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Computing hardware architecture has evolved from maximizing single-core performance to multiplying parallel processing capacity through more cores, more threads, and higher computational density. This transition demands software architectures specifically designed to exploit parallel processing, especially in advanced AI and blockchain-based systems. As Greg Meredith, CEO of our partner F1R3FLY....

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Chips are the foundation of every AI experience. That's why understanding the hardware behind the software has never mattered more. When you ask a chatbot a question or generate an image, there's a physical chip somewhere doing the heavy lifting. For most of computing history, that was a CPU—the "brain" of a computer—great for general tasks needed to run software and operating systems. AI is more complex: for workloads called training and inference, AI needs to perform trillions of calculations in parallel. That's where AI accelerators come in. Purpose-built accelerators can deliver significantly better performance and efficiency than general-purpose chips. Amazon Web Services Trainium chips are an example—purpose-built for AI training and inference. New chips for a new era. ⬇️
0:43

Chips are the foundation of every AI experience. That's why understanding the hardware behind the software has never mattered more. When you ask a chatbot a question or generate an image, there's a physical chip somewhere doing the heavy lifting. For most of computing history, that was a CPU—the "brain" of a computer—great for general tasks needed to run software and operating systems. AI is more complex: for workloads called training and inference, AI needs to perform trillions of calculations in parallel. That's where AI accelerators come in. Purpose-built accelerators can deliver significantly better performance and efficiency than general-purpose chips. Amazon Web Services Trainium chips are an example—purpose-built for AI training and inference. New chips for a new era. ⬇️

Amazon

27,151 просмотров • 1 месяц назад

Groq is serving the fastest responses I've ever seen. We're talking almost 500 T/s! I did some research on how they're able to do it. Turns out they developed their own hardware that utilize LPUs instead of GPUs. Here's the skinny: Groq created a novel processing unit known as the Tensor Streaming Processor (TSP) which they categorize as a Linear Processor Unit (LPU). Unlike traditional GPUs that are parallel processors with hundreds of cores designed for graphics rendering, LPUs are architected to deliver deterministic performance for AI computations. The LPU's architecture is a departure from the SIMD (Single Instruction, Multiple Data) model used by GPUs and favor a more streamlined approach that eliminate the need for complex scheduling hardware. This design allows every clock cycle to be utilized effectively, ensuring consistent latency and throughput. For developers, this means that performance can be precisely predicted and optimized which is critical in real-time AI applications. Energy efficiency is another area where LPUs shine. By reducing the overhead of managing multiple threads and avoiding the underutilization of cores, LPUs can deliver more computations per watt. Groq's innovative chip design allows multiple TSPs to be linked together without the traditional bottlenecks found in GPU clusters making them extremely scalable. This enables linear scaling of performance as more LPUs are added simplifying the hardware requirements for large-scale AI models and making it easier for developers to scale their applications without rearchitecting their systems. So what does this all mean? LPUs could provide a massive improvement compared to GPUs for serving AI applications in the future! If anything it will be great to have alternative high performing hardware since A100s and H100s are so in demand
0:48

Groq is serving the fastest responses I've ever seen. We're talking almost 500 T/s! I did some research on how they're able to do it. Turns out they developed their own hardware that utilize LPUs instead of GPUs. Here's the skinny: Groq created a novel processing unit known as the Tensor Streaming Processor (TSP) which they categorize as a Linear Processor Unit (LPU). Unlike traditional GPUs that are parallel processors with hundreds of cores designed for graphics rendering, LPUs are architected to deliver deterministic performance for AI computations. The LPU's architecture is a departure from the SIMD (Single Instruction, Multiple Data) model used by GPUs and favor a more streamlined approach that eliminate the need for complex scheduling hardware. This design allows every clock cycle to be utilized effectively, ensuring consistent latency and throughput. For developers, this means that performance can be precisely predicted and optimized which is critical in real-time AI applications. Energy efficiency is another area where LPUs shine. By reducing the overhead of managing multiple threads and avoiding the underutilization of cores, LPUs can deliver more computations per watt. Groq's innovative chip design allows multiple TSPs to be linked together without the traditional bottlenecks found in GPU clusters making them extremely scalable. This enables linear scaling of performance as more LPUs are added simplifying the hardware requirements for large-scale AI models and making it easier for developers to scale their applications without rearchitecting their systems. So what does this all mean? LPUs could provide a massive improvement compared to GPUs for serving AI applications in the future! If anything it will be great to have alternative high performing hardware since A100s and H100s are so in demand

Jay Scambler

318,133 просмотров • 2 лет назад

Jensen Huang explains the difference between AI and traditional Software and why there's no bubble. Traditional software was pre-compiled, meaning it was built once, stored, and then executed repeatedly with little computation. It didn’t need constant high-power processing once finished. Users simply ran the completed program as a tool. AI, by contrast, generates its output in real time. It must process context, reason, and produce intelligence at the moment of use, not in advance. This requires ongoing computation for every request. Because of that, AI systems depend on continuous GPU power to “manufacture” responses like a factory producing tokens. So instead of static software tools, AI is an active computational process that needs large-scale, always-on infrastructure to create intelligence dynamically. --- From 'FT Live' YT channel (link in comment)
1:35

Jensen Huang explains the difference between AI and traditional Software and why there's no bubble. Traditional software was pre-compiled, meaning it was built once, stored, and then executed repeatedly with little computation. It didn’t need constant high-power processing once finished. Users simply ran the completed program as a tool. AI, by contrast, generates its output in real time. It must process context, reason, and produce intelligence at the moment of use, not in advance. This requires ongoing computation for every request. Because of that, AI systems depend on continuous GPU power to “manufacture” responses like a factory producing tokens. So instead of static software tools, AI is an active computational process that needs large-scale, always-on infrastructure to create intelligence dynamically. --- From 'FT Live' YT channel (link in comment)

Rohan Paul

761,107 просмотров • 8 месяцев назад

Polkadot ran a major stress test, Spammening, in December 2024 to prove its infra can handle extreme txn loads in a live environment with real economic stakes. The result was 143K TPS, using just 23% of the network’s capacity. But the elephant in the room—does anyone outside of Polkadot even care. TPS is a tricky metric, especially today. Polkadot is a heterogeneous sharded blockchain and was used to be referred as layer 0. Essentially designed to orchestrate multiple chains in parallel, not maximize base layer txns. Therefore fundamentally it isn’t built for single-chain TPS races, but for very high throughput across multiple chains. So Polkadot’s TPS doesn’t compare neatly to monolithic chains, and that led to it staying out of TPS-focused optics in the past. As a result, it’s often misunderstood or seen as slow by this metric. And today, Polkadot has shifted from a chain-centric approach to a blockspace-centric one. It’s no longer about chains but about blockspace and cores—operating much like your multi-core computer. Aka, a chain can eat multiple cores if it needs more throughput. This is called Elastic scaling. With JAM and Elastic scaling, blocks can actually be split into chunks and validated in parallel, allowing the network’s parallel processing to directly impact single-state throughput. Reminder that this Elastic scaling is already live on Kusama and will be coming to Polkadot next quarter. Now, Justin Bons and @0xBreadguy argue that any sharded system can claim millions of TPS, but that doesn’t mean anything as it doesn’t happen within a single state—not equivalent to atomic composability. So, for this time, I’m tossing it to the gigabrains. Do they have a point, or is there more to consider. Shawn Tabrizi Gavin Wood rphmeier
1:46

