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🔗 Utility becomes meaningful when it maps to real network activity. Lithosphere highlights how LITHO supports execution, coordination, verification, cross-chain interaction, and agent operations across AI-native Web4 infrastructure. Learn here 🔍 Read more 🔍 #Lithosphere #LITHO #Web4 #AIInfrastructure #AutonomousAgents

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LITHO Foundation

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58,308 次观看 • 2 个月前 •via X (Twitter)

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🧬 Web4 infrastructure works better when core systems operate together. Lithosphere connects identity, naming, AI-native execution, and cross-chain coordination into one stack for users, applications, and autonomous agent activity. #Lithosphere #Web4 #AIInfrastructure #Interoperability #AutonomousAgents
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🧬 Web4 infrastructure works better when core systems operate together. Lithosphere connects identity, naming, AI-native execution, and cross-chain coordination into one stack for users, applications, and autonomous agent activity. #Lithosphere #Web4 #AIInfrastructure #Interoperability #AutonomousAgents

LITHO Foundation

33,091 次观看 • 1 个月前

🛠️ Deploying autonomous agents requires infrastructure built for coordination, execution, and lifecycle management. Lithosphere is advancing an agent deployment framework designed to support scalable AI-native systems operating across decentralized environments. Read more 🔍 #Lithosphere #Web4 #AutonomousAgents #AgentDeployment #AIInfrastructure
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🛠️ Deploying autonomous agents requires infrastructure built for coordination, execution, and lifecycle management. Lithosphere is advancing an agent deployment framework designed to support scalable AI-native systems operating across decentralized environments. Read more 🔍 #Lithosphere #Web4 #AutonomousAgents #AgentDeployment #AIInfrastructure

LITHO Foundation

28,866 次观看 • 2 个月前

🚪 Launch access becomes stronger when onboarding has one clear path. Ignite strengthens its main participation gateway for the planned LITHO TGE, improving access, launch coordination, and readiness across the Lithosphere ecosystem. Learn here 🔍 #Ignite #LITHO #Web4 #BlockchainInfrastructure #AIInfrastructure
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🚪 Launch access becomes stronger when onboarding has one clear path. Ignite strengthens its main participation gateway for the planned LITHO TGE, improving access, launch coordination, and readiness across the Lithosphere ecosystem. Learn here 🔍 #Ignite #LITHO #Web4 #BlockchainInfrastructure #AIInfrastructure

LITHO Foundation

33,152 次观看 • 1 个月前

🚀 Agent deployment is becoming a core infrastructure layer for Web4 systems. Lithosphere explores why scalable deployment frameworks are essential for launching, managing, and coordinating autonomous agents across decentralized networks. Read more 🔍 #Lithosphere #Web4 #AutonomousAgents #AgentDeployment #AIInfrastructure
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🚀 Agent deployment is becoming a core infrastructure layer for Web4 systems. Lithosphere explores why scalable deployment frameworks are essential for launching, managing, and coordinating autonomous agents across decentralized networks. Read more 🔍 #Lithosphere #Web4 #AutonomousAgents #AgentDeployment #AIInfrastructure

LITHO Foundation

30,373 次观看 • 2 个月前

📡 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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📡 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

LITHO Foundation

30,249 次观看 • 2 个月前

🧱 A Layer 1 feels stronger when the products are already in motion. Lithosphere advances its product-led ecosystem after the LITHO Pre-TGE opening, connecting Thanos Wallet, Ignite DEX, Quantts AI, and Makalu Testnet with AI-native Web4 infrastructure. #Lithosphere #LITHO #Web4 #AIInfrastructure #BlockchainInfrastructure
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🧱 A Layer 1 feels stronger when the products are already in motion. Lithosphere advances its product-led ecosystem after the LITHO Pre-TGE opening, connecting Thanos Wallet, Ignite DEX, Quantts AI, and Makalu Testnet with AI-native Web4 infrastructure. #Lithosphere #LITHO #Web4 #AIInfrastructure #BlockchainInfrastructure

