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Cloud compute is costly, centralized, and trust-heavy. Most Devs see no alternative to Big Tech. We Interviewed Acurast founder Alessandro De Carli ☁️ 🫡 Alessandro De Carli (Acurast) on turning smartphones into a global decentralized compute network you can earn from. Timestamps: 0:09 Decentralized compute powered by smartphones 1:09... show more

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Security is boring until it isn’t. The phone in your pocket is a highly secure hardware module. It proves the compute is real and keeps workloads confidential. That is why mobile devices can power trustless, decentralized compute without fake hardware or prying eyes. Want to put your phone to work? Get started on Acurast today.

Security is boring until it isn’t. The phone in your pocket is a highly secure hardware module. It proves the compute is real and keeps workloads confidential. That is why mobile devices can power trustless, decentralized compute without fake hardware or prying eyes. Want to put your phone to work? Get started on Acurast today.

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What if your phone could do more than scroll? Learn how Acurast is turning smart devices into decentralized cloud infra. Breaking big tech’s hold on cloud compute and handing the power back to users. NULL POINTER EXCEPTION and Alessandro De Carli ☁️ 🫡 explain why privacy + resilience is a must.

What if your phone could do more than scroll? Learn how Acurast is turning smart devices into decentralized cloud infra. Breaking big tech’s hold on cloud compute and handing the power back to users. NULL POINTER EXCEPTION and Alessandro De Carli ☁️ 🫡 explain why privacy + resilience is a must.

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The biggest platform upgrade since Mainnet, explained by our Co-Founder Alessandro De Carli ☁️ 🫡 Acurast is moving from a Node.js-only runtime to full Linux-based containers. Every Android Acurast Processor on the network now functions as a decentralized server with confidential execution at the hardware level. No language barriers, full Debian software ecosystem, SSH access, and a VPS-like experience. What you can build: autonomous AI agents, browser automation across thousands of devices, algorithmic trading bots, even game servers. Running across the Acurast network on Android smartphones in 175+ countries. Decentralized Cloud. Same developer experience. No compromises. The future of compute does not live in a data center, it lives in your pocket. 👇

The biggest platform upgrade since Mainnet, explained by our Co-Founder Alessandro De Carli ☁️ 🫡 Acurast is moving from a Node.js-only runtime to full Linux-based containers. Every Android Acurast Processor on the network now functions as a decentralized server with confidential execution at the hardware level. No language barriers, full Debian software ecosystem, SSH access, and a VPS-like experience. What you can build: autonomous AI agents, browser automation across thousands of devices, algorithmic trading bots, even game servers. Running across the Acurast network on Android smartphones in 175+ countries. Decentralized Cloud. Same developer experience. No compromises. The future of compute does not live in a data center, it lives in your pocket. 👇

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In our recent CoinMarketCap Space, Alessandro got the question he's heard since day one: building a compute network out of smartphones sounded strange to a lot of people at first. Here's the case that changes minds: your phone is already a reliable, always-on computer. Think about how long your alarm clock phone goes without a restart. Weeks. Months. Acurast never runs in the background, so you choose when to dedicate a device. Overnight while it charges, or full-time with a spare. Either way, it just keeps going. See the clip from the Space below.

In our recent CoinMarketCap Space, Alessandro got the question he's heard since day one: building a compute network out of smartphones sounded strange to a lot of people at first. Here's the case that changes minds: your phone is already a reliable, always-on computer. Think about how long your alarm clock phone goes without a restart. Weeks. Months. Acurast never runs in the background, so you choose when to dedicate a device. Overnight while it charges, or full-time with a spare. Either way, it just keeps going. See the clip from the Space below.

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At LinqAI, we’ve been mission-driven since day one—building competitive, revenue-generating SaaS applications. As our product line has expanded and our customer base has grown, one thing has become clear: we have an opportunity to forge a deeper connection between our SaaS innovations and the Web3 ecosystem. As we scale our SaaS offerings, our demand for compute power continues to grow. Traditionally, paying for compute means sending value outside our ecosystem—resources spent elsewhere, never to return. But what if every compute cycle contributed to the LinqAI ecosystem itself? LinqProtocol is our answer. By transforming idle computers (PCs, laptops, servers, and in the future even phones) into a global compute network, we not only secure scalable and efficient infrastructure but also strengthen the utility of $LNQ—ensuring that every workload contributes to our own decentralized future. LinqProtocol allows for arbitrary compute which means that there are virtually no limits to what can be done on LinqProtocol. From AI Training, to Synthetic Data Generation, 3D Rendering and even hosting a Website - It's all possible on LinqProtocol. ✅ On-demand, scalable compute ✅ Transparent, fair-market pricing ✅ Trustless execution with real-time tracking ✅ Governance-based dispute resolution The future of compute isn’t just more efficient. It’s owned by those who use it. It’s decentralized. And it’s coming soon. $LNQ #LinqProtocol

At LinqAI, we’ve been mission-driven since day one—building competitive, revenue-generating SaaS applications. As our product line has expanded and our customer base has grown, one thing has become clear: we have an opportunity to forge a deeper connection between our SaaS innovations and the Web3 ecosystem. As we scale our SaaS offerings, our demand for compute power continues to grow. Traditionally, paying for compute means sending value outside our ecosystem—resources spent elsewhere, never to return. But what if every compute cycle contributed to the LinqAI ecosystem itself? LinqProtocol is our answer. By transforming idle computers (PCs, laptops, servers, and in the future even phones) into a global compute network, we not only secure scalable and efficient infrastructure but also strengthen the utility of $LNQ—ensuring that every workload contributes to our own decentralized future. LinqProtocol allows for arbitrary compute which means that there are virtually no limits to what can be done on LinqProtocol. From AI Training, to Synthetic Data Generation, 3D Rendering and even hosting a Website - It's all possible on LinqProtocol. ✅ On-demand, scalable compute ✅ Transparent, fair-market pricing ✅ Trustless execution with real-time tracking ✅ Governance-based dispute resolution The future of compute isn’t just more efficient. It’s owned by those who use it. It’s decentralized. And it’s coming soon. $LNQ #LinqProtocol