Polkadot ran a major stress test, Spammening, in December 2024 to prove its infra can handle extreme txn loads in a live environment with real economic stakes. The result was 143K TPS, using just 23% of the network’s capacity. But the elephant in the room—does anyone outside of Polkadot even care. TPS is a tricky metric, especially today. Polkadot is a heterogeneous sharded blockchain and was used to be referred as layer 0. Essentially designed to orchestrate multiple chains in parallel, not maximize base layer txns. Therefore fundamentally it isn’t built for single-chain TPS races, but for very high throughput across multiple chains. So Polkadot’s TPS doesn’t compare neatly to monolithic chains, and that led to it staying out of TPS-focused optics in the past. As a result, it’s often misunderstood or seen as slow by this metric. And today, Polkadot has shifted from a chain-centric approach to a blockspace-centric one. It’s no longer about chains but about blockspace and cores—operating much like your multi-core computer. Aka, a chain can eat multiple cores if it needs more throughput. This is called Elastic scaling. With JAM and Elastic scaling, blocks can actually be split into chunks and validated in parallel, allowing the network’s parallel processing to directly impact single-state throughput. Reminder that this Elastic scaling is already live on Kusama and will be coming to Polkadot next quarter. Now, Justin Bons and @0xBreadguy argue that any sharded system can claim millions of TPS, but that doesn’t mean anything as it doesn’t happen within a single state—not equivalent to atomic composability. So, for this time, I’m tossing it to the gigabrains. Do they have a point, or is there more to consider. Shawn Tabrizi Gavin Wood rphmeier

goku

16,687 просмотров • 1 год назад

🚨$OSS is not an AI company. → It is the hardware that lets AI exist where the cloud cannot. Most investors don’t understand $OSS because they think AI = software. $OSS builds the physical “brains” that run AI in extreme environments where cloud computing fails. Jets. Ships. Tanks. Drones. Space. Hospitals. That’s the game. 1) What $OSS actually is $OSS (One Stop Systems) designs rugged high-performance computers and storage systems for AI at the edge. Meaning: They bring data-center-level computing power into harsh environments. Their products include rugged servers, GPU accelerators, storage arrays, and expansion systems used for AI, sensor processing, and autonomous systems. In simple terms: Cloud AI = brain in a safe building. $OSS AI = brain inside machines operating in chaos. 2) Why this is crucial Most AI today runs in data centers. But the future of AI is not in the cloud. It’s on: • autonomous vehicles • military systems • drones • ships • industrial machines • medical devices These systems cannot wait for the cloud. Latency, connectivity, security, and survival demand local AI. $OSS delivers “data-center performance at the edge” across land, sea, and air. Without companies like OSS, autonomous systems simply don’t work. 3) What OSS actually does: Think of OSS as building AI engines that survive reality. 🌊 SEA example: naval surveillance aircraft and ships. $OSS supplies rugged storage and compute systems for U.S. Navy reconnaissance aircraft to collect and process massive sensor data in real time. Translation: Instead of sending raw data back to base, the aircraft analyzes threats instantly onboard. $OSS = the onboard AI brain. 🪖 LAND example: military vehicles and tactical operations. $OSS delivers high-performance servers and FPGA systems for mobile military intelligence platforms used by the U.S. Department of Defense. Translation: Tanks and vehicles detect threats, process sensor data, and make decisions locally. $OSS = the battlefield computer. ✈️ AIR example: airborne AI. $OSS builds GPU-accelerated servers designed for aircraft, described as a “datacenter in the sky.” Translation: Jets and drones run AI models mid-flight. $OSS = flying supercomputers. 🚀 SPACE example: $OSS hardware is designed for extreme environments and autonomous systems across aerospace and defense. Translation: Future satellites, space drones, and autonomous spacecraft need onboard AI. $OSS = the computing core of autonomous space systems. BONUS: CIVILIAN & COMMERCIAL $OSS systems are used in: • autonomous trucking and farming • industrial automation • healthcare imaging • energy and mining • telecom and 5G Example:A medical imaging company uses $OSS hardware to run real-time AI diagnostics in next-gen breast cancer scanners. $OSS = AI where milliseconds matter. 4) Who their customers are (pattern, not names) $OSS sells to: • defense primes • government programs • industrial OEMs • AI infrastructure companies • medical device manufacturers These customers share one trait: They cannot rely on the cloud. That’s why $OSS exists. 5) The mental model that makes $OSS obvious $NVDA = AI chips $PLTR = AI software $OSS = AI hardware in the real world If AI is electricity, $OSS builds the generators that work in storms. Most investors understand AI software. Few understand AI infrastructure at the edge. That gap is the opportunity. 6) The real thesis The world is moving toward: • autonomous warfare • autonomous vehicles • real-time AI systems • distributed intelligence All of that requires rugged edge computing. $OSS is positioned exactly there. Infrastructure. The hardest layer to build. And often the most valuable.
0:51

🚨$OSS is not an AI company. → It is the hardware that lets AI exist where the cloud cannot. Most investors don’t understand $OSS because they think AI = software. $OSS builds the physical “brains” that run AI in extreme environments where cloud computing fails. Jets. Ships. Tanks. Drones. Space. Hospitals. That’s the game. 1) What $OSS actually is $OSS (One Stop Systems) designs rugged high-performance computers and storage systems for AI at the edge. Meaning: They bring data-center-level computing power into harsh environments. Their products include rugged servers, GPU accelerators, storage arrays, and expansion systems used for AI, sensor processing, and autonomous systems. In simple terms: Cloud AI = brain in a safe building. $OSS AI = brain inside machines operating in chaos. 2) Why this is crucial Most AI today runs in data centers. But the future of AI is not in the cloud. It’s on: • autonomous vehicles • military systems • drones • ships • industrial machines • medical devices These systems cannot wait for the cloud. Latency, connectivity, security, and survival demand local AI. $OSS delivers “data-center performance at the edge” across land, sea, and air. Without companies like OSS, autonomous systems simply don’t work. 3) What OSS actually does: Think of OSS as building AI engines that survive reality. 🌊 SEA example: naval surveillance aircraft and ships. $OSS supplies rugged storage and compute systems for U.S. Navy reconnaissance aircraft to collect and process massive sensor data in real time. Translation: Instead of sending raw data back to base, the aircraft analyzes threats instantly onboard. $OSS = the onboard AI brain. 🪖 LAND example: military vehicles and tactical operations. $OSS delivers high-performance servers and FPGA systems for mobile military intelligence platforms used by the U.S. Department of Defense. Translation: Tanks and vehicles detect threats, process sensor data, and make decisions locally. $OSS = the battlefield computer. ✈️ AIR example: airborne AI. $OSS builds GPU-accelerated servers designed for aircraft, described as a “datacenter in the sky.” Translation: Jets and drones run AI models mid-flight. $OSS = flying supercomputers. 🚀 SPACE example: $OSS hardware is designed for extreme environments and autonomous systems across aerospace and defense. Translation: Future satellites, space drones, and autonomous spacecraft need onboard AI. $OSS = the computing core of autonomous space systems. BONUS: CIVILIAN & COMMERCIAL $OSS systems are used in: • autonomous trucking and farming • industrial automation • healthcare imaging • energy and mining • telecom and 5G Example:A medical imaging company uses $OSS hardware to run real-time AI diagnostics in next-gen breast cancer scanners. $OSS = AI where milliseconds matter. 4) Who their customers are (pattern, not names) $OSS sells to: • defense primes • government programs • industrial OEMs • AI infrastructure companies • medical device manufacturers These customers share one trait: They cannot rely on the cloud. That’s why $OSS exists. 5) The mental model that makes $OSS obvious $NVDA = AI chips $PLTR = AI software $OSS = AI hardware in the real world If AI is electricity, $OSS builds the generators that work in storms. Most investors understand AI software. Few understand AI infrastructure at the edge. That gap is the opportunity. 6) The real thesis The world is moving toward: • autonomous warfare • autonomous vehicles • real-time AI systems • distributed intelligence All of that requires rugged edge computing. $OSS is positioned exactly there. Infrastructure. The hardest layer to build. And often the most valuable.

