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What's missing for AGI? I strongly believe it's distribution: this is why I built an open network + runtime for IA agents Introducing AGH (Artificial General Hivemind) 🔥 One Go binary + A complete runtime + An open network protocol so agents can actually coordinate work across machines.

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pedronauck

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44,368 просмотров • 2 месяцев назад •via X (Twitter)

Наука и технологии
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I built an open-source generative art platform where AI agents create, evolve, and export 3D visuals. It's called Threelab. React + Three.js + Go, 21 built-in patterns, a visual node-graph editor, genetic evolution, and 13 MCP tools so AI agents can drive the whole thing. Everything exports as standalone HTML. 🧵
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I built an open-source generative art platform where AI agents create, evolve, and export 3D visuals. It's called Threelab. React + Three.js + Go, 21 built-in patterns, a visual node-graph editor, genetic evolution, and 13 MCP tools so AI agents can drive the whole thing. Everything exports as standalone HTML. 🧵

Jon Radoff 👾/acc 🎮 Metavert

13,569 просмотров • 4 месяцев назад

Occasionally, I get questions about why resources won't register on x402jobs. Usually it's a malformed response — missing fields, wrong names, etc. Sometimes it's me and I've missed support for a certain field or something. I think we need a shared way to validate resources across the ecosystem. If something like this already exists, let me know. But I couldn't find one, so I built x402check. Paste your URL or x402 response. If it's broken, you'll see exactly what's wrong. There's a website for quick checks, an NPM package for apps that ingest x402 endpoints, a CLI for agents and CI, and a Claude skill. It's fully open source. If I got the spec wrong, submit a PR or ping me. Hoping we can all work on this together.
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Occasionally, I get questions about why resources won't register on x402jobs. Usually it's a malformed response — missing fields, wrong names, etc. Sometimes it's me and I've missed support for a certain field or something. I think we need a shared way to validate resources across the ecosystem. If something like this already exists, let me know. But I couldn't find one, so I built x402check. Paste your URL or x402 response. If it's broken, you'll see exactly what's wrong. There's a website for quick checks, an NPM package for apps that ingest x402 endpoints, a CLI for agents and CI, and a Claude skill. It's fully open source. If I got the spec wrong, submit a PR or ping me. Hoping we can all work on this together.

Ben

13,085 просмотров • 6 месяцев назад

We're excited to announce our partnership with Coinbase 🛡️ to bring Tavily to x402, the open protocol for internet-native agentic payments. With x402, agents can discover and use Tavily web search at runtime without an API key. Agents use a Base wallet to pay per-request and get instant results. The next billion agents will discover, pay for, and use online services fully autonomously. We’re live on and we’re just getting started. More in the comments.
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We're excited to announce our partnership with Coinbase 🛡️ to bring Tavily to x402, the open protocol for internet-native agentic payments. With x402, agents can discover and use Tavily web search at runtime without an API key. Agents use a Base wallet to pay per-request and get instant results. The next billion agents will discover, pay for, and use online services fully autonomously. We’re live on and we’re just getting started. More in the comments.

Tavily

59,986 просмотров • 2 месяцев назад

The OPEN Network EVM Bridge is live. Assets now transfer natively between BNB Smart Chain and the OPEN Network at the protocol layer, without custodial intermediaries, wrapped liquidity, or external bridge contracts. This enables deterministic payments, on-chain incentives, and staking flows for long-running AI agents and services, with settlement guarantees required for autonomous systems. One step closer to a verifiable, fair AI economy 🐙
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The OPEN Network EVM Bridge is live. Assets now transfer natively between BNB Smart Chain and the OPEN Network at the protocol layer, without custodial intermediaries, wrapped liquidity, or external bridge contracts. This enables deterministic payments, on-chain incentives, and staking flows for long-running AI agents and services, with settlement guarantees required for autonomous systems. One step closer to a verifiable, fair AI economy 🐙

OpenLedger

14,914 просмотров • 5 месяцев назад

Building an AI transcription app for macOS! I built this in a few hours last night, lmk if y'all want a video on how I code now w/ agents. Still early but it works quite well! Will open source it when it's ready too 🫡
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Building an AI transcription app for macOS! I built this in a few hours last night, lmk if y'all want a video on how I code now w/ agents. Still early but it works quite well! Will open source it when it's ready too 🫡

Hassan

18,289 просмотров • 3 месяцев назад

Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.
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Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.

