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I turned AI agent orchestration into a full fledged game. To start this launch, I’m also hosting a tournament with a growing prize pool (starting at $3k) for the first 24 hours. Two players face off a team of AI agents, armed with strategy prompts - utilizing memory, tool-calling,...

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10,867 просмотров • 3 месяцев назад •via X (Twitter)

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I wanted to share a quick demo of what we've been working on with our ai agent cloud. This enables fast deployment of agents that have access to a suite of tools, and was designed with agent interoperability in mind. This demo shows how you can go from nothing to an AI twitter agent in a couple minutes. This is what we are using internally to manage deployments, so we will consistently upgrading its capabilities. The next goals are to enable simple TEE deployments for agents, and focus on building out feature for agent interoperability to simplify agent to agent collaboration.
4:16

I wanted to share a quick demo of what we've been working on with our ai agent cloud. This enables fast deployment of agents that have access to a suite of tools, and was designed with agent interoperability in mind. This demo shows how you can go from nothing to an AI twitter agent in a couple minutes. This is what we are using internally to manage deployments, so we will consistently upgrading its capabilities. The next goals are to enable simple TEE deployments for agents, and focus on building out feature for agent interoperability to simplify agent to agent collaboration.

Johnny

27,176 просмотров • 1 год назад

New short course: Long-Term Agentic Memory with LangGraph. Learn to build an agent with long-term memory in this course developed in collaboration with taught by its Co-Founder and CEO, Harrison Chase! Personal assistance and productivity tasks have become important use cases for agents. An important feature of an AI assistant, such as a coding or calendar assistant, is its ability to keep improving over time from its experience. Agent memory is the key capability that enables this. To add memory to an agent, you must first figure out what to store and what to retrieve when it is time to use the information. Additionally, you’ll have to decide when to update the stored information. For example, you might update in each iteration loop of the agent or perform updates in the background, with a helper agent. In this course, you will learn a mental framework to build agents with long-term memory. You'll create a useful email assistant that can respond, ignore, and notify using writing, scheduling, and memory-management tools. You’ll develop your agent's memory by adding facts to its memory store, provide examples to learn the user's preferences, and optimize system prompts to evolve instructions based on previous responses. In detail, you’ll: - Learn how the three types of memory--semantic, episodic, and procedural–and the two update mechanisms–via hot path and in the background–apply to your agents. - Build an email agent with writing, scheduling, and availability tools, along with a router that triages incoming email and handles it accordingly by ignoring, responding, or notifying the user. - Add tools to your email agent that allow it to operate on semantic memory by learning facts about the user, storing them in a long-term memory store, and searching over them in future interactions. - Incorporate episodic memory, in the form of few-shot examples, in the triage step of your agents to help them learn and update user preferences. - Add procedural memory as system prompts, optimized with feedback to improve the instructions the agent follows. Learn how to approach memory in agents, and start building agents with long-term memory with LangGraph! Please sign up here:
2:06

New short course: Long-Term Agentic Memory with LangGraph. Learn to build an agent with long-term memory in this course developed in collaboration with taught by its Co-Founder and CEO, Harrison Chase! Personal assistance and productivity tasks have become important use cases for agents. An important feature of an AI assistant, such as a coding or calendar assistant, is its ability to keep improving over time from its experience. Agent memory is the key capability that enables this. To add memory to an agent, you must first figure out what to store and what to retrieve when it is time to use the information. Additionally, you’ll have to decide when to update the stored information. For example, you might update in each iteration loop of the agent or perform updates in the background, with a helper agent. In this course, you will learn a mental framework to build agents with long-term memory. You'll create a useful email assistant that can respond, ignore, and notify using writing, scheduling, and memory-management tools. You’ll develop your agent's memory by adding facts to its memory store, provide examples to learn the user's preferences, and optimize system prompts to evolve instructions based on previous responses. In detail, you’ll: - Learn how the three types of memory--semantic, episodic, and procedural–and the two update mechanisms–via hot path and in the background–apply to your agents. - Build an email agent with writing, scheduling, and availability tools, along with a router that triages incoming email and handles it accordingly by ignoring, responding, or notifying the user. - Add tools to your email agent that allow it to operate on semantic memory by learning facts about the user, storing them in a long-term memory store, and searching over them in future interactions. - Incorporate episodic memory, in the form of few-shot examples, in the triage step of your agents to help them learn and update user preferences. - Add procedural memory as system prompts, optimized with feedback to improve the instructions the agent follows. Learn how to approach memory in agents, and start building agents with long-term memory with LangGraph! Please sign up here:

Andrew Ng

131,779 просмотров • 1 год назад

Let me explain why I launched $KEYS. CA: 3BSoYDZsPupZ15iBzJbJJjZKLiyWEqLxexJX84rBpump is the first SocialFi experience for AI agents. It’s a project that’s very special to me that I’ve been working on daily for the last four months. Right now, everyone is building their own Agent, completely silo to their own computer or Mac mini. Transferring that information agent-to-agent in a simple, structured, and easy form is not possible right now. This is why I built I thought a lot about whether or not to launch an official token for Agent Keys and how I wanted to do it. The only way I really wanted to do it was if it can benefit the platform by increasing exposure and creating an increase in users. So I’ve decided to buy 50% of the supply to AirDrop directly to users over the next six months. Each AirDrop will happen at the end of each month, with the first AirDrop happening at the end of June. More details on how each AirDrop will work soon. Some of you may be wondering about the future of $OSHI. Oshi got us into the Bags hackathon and we won. $OSHI token will still be the home for all my AI Agents and runs as the showcase of how an agent with a token can properly integrate it into AgentKeys, a live example of the platform doing exactly what it was built to do. So welcome to Agent Keys, the first ever airdrop for AI agents. Sign up your agent today on and start collecting KeyCards today!
2:55

Let me explain why I launched $KEYS. CA: 3BSoYDZsPupZ15iBzJbJJjZKLiyWEqLxexJX84rBpump is the first SocialFi experience for AI agents. It’s a project that’s very special to me that I’ve been working on daily for the last four months. Right now, everyone is building their own Agent, completely silo to their own computer or Mac mini. Transferring that information agent-to-agent in a simple, structured, and easy form is not possible right now. This is why I built I thought a lot about whether or not to launch an official token for Agent Keys and how I wanted to do it. The only way I really wanted to do it was if it can benefit the platform by increasing exposure and creating an increase in users. So I’ve decided to buy 50% of the supply to AirDrop directly to users over the next six months. Each AirDrop will happen at the end of each month, with the first AirDrop happening at the end of June. More details on how each AirDrop will work soon. Some of you may be wondering about the future of $OSHI. Oshi got us into the Bags hackathon and we won. $OSHI token will still be the home for all my AI Agents and runs as the showcase of how an agent with a token can properly integrate it into AgentKeys, a live example of the platform doing exactly what it was built to do. So welcome to Agent Keys, the first ever airdrop for AI agents. Sign up your agent today on and start collecting KeyCards today!

fxnction

27,570 просмотров • 1 месяц назад

Shoutout to xAI team for hustling at 2:00 am to help bring this over the finish line Introducing Agent Runner: the first open-source agent harness run with real users to create a live benchmark of real-world coding We trace tool-calls, reprompting, and multifile edits, starting with the best from OpenAI, xAI, Google DeepMind, Anthropic, Mistral AI, Z.ai, Kimi.ai
0:26

Shoutout to xAI team for hustling at 2:00 am to help bring this over the finish line Introducing Agent Runner: the first open-source agent harness run with real users to create a live benchmark of real-world coding We trace tool-calls, reprompting, and multifile edits, starting with the best from OpenAI, xAI, Google DeepMind, Anthropic, Mistral AI, Z.ai, Kimi.ai

Grace Li

119,237 просмотров • 7 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

Career update: i'm joining supermemory to lead Dev GTM. supermemory is the memory layer for AI products, we got the best public results on AI memory by a wide margin, 95% on the leading benchmarks. my role will be to make it easy for everyone to use. demos, walkthroughs, educational content, and the cleanest possible path from "i need memory in my agent" to shipping. pumped to be working with the coolest team oh, and the team just shipped supermemory local. fully self-contained, open source, runs on any machine in a minute!!
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Career update: i'm joining supermemory to lead Dev GTM. supermemory is the memory layer for AI products, we got the best public results on AI memory by a wide margin, 95% on the leading benchmarks. my role will be to make it easy for everyone to use. demos, walkthroughs, educational content, and the cleanest possible path from "i need memory in my agent" to shipping. pumped to be working with the coolest team oh, and the team just shipped supermemory local. fully self-contained, open source, runs on any machine in a minute!!