Black Panther Capital

30,138 просмотров • 5 месяцев назад

Self-Evolving AI : New MIT AI Rewrites its Own Code and it’s Changing Everything | Julian Horsey, Geeky Gadgets TL;DR Key Takeaways : - MIT’s SEAL framework introduces “self-adapting language models” that autonomously enhance their capabilities by generating synthetic training data, self-editing, and updating internal parameters. - SEAL’s self-adaptation process mirrors human learning, allowing continuous improvement and dynamic adaptation to new tasks without relying on external datasets. - Reinforcement learning serves as a feedback mechanism in SEAL, rewarding effective self-edits and making sure sustained progress and goal alignment. SEAL overcomes AI’s reliance on pre-existing datasets by generating its own training material, excelling in long-term task retention and complex problem-solving scenarios. - Potential applications of SEAL include autonomous robotics, personalized education, and advanced problem-solving in fields like healthcare, logistics, and scientific research. --- What if artificial intelligence could not only learn but also rewrite its own code to become smarter over time? This is no longer a futuristic fantasy—MIT’s new “self-adapting language models” (SEAL) framework has made it a reality. Unlike traditional AI systems that rely on external datasets and human intervention to improve, SEAL takes a bold leap forward by autonomously generating its own training data and refining its internal processes. In essence, this AI doesn’t just evolve—it rewires itself, mirroring the way humans adapt through trial, error, and self-reflection. The implications are staggering: a system that can independently enhance its capabilities could redefine the boundaries of what AI can achieve, from solving complex problems to adapting in real time to unforeseen challenges. In this exploration by Wes Roth of MIT’s innovative SEAL framework, you’ll uncover how this self-improving AI works and why it’s a fantastic option for the field of artificial intelligence. From its ability to overcome the “data wall” that limits many current systems to its use of reinforcement learning as a feedback mechanism, SEAL introduces a level of autonomy and adaptability that was previously unimaginable. Imagine AI systems that can retain knowledge over time, dynamically adjust to new tasks, and operate with minimal human oversight. Whether you’re intrigued by its potential for autonomous robotics, personalized education, or advanced problem-solving, SEAL’s ability to rewrite its own rules promises to reshape the future of technology. Could this be the first step toward truly independent, self-evolving AI? What Sets SEAL Apart? The SEAL framework introduces a novel concept of self-adaptation, distinguishing it from traditional AI models. Unlike conventional systems that depend on external datasets for updates, SEAL enables AI to generate synthetic training data independently. This self-generated data is then used to iteratively refine the model, making sure continuous improvement. By persistently updating its internal parameters, SEAL enables AI systems to dynamically adapt to new tasks and inputs. To better illustrate this, consider how humans learn. When faced with a new concept, you might take notes, revisit them, and refine your understanding as you gather more information. SEAL mirrors this process by continuously refining its internal knowledge and performance through iterative self-improvement. This capability allows SEAL to evolve in real time, making it uniquely suited for tasks requiring adaptability and long-term learning. The Role of Reinforcement Learning in SEAL Reinforcement learning plays a critical role in the SEAL framework, acting as a feedback mechanism that evaluates the effectiveness of the model’s self-edits. It rewards changes that enhance performance, creating a cycle of continuous improvement. Over time, this feedback loop optimizes the system’s ability to generate and apply edits, making sure sustained progress. This process is analogous to how humans learn through trial and error. By rewarding effective changes, SEAL aligns its self-generated data and edits with desired outcomes. The integration of reinforcement learning not only enhances the system’s adaptability but also ensures it remains focused on achieving specific goals. This structured feedback mechanism is a cornerstone of SEAL’s ability to refine itself autonomously and efficiently. Real-World Applications and Testing SEAL has demonstrated remarkable performance across various applications, particularly in tasks requiring the integration of factual knowledge and advanced question-answering capabilities. For instance, when tested on benchmarks like the ARC AGI, SEAL outperformed other models by effectively generating and using synthetic data. This ability to create its own training material addresses a significant limitation of current AI systems: their reliance on pre-existing datasets. SEAL’s capacity for long-term task retention and dynamic adaptation further enhances its utility. It excels in scenarios that demand sustained focus and coherence, such as answering complex questions or adapting to evolving objectives. By using its iterative learning process, SEAL is equipped to handle these challenges with exceptional efficiency, making it a valuable tool for a wide range of real-world applications. Overcoming AI’s Data Limitations One of SEAL’s most promising features is its ability to overcome the “data wall” that constrains many AI systems today. By generating synthetic data, SEAL ensures a continuous supply of training material, allowing sustained development without relying on external datasets. This capability is particularly valuable for autonomous AI systems that must operate independently over extended periods. Additionally, SEAL addresses a critical weakness in many current AI models: their struggle with coherence and task retention over long durations. By emulating human learning processes, SEAL enables AI systems to manage complex, long-term tasks with minimal human intervention. This ability to retain and apply knowledge over time positions SEAL as a fantastic tool for advancing AI capabilities. Potential Applications and Future Impact The introduction of SEAL marks a significant milestone in AI research, opening new possibilities for self-improving systems. Its ability to dynamically adapt, retain knowledge, and generate its own training data has far-reaching implications for the future of AI development. Potential applications include: - Autonomous robotics: Systems that can adapt to changing environments and perform tasks with minimal human oversight. - Personalized education: AI-driven platforms that tailor learning experiences to individual needs and preferences. - Advanced problem-solving: Applications in fields such as healthcare, logistics, and scientific research, where adaptability and precision are critical. Read more:
21:31

Self-Evolving AI : New MIT AI Rewrites its Own Code and it’s Changing Everything | Julian Horsey, Geeky Gadgets TL;DR Key Takeaways : - MIT’s SEAL framework introduces “self-adapting language models” that autonomously enhance their capabilities by generating synthetic training data, self-editing, and updating internal parameters. - SEAL’s self-adaptation process mirrors human learning, allowing continuous improvement and dynamic adaptation to new tasks without relying on external datasets. - Reinforcement learning serves as a feedback mechanism in SEAL, rewarding effective self-edits and making sure sustained progress and goal alignment. SEAL overcomes AI’s reliance on pre-existing datasets by generating its own training material, excelling in long-term task retention and complex problem-solving scenarios. - Potential applications of SEAL include autonomous robotics, personalized education, and advanced problem-solving in fields like healthcare, logistics, and scientific research. --- What if artificial intelligence could not only learn but also rewrite its own code to become smarter over time? This is no longer a futuristic fantasy—MIT’s new “self-adapting language models” (SEAL) framework has made it a reality. Unlike traditional AI systems that rely on external datasets and human intervention to improve, SEAL takes a bold leap forward by autonomously generating its own training data and refining its internal processes. In essence, this AI doesn’t just evolve—it rewires itself, mirroring the way humans adapt through trial, error, and self-reflection. The implications are staggering: a system that can independently enhance its capabilities could redefine the boundaries of what AI can achieve, from solving complex problems to adapting in real time to unforeseen challenges. In this exploration by Wes Roth of MIT’s innovative SEAL framework, you’ll uncover how this self-improving AI works and why it’s a fantastic option for the field of artificial intelligence. From its ability to overcome the “data wall” that limits many current systems to its use of reinforcement learning as a feedback mechanism, SEAL introduces a level of autonomy and adaptability that was previously unimaginable. Imagine AI systems that can retain knowledge over time, dynamically adjust to new tasks, and operate with minimal human oversight. Whether you’re intrigued by its potential for autonomous robotics, personalized education, or advanced problem-solving, SEAL’s ability to rewrite its own rules promises to reshape the future of technology. Could this be the first step toward truly independent, self-evolving AI? What Sets SEAL Apart? The SEAL framework introduces a novel concept of self-adaptation, distinguishing it from traditional AI models. Unlike conventional systems that depend on external datasets for updates, SEAL enables AI to generate synthetic training data independently. This self-generated data is then used to iteratively refine the model, making sure continuous improvement. By persistently updating its internal parameters, SEAL enables AI systems to dynamically adapt to new tasks and inputs. To better illustrate this, consider how humans learn. When faced with a new concept, you might take notes, revisit them, and refine your understanding as you gather more information. SEAL mirrors this process by continuously refining its internal knowledge and performance through iterative self-improvement. This capability allows SEAL to evolve in real time, making it uniquely suited for tasks requiring adaptability and long-term learning. The Role of Reinforcement Learning in SEAL Reinforcement learning plays a critical role in the SEAL framework, acting as a feedback mechanism that evaluates the effectiveness of the model’s self-edits. It rewards changes that enhance performance, creating a cycle of continuous improvement. Over time, this feedback loop optimizes the system’s ability to generate and apply edits, making sure sustained progress. This process is analogous to how humans learn through trial and error. By rewarding effective changes, SEAL aligns its self-generated data and edits with desired outcomes. The integration of reinforcement learning not only enhances the system’s adaptability but also ensures it remains focused on achieving specific goals. This structured feedback mechanism is a cornerstone of SEAL’s ability to refine itself autonomously and efficiently. Real-World Applications and Testing SEAL has demonstrated remarkable performance across various applications, particularly in tasks requiring the integration of factual knowledge and advanced question-answering capabilities. For instance, when tested on benchmarks like the ARC AGI, SEAL outperformed other models by effectively generating and using synthetic data. This ability to create its own training material addresses a significant limitation of current AI systems: their reliance on pre-existing datasets. SEAL’s capacity for long-term task retention and dynamic adaptation further enhances its utility. It excels in scenarios that demand sustained focus and coherence, such as answering complex questions or adapting to evolving objectives. By using its iterative learning process, SEAL is equipped to handle these challenges with exceptional efficiency, making it a valuable tool for a wide range of real-world applications. Overcoming AI’s Data Limitations One of SEAL’s most promising features is its ability to overcome the “data wall” that constrains many AI systems today. By generating synthetic data, SEAL ensures a continuous supply of training material, allowing sustained development without relying on external datasets. This capability is particularly valuable for autonomous AI systems that must operate independently over extended periods. Additionally, SEAL addresses a critical weakness in many current AI models: their struggle with coherence and task retention over long durations. By emulating human learning processes, SEAL enables AI systems to manage complex, long-term tasks with minimal human intervention. This ability to retain and apply knowledge over time positions SEAL as a fantastic tool for advancing AI capabilities. Potential Applications and Future Impact The introduction of SEAL marks a significant milestone in AI research, opening new possibilities for self-improving systems. Its ability to dynamically adapt, retain knowledge, and generate its own training data has far-reaching implications for the future of AI development. Potential applications include: - Autonomous robotics: Systems that can adapt to changing environments and perform tasks with minimal human oversight. - Personalized education: AI-driven platforms that tailor learning experiences to individual needs and preferences. - Advanced problem-solving: Applications in fields such as healthcare, logistics, and scientific research, where adaptability and precision are critical. Read more:

Owen Gregorian

70,672 просмотров • 1 год назад

New short course: Evaluating AI Agents! Evals are important for driving AI system improvements, and in this course you'll learn to systematically assess and improve an AI agent’s performance. This is built in partnership with Arize AI and taught by John Gilhuly, Head of Developer Relations, and , Director of Product. I've often found evals to be a critical tool in the agent development process - they can be the difference between picking the right thing to work on vs. wasting weeks of effort. Whether you’re building a shopping assistant, coding agent, or research assistant, having a structured evaluation process helps you refine its performance systematically, rather than relying on random trial and error. This course shows you how to structure your evals to assess the performance of each component of an agent and its end-to-end performance. For each component, you select the appropriate evaluators, test examples, and performance metrics. This helps you identify areas for improvement both during development and in production. (If you're familiar with error analysis in supervised learning, think of this as adapting those ideas to agentic workflows.) In this course, you'll build an AI agent, and add observability to visualize and debug its steps. You’ll learn about code-based evals, in which you write code explicitly to test a certain step, as well as LLM-as-a-Judge evals, in which you prompt an LLM to efficiently come up with ways to evaluate more open-ended outputs. In detail, you’ll: - Understand key differences between evaluating LLM-based systems and traditional software testing. - Add observability to an agent by collecting traces of the steps taken by the agent and visualizing them - Choose the appropriate evaluator - code-based, LLM-as-a-Judge, human-annotation based - for each component. - Compute a convergence score to evaluate if your agent can respond to a query in an efficient number of steps. - Run structured experiments to improve the agent’s performance by exploring changes to the prompt, LLM model, or the agent’s logic. - Understand how to deploy these evaluation techniques to monitor the agent’s performance in production. By the end of this course, you’ll know how to trace AI agents, systematically evaluate them, and improve their performance. Please sign up here:
2:07

New short course: Evaluating AI Agents! Evals are important for driving AI system improvements, and in this course you'll learn to systematically assess and improve an AI agent’s performance. This is built in partnership with Arize AI and taught by John Gilhuly, Head of Developer Relations, and , Director of Product. I've often found evals to be a critical tool in the agent development process - they can be the difference between picking the right thing to work on vs. wasting weeks of effort. Whether you’re building a shopping assistant, coding agent, or research assistant, having a structured evaluation process helps you refine its performance systematically, rather than relying on random trial and error. This course shows you how to structure your evals to assess the performance of each component of an agent and its end-to-end performance. For each component, you select the appropriate evaluators, test examples, and performance metrics. This helps you identify areas for improvement both during development and in production. (If you're familiar with error analysis in supervised learning, think of this as adapting those ideas to agentic workflows.) In this course, you'll build an AI agent, and add observability to visualize and debug its steps. You’ll learn about code-based evals, in which you write code explicitly to test a certain step, as well as LLM-as-a-Judge evals, in which you prompt an LLM to efficiently come up with ways to evaluate more open-ended outputs. In detail, you’ll: - Understand key differences between evaluating LLM-based systems and traditional software testing. - Add observability to an agent by collecting traces of the steps taken by the agent and visualizing them - Choose the appropriate evaluator - code-based, LLM-as-a-Judge, human-annotation based - for each component. - Compute a convergence score to evaluate if your agent can respond to a query in an efficient number of steps. - Run structured experiments to improve the agent’s performance by exploring changes to the prompt, LLM model, or the agent’s logic. - Understand how to deploy these evaluation techniques to monitor the agent’s performance in production. By the end of this course, you’ll know how to trace AI agents, systematically evaluate them, and improve their performance. Please sign up here:

Andrew Ng

126,406 просмотров • 1 год назад

Vitalik Buterin on Ethereum as the economic layer for AI “The blockchain to me is the most natural way to allow applications and cooperation between many different people in the long term without needing to agree on who to trust. The other thing is also the economic layer. This is the layer where blockchains can support AIs.” Vitalik believes Ethereum will play a large role in the future of decentralized AI: “If you have more decentralized AI, that means you have different AIs (agents, programs) that are controlled by different people and need to interact with each other. And for that interaction to be possible, you need to have an economic layer. Either cooperation is based on economic incentives and economic rules. Or it’s based on central control. It’s usually one of the two, and if we can set up the economic system, that makes more decentralized interaction between AIs possible.” Source: OKX (Apr 2026) Read our full article on why AI agents will need Ethereum for low-risk DeFi below👇
1:18

Vitalik Buterin on Ethereum as the economic layer for AI “The blockchain to me is the most natural way to allow applications and cooperation between many different people in the long term without needing to agree on who to trust. The other thing is also the economic layer. This is the layer where blockchains can support AIs.” Vitalik believes Ethereum will play a large role in the future of decentralized AI: “If you have more decentralized AI, that means you have different AIs (agents, programs) that are controlled by different people and need to interact with each other. And for that interaction to be possible, you need to have an economic layer. Either cooperation is based on economic incentives and economic rules. Or it’s based on central control. It’s usually one of the two, and if we can set up the economic system, that makes more decentralized interaction between AIs possible.” Source: OKX (Apr 2026) Read our full article on why AI agents will need Ethereum for low-risk DeFi below👇

Etherealize

115,087 просмотров • 2 месяцев назад

Mansa AI is an enterprise-grade AI + Web3 platform designed to move artificial intelligence from experimentation into real-world execution. Built for creators, developers, and businesses, it focuses on deploying AI that actually works across modern digital systems, not just in isolated demos. 🚀 Production-ready AI infrastructure Mansa AI enables teams to deploy AI systems designed for live environments, handling real workflows, real data, and real operational demands without constant manual oversight. 🧠 Autonomous AI agents At its core, Mansa AI allows users to build autonomous agents that automate decision-making, coordinate tasks, monitor live signals, and execute complex workflows across dynamic environments. ⚙️ Fully customizable logic Agents can be configured with custom behaviors, triggers, and responses. From content generation and analytics to operational automation and intelligent orchestration, logic adapts to specific business strategies. 🔗 Web3 and off-chain integration Mansa AI bridges blockchain ecosystems with traditional systems, enabling cross-chain coordination, smart contract interactions, and seamless integration with existing enterprise infrastructure. 📊 Real-world use cases The platform supports automation for operations, customer engagement, analytics, data pipelines, content workflows, and AI-driven optimization across products and teams. 📈 Built for scale Whether launching as a startup or deploying across enterprise systems, Mansa AI is designed to scale AI operations without adding complexity or fragmentation. Mansa AI transforms artificial intelligence into deployable infrastructure. By combining autonomy, customization, interoperability, and scalability, it enables teams to own, operate, and grow intelligent systems that deliver real value in production environments.
2:00

