Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

Introducing OKX Agentic Wallet. Built for AI agents to execute across ~20 chains using natural language via CLI. TXs on X Layer are gas-free. Before signing, every transaction is simulated, screened, and validated. Execution halts if any risk appears. More info:

OKX Wallet

1,894,531 subscribers

98,443 görüntüleme • 4 ay önce •via X (Twitter)

Bilim & Teknoloji

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

Most agentic wallets still need babysitting. Ours gives agents: • Natural-language execution • Gas-free trades on X Layer • Risk checks before signing See the docs:

Most agentic wallets still need babysitting. Ours gives agents: • Natural-language execution • Gas-free trades on X Layer • Risk checks before signing See the docs:

OKX Wallet

17,459 görüntüleme • 1 ay önce

DAPPOS is bringing Onchain OS Skills from X Layer OKX Wallet into the xBubble ecosystem, making OKX Wallet’s agent-ready wallet, trading, market data, and agentic payments protocol capabilities accessible across xBubble agents. Built on top of Onchain OS Skills, xBubble’s crypto-task SOPs help turn fragmented on-chain flows into a more seamless chat-native experience inside the xBubble app across mobile, desktop, and web. Users can monitor markets, prepare trades, manage wallet activity, and coordinate payment flows through a single conversation. Bubble Engine will continue to use Onchain OS Skills as the baseline for every future SOP iteration and upgrade. With Onchain OS Skills, DAPPOS is making agentic on-chain tasks more conversational, practical, and accessible.

DAPPOS is bringing Onchain OS Skills from X Layer OKX Wallet into the xBubble ecosystem, making OKX Wallet’s agent-ready wallet, trading, market data, and agentic payments protocol capabilities accessible across xBubble agents. Built on top of Onchain OS Skills, xBubble’s crypto-task SOPs help turn fragmented on-chain flows into a more seamless chat-native experience inside the xBubble app across mobile, desktop, and web. Users can monitor markets, prepare trades, manage wallet activity, and coordinate payment flows through a single conversation. Bubble Engine will continue to use Onchain OS Skills as the baseline for every future SOP iteration and upgrade. With Onchain OS Skills, DAPPOS is making agentic on-chain tasks more conversational, practical, and accessible.

DAPPOS

14,289 görüntüleme • 2 ay önce

Quack AI × Avalanche🔺 Q402 is officially expanding into the Avalanche ecosystem. We are pushing toward more scalable, execution-first environments for agents. With Avalanche's sub-second finality and builder-first stack, Q402 now provides a foundation for verifiable, policy-aware execution that scales with real-world usage. What’s new for the Avalanche community? The Q402 signature-based execution layer is now LIVE on the Avalanche C-Chain. Users and builders can now: • Zero Gas Barrier: Settle ERC-20 transfers (USDC, etc.) without holding AVAX for gas. • Sign-to-Pay: Decouple intent signing from transaction execution for a seamless UX. • Production-Ready: High-performance, secure, and auditable execution layer for the agent economy. Contract Deployed on C-Chain: Execution should feel native. Verification should be default. Welcome to the agent-native era on Avalanche.

Quack AI × Avalanche🔺 Q402 is officially expanding into the Avalanche ecosystem. We are pushing toward more scalable, execution-first environments for agents. With Avalanche's sub-second finality and builder-first stack, Q402 now provides a foundation for verifiable, policy-aware execution that scales with real-world usage. What’s new for the Avalanche community? The Q402 signature-based execution layer is now LIVE on the Avalanche C-Chain. Users and builders can now: • Zero Gas Barrier: Settle ERC-20 transfers (USDC, etc.) without holding AVAX for gas. • Sign-to-Pay: Decouple intent signing from transaction execution for a seamless UX. • Production-Ready: High-performance, secure, and auditable execution layer for the agent economy. Contract Deployed on C-Chain: Execution should feel native. Verification should be default. Welcome to the agent-native era on Avalanche.

