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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.... io, notes, "We need software architectures that can eat physical threads of computation and turn that into throughput." Traditional sequential programming models often struggle to effectively leverage the potential of these multi-core systems, creating a bottleneck in performance scaling. Rholang addresses this challenge through its foundation in the Rho-Calculus, a reflective higher-order process calculus specifically designed for inherent concurrency and reflection. Unlike computational models based on sequential execution or those that bolt on concurrency primitives, Rholang can leverage its process-oriented nature to detect and distribute non-interfering logical computation threads across available physical processing units, creating a more direct correlation between hardware resources and performance scaling. The Rho-Calculus was specifically designed to provide the minimal set of operations needed to model autonomous agents operating in parallel while maintaining the ability to represent their own reasoning processes. This structure mirrors the operational characteristics of intelligent systems, where autonomous processes run independently while communicating and coordinating. For our novel decentralized AI platform, MeTTaCycle, this future-proof architectural foundation enables more than just enhanced transactional throughput; it fosters an environment where independent computational processes can efficiently coexist and interact, forming a critical layer for the Artificial Superintelligence Alliance's decentralized AGI infrastructure and products.show more

SingularityNET

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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. ⬇️

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 • 3 месяцев 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

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

318,228 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)

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

761,324 просмотров • 9 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

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.

🚨$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 • 7 месяцев назад

We are turning energy into non-biological Intelligence. Data centers are literally 'factories' that produce Intelligence. Electricity is the essential input. The electricity powers computation, and the computation produces and processes the tokens through which machine Intelligence operates. As these systems become more capable and autonomous, they consume vastly more tokens, driving ever greater demand for computation and electricity. OpenRouter estimates that an agentic request already consumes around 15 times as many tokens as a human-led request on its platform. This fundamentally changes the nature of electricity demand as our demand for useful Intelligence has no obvious ceiling. If more computation can produce more valuable work, we will keep finding more work for machines to do - and so, we will need ever more electricity. We got future energy demand so wrong because we did not foresee that non-biological Intelligence could be produced industrially; and that we would need an entirely new energy paradigm to support that.

We are turning energy into non-biological Intelligence. Data centers are literally 'factories' that produce Intelligence. Electricity is the essential input. The electricity powers computation, and the computation produces and processes the tokens through which machine Intelligence operates. As these systems become more capable and autonomous, they consume vastly more tokens, driving ever greater demand for computation and electricity. OpenRouter estimates that an agentic request already consumes around 15 times as many tokens as a human-led request on its platform. This fundamentally changes the nature of electricity demand as our demand for useful Intelligence has no obvious ceiling. If more computation can produce more valuable work, we will keep finding more work for machines to do - and so, we will need ever more electricity. We got future energy demand so wrong because we did not foresee that non-biological Intelligence could be produced industrially; and that we would need an entirely new energy paradigm to support that.

Nina Schick

209,015 просмотров • 4 дней 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:

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

126,559 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👇

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

115,396 просмотров • 3 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.

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 • 8 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.

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 • 4 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.

💡 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

AI is entering its physics era guys.. A duo refused Bezos' multi-billion-dollar offer, then unveiled an AI model that no PC can run. "They unveiled Accelerated Understanding (AU), a new physics-first AI startup that takes a different approach from traditional language models. Its models are designed to predict how physical systems evolve across space and time." "In testing, the model processed 5 trillion pieces of physical data in a single prompt — roughly 5 million times more data than Google Gemini or Anthropic Claude can handle at once. This isn't 5 trillion words or tokens. It's massive 3D + time (4D) physical data." 👀 "The model uses neural operators instead of conventional Transformer architectures, and it's designed to simulate phenomena such as weather, fluid dynamics, materials, geology and semiconductor behavior." "AU says it has trained models with up to 1 trillion parameters, with scaling experiments reaching 35 trillion parameters. A 5-trillion-point inference can reportedly generate around 22 TB of output." Now, AI is moving from predicting what comes next in a sentence to predicting what happens next in the physical world. Acceleration is everywhere! 👀