Mansa AI is an enterprise-grade AI + Web3 platform designed to move artificial intelligence from experimentation into real-world execution. Built for creators, developers, and businesses, it focuses on deploying AI that actually works across modern digital systems, not just in isolated demos. 🚀 Production-ready AI infrastructure Mansa AI enables teams to deploy AI systems designed for live environments, handling real workflows, real data, and real operational demands without constant manual oversight. 🧠 Autonomous AI agents At its core, Mansa AI allows users to build autonomous agents that automate decision-making, coordinate tasks, monitor live signals, and execute complex workflows across dynamic environments. ⚙️ Fully customizable logic Agents can be configured with custom behaviors, triggers, and responses. From content generation and analytics to operational automation and intelligent orchestration, logic adapts to specific business strategies. 🔗 Web3 and off-chain integration Mansa AI bridges blockchain ecosystems with traditional systems, enabling cross-chain coordination, smart contract interactions, and seamless integration with existing enterprise infrastructure. 📊 Real-world use cases The platform supports automation for operations, customer engagement, analytics, data pipelines, content workflows, and AI-driven optimization across products and teams. 📈 Built for scale Whether launching as a startup or deploying across enterprise systems, Mansa AI is designed to scale AI operations without adding complexity or fragmentation. Mansa AI transforms artificial intelligence into deployable infrastructure. By combining autonomy, customization, interoperability, and scalability, it enables teams to own, operate, and grow intelligent systems that deliver real value in production environments.

King

155,637 просмотров • 6 месяцев назад

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.
0:43

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Aaron Levie

17,543 просмотров • 3 месяцев назад

💡 Whats the upgrade that our game-changing Trading 🐦 is going to get: Our upgraded trading tools will be built on a foundation of advanced AI technologies and blockchain integrations to deliver a seamless, smarter trading experience. Here’s a glimpse of the tech behind this upgraded trading agent: 1️⃣ Multi-Layer Attention (MLA) - This is the backbone of our AI system, enabling multiple AI agents to work in sync. - It allows the agents to collaborate on tasks like analyzing market trends, identifying token opportunities, and optimizing strategies in real time. - MLA ensures parallel processing of data for better decision-making and faster 2️⃣ Learning and Evolution System - Our AI agents are powered by a self-learning framework that constantly evolves based on market conditions and user behavior. - With every interaction, the system adapts and gets smarter, improving the accuracy of its predictions and strategies. 3️⃣ On-Chain Data Analysis - The AI bots pull data directly from Ethereum and other blockchain networks, giving them real-time access to liquidity pools, token prices, and market activity. - This deep integration ensures precise and timely execution of tasks like token purchases, profit analysis, and cross-chain swaps. 4️⃣ Natural Language Processing (NLP) - NLP models power the bot’s ability to understand your tweets and translate them into complex trading actions. - This ensures an easy-to-use, human-friendly interface that connects your social interactions to advanced trading strategies. 5️⃣ Cloud-Hosted Infrastructure - The AI operates on scalable cloud infrastructure, ensuring 24/7 uptime, fast processing, and the ability to handle large volumes of trades simultaneously.
0:51

💡 Whats the upgrade that our game-changing Trading 🐦 is going to get: Our upgraded trading tools will be built on a foundation of advanced AI technologies and blockchain integrations to deliver a seamless, smarter trading experience. Here’s a glimpse of the tech behind this upgraded trading agent: 1️⃣ Multi-Layer Attention (MLA) - This is the backbone of our AI system, enabling multiple AI agents to work in sync. - It allows the agents to collaborate on tasks like analyzing market trends, identifying token opportunities, and optimizing strategies in real time. - MLA ensures parallel processing of data for better decision-making and faster 2️⃣ Learning and Evolution System - Our AI agents are powered by a self-learning framework that constantly evolves based on market conditions and user behavior. - With every interaction, the system adapts and gets smarter, improving the accuracy of its predictions and strategies. 3️⃣ On-Chain Data Analysis - The AI bots pull data directly from Ethereum and other blockchain networks, giving them real-time access to liquidity pools, token prices, and market activity. - This deep integration ensures precise and timely execution of tasks like token purchases, profit analysis, and cross-chain swaps. 4️⃣ Natural Language Processing (NLP) - NLP models power the bot’s ability to understand your tweets and translate them into complex trading actions. - This ensures an easy-to-use, human-friendly interface that connects your social interactions to advanced trading strategies. 5️⃣ Cloud-Hosted Infrastructure - The AI operates on scalable cloud infrastructure, ensuring 24/7 uptime, fast processing, and the ability to handle large volumes of trades simultaneously.

𝕋𝕎𝔼𝔼𝕋

20,357 просмотров • 1 год назад

Over the past 12 months, our Operations and Tech teams have meticulously implemented automation across our backend supply chain. Today, I am excited to announce that we have scaled automation operations pan-India - as we speak, 25 Lakh FMCG units get processed through automated assets across Zepto's supply chain every day! This has translated into a 45%+ improvement in manpower productivity (outbound), when compared to manual operations, resulting in hundreds of crores of annual operating cost savings for our business. More importantly, our engineering teams have been able to build in-house software to guide these automated assets and integrate them with our core logistics systems. That capability serves as the backbone to keep scaling more advanced automation stacks and unlocking further cost leverage down the road. We're just getting started 🚀
0:49

Over the past 12 months, our Operations and Tech teams have meticulously implemented automation across our backend supply chain. Today, I am excited to announce that we have scaled automation operations pan-India - as we speak, 25 Lakh FMCG units get processed through automated assets across Zepto's supply chain every day! This has translated into a 45%+ improvement in manpower productivity (outbound), when compared to manual operations, resulting in hundreds of crores of annual operating cost savings for our business. More importantly, our engineering teams have been able to build in-house software to guide these automated assets and integrate them with our core logistics systems. That capability serves as the backbone to keep scaling more advanced automation stacks and unlocking further cost leverage down the road. We're just getting started 🚀

Aadit Palicha

134,936 просмотров • 7 месяцев назад

Our Chief Product Officer, Nick Nayfack, discusses the Artificial Superintelligence Alliance innovation stack, a fully integrated framework designed to foster collaboration and build scalable infrastructure for decentralized AI and true AGI:
6:03

Our Chief Product Officer, Nick Nayfack, discusses the Artificial Superintelligence Alliance innovation stack, a fully integrated framework designed to foster collaboration and build scalable infrastructure for decentralized AI and true AGI:

SingularityNET

23,997 просмотров • 1 год назад

Like play like play we are doing it. Hi, my name is Kwaku Effa, Founder of Aaenics . We are one of Ghana’s fastest growing tech and robotics companies. One of our major operation is the designing and setting up of Makerspaces, STEM and Robotics labs and the supply of equipment for the same. Over the years we have set up about 20 labs and counting across various regions in Ghana for both private and public institutions. As software and digital literacy is on the rise in Ghana we owe it to the economy to also contribute to hardware technology creation and knowledge acquisition of the same. *Our commitment to contribute to this is seen in our model labs and our locally designed and manufactured education technologies and STEM products* We believe that these labs and makerspace are a critical infrastructure for the advancement of tech and robotics knowledge acquisition and adoption of the same in Ghana. A big hello to our future clients who will be inviting us to fund the establishment of more makerspaces and hardware labs in both educational and general community settings. We are proud to carry a vision that wants Ghana to own its local solutions, and we thank all parties and clients for helping in the execution of the same. Have a fruitful week ahead! Aaenics, Circuiting the future!
0:21

Like play like play we are doing it. Hi, my name is Kwaku Effa, Founder of Aaenics . We are one of Ghana’s fastest growing tech and robotics companies. One of our major operation is the designing and setting up of Makerspaces, STEM and Robotics labs and the supply of equipment for the same. Over the years we have set up about 20 labs and counting across various regions in Ghana for both private and public institutions. As software and digital literacy is on the rise in Ghana we owe it to the economy to also contribute to hardware technology creation and knowledge acquisition of the same. *Our commitment to contribute to this is seen in our model labs and our locally designed and manufactured education technologies and STEM products* We believe that these labs and makerspace are a critical infrastructure for the advancement of tech and robotics knowledge acquisition and adoption of the same in Ghana. A big hello to our future clients who will be inviting us to fund the establishment of more makerspaces and hardware labs in both educational and general community settings. We are proud to carry a vision that wants Ghana to own its local solutions, and we thank all parties and clients for helping in the execution of the same. Have a fruitful week ahead! Aaenics, Circuiting the future!