Quack AI

23,309 görüntüleme • 5 ay önce

Major upgrades are coming with Orbofi AI engine V3, and the utility of $OBI - OBI will be the main currency pair for all ai agents on Orbofi. - Every ai agent transaction, even if purchased using other ERC20s, will flow through OBI -OBI and agent tokens as the main compute currencies to generate anything multimodal engine - A lot more. Bottom line. $OBI powering the compute and the liquidity for all millions of agents on Orbofi. Godspeed

Major upgrades are coming with Orbofi AI engine V3, and the utility of $OBI - OBI will be the main currency pair for all ai agents on Orbofi. - Every ai agent transaction, even if purchased using other ERC20s, will flow through OBI -OBI and agent tokens as the main compute currencies to generate anything multimodal engine - A lot more. Bottom line. $OBI powering the compute and the liquidity for all millions of agents on Orbofi. Godspeed

Orbofi

20,389 görüntüleme • 1 yıl önce

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.

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

142,179 görüntüleme • 4 ay önce

We’re thrilled to launch on Genesis at Virtuals Protocol and want to share what’s next on the technical side. > AI Sniping Engine: In final testing for ultra-fast on-chain reactions to large buys/sells with sub-second execution. > Strategy Scripting: Enhancing natural language parsing so users can define complex market-making strategies more easily. > Wallet Management: Upgrades for dynamic splitting, stealth rotation, and batch execution to reduce detection risk. > Dashboard: Coming soon with advanced analytics, strategy templates, and real-time monitoring. > AI chat: Now the AI can learn your personal responding style in a single conversation. Currently, Dessistant is not yet 100% autonomous. We’re improving it step by step. Starting tomorrow, we’ll be collaborating with other projects to test market-making using small LP allocations on Virtuals-based tokens. This will let us refine the agent in real-world conditions before a broader rollout. Our goal is to continuously improve Dessistant into a fully autonomous AI market-making agent that is adaptive, transparent

We’re thrilled to launch on Genesis at Virtuals Protocol and want to share what’s next on the technical side. > AI Sniping Engine: In final testing for ultra-fast on-chain reactions to large buys/sells with sub-second execution. > Strategy Scripting: Enhancing natural language parsing so users can define complex market-making strategies more easily. > Wallet Management: Upgrades for dynamic splitting, stealth rotation, and batch execution to reduce detection risk. > Dashboard: Coming soon with advanced analytics, strategy templates, and real-time monitoring. > AI chat: Now the AI can learn your personal responding style in a single conversation. Currently, Dessistant is not yet 100% autonomous. We’re improving it step by step. Starting tomorrow, we’ll be collaborating with other projects to test market-making using small LP allocations on Virtuals-based tokens. This will let us refine the agent in real-world conditions before a broader rollout. Our goal is to continuously improve Dessistant into a fully autonomous AI market-making agent that is adaptive, transparent

Dessistant

42,252 görüntüleme • 1 yıl önce

Your trusted Yoroi Wallet is evolving. 📣 Yoroi Wallet is soon becoming SecondFi, a neofinance platform built on everything Yoroi Wallet stands for, and so much more. Here’s what you are getting: 🔹 A self-custody card, because we heard you and it is finally coming 🔹 More yield-bearing options across multiple chains 🔹 Your staking and governance features are staying 🔹 Your self-custody stays intact 🔹 Yoroi DRep and Yoroi stake pool is not changing. To our delegators, everything continues as normal One thing is for sure, your funds are safe throughout the entire transition. SecondFi is built on the same self-custody values Yoroi was founded on, so you remain in full control of your assets at every step. Here is a glimpse of your new life. 👇

