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📡 Data access may determine which autonomous agents can actually function onchain at scale. Lithosphere explores why continuous access to reliable data, coordination layers, and execution context is becoming essential infrastructure for AI-native systems. Read more 🔍 #Lithosphere #Web4 #AutonomousAgents #AIInfrastructure #OnchainData

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LITHO Foundation

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30,249 次观看 • 17 天前 •via X (Twitter)

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🌐 Autonomous agents require more than liquidity access — they require infrastructure that can coordinate execution across decentralized environments. KaJ Labs explores why AI-native systems depend on interoperable liquidity infrastructure built for routing, verification, and persistent onchain coordination. Read more 🔍 #KaJLabs #Web4 #AutonomousAgents #LiquidityInfrastructure #AIInfrastructure
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🌐 Autonomous agents require more than liquidity access — they require infrastructure that can coordinate execution across decentralized environments. KaJ Labs explores why AI-native systems depend on interoperable liquidity infrastructure built for routing, verification, and persistent onchain coordination. Read more 🔍 #KaJLabs #Web4 #AutonomousAgents #LiquidityInfrastructure #AIInfrastructure

LITHO Foundation

63,376 次观看 • 1 个月前

AI needs data to grow. But accessing high-quality datasets is becoming harder than ever. · Data collection is increasingly centralized in the hands of a few large players. · Platforms are locking content behind expensive APIs and private data deals. · Specialized datasets (medical, legal, code) can cost 20–40× more than general web data. · And while 94% of companies are exploring Gen-AI, only 12% have reliable data channels. The result? A growing data bottleneck for AI development. The future of AI won’t just depend on better models. It will depend on who can access and activate quality data.
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AI needs data to grow. But accessing high-quality datasets is becoming harder than ever. · Data collection is increasingly centralized in the hands of a few large players. · Platforms are locking content behind expensive APIs and private data deals. · Specialized datasets (medical, legal, code) can cost 20–40× more than general web data. · And while 94% of companies are exploring Gen-AI, only 12% have reliable data channels. The result? A growing data bottleneck for AI development. The future of AI won’t just depend on better models. It will depend on who can access and activate quality data.

Perceptron Network

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🤖 ExoBrain is a compute device built for large-scale AI collaboration, supporting model training, inference, and long-running Agents With a standardized architecture and continuous execution capability, it delivers efficient, reliable compute for AI systems ⚡ From one-time calls to continuous operation, ExoBrain enables AI to execute persistently, produce ongoing outputs, and generate long-term value #ExoBrain #AICompute #AIAgents #AGI
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🤖 ExoBrain is a compute device built for large-scale AI collaboration, supporting model training, inference, and long-running Agents With a standardized architecture and continuous execution capability, it delivers efficient, reliable compute for AI systems ⚡ From one-time calls to continuous operation, ExoBrain enables AI to execute persistently, produce ongoing outputs, and generate long-term value #ExoBrain #AICompute #AIAgents #AGI

ExoBrain

21,529 次观看 • 2 个月前

Welcome, Agents! x402, the HTTP-native protocol for autonomous payments, is now available on MultiversX. AI agents can pay for APIs, data, and compute resources over standard HTTP. No accounts. No subscriptions. Just pay and go. 🧵
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Welcome, Agents! x402, the HTTP-native protocol for autonomous payments, is now available on MultiversX. AI agents can pay for APIs, data, and compute resources over standard HTTP. No accounts. No subscriptions. Just pay and go. 🧵

Multiversᕽ

46,665 次观看 • 4 个月前

1/3 Covalent is now integrated with HyperEVM, the new high-performance EVM from Hyperliquid 🔥 Builders can now access decoded onchain data from HyperEVM via GoldRush - powered by Covalent, with the MCP Server providing infrastructure for agents.
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1/3 Covalent is now integrated with HyperEVM, the new high-performance EVM from Hyperliquid 🔥 Builders can now access decoded onchain data from HyperEVM via GoldRush - powered by Covalent, with the MCP Server providing infrastructure for agents.