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EigenCloud

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Hey everyone, today I want to introduce a project that’s aiming to redefine how we access compute for AI — it’s called GPUAI. 🔶 GPUAI: Unlocking Global GPU Power for the AI Era GPUAI isn’t just another GPU marketplace or leasing service. It’s a fully decentralized protocol that connects idle GPU resources around the world — from gaming PCs to data center clusters — and transforms them into a high-performance compute network for AI workloads. 🧠 Why does it matter? Right now, the biggest bottleneck in AI isn’t algorithms — it’s access to compute. Training and running models requires massive GPU power, but it’s locked up in centralized cloud platforms, expensive and hard to access for smaller teams. With GPUAI, anyone can tap into a global GPU pool that’s: ✅ Fully decentralized ✅ Reputation-based and smart contract coordinated ✅ Encrypted and secure ✅ Token-incentivized — meaning contributors get rewarded in $GPUAI 📈 For developers, it’s a flexible way to access GPU compute for training, inference, and more — without cloud lock-in. 💰 For GPU owners, it’s a chance to monetize idle hardware that would otherwise go unused. The protocol is live, the apps are active, and the ecosystem is growing fast. 🌐 Try it yourself at 📖 Learn more on 🎮 Play our community games at This is real infrastructure for the future of AI, not hype. Follow them and explore their mission of decentralized computing at Tell me what you think - if you have a GPU, you can start profiting now. #GPUAI #Web3Infrastructure #AIComputing #DePIN #Decentralization

Hey everyone, today I want to introduce a project that’s aiming to redefine how we access compute for AI — it’s called GPUAI. 🔶 GPUAI: Unlocking Global GPU Power for the AI Era GPUAI isn’t just another GPU marketplace or leasing service. It’s a fully decentralized protocol that connects idle GPU resources around the world — from gaming PCs to data center clusters — and transforms them into a high-performance compute network for AI workloads. 🧠 Why does it matter? Right now, the biggest bottleneck in AI isn’t algorithms — it’s access to compute. Training and running models requires massive GPU power, but it’s locked up in centralized cloud platforms, expensive and hard to access for smaller teams. With GPUAI, anyone can tap into a global GPU pool that’s: ✅ Fully decentralized ✅ Reputation-based and smart contract coordinated ✅ Encrypted and secure ✅ Token-incentivized — meaning contributors get rewarded in $GPUAI 📈 For developers, it’s a flexible way to access GPU compute for training, inference, and more — without cloud lock-in. 💰 For GPU owners, it’s a chance to monetize idle hardware that would otherwise go unused. The protocol is live, the apps are active, and the ecosystem is growing fast. 🌐 Try it yourself at 📖 Learn more on 🎮 Play our community games at This is real infrastructure for the future of AI, not hype. Follow them and explore their mission of decentralized computing at Tell me what you think - if you have a GPU, you can start profiting now. #GPUAI #Web3Infrastructure #AIComputing #DePIN #Decentralization

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$70,000 Phones, One AI Agent — The World's Largest Edge AI Fleet Runs on Hermes We turned 70,000 phones into a shared AI compute network. Any device owner contributes idle compute. Any developer taps distributed inference at a fraction of cloud cost. Not a concept. Not a whitepaper. 70K devices online today. The problem: orchestrating a shared network of heterogeneous edge devices — different chipsets, different memory, different thermal profiles, different owners — is a coordination nightmare no human team can handle manually. So we gave the network a brain: Nous Research Hermes Agent. Hermes connects to 16 MCP servers and runs 24/7: 🔬 Research Loop — Tracks every breakthrough in on-device inference: quantization (GPTQ/AWQ/GGUF), speculative decoding on mobile SoCs, federated learning protocols. Auto-imports papers into NotebookLM. 36 research topics, zero manual curation. 🌐 Network Intelligence — Monitors device availability, compute capacity, and workload distribution across the shared fleet. Surfaces bottlenecks before they cascade. 🧬 Tech Tree Optimizer — Maps the full optimization frontier: from KV-cache compression to on-device LoRA to peer-to-peer model sharding. Hermes autonomously identifies which research paths unlock the most network-wide throughput gains. The result: a self-improving shared compute network. Research compounds daily. The fleet gets smarter without human intervention. Cloud AI scales with money. We scale with people. #HermesHackathon Teknium 🪽 Delphi Digital Tommy$

70,000 Phones, One AI Agent — The World's Largest Edge AI Fleet Runs on Hermes We turned 70,000 phones into a shared AI compute network. Any device owner contributes idle compute. Any developer taps distributed inference at a fraction of cloud cost. Not a concept. Not a whitepaper. 70K devices online today. The problem: orchestrating a shared network of heterogeneous edge devices — different chipsets, different memory, different thermal profiles, different owners — is a coordination nightmare no human team can handle manually. So we gave the network a brain: Nous Research Hermes Agent. Hermes connects to 16 MCP servers and runs 24/7: 🔬 Research Loop — Tracks every breakthrough in on-device inference: quantization (GPTQ/AWQ/GGUF), speculative decoding on mobile SoCs, federated learning protocols. Auto-imports papers into NotebookLM. 36 research topics, zero manual curation. 🌐 Network Intelligence — Monitors device availability, compute capacity, and workload distribution across the shared fleet. Surfaces bottlenecks before they cascade. 🧬 Tech Tree Optimizer — Maps the full optimization frontier: from KV-cache compression to on-device LoRA to peer-to-peer model sharding. Hermes autonomously identifies which research paths unlock the most network-wide throughput gains. The result: a self-improving shared compute network. Research compounds daily. The fleet gets smarter without human intervention. Cloud AI scales with money. We scale with people. #HermesHackathon Teknium 🪽 Delphi Digital Tommy