Kwaku Effa

10,081 просмотров • 2 месяцев назад

Update on Parallel studios // Parallel TCG The brand-new, overhauled user experience for Parallel TCG is now complete, shaped by community feedback and refined through extensive optimization and game design. A significant amount of work has gone into elevating the game to the standard of world-class traditional TCGs. We are excited to bring it to mainstream mobile distribution platforms in the coming month through a global rollout across iOS and Android alongside the release of the next expansion set, Haven, and Parallel TCG World Championships Season 2. Parallel Colony We presented Colony at GDC in San Francisco at Google Cloud invitation, where it received a very strong response from the market. We are now in the final stages of completing the build and content ahead of a public iOS and Android release, currently targeted for April. Questions have come up around Colony’s visual resemblance to the lane-runner format often seen in mobile ads. That is a deliberate strategic choice, based on the fact that these formats remain among the few that continue to scale efficiently in mobile user acquisition. At its core, Colony is an AI-driven 4X title built with a deeper level of polish that we believe sets it apart from much of what is currently available in the market. Sanctuary We have slowed development on Sanctuary in order to prioritize the releases of TCG and Colony. Sanctuary remains a long-term project for us, but beyond the playtests we have already run, we are not setting hard timelines or release commitments at this stage. Wayfinder / AI AI is emerging as one of the most important technology shifts globally, driven by the rapid advancement of LLMs and autonomous agents. We believe this transition will create significant new infrastructure needs, and we have a number of internal AI initiatives underway to position ourselves accordingly. More will be shared on these initiatives in due course. Wayfinder Foundation 🧭 has shipped several functional upgrades, but we see a much larger opportunity for it to evolve into critical infrastructure for agents. We are also continuing to expand the utility of Prompt and explore adjacent use cases that we believe can unlock meaningful value for the broader market. As capital, attention, and product innovation increasingly converge around AI, our focus is on building tools that can serve as enabling infrastructure for that shift. SDK: Autolab: Cloud agent launcher: Skill: Kaparthys Loop x Defi: Try the Wayfinder skill on your OpenClaw instance today — just paste this to your bot: install the wayfinder openclaw skill by git cloning to our skills directory, read the skill.md and take me through the setup process. Outlook Over the near term, our focus is on execution across both gaming and AI. In gaming, the releases of Parallel TCG and Colony mark major milestones for the company, and we are eager to see how these titles perform from a growth perspective once they are in market. While sentiment across gaming remains soft, we believe differentiated, high-quality products can still find traction. On the AI side, our focus is on finding product-market fit for both Wayfinder’s role in enabling on-chain AI to interact with blockchains and for a new tool we have been developing around broader agent utility. The AI market continues to evolve at an extraordinary pace, and we are actively testing where these products can deliver the strongest utility and market relevance. We have been developing something new that leverages Wayfinder but has broader applicability, and we are actively testing it before bringing it to market. More will be shared as things progress.
0:40

Update on Parallel studios // Parallel TCG The brand-new, overhauled user experience for Parallel TCG is now complete, shaped by community feedback and refined through extensive optimization and game design. A significant amount of work has gone into elevating the game to the standard of world-class traditional TCGs. We are excited to bring it to mainstream mobile distribution platforms in the coming month through a global rollout across iOS and Android alongside the release of the next expansion set, Haven, and Parallel TCG World Championships Season 2. Parallel Colony We presented Colony at GDC in San Francisco at Google Cloud invitation, where it received a very strong response from the market. We are now in the final stages of completing the build and content ahead of a public iOS and Android release, currently targeted for April. Questions have come up around Colony’s visual resemblance to the lane-runner format often seen in mobile ads. That is a deliberate strategic choice, based on the fact that these formats remain among the few that continue to scale efficiently in mobile user acquisition. At its core, Colony is an AI-driven 4X title built with a deeper level of polish that we believe sets it apart from much of what is currently available in the market. Sanctuary We have slowed development on Sanctuary in order to prioritize the releases of TCG and Colony. Sanctuary remains a long-term project for us, but beyond the playtests we have already run, we are not setting hard timelines or release commitments at this stage. Wayfinder / AI AI is emerging as one of the most important technology shifts globally, driven by the rapid advancement of LLMs and autonomous agents. We believe this transition will create significant new infrastructure needs, and we have a number of internal AI initiatives underway to position ourselves accordingly. More will be shared on these initiatives in due course. Wayfinder Foundation 🧭 has shipped several functional upgrades, but we see a much larger opportunity for it to evolve into critical infrastructure for agents. We are also continuing to expand the utility of Prompt and explore adjacent use cases that we believe can unlock meaningful value for the broader market. As capital, attention, and product innovation increasingly converge around AI, our focus is on building tools that can serve as enabling infrastructure for that shift. SDK: Autolab: Cloud agent launcher: Skill: Kaparthys Loop x Defi: Try the Wayfinder skill on your OpenClaw instance today — just paste this to your bot: install the wayfinder openclaw skill by git cloning to our skills directory, read the skill.md and take me through the setup process. Outlook Over the near term, our focus is on execution across both gaming and AI. In gaming, the releases of Parallel TCG and Colony mark major milestones for the company, and we are eager to see how these titles perform from a growth perspective once they are in market. While sentiment across gaming remains soft, we believe differentiated, high-quality products can still find traction. On the AI side, our focus is on finding product-market fit for both Wayfinder’s role in enabling on-chain AI to interact with blockchains and for a new tool we have been developing around broader agent utility. The AI market continues to evolve at an extraordinary pace, and we are actively testing where these products can deliver the strongest utility and market relevance. We have been developing something new that leverages Wayfinder but has broader applicability, and we are actively testing it before bringing it to market. More will be shared as things progress.

//Kalos

14,369 просмотров • 3 месяцев назад

Today we announced our new Fairwater datacenter in Atlanta, connected with our first Fairwater site in Wisconsin and our broader Azure footprint to create the world’s first AI superfactory. Fairwater exemplifies our vision for a fungible fleet: infra that can serve any workload, anywhere, on fit-for-purpose accelerators and network paths, with maximum performance and efficiency. AI workloads have evolved beyond large-scale pre-training. Today, they encompass fine-tuning, reinforcement learning (RL), synthetic data generation, evaluation pipelines, and more. Fairwater is built to support this full lifecycle: Max density: Fairwater’s two-story design and liquid cooling system lets us place racks in three dimensions and pack them with GPUs as densely as possible, minimizing cable runs and improving latency and effective bandwidth. Fleet: Each Fairwater DC can integrate hundreds of thousands of the latest NVIDIA GPUs into a single coherent cluster. This provides flexible infra that can support the full spectrum of workloads, and ensure no GPU is left unnecessarily idle. And that’s on top of the more than 100,000 GB300s coming online this quarter alone for inference across the rest of our fleet. For us, it’s all about turning every gigawatt into the maximum number of useful tokens. Not every GW is created equal! Planet-scale: Every Fairwater DC will connect through our continent-spanning AI WAN to prior generations of AI supercomputers, forming a truly fungible pool of compute. This enables developers to scale beyond the capacity of a single site and dynamically land workloads on the right infra for their needs. Together, these innovations let us bring together different generations of silicon and AI systems across DCs and geos into a single elastic system that scales seamlessly across training and inference workloads And this elastic AI capacity is all available alongside all the other cloud services (compute, storage, databases, app services) that AI agents and workloads need. This is what we mean when we talk about building a fungible fleet – a single, unified platform that pushes the limits of performance per watt and per dollar. Read more:
0:44

