for consumption by AI agents, with extreme attention paid to things like perfect progressive disclosure, token density, agent-ergonomics, agent-intuitiveness, etc. And also Skills that go way beyond markdown files, with full integration into Claude Code where it makes sense via hooks, sub-agents, and even Python scripts. These kinds of skills are a qualitative difference in expressive power and usefulness and a total game changer. They are also effectively composable, creating almost an algebra of skills that let you use them together in powerful ways. I'm working on a subscription service website and CLI tool now to share what I've learned here most effectively, stay tuned for that in the coming days. Anyway, I now know what to make and how to make it. So, getting back to that screen recording, what does it show that makes me claim recursive self-improvement is here? If you keep your eye on the upper left tmux pane, that's the "controller" agent. It is using ntm to control all the other panes which are also running Claude Code (but ntm fully supports other agent types like Codex and Gemini-CLI, and it's trivially easy to mix and match them if you wanted to have, say, 8 CCs and 6 Codexes for writing the code and 3 Gemini-CLIs for reviewing code.) Now, there's nothing that crazy about this much so far. But where it starts to get very cool is that as the session continues and we encounter real-world problems, things like my ridiculously overloaded computer that keeps hanging for long periods, Claude Code instances that crash and get into a frozen, unresponsive state, it can learn from that. And you can see it using my skill writing skill to refine its ntm vibe coding skill in real time. And then take that skill and refine it to be more intuitive for itself. Or use my cass tool skill to search all the session histories to look for problems that came up and strategize how to solve them. The most useful part was when, towards the end of the session, I told it to reflect on all the things we had done and problems we encountered. One way it can usefully leverage those reflections is by improving its ntm vibe coding skill to make it cover more edge cases and exigencies. But the other, more fundamental, way is for it to conceive of and design the optimal new features and functionality for ntm itself so that the tool embodies those lessons in a first-class way. This offloads cognition from its brain onto its tooling, just like how a person can lean on spellcheck or a calculator. It codifies correct, effective reasoning at the tool level, where it's more reliable and robust and repeatable. And btw, did you notice what code base it was working on the whole time? It was none other than ntm itself! So as it worked on its own tool, it had reflections and ideas about how to further improve the tool. Now, it could have just as easily gotten those insights and ideas while using ntm to work on a different project, but the fact that it was working on itself is almost gloriously meta and recursive. So by the end, after learning from tending to a big group of agent workers (btw, I have previously emphasized doing everything in a really distributed/decentralized way, where each fungible agent gets identical marching orders that tell it to use my bv tool to find the optimal bead to work on. This does work very well, but occasionally results in some contention and overlap from thundering herd, or at least wastes time/tokens/communication in avoiding that before the agents waste time duplicating work. But in this new ntm-oriented workflow, I was able to have the controller agent in the upper left use bv itself and then optimally parcel out the instructions to each agent so that we could know for sure that there's no overlap), I ended up with a ton of new beads for new features, which I had it optimize and polish a few times. Now I can swap to a new Claude Max account and have the swarm implement all those new features! It should only take a couple passes like the one shown in the screen recording to get everything implemented. Then we can rinse and repeat, having the agent read through the full session histories of each agent and its experience from its own session in sending ntm commands and seeing how they worked out in practice, to come up with the next batch of changes to both its ntm vibe coding skill AND to the ntm tool itself. Do you see how rapidly this turns into Skynet? My mistake earlier was in focusing on making myself a "faster horse" as Henry Ford used to joke about customers wanting before he showed them what they should really want (a Model T). That is, something that would make my experience nicer while doing this agent swarm based development workflow. But the obvious lesson is that you should make all your tooling agent-first because the agents are just better at this stuff. You can still watch, and of course I did add a ridiculous number of very nice human-centric features to ntm that you'll be seeing in the next day or two, but those are really kind of "for fun" to make us humans feel better about the process. All the real value-add is happening "by agents, for agents." PS: Towards the end, you can see me switch to my Mac and tell Claude to improve the skill that I made earlier today for taking the mkv screen recording files from OBS Studio and muxing them into MP4 files for sharing, while downloading songs from YouTube to serve as the background music. I made it so it can also grab the thumbnails and generate little song credit cards that show up in the lower right corner. This worked perfectly the first time! I'll include some screenshots in a response post showing how that worked, but it was awesome to witness. Skills are POWERFUL. I'll also post a link to this video on YouTube if you prefer to watch it there.

Jeffrey Emanuel

25,483 次观看 • 7 个月前

$AMD Massive Rotation from $NVDA $INTC🧵 Not Financial Advice! DYOR! 5-10 minutes before the bell today, last trading day of May 2026, massive rotation out of $INTC and $NVDA into $AMD. I wrote this thread this morning on what $TSM said on Energy Efficiency is now TOP Priotity and why AMD is the biggest winner. Of course I did not have influence on this rebalancing, I was just pointing out why Dr. Su saw this coming years ago. (Check the picture to understand more). I been talking about Agentic AI for like 3-4 years now. OpenClaw broke the CPU:GPU Ratio 1:4 narrative to 1:1 to 5:1 in late Jan and Feb 2026. I will link various threads where you can understand the full picture from supply chain, to TSMC expansion, and different Wafer Ratio for EPYC Venice and MI455X. Energy efficiency is a structural, long-term driver behind institutional rotation from $NVDA and $INTC into $AMD (with spillover strength in $AVGO for complementary networking/custom silicon). This isn't just short-term rebalancing, it's a massive bet on the shift from AI training (performance-at-any-cost) to inference, deployment, and embodied/agentic systems (where total cost of ownership, power draw, and scalability dominate). Precisely What I been writing about $AMD for years now, probably at least more than 5,000 threads.This is the FOMO from Institutions to own $AMD. Do know that AMD is the least owned Semi Stock among vs Peers. AI infrastructure is moving beyond massive training clusters to widespread inference for Agentic AI (running models 24/7) and embodied AI (robots, autonomous agents, edge devices). These workloads prioritize: ~Tokens-per-watt and performance-per-watt ~Lower total power consumption for data centers facing grid constraints ~Better economics at scale (cost-per-token, TCO) ~Thermal and power efficiency for on-device/robotics use Hyperscalers are now thinking more about Margin, Profitability, and $/M Tokens At $516/share. AMD Fwd PEG Ratio is still 35/100+= 0.35 AKA very cheap IMO for the growth and potential. A. Why institutions rotated out of $NVDA? Because Agentic AI is going to dominated by CPUs for years to come, moving violently to 5-10-20:1 CPU:GPU Ratio as enterprises are demanding more than 10-20 agents to run tasks. Now, that does not mean training is going away, Inference is just going to grow much faster. B. Why instiutitons rotated out of $INTC? Because AMD x86 unit share is only at 30-31% but Revenue share is already at 46.2% according to Mercury Research. And Dr. Su wants 50-60% market share, and that would mean 60-70%+ Revenue share where the CPUs TAM Is now already at $200B in 2026 and projected to be $500B by 2030. C. Why $AMD? Because AMD secured meaningful 2nm Capacity, Advanced Packaging and Memory through 2027-2028. And TSMC is expanding 2 primary 2nm Fabs toward 60-65k WPM each, and speeding up 5 2nm Fabs in Taiwan. With total up to 12 2nm Fabs through 2027/2028. 2nm Capacity is expected to be 140k+ WPM toward end of 2026, and 220-240k WPM by end of 2027. Apple has secured 35-45k WPM. And AMD does not have to worry about allocation competition until late 2027 from $AVGO for $META and $GOOGL(This may change) D. Agentic AI will evolve to 24/7 Autonomous Agent, and that will become the foundational layer for Robotic or Physical AI. Agentic AI (autonomous systems that plan, reason, use tools, self-correct, pursue long-horizon goals, and adapt) provides the high-level cognitive architecture. It turns raw perception and low-level control into useful, general-purpose behavior in the physical world. Physical AI (or Embodied AI) refers to AI that senses, understands, and acts directly in the real world through robots, actuators, and sensors. Agentic capabilities are what make this scalable and useful beyond narrow, scripted tasks. Reactive/programmed machines → To proactive, goal-oriented autonomous agents. How does this work? Autonomous Agent layer is the brain ~Vision-Language-Action models or robotics foundation models. ~Agentic loops: Planning, chain-of-thought reasoning, reflection, tool use (simulators, APIs), multi-step task decomposition. ~Persistent 24/7 operation with Memory, world modeling, continuous learning. Institutions may not like $AMD from 2022-2025, but they cannot stop this evolution and it is inevitable. Part of my main thesis for AMD to get to $5 Trillion Market Cap Long Term. Conclusion: Institutions are rotating capital toward AMD not merely for tactical rebalancing, but because Dr. Lisa Su and her team anticipated this exact inflection years in advance and have been methodically engineering AMD’s platform to dominate it. Dr. Su has long championed the convergence of Agentic AI as the high-level cognitive foundation for Physical AI and robotics. As far back as her 2023/2024 CES keynote and earlier strategic commentary, she described Physical AI (including humanoid robotics and edge autonomy) as “the next big thing”; a natural extension of agentic workflows moving from digital reasoning to real-world action. She emphasized that enabling persistent, 24/7 autonomous agents requires a full-stack approach: high-performance CPUs for orchestration and motion control, dedicated accelerators for real-time vision and multimodal inference, and open software ecosystems for rapid development. This vision aligns precisely with the structural drivers we’ve discussed. As AI shifts from training to massive-scale inference and embodiment, energy efficiency, total cost of ownership, and heterogeneous compute become first-order advantages. AMD’s Instinct MI350/MI355 series, Ryzen AI Embedded processors, and EPYC platforms deliver superior performance-per-watt and balanced CPU + GPU + NPU integration ideal for power-constrained robots that must run sophisticated agentic reasoning loops without excessive thermal or battery drain. Dr. Su has repeatedly highlighted the rising importance of CPUs in agentic systems (moving toward 1:1 or even CPU-heavy ratios with GPUs), positioning AMD’s strengths in orchestration, memory handling, and efficiency as critical for the next phase of growth. AMD is engineered for the deployment realities of embodied agents: scalable, efficient, and deployable at the edge and in physical systems. The institutional flows out of NVDA and INTC into AMD reflect recognition of this prepared leadership. Dr. Su didn’t just see the future of Agentic AI powering robotics, she has spent years building the silicon, software, and partnerships to make it practical and economically viable. This rotation signals confidence that the companies best positioned for the physical, always-on intelligence layer will capture the highest-volume opportunities in the coming decade. Not Financial Advice! DYOR!

$AMD Massive Rotation from $NVDA $INTC🧵 Not Financial Advice! DYOR! 5-10 minutes before the bell today, last trading day of May 2026, massive rotation out of $INTC and $NVDA into $AMD. I wrote this thread this morning on what $TSM said on Energy Efficiency is now TOP Priotity and why AMD is the biggest winner. Of course I did not have influence on this rebalancing, I was just pointing out why Dr. Su saw this coming years ago. (Check the picture to understand more). I been talking about Agentic AI for like 3-4 years now. OpenClaw broke the CPU:GPU Ratio 1:4 narrative to 1:1 to 5:1 in late Jan and Feb 2026. I will link various threads where you can understand the full picture from supply chain, to TSMC expansion, and different Wafer Ratio for EPYC Venice and MI455X. Energy efficiency is a structural, long-term driver behind institutional rotation from $NVDA and $INTC into $AMD (with spillover strength in $AVGO for complementary networking/custom silicon). This isn't just short-term rebalancing, it's a massive bet on the shift from AI training (performance-at-any-cost) to inference, deployment, and embodied/agentic systems (where total cost of ownership, power draw, and scalability dominate). Precisely What I been writing about $AMD for years now, probably at least more than 5,000 threads.This is the FOMO from Institutions to own $AMD. Do know that AMD is the least owned Semi Stock among vs Peers. AI infrastructure is moving beyond massive training clusters to widespread inference for Agentic AI (running models 24/7) and embodied AI (robots, autonomous agents, edge devices). These workloads prioritize: ~Tokens-per-watt and performance-per-watt ~Lower total power consumption for data centers facing grid constraints ~Better economics at scale (cost-per-token, TCO) ~Thermal and power efficiency for on-device/robotics use Hyperscalers are now thinking more about Margin, Profitability, and $/M Tokens At $516/share. AMD Fwd PEG Ratio is still 35/100+= 0.35 AKA very cheap IMO for the growth and potential. A. Why institutions rotated out of $NVDA? Because Agentic AI is going to dominated by CPUs for years to come, moving violently to 5-10-20:1 CPU:GPU Ratio as enterprises are demanding more than 10-20 agents to run tasks. Now, that does not mean training is going away, Inference is just going to grow much faster. B. Why instiutitons rotated out of $INTC? Because AMD x86 unit share is only at 30-31% but Revenue share is already at 46.2% according to Mercury Research. And Dr. Su wants 50-60% market share, and that would mean 60-70%+ Revenue share where the CPUs TAM Is now already at $200B in 2026 and projected to be $500B by 2030. C. Why $AMD? Because AMD secured meaningful 2nm Capacity, Advanced Packaging and Memory through 2027-2028. And TSMC is expanding 2 primary 2nm Fabs toward 60-65k WPM each, and speeding up 5 2nm Fabs in Taiwan. With total up to 12 2nm Fabs through 2027/2028. 2nm Capacity is expected to be 140k+ WPM toward end of 2026, and 220-240k WPM by end of 2027. Apple has secured 35-45k WPM. And AMD does not have to worry about allocation competition until late 2027 from $AVGO for $META and $GOOGL(This may change) D. Agentic AI will evolve to 24/7 Autonomous Agent, and that will become the foundational layer for Robotic or Physical AI. Agentic AI (autonomous systems that plan, reason, use tools, self-correct, pursue long-horizon goals, and adapt) provides the high-level cognitive architecture. It turns raw perception and low-level control into useful, general-purpose behavior in the physical world. Physical AI (or Embodied AI) refers to AI that senses, understands, and acts directly in the real world through robots, actuators, and sensors. Agentic capabilities are what make this scalable and useful beyond narrow, scripted tasks. Reactive/programmed machines → To proactive, goal-oriented autonomous agents. How does this work? Autonomous Agent layer is the brain ~Vision-Language-Action models or robotics foundation models. ~Agentic loops: Planning, chain-of-thought reasoning, reflection, tool use (simulators, APIs), multi-step task decomposition. ~Persistent 24/7 operation with Memory, world modeling, continuous learning. Institutions may not like $AMD from 2022-2025, but they cannot stop this evolution and it is inevitable. Part of my main thesis for AMD to get to $5 Trillion Market Cap Long Term. Conclusion: Institutions are rotating capital toward AMD not merely for tactical rebalancing, but because Dr. Lisa Su and her team anticipated this exact inflection years in advance and have been methodically engineering AMD’s platform to dominate it. Dr. Su has long championed the convergence of Agentic AI as the high-level cognitive foundation for Physical AI and robotics. As far back as her 2023/2024 CES keynote and earlier strategic commentary, she described Physical AI (including humanoid robotics and edge autonomy) as “the next big thing”; a natural extension of agentic workflows moving from digital reasoning to real-world action. She emphasized that enabling persistent, 24/7 autonomous agents requires a full-stack approach: high-performance CPUs for orchestration and motion control, dedicated accelerators for real-time vision and multimodal inference, and open software ecosystems for rapid development. This vision aligns precisely with the structural drivers we’ve discussed. As AI shifts from training to massive-scale inference and embodiment, energy efficiency, total cost of ownership, and heterogeneous compute become first-order advantages. AMD’s Instinct MI350/MI355 series, Ryzen AI Embedded processors, and EPYC platforms deliver superior performance-per-watt and balanced CPU + GPU + NPU integration ideal for power-constrained robots that must run sophisticated agentic reasoning loops without excessive thermal or battery drain. Dr. Su has repeatedly highlighted the rising importance of CPUs in agentic systems (moving toward 1:1 or even CPU-heavy ratios with GPUs), positioning AMD’s strengths in orchestration, memory handling, and efficiency as critical for the next phase of growth. AMD is engineered for the deployment realities of embodied agents: scalable, efficient, and deployable at the edge and in physical systems. The institutional flows out of NVDA and INTC into AMD reflect recognition of this prepared leadership. Dr. Su didn’t just see the future of Agentic AI powering robotics, she has spent years building the silicon, software, and partnerships to make it practical and economically viable. This rotation signals confidence that the companies best positioned for the physical, always-on intelligence layer will capture the highest-volume opportunities in the coming decade. Not Financial Advice! DYOR!