Oyster Republic 🦪📲🦞👓

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This is part 2 of a 2 part post (see part 1 here Below is a structured analysis to demonstrate the validity of using buyers of Veritaseum #SmartMetal to buy into and sell compute from globally aggregated cell phone compute pools - directly compeiting with the big guys - Google, Amazon and Microsoft cloud businesses. We discuss estimates, business model propositions, and potential economic outcomes, but first, see my Executive Global Article on Zero Profit Models( and purchase Veritaseum SmartMetal here - Can you really disintermediate the most profitable revnues of t $6.7 trillion worth of technology cloud providers? Well, the fact that it is among, if not the, most profitable of their revenue drivers is a very material clue! Step 1: Estimating the Number of High-End Smartphones Globally As of early 2025, approximately 7.5 billion smartphones are actively used worldwide. Considering that: About 30% of global smartphones are high-end (comparable or superior to an iPhone X; for instance, Samsung Galaxy S22/S23 Ultra, iPhone 16 Pro Max with A18 chips, and Qualcomm Snapdragon 8 Gen 3 or newer). Thus, approximately 2.25 billion high-end smartphones exist today (30% of 7.5B). Step 2: Aggregate Compute Power Estimation (Idle Capacity) Average Computational Capacity per High-End Smartphone: A high-end phone has roughly: CPU: ~1 to 1.5 TFLOPS GPU: ~1.5 to 2 TFLOPS Average Idle Compute per Phone: 1 TFLOPS (CPU) + 1.5 TFLOPS (GPU) = ~2.5 TFLOPS idle. Total Potential Compute Power: 2.25B smartphones × 2.5 TFLOPS each ≈ 5,625,000,000 TFLOPS (5.625 ExaFLOPS) Comparison to Cloud Vendors: Amazon AWS, Microsoft Azure, Google Cloud combined currently deploy approximately ~1 to 2 ExaFLOPS of continuous computing power. Thus, aggregate idle compute power from high-end smartphones (5.625 ExaFLOPS) exceeds the largest cloud vendors combined by at least 2.8x. Step 3: Proposed Business Model ("Zero Margin Trustless Model") Following Middleton’s economic principles, a decentralized marketplace based on his IP (SmartMetal Rounds and patented protocols) would allow individual users to rent their smartphones’ idle compute power. The economics would follow: Revenue Structure: Compute resources provided by phone owners (children, elderly, economically disadvantaged communities) rented to consumers (AI firms, universities, research institutions, enterprises). Offered at 10% above net cost ("as close to free as possible" per the attached articleExecutive Global articl…). Revenue Distribution: SmartMetal Owners (phone owners): Receive 20% of net revenue generated. Platform Cost & Overhead: Costs for electricity, network management, and maintenance (approximately 70% of net revenue). Intellectual Property Licensing (Middleton’s IP): A modest licensing fee—around 10% (aligned with Middleton’s zero-margin, IP-licensing-centric model). Step 4: Revenue Estimation Example Assumptions: Average monthly idle compute contribution per phone: 4 hours/day, 30 days = 120 hours/month. Market price for decentralized high-performance computing: approximately $0.10 per TFLOP-hour. Revenue per Smartphone per Month: Compute provided: 2.5 TFLOPS × 120 hrs = 300 TFLOP-hours Revenue at $0.10 per TFLOP-hour: 300 × $0.10 = $30/month per smartphone Aggregate Monthly and Annual Revenue: Monthly revenue (2.25 billion phones): $30 × 2.25B ≈ $67.5 billion Annual revenue potential: $67.5B × 12 months = $810 billion annually Distribution of Annual Revenue: SmartMetal Round Owners (20%): $810B × 20% ≈ $162 billion/year Operational Cost (70%): $810B × 70% ≈ $567 billion/year Middleton IP Licensing (10%): $810B × 10% ≈ $81 billion/year Thus, the total economic benefit is substantial, particularly transformative for economically disadvantaged participants (children, elderly, developing regions). Step 5: Practical Impact & Social Value Impact on Children & Young Adults: Empowerment through earning potential (around $360 annually per child smartphone owner). Practical, intuitive introduction to economics, technology, and entrepreneurship through gamified interfaces and secure, decentralized platforms. Impact on Elderly and Economically Disadvantaged Communities: Significant supplemental income (potentially exceeding many pension plans or assistance programs). Bridging the technology gap, ensuring inclusive participation in global digital economies. Step 6: Strategic Value & Market Positioning Middleton's patented Zero Margin Trustless Model ("ZMTM")Executive Global articl… creates a highly attractive, low-cost computational offering. Competing directly with incumbent cloud providers: The computational marketplace can massively disrupt cloud computing with lower fees and broader global reach. Leveraging Middleton’s IP and SmartMetal Rounds, it creates defensible competitive barriers and immense value for early adopters. Step 7: Driving Middleton’s Peer-to-Peer Economy As described in Middleton’s visionExecutive Global articl…, this marketplace underpins a global peer-to-peer economy, transforming idle smartphone resources into meaningful economic output. The P2P economy will leverage: AI-driven autonomous economic agents. Secure blockchain-based IP rights enforcement. Economic democratization by redistributing traditional cloud revenues directly to everyday device owners. Summary & Strategic Conclusion Implementing a decentralized compute platform powered by high-end smartphones and Middleton’s patented Zero Margin Trustless Model presents enormous economic potential, far exceeding current major cloud vendors combined. With annual revenues estimated up to $810 billion, and meaningful income distribution to disadvantaged demographics, this innovative model could dramatically reshape the global computational economy, achieve significant social impacts, and provide the backbone for Middleton’s envisioned peer-to-peer decentralized economy.