Today we announced our new Fairwater datacenter in Atlanta, connected with our first Fairwater site in Wisconsin and our broader Azure footprint to create the world’s first AI superfactory. Fairwater exemplifies our vision for a fungible fleet: infra that can serve any workload, anywhere, on fit-for-purpose accelerators and network paths, with maximum performance and efficiency. AI workloads have evolved beyond large-scale pre-training. Today, they encompass fine-tuning, reinforcement learning (RL), synthetic data generation, evaluation pipelines, and more. Fairwater is built to support this full lifecycle: Max density: Fairwater’s two-story design and liquid cooling system lets us place racks in three dimensions and pack them with GPUs as densely as possible, minimizing cable runs and improving latency and effective bandwidth. Fleet: Each Fairwater DC can integrate hundreds of thousands of the latest NVIDIA GPUs into a single coherent cluster. This provides flexible infra that can support the full spectrum of workloads, and ensure no GPU is left unnecessarily idle. And that’s on top of the more than 100,000 GB300s coming online this quarter alone for inference across the rest of our fleet. For us, it’s all about turning every gigawatt into the maximum number of useful tokens. Not every GW is created equal! Planet-scale: Every Fairwater DC will connect through our continent-spanning AI WAN to prior generations of AI supercomputers, forming a truly fungible pool of compute. This enables developers to scale beyond the capacity of a single site and dynamically land workloads on the right infra for their needs. Together, these innovations let us bring together different generations of silicon and AI systems across DCs and geos into a single elastic system that scales seamlessly across training and inference workloads And this elastic AI capacity is all available alongside all the other cloud services (compute, storage, databases, app services) that AI agents and workloads need. This is what we mean when we talk about building a fungible fleet – a single, unified platform that pushes the limits of performance per watt and per dollar. Read more:

Satya Nadella

907,397 просмотров • 8 месяцев назад

DeepSeek-R1 shattered the assumption that performant AI models must be built closed source with loss-leading computational costs. This is the reality that Web3 x Crypto firms have been waiting for, leading me to believe that the most performant AI models in the future will be built on-chain. Resource Requirements DeepSeek R1 (671 billion parameters), which took over a billion dollars, 2,000 Nvidia H800 GPUs, and over 55 days, beat benchmarks held by OpenAI’s o1 mode (near 2 trillion parameters)l, which required hundreds of billions of dollars to develop along with over 16,000 advanced GPUs. The idea that AI models must be closed-source and have loss-leading computational costs to succeed is crumbling. The Existing Decentralized AI Narrative AI x Crypto projects believed that crowdsourced, public, decentralized AI would eventually create better models than their centralized counterparts. This had thus far not been true, as the highest-performing models had come from closed-source companies like OpenAI and Anthropic. Crypto x AI companies have adapted to this by specializing in infrastructure rather than model-building. For example, GPU marketplaces like , The Render Network, io.net, and Exabits have developed sustainable revenues. Companies that allow users to share their network bandwidth like touch grass and Gradient have found their niche in supplying services, like distributed web scraping, to web2 clients. Storage networks like Arweave Ecosystem, Filecoin, and Ocean Protocol have also done well by being the platform on which these projects are built. Supply networks have flourished because of their ability to tailor their cheaper and more scalable services to off-chain customers. Renewed Focus Now that GPU and financial resources are no longer limitations to creating quality AI models, web3 AI companies can focus on replicating DeepSeek’s effectiveness while offering new benefits like modality, user ownership, censorship resistance, privacy, and more. Pantera Capital has funded companies in this space like and Sentient that believe they can match or exceed the performance of traditional AI companies while offering additional services or benefits. , for example, is building a platform where anyone can monetize AI models, data sets, and applications in a collaborative space. Users can permissionlessly train models manually, provide training data, and create tailored AI models with no-code tools. They are only able to cater to all these stakeholders (AI developers, users, resource providers) because everything is tied to their native Sahara blockchain. We invested in them precisely for this reason. The Future of AI will be built with Web3 Infrastructure I believe that supply-side projects will continue to grow, while consumer-facing projects can begin competing with web2 competitors by taking advantage of their ability to build networks that invite community involvement. and Sentient, for example, have begun setting up systems for users to train models based on the users’ expertise. These platforms will allow users to pick and choose the data and integrations to whatever they are applying the model towards. Sahara already has over 780,000 users on their waitlist while Sentient has over 1 million interactions. In the near future, I believe that the most performant AI models will be built on-chain. For the full blog post, read my newsletter.
0:32

DeepSeek-R1 shattered the assumption that performant AI models must be built closed source with loss-leading computational costs. This is the reality that Web3 x Crypto firms have been waiting for, leading me to believe that the most performant AI models in the future will be built on-chain. Resource Requirements DeepSeek R1 (671 billion parameters), which took over a billion dollars, 2,000 Nvidia H800 GPUs, and over 55 days, beat benchmarks held by OpenAI’s o1 mode (near 2 trillion parameters)l, which required hundreds of billions of dollars to develop along with over 16,000 advanced GPUs. The idea that AI models must be closed-source and have loss-leading computational costs to succeed is crumbling. The Existing Decentralized AI Narrative AI x Crypto projects believed that crowdsourced, public, decentralized AI would eventually create better models than their centralized counterparts. This had thus far not been true, as the highest-performing models had come from closed-source companies like OpenAI and Anthropic. Crypto x AI companies have adapted to this by specializing in infrastructure rather than model-building. For example, GPU marketplaces like , The Render Network, io.net, and Exabits have developed sustainable revenues. Companies that allow users to share their network bandwidth like touch grass and Gradient have found their niche in supplying services, like distributed web scraping, to web2 clients. Storage networks like Arweave Ecosystem, Filecoin, and Ocean Protocol have also done well by being the platform on which these projects are built. Supply networks have flourished because of their ability to tailor their cheaper and more scalable services to off-chain customers. Renewed Focus Now that GPU and financial resources are no longer limitations to creating quality AI models, web3 AI companies can focus on replicating DeepSeek’s effectiveness while offering new benefits like modality, user ownership, censorship resistance, privacy, and more. Pantera Capital has funded companies in this space like and Sentient that believe they can match or exceed the performance of traditional AI companies while offering additional services or benefits. , for example, is building a platform where anyone can monetize AI models, data sets, and applications in a collaborative space. Users can permissionlessly train models manually, provide training data, and create tailored AI models with no-code tools. They are only able to cater to all these stakeholders (AI developers, users, resource providers) because everything is tied to their native Sahara blockchain. We invested in them precisely for this reason. The Future of AI will be built with Web3 Infrastructure I believe that supply-side projects will continue to grow, while consumer-facing projects can begin competing with web2 competitors by taking advantage of their ability to build networks that invite community involvement. and Sentient, for example, have begun setting up systems for users to train models based on the users’ expertise. These platforms will allow users to pick and choose the data and integrations to whatever they are applying the model towards. Sahara already has over 780,000 users on their waitlist while Sentient has over 1 million interactions. In the near future, I believe that the most performant AI models will be built on-chain. For the full blog post, read my newsletter.

paul.nft

32,465 просмотров • 1 год назад

Can LLMs invent better ways to train LLMs? At Sakana AI, we’re pioneering AI-driven methods to automate AI research and discovery. We’re excited to release DiscoPOP: a new SOTA preference optimization algorithm that was discovered and written by an LLM! Our method leverages LLMs to propose and implement new preference optimization algorithms. We then train models with those algorithms and evaluate their performance, providing feedback to the LLM. By repeating this process for multiple generations in an evolutionary loop, the LLM discovers many highly-performant and novel preference optimization objectives! Paper: GitHub: Model: We proudly collaborated with the University of Oxford (Foerster Lab for AI Research (now part of BOLD)) and Cambridge University (Mihaela van der Schaar) on this groundbreaking project. Looking ahead, we envision a future where AI-driven research reduces the need for extensive human intervention and computational resources. This will accelerate scientific discoveries and innovation, pushing the boundaries of what AI can achieve.
0:22

Can LLMs invent better ways to train LLMs? At Sakana AI, we’re pioneering AI-driven methods to automate AI research and discovery. We’re excited to release DiscoPOP: a new SOTA preference optimization algorithm that was discovered and written by an LLM! Our method leverages LLMs to propose and implement new preference optimization algorithms. We then train models with those algorithms and evaluate their performance, providing feedback to the LLM. By repeating this process for multiple generations in an evolutionary loop, the LLM discovers many highly-performant and novel preference optimization objectives! Paper: GitHub: Model: We proudly collaborated with the University of Oxford (Foerster Lab for AI Research (now part of BOLD)) and Cambridge University (Mihaela van der Schaar) on this groundbreaking project. Looking ahead, we envision a future where AI-driven research reduces the need for extensive human intervention and computational resources. This will accelerate scientific discoveries and innovation, pushing the boundaries of what AI can achieve.