AI is entering its physics era guys.. A duo refused Bezos' multi-billion-dollar offer, then unveiled an AI model that no PC can run. "They unveiled Accelerated Understanding (AU), a new physics-first AI startup that takes a different approach from traditional language models. Its models are designed to predict how physical systems evolve across space and time." "In testing, the model processed 5 trillion pieces of physical data in a single prompt — roughly 5 million times more data than Google Gemini or Anthropic Claude can handle at once. This isn't 5 trillion words or tokens. It's massive 3D + time (4D) physical data." 👀 "The model uses neural operators instead of conventional Transformer architectures, and it's designed to simulate phenomena such as weather, fluid dynamics, materials, geology and semiconductor behavior." "AU says it has trained models with up to 1 trillion parameters, with scaling experiments reaching 35 trillion parameters. A 5-trillion-point inference can reportedly generate around 22 TB of output." Now, AI is moving from predicting what comes next in a sentence to predicting what happens next in the physical world. Acceleration is everywhere! 👀

SciTech Era

19,294 просмотров • 5 дней 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 🚀

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

135,431 просмотров • 9 месяцев назад

What a time to be alive! We are entering the era of machines that discover and build. Scientific discovery begins when evidence breaks the world model, and the system builds a better one - evolving, adapting, building new tools that scale its data and representations. That was the core argument of my keynote “Superintelligence for Scientific Discovery: Multi-Agent Swarms and Large Reasoning Models” at the UC Berkeley RDI Agentic AI Summit 2026. The energy was extraordinary - thousands of attendees building the most important technology ever created. Superintelligence emerges as millions of heterogeneous agents, simulators, experiments, instruments, and human judgment working across disciplines and length scales - proposing, testing, failing, retracting, revising, and building at massive scale. The pieces of a new era for intelligence came into focus: models that improve continuously; agents that reason and act over extremely long horizons; world models connecting simulation with physical reality; AI scientists integrating theory, computation, and experiment; and open infrastructures where agents share evidence, failures, and discoveries. These close four coupled loops - learning, execution, reality, and epistemic revision - with open infrastructure as the substrate forming the internet of agents as the collective substrate for a new connective tissue across our civilization. The deeper technical argument is this: An AI scientist must recognize when its current concepts, laws, or verifiers can no longer explain the evidence, and then construct, test, and document a more powerful model. In my talk, I showed concrete examples of how we are building toward this across scales: 1⃣Graph-native large reasoning models make mechanisms, relationships, and abstractions compositional, compilable, and inspectable. 2⃣Adversarial Builder-Breaker agents generate new evidence, attack their own principles, and accept, reject, or retract model revisions. 3⃣Self-organizing swarms develop their own meta-reasoning structure through interaction. ScienceClaw × Infinite (arXiv:2603.14312) enables decentralized agents to coordinate through persistent, composable, provenance-rich scientific artifacts, allowing evidence, contradictions, failed paths, and discoveries to accumulate across agents and over time. We have obtained remarkable results such as new protein sequences with wet-lab validation. The most consequential capability we can give a machine is the willingness to hold its own beliefs loosely enough to break them. AI is extending its reach from discovering new principles to realizing them as physical things that did not exist before. Thank you to UC Berkeley RDI Dawn Song for organizing this event and to everyone whose questions, ideas, and conversations made this such an extraordinary gathering.

What a time to be alive! We are entering the era of machines that discover and build. Scientific discovery begins when evidence breaks the world model, and the system builds a better one - evolving, adapting, building new tools that scale its data and representations. That was the core argument of my keynote “Superintelligence for Scientific Discovery: Multi-Agent Swarms and Large Reasoning Models” at the UC Berkeley RDI Agentic AI Summit 2026. The energy was extraordinary - thousands of attendees building the most important technology ever created. Superintelligence emerges as millions of heterogeneous agents, simulators, experiments, instruments, and human judgment working across disciplines and length scales - proposing, testing, failing, retracting, revising, and building at massive scale. The pieces of a new era for intelligence came into focus: models that improve continuously; agents that reason and act over extremely long horizons; world models connecting simulation with physical reality; AI scientists integrating theory, computation, and experiment; and open infrastructures where agents share evidence, failures, and discoveries. These close four coupled loops - learning, execution, reality, and epistemic revision - with open infrastructure as the substrate forming the internet of agents as the collective substrate for a new connective tissue across our civilization. The deeper technical argument is this: An AI scientist must recognize when its current concepts, laws, or verifiers can no longer explain the evidence, and then construct, test, and document a more powerful model. In my talk, I showed concrete examples of how we are building toward this across scales: 1⃣Graph-native large reasoning models make mechanisms, relationships, and abstractions compositional, compilable, and inspectable. 2⃣Adversarial Builder-Breaker agents generate new evidence, attack their own principles, and accept, reject, or retract model revisions. 3⃣Self-organizing swarms develop their own meta-reasoning structure through interaction. ScienceClaw × Infinite (arXiv:2603.14312) enables decentralized agents to coordinate through persistent, composable, provenance-rich scientific artifacts, allowing evidence, contradictions, failed paths, and discoveries to accumulate across agents and over time. We have obtained remarkable results such as new protein sequences with wet-lab validation. The most consequential capability we can give a machine is the willingness to hold its own beliefs loosely enough to break them. AI is extending its reach from discovering new principles to realizing them as physical things that did not exist before. Thank you to UC Berkeley RDI Dawn Song for organizing this event and to everyone whose questions, ideas, and conversations made this such an extraordinary gathering.