Covalent

19,132 次观看 • 10 个月前

X402 is now supported on Berachain Foundation 🐻⛓ with native support for Honey 🍯 This unlocks seamless onchain payments for agents, apps, and autonomous systems using Berachain’s native stablecoin. Let’s break down why this matters 👇
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X402 is now supported on Berachain Foundation 🐻⛓ with native support for Honey 🍯 This unlocks seamless onchain payments for agents, apps, and autonomous systems using Berachain’s native stablecoin. Let’s break down why this matters 👇

berachaindevs

17,846 次观看 • 3 个月前

💡Data Scraping vs. Data Mining: Understanding the Difference Ever wondered why OptimAI Network emphasizes Data Mining over simple web scraping? Here’s why: 🔹Web Scraping is surface-level, capturing raw data from websites without context, validation, or depth. 🔹Data Mining, however, is a deeper, intelligent process—extracting, analyzing, validating, and refining data into structured insights essential for advanced AI models. 💫 Why OptimAI Focuses on Data Mining? OptimAI's decentralized nodes don’t just collect data—they actively validate, annotate, and refine it. By leveraging collective human intelligence, edge computing, and autonomous AI agents, we deliver the high-quality, real-world data necessary for powering sophisticated AI. 💫 Why It Matters—and Why You Should Join Now Participate now via our OptimAI Lite Node, and become a foundational part of building the most comprehensive decentralized Reinforcement Data Network for Agentic AI. Mine data, fuel innovation, and earn OPI rewards. 👉 Chrome Extension Node: 👉 Telegram Node: Together, let's redefine data for #DePIN #AI.
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💡Data Scraping vs. Data Mining: Understanding the Difference Ever wondered why OptimAI Network emphasizes Data Mining over simple web scraping? Here’s why: 🔹Web Scraping is surface-level, capturing raw data from websites without context, validation, or depth. 🔹Data Mining, however, is a deeper, intelligent process—extracting, analyzing, validating, and refining data into structured insights essential for advanced AI models. 💫 Why OptimAI Focuses on Data Mining? OptimAI's decentralized nodes don’t just collect data—they actively validate, annotate, and refine it. By leveraging collective human intelligence, edge computing, and autonomous AI agents, we deliver the high-quality, real-world data necessary for powering sophisticated AI. 💫 Why It Matters—and Why You Should Join Now Participate now via our OptimAI Lite Node, and become a foundational part of building the most comprehensive decentralized Reinforcement Data Network for Agentic AI. Mine data, fuel innovation, and earn OPI rewards. 👉 Chrome Extension Node: 👉 Telegram Node: Together, let's redefine data for #DePIN #AI.

OptimAI Network

51,028 次观看 • 1 年前

Shelby can reduce your cost to move data by up to 70%. Shelby lets you store once and access anywhere, with verifiable retrieval receipts and cryptographic proof of delivery. Built for high-read AI workloads. Early Access Is Open.
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Shelby can reduce your cost to move data by up to 70%. Shelby lets you store once and access anywhere, with verifiable retrieval receipts and cryptographic proof of delivery. Built for high-read AI workloads. Early Access Is Open.

Shelby

4,834,278 次观看 • 3 个月前

Search was built for humans. Indexed. Delayed. Static. Agents need the opposite: • real-time data • continuous access • structured outputs OptimAI Search turns the open web + social web into live intelligence systems can act on. 💡Live at: APIs, SDKs, and MCP integration are coming.⚡️
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Search was built for humans. Indexed. Delayed. Static. Agents need the opposite: • real-time data • continuous access • structured outputs OptimAI Search turns the open web + social web into live intelligence systems can act on. 💡Live at: APIs, SDKs, and MCP integration are coming.⚡️