Mike

104,109 次观看 • 3 个月前

The most epic 13 minute AI rant I've heard in 2026 PS: My parent's heard this when I was playing it in the car and thought Jason ✨👾SaaStr.Ai✨ Lemkin went OFF like Stephen A Smith does on first take PPS: Full transcript below [17:00] Harry Stebbings: I I just wanted to ask Jason, if the people that we want are fundamentally different, the developers that we used to hire, we don't because AI writes the code for us. The marketers we don't want, the sales people we don't want—who who do we want genuinely? Like what is the attractive profile? Because your Anthropic’s and your OpenAIs are hiring, so so what are the people that we want in the companies of the future? [17:18] Jason Lemkin: Look, I know it sounds trite, but but the answer is simple. It's just the expression each year changes. We want folks that are genuinely AI fluent. It's pretty simple. Now you know, maybe last year we called them prompt engineers, right? That used to be a job. I don't know if you remember that actually used to be the hottest job on planet earth. Now no one needs a prompt engineer because it's pretty easy to prompt all these tools. That job died. Okay. Um and now we need go-to-market engineers. Um I think that job's going to die. We need—everyone needs so many forward deployed engineers. Like you can't hire enough forward deployed engineers. But uh you know um but Palantir just announced in whatever their their big their big event—they've gotten their deployment times down over 90% with forward deployed engineers. So that may become—so the this wave of disruption for the titles and the specificity, it's also exhaustingly accelerating. But it's really simple. You meet anyone for any role—sales, marketing, engineering, product, QA—they're they're either they're either they can't keep all of the ways they use AI to accelerate their job from spewing out of their mouth, or they're staring at you. It's there's nowhere in the middle. Like, and the person that comes in and says—it's it's it sounds Captain Obvious—but like, you know, you just had the whatever from Lovable, the the marketing head that was super popular on the show, right? She's just spewing AI-native insights into Lovable, right? It's not that complicated. You hire her, Elena, or whatever it is. You just hire her. It doesn't matter whether she's still in college or a junior or a senior or a middler, a left or right. And honestly, if you interview people, I would say of all even of the best startups I've invested in, maybe 30% of the management team meets this standard at best. 30%. Maybe less. And of the interviews I do in general, it's single-digit percents. It's just and in in that sense, it's the same as ever. Like you either lower the bar in hiring or you hire someone that's actually great. And someone that's actually great is so far ahead of you in how to apply to to employ the efficiencies of AI in their role, your jaw falls on the table. The difference is we used to need warm bodies. That's what's changing. We used to need warm bodies to answer the call, to do QA, to do code review, to to get the blue pixel to go from the upper left to the lower right. You laugh, but you need you literally needed to brute force this with humans. With AI, every day that goes by, the AI—you do not need brute force human beings on your team. And that's another reason they're shrinking. Why are all these new companies so efficient? They're just not brute forcing things with humans. They're just not. They're choosing not to. And so these team—all the brute forcers out there—everyone talks about how bloated teams got in 2021. I don't agree with that. I think they got as big as they needed to be when growth was high and you needed humans to do everything. All you look at these teams that that doubled—well if growth continued at 60% like the rate in early 2021 for 5 years or can help me do the math and every single thing a software company did required a human. You were understaffed by your 2021 headcount. You'd be sitting here in 2026. You every office in SoMa would be triple packed and you there wouldn't be enough humans to staff your company. It's just the world changed. [20:33] Harry Stebbings: Jason, you live on the bleeding edge. I think me and Rory see that and I think the world sees that when they hear you every week in terms of how you run SaaS. For all of the CEOs and execs who listen to the show, what would you advise them in terms of determining whether someone is AI fluent when they meet them for jobs, for talent? [20:51] Jason Lemkin: Here's I realized I was just asked this. I just did a review with a super fast startup growing just crossing 100 million and I was asked this question. And one of my favorite executives, I thought his answer was pretty dated and because he gave me an answer that was about 6 months old. The answer 6 months old is: "I look for folks in my team, I look for you know at what tools they play with." Okay, that was a great answer in like summer of 2025. Okay, I tried Lovable last week. Okay, the answer in 2026 is: "What commercial AI tool have you brought into your organization this month?" That's the test. Anyone that is on the bleeding edge that you would want to hire—now there are so many great products in the market. Okay, there is no excuse in any role to have not brought one tool a month into your organization. Okay, there—now there's going to be better and better tools and better and better products as the year goes on. What's the one you did? And you will see folks with their deer in the headlights to this question. What what sales tool? What marketing tool? What product tool? What engineering tool? What did you bring in? Why did you pick it? How does it working? Because if you're at remotely at the cutting edge, you're all over this. You're looking for the next agentic tools that will radically improve how you do business. This is—you think everyone thinks SaaS is at the bleeding edge, right? You know, you know, all we do is we're just looking for the tools and trying them. Okay? Okay, we're one year ahead of everybody else because we did the simplest thing in the world. Like we tried the tools early and we trained them. We trained them for a month. Okay, I'll give you—want hear a horrible example from this week? Super hot AI company valued at 6 billion. Okay, I'm not going to name it. Um, this week yesterday told us we had to quadruple what we spent on their product. Okay, their agent told us, right? And why did this happen? Okay. Well, at this $6 billion company, no one had trained the agent on its pricing properly. No one had tested it. They said, "Well, well, we've been in beta." And we said, "Well, when did the beta launch? A year ago." Okay, these are people asleep at at the wheel. You want somebody who the instant this comes up, they exactly know what the issue is. And "Hey, when I was at Lovable Replit, we trained the agent. This is how we did it. I brought in this tool. I brought in this tool that that Rory invested in last week. It solved all these issues." That's what you want to hear. And if they haven't brought in a tool in the last 30 days, at least deeply evaluated it. I don't really care whether they bought it, but gone so far down the funnel they can tell you—pick whatever tool: Fixie, Regie, GC, AIGC—I don't care how you went through it, you looked at it, you can tell me the eight ways it would improve the productivity of your business and three you didn't. Just don't hire that person because they're going to run your company to the ground. This is the job today. The job today is not to screw around on ChatGPT and to be a prompt engineer. The job today is to bring the best AI and agentic products into your organization and leverage all the hard work that the engineers have done building those products. That's your job. You don't have to screw around. You don't have to be a prompt engineer anymore. You have to be an agent deployment expert. A—this is the new job we're making up today. An Agentic Deployment Expert. That's your job from C-level to junior. Agentic Deployment Expert. Don't hire anybody else. You're going to regret it. They're going to stare at the camera. He's good. Stare at the camera. He's honorable. We could probably just I could slip away, get a coffee, and come back. No. And I I sound exasperated, Rory. And I—but the reason I am is I can just see I can see my best companies doing it. And I can see some companies I've invested in not doing it. And I want to cry. I just want to cry when they have no ADs on their team. I just—like you're flushing your years of your life down the toilet by not approaching your how you're building this company this way. [24:33] Rory: Yes. And at the risk of being positive, it's worth pointing out two things he didn't say. Well, something implicit why he said—Jason didn't do the only hire, you know, he didn't commit the um employment law, I think it's a civil penalty of saying only employ people below X who get the new new thing because he implicitly said anyone can do it provided you're willing to learn. And I think that's the big aha that's one of the positive statements to make here right? Look and I think it applies—I'm always wary of being "Hey, coming across, hey this this is the things that you all have to do." I think it applies to everyone including investors right? I mean I will say I have found that unless you're willing to invest the time learning these tools you actually shouldn't be investing in them. One of my partners Andy had this expression: "You know, if you decide you want to stop learning new things you probably should retire within 6 to 12 months and never write another check again." Maybe that's down to 3 to 6 months at this stage, right? And I think, you know, it's— [25:27] Harry Stebbings: Yeah, I actually I actually had a meeting with mine and Jason's biggest investor the other day and I—pretend he's not here—I said I think he's the most equipped investor for this generation of investing because I don't think anyone quite sits at the bleeding edge like he does on the investor side. [25:42] Harry Stebbings: Why in terms of using the equip stuff? Yeah. Yeah. In terms of using the stuff, understanding understanding bottlenecks, constraints. For sure. [25:51] Jason Lemkin: But can I just add one point? We can just cuz it's so important if it helps people. Okay, we are—and thank you Harry. We're going through these phases. Okay, and when AI started to blow up for real for us, uh call it early 2024, right? Maybe late '23, I wasn't equipped. It was too technical. I wasn't going to go in and figure out—I wasn't smart enough to figure out how to deal with a massively hallucinating LLM API and turn that and turn that into something magical. Kudos to investors and others that that got it in early '23, '22. I mean I remember I—I guess it was maybe SaaStr Annual '23. I was with David Sacks and I did a Q&A and I said, "How you thinking about AI at Craft?" He's like, "Well we're all in. We want 80% of '23 of investments to be AI." I'm like, "Great but like show me the show me the great ones in market." He's like, "They're all prototypes. We're all they're all they're all proof of concepts but we're all in anyway." That's where you kind of had to be in '23 if you weren't investing at like the LLM level. Okay, I wasn't smart enough. Then we went through this weird-ass prompt engineer era where like you you could torture these products to do something good, right? But you had to torture them. You had to like craft these crazy things that made no sense. Now we are in the era where mere ordinarily smart generalists can make these tools do magical things. And literally I go to these meetings and people be like, "I don't know how to like this is so scary. I don't know how to do this." And we show them our backends. Do you know how to do a workflow generator? Do you know how to do a a decision tree? Like we've been building these since software in the '90s. Okay, if you—I can show you all of our agents. The how they work is novel. They do have to be trained. You can't be lazy and have these agents work. But honestly, the the UI, the UX, the way we interact with them, it's just software. And so my point is: Pick yourself off the ground. This is your time now. If you felt lost in AI era, if you felt like you're behind, you don't understand what all these people are saying on X and Twitter and their Claude and and their and talking about all the 4.6 point Nano point and it's over—like you just it's not your world. This is your time. This is your time for the generalist that knows how to use software tools really really well. And I—this is my last point but it's so important. If ever in your recent life—and this is why you could be all you need to be is young at heart to Rory's point—if in the last three to five years you have successfully deployed a piece of enterprise software of any sort you yourself, not some agency you hired, but if you have deployed it, you can deploy any agentic tool. Any. And you can become the hero in your company and you can become the hero in your functional area. But I watch folks—I'm literally helping a company now that they're adding hundreds of sales folks this year with a new pre-IPO COO—he's not hasn't brought in a single tool, totally scared of it. Okay, it's not that hard. Did you use SalesLoft? Did you use Outreach? Did you use HubSpot? Do you know these tools? If you can deploy these tools, you can deploy a world-changing AI agent. And so this is the time for people like the folks that that were shut out of the AI revolution right now. The generalist folks that are not that know how to deploy software that don't even know how to build software. Like vibe coding for me was folks who knew how to build software, but you didn't have to be an engineer. Now, you just need to know how to deploy software to win with AI agents. That's all you need to know. So many people have these skills and they're petrified of AI. "How did you do that? How did you deploy an AI BDR?" Well, we bought a piece of software, we figured out how it worked for a day, we set it up in an afternoon, and then and then we did spend 30 months training it, which you didn't do with this old software because in the old days, we just had to manually upload all the data, right? And there was no training. The the only non-intuitive part is training these things. And it's it's it's just work. So that's why when I see folks on the management team not doing this, there's no excuse. You do not need to be technical to win with AI agents in Q2 of '26. You do not need to be even 1% technical. Not at all. So it's your time. Or you're going to get laid off. Or you're going to get laid off because you're not going to matter.

