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Introducing Eigen Trace Mirror - a verifiable AI agent telemetry powered by EigenCloud In an era where your agent runs everything but there is no way to verify what your agent said it ran > Runs on EigenCompute inside a TDX enclave. Every OTel span signed with a secp256k1...

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Zeeshan

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11,254 次观看 • 4 个月前 •via X (Twitter)

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Internet Identity is getting an MCP server. We sat down with Arshavir Ter-Gabrielyan to understand what it is and why it matters. He starts with the problem: an AI agent runs in a cloud sandbox. No access to your device. No safe way to hold your keys. The MCP server is the answer. Keys stay in a trusted execution environment, and the agent asks permission to act in each app. Full conversation below.
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Internet Identity is getting an MCP server. We sat down with Arshavir Ter-Gabrielyan to understand what it is and why it matters. He starts with the problem: an AI agent runs in a cloud sandbox. No access to your device. No safe way to hold your keys. The MCP server is the answer. Keys stay in a trusted execution environment, and the agent asks permission to act in each app. Full conversation below.

DFINITY Foundation

32,966 次观看 • 2 个月前

Introducing Agent Tincan. Let your AI agents ask each other for help. Your agents each have a superpower. Grok Bot is always on in the cloud. Muse makes phone calls and has access to Meta. Instinct runs in iMessage. Codex and Claude Code have your code. ChatGPT and Claude have your chats. But they can't talk to each other, so you're the copy-paste. Now they can, all securely over Tailscale. 📞 Ask Grok Bot to have Muse call the restaurant 🖼 Get an image from ChatGPT, from any agent 🔎 Pull a detail out of an old Claude chat 🛠 Talk to Codex on your primary development machine Runs on your own private Tailscale network. No open ports, nothing leaves it. Open source. Setup is agent-first: paste one message into your always-on agent and it sets up the rest.
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Introducing Agent Tincan. Let your AI agents ask each other for help. Your agents each have a superpower. Grok Bot is always on in the cloud. Muse makes phone calls and has access to Meta. Instinct runs in iMessage. Codex and Claude Code have your code. ChatGPT and Claude have your chats. But they can't talk to each other, so you're the copy-paste. Now they can, all securely over Tailscale. 📞 Ask Grok Bot to have Muse call the restaurant 🖼 Get an image from ChatGPT, from any agent 🔎 Pull a detail out of an old Claude chat 🛠 Talk to Codex on your primary development machine Runs on your own private Tailscale network. No open ports, nothing leaves it. Open source. Setup is agent-first: paste one message into your always-on agent and it sets up the rest.

Matt Van Horn

43,217 次观看 • 3 天前

$FET just quietly built the internet for AI agents. 3,000,000 agents. One network. One token. Here's what nobody is explaining: Every AI agent needs 3 things to be useful: An address. A way to be found. A way to get paid. Agentverse gives agents all three. Your agent becomes discoverable on ASI: One. Other agents can find it, call it, or hire it. Automatically. No human in the loop. This is agent-to-agent commerce. And every transaction settles in $FET. More agents = more activity = more demand. The infrastructure is already live. The economy is just getting started.
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$FET just quietly built the internet for AI agents. 3,000,000 agents. One network. One token. Here's what nobody is explaining: Every AI agent needs 3 things to be useful: An address. A way to be found. A way to get paid. Agentverse gives agents all three. Your agent becomes discoverable on ASI: One. Other agents can find it, call it, or hire it. Automatically. No human in the loop. This is agent-to-agent commerce. And every transaction settles in $FET. More agents = more activity = more demand. The infrastructure is already live. The economy is just getting started.

2xnmore

12,894 次观看 • 5 个月前

OpenClaw in your pocket — powered by Solana 🦞 (Video sent by one of our .molt domain users minting AI agent on Seeker | Solana Mobile ) Connect wallet → pick a name → mint → your AI agent is live. That's it. No servers, no CLI, no Docker, no monthly bills, no coding. Every .molt domain is an NFT with a built-in wallet, zero private keys, and full on-chain identity. One mint and you own an agent that can launch tokens, mint NFTs, host x402 services, and chat on Telegram, Discord & Slack. OpenClaw runs for free — persistent memory, sleeps when idle, wakes on demand. Your agent is always there when you need it. No $600 MacBook. No $15/month VPS. Just your phone and a Solana wallet. This is what building on Solana feels like — fast, cheap, and instant. A Solana Seeker phone with a built-in wallet is all you need. Mint your AI agent in seconds, straight from your pocket.
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OpenClaw in your pocket — powered by Solana 🦞 (Video sent by one of our .molt domain users minting AI agent on Seeker | Solana Mobile ) Connect wallet → pick a name → mint → your AI agent is live. That's it. No servers, no CLI, no Docker, no monthly bills, no coding. Every .molt domain is an NFT with a built-in wallet, zero private keys, and full on-chain identity. One mint and you own an agent that can launch tokens, mint NFTs, host x402 services, and chat on Telegram, Discord & Slack. OpenClaw runs for free — persistent memory, sleeps when idle, wakes on demand. Your agent is always there when you need it. No $600 MacBook. No $15/month VPS. Just your phone and a Solana wallet. This is what building on Solana feels like — fast, cheap, and instant. A Solana Seeker phone with a built-in wallet is all you need. Mint your AI agent in seconds, straight from your pocket.