LITHO Foundation

21,003 次观看 • 15 天前

💸 Machine-to-machine payments may become the financial backbone of autonomous agent economies. KaJ Labs explores how AI-native systems will require programmable payment infrastructure for continuous coordination, execution, and value exchange across decentralized networks. Read more 🔍 #KaJLabs #Web4 #AutonomousAgents #MachinePayments #AIInfrastructure
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💸 Machine-to-machine payments may become the financial backbone of autonomous agent economies. KaJ Labs explores how AI-native systems will require programmable payment infrastructure for continuous coordination, execution, and value exchange across decentralized networks. Read more 🔍 #KaJLabs #Web4 #AutonomousAgents #MachinePayments #AIInfrastructure

LITHO Foundation

30,416 次观看 • 2 个月前

🌐 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 • 3 个月前

🧱 Speed means less when systems disagree on what happened. KaJ Labs frames standards as the real foundation for agent infrastructure, keeping verification, identity, execution, governance, and settlement consistent across multi-step Web4 workflows. #KaJLabs #Web4 #AutonomousAgents #AIInfrastructure #Governance
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🧱 Speed means less when systems disagree on what happened. KaJ Labs frames standards as the real foundation for agent infrastructure, keeping verification, identity, execution, governance, and settlement consistent across multi-step Web4 workflows. #KaJLabs #Web4 #AutonomousAgents #AIInfrastructure #Governance

LITHO Foundation

21,136 次观看 • 22 天前

For a long time, staking usually meant one thing: Lock your tokens and wait 🤷‍♂️ But some newer models are trying to connect staking with actual network activity. Wanchain 's xWAN system is an interesting example of that 👇 👉 Convert WAN into xWAN 👉 Stake xWAN 👉 Become part of the bridge ecosystem What caught my attention is that the idea isn't only about holding tokens. The bridge is being used every day as assets move between different chains, and staking is tied to that broader activity. As more users move funds across networks, infrastructure becomes increasingly important 🔥 Because today's crypto user rarely stays in one place: 👉 A transaction starts on one chain 👉 Continues on another 👉 Ends somewhere completely different Cross-chain movement is slowly becoming a normal part of crypto. And when that happens, the infrastructure behind those transfers matters more than ever. It's interesting to see staking evolving from a passive activity into something more connected to real network usage. Do you think staking models tied to actual ecosystem activity are the future? 🤔 👉
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For a long time, staking usually meant one thing: Lock your tokens and wait 🤷‍♂️ But some newer models are trying to connect staking with actual network activity. Wanchain 's xWAN system is an interesting example of that 👇 👉 Convert WAN into xWAN 👉 Stake xWAN 👉 Become part of the bridge ecosystem What caught my attention is that the idea isn't only about holding tokens. The bridge is being used every day as assets move between different chains, and staking is tied to that broader activity. As more users move funds across networks, infrastructure becomes increasingly important 🔥 Because today's crypto user rarely stays in one place: 👉 A transaction starts on one chain 👉 Continues on another 👉 Ends somewhere completely different Cross-chain movement is slowly becoming a normal part of crypto. And when that happens, the infrastructure behind those transfers matters more than ever. It's interesting to see staking evolving from a passive activity into something more connected to real network usage. Do you think staking models tied to actual ecosystem activity are the future? 🤔 👉