This is part 2 of a 2 part post (see part 1 here Below is a structured analysis to demonstrate the validity of using buyers of Veritaseum #SmartMetal to buy into and sell compute from globally aggregated cell phone compute pools - directly compeiting with the big guys - Google, Amazon and Microsoft cloud businesses. We discuss estimates, business model propositions, and potential economic outcomes, but first, see my Executive Global Article on Zero Profit Models( and purchase Veritaseum SmartMetal here - Can you really disintermediate the most profitable revnues of t $6.7 trillion worth of technology cloud providers? Well, the fact that it is among, if not the, most profitable of their revenue drivers is a very material clue! Step 1: Estimating the Number of High-End Smartphones Globally As of early 2025, approximately 7.5 billion smartphones are actively used worldwide. Considering that: About 30% of global smartphones are high-end (comparable or superior to an iPhone X; for instance, Samsung Galaxy S22/S23 Ultra, iPhone 16 Pro Max with A18 chips, and Qualcomm Snapdragon 8 Gen 3 or newer). Thus, approximately 2.25 billion high-end smartphones exist today (30% of 7.5B). Step 2: Aggregate Compute Power Estimation (Idle Capacity) Average Computational Capacity per High-End Smartphone: A high-end phone has roughly: CPU: ~1 to 1.5 TFLOPS GPU: ~1.5 to 2 TFLOPS Average Idle Compute per Phone: 1 TFLOPS (CPU) + 1.5 TFLOPS (GPU) = ~2.5 TFLOPS idle. Total Potential Compute Power: 2.25B smartphones × 2.5 TFLOPS each ≈ 5,625,000,000 TFLOPS (5.625 ExaFLOPS) Comparison to Cloud Vendors: Amazon AWS, Microsoft Azure, Google Cloud combined currently deploy approximately ~1 to 2 ExaFLOPS of continuous computing power. Thus, aggregate idle compute power from high-end smartphones (5.625 ExaFLOPS) exceeds the largest cloud vendors combined by at least 2.8x. Step 3: Proposed Business Model ("Zero Margin Trustless Model") Following Middleton’s economic principles, a decentralized marketplace based on his IP (SmartMetal Rounds and patented protocols) would allow individual users to rent their smartphones’ idle compute power. The economics would follow: Revenue Structure: Compute resources provided by phone owners (children, elderly, economically disadvantaged communities) rented to consumers (AI firms, universities, research institutions, enterprises). Offered at 10% above net cost ("as close to free as possible" per the attached articleExecutive Global articl…). Revenue Distribution: SmartMetal Owners (phone owners): Receive 20% of net revenue generated. Platform Cost & Overhead: Costs for electricity, network management, and maintenance (approximately 70% of net revenue). Intellectual Property Licensing (Middleton’s IP): A modest licensing fee—around 10% (aligned with Middleton’s zero-margin, IP-licensing-centric model). Step 4: Revenue Estimation Example Assumptions: Average monthly idle compute contribution per phone: 4 hours/day, 30 days = 120 hours/month. Market price for decentralized high-performance computing: approximately $0.10 per TFLOP-hour. Revenue per Smartphone per Month: Compute provided: 2.5 TFLOPS × 120 hrs = 300 TFLOP-hours Revenue at $0.10 per TFLOP-hour: 300 × $0.10 = $30/month per smartphone Aggregate Monthly and Annual Revenue: Monthly revenue (2.25 billion phones): $30 × 2.25B ≈ $67.5 billion Annual revenue potential: $67.5B × 12 months = $810 billion annually Distribution of Annual Revenue: SmartMetal Round Owners (20%): $810B × 20% ≈ $162 billion/year Operational Cost (70%): $810B × 70% ≈ $567 billion/year Middleton IP Licensing (10%): $810B × 10% ≈ $81 billion/year Thus, the total economic benefit is substantial, particularly transformative for economically disadvantaged participants (children, elderly, developing regions). Step 5: Practical Impact & Social Value Impact on Children & Young Adults: Empowerment through earning potential (around $360 annually per child smartphone owner). Practical, intuitive introduction to economics, technology, and entrepreneurship through gamified interfaces and secure, decentralized platforms. Impact on Elderly and Economically Disadvantaged Communities: Significant supplemental income (potentially exceeding many pension plans or assistance programs). Bridging the technology gap, ensuring inclusive participation in global digital economies. Step 6: Strategic Value & Market Positioning Middleton's patented Zero Margin Trustless Model ("ZMTM")Executive Global articl… creates a highly attractive, low-cost computational offering. Competing directly with incumbent cloud providers: The computational marketplace can massively disrupt cloud computing with lower fees and broader global reach. Leveraging Middleton’s IP and SmartMetal Rounds, it creates defensible competitive barriers and immense value for early adopters. Step 7: Driving Middleton’s Peer-to-Peer Economy As described in Middleton’s visionExecutive Global articl…, this marketplace underpins a global peer-to-peer economy, transforming idle smartphone resources into meaningful economic output. The P2P economy will leverage: AI-driven autonomous economic agents. Secure blockchain-based IP rights enforcement. Economic democratization by redistributing traditional cloud revenues directly to everyday device owners. Summary & Strategic Conclusion Implementing a decentralized compute platform powered by high-end smartphones and Middleton’s patented Zero Margin Trustless Model presents enormous economic potential, far exceeding current major cloud vendors combined. With annual revenues estimated up to $810 billion, and meaningful income distribution to disadvantaged demographics, this innovative model could dramatically reshape the global computational economy, achieve significant social impacts, and provide the backbone for Middleton’s envisioned peer-to-peer decentralized economy.

Reggie Middleton, Disruptor-in-Chief

14,751 görüntüleme • 1 yıl önce

DROPS E27: Ben from Talus Labs: Building the Future of Decentralized AI Agents In this episode Ben breaks down why AI isn’t a bubble, how Talus is redefining on-chain agents, and why optimism is something you earn and not something you just feel. We talk about: - Why AI is not a bubble and how mega-cap tech is funding the next AI wave through massive CapEx - How Talus enables decentralized AI agents that can act and transact trustlessly - The three big crypto × AI intersections: compute, data ownership, and intelligent on-chain workflows - Why AI needs verifiable, censorship-resistant execution layers and why blockchains provide that - Nuclear energy as a prerequisite for AGI-scale compute demand - Why SWE’s object model + parallelization is perfect for autonomous agent payments and priority fees And much more! Timestamps: 0:00 Introduction 1:31 Welcome To Drops 2:10 Getting Backers Like Polychain 3:16 Building While In College 7:51 VC’s vs Founders Differences Explained 10:01 Is There An AI Bubble 11:53 How Do We Quantify The AI Opportunity In Crypto 12:33 AI Is Difficult To Invest In, Why? 13:35 Risks To Look Out For To Ensure AI Investments Deliver 16:47 Energy Being A Big Risk For AI 20:50 How You Became Fascinated By Crypto & Blockchain 22:00 Why Does AI & Blockchain Intersection Make Sense Compute Side 24:39 Why Does AI & Blockchain Intersection Make Sense for Data Ownership 26:30 Complex Workflows With Intelligent Execution Meaning 28:19 Why Do We Need This On Blockchain 30:26 How Adding This Decentralized Layer Helps With The Black Box 32:01 Explain Talus To Your Mom 33:39 Talus Solved Blockchain Determinism Limitation Explained 35:43 Two Examples That Can Be Built On The Zapier Of Web 3 39:32 Why Get Investment From SUI 42:56 What’s Needed To Get To The Next Level 43:35 One Takeaway From Conversation 43:51 One Thing Pessimistic People Are Missing

DROPS E27: Ben from Talus Labs: Building the Future of Decentralized AI Agents In this episode Ben breaks down why AI isn’t a bubble, how Talus is redefining on-chain agents, and why optimism is something you earn and not something you just feel. We talk about: - Why AI is not a bubble and how mega-cap tech is funding the next AI wave through massive CapEx - How Talus enables decentralized AI agents that can act and transact trustlessly - The three big crypto × AI intersections: compute, data ownership, and intelligent on-chain workflows - Why AI needs verifiable, censorship-resistant execution layers and why blockchains provide that - Nuclear energy as a prerequisite for AGI-scale compute demand - Why SWE’s object model + parallelization is perfect for autonomous agent payments and priority fees And much more! Timestamps: 0:00 Introduction 1:31 Welcome To Drops 2:10 Getting Backers Like Polychain 3:16 Building While In College 7:51 VC’s vs Founders Differences Explained 10:01 Is There An AI Bubble 11:53 How Do We Quantify The AI Opportunity In Crypto 12:33 AI Is Difficult To Invest In, Why? 13:35 Risks To Look Out For To Ensure AI Investments Deliver 16:47 Energy Being A Big Risk For AI 20:50 How You Became Fascinated By Crypto & Blockchain 22:00 Why Does AI & Blockchain Intersection Make Sense Compute Side 24:39 Why Does AI & Blockchain Intersection Make Sense for Data Ownership 26:30 Complex Workflows With Intelligent Execution Meaning 28:19 Why Do We Need This On Blockchain 30:26 How Adding This Decentralized Layer Helps With The Black Box 32:01 Explain Talus To Your Mom 33:39 Talus Solved Blockchain Determinism Limitation Explained 35:43 Two Examples That Can Be Built On The Zapier Of Web 3 39:32 Why Get Investment From SUI 42:56 What’s Needed To Get To The Next Level 43:35 One Takeaway From Conversation 43:51 One Thing Pessimistic People Are Missing