Your trusted Yoroi Wallet is evolving. 📣 Yoroi Wallet is soon becoming SecondFi, a neofinance platform built on everything Yoroi Wallet stands for, and so much more. Here’s what you are getting: 🔹 A self-custody card, because we heard you and it is finally coming 🔹 More yield-bearing options across multiple chains 🔹 Your staking and governance features are staying 🔹 Your self-custody stays intact 🔹 Yoroi DRep and Yoroi stake pool is not changing. To our delegators, everything continues as normal One thing is for sure, your funds are safe throughout the entire transition. SecondFi is built on the same self-custody values Yoroi was founded on, so you remain in full control of your assets at every step. Here is a glimpse of your new life. 👇

Yoroi

39,088 görüntüleme • 3 ay önce

The AI Agent Terminal (Public Beta) is LIVE. Trade, analyze, and strategize, all in natural language. 🚀 With Terminal Public Beta: ✨ Revamped platform + Lander page 📖 New docs + Educational content pages from Gen3 ⚡ Streamlined UX + UI What can the AI Agent Terminal do? - AI-Powered Swaps → Trade instantly with plain text. - AI Market Analysis → Hourly updates + daily recaps. - Automated Tasks → Price alerts, rebalancing, recurring actions. - Strategy Building → Create + run strategies in natural language. - Advanced Transactions → Complex XRPL ops made effortless. - External Wallet Integrations → Works with Joey Wallet, Xaman® Wallet 🪝 + more. Pricing: - Terminal = Free - Inference = Very cheap, we are always looking for the best model rates - Link your Aigent Pass NFT holdings for discounted inference costs - Pay for inference balances directly with $AIXRP 🌍 Why it matters: AI + DeFi aren’t hype anymore, they’re live, accessible, and ready for you on XRPL. This is just the beginning. More agentic power is coming! Are you ready to enter the Terminal? Please bring all feedback to our Discord or Telegram.. we’re building this together. 💬

The AI Agent Terminal (Public Beta) is LIVE. Trade, analyze, and strategize, all in natural language. 🚀 With Terminal Public Beta: ✨ Revamped platform + Lander page 📖 New docs + Educational content pages from Gen3 ⚡ Streamlined UX + UI What can the AI Agent Terminal do? - AI-Powered Swaps → Trade instantly with plain text. - AI Market Analysis → Hourly updates + daily recaps. - Automated Tasks → Price alerts, rebalancing, recurring actions. - Strategy Building → Create + run strategies in natural language. - Advanced Transactions → Complex XRPL ops made effortless. - External Wallet Integrations → Works with Joey Wallet, Xaman® Wallet 🪝 + more. Pricing: - Terminal = Free - Inference = Very cheap, we are always looking for the best model rates - Link your Aigent Pass NFT holdings for discounted inference costs - Pay for inference balances directly with $AIXRP 🌍 Why it matters: AI + DeFi aren’t hype anymore, they’re live, accessible, and ready for you on XRPL. This is just the beginning. More agentic power is coming! Are you ready to enter the Terminal? Please bring all feedback to our Discord or Telegram.. we’re building this together. 💬

aigent.run

25,889 görüntüleme • 11 ay önce

Introducing Zest Protocol Stacks Swap, The ultimate swap experience for the stacks.btc ecosystem. Live now. Stacks swap is built with more routes, faster backend and UX than any DEx product previously built on Stacks. Stacks Swap compares hundreds of liquidity pools across every major DEX on Stacks and returns the route with the best output for every trade. How it works: 🟠 Smart order routing. Large swaps split across multiple pools and multiple DEXes at once. When no direct pool exists, the router chains hops through intermediate tokens. The full route executes as a single transaction. 🟠 All or nothing. Every swap is one atomic Stacks transaction. Either the full route completes and you receive at least the guaranteed minimum, or the entire transaction reverts and no tokens leave your wallet. 🟠 Price protection enforced on-chain. Every swap carries a minimum-received amount written into the transaction itself. If the market moves before confirmation, the swap reverts instead of filling at a worse price. No app, server, or operator can override this check. 🟠 Non-custodial by design. Tokens move from your wallet, through the pools, and back to your wallet within a single transaction. No deposits, no balances, no accounts. 🟠 Zero additional fees. Pool fees are set by the DEXes and already priced into every quote. Zest Protocol charges nothing on top. 🟠 Supported liquidity sources: Bitflow , Velar, ALEX 🟧 No. 1 Bitcoin DeFi, and Arkadiko Protocol 🟧🗿, plus direct protocol conversion through Stacking DAO for stSTXbtc swaps. The quote you approve is the worst case you can receive. The swap fills at that amount or better, or it does not happen at all. We built Stacks Swap using Zest Protocol's existing internal infrastructure. When we realised that it performed much better than existing DEx products, we decided it made sense to release it for the benefit of the ecosystem. Happy swapping.