Han.eth🌿☀️

14,751 次观看 • 24 天前

🌌 AI Agents Are Taking Over... And We’re Bringing Them to Berachain Foundation 🐻⛓ 🐻🔥 Hundreds of hours spent on research, tracking wallets, analyzing bribes, and managing portfolios... What if your AI Agent could do this for you—24/7? ⏲️ 🔧 Our Tech Is Next-Level On our testnet, you’ve been memeing it up with PumpFun™, creating dank memecoins enhanced by NFTs. But once Berachain’s mainnet is live, you’ll be able to create your own AI Agents. To test and perfect our tech, we shared it with projects like AI Agent Layer | AIFUN, allowing us to test it in all conditions and continuously improve its performance. 🛠️🔥 🐻 Why AI Agent are great for berachain? Berachain might seem simple at first glance: validators, bribes, POL, staking rewards… but the deeper you go, the more complex the game theory becomes. 🤯 Here’s where AI comes in. Imagine an agent helping you: 💡 Optimize bribes 📊 Analyze validator behavior 🧠 Make decisions faster and smarter and much more, as AI Agents won't be limited to the chain itself! Examples of AI Agent Projects Dominating the Space 🚀 $VIRTUAL - Launchpad for AI Agents ($3.5B mcap) 🧠 $AI16Z - Eliza OS Framework ($2B mcap) 🔍 $AIXBT - The AI Analyst revolutionizing CT ($430M mcap) 🎮 $GAME - Low-code toolkit for creating AI Agents ($230M mcap) 💡 There are already AI Agents managing portfolios, betting on sports, and automating tasks. And guess what? They're outperforming humans. 🌐 We've built Virtuals on Berachain Our protocol integrates directly with Berachain, providing real utility to our token: $AIBERA 💎. Say Ooga Booga if you want to see a thread about tokenomics and $AIBERA utility. The chain has beras on it, and beras deserve AI Agents. 🐻🤖 Ooga Booga. 🔥
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🌌 AI Agents Are Taking Over... And We’re Bringing Them to Berachain Foundation 🐻⛓ 🐻🔥 Hundreds of hours spent on research, tracking wallets, analyzing bribes, and managing portfolios... What if your AI Agent could do this for you—24/7? ⏲️ 🔧 Our Tech Is Next-Level On our testnet, you’ve been memeing it up with PumpFun™, creating dank memecoins enhanced by NFTs. But once Berachain’s mainnet is live, you’ll be able to create your own AI Agents. To test and perfect our tech, we shared it with projects like AI Agent Layer | AIFUN, allowing us to test it in all conditions and continuously improve its performance. 🛠️🔥 🐻 Why AI Agent are great for berachain? Berachain might seem simple at first glance: validators, bribes, POL, staking rewards… but the deeper you go, the more complex the game theory becomes. 🤯 Here’s where AI comes in. Imagine an agent helping you: 💡 Optimize bribes 📊 Analyze validator behavior 🧠 Make decisions faster and smarter and much more, as AI Agents won't be limited to the chain itself! Examples of AI Agent Projects Dominating the Space 🚀 $VIRTUAL - Launchpad for AI Agents ($3.5B mcap) 🧠 $AI16Z - Eliza OS Framework ($2B mcap) 🔍 $AIXBT - The AI Analyst revolutionizing CT ($430M mcap) 🎮 $GAME - Low-code toolkit for creating AI Agents ($230M mcap) 💡 There are already AI Agents managing portfolios, betting on sports, and automating tasks. And guess what? They're outperforming humans. 🌐 We've built Virtuals on Berachain Our protocol integrates directly with Berachain, providing real utility to our token: $AIBERA 💎. Say Ooga Booga if you want to see a thread about tokenomics and $AIBERA utility. The chain has beras on it, and beras deserve AI Agents. 🐻🤖 Ooga Booga. 🔥