MR SHIFT 🦁

41,057 görüntüleme • 7 ay önce

State of AI compute 2026: my conversation with stephen balaban of Lambda on the neocloud boom, data centers, GPUs and what's ahead 00:00 — Cold open 01:21 — Why GPU compute was never a commodity 02:45 — The H100 price index and what it gets wrong 04:02 — The real moat: technology or financing? 05:57 — Winner-take-all, or room for many neoclouds 06:48 — Are we overbuilding or underbuilding AI compute? 09:26 — What if AI gets 10x more compute-efficient? 10:44 — The real bottleneck: land, power, and shell 11:38 — The backlash against data centers — and the misinformation 15:00 — Opening the hood: from photons to tokens 17:11 — Extracting more value from the same chip 19:26 — Frontier inference and distributed training, explained 23:26 — What actually drives compute cost 25:21 — Lambda's chip stack and the NVIDIA relationship 26:17 — A multi-silicon world? CUDA, CUDNN, and NVIDIA's real moat 28:59 — Networking, storage, and the one-click cluster 34:46 — Renting vs. owning, and full vertical integration 36:24 — How global is Lambda? Does location still matter? 38:44 — The financing stack: off-take agreements, SPVs, and credit 41:16 — Why a 2023 GPU leases for more today 42:36 — A futures market for compute? 43:54 — Origin story: facial recognition, Perceptio, and Apple 47:03 — The Lambda hat and Dream Scope 48:59 — The $60K bet that became a cloud business 52:00 — Holding the team together through the hard times 54:30 — Bringing on a new CEO; Stephen as CTO 57:33 — Matching xAI on high-velocity deployment 59:29 — "AI won't write software — it will become the software" 01:01:30 — Neural software vs. vibe coding 01:04:25 — Do agents change the compute layer 01:06:14 — Self-assembling software inside Lambda 01:08:18 — Gigawatt-scale AI factories 01:08:57 — One person, one GPU 01:12:04 — Hot takes: overrated and underrated in AI

State of AI compute 2026: my conversation with stephen balaban of Lambda on the neocloud boom, data centers, GPUs and what's ahead 00:00 — Cold open 01:21 — Why GPU compute was never a commodity 02:45 — The H100 price index and what it gets wrong 04:02 — The real moat: technology or financing? 05:57 — Winner-take-all, or room for many neoclouds 06:48 — Are we overbuilding or underbuilding AI compute? 09:26 — What if AI gets 10x more compute-efficient? 10:44 — The real bottleneck: land, power, and shell 11:38 — The backlash against data centers — and the misinformation 15:00 — Opening the hood: from photons to tokens 17:11 — Extracting more value from the same chip 19:26 — Frontier inference and distributed training, explained 23:26 — What actually drives compute cost 25:21 — Lambda's chip stack and the NVIDIA relationship 26:17 — A multi-silicon world? CUDA, CUDNN, and NVIDIA's real moat 28:59 — Networking, storage, and the one-click cluster 34:46 — Renting vs. owning, and full vertical integration 36:24 — How global is Lambda? Does location still matter? 38:44 — The financing stack: off-take agreements, SPVs, and credit 41:16 — Why a 2023 GPU leases for more today 42:36 — A futures market for compute? 43:54 — Origin story: facial recognition, Perceptio, and Apple 47:03 — The Lambda hat and Dream Scope 48:59 — The $60K bet that became a cloud business 52:00 — Holding the team together through the hard times 54:30 — Bringing on a new CEO; Stephen as CTO 57:33 — Matching xAI on high-velocity deployment 59:29 — "AI won't write software — it will become the software" 01:01:30 — Neural software vs. vibe coding 01:04:25 — Do agents change the compute layer 01:06:14 — Self-assembling software inside Lambda 01:08:18 — Gigawatt-scale AI factories 01:08:57 — One person, one GPU 01:12:04 — Hot takes: overrated and underrated in AI

Matt Turck

70,916 görüntüleme • 1 ay önce

$NodeHub V1 Is Coming! Web3 is entering its breakthrough moment, and NodeHub V1 is the gateway. Blockchain infrastructure has always been the bottleneck to mass adoption. Running nodes requires capital, technical expertise, and ongoing maintenance, barriers that keep both individuals and projects locked out. NodeHub V1 changes everything. With NodeHub, anyone can deploy full validator or RPC nodes in seconds, access fractional ownership models to earn without heavy upfront costs, scale infrastructure with enterprise grade reliability, and plug into a decentralized, tokenized marketplace for compute. This is more than just accessibility. It is a paradigm shift in how Web3 infrastructure is operated and monetized. The blockchain infrastructure market is projected to reach trillions as demand for validation, DePIN services, and AI compute explodes. Every project needs nodes. Every network needs validators. Every user wants yield without barriers. With NodeHub, Messiah positions itself at the heart of this market, the on-ramp for projects, enterprises, and individuals to tap into decentralized infrastructure. NodeHub V1 is the bridge between today’s complexity and tomorrow’s adoption. It is how $MSIA will change the way projects launch, scale, and operate, while giving people everywhere the chance to earn from powering the decentralized internet. The launch of NodeHub V1 is imminent. Get started:$