Molt.id

78,186 次观看 • 7 个月前

Run your coding agent on a local model, with QVAC. OpenCode now plugs into a model on your own machine through our official plugin. That changes two things. Privacy by default. Your repo, your prompts, and every command the agent runs stay on your device. No third-party model ever sees your code. For an agent that reads your codebase and runs commands, that is the point, not a nice-to-have. New workflows. With no meter and no rate limit, the agent does not have to stop. Let it loop on a task, refactor a whole repo overnight, or run a watch-build-fix loop that keeps going. Setup takes a few minutes: one line in opencode.json and OpenCode runs on a local QVAC model. In the video demo, it handles three real developer tasks back to back, fully offline.
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Run your coding agent on a local model, with QVAC. OpenCode now plugs into a model on your own machine through our official plugin. That changes two things. Privacy by default. Your repo, your prompts, and every command the agent runs stay on your device. No third-party model ever sees your code. For an agent that reads your codebase and runs commands, that is the point, not a nice-to-have. New workflows. With no meter and no rate limit, the agent does not have to stop. Let it loop on a task, refactor a whole repo overnight, or run a watch-build-fix loop that keeps going. Setup takes a few minutes: one line in opencode.json and OpenCode runs on a local QVAC model. In the video demo, it handles three real developer tasks back to back, fully offline.

QVAC

11,382,936 次观看 • 3 个月前

Comfy MCP is now local and open-source! The #1 ask after Comfy Cloud MCP shipped in June. Your agent sees your actual install. So every node, custom nodes included, and every model on disk. It fetches files, starts your instance, gets a workflow to the point where it runs. It is now the easiest way to help with your local Minimax H3 workflows! Cloud MCP still does everything it did. Tell your agent where a job goes, or let it decide.
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Comfy MCP is now local and open-source! The #1 ask after Comfy Cloud MCP shipped in June. Your agent sees your actual install. So every node, custom nodes included, and every model on disk. It fetches files, starts your instance, gets a workflow to the point where it runs. It is now the easiest way to help with your local Minimax H3 workflows! Cloud MCP still does everything it did. Tell your agent where a job goes, or let it decide.

ComfyUI

184,611 次观看 • 1 个月前

NEAR Intents for AI Agents by OutLayer Give any AI agent a multi-chain wallet with gasless swaps for all NEAR Intents assets. The agent reads the skill URL and creates a wallet inside Intel TDX. Wallet keys are generated by the NEAR MPC network, enabling operations across 35 chains. No seed phrases, no setup, no gas fees. The agent only holds an API key. Swap 200+ tokens, get quotes, deposit to Intents, withdraw, transfer tokens - all through a simple REST API. You stay in control: spending limits, address whitelists, multisig approvals, emergency freeze, full audit log - all enforced by the enclave, not by the agent. Without an Intents API key every swap costs 0.2%. Through OutLayer - 0.0001%. Your agent can't register for one on its own. We did it for you. Live on NEAR mainnet. Links 👇
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NEAR Intents for AI Agents by OutLayer Give any AI agent a multi-chain wallet with gasless swaps for all NEAR Intents assets. The agent reads the skill URL and creates a wallet inside Intel TDX. Wallet keys are generated by the NEAR MPC network, enabling operations across 35 chains. No seed phrases, no setup, no gas fees. The agent only holds an API key. Swap 200+ tokens, get quotes, deposit to Intents, withdraw, transfer tokens - all through a simple REST API. You stay in control: spending limits, address whitelists, multisig approvals, emergency freeze, full audit log - all enforced by the enclave, not by the agent. Without an Intents API key every swap costs 0.2%. Through OutLayer - 0.0001%. Your agent can't register for one on its own. We did it for you. Live on NEAR mainnet. Links 👇

Vadim (AI, ⋈)

22,024 次观看 • 6 个月前

Claude Fable 5 is back! GPT- 5.6 is coming soon! But how much do you trust your Al Agent or chatbot? Are you sure its performance aligns with your intentions? As of today iFixAi is available in Claude Code, Open AI Codex, Cursor and many more! And it is open-source! iFixAi is where Al red teaming meets Operational Assurance. It runs in auto, and in under 5 mins you can have a good idea if you can trust your Agent/chatbot. Key Features: 🕵️‍♂️ Setup Discovery Reads your agent's real configuration, its tools, rules, and permissions. 👷 Fixture Builder Turns that setup into a complete test. No YAML, no flags, no config to write. 🌐 Any Model, Any Provider Runs the 45 inspections on the model you actually ship. ⚖️ A Independent Grading Your agent is judged by a different vendor's model, never itself. No Al grading its own homework. 📊 Interactive Scorecard An A to F grade in minutes, with exactly what failed and why. Repo in the comments below 👇👇👇
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Claude Fable 5 is back! GPT- 5.6 is coming soon! But how much do you trust your Al Agent or chatbot? Are you sure its performance aligns with your intentions? As of today iFixAi is available in Claude Code, Open AI Codex, Cursor and many more! And it is open-source! iFixAi is where Al red teaming meets Operational Assurance. It runs in auto, and in under 5 mins you can have a good idea if you can trust your Agent/chatbot. Key Features: 🕵️‍♂️ Setup Discovery Reads your agent's real configuration, its tools, rules, and permissions. 👷 Fixture Builder Turns that setup into a complete test. No YAML, no flags, no config to write. 🌐 Any Model, Any Provider Runs the 45 inspections on the model you actually ship. ⚖️ A Independent Grading Your agent is judged by a different vendor's model, never itself. No Al grading its own homework. 📊 Interactive Scorecard An A to F grade in minutes, with exactly what failed and why. Repo in the comments below 👇👇👇