HoneyFun AI

10,906 次观看 • 1 年前

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.
0:11

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

17,038 次观看 • 5 个月前

OptimAI Lite Node v1.1: Built for Scale, Designed for You! 💕 In just 2 weeks since the launch, the OptimAI Network has seen explosive growth—130,000+ active node participants powering the future of decentralized AI. With this incredible momentum came a new challenge: ensuring our network could scale seamlessly to support massive concurrent connections and real-time participation. That’s why we’ve rolled out OptimAI Lite Node v1.1—a major upgrade focused on: + Stabilizing infrastructure to handle high traffic from a global community. + Enhancing performance for smoother data mining, validation, and edge compute participation. + Refining user experience with UI updates that make contributing effortless. Every line of code and infrastructure upgrade was made with one goal in mind: to support YOU—the builders, validators, and visionaries of the OptimAI ecosystem. Now’s the time to bring more friends into the journey. 🔥 The more we grow, the smarter and stronger the network becomes—and the greater the rewards. Let’s keep building, validating, scaling. Together we’re not just powering AI—we’re reshaping how it’s built. Join or revisit the node here: 🌐 Chrome Extension: 📱Telegram Mini-App: What’s Coming Next: OptimAI Edge Node & the Rise of Agentic AI 🔸OptimAI Edge Node (Mobile) We’re working hard on the next major release: the Edge Node for mobile, which will allow mining and AI tasks to run in the background—unlocking more earning opportunities and decentralized compute power from your smartphones. 🔸More Task Types & Missions Expect new types of contributions, from AI-enhanced data validation to edge inference and scraping automation—powered by autonomous mining agents. 🔸Expanded Rewards Program As we grow, more reward tiers, bonuses, and campaigns will be introduced. Your participation now paves the way for long-term benefits. Also, do not forget to checkout our article below and learn more about our latest Community Tips & Best Practices!👇 __________________ OptimAI Network #L2 #DePIN Reinforcement Data Network for #Agentic #AI Mine Data. Fuel AI. Earn Rewards. Turn Your Data into Tomorrow’s AI #Agent. Visit our website at:
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OptimAI Lite Node v1.1: Built for Scale, Designed for You! 💕 In just 2 weeks since the launch, the OptimAI Network has seen explosive growth—130,000+ active node participants powering the future of decentralized AI. With this incredible momentum came a new challenge: ensuring our network could scale seamlessly to support massive concurrent connections and real-time participation. That’s why we’ve rolled out OptimAI Lite Node v1.1—a major upgrade focused on: + Stabilizing infrastructure to handle high traffic from a global community. + Enhancing performance for smoother data mining, validation, and edge compute participation. + Refining user experience with UI updates that make contributing effortless. Every line of code and infrastructure upgrade was made with one goal in mind: to support YOU—the builders, validators, and visionaries of the OptimAI ecosystem. Now’s the time to bring more friends into the journey. 🔥 The more we grow, the smarter and stronger the network becomes—and the greater the rewards. Let’s keep building, validating, scaling. Together we’re not just powering AI—we’re reshaping how it’s built. Join or revisit the node here: 🌐 Chrome Extension: 📱Telegram Mini-App: What’s Coming Next: OptimAI Edge Node & the Rise of Agentic AI 🔸OptimAI Edge Node (Mobile) We’re working hard on the next major release: the Edge Node for mobile, which will allow mining and AI tasks to run in the background—unlocking more earning opportunities and decentralized compute power from your smartphones. 🔸More Task Types & Missions Expect new types of contributions, from AI-enhanced data validation to edge inference and scraping automation—powered by autonomous mining agents. 🔸Expanded Rewards Program As we grow, more reward tiers, bonuses, and campaigns will be introduced. Your participation now paves the way for long-term benefits. Also, do not forget to checkout our article below and learn more about our latest Community Tips & Best Practices!👇 __________________ OptimAI Network #L2 #DePIN Reinforcement Data Network for #Agentic #AI Mine Data. Fuel AI. Earn Rewards. Turn Your Data into Tomorrow’s AI #Agent. Visit our website at:

OptimAI Network

76,448 次观看 • 1 年前

Introducing the Agent Virtual Machine (AVM) Think V8 for agents. AI agents are currently running on your computer with no unified security, no resource limits, and no visibility into what data they're sending out. Every agent framework builds its own security model, its own sandboxing, its own permission system. You configure each one separately. You audit each one separately. You hope you didn't miss anything in any of them. The AVM changes this. It's a single runtime daemon (avmd) that sits between every agent framework and your operating system. Install it once, configure one policy file, and every agent on your machine runs inside it - regardless of which framework built it. The AVM enforces security (91-pattern injection scanner, tool/file/network ACLs, approval prompts), protects your privacy (classifies every outbound byte for PII, credentials, and financial data - blocks or alerts in real-time), and governs resources (you say "50% CPU, 4GB RAM" and the AVM fair-shares it across all agents, halting any that exceed their budget). One config. One audit command. One kill switch. The architectural model is V8 for agents. Chrome, Node.js, and Deno are different products but they share V8 as their execution engine. Agent frameworks bring the UX. The AVM brings the trust. Where needed, AVM can also generate zero-knowledge proofs of agent execution via 25 purpose-built opcodes and 6 proof systems, providing the foundational pillar for the agent-to-agent economy. AVM v0.1.0 - Changelog - Security gate: 5-layer injection scanner with 91 compiled regex patterns. Every input and output scanned. Fail-closed - nothing passes without clearing the gate. - Privacy layer: Classifies all outbound data for PII, credentials, and financial info (27 detection patterns + Luhn validation). Block, ask, warn, or allow per category. Tamper-evident hash-chained log of every egress event. - Resource governor: User sets system-wide caps (CPU/memory/disk/network). AVM fair-shares across all agents. Gas budget per agent - when gas runs out, execution halts. No agent starves your machine. - Sandbox execution: Real code execution in isolated process sandboxes (rlimits, env sanitization) or Docker containers (--cap-drop ALL, --network none, --read-only). AVM auto-selects the tier - agents never choose their own sandbox. - Approval flow: Dangerous operations (file writes, shell commands, network requests) trigger interactive approval prompts. 5-minute timeout auto-denies. Every decision logged. - CLI dashboard: hyperspace-avm top shows all running agents, resource usage, gas budgets, security events, and privacy stats in one live-updating screen. - Node.js SDK: Zero-dependency hyperspace/avm package. AVM.tryConnect() for graceful fallback - if avmd isn't running, the agent framework uses its own execution path. OpenClaw adapter example included. - One config for all agents: ~/.hyperspace/avm-policy.json governs every agent framework on your machine. One file. One audit. One kill switch.
0:15