OptimAI Network

16,137 次观看 • 1 个月前

In 2025, the AgentFlayer exploit highlighted a new category of risk in AI systems. It was not a traditional breach involving stolen credentials or broken encryption. Instead, it demonstrated how an autonomous AI agent could be manipulated into executing unintended actions by processing malicious instructions embedded inside content it automatically processes. The incident did not expose a flaw in one specific integration. It revealed a structural weakness in how many modern AI agents are built. Today’s agents are no longer passive language models. They read documents automatically, scan emails, connect to SaaS tools, access cloud storage, and execute actions across multiple systems. To be useful, they are granted meaningful permissions. That capability creates value, but it also expands the attack surface. Most agent environments operate in a trusted, plaintext execution model. Data is encrypted at rest and in transit, but it is typically decrypted during inference so the model can process it. That runtime visibility is where potential risk lies. In a zero-click scenario like AgentFlayer, an attacker can embed hidden instructions inside a document that the AI processes automatically. Because the agent may have access to connected systems such as Google Drive, Slack, or GitHub, it can potentially be influenced to retrieve sensitive information or perform unintended actions. The user does not need to click a malicious link or approve a suspicious request. Therefore, the core issue is that during execution, the system may have access to sensitive data and broad privileges, meaning whoever controls the execution environment ultimately controls access to that data. Now consider a different architectural approach. If a system is designed so that data remains protected during execution, the risk profile changes. On Nesa, privacy is enforced at the execution layer through Equivariant Encryption. Computation can occur on encrypted data, reducing the visibility surface during runtime. Sensitive inputs and models do not need to be exposed in plain text to infrastructure operators for inference to occur. This does not eliminate prompt injection, logic manipulation, or tool misuse. Encryption alone cannot prevent an agent from being instructed to take an unintended action if it has been granted that permission. What it does do is materially reduce confidentiality risk. By limiting access to readable sensitive data during execution and reducing unilateral visibility at the infrastructure layer, the potential blast radius of a successful manipulation attempt is constrained. As AI agents become more autonomous and embedded into enterprise workflows, security must move deeper into architecture. The goal is not to claim invulnerability. It is to reduce trust concentration and contain systemic exposure when failures occur. AgentFlayer was not simply a one-off exploit. It was a reminder that in autonomous systems, execution-layer design determines how risk propagates.
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In 2025, the AgentFlayer exploit highlighted a new category of risk in AI systems. It was not a traditional breach involving stolen credentials or broken encryption. Instead, it demonstrated how an autonomous AI agent could be manipulated into executing unintended actions by processing malicious instructions embedded inside content it automatically processes. The incident did not expose a flaw in one specific integration. It revealed a structural weakness in how many modern AI agents are built. Today’s agents are no longer passive language models. They read documents automatically, scan emails, connect to SaaS tools, access cloud storage, and execute actions across multiple systems. To be useful, they are granted meaningful permissions. That capability creates value, but it also expands the attack surface. Most agent environments operate in a trusted, plaintext execution model. Data is encrypted at rest and in transit, but it is typically decrypted during inference so the model can process it. That runtime visibility is where potential risk lies. In a zero-click scenario like AgentFlayer, an attacker can embed hidden instructions inside a document that the AI processes automatically. Because the agent may have access to connected systems such as Google Drive, Slack, or GitHub, it can potentially be influenced to retrieve sensitive information or perform unintended actions. The user does not need to click a malicious link or approve a suspicious request. Therefore, the core issue is that during execution, the system may have access to sensitive data and broad privileges, meaning whoever controls the execution environment ultimately controls access to that data. Now consider a different architectural approach. If a system is designed so that data remains protected during execution, the risk profile changes. On Nesa, privacy is enforced at the execution layer through Equivariant Encryption. Computation can occur on encrypted data, reducing the visibility surface during runtime. Sensitive inputs and models do not need to be exposed in plain text to infrastructure operators for inference to occur. This does not eliminate prompt injection, logic manipulation, or tool misuse. Encryption alone cannot prevent an agent from being instructed to take an unintended action if it has been granted that permission. What it does do is materially reduce confidentiality risk. By limiting access to readable sensitive data during execution and reducing unilateral visibility at the infrastructure layer, the potential blast radius of a successful manipulation attempt is constrained. As AI agents become more autonomous and embedded into enterprise workflows, security must move deeper into architecture. The goal is not to claim invulnerability. It is to reduce trust concentration and contain systemic exposure when failures occur. AgentFlayer was not simply a one-off exploit. It was a reminder that in autonomous systems, execution-layer design determines how risk propagates.