Dimitris Neocleous

5,800,937 次观看 • 2 个月前

AgentCRM is the first CRM built for AI agents, not humans. Your agent can synthesize real-time data, curate it, format it into an email, and send it to your list. On its own, every day. What makes it different? 3 API calls to go from zero to sending a newsletter. Create org, add contacts, send. No onboarding wizard, no domain setup, no OAuth. Agent-first SDK. pip install agentcrm Built on AgentMail (YC S25) for delivery. No SMTP config. Connect your AgentMail key and emails go out. Suppression is automatic. Bounces, complaints, and unsubscribes are handled without the agent doing anything. DM me for developer access or questions.
4:50

AgentCRM is the first CRM built for AI agents, not humans. Your agent can synthesize real-time data, curate it, format it into an email, and send it to your list. On its own, every day. What makes it different? 3 API calls to go from zero to sending a newsletter. Create org, add contacts, send. No onboarding wizard, no domain setup, no OAuth. Agent-first SDK. pip install agentcrm Built on AgentMail (YC S25) for delivery. No SMTP config. Connect your AgentMail key and emails go out. Suppression is automatic. Bounces, complaints, and unsubscribes are handled without the agent doing anything. DM me for developer access or questions.

Rani Haddad

29,492 次观看 • 5 个月前

My agent is now paying for its own data. No API key. No account. No credit card. Check the video I recorded. This uses the x402 open protocol built by Coinbase, along with a simple skill. This is an open protocol governed by the Linux Foundation. To complete a goal, an agent can now search for an actor on the Apify Store, pay for it, and use it without any human intervention. The way it works is pretty simple: 1. Your agent finds an Actor it wants to use 2. It sends a request 3. The Actor sends back an HTTP 402 "Payment Required" response 4. Agent authorizes payment from a wallet in USDC on Base 5. The Actor runs 6. User receives the result The model is pay-as-you-go: the agent authorizes a spending ceiling, pays only for usage, and the remaining balance is automatically settled. This brings an entire marketplace of AI tools into the agentic economy. Here is the skill you need. Add it to your agent, and it will take care of everything for you: If you want to know how this works, including what you can do with $1, read this post: Thanks to the Apify team for partnering with me on this post.
5:56

My agent is now paying for its own data. No API key. No account. No credit card. Check the video I recorded. This uses the x402 open protocol built by Coinbase, along with a simple skill. This is an open protocol governed by the Linux Foundation. To complete a goal, an agent can now search for an actor on the Apify Store, pay for it, and use it without any human intervention. The way it works is pretty simple: 1. Your agent finds an Actor it wants to use 2. It sends a request 3. The Actor sends back an HTTP 402 "Payment Required" response 4. Agent authorizes payment from a wallet in USDC on Base 5. The Actor runs 6. User receives the result The model is pay-as-you-go: the agent authorizes a spending ceiling, pays only for usage, and the remaining balance is automatically settled. This brings an entire marketplace of AI tools into the agentic economy. Here is the skill you need. Add it to your agent, and it will take care of everything for you: If you want to know how this works, including what you can do with $1, read this post: Thanks to the Apify team for partnering with me on this post.

Santiago

26,981 次观看 • 2 个月前

Introducing Claude for reading social media. By default your agent is blind out there. "Log in to continue." "Verify you are human." Every feed is a wall. One skill gives it eyes: > TikTok, Instagram, Reddit, YouTube > live posts, real numbers, structured JSON > $0.0015 per call 4 feeds, one message, $0.006 total. No logins. No bot walls. No subscription.
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Introducing Claude for reading social media. By default your agent is blind out there. "Log in to continue." "Verify you are human." Every feed is a wall. One skill gives it eyes: > TikTok, Instagram, Reddit, YouTube > live posts, real numbers, structured JSON > $0.0015 per call 4 feeds, one message, $0.006 total. No logins. No bot walls. No subscription.

Jasper Li

20,373 次观看 • 1 个月前

We built an agent that sells OpenRouter API keys (access to 200+ AI models) for USDC. No human in the loop. It earns money 24/7 on Base. Now we're turning that into a market where any agent can do the same. → openagent.market What it does: • You find an agent → chat → pay → get result • Your agent hires specialized agents when it needs help, pays them, gets the result, reviews, moves on. • Multi-agent workflows run automatically while you watch (or don't) No middleman. Pay only for results. Why not just install skills, scripts? Current way to extend an agent: install random scripts. That's risky. Our way: hire another agent. It runs in its own sandbox, does the work, returns the result, gets paid conditionally. No downloads. No setup. No blind trust. Hire, don't install. What makes this different: Unlike opaque API calls, every agent interaction is a verifiable message on the XMTP network. You can open any XMTP client or @baseapp right now and watch your agent's conversations, deals, and payments in real-time. Humans are always in the loop. For builders: Turn any service into a paid agent in ~10 lines of code. Or scaffold a full project: npx openagent.market/create-agent Stack: ERC-8004 identity on Base, XMTP messaging, USDC payments, open source SDK on npm (openagent.market/nodejs). Others give agents skills. We're giving them an economy. Still very early. Building in public. Come build with us.
1:21