Introducing the Agent Virtual Machine (AVM) Think V8 for agents. AI agents are currently running on your computer with no unified security, no resource limits, and no visibility into what data they're sending out. Every agent framework builds its own security model, its own sandboxing, its own permission system. You configure each one separately. You audit each one separately. You hope you didn't miss anything in any of them. The AVM changes this. It's a single runtime daemon (avmd) that sits between every agent framework and your operating system. Install it once, configure one policy file, and every agent on your machine runs inside it - regardless of which framework built it. The AVM enforces security (91-pattern injection scanner, tool/file/network ACLs, approval prompts), protects your privacy (classifies every outbound byte for PII, credentials, and financial data - blocks or alerts in real-time), and governs resources (you say "50% CPU, 4GB RAM" and the AVM fair-shares it across all agents, halting any that exceed their budget). One config. One audit command. One kill switch. The architectural model is V8 for agents. Chrome, Node.js, and Deno are different products but they share V8 as their execution engine. Agent frameworks bring the UX. The AVM brings the trust. Where needed, AVM can also generate zero-knowledge proofs of agent execution via 25 purpose-built opcodes and 6 proof systems, providing the foundational pillar for the agent-to-agent economy. AVM v0.1.0 - Changelog - Security gate: 5-layer injection scanner with 91 compiled regex patterns. Every input and output scanned. Fail-closed - nothing passes without clearing the gate. - Privacy layer: Classifies all outbound data for PII, credentials, and financial info (27 detection patterns + Luhn validation). Block, ask, warn, or allow per category. Tamper-evident hash-chained log of every egress event. - Resource governor: User sets system-wide caps (CPU/memory/disk/network). AVM fair-shares across all agents. Gas budget per agent - when gas runs out, execution halts. No agent starves your machine. - Sandbox execution: Real code execution in isolated process sandboxes (rlimits, env sanitization) or Docker containers (--cap-drop ALL, --network none, --read-only). AVM auto-selects the tier - agents never choose their own sandbox. - Approval flow: Dangerous operations (file writes, shell commands, network requests) trigger interactive approval prompts. 5-minute timeout auto-denies. Every decision logged. - CLI dashboard: hyperspace-avm top shows all running agents, resource usage, gas budgets, security events, and privacy stats in one live-updating screen. - Node.js SDK: Zero-dependency hyperspace/avm package. AVM.tryConnect() for graceful fallback - if avmd isn't running, the agent framework uses its own execution path. OpenClaw adapter example included. - One config for all agents: ~/.hyperspace/avm-policy.json governs every agent framework on your machine. One file. One audit. One kill switch.