Introducing Zest Protocol Stacks Swap, The ultimate swap experience for the stacks.btc ecosystem. Live now. Stacks swap is built with more routes, faster backend and UX than any DEx product previously built on Stacks. Stacks Swap compares hundreds of liquidity pools across every major DEX on Stacks and returns the route with the best output for every trade. How it works: 🟠 Smart order routing. Large swaps split across multiple pools and multiple DEXes at once. When no direct pool exists, the router chains hops through intermediate tokens. The full route executes as a single transaction. 🟠 All or nothing. Every swap is one atomic Stacks transaction. Either the full route completes and you receive at least the guaranteed minimum, or the entire transaction reverts and no tokens leave your wallet. 🟠 Price protection enforced on-chain. Every swap carries a minimum-received amount written into the transaction itself. If the market moves before confirmation, the swap reverts instead of filling at a worse price. No app, server, or operator can override this check. 🟠 Non-custodial by design. Tokens move from your wallet, through the pools, and back to your wallet within a single transaction. No deposits, no balances, no accounts. 🟠 Zero additional fees. Pool fees are set by the DEXes and already priced into every quote. Zest Protocol charges nothing on top. 🟠 Supported liquidity sources: Bitflow , Velar, ALEX 🟧 No. 1 Bitcoin DeFi, and Arkadiko Protocol 🟧🗿, plus direct protocol conversion through Stacking DAO for stSTXbtc swaps. The quote you approve is the worst case you can receive. The swap fills at that amount or better, or it does not happen at all. We built Stacks Swap using Zest Protocol's existing internal infrastructure. When we realised that it performed much better than existing DEx products, we decided it made sense to release it for the benefit of the ecosystem. Happy swapping.

Zest Protocol

17,391 görüntüleme • 13 gün önce

1/3 🤖 Meet AgentOS: A Token-efficient, Microkernel AI agent with on-device model routing across CLI, Web UI, and chat. A local router reads every message on your device and sends it to the cheapest model that can still do the job well. You stop overpaying for AI. ⚡ What makes it stand out: ■ Smart on-device model routing across 20+ providers ( bankrbot LLM Gateway, OpenRouter, OpenAI, Anthropic, Ollama, and more). ■ Persistent local memory that survives restarts. ■ A layered security sandbox (Standard, Strict, Locked). ■ 37 built-in skills plus MCP, loaded only when a task needs them, and more ■ One unified gateway for CLI, Web UI, Slack, Telegram, Discord, and more Remember this: AgentOS has been integrated with the Bankr LLM Gateway since day one. That means any Bankr user with Bankr API key can start using AgentOS in minutes. Let the router cook.

1/3 🤖 Meet AgentOS: A Token-efficient, Microkernel AI agent with on-device model routing across CLI, Web UI, and chat. A local router reads every message on your device and sends it to the cheapest model that can still do the job well. You stop overpaying for AI. ⚡ What makes it stand out: ■ Smart on-device model routing across 20+ providers ( bankrbot LLM Gateway, OpenRouter, OpenAI, Anthropic, Ollama, and more). ■ Persistent local memory that survives restarts. ■ A layered security sandbox (Standard, Strict, Locked). ■ 37 built-in skills plus MCP, loaded only when a task needs them, and more ■ One unified gateway for CLI, Web UI, Slack, Telegram, Discord, and more Remember this: AgentOS has been integrated with the Bankr LLM Gateway since day one. That means any Bankr user with Bankr API key can start using AgentOS in minutes. Let the router cook.