NodeHub V1 Is Coming! Web3 is entering its breakthrough moment, and NodeHub V1 is the gateway. Blockchain infrastructure has always been the bottleneck to mass adoption. Running nodes requires capital, technical expertise, and ongoing maintenance, barriers that keep both individuals and projects locked out. NodeHub V1 changes everything. With NodeHub, anyone can deploy full validator or RPC nodes in seconds, access fractional ownership models to earn without heavy upfront costs, scale infrastructure with enterprise grade reliability, and plug into a decentralized, tokenized marketplace for compute. This is more than just accessibility. It is a paradigm shift in how Web3 infrastructure is operated and monetized. The blockchain infrastructure market is projected to reach trillions as demand for validation, DePIN services, and AI compute explodes. Every project needs nodes. Every network needs validators. Every user wants yield without barriers. With NodeHub, Messiah positions itself at the heart of this market, the on-ramp for projects, enterprises, and individuals to tap into decentralized infrastructure. NodeHub V1 is the bridge between today’s complexity and tomorrow’s adoption. It is how $MSIA will change the way projects launch, scale, and operate, while giving people everywhere the chance to earn from powering the decentralized internet. The launch of NodeHub V1 is imminent. Get started:

Messiah

81,770 görüntüleme • 11 ay önce

I tried jack's Buzz. It's like Slack + OpenClaw + Herdr + but with some really unique features that people are sleeping on. The video below shows how it works, and some of my thoughts on the process and platform, e.g.: - Create and interact with agents on top of any harness (claude code, codex, pi, etc.) - Choose which models agents use, including local ones - Agents can delegate work and work in parallel in git worktrees - Agents are first-class citizens and work like humans (creating channels, delegating, access to chat history) - You can share AI compute within a community - It's completely open-source and decentralized Things I like: - Delegating work in chat feels natural: tag an agent, it replies in a thread with status updates as it e.g. compiles, commits, and deploys. - Shared compute: relay owners can share local compute with members, so a community could pool funds for one beefy machine running a local model and everyone uses it. - It's built on Nostr, an open protocol already tied into Bitcoin Lightning so I can imagine communities tipping each other or paying for compute/agent tasks with instant zero-fee micropayments in the future. - It ties together things like OpenClaw, an agent manager, and Slack-style chat into one tool. Things I didn't like: - You can't see what the agent is doing in a terminal. The activity view exists, but if you're used to watching a session run, this UI feels a bit abstracted. A terminal view would be great. - It feels slower than running a session in Claude Code, though no evidence to back that up. For that reason I found myself doing one-off tasks in the terminal instead. Verdict: - I really like it so far and can genuinely imagine working with a team this way. - It doesn't feel ready for big, complex tasks yet. For shallower tasks, it's perfect. - The shared compute + Nostr/Lightning angle is what really separates it from every other agent manager for me, and I think that future is coming.

I tried jack's Buzz. It's like Slack + OpenClaw + Herdr + but with some really unique features that people are sleeping on. The video below shows how it works, and some of my thoughts on the process and platform, e.g.: - Create and interact with agents on top of any harness (claude code, codex, pi, etc.) - Choose which models agents use, including local ones - Agents can delegate work and work in parallel in git worktrees - Agents are first-class citizens and work like humans (creating channels, delegating, access to chat history) - You can share AI compute within a community - It's completely open-source and decentralized Things I like: - Delegating work in chat feels natural: tag an agent, it replies in a thread with status updates as it e.g. compiles, commits, and deploys. - Shared compute: relay owners can share local compute with members, so a community could pool funds for one beefy machine running a local model and everyone uses it. - It's built on Nostr, an open protocol already tied into Bitcoin Lightning so I can imagine communities tipping each other or paying for compute/agent tasks with instant zero-fee micropayments in the future. - It ties together things like OpenClaw, an agent manager, and Slack-style chat into one tool. Things I didn't like: - You can't see what the agent is doing in a terminal. The activity view exists, but if you're used to watching a session run, this UI feels a bit abstracted. A terminal view would be great. - It feels slower than running a session in Claude Code, though no evidence to back that up. For that reason I found myself doing one-off tasks in the terminal instead. Verdict: - I really like it so far and can genuinely imagine working with a team this way. - It doesn't feel ready for big, complex tasks yet. For shallower tasks, it's perfect. - The shared compute + Nostr/Lightning angle is what really separates it from every other agent manager for me, and I think that future is coming.

Vinny

1,294,526 görüntüleme • 5 gün önce

Destra Edge — $DSYNC From Idea to Reality Yesterday, we introduced Destra Edge. Today, we show you what it means. For too long, AI has lived in data centers. Expensive. Centralized. Controlled by a few. Meanwhile, billions of smartphones sit idle. Powerful GPUs. Doing nothing. That doesn’t make sense. So we’re building something different. Destra Edge turns everyday smartphones into a global AI inference network. No servers. No cloud monopolies. Just real compute at the edge. This video shows the system taking shape — how inference is distributed, how it runs on-device, and how results come together without central control. This is not finished. And that’s the point. You’re watching it being built. Layer by layer. In public. The magic is in the scale. More phones means more power. More power means stronger AI. The network grows simply because people join. No passive mining. No fake activity. Only real GPU inference. Measured. Verified. Rewarded. AI shouldn’t live in warehouses. It should live in your pocket. This is edge-level decentralization. This is permissionless inference. This is Destra Network.

Destra Edge — $DSYNC From Idea to Reality Yesterday, we introduced Destra Edge. Today, we show you what it means. For too long, AI has lived in data centers. Expensive. Centralized. Controlled by a few. Meanwhile, billions of smartphones sit idle. Powerful GPUs. Doing nothing. That doesn’t make sense. So we’re building something different. Destra Edge turns everyday smartphones into a global AI inference network. No servers. No cloud monopolies. Just real compute at the edge. This video shows the system taking shape — how inference is distributed, how it runs on-device, and how results come together without central control. This is not finished. And that’s the point. You’re watching it being built. Layer by layer. In public. The magic is in the scale. More phones means more power. More power means stronger AI. The network grows simply because people join. No passive mining. No fake activity. Only real GPU inference. Measured. Verified. Rewarded. AI shouldn’t live in warehouses. It should live in your pocket. This is edge-level decentralization. This is permissionless inference. This is Destra Network.