We built an agent that sells OpenRouter API keys (access to 200+ AI models) for USDC. No human in the loop. It earns money 24/7 on Base. Now we're turning that into a market where any agent can do the same. → openagent.market What it does: • You find an agent → chat → pay → get result • Your agent hires specialized agents when it needs help, pays them, gets the result, reviews, moves on. • Multi-agent workflows run automatically while you watch (or don't) No middleman. Pay only for results. Why not just install skills, scripts? Current way to extend an agent: install random scripts. That's risky. Our way: hire another agent. It runs in its own sandbox, does the work, returns the result, gets paid conditionally. No downloads. No setup. No blind trust. Hire, don't install. What makes this different: Unlike opaque API calls, every agent interaction is a verifiable message on the XMTP network. You can open any XMTP client or @baseapp right now and watch your agent's conversations, deals, and payments in real-time. Humans are always in the loop. For builders: Turn any service into a paid agent in ~10 lines of code. Or scaffold a full project: npx openagent.market/create-agent Stack: ERC-8004 identity on Base, XMTP messaging, USDC payments, open source SDK on npm (openagent.market/nodejs). Others give agents skills. We're giving them an economy. Still very early. Building in public. Come build with us.

applefather

118,226 次观看 • 7 个月前

Today, the most important technology of our century runs inside about five buildings. Halo is now live on Base, a peer-to-peer marketplace for verifiable AI inference where both sides can be a human or an AI agent. Have a model, serve it. Need one, use it. Every payment settles onchain in USDC in ~2 seconds. AI that lives everywhere and belongs to no one. Learn more 👇
0:27

Today, the most important technology of our century runs inside about five buildings. Halo is now live on Base, a peer-to-peer marketplace for verifiable AI inference where both sides can be a human or an AI agent. Have a model, serve it. Need one, use it. Every payment settles onchain in USDC in ~2 seconds. AI that lives everywhere and belongs to no one. Learn more 👇

Warden / Halo

20,554 次观看 • 1 个月前

You shipped your app. Now what? Your app may look great, but if no one can find it, it stays invisible Publishing is only the beginning Meet SEO Agent. It runs a scan for you and suggests fixes to help your app get discovered in web & AI search
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You shipped your app. Now what? Your app may look great, but if no one can find it, it stays invisible Publishing is only the beginning Meet SEO Agent. It runs a scan for you and suggests fixes to help your app get discovered in web & AI search

Replit ⠕

719,858 次观看 • 3 个月前

Another blow to Anthropic! They spent months building what's now fully open-source. Anthropic recently put Claude inside Slack, where you can tag it in a channel. It reads the thread, breaks the task into steps, and posts the result back. The problem is that it only runs Claude and only in the channels Anthropic supports. Running your own agent there is harder. The reasoning, tool calls, and state management are mostly handled by the framework. Connecting that agent to a messaging platform is not. Moreover, each platform has a different integration: - Slack renders messages with Block Kit - Teams uses Adaptive Cards - and each has its own SDK, auth flow, and delivery model. If an agent needs to run on three platforms, one must write three separate integrations against the same agent logic. That overhead explains why most custom agents never get deployed to Slack, and why the ones that do are usually a single vendor's hosted assistant. The alternative is to keep the agent in one place and add a per-platform adapter that translates its output into each platform's native format. The agent is written once, and each channel requires just another output target instead of a separate build. CopilotKit open-sourced this full implementation in the Channels SDK. Essentially, any agent that implements AG-UI can run in a messaging platform in a few lines of code, like Slack, Teams, Discord, WhatsApp, and many more. Because the agent runs inside the thread, it has that conversation's context, so it can summarize the discussion, open a ticket, or route to the right person. It works with any backend, so LangGraph, CrewAI, Mastra, Google ADK, or a plain HTTP agent can connect through an existing endpoint. The same message can render as a Block Kit in Slack and as Adaptive Cards in Teams. In practice, the model and orchestration stay the same; it requires no migration or rewrite. It also handles human-in-the-loop approvals, persistence, and transcripts that carry state across platforms, so a thread started in Teams can continue in Slack. CopilotKit is open-source, and AG-UI is supported across every major agent framework, including LangGraph, CrewAI, Mastra, and Google ADK. Here's the repo: (don't forget to star it ⭐) The agent running in Slack no longer has to be a vendor's. It can be the one you already built. The video below shows this in action. Thanks to CopilotKit for working with me on this launch.
0:30

Another blow to Anthropic! They spent months building what's now fully open-source. Anthropic recently put Claude inside Slack, where you can tag it in a channel. It reads the thread, breaks the task into steps, and posts the result back. The problem is that it only runs Claude and only in the channels Anthropic supports. Running your own agent there is harder. The reasoning, tool calls, and state management are mostly handled by the framework. Connecting that agent to a messaging platform is not. Moreover, each platform has a different integration: - Slack renders messages with Block Kit - Teams uses Adaptive Cards - and each has its own SDK, auth flow, and delivery model. If an agent needs to run on three platforms, one must write three separate integrations against the same agent logic. That overhead explains why most custom agents never get deployed to Slack, and why the ones that do are usually a single vendor's hosted assistant. The alternative is to keep the agent in one place and add a per-platform adapter that translates its output into each platform's native format. The agent is written once, and each channel requires just another output target instead of a separate build. CopilotKit open-sourced this full implementation in the Channels SDK. Essentially, any agent that implements AG-UI can run in a messaging platform in a few lines of code, like Slack, Teams, Discord, WhatsApp, and many more. Because the agent runs inside the thread, it has that conversation's context, so it can summarize the discussion, open a ticket, or route to the right person. It works with any backend, so LangGraph, CrewAI, Mastra, Google ADK, or a plain HTTP agent can connect through an existing endpoint. The same message can render as a Block Kit in Slack and as Adaptive Cards in Teams. In practice, the model and orchestration stay the same; it requires no migration or rewrite. It also handles human-in-the-loop approvals, persistence, and transcripts that carry state across platforms, so a thread started in Teams can continue in Slack. CopilotKit is open-source, and AG-UI is supported across every major agent framework, including LangGraph, CrewAI, Mastra, and Google ADK. Here's the repo: (don't forget to star it ⭐) The agent running in Slack no longer has to be a vendor's. It can be the one you already built. The video below shows this in action. Thanks to CopilotKit for working with me on this launch.