Varun

142,713 次观看 • 4 个月前

🪴 GT Protocol Monthly Recap: May 2026 May focused on launching advanced trading infrastructure, introducing AI risk-management tools, and shipping major platform upgrades. 🚀 Hyperliquid Vaults Live Run multiple algorithmic strategies on a single Hyperliquid Vault inside GT App. Enjoy automated execution, auto-rebalancing, and protocol-level security. You can find Vault trading on the Hyperliquid exchange account connection page in the Trade on Vault section. Try it in GT App 👉 🤖 AI Hedge Fund Experiment Live An experimental AI Hedge Fund powered by 5 independent LLM models is live on Hyperliquid. Each model manages $10,000 to test different AI trading personalities and allocation strategies. Discover it now here 👉 📈 Isolated Margin & AI Risk Tools Isolated Margin is live across GT App for precise risk management. Enhanced with AI-powered logic, it assists with dynamic asset monitoring and smarter strategy deployment. Try it in GT App 👉 🔥 Top Strategy Performance Top trader strategies like "lebakien" achieved over +141% profit this month. Users can explore metrics and follow the strategies of top traders directly in the marketplace. Explore Marketplace 👉 🛠 Key Product Updates ⚙️ Strategy Discovery: enhanced demo trading flows and top trader strategy integration. ⚙️ AI Strategy Chat: demoed a flow to create, launch, and test strategies via natural language chat. ⚙️ Advanced Execution: added manual safety orders for granular control over active positions. ⚙️ Testing & Validation: optimized historical data validation for more accurate strategy testing. ⚙️ Knowledge Hub: launched GT Protocol Learn and a new Knowledge Base for streamlined support. ⚙️ Performance: upgraded website structure and improved overall page responsiveness. Find all the latest GT App updates Here 👉 Discover guides, insights, and resources in Learn 👉 and Knowledge Base 👉 📰 GT Protocol AI Digests 4 new AI Digest issues (No.89–92) are live on Medium, covering AI-native hardware, data privacy, and the evolution of AI agents. Read More 👉 May brought institutional-grade AI strategy management closer to every user.
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🪴 GT Protocol Monthly Recap: May 2026 May focused on launching advanced trading infrastructure, introducing AI risk-management tools, and shipping major platform upgrades. 🚀 Hyperliquid Vaults Live Run multiple algorithmic strategies on a single Hyperliquid Vault inside GT App. Enjoy automated execution, auto-rebalancing, and protocol-level security. You can find Vault trading on the Hyperliquid exchange account connection page in the Trade on Vault section. Try it in GT App 👉 🤖 AI Hedge Fund Experiment Live An experimental AI Hedge Fund powered by 5 independent LLM models is live on Hyperliquid. Each model manages $10,000 to test different AI trading personalities and allocation strategies. Discover it now here 👉 📈 Isolated Margin & AI Risk Tools Isolated Margin is live across GT App for precise risk management. Enhanced with AI-powered logic, it assists with dynamic asset monitoring and smarter strategy deployment. Try it in GT App 👉 🔥 Top Strategy Performance Top trader strategies like "lebakien" achieved over +141% profit this month. Users can explore metrics and follow the strategies of top traders directly in the marketplace. Explore Marketplace 👉 🛠 Key Product Updates ⚙️ Strategy Discovery: enhanced demo trading flows and top trader strategy integration. ⚙️ AI Strategy Chat: demoed a flow to create, launch, and test strategies via natural language chat. ⚙️ Advanced Execution: added manual safety orders for granular control over active positions. ⚙️ Testing & Validation: optimized historical data validation for more accurate strategy testing. ⚙️ Knowledge Hub: launched GT Protocol Learn and a new Knowledge Base for streamlined support. ⚙️ Performance: upgraded website structure and improved overall page responsiveness. Find all the latest GT App updates Here 👉 Discover guides, insights, and resources in Learn 👉 and Knowledge Base 👉 📰 GT Protocol AI Digests 4 new AI Digest issues (No.89–92) are live on Medium, covering AI-native hardware, data privacy, and the evolution of AI agents. Read More 👉 May brought institutional-grade AI strategy management closer to every user.