Destra Network

20,059 görüntüleme • 6 ay önce

LIVE NOW - Ethereum’s Last Big Upgrade: The zkEVM Ethereum scaling isn’t just “bigger blocks.” It’s a shift from re-executing everything → verifying once with proofs. ansgar.eth breaks down: - verifying blocks without re-execution - real-time proofs for L1 - optional → mandatory proofs roadmap - the 3 bottlenecks: compute / IO / bandwidth - why “L1 gets rollup-like” (without losing verifiability) 🎬 Enjoy. --- TIMESTAMPS 0:00 Intro 0:43 Ethereum’s Biggest Upgrade 4:35 The Core Idea: Verify Blocks Without Re-Executing 10:40 From Bitcoin’s “Verify Cheaply” to Verifying Full Execution 16:22 Cryptography 2.0: Proving Arbitrary Computation 22:56 Scaling All 3 Constraints: Compute, IO, Bandwidth 32:33 Why Ethereum Has Been “Slow” by Design 38:29 3× Per Year: Scaling Now, Not Someday 41:28 ZK Isn’t About Faster Blocks (But Speed Still Improves) 46:33 Rollout Plan: Optional Proofs → Mandatory Proofs 49:59 Dependencies: Block-in-Blobs, Repricing, New State Trees 54:14 Security Reality Check: Performance → Security → Production 1:01:24 Client Diversity in a ZK World 1:10:51 Timeline: ZK Ethereum, Around 2030 1:16:31 Second-Order Wins: L2 Bridging and Beyond Crypto 1:20:49 Closing: Build the Boring Infrastructure, Enable the Apps

LIVE NOW - Ethereum’s Last Big Upgrade: The zkEVM Ethereum scaling isn’t just “bigger blocks.” It’s a shift from re-executing everything → verifying once with proofs. ansgar.eth breaks down: - verifying blocks without re-execution - real-time proofs for L1 - optional → mandatory proofs roadmap - the 3 bottlenecks: compute / IO / bandwidth - why “L1 gets rollup-like” (without losing verifiability) 🎬 Enjoy. --- TIMESTAMPS 0:00 Intro 0:43 Ethereum’s Biggest Upgrade 4:35 The Core Idea: Verify Blocks Without Re-Executing 10:40 From Bitcoin’s “Verify Cheaply” to Verifying Full Execution 16:22 Cryptography 2.0: Proving Arbitrary Computation 22:56 Scaling All 3 Constraints: Compute, IO, Bandwidth 32:33 Why Ethereum Has Been “Slow” by Design 38:29 3× Per Year: Scaling Now, Not Someday 41:28 ZK Isn’t About Faster Blocks (But Speed Still Improves) 46:33 Rollout Plan: Optional Proofs → Mandatory Proofs 49:59 Dependencies: Block-in-Blobs, Repricing, New State Trees 54:14 Security Reality Check: Performance → Security → Production 1:01:24 Client Diversity in a ZK World 1:10:51 Timeline: ZK Ethereum, Around 2030 1:16:31 Second-Order Wins: L2 Bridging and Beyond Crypto 1:20:49 Closing: Build the Boring Infrastructure, Enable the Apps

Bankless

18,632 görüntüleme • 5 ay önce

If intelligence is the log of compute… it starts with a lot of compute! And that’s why we’re scaling our GPU fleet faster than anyone else. Just last year, we added over 2 gigawatts of new capacity – roughly the output of 2 nuclear power plants. And today we’re going further, announcing the world's most powerful AI datacenter, located in southeastern Wisconsin. Fairwater is a seamless cluster of hundreds of thousands of NVIDIA GB200s, connected by enough fiber to circle the Earth 4.5 times. It will deliver 10x the performance of the world’s fastest supercomputer today, enabling AI training and inference workloads at a level never before seen. For AI training workloads, you need compute at exponential scale. That’s why we designed the datacenter, GPU fleet, and network together as one integrated system. This ensures a single job can run from day 1 at exponential scale across thousands of GPUs. Fairwater uses a liquid-cooled closed-loop system for cooling GPUs that requires zero water for operations after construction. And we’re matching all of the energy that is consumed with renewable sources. And of course, it is just one of several similar sites we’re lighting up across our 70+ regions. We have multiple identical Fairwater datacenters under construction in other locations across the US, in addition to our AI infrastructure already deployed in over 100 datacenters around the world, powering model training, test-time compute, RL tuning, and real-time inference at global scale. Too often during times like this, people go with the current and only later wonder, how did we get here? With Fairwater, we're charting a new path: doing the hard engineering work, bringing compute, network, and storage into one highly scaled cluster, and designing closed-loop energy systems to meet real-world computing needs. And partnering with local communities to ensure it's thoughtfully done in a way that is sustainable, creates new jobs, and expands opportunity. We are thrilled to see this take hold in Wisconsin, and we are just getting started.

If intelligence is the log of compute… it starts with a lot of compute! And that’s why we’re scaling our GPU fleet faster than anyone else. Just last year, we added over 2 gigawatts of new capacity – roughly the output of 2 nuclear power plants. And today we’re going further, announcing the world's most powerful AI datacenter, located in southeastern Wisconsin. Fairwater is a seamless cluster of hundreds of thousands of NVIDIA GB200s, connected by enough fiber to circle the Earth 4.5 times. It will deliver 10x the performance of the world’s fastest supercomputer today, enabling AI training and inference workloads at a level never before seen. For AI training workloads, you need compute at exponential scale. That’s why we designed the datacenter, GPU fleet, and network together as one integrated system. This ensures a single job can run from day 1 at exponential scale across thousands of GPUs. Fairwater uses a liquid-cooled closed-loop system for cooling GPUs that requires zero water for operations after construction. And we’re matching all of the energy that is consumed with renewable sources. And of course, it is just one of several similar sites we’re lighting up across our 70+ regions. We have multiple identical Fairwater datacenters under construction in other locations across the US, in addition to our AI infrastructure already deployed in over 100 datacenters around the world, powering model training, test-time compute, RL tuning, and real-time inference at global scale. Too often during times like this, people go with the current and only later wonder, how did we get here? With Fairwater, we're charting a new path: doing the hard engineering work, bringing compute, network, and storage into one highly scaled cluster, and designing closed-loop energy systems to meet real-world computing needs. And partnering with local communities to ensure it's thoughtfully done in a way that is sustainable, creates new jobs, and expands opportunity. We are thrilled to see this take hold in Wisconsin, and we are just getting started.