Akshay 🚀

244,298 次观看 • 1 个月前

Astarter s agent beta is open. An autonomous trading agent for BNB Smart Chain. Your funds stay in an account only you own. You grant the agent a bounded, expiring permission, you can revoke it at any time, and proceeds can only ever return to you. AI proposes. Code verifies. You set the limits. The model reads the market and builds a case. Deterministic code checks that case against the limits you signed, and decides whether it may execute at all. The model cannot widen a limit, because no code path widens one. Every decision is recorded with its reasons, including the decision not to trade. In a quiet market that is the correct output, and you will see exactly why it was reached. And you can walk away whenever you want, taking your funds with you, without asking us for anything. Try it, and tell us what to fix. We are improving it continuously. Trading involves risk. No return is promised.
0:26

Astarter s agent beta is open. An autonomous trading agent for BNB Smart Chain. Your funds stay in an account only you own. You grant the agent a bounded, expiring permission, you can revoke it at any time, and proceeds can only ever return to you. AI proposes. Code verifies. You set the limits. The model reads the market and builds a case. Deterministic code checks that case against the limits you signed, and decides whether it may execute at all. The model cannot widen a limit, because no code path widens one. Every decision is recorded with its reasons, including the decision not to trade. In a quiet market that is the correct output, and you will see exactly why it was reached. And you can walk away whenever you want, taking your funds with you, without asking us for anything. Try it, and tell us what to fix. We are improving it continuously. Trading involves risk. No return is promised.

Astarter

20,500 次观看 • 20 天前

Everyone's building AI agents that run on someone else's server, store memory in someone else's database, and can be shut down by someone else's terms of service. I built one that can't be. FlowClaw is an AI agent that runs on a decentralized distributed computer. Your agent, your conversations, your memory, your tools — all stored onchain on Flow, a distributed network of validator nodes across the world. Not a centralized cloud. Not someone's S3 bucket. A blockchain that functions as censorship-resistant compute and storage for your AI. This isn't a wrapper. Your agent is a Resource — a first-class programmable object in Cadence (Flow's smart contract language) that physically lives in your account's on-chain storage. It can't be duplicated, seized, or deleted by anyone except you. Your encrypted messages, your cognitive memory, your scheduled tasks — they persist on a global distributed ledger that no single entity controls. It's an alpha build. It will break. But it works today on mainnet and I want people to push it this weekend. What it does: You go to authenticate with a passkey (Face ID, Touch ID), and you have a blockchain account in seconds. No wallet. No seed phrase. No tokens needed — gas is sponsored. You're immediately chatting with an AI agent that has real tool execution: live web data, token prices, on-chain balances, Cadence script execution, FLOW transfers. Every message is encrypted client-side before it touches the chain. The agent has a cognitive memory system — it doesn't just remember your last message, it builds molecular memory clusters where related knowledge bonds together for contextual retrieval across sessions. You can spawn sub-agents from a visual canvas to run parallel research. The memory tab shows you exactly what your agent knows. Everything is transparent and everything is yours. 11 smart contracts. No external dependencies. No keeper networks. No account abstraction hacks. Here's the part that matters for the censorship-resistance crowd: FlowClaw supports BYOK — bring your own key. You can plug in any LLM provider. But pair it with Venice and you get the full stack: a censorship-resistant AI model running inference with no content filtering, connected to an agent whose state lives on a decentralized network that no company can shut down, with end-to-end encrypted conversations that nobody can read — not the relay operator, not the LLM provider, not the blockchain validators. Venice doesn't log prompts. Flow can't read your encrypted storage. The relay never sees your plaintext. That's not a privacy policy. That's architecture. You can also use OpenAI, Anthropic, or any OpenAI-compatible provider. The agent platform doesn't care — it's model-agnostic. But the Venice pairing is the one that closes every gap in the stack. For the people tinkering with OpenClaw and the broader open-source agent ecosystem — FlowClaw is exploring what happens when you take the agent off the cloud entirely. Not just open-sourcing the code (though it is), but putting the actual runtime state on a distributed computer. Your agent's memory isn't in a SQLite file on your laptop or a Pinecone index on someone's cluster. It's on-chain, encrypted, and replicated across every validator node on Flow. You own it the way you own a private key — mathematically, not contractually. The blockchain here isn't a gimmick bolted onto an agent for token speculation. It's functioning as the infrastructure layer that replaces AWS. Flow accounts are programmable containers with their own storage, keys, and security capabilities. Passkey authentication works natively because Flow supports P-256 keys at the protocol level — the same curve your phone uses for biometrics. Gas sponsorship works natively because Flow transactions have separate proposer, authorizer, and payer roles built into the protocol. No proxy contracts. No relayers. No ERC-4337. Now here's the part that interests me economically. Every FlowClaw interaction is an on-chain transaction. Every message stored, every memory committed, every session created, every sub-agent spawned. An active user might generate dozens of transactions in a single conversation. Scale that and FlowClaw becomes a real contributor to Flow's transaction volume. Flow.com becomes deflationary at 250 TPS. Applications like FlowClaw that generate high-frequency, storage-heavy transactions are exactly what moves the needle. Every encrypted message uses account storage, which requires FLOW balance to back it. Every transaction burns fees. The more agents running, the more demand for $FLOW — not because of a tokenomics gimmick, but because the protocol literally requires it for compute and storage. FlowClaw doesn't have its own token. The token is $FLOW. The entire platform runs natively on the network — using Flow storage, paying Flow transaction fees, backed by Flow account balances. If FlowClaw succeeds, FLOW captures that value directly. I'm sharing this early because the AI agent space is moving fast and I think the decentralized infrastructure angle is underexplored. Most "crypto AI" projects are tokens with a chatbot attached. FlowClaw is the opposite — it's an agent platform that happens to use a blockchain because the blockchain solves real engineering problems that centralized infrastructure can't. Try it: Github: Create an agent, ask it something, spawn a sub-agent, check your memory tab, pair it with Venice for the full censorship-resistant stack. Break it and tell me what broke. If you think this direction matters, the best thing you can do is use it and give feedback. Your AI agent should be yours. Not your provider's. Not your platform's. Yours.
0:46