Arjun Mahadevan (Mr. LLC 🇺🇸)

37,744 次观看 • 5 个月前

I Cracked Polymarket Using Claude Opus 4.6: The 96,000 Dollar Script For 5 Minute High Leverage Windows most traders are currently sitting at their desks fighting a losing battle against a digital wall because they do not realize the house always wins against human emotion. while the crowd is busy chasing the next meme coin or getting washed out in a single wick an automated agent just pulled nearly a hundred thousand dollars out of thin air using nothing but raw logic. i have seen people blow their life savings in these five minute windows because they treated a high leverage prediction market like a playground instead of a laboratory i am moon dev and 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. in the past i spent hundreds of thousands on devs for apps thinking i would not be able to code myself which was a massive waste of my time and resources. with bots you must iterate to success so i decided to learn live on youtube and now we are here with fully automated systems trading for me instead of getting liquidated by the market the five minute markets on polymarket are essentially a high speed game of musical chairs where the person left standing is usually the one with the fastest script. leverage makes these markets extremely dangerous because it amplifies every mistake you make until your account is completely empty. the only way to survive this environment is to stop trading based on a gut feeling and start trading based on a stress tested mathematical edge the real breakthrough happened when i started using claude opus four point six to write the execution code for these specific five minute windows. having an ai agent that can analyze microstructure data means you can find trends that are completely invisible to the naked eye. it is essentially like having a team of twenty engineers working for you around the clock without the communication lag or the massive overhead costs some of our back tests show a sixty four percent win rate which sounds like a dream to anyone who has ever spent a night staring at a red screen. however the return on these tests varies wildly based on a few specific changes to the strategy parameters and histogram filters. i found that a return of forty one thousand dollars can jump to nearly double that just by adjusting how the bot handles the macd histogram threshold the trap that most people fall into is thinking that a good back test is a license to print money immediately without any further validation. this is a dangerous lie that leads to huge losses because the market in the past is not a perfect mirror of what is going to happen today. that is why i never launch a bot with full size until it has survived the incubation phase where it trades with ten dollars at a time incubation is the ultimate reality check for any trading strategy regardless of how good the numbers look on a computer screen. it is nerve wracking to watch a bot enter its first real trade even if the size is small because that is the moment theory meets reality. most of the bots that pass a back test will fail during the first forty eight hours of incubation and that is exactly why this step is non negotiable the data i use to build these systems covers over two hundred weeks of historical one minute candles to ensure the results are robust and not just luck. we are currently moving toward a machine learning approach where the system can adapt to changing market conditions without me having to intervene. this means the bot will eventually be able to recognize when a high leverage window is too risky and simply wait for a better entry the strategy itself relies heavily on macd variations which is a well known indicator but it is used here with a very specific and proprietary twist. by filtering for trades that hit a specific threshold we can ignore the random price noise that usually liquidates manual traders. we look for an edge of at least six percent which is enough to cover all platform fees and still leave a significant profit on the table i used to think that being a successful trader meant being a genius who could predict the future with a magical crystal ball. the truth is far more boring because success is just about researching an idea and testing it until the data proves it works in the past. then you just let the bot do the work while you go live your life instead of being a slave to the candle sticks and charts this world is changing fast and the people who learn to leverage ai to automate their thinking are going to be the ones who win the next decade. i am not asking you to trust a back test or a screenshot from a website because i want you to trust the process of testing it yourself. code allows you to take your life back from the screens and finally stop the cycle of over trading and emotional liquidations the difference between the traders who make it and the people who blow up is simply the willingness to iterate on their ideas daily. you might fail on your first ten bots but the eleventh one might be the script that changes your entire financial trajectory forever. it is about staying in the game long enough for the math to finally work in your favor and removing the human heart from the execution every day i am back testing and researching new ideas to see if they can survive the stress of real market data. i launch these live bots and let them run for seventy two hours to see if they can handle the pressure of the current market trend. while everyone else is coping and complaining about market volatility we are just adjusting our parameters and letting the ai find the next profitable window vibe coding with claude opus four point six has changed the speed at which i can deploy a new strategy from weeks down to just a few minutes. you can give the ai a general strategy idea and it builds the entire trading infrastructure for you while you focus on the logic. this speed is the ultimate advantage in a market that moves as fast as a five minute prediction window on the blockchain the future of trading is not found in a chat room or a paid signal group but in the code you write and the data you process. i believe that everyone has the ability to become an automated trader if they are willing to put in the work to learn the scripts. it is the only way to escape the trap of the nine to five and the anxiety of manual hand trading in a manipulated market i want you to understand that the ninety six thousand dollar returns i see are the result of hundreds of failed tests that never saw the light of day. you have to be willing to look at a failing bot and kill it without emotion so you can move on to the next research project. that is the quantitative mindset that separates the winners from the people who are just gambling with their savings if you are ready to stop being the liquidity for the big players then it is time to start building your own automated army of bots. for the cost of a few cups of coffee you can get access to the road map and the scripts that are driving these results. i am here every day showing you the process because i want to see more people use code to find their financial freedom and beat the house at its own game

I Cracked Polymarket Using Claude Opus 4.6: The 96,000 Dollar Script For 5 Minute High Leverage Windows most traders are currently sitting at their desks fighting a losing battle against a digital wall because they do not realize the house always wins against human emotion. while the crowd is busy chasing the next meme coin or getting washed out in a single wick an automated agent just pulled nearly a hundred thousand dollars out of thin air using nothing but raw logic. i have seen people blow their life savings in these five minute windows because they treated a high leverage prediction market like a playground instead of a laboratory i am moon dev and 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. in the past i spent hundreds of thousands on devs for apps thinking i would not be able to code myself which was a massive waste of my time and resources. with bots you must iterate to success so i decided to learn live on youtube and now we are here with fully automated systems trading for me instead of getting liquidated by the market the five minute markets on polymarket are essentially a high speed game of musical chairs where the person left standing is usually the one with the fastest script. leverage makes these markets extremely dangerous because it amplifies every mistake you make until your account is completely empty. the only way to survive this environment is to stop trading based on a gut feeling and start trading based on a stress tested mathematical edge the real breakthrough happened when i started using claude opus four point six to write the execution code for these specific five minute windows. having an ai agent that can analyze microstructure data means you can find trends that are completely invisible to the naked eye. it is essentially like having a team of twenty engineers working for you around the clock without the communication lag or the massive overhead costs some of our back tests show a sixty four percent win rate which sounds like a dream to anyone who has ever spent a night staring at a red screen. however the return on these tests varies wildly based on a few specific changes to the strategy parameters and histogram filters. i found that a return of forty one thousand dollars can jump to nearly double that just by adjusting how the bot handles the macd histogram threshold the trap that most people fall into is thinking that a good back test is a license to print money immediately without any further validation. this is a dangerous lie that leads to huge losses because the market in the past is not a perfect mirror of what is going to happen today. that is why i never launch a bot with full size until it has survived the incubation phase where it trades with ten dollars at a time incubation is the ultimate reality check for any trading strategy regardless of how good the numbers look on a computer screen. it is nerve wracking to watch a bot enter its first real trade even if the size is small because that is the moment theory meets reality. most of the bots that pass a back test will fail during the first forty eight hours of incubation and that is exactly why this step is non negotiable the data i use to build these systems covers over two hundred weeks of historical one minute candles to ensure the results are robust and not just luck. we are currently moving toward a machine learning approach where the system can adapt to changing market conditions without me having to intervene. this means the bot will eventually be able to recognize when a high leverage window is too risky and simply wait for a better entry the strategy itself relies heavily on macd variations which is a well known indicator but it is used here with a very specific and proprietary twist. by filtering for trades that hit a specific threshold we can ignore the random price noise that usually liquidates manual traders. we look for an edge of at least six percent which is enough to cover all platform fees and still leave a significant profit on the table i used to think that being a successful trader meant being a genius who could predict the future with a magical crystal ball. the truth is far more boring because success is just about researching an idea and testing it until the data proves it works in the past. then you just let the bot do the work while you go live your life instead of being a slave to the candle sticks and charts this world is changing fast and the people who learn to leverage ai to automate their thinking are going to be the ones who win the next decade. i am not asking you to trust a back test or a screenshot from a website because i want you to trust the process of testing it yourself. code allows you to take your life back from the screens and finally stop the cycle of over trading and emotional liquidations the difference between the traders who make it and the people who blow up is simply the willingness to iterate on their ideas daily. you might fail on your first ten bots but the eleventh one might be the script that changes your entire financial trajectory forever. it is about staying in the game long enough for the math to finally work in your favor and removing the human heart from the execution every day i am back testing and researching new ideas to see if they can survive the stress of real market data. i launch these live bots and let them run for seventy two hours to see if they can handle the pressure of the current market trend. while everyone else is coping and complaining about market volatility we are just adjusting our parameters and letting the ai find the next profitable window vibe coding with claude opus four point six has changed the speed at which i can deploy a new strategy from weeks down to just a few minutes. you can give the ai a general strategy idea and it builds the entire trading infrastructure for you while you focus on the logic. this speed is the ultimate advantage in a market that moves as fast as a five minute prediction window on the blockchain the future of trading is not found in a chat room or a paid signal group but in the code you write and the data you process. i believe that everyone has the ability to become an automated trader if they are willing to put in the work to learn the scripts. it is the only way to escape the trap of the nine to five and the anxiety of manual hand trading in a manipulated market i want you to understand that the ninety six thousand dollar returns i see are the result of hundreds of failed tests that never saw the light of day. you have to be willing to look at a failing bot and kill it without emotion so you can move on to the next research project. that is the quantitative mindset that separates the winners from the people who are just gambling with their savings if you are ready to stop being the liquidity for the big players then it is time to start building your own automated army of bots. for the cost of a few cups of coffee you can get access to the road map and the scripts that are driving these results. i am here every day showing you the process because i want to see more people use code to find their financial freedom and beat the house at its own game

Moon Dev

10,921 次观看 • 5 个月前

I agree with Trevor Noah’s analysis of the immigration debate in South Africa, and I also agree with Julius Malema’s noble desire for Africa to be one. From the outset, I must be clear that the biggest obstacle to African unity has been African leadership. Some of our countries have been independent for more than 60 years, yet we are still far from achieving the level of integration many Pan-Africanists envisioned. The failure to get there is fundamentally a leadership issue. I want to focus on what Julius Malema has said. He is one of the continent’s most outspoken Pan-Africanists, and his vision of a more united Africa is both admirable and inspiring. Unfortunately, because of the dysfunctionality of leadership across much of the continent, Pan-Africanism has, in some circles in South Africa, become a dirty word. That is a tragedy because the principle itself is not the problem. The problem is that many African leaders have failed to create the political, economic, and institutional conditions necessary to make that vision a reality. So let us look carefully at what Julius Malema is saying. I have great respect for Julius Malema when it comes to his Pan-African outlook, but I am afraid to say that the idea of an Africa with one passport, one currency, and a fully integrated political and economic system is unlikely to happen within our lifetime. It is good to dream and to idealise the kind of Africa we would like to see, but in its current political and economic format, the continent is nowhere near achieving that goal. I am 55 years old, so when I talk about a lifetime, I am talking about the next 25 years. If I live to 80, that would be wonderful, but I do not believe Africa will achieve that level of integration within that timeframe. The reason is quite simple. If you look at the European Union, countries do not simply join because they want to. They must first meet a long list of requirements and benchmarks. These include economic standards, institutional capacity, governance standards, judicial independence, and human rights protections. Even if we set aside the human rights question in Africa, because we know that remains a long journey, the economic question alone presents a major obstacle. A truly united continent can only emerge if its member states are led by competent, educated, and trustworthy leaders who build functioning economies capable of providing opportunities for their own citizens. The current xenophobic, Afrophobic, and anti-immigration discourse taking place in South Africa is often crude and sometimes ugly. However, stripped of the crudeness, there is an important point being raised that cannot simply be ignored. For Africans to unite successfully, they cannot first unite in one country. They must first unite across the continent by creating broadly comparable economic opportunities and living standards. For example, a Ghanaian should be able to travel to Zimbabwe visa-free. That is largely a political decision. But if that Ghanaian wants to relocate permanently to Zimbabwe, then the economies of Ghana and Zimbabwe should have a reasonable degree of parity. People should not be compelled to migrate primarily because one country is functioning while another is failing. The same applies across the continent. Someone should not feel forced to leave the Democratic Republic of Congo for South Africa purely because of economic collapse at home. If integration is driven solely by economics, then the countries that are relatively well managed will inevitably carry the burden of those that are not. This is an intellectual discussion that Africa cannot avoid. Resource competition is often what inflames tensions. If someone moves from a poor community in Mozambique to a poor community in South Africa, both groups are competing for the same clinics, schools, housing, jobs, and social services. That is where tensions arise. Interestingly, illegal immigrants from Europe are rarely part of the immigration debate in South Africa. Many people immediately attribute this to race, but there is another factor that deserves consideration. Wealthy immigrants generally live in affluent communities where there is little or no competition for scarce public resources. Take Chatunga Mugabe, for example. He lived in Hyde Park, drove expensive cars, and socialised in Sandton. Nobody was concerned about his immigration status. Likewise, where I live in South Africa, there are immigrants from the United Kingdom, Spain, Germany, Kenya, and elsewhere. They are largely affluent people. The South Africans living there are often excited when newcomers arrive. When I moved from Zimbabwe and bought a house on my road, both black and white South Africans invited me into their homes for dinner and wine. There was no hostility because there was no competition for resources. That reality matters. If Africa is ever going to have one passport and one currency, we must first deal with the economic fundamentals. Most Africans do not realise that this is not primarily a political project. It is an economic one. Turkey, for example, has spent decades seeking membership of the European Union but has not been admitted because it has not met all the requirements. Countries such as Bulgaria and Romania had to meet strict standards before joining. Their judicial systems, governance structures, healthcare systems, and institutions had to reach certain benchmarks. The same logic applies to Africa. If every African citizen were suddenly free to seek healthcare anywhere on the continent, countries with stronger healthcare systems such as South Africa, Botswana, and Namibia would immediately face enormous pressure from people seeking treatment, including specialised care for conditions such as cancer. That is why this discussion is important. We must have it honestly and without slogans. We must discuss it not only in universities and intellectual circles but also in townships, villages, and communities across Africa. The dream of one Africa is a noble one. I support it. But before we get there, we must first address the economic, institutional, and governance realities that stand in the way. Until those challenges are resolved, the vision Julius Malema speaks about will remain an aspiration rather than a practical reality. The tragedy we face today is that we are focusing on the sideshows created by tribalists and rogue political actors who are taking advantage of a genuine problem that exists in South Africa and, indeed, in other parts of Africa as well. We amplify their voices and focus on what they are saying instead of focusing on the real issue. We should be asking ourselves a simple question. Julius Malema is right about the ideal he is advocating, but why are we not getting to where he wants us to get? Once we ask that question honestly, we are forced to examine the root causes. There can be no economic harmony, political harmony, or any other form of harmony between countries that are operating at vastly different levels of development and functionality. Take Zimbabwe and South Africa as an example. Zimbabwe has not had a working radiotherapy machine in its public healthcare system for more than four years. The country’s largest hospital has only one maternity theatre, built in 1977. Then look at South Africa. Its public healthcare system has some problems and could be much better, but by African standards it remains among the most advanced on the continent. If those two countries stand side by side, as they physically do, how do you integrate them when one is dysfunctional and the other remains a functioning state? These are the root causes we need to confront. This discussion must be held in a comprehensive and honest manner, not in fragments. We can speak about the noble aspirations of Pan-Africanism, and we can also discuss the obstacles that stand in its way. Both conversations must be held together. Only then can we identify what needs to be done and begin serious scenario planning around how to get there. Instead, we often get beautiful speeches delivered at the African Union, one of the most ineffective continental organisations in the world. People make grand declarations, earn generous salaries, and then nothing happens. Great speeches have been delivered since the days of the Organisation of African Unity. One of those speeches was even immortalised by Bob Marley in his song War. Yet more than 60 years later, many of the same challenges remain unresolved. That is an indictment not only of African leaders but also of African elites. Too many are content to make money while ignoring the underlying governance failures that hold the continent back. Consider Aliko Dangote, the richest black man in the world and Africa’s most successful entrepreneur. He requires 34 visas to enter dozens of African countries. Yet if I hold a British passport, my movement across much of Africa can often be easier than his. How can Africa speak seriously about integration when one of its own leading business figures faces such barriers within the continent? Until influential African business leaders such as Aliko Dangote, Strive Masiyiwa, Patrice Motsepe, and others begin speaking more forcefully about governance, corruption, economic mismanagement, and state dysfunction, progress will remain slow. As long as these issues are accommodated because money can still be made, Africa will continue to talk about unity without creating the conditions necessary to achieve it. So, back in the townships of South Africa, there is a crisis. I have always said that Zimbabwe is no longer a foreign policy issue. It is a domestic issue because the South African government must deal with its consequences in hospitals, social services, employment, housing, education, and many other facets of daily life. If the South African government does not have the courage to stand up to leaders such as Emmerson Mnangagwa and Mozambique’s President, Daniel Chapo, and say, “The way you are running your economies is creating problems for us,” then the situation will continue to deteriorate. The tragedy is that it is always the poor, the ordinary, and those living in abject poverty who end up fighting amongst themselves. Yet the root causes of these tensions are often created at the highest levels of political leadership. The people competing for jobs, housing, healthcare, and other scarce resources did not create the conditions that led to mass migration. Those conditions were created by policy failures, corruption, poor governance, and economic mismanagement. I would go even further and say that this is also an indictment of South African leadership. SADC already has protocols, principles, and governance frameworks that were specifically designed to prevent member states from becoming dysfunctional and destabilising their neighbours. The problem is not the absence of rules. The problem is the absence of enforcement. Those protocols exist on paper, but too often they are ignored in practice. When governance standards are violated, when economies collapse, when democratic institutions are weakened, and when corruption flourishes, there is rarely any meaningful consequence from the region. As a result, the effects spill across borders and eventually become someone else’s problem. That is why the immigration debate cannot be separated from the governance debate. They are two sides of the same coin. If African leaders are serious about reducing migration pressures, they must first address the political and economic failures that are pushing people to leave their countries in the first place. We all know why that conversation is avoided. So, coming back to Trevor Noah’s analogy, it is ultimately a human analogy. It reflects a reality that has existed throughout history and even in nature itself. If lions have abundant access to zebras and other prey, there is very little competition between lions, leopards, and other predators. But when food becomes scarce, competition intensifies. The struggle is no longer about identity. It becomes a struggle over limited resources. The same principle applies in human societies. When jobs are plentiful, when healthcare functions, when housing is available, and when opportunities are expanding, people are generally more tolerant and welcoming. But the moment resources become scarce, tensions rise. People begin competing for the same opportunities, and that competition often manifests itself through politics, nationalism, tribalism, xenophobia, or other forms of social conflict. This is not unique to South Africa. It is not unique to Africa. It is part of the human condition. In many ways, what we are witnessing is both a human story and an animal kingdom story. The underlying dynamic is remarkably similar. Scarcity creates competition. Competition creates tension. Tension creates conflict. That is why discussions about immigration cannot be separated from discussions about governance, economic growth, service delivery, and opportunity. If we focus only on the symptoms while ignoring the underlying causes, we will never solve the problem. The real challenge is not merely getting people to live together. The real challenge is creating societies and economies that produce enough opportunity for people to live together peacefully.