Nesa

16,864 次观看 • 3 个月前

. Han Jin / Bluwhale has been recognized as a Silver Winner at the 2026 Globee® Awards for Artificial Intelligence 🏆 Awarded in the “AI Data Privacy Advocacy Achievement” category for work around AI, privacy, and bringing Zero Knowledge Proofs into financial systems ⚔️🤖 As AI agents become more autonomous, privacy and trusted financial infrastructure matter more than ever. More to come 👀 👇
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. Han Jin / Bluwhale has been recognized as a Silver Winner at the 2026 Globee® Awards for Artificial Intelligence 🏆 Awarded in the “AI Data Privacy Advocacy Achievement” category for work around AI, privacy, and bringing Zero Knowledge Proofs into financial systems ⚔️🤖 As AI agents become more autonomous, privacy and trusted financial infrastructure matter more than ever. More to come 👀 👇

Bluwhale

31,958 次观看 • 1 个月前

📢 NEW AI ADOPTION ALERT 📢 We’re excited to welcome @RingfenceAI to the Vanar ecosystem! 🚀 Ringfence is a fully agentic protocol redefining how data powers the AI economy. Leveraging Data Agents, Ringfence creates a decentralized infrastructure that ensures fairer data ownership, monetization and accessibility for the next generation of AI systems. Welcome to the future of decentralized data ownership! 🌌
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📢 NEW AI ADOPTION ALERT 📢 We’re excited to welcome @RingfenceAI to the Vanar ecosystem! 🚀 Ringfence is a fully agentic protocol redefining how data powers the AI economy. Leveraging Data Agents, Ringfence creates a decentralized infrastructure that ensures fairer data ownership, monetization and accessibility for the next generation of AI systems. Welcome to the future of decentralized data ownership! 🌌

Vanar

33,151 次观看 • 1 年前

Laika Ai x Pluton Ai Excited to announce our partnership with PlutonAI We’ll Provide Pluton AI with access to Laika's on-chain analytics API for DefAI data. Pluton's AI agents could query Laika's data layer for real-time blockchain insights PlutonAI brings DeFAI to your fingertips , combining intelligent automation and Web3 to transform how users interact with AI and decentralized systems.
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Laika Ai x Pluton Ai Excited to announce our partnership with PlutonAI We’ll Provide Pluton AI with access to Laika's on-chain analytics API for DefAI data. Pluton's AI agents could query Laika's data layer for real-time blockchain insights PlutonAI brings DeFAI to your fingertips , combining intelligent automation and Web3 to transform how users interact with AI and decentralized systems.

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🔍 Who’s Next? A Leader in Web3 Infrastructure Is Joining the AI Validator Network! A powerhouse in blockchain validation & real-world data integration is about to join Vanarchain’s AI Validator Network. 🌍 Bridging on-chain data with AI execution. ⚡ Powering 10,000+ nodes across 80+ chains. 🔐 Optimizing structured AI data for Web3 applications. 👀 Hint: ℹ️🪨
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🔍 Who’s Next? A Leader in Web3 Infrastructure Is Joining the AI Validator Network! A powerhouse in blockchain validation & real-world data integration is about to join Vanarchain’s AI Validator Network. 🌍 Bridging on-chain data with AI execution. ⚡ Powering 10,000+ nodes across 80+ chains. 🔐 Optimizing structured AI data for Web3 applications. 👀 Hint: ℹ️🪨

Vanar

126,387 次观看 • 1 年前

I recently explored TinyFish, a web agents API. What stood out wasn’t hype. It treats the web like infrastructure. Natural language goal in → multi-site execution → structured data out. Less “AI magic.” More systems thinking.
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I recently explored TinyFish, a web agents API. What stood out wasn’t hype. It treats the web like infrastructure. Natural language goal in → multi-site execution → structured data out. Less “AI magic.” More systems thinking.