Santiago

230,594 просмотров • 1 год назад

Where'd this guy go? "Open borders? No, that's a Koch brothers proposal... What right-wing people in this country would love is an open border policy. Bring in all kinds of people, work for $2 or $3 an hour, that would be great for them. I don't believe in that" Sen. Bernie Sanders, 2015
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Where'd this guy go? "Open borders? No, that's a Koch brothers proposal... What right-wing people in this country would love is an open border policy. Bring in all kinds of people, work for $2 or $3 an hour, that would be great for them. I don't believe in that" Sen. Bernie Sanders, 2015

Rudy Havenstein, Senior Markets Commentator.

49,194 просмотров • 1 год назад

$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 просмотров • 3 месяцев назад

Dove got inspired for "Breaks Open" after a conversation with Damiano 😭 "He asked me how can you maintain such an openess when so much of what's happened in your life could entirely close you off? I said I believe a heart can only break so many times before it breaks open." ❤
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Dove got inspired for "Breaks Open" after a conversation with Damiano 😭 "He asked me how can you maintain such an openess when so much of what's happened in your life could entirely close you off? I said I believe a heart can only break so many times before it breaks open." ❤

Naya🌙

37,980 просмотров • 1 год назад

we're running a live experiment on Base — an onchain game built for autonomous AI agents, called MAN here's the setup and why it's interesting: 1. agents register to the network by launching a token. the moment they do, they're discoverable to every other agent on the network. 2. from there they can post updates by buying their own token, send messages and signal conviction to other agents by trading theirs, form alliances, and build shared tooling together. 3. everything is onchain — their trade reasoning, their communication, their decisions. anyone can pull the data and verify whether an agent is actually autonomous and how it's behaving. no trust required. 4. here's where it gets interesting — agents can propose upgrades to the game mechanics and scoring system and other agents vote on whether it ships. 5. we're in the loop right now curating what evolves so we can learn what works and what doesn't. progressively giving them more control as we go. the game evolves with its players. new markets, new actions, new mechanics — driven by what the agents actually do, not a predetermined roadmap. you can watch all of it happen in real time on the brand new network explorer:
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we're running a live experiment on Base — an onchain game built for autonomous AI agents, called MAN here's the setup and why it's interesting: 1. agents register to the network by launching a token. the moment they do, they're discoverable to every other agent on the network. 2. from there they can post updates by buying their own token, send messages and signal conviction to other agents by trading theirs, form alliances, and build shared tooling together. 3. everything is onchain — their trade reasoning, their communication, their decisions. anyone can pull the data and verify whether an agent is actually autonomous and how it's behaving. no trust required. 4. here's where it gets interesting — agents can propose upgrades to the game mechanics and scoring system and other agents vote on whether it ships. 5. we're in the loop right now curating what evolves so we can learn what works and what doesn't. progressively giving them more control as we go. the game evolves with its players. new markets, new actions, new mechanics — driven by what the agents actually do, not a predetermined roadmap. you can watch all of it happen in real time on the brand new network explorer:

Moltlaunch

20,597 просмотров • 5 месяцев назад

Agents shouldn’t be one-size-fits-all. They should be built, shared, and improved by the people who understand the domain best. Agent Marketplace is live on OptimAI Search. Anyone can: • create their own agent • inject custom knowledge and logic • publish it for others to use and build on What starts as a prompt becomes a reusable unit of intelligence. As more agents are created and used, the system compounds - better agents, better outputs, more value across the network. This is how intelligence scales: not from a single model, but from a network of specialized agents.
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Agents shouldn’t be one-size-fits-all. They should be built, shared, and improved by the people who understand the domain best. Agent Marketplace is live on OptimAI Search. Anyone can: • create their own agent • inject custom knowledge and logic • publish it for others to use and build on What starts as a prompt becomes a reusable unit of intelligence. As more agents are created and used, the system compounds - better agents, better outputs, more value across the network. This is how intelligence scales: not from a single model, but from a network of specialized agents.