I agree with Trevor Noah’s analysis of the immigration debate in South Africa, and I also agree with Julius Malema’s noble desire for Africa to be one. From the outset, I must be clear that the biggest obstacle to African unity has been African leadership. Some of our countries have been independent for more than 60 years, yet we are still far from achieving the level of integration many Pan-Africanists envisioned. The failure to get there is fundamentally a leadership issue. I want to focus on what Julius Malema has said. He is one of the continent’s most outspoken Pan-Africanists, and his vision of a more united Africa is both admirable and inspiring. Unfortunately, because of the dysfunctionality of leadership across much of the continent, Pan-Africanism has, in some circles in South Africa, become a dirty word. That is a tragedy because the principle itself is not the problem. The problem is that many African leaders have failed to create the political, economic, and institutional conditions necessary to make that vision a reality. So let us look carefully at what Julius Malema is saying. I have great respect for Julius Malema when it comes to his Pan-African outlook, but I am afraid to say that the idea of an Africa with one passport, one currency, and a fully integrated political and economic system is unlikely to happen within our lifetime. It is good to dream and to idealise the kind of Africa we would like to see, but in its current political and economic format, the continent is nowhere near achieving that goal. I am 55 years old, so when I talk about a lifetime, I am talking about the next 25 years. If I live to 80, that would be wonderful, but I do not believe Africa will achieve that level of integration within that timeframe. The reason is quite simple. If you look at the European Union, countries do not simply join because they want to. They must first meet a long list of requirements and benchmarks. These include economic standards, institutional capacity, governance standards, judicial independence, and human rights protections. Even if we set aside the human rights question in Africa, because we know that remains a long journey, the economic question alone presents a major obstacle. A truly united continent can only emerge if its member states are led by competent, educated, and trustworthy leaders who build functioning economies capable of providing opportunities for their own citizens. The current xenophobic, Afrophobic, and anti-immigration discourse taking place in South Africa is often crude and sometimes ugly. However, stripped of the crudeness, there is an important point being raised that cannot simply be ignored. For Africans to unite successfully, they cannot first unite in one country. They must first unite across the continent by creating broadly comparable economic opportunities and living standards. For example, a Ghanaian should be able to travel to Zimbabwe visa-free. That is largely a political decision. But if that Ghanaian wants to relocate permanently to Zimbabwe, then the economies of Ghana and Zimbabwe should have a reasonable degree of parity. People should not be compelled to migrate primarily because one country is functioning while another is failing. The same applies across the continent. Someone should not feel forced to leave the Democratic Republic of Congo for South Africa purely because of economic collapse at home. If integration is driven solely by economics, then the countries that are relatively well managed will inevitably carry the burden of those that are not. This is an intellectual discussion that Africa cannot avoid. Resource competition is often what inflames tensions. If someone moves from a poor community in Mozambique to a poor community in South Africa, both groups are competing for the same clinics, schools, housing, jobs, and social services. That is where tensions arise. Interestingly, illegal immigrants from Europe are rarely part of the immigration debate in South Africa. Many people immediately attribute this to race, but there is another factor that deserves consideration. Wealthy immigrants generally live in affluent communities where there is little or no competition for scarce public resources. Take Chatunga Mugabe, for example. He lived in Hyde Park, drove expensive cars, and socialised in Sandton. Nobody was concerned about his immigration status. Likewise, where I live in South Africa, there are immigrants from the United Kingdom, Spain, Germany, Kenya, and elsewhere. They are largely affluent people. The South Africans living there are often excited when newcomers arrive. When I moved from Zimbabwe and bought a house on my road, both black and white South Africans invited me into their homes for dinner and wine. There was no hostility because there was no competition for resources. That reality matters. If Africa is ever going to have one passport and one currency, we must first deal with the economic fundamentals. Most Africans do not realise that this is not primarily a political project. It is an economic one. Turkey, for example, has spent decades seeking membership of the European Union but has not been admitted because it has not met all the requirements. Countries such as Bulgaria and Romania had to meet strict standards before joining. Their judicial systems, governance structures, healthcare systems, and institutions had to reach certain benchmarks. The same logic applies to Africa. If every African citizen were suddenly free to seek healthcare anywhere on the continent, countries with stronger healthcare systems such as South Africa, Botswana, and Namibia would immediately face enormous pressure from people seeking treatment, including specialised care for conditions such as cancer. That is why this discussion is important. We must have it honestly and without slogans. We must discuss it not only in universities and intellectual circles but also in townships, villages, and communities across Africa. The dream of one Africa is a noble one. I support it. But before we get there, we must first address the economic, institutional, and governance realities that stand in the way. Until those challenges are resolved, the vision Julius Malema speaks about will remain an aspiration rather than a practical reality. The tragedy we face today is that we are focusing on the sideshows created by tribalists and rogue political actors who are taking advantage of a genuine problem that exists in South Africa and, indeed, in other parts of Africa as well. We amplify their voices and focus on what they are saying instead of focusing on the real issue. We should be asking ourselves a simple question. Julius Malema is right about the ideal he is advocating, but why are we not getting to where he wants us to get? Once we ask that question honestly, we are forced to examine the root causes. There can be no economic harmony, political harmony, or any other form of harmony between countries that are operating at vastly different levels of development and functionality. Take Zimbabwe and South Africa as an example. Zimbabwe has not had a working radiotherapy machine in its public healthcare system for more than four years. The country’s largest hospital has only one maternity theatre, built in 1977. Then look at South Africa. Its public healthcare system has some problems and could be much better, but by African standards it remains among the most advanced on the continent. If those two countries stand side by side, as they physically do, how do you integrate them when one is dysfunctional and the other remains a functioning state? These are the root causes we need to confront. This discussion must be held in a comprehensive and honest manner, not in fragments. We can speak about the noble aspirations of Pan-Africanism, and we can also discuss the obstacles that stand in its way. Both conversations must be held together. Only then can we identify what needs to be done and begin serious scenario planning around how to get there. Instead, we often get beautiful speeches delivered at the African Union, one of the most ineffective continental organisations in the world. People make grand declarations, earn generous salaries, and then nothing happens. Great speeches have been delivered since the days of the Organisation of African Unity. One of those speeches was even immortalised by Bob Marley in his song War. Yet more than 60 years later, many of the same challenges remain unresolved. That is an indictment not only of African leaders but also of African elites. Too many are content to make money while ignoring the underlying governance failures that hold the continent back. Consider Aliko Dangote, the richest black man in the world and Africa’s most successful entrepreneur. He requires 34 visas to enter dozens of African countries. Yet if I hold a British passport, my movement across much of Africa can often be easier than his. How can Africa speak seriously about integration when one of its own leading business figures faces such barriers within the continent? Until influential African business leaders such as Aliko Dangote, Strive Masiyiwa, Patrice Motsepe, and others begin speaking more forcefully about governance, corruption, economic mismanagement, and state dysfunction, progress will remain slow. As long as these issues are accommodated because money can still be made, Africa will continue to talk about unity without creating the conditions necessary to achieve it. So, back in the townships of South Africa, there is a crisis. I have always said that Zimbabwe is no longer a foreign policy issue. It is a domestic issue because the South African government must deal with its consequences in hospitals, social services, employment, housing, education, and many other facets of daily life. If the South African government does not have the courage to stand up to leaders such as Emmerson Mnangagwa and Mozambique’s President, Daniel Chapo, and say, “The way you are running your economies is creating problems for us,” then the situation will continue to deteriorate. The tragedy is that it is always the poor, the ordinary, and those living in abject poverty who end up fighting amongst themselves. Yet the root causes of these tensions are often created at the highest levels of political leadership. The people competing for jobs, housing, healthcare, and other scarce resources did not create the conditions that led to mass migration. Those conditions were created by policy failures, corruption, poor governance, and economic mismanagement. I would go even further and say that this is also an indictment of South African leadership. SADC already has protocols, principles, and governance frameworks that were specifically designed to prevent member states from becoming dysfunctional and destabilising their neighbours. The problem is not the absence of rules. The problem is the absence of enforcement. Those protocols exist on paper, but too often they are ignored in practice. When governance standards are violated, when economies collapse, when democratic institutions are weakened, and when corruption flourishes, there is rarely any meaningful consequence from the region. As a result, the effects spill across borders and eventually become someone else’s problem. That is why the immigration debate cannot be separated from the governance debate. They are two sides of the same coin. If African leaders are serious about reducing migration pressures, they must first address the political and economic failures that are pushing people to leave their countries in the first place. We all know why that conversation is avoided. So, coming back to Trevor Noah’s analogy, it is ultimately a human analogy. It reflects a reality that has existed throughout history and even in nature itself. If lions have abundant access to zebras and other prey, there is very little competition between lions, leopards, and other predators. But when food becomes scarce, competition intensifies. The struggle is no longer about identity. It becomes a struggle over limited resources. The same principle applies in human societies. When jobs are plentiful, when healthcare functions, when housing is available, and when opportunities are expanding, people are generally more tolerant and welcoming. But the moment resources become scarce, tensions rise. People begin competing for the same opportunities, and that competition often manifests itself through politics, nationalism, tribalism, xenophobia, or other forms of social conflict. This is not unique to South Africa. It is not unique to Africa. It is part of the human condition. In many ways, what we are witnessing is both a human story and an animal kingdom story. The underlying dynamic is remarkably similar. Scarcity creates competition. Competition creates tension. Tension creates conflict. That is why discussions about immigration cannot be separated from discussions about governance, economic growth, service delivery, and opportunity. If we focus only on the symptoms while ignoring the underlying causes, we will never solve the problem. The real challenge is not merely getting people to live together. The real challenge is creating societies and economies that produce enough opportunity for people to live together peacefully.