Everyone's building AI agents that run on someone else's server, store memory in someone else's database, and can be shut down by someone else's terms of service. I built one that can't be. FlowClaw is an AI agent that runs on a decentralized distributed computer. Your agent, your conversations, your memory, your tools — all stored onchain on Flow, a distributed network of validator nodes across the world. Not a centralized cloud. Not someone's S3 bucket. A blockchain that functions as censorship-resistant compute and storage for your AI. This isn't a wrapper. Your agent is a Resource — a first-class programmable object in Cadence (Flow's smart contract language) that physically lives in your account's on-chain storage. It can't be duplicated, seized, or deleted by anyone except you. Your encrypted messages, your cognitive memory, your scheduled tasks — they persist on a global distributed ledger that no single entity controls. It's an alpha build. It will break. But it works today on mainnet and I want people to push it this weekend. What it does: You go to authenticate with a passkey (Face ID, Touch ID), and you have a blockchain account in seconds. No wallet. No seed phrase. No tokens needed — gas is sponsored. You're immediately chatting with an AI agent that has real tool execution: live web data, token prices, on-chain balances, Cadence script execution, FLOW transfers. Every message is encrypted client-side before it touches the chain. The agent has a cognitive memory system — it doesn't just remember your last message, it builds molecular memory clusters where related knowledge bonds together for contextual retrieval across sessions. You can spawn sub-agents from a visual canvas to run parallel research. The memory tab shows you exactly what your agent knows. Everything is transparent and everything is yours. 11 smart contracts. No external dependencies. No keeper networks. No account abstraction hacks. Here's the part that matters for the censorship-resistance crowd: FlowClaw supports BYOK — bring your own key. You can plug in any LLM provider. But pair it with Venice and you get the full stack: a censorship-resistant AI model running inference with no content filtering, connected to an agent whose state lives on a decentralized network that no company can shut down, with end-to-end encrypted conversations that nobody can read — not the relay operator, not the LLM provider, not the blockchain validators. Venice doesn't log prompts. Flow can't read your encrypted storage. The relay never sees your plaintext. That's not a privacy policy. That's architecture. You can also use OpenAI, Anthropic, or any OpenAI-compatible provider. The agent platform doesn't care — it's model-agnostic. But the Venice pairing is the one that closes every gap in the stack. For the people tinkering with OpenClaw and the broader open-source agent ecosystem — FlowClaw is exploring what happens when you take the agent off the cloud entirely. Not just open-sourcing the code (though it is), but putting the actual runtime state on a distributed computer. Your agent's memory isn't in a SQLite file on your laptop or a Pinecone index on someone's cluster. It's on-chain, encrypted, and replicated across every validator node on Flow. You own it the way you own a private key — mathematically, not contractually. The blockchain here isn't a gimmick bolted onto an agent for token speculation. It's functioning as the infrastructure layer that replaces AWS. Flow accounts are programmable containers with their own storage, keys, and security capabilities. Passkey authentication works natively because Flow supports P-256 keys at the protocol level — the same curve your phone uses for biometrics. Gas sponsorship works natively because Flow transactions have separate proposer, authorizer, and payer roles built into the protocol. No proxy contracts. No relayers. No ERC-4337. Now here's the part that interests me economically. Every FlowClaw interaction is an on-chain transaction. Every message stored, every memory committed, every session created, every sub-agent spawned. An active user might generate dozens of transactions in a single conversation. Scale that and FlowClaw becomes a real contributor to Flow's transaction volume. Flow.com becomes deflationary at 250 TPS. Applications like FlowClaw that generate high-frequency, storage-heavy transactions are exactly what moves the needle. Every encrypted message uses account storage, which requires FLOW balance to back it. Every transaction burns fees. The more agents running, the more demand for $FLOW — not because of a tokenomics gimmick, but because the protocol literally requires it for compute and storage. FlowClaw doesn't have its own token. The token is $FLOW. The entire platform runs natively on the network — using Flow storage, paying Flow transaction fees, backed by Flow account balances. If FlowClaw succeeds, FLOW captures that value directly. I'm sharing this early because the AI agent space is moving fast and I think the decentralized infrastructure angle is underexplored. Most "crypto AI" projects are tokens with a chatbot attached. FlowClaw is the opposite — it's an agent platform that happens to use a blockchain because the blockchain solves real engineering problems that centralized infrastructure can't. Try it: Github: Create an agent, ask it something, spawn a sub-agent, check your memory tab, pair it with Venice for the full censorship-resistant stack. Break it and tell me what broke. If you think this direction matters, the best thing you can do is use it and give feedback. Your AI agent should be yours. Not your provider's. Not your platform's. Yours.