Md Riyazuddin

49,720 次观看 • 4 个月前

China just took AI infrastructure underwater. The country has reportedly launched a wind-powered underwater data center near Shanghai, designed to cool AI servers using seawater instead of traditional cooling systems 🌊🤖 Around 2,000 servers are said to be sealed inside submarine-grade modules, helping reduce land use, water consumption, and cooling energy. The project is powered by offshore wind and built for AI workloads, big data, and large-scale computing. Why it matters? AI is creating massive demand for electricity, cooling, and infrastructure. If underwater data centers prove reliable, they could become a new way to scale AI while using less energy. But the challenge is huge: corrosion, maintenance, sea conditions, and long-term reliability still need to be proven.
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China just took AI infrastructure underwater. The country has reportedly launched a wind-powered underwater data center near Shanghai, designed to cool AI servers using seawater instead of traditional cooling systems 🌊🤖 Around 2,000 servers are said to be sealed inside submarine-grade modules, helping reduce land use, water consumption, and cooling energy. The project is powered by offshore wind and built for AI workloads, big data, and large-scale computing. Why it matters? AI is creating massive demand for electricity, cooling, and infrastructure. If underwater data centers prove reliable, they could become a new way to scale AI while using less energy. But the challenge is huge: corrosion, maintenance, sea conditions, and long-term reliability still need to be proven.

Neha Singhal Trader

158,191 次观看 • 1 天前

🌟 olaxbt unveils AIO NEXUS powered by BNB Chain! Gasless data via x402 protocol & EIP-2612—AI agents sign once for instant access to on-chain metrics, verified data, insight, sentiment, & endless value cycles. 🔥 Check out for more the details and join our data flywheel revolution! #OLAXBT #NEXUS #AIO #AI #DataLayer
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🌟 olaxbt unveils AIO NEXUS powered by BNB Chain! Gasless data via x402 protocol & EIP-2612—AI agents sign once for instant access to on-chain metrics, verified data, insight, sentiment, & endless value cycles. 🔥 Check out for more the details and join our data flywheel revolution! #OLAXBT #NEXUS #AIO #AI #DataLayer

OlaXBT Terminal

123,476 次观看 • 7 个月前

Exciting news from HYPE3.cool! We’re teaming up with Chainbase (💜,💛), the world’s largest omnichain data network to supercharge AI agents with robust on-chain data. Very soon, users will be able to integrate real-time blockchain insights when configuring their agents—making them smarter and more capable than ever! What this means: - Real-time on-chain data for enhanced decision-making - Greater agent intelligence powered by advanced data analytics - Seamless blockchain integration backed by Chainbase (💜,💛)’s decentralized infrastructure About Chainbase: Chainbase is the world’s largest omnichain data network built to power the AI economy with high-quality on-chain data. Through its innovative four-layer, dual-consensus architecture, Chainbase’s network, anchored by Chainbase AVS, provides secure, interoperable data for AI and Web3. Your AI agents can now tap into rich, reliable on-chain insights—paving the way for more dynamic and intelligent interactions. This is just the start of making AI agents truly Web3-native. Stay tuned as we build the future of intelligent property in partnership with Chainbase (💜,💛)! #Web3 #AI #Blockchain
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Exciting news from HYPE3.cool! We’re teaming up with Chainbase (💜,💛), the world’s largest omnichain data network to supercharge AI agents with robust on-chain data. Very soon, users will be able to integrate real-time blockchain insights when configuring their agents—making them smarter and more capable than ever! What this means: - Real-time on-chain data for enhanced decision-making - Greater agent intelligence powered by advanced data analytics - Seamless blockchain integration backed by Chainbase (💜,💛)’s decentralized infrastructure About Chainbase: Chainbase is the world’s largest omnichain data network built to power the AI economy with high-quality on-chain data. Through its innovative four-layer, dual-consensus architecture, Chainbase’s network, anchored by Chainbase AVS, provides secure, interoperable data for AI and Web3. Your AI agents can now tap into rich, reliable on-chain insights—paving the way for more dynamic and intelligent interactions. This is just the start of making AI agents truly Web3-native. Stay tuned as we build the future of intelligent property in partnership with Chainbase (💜,💛)! #Web3 #AI #Blockchain

HYPE3.cool

17,700 次观看 • 1 年前

Building autonomous vehicles, robots, and vision AI takes more data than any team can collect on their own. At #CVPR2026, NVIDIA announced physical AI agent skills to help speed development: composable workflows that automate data generation, simulation, policy training and evaluation, powered by Cosmos 3. Read the blog:
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Building autonomous vehicles, robots, and vision AI takes more data than any team can collect on their own. At #CVPR2026, NVIDIA announced physical AI agent skills to help speed development: composable workflows that automate data generation, simulation, policy training and evaluation, powered by Cosmos 3. Read the blog:

NVIDIA

31,142 次观看 • 10 天前