Hopewell Chin’ono

91,527 次观看 • 2 个月前

Just in $AMD Anush "Speed is the moat"|ROCm🎙️ In the race to define the future of AI, what's the one advantage that truly lasts? It's not proprietary tech, argues Anush Elangovan Elangovan, VP of AI Software at AMD , but the sustainable speed of innovation. He explains why AMD is rejecting the "walled garden" model for its open source ROCm stack, betting that an open community flywheel is the key to victory. Listen to understand how this open strategy is designed to out-innovate closed systems by empowering developers to solve everything from frontier-model challenges to the mundane, everyday problems that define the "last mile" of AI. AMD ROCm Software: Part 1 Transcript [00:00:00] Andrew Zigler: Joining me is Anush Elangovan, VP of AI software at AMD. And when people talk about AI compute, the conversation often stops at hardware specs, but it's more than just physical chips that win the game. It's also the software ecosystems supporting them. [00:00:18] Andrew Zigler: The prevailing strategy in the industry has been to build something like a walled garden. You know, something closed, proprietary locks, developers in. But AMD is betting on an entirely different play, open source acceleration, and with rock, their open source AI software stack. AMD is building not just hardware parity, but an innovation flywheel that's powered by the community with interoperability and the freedom to scale without all of that pesky lockin. [00:00:48] Andrew Zigler: And in this world, speed is your moat and how fast you can innovate while your platform remains open, flexible, and standardize across all of its applications. That's what we're gonna explore [00:01:00] today. So Anush, I'm really excited to have you here. Welcome to Dev Interrupted. [00:01:04] Anush Elangovan: Thanks for having me. Uh, super excited to chat about it. [00:01:07] Andrew Zigler: Amazing. Well, let's go ahead and dive right in with kind of what I laid it out with in the beginning, the idea of the moat and it being about speed. I wanna unpack that a bit because that came from you when you and I first spoke. And I, and I want to know, you know, how do you define speed inside of AMD beyond just things like hardware, benchmarks. [00:01:27] Anush Elangovan: Yeah, that's a very good question. So when we typically talk about speed, everyone's like, Hey, hardware benchmark specs, right? Like, uh, memory bandwidth or, or flops. And that is one important part of it, uh, AMD does very well. With that, we do have, a, a very good history of executing on that axis. [00:01:47] Anush Elangovan: But when I say speed is the moat, it is about, uh, how we prepare, how we build the muscle to run the race for a long time and run it fast. And it is [00:02:00] not about a single point in time that you've, you've beat some you know, benchmark and, and you declare victory. It's about building the ability to consistently develop and deliver. [00:02:13] Anush Elangovan: Both hardware and software innovation at scale and do it fast, right? Like, you know, we we're increasingly getting to a point where models come out and they're, uh, you know, a year or two ago it was like, Hey, they work on AMD on day zero, which is great, but now they are performing on AMD the day it releases, right? [00:02:32] Anush Elangovan: So, what does it take to Prefetch where the industry is going? Be prepared to intercept. At that point is what you know, I, I refer to as you know, the, the speed factor in, in creating this mode, right? And the mode is just shed all things that hold you back and run as fast as you can. [00:02:53] Anush Elangovan: Uh, because the pace of innovation that is, uh, being seen in, in AI [00:03:00] industries is just. Amazing. Right? And it's like, it's transformational at at how you generate electricity. It's transformational as at how you build data centers. It's transformational at how you deploy compute, networking. It's transformational at what kind of use cases you, you know, uh, use AI for. [00:03:17] Anush Elangovan: Uh, and for that, you need to be prepared to, see what comes tomorrow and be prepared to run the race tomorrow. [00:03:23] Andrew Zigler: Yeah, it's a really great perspective because it highlights that it's not just like a checkpoint that you run through. I like how you called out, like it's not just hitting that benchmark or being the best in class at that moment, in that snapshot, it's about having a. The throughput and about having that dedication to the idea and continuing to deliver on it. [00:03:43] Andrew Zigler: It's not just crossing the threshold, but it's also being the engine. And that's what, that's what protects a business. That is the moat, because the moat is that innovation layer, the faster and more, uh, future forward. That you can work and think, [00:04:00] you know, the better. Uh, we, we talk a lot about like future forward work styles. [00:04:04] Andrew Zigler: Like what are the things I could be doing right now today that are gonna be like, way more useful tomorrow? Let, let's abandon those, workflows that are older and that kind of like, that translates into. An advantage when you work that way. You know, what kind of things have you learned working with, uh, like across all spectrums of people who would use ROCm, right? [00:04:23] Andrew Zigler: You have like the developers, but then you also have the enterprises and you have this large span of adoptees, right? So what is the, what does that look like that you learn? [00:04:32] Anush Elangovan: Yeah, so, so the way I look at it is there are gonna be pockets of different, uh, you know, cadences, right? Like, so people who are deploying in enterprises, for example, right? The validation and how long it takes for them to deploy an LLM that's secure. It's, with guardrails, et cetera, maybe longer. [00:04:52] Anush Elangovan: but you still have to go through the process and you have to be prepared to like, walk that walk to deploy an enterprises. That doesn't mean it's [00:05:00] not fast, that's as fast as you can do for that industry, right? And if you are deploying AI in healthcare, right, it's, it's got its own, uh, cycle. [00:05:07] Anush Elangovan: but in each one of these, you want to see how, like, go down to the essence of what is it that you actually have to do. And, you know, I, I, I like how you framed it. It's like it's, you shed your prior assumptions of how things are done, right. And, and you kind of build up from a, uh, first principles, uh, approach to say, this is how I could use AI to unlock, whatever I'm doing. [00:05:33] Anush Elangovan: And, and, some of it, you know, it's good to really step back and look at. Just question every part of it, right? Like right now you're getting chat GPT and, Gemini competing for like, math, olympiads and, and, uh, college, uh, reasoning, uh, tests. Right? And, and those are like that, that is amazing and increasingly like complex tasks that they're trying to do. [00:05:58] Anush Elangovan: But there may also be like. [00:06:00] More mundane things that AI could, could get applied to. Right? And, and so when we think about shedding old ways, you wanna shed it not just in like the tip of the spear. It's like, you know, I'm gonna see what's the frontier model. It's also, it could be something as simple as. [00:06:18] Anush Elangovan: How do you choose a, a movie, uh, you know, like a recommendation system, right? Or, or, uh, an automated, uh, flight, uh, rebooking system. So the moment, you know, your flight is late, uh, right now it's a notification, right? It's like, oh, you got a text message saying your flight's late. And I got that like three times this week. [00:06:38] Anush Elangovan: But anyway, uh, and, and, and, and, I was just like, okay, so if I were to rethink this. All this MCPs that we have that should be hooked up into an MCP that says, your flight's delayed. Here are your options. If you want, you know, these are the paid options. Yeah. Here are the free options. This will get you back into your you know, Toronto airport [00:07:00] tonight. [00:07:00] Anush Elangovan: Or if you stay, here's a hotel plus this, plus this, plus. It's just like, go ahead is all I should say. Versus now I'm like, okay, can someone, you know, can I call a travel agent? Can I do this? Can I go online and log into And you know, so we gotta fundamentally rethink even those like small, nuances of, things that we do that can be automated out and AI is really, really good at doing something like this, right? Maybe I just explained an AI startup idea right now. Somebody should just start that. [00:07:29] Andrew Zigler: I think you did. Yeah, you definitely did. Someone, one of our listeners is definitely going to lift that off of you. I, I, I, you know, I hate being on the receiving end of those. You feel a little helpless and then you have to like, follow the whole flow. So I know what you mean. Like I, I like how you called out that the build and this like. [00:07:45] Andrew Zigler: Where speed is your moat and the innovation layer is protecting you, is what makes you better than your competitors. How you scale that and you bring that to market. So by understanding the problems that you're solving, uh, throwing away those older assumptions, but also [00:08:00] recognizing that like. We're building every single day, new things and new ways of using stuff that we're still figuring out the implications of. [00:08:08] Andrew Zigler: And so when you have a lot of velocity and you're introducing a lot of new ideas, and maybe you have that workflow now that automatically rebook your flight off of your late flight text message, and uh, I know I would certainly use it, but you know, what kind of philosophies guide the way that y'all think about building this ecosystem to manage that stability while letting folks. [00:08:29] Andrew Zigler: Play with the speed and the assumptions and the airplane re bookings. [00:08:34] Anush Elangovan: so, so I think, you know, we need to peel one layer down, right? and the philosophy is, Hey, we, we just discovered electricity, right? And you know what we're gonna do? We are gonna make motors, uh, or dynamos, right? Like engines. Uh, sure. We don't know if it's gonna be a Ferrari that you're gonna make, or it's a a a a dump truck. [00:08:57] Anush Elangovan: That's good for doing this. But let's [00:09:00] let, which is also required, right? You need a dump truck. You need a garbage truck. And, [00:09:04] Andrew Zigler: Yeah. You need the [00:09:04] Anush Elangovan: course you need, uh, a Ferrari for a midlife crisis, right? So, [00:09:09] Andrew Zigler: precisely. [00:09:10] Anush Elangovan: But, but my, uh, point is what do we build next? And, uh, and this is what I meant by like, okay, let's, let's take those baby steps to build the. [00:09:20] Anush Elangovan: Infrastructure that's required that we know we'll have to use, right? So, so if I just discovered electricity, okay, great. Now one, how do I save this electricity and how do I use it? So there's battery technology, so you need to do something like that, right? Like so. But then you also want to make it into an actionable thing. [00:09:37] Anush Elangovan: You want to make it for like automobiles, or you wanna use it for, you know, powering, uh, entire cities. So it is that transformational. So, uh, AI is that transformational. So, if you distill down, it'll, it'll come down to how do we think about, what we can do with this this fundamental technology that, We may not be aware of what it [00:10:00] is gonna unlock next, but at least you know the next step is clear, right? It's like a dense fog, you know, it's gonna be like, it, it's the right path. You see the light, but it's kind of like out there and, and the steps you're taking are concrete and you're like, okay, this is good. [00:10:16] Anush Elangovan: I, this is better than where I was or where we were. So we are moving forward. So you can build with the. Intuition from what you see in the short term and a tactical view, but towards what you think the future is gonna be. [00:10:28] Andrew Zigler: Right. You almost like we're all in this like fog of war, right? And like you said, you're reaching out and you're trying to step through it. You could think of it too, as like you're in the dark and your hands are up in front of you and you know that. You're, you're not gonna run your face into a wall because your hands are out in front of you, but you're not gonna maybe do much better than that. [00:10:45] Andrew Zigler: So that's kind of like, I think the eco, the, the industry, the world that we find ourselves in, uh, and we all have to, then this becomes the power of an ecosystem, of a group of people working together to create that layer of, [00:11:00] uh, of establishing the [00:11:01] Anush Elangovan: exactly. And I, I, I just, instead of, you know, saying fog of war I describe it as like, you're in this. Beautiful valley with like a morning, uh, fog that's in. You can smell the flowers. You, you hear the birds. You are like, okay, it's, we are in like, uh, utopian paradise and yes, I just need to like, continue the walk, right? [00:11:24] Anush Elangovan: and then move forward with that, conviction that you're in the right spot. [00:11:27] Andrew Zigler: Yeah. So let's talk about that ecosystem world. This nice, I love how you describe it, this grassy side of a hill in the morning that's covered in some mist and maybe we can't see 30 feet in one direction, but it sure is a beautiful hill and it smells nice. And so we're all here. And why is, in that world, why is. [00:11:44] Andrew Zigler: You know, open source, their strategic advantage that y'all are going for in the AI hardware market. And, and then how does like ROCm turn that into wins for people within that ecosystem? [00:11:56] Anush Elangovan: you know, the, the way we look at it is this, is kind of like how I view [00:12:00] AI and the ecosystem, right? But, but it is for everyone to enjoy. Uh, and so we do want to make sure that. You know, it is, uh, beneficial for everyone. [00:12:09] Anush Elangovan: The ecosystem can come in and, and innovate. It's an open innovation engine. and uh, it is very different from, you know, having a walled garden with, Hey, only I know how to do this and I'm gonna do it and throw it over the fence and you can use it or keep walking, right? So we'd like to be good citizens that way, but also. [00:12:30] Anush Elangovan: Uh, it is self-fulfilling in a way, right? Like it, the, the pace at which we innovate with open source is unmatched. Like, you know, our serving engines are like VLLM and, and sg l. Those things, uh, those frameworks are like super, super aggressive in terms of how fast they come out with features and how fast they can you know, get performant models out. [00:12:52] Anush Elangovan: And that compared with what, uh, you'd get from, you know, the likes of like T-R-T-L-L-M or something is always lagging, right? Because you [00:13:00] just can't keep up with you know, 200 commits a week just on one particular model to get that model really performant [00:13:06] Andrew Zigler: And, and, and in that world where, you know, everyone can enjoy the winds of this, what kind of customer stories or innovation stories have really stood out to you and excite you about building and creating this place for developers? [00:13:19] Anush Elangovan: Yeah. So I think the parts that are super exciting for me are when when we get to see a customer that is first skeptical. Then they start a little like, okay, fine, we'll give you a chance. Uh, we do a simple, uh, POC and then they're like, huh, this seems to work. Yeah, we told you it works. [00:13:42] Anush Elangovan: You don't have to change one line of code. Really? Yes, no need to change one line of code. Okay, let's try a production workload. So then they try it. Oh, you're more performant than the competition. Yes. We're more performant than, than the competition. So how much does it cost? And we're like, oh, it's your TCO is better with, uh, [00:14:00] AMD. [00:14:00] Anush Elangovan: So again, they're like, wow, okay, good. So now how do we deploy at scale? And then we go deploy it at scale. And when they give a thumbs up on that and they say, this is good, right? That's when you know, you, you see it go full circle from like, oh, we, we've never heard about AMD to like actually deploy to tens of thousands of GPUs In the order of a few months, right? It, it, it really is fascinating to see and very exciting and invigorating to [00:14:28] Andrew Zigler: Yeah. At like a great exposure to a lot of interesting problems. And, and then people using the infrastructure, the, the technology available to solve those problems. Really specific problems by the way, that's often why they're bringing their data and AI to it, uh, is because it is really specific and important for them. [00:14:45] Andrew Zigler: And there's a, a lot I think that other engineering orgs can learn and even emulate from AMD's success and, and having this open source ecosystem and it causing this acceleration within. You [00:15:00] know, uh, customers and enterprises that use and adopt the tools and, and, and that creates an advantage. And that goes back to why we're talking and like the real thesis of our conversation today. [00:15:10] Andrew Zigler: So how do you think engineering leaders that are listening to this and obviously tapping into this great success AMD has from an open source flywheel, how do you think other, other folks building in the same space can foster that open, first, that open source oriented culture in order to, you know, accelerate their innovation goals? [00:15:29] Anush Elangovan: Yeah, that's a very good question. So the startup that um, was acquired by AMD we, we built, I mean, we started off doing iot stuff and you know, smart ring and all that, right? But in the, the end of like, uh, and not the end, the last six years of the company was building ML compilers. [00:15:47] Anush Elangovan: And ml, ML compilers are like super, uh, complicated, sophisticated, advanced algorithms, dah, dah, dah. but it was all open source, right? So our VCs were like, wait, what do you mean your core [00:16:00] IP is open source? And um, the speed is the moat applied even then, right? It was just like, yes, if you have an idea that. [00:16:08] Anush Elangovan: Because someone saw this idea that you are, they're gonna be able to catch up, then you probably have the wrong idea anyway. But if they are, you know, you execute and they're gonna catch up, that you should assume they're gonna catch up. Right? So you gotta move forward. So keeping it open source is super important. [00:16:25] Anush Elangovan: But also to your question on like, you know, the learnings from an AMD standpoint, right? If there are, hard problems, I'd say dig in and work through it, right? Like there's no way but through it, right? That should be the simple mentality. And more, uh, frequently than not. you'll see that you'll just make it through in a, in, in good form. [00:16:52] Anush Elangovan: But if you doubt it and you're like, oh, I don't know if I should commit, if I'm, I, you know, what should just commit to do the right thing [00:17:00] every step, right? Every step, and just keep taking one step in front of the other. And in no time you'll see that you'll be running. Right. And, and yes, the first few steps will be like, yeah, everyone's complaining about your software quality. [00:17:15] Anush Elangovan: Everyone's complaining about this and that, and it doesn't work. And, and a few steps in, you know, you get, you get the hang of all the complaints that are coming in. You get the feedback loop. You're like, okay, what, what are you prioritizing again? One step in front of the other, right? You just keep knocking that out and then you get to a point where you're, it just becomes second nature, right? To do the, to do the right thing. And, and then yes, if someone gives you two options, you'll be like, fine. This is, uh, you know, there's always the resource trade off. There's always a human capital trade off, but what's the right thing to do? of course, I, I'm pragmatic about what we choose, but, but if the right thing for your long-term success is dig in, go first, principles, make it [00:18:00] happen. [00:18:00] Anush Elangovan: Well. Then just go for that. There's, there is no shortcut to [00:18:04] Andrew Zigler: acknowledging, you know, how it aligns with your mission, your core company goals, and what you're looking to achieve. And, and I, I love how you rightfully called out that in the open source world and you know, you have your technology that you've built, what you think is your moat upon, right? [00:18:22] Andrew Zigler: It's your code and, and to open source that, or to just make it where anyone could peer in is, you know. Scary in one regard, but two, it just kind of feels like you're handing away your throne room in some kind of sense, a very direct feeling sense. But the ultimately, you were really right to call out, and this is something I think about all the time, that the real power there is still the speed This the speed. [00:18:42] Andrew Zigler: That was the moat at the beginning of our conversation. It's the speed in combination with your. Very specific domain understanding of what you're building and what you're creating, and your new role as the steward of that world and how people plug into it, which [00:19:00] has frankly, a lot more influence and power than lording over a closed. [00:19:04] Andrew Zigler: You know, repository or an ecosystem, and like you said, like throwing things over the wall. Sure. There, there might be people always on the other side of that wall, but you're not gonna have a great connection with them. You're not gonna be able to really clearly understand them. I, I like your metaphor of the side of the field of the mountain a lot more. [00:19:23] Andrew Zigler: But, but in the, in this world, you know, where. That speed is, is the power and, and open source is just one way that you can harness that speed to get really far ahead and to innovate. , There's other parts of this equation that you can be experimenting with too, and I'd love to pick your brain about them as a software leader and, and, and one of them is about looking forward and kind of understanding that future that we're all building towards and beyond today's models and hardware. [00:19:48] Andrew Zigler: You know, what do you see as the next major bottleneck or opportunity in the AI compute space? As, as you know, enterprises and folks start to get a little more mature about what's available to [00:20:00] them. [00:20:00] Anush Elangovan: Yeah, I think, the bottleneck and opportunity is, uh, what I'd call, call walking the last mile of ai. Right. Uh, and like I I, I gave you an example, uh, previously, but, but it's similar to that. It's like there are cases where Humans have so many, uh, things to do in your day. You know, like the, if we sit down and actually had a customer focus like, okay, these customers lives, I'm gonna save four hours of this customer's life. And if you actually sit down and look at all of that, it'll be. Easily automatable, easily you know, uh, applicable, uh, for ai, right? [00:20:39] Anush Elangovan: Like, but then making it happen is gonna take a little bit, right? It's like maybe it's, uh, paying your utility bill, right? Or something like that, right? Or, or, your healthcare explanation of benefits. Uh, like, I'm sure you get an explanation of benefits, and I'm like, I, I don't even know what that thing is. [00:20:55] Anush Elangovan: It's just like EOB and like. [00:20:57] Andrew Zigler: it's a big, a big old PDF. Yeah, [00:21:00] exactly. [00:21:01] Anush Elangovan: Like, like, I'm like great straight to the, uh, shredder, right? And but that could be, you know, automated with the ai, right? It, it, it'd be like, Hey, the summary of this thing is you went and visited this day. Everything is okay. Everything is paid for, so don't worry, it's not a bill. [00:21:17] Anush Elangovan: That again, the same, uh, thing, but the sense of what that information overload is could be. Digested by ai, uh, accumulated over time and retrieved when you need it. Like, I don't, I actually don't even need to know this EOB right now, unless of course, whenever I need to know it, that maybe, you know, like for some benefits I need to figure out what do, what did I do over the past year and how do I apply it? Source:

Just in $AMD Anush "Speed is the moat"|ROCm🎙️ In the race to define the future of AI, what's the one advantage that truly lasts? It's not proprietary tech, argues Anush Elangovan Elangovan, VP of AI Software at AMD , but the sustainable speed of innovation. He explains why AMD is rejecting the "walled garden" model for its open source ROCm stack, betting that an open community flywheel is the key to victory. Listen to understand how this open strategy is designed to out-innovate closed systems by empowering developers to solve everything from frontier-model challenges to the mundane, everyday problems that define the "last mile" of AI. AMD ROCm Software: Part 1 Transcript [00:00:00] Andrew Zigler: Joining me is Anush Elangovan, VP of AI software at AMD. And when people talk about AI compute, the conversation often stops at hardware specs, but it's more than just physical chips that win the game. It's also the software ecosystems supporting them. [00:00:18] Andrew Zigler: The prevailing strategy in the industry has been to build something like a walled garden. You know, something closed, proprietary locks, developers in. But AMD is betting on an entirely different play, open source acceleration, and with rock, their open source AI software stack. AMD is building not just hardware parity, but an innovation flywheel that's powered by the community with interoperability and the freedom to scale without all of that pesky lockin. [00:00:48] Andrew Zigler: And in this world, speed is your moat and how fast you can innovate while your platform remains open, flexible, and standardize across all of its applications. That's what we're gonna explore [00:01:00] today. So Anush, I'm really excited to have you here. Welcome to Dev Interrupted. [00:01:04] Anush Elangovan: Thanks for having me. Uh, super excited to chat about it. [00:01:07] Andrew Zigler: Amazing. Well, let's go ahead and dive right in with kind of what I laid it out with in the beginning, the idea of the moat and it being about speed. I wanna unpack that a bit because that came from you when you and I first spoke. And I, and I want to know, you know, how do you define speed inside of AMD beyond just things like hardware, benchmarks. [00:01:27] Anush Elangovan: Yeah, that's a very good question. So when we typically talk about speed, everyone's like, Hey, hardware benchmark specs, right? Like, uh, memory bandwidth or, or flops. And that is one important part of it, uh, AMD does very well. With that, we do have, a, a very good history of executing on that axis. [00:01:47] Anush Elangovan: But when I say speed is the moat, it is about, uh, how we prepare, how we build the muscle to run the race for a long time and run it fast. And it is [00:02:00] not about a single point in time that you've, you've beat some you know, benchmark and, and you declare victory. It's about building the ability to consistently develop and deliver. [00:02:13] Anush Elangovan: Both hardware and software innovation at scale and do it fast, right? Like, you know, we we're increasingly getting to a point where models come out and they're, uh, you know, a year or two ago it was like, Hey, they work on AMD on day zero, which is great, but now they are performing on AMD the day it releases, right? [00:02:32] Anush Elangovan: So, what does it take to Prefetch where the industry is going? Be prepared to intercept. At that point is what you know, I, I refer to as you know, the, the speed factor in, in creating this mode, right? And the mode is just shed all things that hold you back and run as fast as you can. [00:02:53] Anush Elangovan: Uh, because the pace of innovation that is, uh, being seen in, in AI [00:03:00] industries is just. Amazing. Right? And it's like, it's transformational at at how you generate electricity. It's transformational as at how you build data centers. It's transformational at how you deploy compute, networking. It's transformational at what kind of use cases you, you know, uh, use AI for. [00:03:17] Anush Elangovan: Uh, and for that, you need to be prepared to, see what comes tomorrow and be prepared to run the race tomorrow. [00:03:23] Andrew Zigler: Yeah, it's a really great perspective because it highlights that it's not just like a checkpoint that you run through. I like how you called out, like it's not just hitting that benchmark or being the best in class at that moment, in that snapshot, it's about having a. The throughput and about having that dedication to the idea and continuing to deliver on it. [00:03:43] Andrew Zigler: It's not just crossing the threshold, but it's also being the engine. And that's what, that's what protects a business. That is the moat, because the moat is that innovation layer, the faster and more, uh, future forward. That you can work and think, [00:04:00] you know, the better. Uh, we, we talk a lot about like future forward work styles. [00:04:04] Andrew Zigler: Like what are the things I could be doing right now today that are gonna be like, way more useful tomorrow? Let, let's abandon those, workflows that are older and that kind of like, that translates into. An advantage when you work that way. You know, what kind of things have you learned working with, uh, like across all spectrums of people who would use ROCm, right? [00:04:23] Andrew Zigler: You have like the developers, but then you also have the enterprises and you have this large span of adoptees, right? So what is the, what does that look like that you learn? [00:04:32] Anush Elangovan: Yeah, so, so the way I look at it is there are gonna be pockets of different, uh, you know, cadences, right? Like, so people who are deploying in enterprises, for example, right? The validation and how long it takes for them to deploy an LLM that's secure. It's, with guardrails, et cetera, maybe longer. [00:04:52] Anush Elangovan: but you still have to go through the process and you have to be prepared to like, walk that walk to deploy an enterprises. That doesn't mean it's [00:05:00] not fast, that's as fast as you can do for that industry, right? And if you are deploying AI in healthcare, right, it's, it's got its own, uh, cycle. [00:05:07] Anush Elangovan: but in each one of these, you want to see how, like, go down to the essence of what is it that you actually have to do. And, you know, I, I, I like how you framed it. It's like it's, you shed your prior assumptions of how things are done, right. And, and you kind of build up from a, uh, first principles, uh, approach to say, this is how I could use AI to unlock, whatever I'm doing. [00:05:33] Anush Elangovan: And, and, some of it, you know, it's good to really step back and look at. Just question every part of it, right? Like right now you're getting chat GPT and, Gemini competing for like, math, olympiads and, and, uh, college, uh, reasoning, uh, tests. Right? And, and those are like that, that is amazing and increasingly like complex tasks that they're trying to do. [00:05:58] Anush Elangovan: But there may also be like. [00:06:00] More mundane things that AI could, could get applied to. Right? And, and so when we think about shedding old ways, you wanna shed it not just in like the tip of the spear. It's like, you know, I'm gonna see what's the frontier model. It's also, it could be something as simple as. [00:06:18] Anush Elangovan: How do you choose a, a movie, uh, you know, like a recommendation system, right? Or, or, uh, an automated, uh, flight, uh, rebooking system. So the moment, you know, your flight is late, uh, right now it's a notification, right? It's like, oh, you got a text message saying your flight's late. And I got that like three times this week. [00:06:38] Anush Elangovan: But anyway, uh, and, and, and, and, I was just like, okay, so if I were to rethink this. All this MCPs that we have that should be hooked up into an MCP that says, your flight's delayed. Here are your options. If you want, you know, these are the paid options. Yeah. Here are the free options. This will get you back into your you know, Toronto airport [00:07:00] tonight. [00:07:00] Anush Elangovan: Or if you stay, here's a hotel plus this, plus this, plus. It's just like, go ahead is all I should say. Versus now I'm like, okay, can someone, you know, can I call a travel agent? Can I do this? Can I go online and log into And you know, so we gotta fundamentally rethink even those like small, nuances of, things that we do that can be automated out and AI is really, really good at doing something like this, right? Maybe I just explained an AI startup idea right now. Somebody should just start that. [00:07:29] Andrew Zigler: I think you did. Yeah, you definitely did. Someone, one of our listeners is definitely going to lift that off of you. I, I, I, you know, I hate being on the receiving end of those. You feel a little helpless and then you have to like, follow the whole flow. So I know what you mean. Like I, I like how you called out that the build and this like. [00:07:45] Andrew Zigler: Where speed is your moat and the innovation layer is protecting you, is what makes you better than your competitors. How you scale that and you bring that to market. So by understanding the problems that you're solving, uh, throwing away those older assumptions, but also [00:08:00] recognizing that like. We're building every single day, new things and new ways of using stuff that we're still figuring out the implications of. [00:08:08] Andrew Zigler: And so when you have a lot of velocity and you're introducing a lot of new ideas, and maybe you have that workflow now that automatically rebook your flight off of your late flight text message, and uh, I know I would certainly use it, but you know, what kind of philosophies guide the way that y'all think about building this ecosystem to manage that stability while letting folks. [00:08:29] Andrew Zigler: Play with the speed and the assumptions and the airplane re bookings. [00:08:34] Anush Elangovan: so, so I think, you know, we need to peel one layer down, right? and the philosophy is, Hey, we, we just discovered electricity, right? And you know what we're gonna do? We are gonna make motors, uh, or dynamos, right? Like engines. Uh, sure. We don't know if it's gonna be a Ferrari that you're gonna make, or it's a a a a dump truck. [00:08:57] Anush Elangovan: That's good for doing this. But let's [00:09:00] let, which is also required, right? You need a dump truck. You need a garbage truck. And, [00:09:04] Andrew Zigler: Yeah. You need the [00:09:04] Anush Elangovan: course you need, uh, a Ferrari for a midlife crisis, right? So, [00:09:09] Andrew Zigler: precisely. [00:09:10] Anush Elangovan: But, but my, uh, point is what do we build next? And, uh, and this is what I meant by like, okay, let's, let's take those baby steps to build the. [00:09:20] Anush Elangovan: Infrastructure that's required that we know we'll have to use, right? So, so if I just discovered electricity, okay, great. Now one, how do I save this electricity and how do I use it? So there's battery technology, so you need to do something like that, right? Like so. But then you also want to make it into an actionable thing. [00:09:37] Anush Elangovan: You want to make it for like automobiles, or you wanna use it for, you know, powering, uh, entire cities. So it is that transformational. So, uh, AI is that transformational. So, if you distill down, it'll, it'll come down to how do we think about, what we can do with this this fundamental technology that, We may not be aware of what it [00:10:00] is gonna unlock next, but at least you know the next step is clear, right? It's like a dense fog, you know, it's gonna be like, it, it's the right path. You see the light, but it's kind of like out there and, and the steps you're taking are concrete and you're like, okay, this is good. [00:10:16] Anush Elangovan: I, this is better than where I was or where we were. So we are moving forward. So you can build with the. Intuition from what you see in the short term and a tactical view, but towards what you think the future is gonna be. [00:10:28] Andrew Zigler: Right. You almost like we're all in this like fog of war, right? And like you said, you're reaching out and you're trying to step through it. You could think of it too, as like you're in the dark and your hands are up in front of you and you know that. You're, you're not gonna run your face into a wall because your hands are out in front of you, but you're not gonna maybe do much better than that. [00:10:45] Andrew Zigler: So that's kind of like, I think the eco, the, the industry, the world that we find ourselves in, uh, and we all have to, then this becomes the power of an ecosystem, of a group of people working together to create that layer of, [00:11:00] uh, of establishing the [00:11:01] Anush Elangovan: exactly. And I, I, I just, instead of, you know, saying fog of war I describe it as like, you're in this. Beautiful valley with like a morning, uh, fog that's in. You can smell the flowers. You, you hear the birds. You are like, okay, it's, we are in like, uh, utopian paradise and yes, I just need to like, continue the walk, right? [00:11:24] Anush Elangovan: and then move forward with that, conviction that you're in the right spot. [00:11:27] Andrew Zigler: Yeah. So let's talk about that ecosystem world. This nice, I love how you describe it, this grassy side of a hill in the morning that's covered in some mist and maybe we can't see 30 feet in one direction, but it sure is a beautiful hill and it smells nice. And so we're all here. And why is, in that world, why is. [00:11:44] Andrew Zigler: You know, open source, their strategic advantage that y'all are going for in the AI hardware market. And, and then how does like ROCm turn that into wins for people within that ecosystem? [00:11:56] Anush Elangovan: you know, the, the way we look at it is this, is kind of like how I view [00:12:00] AI and the ecosystem, right? But, but it is for everyone to enjoy. Uh, and so we do want to make sure that. You know, it is, uh, beneficial for everyone. [00:12:09] Anush Elangovan: The ecosystem can come in and, and innovate. It's an open innovation engine. and uh, it is very different from, you know, having a walled garden with, Hey, only I know how to do this and I'm gonna do it and throw it over the fence and you can use it or keep walking, right? So we'd like to be good citizens that way, but also. [00:12:30] Anush Elangovan: Uh, it is self-fulfilling in a way, right? Like it, the, the pace at which we innovate with open source is unmatched. Like, you know, our serving engines are like VLLM and, and sg l. Those things, uh, those frameworks are like super, super aggressive in terms of how fast they come out with features and how fast they can you know, get performant models out. [00:12:52] Anush Elangovan: And that compared with what, uh, you'd get from, you know, the likes of like T-R-T-L-L-M or something is always lagging, right? Because you [00:13:00] just can't keep up with you know, 200 commits a week just on one particular model to get that model really performant [00:13:06] Andrew Zigler: And, and, and in that world where, you know, everyone can enjoy the winds of this, what kind of customer stories or innovation stories have really stood out to you and excite you about building and creating this place for developers? [00:13:19] Anush Elangovan: Yeah. So I think the parts that are super exciting for me are when when we get to see a customer that is first skeptical. Then they start a little like, okay, fine, we'll give you a chance. Uh, we do a simple, uh, POC and then they're like, huh, this seems to work. Yeah, we told you it works. [00:13:42] Anush Elangovan: You don't have to change one line of code. Really? Yes, no need to change one line of code. Okay, let's try a production workload. So then they try it. Oh, you're more performant than the competition. Yes. We're more performant than, than the competition. So how much does it cost? And we're like, oh, it's your TCO is better with, uh, [00:14:00] AMD. [00:14:00] Anush Elangovan: So again, they're like, wow, okay, good. So now how do we deploy at scale? And then we go deploy it at scale. And when they give a thumbs up on that and they say, this is good, right? That's when you know, you, you see it go full circle from like, oh, we, we've never heard about AMD to like actually deploy to tens of thousands of GPUs In the order of a few months, right? It, it, it really is fascinating to see and very exciting and invigorating to [00:14:28] Andrew Zigler: Yeah. At like a great exposure to a lot of interesting problems. And, and then people using the infrastructure, the, the technology available to solve those problems. Really specific problems by the way, that's often why they're bringing their data and AI to it, uh, is because it is really specific and important for them. [00:14:45] Andrew Zigler: And there's a, a lot I think that other engineering orgs can learn and even emulate from AMD's success and, and having this open source ecosystem and it causing this acceleration within. You [00:15:00] know, uh, customers and enterprises that use and adopt the tools and, and, and that creates an advantage. And that goes back to why we're talking and like the real thesis of our conversation today. [00:15:10] Andrew Zigler: So how do you think engineering leaders that are listening to this and obviously tapping into this great success AMD has from an open source flywheel, how do you think other, other folks building in the same space can foster that open, first, that open source oriented culture in order to, you know, accelerate their innovation goals? [00:15:29] Anush Elangovan: Yeah, that's a very good question. So the startup that um, was acquired by AMD we, we built, I mean, we started off doing iot stuff and you know, smart ring and all that, right? But in the, the end of like, uh, and not the end, the last six years of the company was building ML compilers. [00:15:47] Anush Elangovan: And ml, ML compilers are like super, uh, complicated, sophisticated, advanced algorithms, dah, dah, dah. but it was all open source, right? So our VCs were like, wait, what do you mean your core [00:16:00] IP is open source? And um, the speed is the moat applied even then, right? It was just like, yes, if you have an idea that. [00:16:08] Anush Elangovan: Because someone saw this idea that you are, they're gonna be able to catch up, then you probably have the wrong idea anyway. But if they are, you know, you execute and they're gonna catch up, that you should assume they're gonna catch up. Right? So you gotta move forward. So keeping it open source is super important. [00:16:25] Anush Elangovan: But also to your question on like, you know, the learnings from an AMD standpoint, right? If there are, hard problems, I'd say dig in and work through it, right? Like there's no way but through it, right? That should be the simple mentality. And more, uh, frequently than not. you'll see that you'll just make it through in a, in, in good form. [00:16:52] Anush Elangovan: But if you doubt it and you're like, oh, I don't know if I should commit, if I'm, I, you know, what should just commit to do the right thing [00:17:00] every step, right? Every step, and just keep taking one step in front of the other. And in no time you'll see that you'll be running. Right. And, and yes, the first few steps will be like, yeah, everyone's complaining about your software quality. [00:17:15] Anush Elangovan: Everyone's complaining about this and that, and it doesn't work. And, and a few steps in, you know, you get, you get the hang of all the complaints that are coming in. You get the feedback loop. You're like, okay, what, what are you prioritizing again? One step in front of the other, right? You just keep knocking that out and then you get to a point where you're, it just becomes second nature, right? To do the, to do the right thing. And, and then yes, if someone gives you two options, you'll be like, fine. This is, uh, you know, there's always the resource trade off. There's always a human capital trade off, but what's the right thing to do? of course, I, I'm pragmatic about what we choose, but, but if the right thing for your long-term success is dig in, go first, principles, make it [00:18:00] happen. [00:18:00] Anush Elangovan: Well. Then just go for that. There's, there is no shortcut to [00:18:04] Andrew Zigler: acknowledging, you know, how it aligns with your mission, your core company goals, and what you're looking to achieve. And, and I, I love how you rightfully called out that in the open source world and you know, you have your technology that you've built, what you think is your moat upon, right? [00:18:22] Andrew Zigler: It's your code and, and to open source that, or to just make it where anyone could peer in is, you know. Scary in one regard, but two, it just kind of feels like you're handing away your throne room in some kind of sense, a very direct feeling sense. But the ultimately, you were really right to call out, and this is something I think about all the time, that the real power there is still the speed This the speed. [00:18:42] Andrew Zigler: That was the moat at the beginning of our conversation. It's the speed in combination with your. Very specific domain understanding of what you're building and what you're creating, and your new role as the steward of that world and how people plug into it, which [00:19:00] has frankly, a lot more influence and power than lording over a closed. [00:19:04] Andrew Zigler: You know, repository or an ecosystem, and like you said, like throwing things over the wall. Sure. There, there might be people always on the other side of that wall, but you're not gonna have a great connection with them. You're not gonna be able to really clearly understand them. I, I like your metaphor of the side of the field of the mountain a lot more. [00:19:23] Andrew Zigler: But, but in the, in this world, you know, where. That speed is, is the power and, and open source is just one way that you can harness that speed to get really far ahead and to innovate. , There's other parts of this equation that you can be experimenting with too, and I'd love to pick your brain about them as a software leader and, and, and one of them is about looking forward and kind of understanding that future that we're all building towards and beyond today's models and hardware. [00:19:48] Andrew Zigler: You know, what do you see as the next major bottleneck or opportunity in the AI compute space? As, as you know, enterprises and folks start to get a little more mature about what's available to [00:20:00] them. [00:20:00] Anush Elangovan: Yeah, I think, the bottleneck and opportunity is, uh, what I'd call, call walking the last mile of ai. Right. Uh, and like I I, I gave you an example, uh, previously, but, but it's similar to that. It's like there are cases where Humans have so many, uh, things to do in your day. You know, like the, if we sit down and actually had a customer focus like, okay, these customers lives, I'm gonna save four hours of this customer's life. And if you actually sit down and look at all of that, it'll be. Easily automatable, easily you know, uh, applicable, uh, for ai, right? [00:20:39] Anush Elangovan: Like, but then making it happen is gonna take a little bit, right? It's like maybe it's, uh, paying your utility bill, right? Or something like that, right? Or, or, your healthcare explanation of benefits. Uh, like, I'm sure you get an explanation of benefits, and I'm like, I, I don't even know what that thing is. [00:20:55] Anush Elangovan: It's just like EOB and like. [00:20:57] Andrew Zigler: it's a big, a big old PDF. Yeah, [00:21:00] exactly. [00:21:01] Anush Elangovan: Like, like, I'm like great straight to the, uh, shredder, right? And but that could be, you know, automated with the ai, right? It, it, it'd be like, Hey, the summary of this thing is you went and visited this day. Everything is okay. Everything is paid for, so don't worry, it's not a bill. [00:21:17] Anush Elangovan: That again, the same, uh, thing, but the sense of what that information overload is could be. Digested by ai, uh, accumulated over time and retrieved when you need it. Like, I don't, I actually don't even need to know this EOB right now, unless of course, whenever I need to know it, that maybe, you know, like for some benefits I need to figure out what do, what did I do over the past year and how do I apply it? Source:

Mike

15,145 次观看 • 9 个月前

The 118,000% Alpha: Building a High-Frequency AI Trading Floor with Claude Code if you think claude code is just for writing simple scripts then you are already losing to the bots that are hunting your liquidity right now. most traders are still clicking buttons while i have an ai employee running backtests on twenty eight different data sources simultaneously. i am going to show you how a strategy that returned over four hundred thousand percent was built in minutes using a secret sub agent workflow most people treat ai like a chatbot but i treat it like a quant architect that builds systems better than the devs i used to pay hundreds of thousands of dollars. there is one specific indicator combo that actually survived a stress test across tesla and bitcoin at the same time and i will reveal that logic further down. we have to talk about why your current backtests are probably lying to you before we get into the code my name is moon dev and i truly believe that code is the great equalizer in this world. for years i was the guy getting liquidated and overtrading because i was letting my emotions drive the wheel. i spent an insane amount of money hiring developers to build apps for me because i thought i was not smart enough to code myself. through that pain i realized that if i wanted to win i had to automate everything and learn to do it live on youtube for the world to see the secret to trading with claude code is not asking it for a strategy but using it to build a backtest architect. this sub agent acts as a consistent employee that understands how to test against massive datasets without getting tired. it allows me to iterate through hundreds of ideas in the time it used to take me to write one single line of python. this is how i found the strategy that hit a one hundred and eighteen thousand percent return on a single run there is a massive trap that almost every beginner falls into when they start using ai for trading. they find a strategy that looks amazing on one chart and they think they found the holy grail of wealth. that is usually just a lucky fluke or a curve fit mess that will blow up your account next week. the real secret to staying alive is the multi data testing system that claude built for me today we test every single idea against bitcoin and ethereum and solana but we also throw in apple and tesla and nvidia. if a strategy only works on crypto it is probably just riding a trend that is already over. i want to find the logic that is robust enough to handle the volatility of a meme coin and the steady grind of a blue chip stock. this is the only way to prove that the code actually has an edge in the market before we dive into the kalman filter logic i have to tell you about the dca bot i have running on solana right now. it is called housecoin and the thesis behind it is either going to make me a genius or leave me with nothing. it is buying every time we are under the five minute sma and i have been checking the transactions live. i will explain the risk management behind this "all or nothing" play shortly but first we need to look at the winners the winner of today was the acceleration bands combined with a kalman filter. the kalman filter is incredible because it helps remove the noise and lag that you get with standard moving averages. most indicators repaint which means they change their past values to look better after the price has already moved. the way i have implemented this filter prevents that trap so the results you see in the backtest are actually tradable when we ran the acceleration bands across the hourly nvidia chart it returned over two hundred percent while the underlying asset was down forty percent. that is a massive alpha gap that most people will never see because they are stuck using standard rsi settings. i have found that adding a volatility breakout with atr to this setup helps catch the moves that the banks are trying to hide. the math behind the atr breakout is what kept me from getting chopped up in the sideway ranges you might be wondering why i am giving all this code away for free on github instead of keeping it in a vault. it is because i remember what it felt like to be on the other side of the trade losing money every single day. i want to build a community of quads that are all researching and backtesting together. the goal is to chase the legacy of jim simons who proved that math and code are the only things that matter in the long run the rbi system is the framework that i follow every single day without exception. it stands for research and backtest and implement. most traders skip the middle step because they are too impatient to see the results. they hear a rumor on twitter and they buy the top only to get liquidated when the whales decide to take profits. if you do not backtest your ideas then you are just gambling with your life savings i am spending around forty to one hundred dollars a day on claude opus tokens because it is a drop in the bucket compared to what a developer would charge. this ai does not need a lunch break and it does not get bored when i ask it to create sixty different variations of a strategy. we just created five different parabolic sar versions today and found that the long only setup was the only one worth keeping. it returned sixteen thousand percent on the soul data set because it stayed out of the short side traps shorting crypto is extremely dangerous and usually not worth the stress for most people. i have found that focusing on long only strategies with a tight trail stop is the most consistent way to grow an account. the sub agent architect allowed me to verify this across twenty five data sources in less than ten minutes. this speed of iteration is the only way to stay ahead of the curve in an industry that changes every few seconds the dca bot i mentioned earlier is still grinding away and buying the dips as we speak. i have built it to be a long term play where i am slowly accumulating a position in housecoin based on smas. if the price stays under the moving average the bot keeps buying and if it goes above then it sits on its hands. it is a simple logic but it removes the human desire to "buy the moon" when the price is already overextended i found that the camarilla pivot indicator was mostly trash today when we ran the numbers. even though it looks fancy on a chart the backtest showed negative expectancy across almost every asset we tried. this is why backtesting is so important because it kills the "indicator porn" that influencers use to sell you courses. i would much rather know that a strategy is a loser now than find out after i put real money on the line the true secret to using claude code is to treat it like a partner and not just a tool. i ask it to find anomalies and then i ask it to prove me wrong by testing it against the worst market conditions in history. if a strategy can survive the 2022 crypto crash and the 2020 stock market dip then i might consider it for a live run. we are stepping on the gas every single day because there are always new anomalies popping up if you are fast enough to find them i have uploaded over twenty five new backtests to the github today for everyone to use. code is the equalizer because it does not care about your background or how much money you started with. if you can write the logic and prove the edge then the market has to pay you. i am going to keep building in public and showing the wins and the losses because that is the only way to stay real in this space the final piece of the puzzle is the mindset of iteration over perfection. i would rather run a hundred messy backtests today than spend a month trying to write one perfect script. the ai allows me to fail fast so that i can find the winners that actually move the needle. my housecoin dca bot is a testament to that philosophy of just building and letting the systems do the heavy lifting for me if you are still trading by hand you are playing a game that is rigged against you by the biggest firms in the world. they have the best servers and the best data and the best phds but they do not have your specific creativity. when you combine your ideas with the power of claude code you are creating a custom weapon that they have never seen before. i will see you in the code and we will keep chasing the goat until we find that ultimate edge