AgentOS

192,535 görüntüleme • 22 gün önce

NEW favorite artifact. I read this every morning to catch up on AI news from high-signal X accounts. It's an HTML artifact that curates X posts using the X MCP tools. Composed by my research agents. I have a daily automation that goes through curated X accounts and captures AI papers, projects, and more. I feel like this is closer to the "For You" feed that we all want. Happy to share the artifact and automation instructions if anyone thinks it would be useful for them.

NEW favorite artifact. I read this every morning to catch up on AI news from high-signal X accounts. It's an HTML artifact that curates X posts using the X MCP tools. Composed by my research agents. I have a daily automation that goes through curated X accounts and captures AI papers, projects, and more. I feel like this is closer to the "For You" feed that we all want. Happy to share the artifact and automation instructions if anyone thinks it would be useful for them.

elvis

49,610 görüntüleme • 18 gün önce

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 görüntüleme • 5 ay önce

LayerAI Network Mainnet is Live: First Layer-2 for the Future of AI Value Chains 🧬 The 1st phase of our mainnet is now live: the biggest exchanges are integrating LayerAI mainnet network. This is an alpha version of the network, and allows us to test parameters before pushing for maximum traction with live users and applications. This is the 1st step in paving adoption of $LAI as gas. Over the next 2 months, we will begin onramping network adopters and applications, starting with our own major products with millions of users, to the LayerAI mainnet. 👉 Add LayerAI to your wallet, onboard, and explore the future of Web3xAI: 👉 Bridge assets to LayerAI: 👉 Claim gas from faucet: 👉 Once you bridge LAI, migrate LAI to LAI as gas using the Migrator on LayerAI Dashboard:

LayerAI Network Mainnet is Live: First Layer-2 for the Future of AI Value Chains 🧬 The 1st phase of our mainnet is now live: the biggest exchanges are integrating LayerAI mainnet network. This is an alpha version of the network, and allows us to test parameters before pushing for maximum traction with live users and applications. This is the 1st step in paving adoption of $LAI as gas. Over the next 2 months, we will begin onramping network adopters and applications, starting with our own major products with millions of users, to the LayerAI mainnet. 👉 Add LayerAI to your wallet, onboard, and explore the future of Web3xAI: 👉 Bridge assets to LayerAI: 👉 Claim gas from faucet: 👉 Once you bridge LAI, migrate LAI to LAI as gas using the Migrator on LayerAI Dashboard:

LayerAI | AI2Earn

220,924 görüntüleme • 1 yıl önce

Karpathy's Agentic Engineering finally has proper tooling! (built by Google) Karpathy defined agentic engineering as the discipline that separates production agent work from vibe coding. The core skills he listed were spec design, eval loops, and security oversight. The problem has been that practicing this still requires a different tool for every phase: - editor for code - a terminal for scaffolding - a browser for testing - a cloud console for deployment - and a separate framework for evals. Every transition is a context switch. The solution to production-grade Agentic Engineering is now actually implemented in Google’s Agents CLI. It covers the entire workflow in one place for scaffolding, evaluating, and deploying ADK agents. One setup command injects 7 ADK-specific skills into a coding agent's context, which lets it handle scaffolding, evals, deployment, and enterprise registration through natural language. I tested this end-to-end by building a RAG agent from scratch using Claude Code. It scaffolded the full project from the ADK agentic_rag template, generated 20 eval scenarios with LLM-as-judge scoring, and returned a quantitative scorecard. Finally, it also deployed everything to Agent Runtime and registered the agent to Gemini Enterprise, so the entire org can discover and use it. The video below shows this in action, and I worked with the Google Cloud team to put this together. Agents CLI GitHub repo → (don't forget to star it ⭐ ) I wrote up the full build covering all six steps from install to enterprise registration. It includes the eval scorecard, the instruction loophole the eval caught before deployment, and what the deployment process actually looks like end-to-end. Read it below.