doodlifts

12,172 次观看 • 7 个月前

AG-UI makes building agentic applications dramatically easier. Here's how it works. This is a model for a simple chatbot: User → LLM → Response But interactive agents that render UI, pause for approvals, and ask users for input need a much more complex model. When building these agents, a response from the LLM will include a series of state changes as the agent runs: • Agent started a task • Agent called a tool • Agent updated its state • Agent streams these tokens • Agent is waiting on a human • Agent is resuming the task The Agent-User Interaction Protocol (AG-UI) treats the LLM response as a stream of events rather than a text endpoint. In practice, here is what you get as an agent runs: 1. Lifecycle events so your UI knows where the agent is. 2. Text messages that stream tokens. 3. Tool calls so your UI can prefill a form with any required arguments. 4. State updates that keep your UI in sync with the agent. 5. Special events for human approvals, rich media, and custom needs. All of these events travel over standard transports (SSE, WebSockets, or plain HTTP) as JSON. As a result, you can build a frontend that stays in sync with the agent's progress without having to invent a custom process to make this happen. For example, building a human-in-the-loop workflow becomes an off-the-shelf component you can integrate rather than build from scratch. CopilotKit🪁 is the creator of AG-UI, and you can use it when building frontend applications pretty much anywhere: • React • Angular • Vue • React Native • Slack • Teams • Discord • WhatsApp • Telegram Here is the link for you to check it out: Thanks to the CopilotKit team for partnering with me on this post.
0:22

AG-UI makes building agentic applications dramatically easier. Here's how it works. This is a model for a simple chatbot: User → LLM → Response But interactive agents that render UI, pause for approvals, and ask users for input need a much more complex model. When building these agents, a response from the LLM will include a series of state changes as the agent runs: • Agent started a task • Agent called a tool • Agent updated its state • Agent streams these tokens • Agent is waiting on a human • Agent is resuming the task The Agent-User Interaction Protocol (AG-UI) treats the LLM response as a stream of events rather than a text endpoint. In practice, here is what you get as an agent runs: 1. Lifecycle events so your UI knows where the agent is. 2. Text messages that stream tokens. 3. Tool calls so your UI can prefill a form with any required arguments. 4. State updates that keep your UI in sync with the agent. 5. Special events for human approvals, rich media, and custom needs. All of these events travel over standard transports (SSE, WebSockets, or plain HTTP) as JSON. As a result, you can build a frontend that stays in sync with the agent's progress without having to invent a custom process to make this happen. For example, building a human-in-the-loop workflow becomes an off-the-shelf component you can integrate rather than build from scratch. CopilotKit🪁 is the creator of AG-UI, and you can use it when building frontend applications pretty much anywhere: • React • Angular • Vue • React Native • Slack • Teams • Discord • WhatsApp • Telegram Here is the link for you to check it out: Thanks to the CopilotKit team for partnering with me on this post.

Santiago

17,438 次观看 • 2 个月前

Would you believe an AI agent can test a real VR action game in real time, the way a person plays it? Meta XR Operator makes it possible. As far as I know, this is the first time. I am not talking about tapping a menu or replaying a recorded click path, but genuinely moving, shooting, and using the same game mechanics a human player does. In NeonReach VR, which is a real (and open source) action game, rings spawn 12m out and come at you somewhere between 1.5 and 5.5 m/s, getting faster over a 90 second ramp. There are three kinds: straight, weaving side to side, and spinning. Every shot is a full slingshot cycle, so you press, pull back, aim, then release. Obstacles arrive at head height and cost you a life if you don't get out of the way. You have ten lives. Here is why the game is hard for an AI agent. Even though Meta XR Operator gives the agent everything it needs to observe the app and act inside it, the agent still cannot play. One agent turn takes 10 to 15 seconds. One throw is four steps that have to happen in order, because the press has to latch before the pull, and they cannot be batched into a single call. So a throw costs about 45 seconds. A fast ring only exists for 2.3 seconds. One action takes 20x longer than the target is alive. Prompt tuning does not close a gap that size. What works is a three stage path: EXPLORATION, then SKILL, then SCRIPT. 1/ EXPLORATION. The agent drives the live app and works the game out on its own. It verified the coordinate mapping by setting a pose and reading it back, then derived the launch model. The more useful output was the traps it found. For example, the player's own body collider silently deflects a ball released inside it, with no error and no log line. That produced two confident wrong conclusions before anyone caught them. 2/ SKILL. All of that gets written down as a reusable SKILL.md plus an aim solver. There is a section that separates what was actually verified from what was assumed, so a wrong conclusion cannot quietly turn into doctrine. This stage also produced the trick that mattered. Set timeScale to 0 and a throw becomes atomic in game time, so however long the agent spends thinking never shows up in the shot. 3/ SCRIPT. The agent then compiles everything into a player script, a loop that observes, decides, and throws, calling the MCP servers directly from Python with no model in the hot path. Round trips drop from 10 to 15 seconds down to something between 1 and 16 milliseconds. The loop runs at 23 Hz, about 0.75 seconds per throw, roughly 60x faster than the agent doing it turn by turn. The result is that it plays like a person, which you can see from the attached video. It tracks the rings, works out where each one is going, throws with whichever hand is free, moves out of the way of the obstacles, and does not wait around to see whether the last throw landed. Shipping settings, no difficulty edits, no health locks, no slow motion. It plays until it actually loses. The takeaway generalizes beyond games: an agent does not have to be the player. Even following the same rules as a player, it has too much latency between moves. Having the agent write the thing that acts bypasses that constraint entirely. Try it yourself: or explore the agent-created skill and scripts: Based on NeonReach VR by Dilmer, with no code changes. I only upgraded its Meta XR Core SDK to v205, which ships Meta XR Operator. Our blog post, Introducing Meta XR Operator: Close the Build-Test-Verify Loop for VR: Disclosure: I work at Meta. And this represents my own opinion. #XR #VR #AI #MetaQuest #Unity #GameDev
1:21