The 118,000% Alpha: Building a High-Frequency AI Trading Floor with Claude Code if you think claude code is just for writing simple scripts then you are already losing to the bots that are hunting your liquidity right now. most traders are still clicking buttons while i have an ai employee running backtests on twenty eight different data sources simultaneously. i am going to show you how a strategy that returned over four hundred thousand percent was built in minutes using a secret sub agent workflow most people treat ai like a chatbot but i treat it like a quant architect that builds systems better than the devs i used to pay hundreds of thousands of dollars. there is one specific indicator combo that actually survived a stress test across tesla and bitcoin at the same time and i will reveal that logic further down. we have to talk about why your current backtests are probably lying to you before we get into the code my name is moon dev and i truly believe that code is the great equalizer in this world. for years i was the guy getting liquidated and overtrading because i was letting my emotions drive the wheel. i spent an insane amount of money hiring developers to build apps for me because i thought i was not smart enough to code myself. through that pain i realized that if i wanted to win i had to automate everything and learn to do it live on youtube for the world to see the secret to trading with claude code is not asking it for a strategy but using it to build a backtest architect. this sub agent acts as a consistent employee that understands how to test against massive datasets without getting tired. it allows me to iterate through hundreds of ideas in the time it used to take me to write one single line of python. this is how i found the strategy that hit a one hundred and eighteen thousand percent return on a single run there is a massive trap that almost every beginner falls into when they start using ai for trading. they find a strategy that looks amazing on one chart and they think they found the holy grail of wealth. that is usually just a lucky fluke or a curve fit mess that will blow up your account next week. the real secret to staying alive is the multi data testing system that claude built for me today we test every single idea against bitcoin and ethereum and solana but we also throw in apple and tesla and nvidia. if a strategy only works on crypto it is probably just riding a trend that is already over. i want to find the logic that is robust enough to handle the volatility of a meme coin and the steady grind of a blue chip stock. this is the only way to prove that the code actually has an edge in the market before we dive into the kalman filter logic i have to tell you about the dca bot i have running on solana right now. it is called housecoin and the thesis behind it is either going to make me a genius or leave me with nothing. it is buying every time we are under the five minute sma and i have been checking the transactions live. i will explain the risk management behind this "all or nothing" play shortly but first we need to look at the winners the winner of today was the acceleration bands combined with a kalman filter. the kalman filter is incredible because it helps remove the noise and lag that you get with standard moving averages. most indicators repaint which means they change their past values to look better after the price has already moved. the way i have implemented this filter prevents that trap so the results you see in the backtest are actually tradable when we ran the acceleration bands across the hourly nvidia chart it returned over two hundred percent while the underlying asset was down forty percent. that is a massive alpha gap that most people will never see because they are stuck using standard rsi settings. i have found that adding a volatility breakout with atr to this setup helps catch the moves that the banks are trying to hide. the math behind the atr breakout is what kept me from getting chopped up in the sideway ranges you might be wondering why i am giving all this code away for free on github instead of keeping it in a vault. it is because i remember what it felt like to be on the other side of the trade losing money every single day. i want to build a community of quads that are all researching and backtesting together. the goal is to chase the legacy of jim simons who proved that math and code are the only things that matter in the long run the rbi system is the framework that i follow every single day without exception. it stands for research and backtest and implement. most traders skip the middle step because they are too impatient to see the results. they hear a rumor on twitter and they buy the top only to get liquidated when the whales decide to take profits. if you do not backtest your ideas then you are just gambling with your life savings i am spending around forty to one hundred dollars a day on claude opus tokens because it is a drop in the bucket compared to what a developer would charge. this ai does not need a lunch break and it does not get bored when i ask it to create sixty different variations of a strategy. we just created five different parabolic sar versions today and found that the long only setup was the only one worth keeping. it returned sixteen thousand percent on the soul data set because it stayed out of the short side traps shorting crypto is extremely dangerous and usually not worth the stress for most people. i have found that focusing on long only strategies with a tight trail stop is the most consistent way to grow an account. the sub agent architect allowed me to verify this across twenty five data sources in less than ten minutes. this speed of iteration is the only way to stay ahead of the curve in an industry that changes every few seconds the dca bot i mentioned earlier is still grinding away and buying the dips as we speak. i have built it to be a long term play where i am slowly accumulating a position in housecoin based on smas. if the price stays under the moving average the bot keeps buying and if it goes above then it sits on its hands. it is a simple logic but it removes the human desire to "buy the moon" when the price is already overextended i found that the camarilla pivot indicator was mostly trash today when we ran the numbers. even though it looks fancy on a chart the backtest showed negative expectancy across almost every asset we tried. this is why backtesting is so important because it kills the "indicator porn" that influencers use to sell you courses. i would much rather know that a strategy is a loser now than find out after i put real money on the line the true secret to using claude code is to treat it like a partner and not just a tool. i ask it to find anomalies and then i ask it to prove me wrong by testing it against the worst market conditions in history. if a strategy can survive the 2022 crypto crash and the 2020 stock market dip then i might consider it for a live run. we are stepping on the gas every single day because there are always new anomalies popping up if you are fast enough to find them i have uploaded over twenty five new backtests to the github today for everyone to use. code is the equalizer because it does not care about your background or how much money you started with. if you can write the logic and prove the edge then the market has to pay you. i am going to keep building in public and showing the wins and the losses because that is the only way to stay real in this space the final piece of the puzzle is the mindset of iteration over perfection. i would rather run a hundred messy backtests today than spend a month trying to write one perfect script. the ai allows me to fail fast so that i can find the winners that actually move the needle. my housecoin dca bot is a testament to that philosophy of just building and letting the systems do the heavy lifting for me if you are still trading by hand you are playing a game that is rigged against you by the biggest firms in the world. they have the best servers and the best data and the best phds but they do not have your specific creativity. when you combine your ideas with the power of claude code you are creating a custom weapon that they have never seen before. i will see you in the code and we will keep chasing the goat until we find that ultimate edge

Moon Dev

18,650 次观看 • 6 个月前

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 • 6 个月前

The 40,000% ROI "Bug": How Claude Code Cracked the TradingView Holy Grail most people think the elite traders at the top of the mountain have some secret indicator or a hidden math formula that gives them a forty thousand percent return. they assume the game is rigged against the small player and that you need a multi million dollar budget just to get a seat at the table. the truth is that the holy grail of trading is actually hidden in plain sight inside a community tab that most people scroll past every single day i spent years losing money to liquidations and over trading because i thought i had to manually predict where the price was going next. i even spent hundreds of thousands of dollars on developers to build apps for me because i was convinced that i would never be able to code the systems myself. it turns out that once you stop trying to be a genius and start using the tools that are already available you can crack the code to unlimited trading strategies the secret is not in a single indicator but in the process of research back test and implement. if you go to the community section of trading view you will find an endless stream of source code for indicators that people have built over decades. most traders just slap these on a chart and hope for the best but if you are a data dog like me you know that a chart is just a pretty picture that lies to you i believe that code is the great equalizer because it allows us to take these public ideas and turn them into fully automated systems that trade for us while we sleep. i decided to learn to code live on youtube to show everyone that you can iterate your way to success without being a math wizard or a stanford graduate. now i have fully automated systems that manage my capital instead of getting liquidated by emotional decisions in the middle of the night the biggest trap in the trading world is something called repainting and it is the reason why so many strategy back tests look like they are printing money when they are actually just a scam. repainting happens when an indicator looks at future data to tell you what happened in the past which makes every buy and sell signal look like a perfect entry at the top and bottom. if you trust a back test on a basic chart without understanding the logic underneath you are just building a house on a foundation of sand this is why i transitioned all of my serious work into python because python does not lie to you. in python you can control the data flow tick by tick and bar by bar to ensure that no future data is leaking into your strategy. i built a back test architect which is a specialized sub agent that knows exactly how to take a simple idea and test it against twenty five different data sources all at once when you run a strategy across btc eth apple google and tesla you start to see the real truth about whether a strategy has an edge or if it was just a lucky fluke on one chart. i saw one strategy this week that showed a one million percent return which sounds like a total lie but the data does not have an ego. even if a number looks insane you have to investigate it and incubate it with tiny size to see if it holds up in the live market you must treat your trading like a business where you are the manager and the code is your team of tireless employees. i have sub agents running for me right now that act as masters of specific tasks like converting pine script into python or optimizing exit logic. if you are not using these specialized ai assistants in your workflow you are essentially trying to build a skyscraper with a hand saw while everyone else is using heavy machinery most people get stuck in the beginner phase because they think they need to write every single line of code from scratch. the reality is that the best developers are just really good at importing the hard work of others and connecting it like lego blocks. i use a library called ccxt that allows my bots to communicate with every major exchange in the world with just a few lines of script which saves me months of development time the reason i show everything live is because the industry is filled with gatekeepers who want to keep the secrets of automation to themselves. they want you to stay as a manual trader who pays high fees and provides liquidity for their algorithms. once you learn to automate you are no longer a victim of the market but a participant in the architecture of the financial system if you are sitting there right now feeling defeated because you just got smoked on a trade or you missed a massive pump you have to realize that those emotions are your greatest enemy. a computer does not feel fomo and it does not get tilted after a loss; it just waits for the next signal that fits the parameters you defined. my mission is to help you get to a place where you can walk away from the screen and let the machines do the heavy lifting learning to code is actually much easier than learning a second language because the syntax is logical and the feedback is immediate. i spent ten years in tech scared to touch a keyboard for anything other than emails because i thought i was not smart enough for engineering. once i realized that code is just logic i was able to build my first profitable bot within a few months and i have never looked back the transition from a manual trader to an algorithmic expert is about building a robust framework for testing your ideas as fast as possible. you want to be able to find an indicator on trading view convert it to python and run it against years of historical data in less than five minutes. if you can do that you have a higher chance of success than ninety nine percent of the people who are just drawing lines on a screen one of the most powerful strategies i found recently combines the squeeze momentum indicator with smart money concepts. when you test these individually they might show a decent return but when you combine them and add a filter like the adx you can find setups that have a massive expectancy. the key is to look for strategies that show positive returns across multiple different asset classes and time frames simultaneously even if a strategy looks like it is printing a forty thousand percent return you must always remain skeptical and look for the catch. i always incubate my new ideas with tiny capital for at least a few weeks to see how they handle real world slippage and fees. a back test is a map of the past but the live market is a wilderness that changes every single day this is why i believe in the rbi method which stands for research back test and implement. you spend your mornings looking for new ideas your afternoons stress testing them with ai and your evenings deploying the winners to the market. it is a systematic approach to wealth that removes the need for luck or guessing what a celebrity is going to tweet next the most successful traders in history like jim simons did not sit around looking at rsi levels on a fifteen minute chart. they built systems that identified mathematical edges and then scaled those systems until they were managing billions of dollars. you do not need thirty one billion dollars to change your life but you do need the discipline to stop trading like a human and start thinking like a system i give away so much for free on youtube because i want to build a community of data dogs who are all chasing the same goal of financial freedom through automation. when we work together and share our findings we can collectively identify edges that nobody else is looking at. the world is moving towards an ai dominated economy and if you are not learning to control the machines you are going to be controlled by them the road to automation is not a straight line and you will run into bugs that make you want to throw your computer out the window. but every time you fix an error and every time you optimize a script you are getting one step closer to a life where you own your time. code really is the great equalizer and it is waiting for you to pick it up and start building your own future if you can fly then run and if you can run then walk but whatever you do you must keep moving forward in this journey. trading can be heartless but the logic of code is always fair and consistent. stop being the liquidity for someone else's bot and start building the walls that will protect your capital forever

The 40,000% ROI "Bug": How Claude Code Cracked the TradingView Holy Grail most people think the elite traders at the top of the mountain have some secret indicator or a hidden math formula that gives them a forty thousand percent return. they assume the game is rigged against the small player and that you need a multi million dollar budget just to get a seat at the table. the truth is that the holy grail of trading is actually hidden in plain sight inside a community tab that most people scroll past every single day i spent years losing money to liquidations and over trading because i thought i had to manually predict where the price was going next. i even spent hundreds of thousands of dollars on developers to build apps for me because i was convinced that i would never be able to code the systems myself. it turns out that once you stop trying to be a genius and start using the tools that are already available you can crack the code to unlimited trading strategies the secret is not in a single indicator but in the process of research back test and implement. if you go to the community section of trading view you will find an endless stream of source code for indicators that people have built over decades. most traders just slap these on a chart and hope for the best but if you are a data dog like me you know that a chart is just a pretty picture that lies to you i believe that code is the great equalizer because it allows us to take these public ideas and turn them into fully automated systems that trade for us while we sleep. i decided to learn to code live on youtube to show everyone that you can iterate your way to success without being a math wizard or a stanford graduate. now i have fully automated systems that manage my capital instead of getting liquidated by emotional decisions in the middle of the night the biggest trap in the trading world is something called repainting and it is the reason why so many strategy back tests look like they are printing money when they are actually just a scam. repainting happens when an indicator looks at future data to tell you what happened in the past which makes every buy and sell signal look like a perfect entry at the top and bottom. if you trust a back test on a basic chart without understanding the logic underneath you are just building a house on a foundation of sand this is why i transitioned all of my serious work into python because python does not lie to you. in python you can control the data flow tick by tick and bar by bar to ensure that no future data is leaking into your strategy. i built a back test architect which is a specialized sub agent that knows exactly how to take a simple idea and test it against twenty five different data sources all at once when you run a strategy across btc eth apple google and tesla you start to see the real truth about whether a strategy has an edge or if it was just a lucky fluke on one chart. i saw one strategy this week that showed a one million percent return which sounds like a total lie but the data does not have an ego. even if a number looks insane you have to investigate it and incubate it with tiny size to see if it holds up in the live market you must treat your trading like a business where you are the manager and the code is your team of tireless employees. i have sub agents running for me right now that act as masters of specific tasks like converting pine script into python or optimizing exit logic. if you are not using these specialized ai assistants in your workflow you are essentially trying to build a skyscraper with a hand saw while everyone else is using heavy machinery most people get stuck in the beginner phase because they think they need to write every single line of code from scratch. the reality is that the best developers are just really good at importing the hard work of others and connecting it like lego blocks. i use a library called ccxt that allows my bots to communicate with every major exchange in the world with just a few lines of script which saves me months of development time the reason i show everything live is because the industry is filled with gatekeepers who want to keep the secrets of automation to themselves. they want you to stay as a manual trader who pays high fees and provides liquidity for their algorithms. once you learn to automate you are no longer a victim of the market but a participant in the architecture of the financial system if you are sitting there right now feeling defeated because you just got smoked on a trade or you missed a massive pump you have to realize that those emotions are your greatest enemy. a computer does not feel fomo and it does not get tilted after a loss; it just waits for the next signal that fits the parameters you defined. my mission is to help you get to a place where you can walk away from the screen and let the machines do the heavy lifting learning to code is actually much easier than learning a second language because the syntax is logical and the feedback is immediate. i spent ten years in tech scared to touch a keyboard for anything other than emails because i thought i was not smart enough for engineering. once i realized that code is just logic i was able to build my first profitable bot within a few months and i have never looked back the transition from a manual trader to an algorithmic expert is about building a robust framework for testing your ideas as fast as possible. you want to be able to find an indicator on trading view convert it to python and run it against years of historical data in less than five minutes. if you can do that you have a higher chance of success than ninety nine percent of the people who are just drawing lines on a screen one of the most powerful strategies i found recently combines the squeeze momentum indicator with smart money concepts. when you test these individually they might show a decent return but when you combine them and add a filter like the adx you can find setups that have a massive expectancy. the key is to look for strategies that show positive returns across multiple different asset classes and time frames simultaneously even if a strategy looks like it is printing a forty thousand percent return you must always remain skeptical and look for the catch. i always incubate my new ideas with tiny capital for at least a few weeks to see how they handle real world slippage and fees. a back test is a map of the past but the live market is a wilderness that changes every single day this is why i believe in the rbi method which stands for research back test and implement. you spend your mornings looking for new ideas your afternoons stress testing them with ai and your evenings deploying the winners to the market. it is a systematic approach to wealth that removes the need for luck or guessing what a celebrity is going to tweet next the most successful traders in history like jim simons did not sit around looking at rsi levels on a fifteen minute chart. they built systems that identified mathematical edges and then scaled those systems until they were managing billions of dollars. you do not need thirty one billion dollars to change your life but you do need the discipline to stop trading like a human and start thinking like a system i give away so much for free on youtube because i want to build a community of data dogs who are all chasing the same goal of financial freedom through automation. when we work together and share our findings we can collectively identify edges that nobody else is looking at. the world is moving towards an ai dominated economy and if you are not learning to control the machines you are going to be controlled by them the road to automation is not a straight line and you will run into bugs that make you want to throw your computer out the window. but every time you fix an error and every time you optimize a script you are getting one step closer to a life where you own your time. code really is the great equalizer and it is waiting for you to pick it up and start building your own future if you can fly then run and if you can run then walk but whatever you do you must keep moving forward in this journey. trading can be heartless but the logic of code is always fair and consistent. stop being the liquidity for someone else's bot and start building the walls that will protect your capital forever

Moon Dev

245,471 次观看 • 6 个月前