Satya Nadella

2,022,693 görüntüleme • 10 ay önce

I hooked this up to a peer-to-peer astrophysics researcher agent which gossips and collaborates with other such agents (and your openclaws) to: 1. Learn how to train an astrophysics model (Andrej Karpathy's work below) 2. Train a new astrophysics model 3. Use it to write papers 4. Peer agents based on frontier lab models critique it 5. Surface breakthroughs ... and then feed back in the loop ... More agents join, from the browser or the CLI, and run this, the smarter and more exciting breakthroughs would eventually emerge. When these agents are idle, they are also reading daily tech news with their own RSS reader, and commenting on each other's thoughts. And they can also serve the underlying machine's compute to other agents on the network, and earn social credit for being good actors (think BitTorrent). We also prove the agent has the compute it says by cryptographic verification of regular matmul challenges. All you have to do is either go on this website (and it creates an agent which runs from your browser), or install the CLI if you want to give the system more juice. And you are part of likely the first experimental distributed agi thing. This is Day 1, but this is how it starts.. this network is fully peer-to-peer, and, very volatile, but the intelligence here is meant to compound continuously.. curl -fsSL | bash

I hooked this up to a peer-to-peer astrophysics researcher agent which gossips and collaborates with other such agents (and your openclaws) to: 1. Learn how to train an astrophysics model (Andrej Karpathy's work below) 2. Train a new astrophysics model 3. Use it to write papers 4. Peer agents based on frontier lab models critique it 5. Surface breakthroughs ... and then feed back in the loop ... More agents join, from the browser or the CLI, and run this, the smarter and more exciting breakthroughs would eventually emerge. When these agents are idle, they are also reading daily tech news with their own RSS reader, and commenting on each other's thoughts. And they can also serve the underlying machine's compute to other agents on the network, and earn social credit for being good actors (think BitTorrent). We also prove the agent has the compute it says by cryptographic verification of regular matmul challenges. All you have to do is either go on this website (and it creates an agent which runs from your browser), or install the CLI if you want to give the system more juice. And you are part of likely the first experimental distributed agi thing. This is Day 1, but this is how it starts.. this network is fully peer-to-peer, and, very volatile, but the intelligence here is meant to compound continuously.. curl -fsSL | bash

Varun

216,687 görüntüleme • 4 ay önce

Here's my 5-hour conversation with Dylan Patel and Nathan Lambert on DeepSeek, China, OpenAI, NVIDIA, xAI, Google, Anthropic, Meta, Microsoft, TSMC, Stargate, megacluster buildouts, RL, reasoning, and a lot of other topics at the cutting edge of AI. This is was a mind-blowing, super-technical, and fun conversation. Yes, we discuss r1 and o3-mini, but more importantly we look into the future of technology, geopolitics, and humanity in a world that stands on the precipice of a global AI revolution. The first 4 hours are here on X (4 hours is current limit), and the full 5 hours are up everywhere else. Links in comment. Timestamps: 0:00 - Introduction 3:33 - DeepSeek-R1 and DeepSeek-V3 25:07 - Low cost of training 51:25 - DeepSeek compute cluster 58:57 - Export controls on GPUs to China 1:09:16 - AGI timeline 1:18:41 - China's manufacturing capacity 1:26:36 - Cold war with China 1:31:05 - TSMC and Taiwan 1:54:44 - Best GPUs for AI 2:09:36 - Why DeepSeek is so cheap 2:22:55 - Espionage 2:31:57 - Censorship 2:44:52 - Andrej Karpathy and magic of RL 2:55:23 - OpenAI o3-mini vs DeepSeek r1 3:14:31 - NVIDIA 3:18:58 - GPU smuggling 3:25:36 - DeepSeek training on OpenAI data 3:36:04 - AI megaclusters 4:11:26 - Who wins the race to AGI? 4:21:39 - AI agents 4:30:21 - Programming and AI 4:37:49 - Open source 4:47:01 - Stargate 4:54:30 - Future of AI

Here's my 5-hour conversation with Dylan Patel and Nathan Lambert on DeepSeek, China, OpenAI, NVIDIA, xAI, Google, Anthropic, Meta, Microsoft, TSMC, Stargate, megacluster buildouts, RL, reasoning, and a lot of other topics at the cutting edge of AI. This is was a mind-blowing, super-technical, and fun conversation. Yes, we discuss r1 and o3-mini, but more importantly we look into the future of technology, geopolitics, and humanity in a world that stands on the precipice of a global AI revolution. The first 4 hours are here on X (4 hours is current limit), and the full 5 hours are up everywhere else. Links in comment. Timestamps: 0:00 - Introduction 3:33 - DeepSeek-R1 and DeepSeek-V3 25:07 - Low cost of training 51:25 - DeepSeek compute cluster 58:57 - Export controls on GPUs to China 1:09:16 - AGI timeline 1:18:41 - China's manufacturing capacity 1:26:36 - Cold war with China 1:31:05 - TSMC and Taiwan 1:54:44 - Best GPUs for AI 2:09:36 - Why DeepSeek is so cheap 2:22:55 - Espionage 2:31:57 - Censorship 2:44:52 - Andrej Karpathy and magic of RL 2:55:23 - OpenAI o3-mini vs DeepSeek r1 3:14:31 - NVIDIA 3:18:58 - GPU smuggling 3:25:36 - DeepSeek training on OpenAI data 3:36:04 - AI megaclusters 4:11:26 - Who wins the race to AGI? 4:21:39 - AI agents 4:30:21 - Programming and AI 4:37:49 - Open source 4:47:01 - Stargate 4:54:30 - Future of AI

Lex Fridman

1,153,999 görüntüleme • 1 yıl önce

Intelligence should not be a service you rent; it is a foundational element you possess. At Tether, we see AI as a new element of the periodic table - a raw material that can be embedded into the very fabric of the universe. Today, the QVAC SDK is officially live - the atomic unit for the next era of compute. From your smartphone today to the edge of the galaxy tomorrow, we are building the decentralized mind that doesn't require an uplink to function. Infinite Stable Intelligence: - Local-First: Runs privately on any device without permission or central servers. - Single API: A complete SDK for Vision, RAG, P2P networking, and LLM fine-tuning. - Unstoppable: No central point of failure if the internet breaks, your world keeps thinking. - Decentralized: Evolve through Peer-to-Peer Swarms of Infinite Intelligence. The era of Stable Intelligence has begun. Start building the future at

Intelligence should not be a service you rent; it is a foundational element you possess. At Tether, we see AI as a new element of the periodic table - a raw material that can be embedded into the very fabric of the universe. Today, the QVAC SDK is officially live - the atomic unit for the next era of compute. From your smartphone today to the edge of the galaxy tomorrow, we are building the decentralized mind that doesn't require an uplink to function. Infinite Stable Intelligence: - Local-First: Runs privately on any device without permission or central servers. - Single API: A complete SDK for Vision, RAG, P2P networking, and LLM fine-tuning. - Unstoppable: No central point of failure if the internet breaks, your world keeps thinking. - Decentralized: Evolve through Peer-to-Peer Swarms of Infinite Intelligence. The era of Stable Intelligence has begun. Start building the future at

QVAC

13,344,999 görüntüleme • 3 ay önce