Would you believe an AI agent can test a real VR action game in real time, the way a person plays it? Meta XR Operator makes it possible. As far as I know, this is the first time. I am not talking about tapping a menu or replaying a recorded click path, but genuinely moving, shooting, and using the same game mechanics a human player does. In NeonReach VR, which is a real (and open source) action game, rings spawn 12m out and come at you somewhere between 1.5 and 5.5 m/s, getting faster over a 90 second ramp. There are three kinds: straight, weaving side to side, and spinning. Every shot is a full slingshot cycle, so you press, pull back, aim, then release. Obstacles arrive at head height and cost you a life if you don't get out of the way. You have ten lives. Here is why the game is hard for an AI agent. Even though Meta XR Operator gives the agent everything it needs to observe the app and act inside it, the agent still cannot play. One agent turn takes 10 to 15 seconds. One throw is four steps that have to happen in order, because the press has to latch before the pull, and they cannot be batched into a single call. So a throw costs about 45 seconds. A fast ring only exists for 2.3 seconds. One action takes 20x longer than the target is alive. Prompt tuning does not close a gap that size. What works is a three stage path: EXPLORATION, then SKILL, then SCRIPT. 1/ EXPLORATION. The agent drives the live app and works the game out on its own. It verified the coordinate mapping by setting a pose and reading it back, then derived the launch model. The more useful output was the traps it found. For example, the player's own body collider silently deflects a ball released inside it, with no error and no log line. That produced two confident wrong conclusions before anyone caught them. 2/ SKILL. All of that gets written down as a reusable SKILL.md plus an aim solver. There is a section that separates what was actually verified from what was assumed, so a wrong conclusion cannot quietly turn into doctrine. This stage also produced the trick that mattered. Set timeScale to 0 and a throw becomes atomic in game time, so however long the agent spends thinking never shows up in the shot. 3/ SCRIPT. The agent then compiles everything into a player script, a loop that observes, decides, and throws, calling the MCP servers directly from Python with no model in the hot path. Round trips drop from 10 to 15 seconds down to something between 1 and 16 milliseconds. The loop runs at 23 Hz, about 0.75 seconds per throw, roughly 60x faster than the agent doing it turn by turn. The result is that it plays like a person, which you can see from the attached video. It tracks the rings, works out where each one is going, throws with whichever hand is free, moves out of the way of the obstacles, and does not wait around to see whether the last throw landed. Shipping settings, no difficulty edits, no health locks, no slow motion. It plays until it actually loses. The takeaway generalizes beyond games: an agent does not have to be the player. Even following the same rules as a player, it has too much latency between moves. Having the agent write the thing that acts bypasses that constraint entirely. Try it yourself: or explore the agent-created skill and scripts: Based on NeonReach VR by Dilmer, with no code changes. I only upgraded its Meta XR Core SDK to v205, which ships Meta XR Operator. Our blog post, Introducing Meta XR Operator: Close the Build-Test-Verify Loop for VR: Disclosure: I work at Meta. And this represents my own opinion. #XR #VR #AI #MetaQuest #Unity #GameDev

Xiang Wei

54,900 次观看 • 1 个月前

anthropic just removed the biggest excuse people had for not building ai agents. “it’s too hard.” not anymore. a few months ago, building an ai agent meant learning how to code, setting up apis, configuring servers, and fixing endless errors. today, all you need is one github repo. anthropic open-sourced launch your agent. it asks what you want to build. then it does the rest. it builds your agent, deploys it to the cloud, tests it, improves it, and keeps it running even after you close your laptop. no fake demos. no complicated setup. just a real ai agent running in your own account. the best part. it doesn’t run on your computer. it runs inside claude managed agents, works 24/7, and costs just a few cents per run. people are already building research agents, lead generation systems, content pipelines, and customer support agents that save hours of work every day. the gap between people who use ai and people who build ai workers is getting bigger every week. that’s usually where the biggest opportunities are. build your first ai agent before everyone else does. watch the video and read the full guide below 👇
16:19

anthropic just removed the biggest excuse people had for not building ai agents. “it’s too hard.” not anymore. a few months ago, building an ai agent meant learning how to code, setting up apis, configuring servers, and fixing endless errors. today, all you need is one github repo. anthropic open-sourced launch your agent. it asks what you want to build. then it does the rest. it builds your agent, deploys it to the cloud, tests it, improves it, and keeps it running even after you close your laptop. no fake demos. no complicated setup. just a real ai agent running in your own account. the best part. it doesn’t run on your computer. it runs inside claude managed agents, works 24/7, and costs just a few cents per run. people are already building research agents, lead generation systems, content pipelines, and customer support agents that save hours of work every day. the gap between people who use ai and people who build ai workers is getting bigger every week. that’s usually where the biggest opportunities are. build your first ai agent before everyone else does. watch the video and read the full guide below 👇

MIKE

26,662 次观看 • 2 个月前