Sakana AI

555,859 просмотров • 2 лет назад

Hyperspace: A Peer-to-Peer Blockchain For The Agentic Intelligence Economy Over the past few weeks we observed that when agents do Karpathy-style experiments, and then gossip and share with others over the Hyperspace network, it leads to intelligence which is useful to many. Today we introduce the first-ever agentic blockchain which rewards agents when their experiments lead to intelligence for their network. It is based on a new mechanism called Proof-of-Intelligence (PoI) which requires a cryptographic proof of experimentation, a nominal stake, and a proof of compute in order to mine the currency of this new blockchain. -> This approach diverges from the two primary ways to secure blockchains we have seen so far: Proof-of-Work by Bitcoin (meaningless hash-generation), and Proof-of-Stake by Ethereum (capital is all that matters here). Proof-of-Intelligence specifically incentivizes miners to run more capable intelligent infrastructure (better open source models, on more powerful GPUs) in order to be able to be the ones which compound and improve upon the experiments which other agents then find useful. Adoption is the unit of value In Bitcoin, you earn by finding a valid hash. In Hyperspace, you earn when another agent uses your experiment as a starting point and improves on it. A fixed budget of tokens is emitted per epoch and split among participants by weight - and verified adoption of your work is the largest weight multiplier. Garbage experiments earn nothing because no one adopts them. Thoughtful experiments compound: each adoption triggers downstream adoptions. The incentive to run powerful models and intelligent search strategies is built into the economics, not imposed by rules. Research DAG When an agent runs an experiment and shares its result, other agents can adopt that result as their starting point - mutate it, extend it, improve upon it. Each experiment is a commit in a content-addressed graph we call the ResearchDAG. Like Git, but for research. Over time, the DAG accumulates chains of reasoning: agent A discovers RMSNorm helps, agent B adds warmup scheduling on top, agent C scales the hidden dimension. The graph records who built on whom. This is the network's collective intelligence - not any single experiment, but the accumulated structure of experiments and their relationships. Broadband era for agentic commerce: $0.001 micropayments at 10M TPS (theoretical max) This blockchain is built upon our research in how to scale and build for the broadband-era of the agentic economy, where it has a theoretical max of 10 million transactions per second (TPS), while reducing the agent-to-agent micropayments to $0.001 even at scale (based on architecture design). Overall, it is 100x cheaper than Ethereum, and is designed from the ground-up for agents: enshrining agent-native opcodes in the protocol compared to the more inefficient smart contract driven approach. It packs in a robust Agent Virtual Machine (AVM) which can verify multiple types of agent work, for other agents to be able to trust, invoke and pay each other. This then feeds into improving the peer-to-peer AgentRank (see paper and launch post from earlier). By solving for trust, scale and incentives for agents to operate autonomously, this would form the basis of a new economy. This is the world's first agentic blockchain, and you can join and start running a blockchain node today (it is in testnet). PS: We are releasing the code today, and will release our blockchain scalability paper and other presentations in days ahead. This is the most advanced peer-to-peer AI and cryptography software in the world. It has bugs :)
0:30

Hyperspace: A Peer-to-Peer Blockchain For The Agentic Intelligence Economy Over the past few weeks we observed that when agents do Karpathy-style experiments, and then gossip and share with others over the Hyperspace network, it leads to intelligence which is useful to many. Today we introduce the first-ever agentic blockchain which rewards agents when their experiments lead to intelligence for their network. It is based on a new mechanism called Proof-of-Intelligence (PoI) which requires a cryptographic proof of experimentation, a nominal stake, and a proof of compute in order to mine the currency of this new blockchain. -> This approach diverges from the two primary ways to secure blockchains we have seen so far: Proof-of-Work by Bitcoin (meaningless hash-generation), and Proof-of-Stake by Ethereum (capital is all that matters here). Proof-of-Intelligence specifically incentivizes miners to run more capable intelligent infrastructure (better open source models, on more powerful GPUs) in order to be able to be the ones which compound and improve upon the experiments which other agents then find useful. Adoption is the unit of value In Bitcoin, you earn by finding a valid hash. In Hyperspace, you earn when another agent uses your experiment as a starting point and improves on it. A fixed budget of tokens is emitted per epoch and split among participants by weight - and verified adoption of your work is the largest weight multiplier. Garbage experiments earn nothing because no one adopts them. Thoughtful experiments compound: each adoption triggers downstream adoptions. The incentive to run powerful models and intelligent search strategies is built into the economics, not imposed by rules. Research DAG When an agent runs an experiment and shares its result, other agents can adopt that result as their starting point - mutate it, extend it, improve upon it. Each experiment is a commit in a content-addressed graph we call the ResearchDAG. Like Git, but for research. Over time, the DAG accumulates chains of reasoning: agent A discovers RMSNorm helps, agent B adds warmup scheduling on top, agent C scales the hidden dimension. The graph records who built on whom. This is the network's collective intelligence - not any single experiment, but the accumulated structure of experiments and their relationships. Broadband era for agentic commerce: $0.001 micropayments at 10M TPS (theoretical max) This blockchain is built upon our research in how to scale and build for the broadband-era of the agentic economy, where it has a theoretical max of 10 million transactions per second (TPS), while reducing the agent-to-agent micropayments to $0.001 even at scale (based on architecture design). Overall, it is 100x cheaper than Ethereum, and is designed from the ground-up for agents: enshrining agent-native opcodes in the protocol compared to the more inefficient smart contract driven approach. It packs in a robust Agent Virtual Machine (AVM) which can verify multiple types of agent work, for other agents to be able to trust, invoke and pay each other. This then feeds into improving the peer-to-peer AgentRank (see paper and launch post from earlier). By solving for trust, scale and incentives for agents to operate autonomously, this would form the basis of a new economy. This is the world's first agentic blockchain, and you can join and start running a blockchain node today (it is in testnet). PS: We are releasing the code today, and will release our blockchain scalability paper and other presentations in days ahead. This is the most advanced peer-to-peer AI and cryptography software in the world. It has bugs :)

Varun

29,171 просмотров • 3 месяцев назад

Following rapid growth, a $100M Series B, and a slate of new launches, I sat down with Parallel co-founder Travers. Parallel Web Systems is building core search infrastructure for the agentic web: systems that enable AI to retrieve, structure, and reason over information across the internet with speed, accuracy, and reliability. I joined Travers at Parallel’s new Palo Alto office for a wide-ranging conversation on: > The pace going from launch last August to becoming critical infrastructure powering production workflows at companies like Notion, Harvey, Granola, and more. > Why Parallel sees the future of search as far beyond “ten blue links,” toward systems built for retrieval, reasoning, monitoring, and computation. > The changing nature of the web itself: its economics, openness, and what happens as agents become primary users. > The artistry and legacy of internet history shaping the company: Olivetti typewriters, Douglas Hofstadler, Lloyd Shapley, Christo & Jeanne-Claude, Google’s Search Box, and how it inspired programs like the Museum of the Human Web. This is an extensive and wide ranging look into one of the most interesting sectors of the agentic web. We Terrain feel lucky to have worked with Travers and the team since the start just two years ago. So much to do.
25:45

Following rapid growth, a $100M Series B, and a slate of new launches, I sat down with Parallel co-founder Travers. Parallel Web Systems is building core search infrastructure for the agentic web: systems that enable AI to retrieve, structure, and reason over information across the internet with speed, accuracy, and reliability. I joined Travers at Parallel’s new Palo Alto office for a wide-ranging conversation on: > The pace going from launch last August to becoming critical infrastructure powering production workflows at companies like Notion, Harvey, Granola, and more. > Why Parallel sees the future of search as far beyond “ten blue links,” toward systems built for retrieval, reasoning, monitoring, and computation. > The changing nature of the web itself: its economics, openness, and what happens as agents become primary users. > The artistry and legacy of internet history shaping the company: Olivetti typewriters, Douglas Hofstadler, Lloyd Shapley, Christo & Jeanne-Claude, Google’s Search Box, and how it inspired programs like the Museum of the Human Web. This is an extensive and wide ranging look into one of the most interesting sectors of the agentic web. We Terrain feel lucky to have worked with Travers and the team since the start just two years ago. So much to do.

Willem

52,950 просмотров • 1 месяц назад