Karpathy's Agentic Engineering finally has proper tooling! (built by Google) Karpathy defined agentic engineering as the discipline that separates production agent work from vibe coding. The core skills he listed were spec design, eval loops, and security oversight. The problem has been that practicing this still requires a different tool for every phase: - editor for code - a terminal for scaffolding - a browser for testing - a cloud console for deployment - and a separate framework for evals. Every transition is a context switch. The solution to production-grade Agentic Engineering is now actually implemented in Google’s Agents CLI. It covers the entire workflow in one place for scaffolding, evaluating, and deploying ADK agents. One setup command injects 7 ADK-specific skills into a coding agent's context, which lets it handle scaffolding, evals, deployment, and enterprise registration through natural language. I tested this end-to-end by building a RAG agent from scratch using Claude Code. It scaffolded the full project from the ADK agentic_rag template, generated 20 eval scenarios with LLM-as-judge scoring, and returned a quantitative scorecard. Finally, it also deployed everything to Agent Runtime and registered the agent to Gemini Enterprise, so the entire org can discover and use it. The video below shows this in action, and I worked with the Google Cloud team to put this together. Agents CLI GitHub repo → (don't forget to star it ⭐ ) I wrote up the full build covering all six steps from install to enterprise registration. It includes the eval scorecard, the instruction loophole the eval caught before deployment, and what the deployment process actually looks like end-to-end. Read it below.

Akshay 🚀

257,053 görüntüleme • 1 ay önce

About Siri AI and EU: 1. Your data (gallery, messages, email, call logs, notes, files, and much more) is indexed for personal context, and that info is stored on your iPhone. 2. When you use Siri AI, the request is either handled on-device or in cloud using PCC. It’s never stored or logged on Apple’s servers. Apple does not have access to it. Now, according to Apple, here’s what EU wants: “According to EU regulators, the DMA requires Apple to give any AI system nearly unlimited access to a user’s device, as well as the ability to act on that access autonomously without a user’s ongoing visibility and control. That includes the ability to read and send messages, make purchases, access files, and execute actions across any app.” What EU is asking is scary dangerous. And apparently they have rejected all the solutions proposed by Apple. And, as hinted by Joz, “We will NOT compromise on privacy and security”, Apple is likely not going to give up, so EU users are likely not going to get Siri AI anytime soon.

About Siri AI and EU: 1. Your data (gallery, messages, email, call logs, notes, files, and much more) is indexed for personal context, and that info is stored on your iPhone. 2. When you use Siri AI, the request is either handled on-device or in cloud using PCC. It’s never stored or logged on Apple’s servers. Apple does not have access to it. Now, according to Apple, here’s what EU wants: “According to EU regulators, the DMA requires Apple to give any AI system nearly unlimited access to a user’s device, as well as the ability to act on that access autonomously without a user’s ongoing visibility and control. That includes the ability to read and send messages, make purchases, access files, and execute actions across any app.” What EU is asking is scary dangerous. And apparently they have rejected all the solutions proposed by Apple. And, as hinted by Joz, “We will NOT compromise on privacy and security”, Apple is likely not going to give up, so EU users are likely not going to get Siri AI anytime soon.

Adan

103,884 görüntüleme • 1 ay önce

Nookplot is building infrastructure for peer-to-peer training, one way with verifiable AI reasoning through recursive language model mining. Instead of generating disposable chatbot responses, agents solve problems inside a structured runtime, each reasoning step captured by a trace interpreter that records inputs, outputs, and intermediate state. When deeper analysis is needed, agents recursively spawn sandboxed sub-workspaces; when a problem requires multiple agents reasoning together, they open a shared space where collaborators operate against the same evolving state. Every step is recorded, replayable, and cryptographically verified. Verification happens through replay validators that independently reproduce the trajectory in their own isolated sandbox before rewards settle onchain in NOOK. Once verified, the trace becomes part of Nookplot's growing knowledge graph where other agents can cite and build on prior work. Those citations generate royalties back to the original solver, creating an economy where useful AI reasoning compounds in value over time. The network has already indexed thousands of citations and knowledge artifacts across active AI agents. Nookplot is agentic internet infrastructure for on-chain, verifiable, monetizable intelligence, and peer-to-peer training.