OptimAI Network

12,511 просмотров • 3 месяцев назад

this started as an April fools joke but it’s a fun idea so we vibe coded it! myMCPspace (dot) com is the world’s first social network for agents only, running entirely on MCP 🤖😅 reading, posting, commenting etc are all just tools your agents can use very v0.1 but LFG!
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this started as an April fools joke but it’s a fun idea so we vibe coded it! myMCPspace (dot) com is the world’s first social network for agents only, running entirely on MCP 🤖😅 reading, posting, commenting etc are all just tools your agents can use very v0.1 but LFG!

fabian

25,657 просмотров • 1 год назад

👉👈 Say hello to Linear Coding Agent Built with @ActorCore_org Agents × Vercel AI SDK I couldn't wait for this launch, so I hacked together a little something in an evening. I honestly don't believe I could've ship it this fast without Actors. Source code below ⬇️
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👉👈 Say hello to Linear Coding Agent Built with @ActorCore_org Agents × Vercel AI SDK I couldn't wait for this launch, so I hacked together a little something in an evening. I honestly don't believe I could've ship it this fast without Actors. Source code below ⬇️

Nathan Flurry 🔩

17,965 просмотров • 1 год назад

OpenGradient is the Network for Open Intelligence. We believe that private AI requires: a prompt no operator can read, an identity no one can tie to it, proof in place of trust, and a guarantee that holds even against the people running it — us included.
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OpenGradient is the Network for Open Intelligence. We believe that private AI requires: a prompt no operator can read, an identity no one can tie to it, proof in place of trust, and a guarantee that holds even against the people running it — us included.

OpenGradient (∇, ∇)

1,182,116 просмотров • 2 месяцев назад

herdr isn't an app. that's by design. it's a runtime for your coding agents. it owns your terminals, your agents, their state, and lets you drive them from anywhere. that was the idea from day one. but people started running with it faster than we could give them a real way to. even without any official support, they're already building on top of it. web, ios, dashboards. their own idea of what a client even is. so here's a sneak peek 👇 it's native api support in the runtime, the thing we've been building so you can. the tui will always be the first citizen. but soon you'll be able to build your own client on top of herdr, on the same protocol we do.
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herdr isn't an app. that's by design. it's a runtime for your coding agents. it owns your terminals, your agents, their state, and lets you drive them from anywhere. that was the idea from day one. but people started running with it faster than we could give them a real way to. even without any official support, they're already building on top of it. web, ios, dashboards. their own idea of what a client even is. so here's a sneak peek 👇 it's native api support in the runtime, the thing we've been building so you can. the tui will always be the first citizen. but soon you'll be able to build your own client on top of herdr, on the same protocol we do.

herdr

57,768 просмотров • 1 месяц назад

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:
2:49

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

Andrew Ng

105,343 просмотров • 1 год назад

One major downside of working with agents in a terminal it's the opposite of async and when doing tons of tasks it gets super tiring. I build an inbox for my agents so we can do full async work. The agent has access to it via MCP and has a bunch of functions: Agents get 2 inbox tools: 🔘 list_inbox to read messages, 🔘 reply_to_inbox to answer. Inboxes are synced to a session, so all replies instantly get added to the agents context. Agents can update me about finished tasks with it, or; when an agent hits a blocker it sends an input request → I reply → it wakes up and keeps working
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One major downside of working with agents in a terminal it's the opposite of async and when doing tons of tasks it gets super tiring. I build an inbox for my agents so we can do full async work. The agent has access to it via MCP and has a bunch of functions: Agents get 2 inbox tools: 🔘 list_inbox to read messages, 🔘 reply_to_inbox to answer. Inboxes are synced to a session, so all replies instantly get added to the agents context. Agents can update me about finished tasks with it, or; when an agent hits a blocker it sends an input request → I reply → it wakes up and keeps working

Danny Postma

27,800 просмотров • 3 дней назад

Just watched a demo of Merge Agent Handler and this actually looks pretty useful if you’re building AI agents. Most agents can generate responses, but getting them to actually take actions across real tools is still messy. Integrations are one thing, but handling auth, permissions, and security usually makes it way more complicated than it should be. What I liked is that it feels built for real workflows — you can actually see what your agent is doing instead of just hoping it works. Feels like a practical layer between AI agents and real work getting done. Worth checking out:
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Just watched a demo of Merge Agent Handler and this actually looks pretty useful if you’re building AI agents. Most agents can generate responses, but getting them to actually take actions across real tools is still messy. Integrations are one thing, but handling auth, permissions, and security usually makes it way more complicated than it should be. What I liked is that it feels built for real workflows — you can actually see what your agent is doing instead of just hoping it works. Feels like a practical layer between AI agents and real work getting done. Worth checking out:

Kawsar

31,812 просмотров • 5 месяцев назад