Markus J. Buehler

19,212 просмотров • 28 дней назад

Everyone wrote Apple off as the AI loser, but one hardware spec might flip that story upside down (Save this). @jason called Apple a screaming buy on the back of a single chip detail. The rumored M7 Ultra, expected around 2028, is designed to support up to 1.5TB of unified memory, enough to run frontier class trillion parameter AI models locally, with no cloud required. The Street's bear case on Apple is straightforward. Apple has no frontier model of its own, Siri has stumbled for years and the company effectively rents OpenAI's models for its hardest queries. That narrative treats Apple as the one Magnificent Seven name that missed the AI wave entirely but the bull case flips that framing on its head. If frontier AI models keep shrinking and getting cheaper to run, Apple doesn't need the smartest model in the world, it just needs to own the device that model runs on. And unified memory is the mechanism that makes this possible. Unlike traditional systems where the CPU and GPU each need separate memory, Apple's architecture lets the CPU, GPU and Neural Engine draw from one shared pool. A fully specced M7 Ultra could theoretically run something on the scale of a 1.2 trillion parameter model locally and that capability plugs directly into the one advantage Apple has spent over a decade building: privacy. Apple has already shipped Private Cloud Compute, a system designed so even Apple can't access user data processed off device. Apple doubled down on this at WWDC 2026, framing on device privacy as non-negotiable while rivals default to the cloud. If the best AI models get small enough to run on Apple silicon, the moat stops being the model and becomes the hardware it has to sit on. Milk Road Pro remains bullish on Apple and it remains as one of our core positions, if you want the full thesis + our full AI trades, come join us using the link below for just a $1.

Everyone wrote Apple off as the AI loser, but one hardware spec might flip that story upside down (Save this). @jason called Apple a screaming buy on the back of a single chip detail. The rumored M7 Ultra, expected around 2028, is designed to support up to 1.5TB of unified memory, enough to run frontier class trillion parameter AI models locally, with no cloud required. The Street's bear case on Apple is straightforward. Apple has no frontier model of its own, Siri has stumbled for years and the company effectively rents OpenAI's models for its hardest queries. That narrative treats Apple as the one Magnificent Seven name that missed the AI wave entirely but the bull case flips that framing on its head. If frontier AI models keep shrinking and getting cheaper to run, Apple doesn't need the smartest model in the world, it just needs to own the device that model runs on. And unified memory is the mechanism that makes this possible. Unlike traditional systems where the CPU and GPU each need separate memory, Apple's architecture lets the CPU, GPU and Neural Engine draw from one shared pool. A fully specced M7 Ultra could theoretically run something on the scale of a 1.2 trillion parameter model locally and that capability plugs directly into the one advantage Apple has spent over a decade building: privacy. Apple has already shipped Private Cloud Compute, a system designed so even Apple can't access user data processed off device. Apple doubled down on this at WWDC 2026, framing on device privacy as non-negotiable while rivals default to the cloud. If the best AI models get small enough to run on Apple silicon, the moat stops being the model and becomes the hardware it has to sit on. Milk Road Pro remains bullish on Apple and it remains as one of our core positions, if you want the full thesis + our full AI trades, come join us using the link below for just a $1.

Milk Road AI

37,459 просмотров • 1 месяц 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:

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

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

14,508 просмотров • 5 месяцев 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!

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

10,117 просмотров • 4 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:

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

908,065 просмотров • 9 месяцев назад