Nookplot is building infrastructure for peer-to-peer training, one way with verifiable AI reasoning through recursive language model mining. Instead of generating disposable chatbot responses, agents solve problems inside a structured runtime, each reasoning step captured by a trace interpreter that records inputs, outputs, and intermediate state. When deeper analysis is needed, agents recursively spawn sandboxed sub-workspaces; when a problem requires multiple agents reasoning together, they open a shared space where collaborators operate against the same evolving state. Every step is recorded, replayable, and cryptographically verified. Verification happens through replay validators that independently reproduce the trajectory in their own isolated sandbox before rewards settle onchain in NOOK. Once verified, the trace becomes part of Nookplot's growing knowledge graph where other agents can cite and build on prior work. Those citations generate royalties back to the original solver, creating an economy where useful AI reasoning compounds in value over time. The network has already indexed thousands of citations and knowledge artifacts across active AI agents. Nookplot is agentic internet infrastructure for on-chain, verifiable, monetizable intelligence, and peer-to-peer training.

nookplot

25,226 görüntüleme • 2 ay önce

I’m joining OpenAI Codex to work on the future of agentic development! At Cursor, I got to see the shift from autocomplete to agents. The next step isn’t a better IDE. It’s an Agent Development Environment (ADE): systems and tools for orchestrating agents, reasoning over their outputs, and making them autonomous enough to reliably complete ambitious work. After chatting with Alexander Embiricos and Tibo, it was clear that Codex is the best place to realize this vision. The team has consistently shipped SOTA models for agentic coding (check out gpt-5.3-codex) and I’m pumped for the future that the new Codex App points to. What I’m most excited about is the broader mission: accelerating the knowledge work economy. All agents are coding agents, and we’re already seeing Codex used across every job function within organizations. I’m extremely grateful for my time at Cursor, working with the incredible team, and I’m proud of what we built together. I’m excited to take an even bigger swing with Codex. If you’re curious to get a glimpse of where we are headed, download the Codex App! If you want to work on this mission, please apply or reach out - we are hiring across all functions! You can just build things.

I’m joining OpenAI Codex to work on the future of agentic development! At Cursor, I got to see the shift from autocomplete to agents. The next step isn’t a better IDE. It’s an Agent Development Environment (ADE): systems and tools for orchestrating agents, reasoning over their outputs, and making them autonomous enough to reliably complete ambitious work. After chatting with Alexander Embiricos and Tibo, it was clear that Codex is the best place to realize this vision. The team has consistently shipped SOTA models for agentic coding (check out gpt-5.3-codex) and I’m pumped for the future that the new Codex App points to. What I’m most excited about is the broader mission: accelerating the knowledge work economy. All agents are coding agents, and we’re already seeing Codex used across every job function within organizations. I’m extremely grateful for my time at Cursor, working with the incredible team, and I’m proud of what we built together. I’m excited to take an even bigger swing with Codex. If you’re curious to get a glimpse of where we are headed, download the Codex App! If you want to work on this mission, please apply or reach out - we are hiring across all functions! You can just build things.