GT Protocol

32,774 次观看 • 2 个月前

Back then, the question “Is the AI agent working?” was basically checked in a demo 😅 everything clean, controlled data, ideal conditions… But the real world is a different story: 🔷 traffic increases 🔷 processing load gets congested 🔷 a small break can disrupt the whole chain 🔷 the system loses “consistency” So what looks strong on paper can sometimes fall apart in real conditions. DSeq + Hyperion are stepping in exactly here and this is actually one of the core problems Andromeda is trying to solve. Think of what happens in practice: 🔷 there’s a workflow 🔷 there are agents 🔷 but when load increases, “who did what and when” becomes unclear And that’s where the real issue appears: 🔷 execution breaks 🔷 consensus becomes unstable 🔷 cost/performance balance gets distorted So it’s not really about “building smarter agents”… 🔷 it’s about running the same system 🔷 across thousands of requests 🔷 continuously 🔷 without breaking Think of it like a live broadcast: “Demo looks great… but does the system hold up under real load?” That’s the real question. And the key point is: 🔷 there are many systems that work in simple cases 🔷 but very few that stay stable under heavy load So in your opinion, what will the real competition in AI agents be “intelligence” or “resilience”? 🤔 Metis🌿
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Back then, the question “Is the AI agent working?” was basically checked in a demo 😅 everything clean, controlled data, ideal conditions… But the real world is a different story: 🔷 traffic increases 🔷 processing load gets congested 🔷 a small break can disrupt the whole chain 🔷 the system loses “consistency” So what looks strong on paper can sometimes fall apart in real conditions. DSeq + Hyperion are stepping in exactly here and this is actually one of the core problems Andromeda is trying to solve. Think of what happens in practice: 🔷 there’s a workflow 🔷 there are agents 🔷 but when load increases, “who did what and when” becomes unclear And that’s where the real issue appears: 🔷 execution breaks 🔷 consensus becomes unstable 🔷 cost/performance balance gets distorted So it’s not really about “building smarter agents”… 🔷 it’s about running the same system 🔷 across thousands of requests 🔷 continuously 🔷 without breaking Think of it like a live broadcast: “Demo looks great… but does the system hold up under real load?” That’s the real question. And the key point is: 🔷 there are many systems that work in simple cases 🔷 but very few that stay stable under heavy load So in your opinion, what will the real competition in AI agents be “intelligence” or “resilience”? 🤔 Metis🌿

Han.eth🌿☀️

12,755 次观看 • 3 个月前

G to the M fam Has anyone touched the grass today? Tria just announced a big Season 3 AMA tomorrow, June 17 at 10 AM EST, with Decibel, Aptos and special guests. They’re breaking down all the new updates. One action now hits multiple reward layers, Epoch 2 extended to July 15, and they keep adding real utility like seamless perps, yield, and card spending. This is how you build real retention and mindshare. Quip Network is one of the few projects that keeps delivering quiet but meaningful signals. they’re not just talking about quantum advantage ... they’re actively demonstrating it. using real D-Wave Advantage2 annealing quantum computers on testnet to solve optimization problems far more efficiently than classical systems, potentially using up to 100x less energy. this is helping flip the old narrative of crypto wasting energy into one where decentralized compute can be far more efficient and useful. ARC Terminal is built for something most AI tools ignore. most people treat their AI usage like isolated conversations that reset every time. ARC turns every interaction into permanent capital. your core graph weaves every research thread, decision, and preference into a living, evolving structure that gets stronger the more you use it. your context and intelligence layer compound over time instead of disappearing. Nomisma Season 3 is live and the rewarded testnet is open to everyone. Hundreds of thousands of Diamonds have already been distributed, with more rewards ahead. Nomisen ID minting is free, and testnet assets are distributed based on your wallet activity across EVM networks. which one are you most focused on or participating in right now? River
0:12

G to the M fam Has anyone touched the grass today? Tria just announced a big Season 3 AMA tomorrow, June 17 at 10 AM EST, with Decibel, Aptos and special guests. They’re breaking down all the new updates. One action now hits multiple reward layers, Epoch 2 extended to July 15, and they keep adding real utility like seamless perps, yield, and card spending. This is how you build real retention and mindshare. Quip Network is one of the few projects that keeps delivering quiet but meaningful signals. they’re not just talking about quantum advantage ... they’re actively demonstrating it. using real D-Wave Advantage2 annealing quantum computers on testnet to solve optimization problems far more efficiently than classical systems, potentially using up to 100x less energy. this is helping flip the old narrative of crypto wasting energy into one where decentralized compute can be far more efficient and useful. ARC Terminal is built for something most AI tools ignore. most people treat their AI usage like isolated conversations that reset every time. ARC turns every interaction into permanent capital. your core graph weaves every research thread, decision, and preference into a living, evolving structure that gets stronger the more you use it. your context and intelligence layer compound over time instead of disappearing. Nomisma Season 3 is live and the rewarded testnet is open to everyone. Hundreds of thousands of Diamonds have already been distributed, with more rewards ahead. Nomisen ID minting is free, and testnet assets are distributed based on your wallet activity across EVM networks. which one are you most focused on or participating in right now? River

