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๐Ÿ”— 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 ๐Ÿ” Read more ๐Ÿ” #Lithosphere #LITHO #Web4 #AIInfrastructure #AutonomousAgents

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LITHO Foundation

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58,308 views โ€ข 2 months ago โ€ขvia X (Twitter)

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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. Todayโ€™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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76,446 views โ€ข 1 year ago

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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 views โ€ข 4 months ago

๐Ÿชด GT Protocol Monthly Recap: May 2026 May focused on launching advanced trading infrastructure, introducing AI risk-management tools, and shipping major platform upgrades. ๐Ÿš€ 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 ๐Ÿ‘‰ ๐Ÿค– 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 ๐Ÿ‘‰ ๐Ÿ“ˆ 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 ๐Ÿ‘‰ ๐Ÿ”ฅ 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 ๐Ÿ‘‰ ๐Ÿ›  Key Product Updates โš™๏ธ Strategy Discovery: enhanced demo trading flows and top trader strategy integration. โš™๏ธ AI Strategy Chat: demoed a flow to create, launch, and test strategies via natural language chat. โš™๏ธ Advanced Execution: added manual safety orders for granular control over active positions. โš™๏ธ Testing & Validation: optimized historical data validation for more accurate strategy testing. โš™๏ธ Knowledge Hub: launched GT Protocol Learn and a new Knowledge Base for streamlined support. โš™๏ธ Performance: upgraded website structure and improved overall page responsiveness. Find all the latest GT App updates Here ๐Ÿ‘‰ Discover guides, insights, and resources in Learn ๐Ÿ‘‰ and Knowledge Base ๐Ÿ‘‰ ๐Ÿ“ฐ GT Protocol AI Digests 4 new AI Digest issues (No.89โ€“92) are live on Medium, covering AI-native hardware, data privacy, and the evolution of AI agents. Read More ๐Ÿ‘‰ May brought institutional-grade AI strategy management closer to every user.
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๐Ÿชด GT Protocol Monthly Recap: May 2026 May focused on launching advanced trading infrastructure, introducing AI risk-management tools, and shipping major platform upgrades. ๐Ÿš€ 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 ๐Ÿ‘‰ ๐Ÿค– 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 ๐Ÿ‘‰ ๐Ÿ“ˆ 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 ๐Ÿ‘‰ ๐Ÿ”ฅ 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 ๐Ÿ‘‰ ๐Ÿ›  Key Product Updates โš™๏ธ Strategy Discovery: enhanced demo trading flows and top trader strategy integration. โš™๏ธ AI Strategy Chat: demoed a flow to create, launch, and test strategies via natural language chat. โš™๏ธ Advanced Execution: added manual safety orders for granular control over active positions. โš™๏ธ Testing & Validation: optimized historical data validation for more accurate strategy testing. โš™๏ธ Knowledge Hub: launched GT Protocol Learn and a new Knowledge Base for streamlined support. โš™๏ธ Performance: upgraded website structure and improved overall page responsiveness. Find all the latest GT App updates Here ๐Ÿ‘‰ Discover guides, insights, and resources in Learn ๐Ÿ‘‰ and Knowledge Base ๐Ÿ‘‰ ๐Ÿ“ฐ GT Protocol AI Digests 4 new AI Digest issues (No.89โ€“92) are live on Medium, covering AI-native hardware, data privacy, and the evolution of AI agents. Read More ๐Ÿ‘‰ May brought institutional-grade AI strategy management closer to every user.

GT Protocol

32,774 views โ€ข 2 months ago

Back then, the question โ€œIs the AI agent working?โ€ was basically checked in a demo ๐Ÿ˜… everything clean, controlled data, ideal conditionsโ€ฆ But the real world is a different story: ๐Ÿ”ท traffic increases ๐Ÿ”ท processing load gets congested ๐Ÿ”ท a small break can disrupt the whole chain ๐Ÿ”ท the system loses โ€œconsistencyโ€ 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: ๐Ÿ”ท thereโ€™s a workflow ๐Ÿ”ท there are agents ๐Ÿ”ท but when load increases, โ€œwho did what and whenโ€ becomes unclear And thatโ€™s where the real issue appears: ๐Ÿ”ท execution breaks ๐Ÿ”ท consensus becomes unstable ๐Ÿ”ท cost/performance balance gets distorted So itโ€™s not really about โ€œbuilding smarter agentsโ€โ€ฆ ๐Ÿ”ท itโ€™s about running the same system ๐Ÿ”ท across thousands of requests ๐Ÿ”ท continuously ๐Ÿ”ท without breaking Think of it like a live broadcast: โ€œDemo looks greatโ€ฆ but does the system hold up under real load?โ€ Thatโ€™s the real question. And the key point is: ๐Ÿ”ท there are many systems that work in simple cases ๐Ÿ”ท but very few that stay stable under heavy load So in your opinion, what will the real competition in AI agents be โ€œintelligenceโ€ or โ€œresilienceโ€? ๐Ÿค” Metis๐ŸŒฟ
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Back then, the question โ€œIs the AI agent working?โ€ was basically checked in a demo ๐Ÿ˜… everything clean, controlled data, ideal conditionsโ€ฆ But the real world is a different story: ๐Ÿ”ท traffic increases ๐Ÿ”ท processing load gets congested ๐Ÿ”ท a small break can disrupt the whole chain ๐Ÿ”ท the system loses โ€œconsistencyโ€ 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: ๐Ÿ”ท thereโ€™s a workflow ๐Ÿ”ท there are agents ๐Ÿ”ท but when load increases, โ€œwho did what and whenโ€ becomes unclear And thatโ€™s where the real issue appears: ๐Ÿ”ท execution breaks ๐Ÿ”ท consensus becomes unstable ๐Ÿ”ท cost/performance balance gets distorted So itโ€™s not really about โ€œbuilding smarter agentsโ€โ€ฆ ๐Ÿ”ท itโ€™s about running the same system ๐Ÿ”ท across thousands of requests ๐Ÿ”ท continuously ๐Ÿ”ท without breaking Think of it like a live broadcast: โ€œDemo looks greatโ€ฆ but does the system hold up under real load?โ€ Thatโ€™s the real question. And the key point is: ๐Ÿ”ท there are many systems that work in simple cases ๐Ÿ”ท but very few that stay stable under heavy load So in your opinion, what will the real competition in AI agents be โ€œintelligenceโ€ or โ€œresilienceโ€? ๐Ÿค” Metis๐ŸŒฟ

Han.eth๐ŸŒฟโ˜€๏ธ

12,755 views โ€ข 3 months ago

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. Theyโ€™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. theyโ€™re not just talking about quantum advantage ... theyโ€™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
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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. Theyโ€™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. theyโ€™re not just talking about quantum advantage ... theyโ€™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 views โ€ข 1 month ago

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 scalableโ€”from casual to the most demanding games. Our SDK will seamlessly integrate into many platforms, tech stacks, and ecosystem โ€“ 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 intelligenceโ€”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 journeyโ€”the next chapter will be the most exciting yet!
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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 scalableโ€”from casual to the most demanding games. Our SDK will seamlessly integrate into many platforms, tech stacks, and ecosystem โ€“ 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 intelligenceโ€”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 journeyโ€”the next chapter will be the most exciting yet!

NRN Agents

20,762 views โ€ข 1 year ago