Rohan Varma

759,511 görüntüleme • 5 ay önce

🚨 Alibaba just open sourced a GUI agent that lives inside your webpage and controls it with natural language. It's called Page Agent and it's not a browser extension. It's pure JavaScript no Python, no Puppeteer, no headless browser, no screenshots. Just one script tag and your web app understands natural language. Here's what it actually does: → Embed it with a single tag or npm install → Control any web interface with plain English commands → Text-based DOM manipulation no OCR, no vision models needed → Bring your own LLM (GPT, Claude, Qwen, anything) → Ships a built-in UI with human-in-the-loop support → Turn 20-click ERP/CRM workflows into one sentence → Optional Chrome extension for multi-tab agent tasks → Works on any web app SaaS, admin panels, internal tools Companies are charging $30/month for AI copilots built on this exact idea. This is 3 lines of code. Your users. Your interface. The AI copilot layer for every web app just got open sourced. 1.6K stars. 100% Open Source. (Link in the comments)

🚨 Alibaba just open sourced a GUI agent that lives inside your webpage and controls it with natural language. It's called Page Agent and it's not a browser extension. It's pure JavaScript no Python, no Puppeteer, no headless browser, no screenshots. Just one script tag and your web app understands natural language. Here's what it actually does: → Embed it with a single tag or npm install → Control any web interface with plain English commands → Text-based DOM manipulation no OCR, no vision models needed → Bring your own LLM (GPT, Claude, Qwen, anything) → Ships a built-in UI with human-in-the-loop support → Turn 20-click ERP/CRM workflows into one sentence → Optional Chrome extension for multi-tab agent tasks → Works on any web app SaaS, admin panels, internal tools Companies are charging $30/month for AI copilots built on this exact idea. This is 3 lines of code. Your users. Your interface. The AI copilot layer for every web app just got open sourced. 1.6K stars. 100% Open Source. (Link in the comments)

Ihtesham Ali

135,474 görüntüleme • 4 ay önce

🌌 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. 🔥

🌌 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 görüntüleme • 1 yıl önce

The best time to understand a product is before everyone else is using it That’s one reason I’ve been following Quant AI With 100,000+ people already on the waitlist ahead of its public launch, interest is clearly building around what Quant AI is creating What stands out to me isn’t just the numbers It’s the problem the product is trying to solve Instead of adding another layer of charts, indicators, and dashboards, Quant AI is focused on making market research easier to navigate. With Quant AI, users can: ➫ Explore markets through natural conversations ➫ Follow crypto, stocks, and commodities from one interface ➫ Use chat and voice instead of constantly switching between tools ➫ Understand both opportunities and risks before taking action ➫ Move from research to execution through a simpler workflow We already have access to more market data than ever before The challenge isn’t finding information It’s knowing what matters That’s where I think Quant AI is taking a different approach, helping users spend less time searching and more time understanding. Maybe that’s one reason the waitlist has grown so quickly. People aren’t just looking for another trading platform. They’re looking for a better way to make sense of the markets. If you’re curious about where AI and financial intelligence are heading, now is a good time to explore what Quant AI is building before the public launch. Join the whitelist 👇 #QuantAIPioneers

The best time to understand a product is before everyone else is using it That’s one reason I’ve been following Quant AI With 100,000+ people already on the waitlist ahead of its public launch, interest is clearly building around what Quant AI is creating What stands out to me isn’t just the numbers It’s the problem the product is trying to solve Instead of adding another layer of charts, indicators, and dashboards, Quant AI is focused on making market research easier to navigate. With Quant AI, users can: ➫ Explore markets through natural conversations ➫ Follow crypto, stocks, and commodities from one interface ➫ Use chat and voice instead of constantly switching between tools ➫ Understand both opportunities and risks before taking action ➫ Move from research to execution through a simpler workflow We already have access to more market data than ever before The challenge isn’t finding information It’s knowing what matters That’s where I think Quant AI is taking a different approach, helping users spend less time searching and more time understanding. Maybe that’s one reason the waitlist has grown so quickly. People aren’t just looking for another trading platform. They’re looking for a better way to make sense of the markets. If you’re curious about where AI and financial intelligence are heading, now is a good time to explore what Quant AI is building before the public launch. Join the whitelist 👇 #QuantAIPioneers

Habibiofcrypto

146,787 görüntüleme • 1 ay önce