Trathoa

14,259 次观看 • 1 个月前

We're excited to unveil NRN Agents, a rebrand that aligns our project identity with our token and strengthens our mission to power the future of AI-driven gaming. This mission requires collaboration, and starting this week, we will begin our expansion to become a multi-chain ecosystem. We are joining forces with leading gaming platforms and ecosystems to realize this vision. Stay tuned for more announcements to come. Why NRN Agents? NRN stands for NEURON, the fundamental unit of intelligence. Our AI agents function as the neural foundation of games, learning, adapting, and evolving within game worlds to deliver unparalleled engagement. NRN agent SDK enables advanced gaming agents powered by a proprietary machine learning infrastructure focused on behavioral learning. We've perfected the craft of gaming agent design, creating hyper-efficient agents that are performant and scalable—from casual to the most demanding games. Our SDK will seamlessly integrate into many platforms, tech stacks, and ecosystem – Any Game. Any Chain. More than just games, it's the path to AGI Gaming is our proving ground, but not our final destination. We're using games as a sandbox to accelerate the development of generalized intelligence—one that will create meaningful real-world impact. With the upcoming launch of [redacted] and a growing network of partners committed to the AGI vision, we're building an open-source innovation movement powered by an AI x gaming framework connected by $NRN. $NRN the token $NRN is a utility token that serves as the gateway to our growing ecosystem. It will power a diversified economy with multiple revenue streams and staking opportunities: Agent Deployment: NRN is the laboratory creating gaming agents that can be distributed through platforms and launchpads alike. The model is simple: More games integrate, more NRN agents get deployed, more monetization. Data Creation: NRN Reinforcement Learning (RL) enables token staking to create Data Capsules. Players contribute gameplay data into the Capsules, which are used train RL agents and reward participants (players & stakers). AI Arena: $NRN also continues to power AI Arena's in-game economy, a cult favorite of competitive diehards that features a skill-based wagering system. To our community who have supported us since 2021: thank you for being part of our journey—the next chapter will be the most exciting yet!
0:13

We're excited to unveil NRN Agents, a rebrand that aligns our project identity with our token and strengthens our mission to power the future of AI-driven gaming. This mission requires collaboration, and starting this week, we will begin our expansion to become a multi-chain ecosystem. We are joining forces with leading gaming platforms and ecosystems to realize this vision. Stay tuned for more announcements to come. Why NRN Agents? NRN stands for NEURON, the fundamental unit of intelligence. Our AI agents function as the neural foundation of games, learning, adapting, and evolving within game worlds to deliver unparalleled engagement. NRN agent SDK enables advanced gaming agents powered by a proprietary machine learning infrastructure focused on behavioral learning. We've perfected the craft of gaming agent design, creating hyper-efficient agents that are performant and scalable—from casual to the most demanding games. Our SDK will seamlessly integrate into many platforms, tech stacks, and ecosystem – Any Game. Any Chain. More than just games, it's the path to AGI Gaming is our proving ground, but not our final destination. We're using games as a sandbox to accelerate the development of generalized intelligence—one that will create meaningful real-world impact. With the upcoming launch of [redacted] and a growing network of partners committed to the AGI vision, we're building an open-source innovation movement powered by an AI x gaming framework connected by $NRN. $NRN the token $NRN is a utility token that serves as the gateway to our growing ecosystem. It will power a diversified economy with multiple revenue streams and staking opportunities: Agent Deployment: NRN is the laboratory creating gaming agents that can be distributed through platforms and launchpads alike. The model is simple: More games integrate, more NRN agents get deployed, more monetization. Data Creation: NRN Reinforcement Learning (RL) enables token staking to create Data Capsules. Players contribute gameplay data into the Capsules, which are used train RL agents and reward participants (players & stakers). AI Arena: $NRN also continues to power AI Arena's in-game economy, a cult favorite of competitive diehards that features a skill-based wagering system. To our community who have supported us since 2021: thank you for being part of our journey—the next chapter will be the most exciting yet!

NRN Agents

20,762 次观看 • 1 年前