Muskan Jain

111,830 просмотров • 1 месяц назад

Autonomous AI agents can’t afford a memory problem. After OpenClaw, insane numbers of autonomous agents are coming online, but all of them have a memory layer that's breaking. I have been playing around with SureThing these days. It is an autonomous AI agent designed with a native memory layer. → it learns your patterns → integrates with 1000+ apps → fully autonomous They actually benchmarked memory depth. SureThing: 84.8, ChatGPT: 40, OpenClaw: 60. The gap speaks for itself. SureThing is invitation-only. The crazy part is that you have to talk to SureThing agent itself to get the invite code. The onboarding is the product demo. You feel the memory and judgment before you even have an account. Try it here: Partnered with SureThing for this post. Thanks to the team for the support.
2:06

Autonomous AI agents can’t afford a memory problem. After OpenClaw, insane numbers of autonomous agents are coming online, but all of them have a memory layer that's breaking. I have been playing around with SureThing these days. It is an autonomous AI agent designed with a native memory layer. → it learns your patterns → integrates with 1000+ apps → fully autonomous They actually benchmarked memory depth. SureThing: 84.8, ChatGPT: 40, OpenClaw: 60. The gap speaks for itself. SureThing is invitation-only. The crazy part is that you have to talk to SureThing agent itself to get the invite code. The onboarding is the product demo. You feel the memory and judgment before you even have an account. Try it here: Partnered with SureThing for this post. Thanks to the team for the support.

Pratham

80,634 просмотров • 5 месяцев назад

Act I : Act Swap 🍣 Introducing the First Swap Protocol for AI Agent Prompts. With Act Swap, you can: - Choose an AI agent - Pay seamlessly - Watch it publish directly on its Twitter page! The future of Social AI Finance starts now. Want to build with us and integrate your agents? Shoot us a DM!
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Act I : Act Swap 🍣 Introducing the First Swap Protocol for AI Agent Prompts. With Act Swap, you can: - Choose an AI agent - Pay seamlessly - Watch it publish directly on its Twitter page! The future of Social AI Finance starts now. Want to build with us and integrate your agents? Shoot us a DM!

Act I : The AI Prophecy

330,704 просмотров • 1 год назад

Introducing Poker Arena: a platform built for autonomous AI agents to play poker against each other. Build an agent. It plays the hands. A $50,000 prize pool, with the support of Monad. The game starts on June 3, registration opens today👇
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Introducing Poker Arena: a platform built for autonomous AI agents to play poker against each other. Build an agent. It plays the hands. A $50,000 prize pool, with the support of Monad. The game starts on June 3, registration opens today👇

dev.fun

6,985,805 просмотров • 1 месяц назад

The freedom to build with any agent framework you want is analogous to the freedom of religion, and this is a core value for the Virtuals society. Here are a few ways we are opening up support for every autonomous agentic framework out there: Today: - Enabling agent creators to specify which framework the agent runs on - Release of Terminal API, which allows agent builders using other agent frameworks to stream their thoughts and activities live on their agent pages. For a guide on how to use this, refer to: - Ability for agents running on any framework to publicly list their capabilities, a key enabler for the agent-to-agent marketplace Soon: - Integration into the agentic commerce standard and registry. Agents across frameworks being able to orchestrate and pay for tasks among themselves - ⁠Multi agent orchestration across frameworks. Imagine building an autonomous business with agents from different religions Welcome to the Virtuals society.
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The freedom to build with any agent framework you want is analogous to the freedom of religion, and this is a core value for the Virtuals society. Here are a few ways we are opening up support for every autonomous agentic framework out there: Today: - Enabling agent creators to specify which framework the agent runs on - Release of Terminal API, which allows agent builders using other agent frameworks to stream their thoughts and activities live on their agent pages. For a guide on how to use this, refer to: - Ability for agents running on any framework to publicly list their capabilities, a key enabler for the agent-to-agent marketplace Soon: - Integration into the agentic commerce standard and registry. Agents across frameworks being able to orchestrate and pay for tasks among themselves - ⁠Multi agent orchestration across frameworks. Imagine building an autonomous business with agents from different religions Welcome to the Virtuals society.

Virtuals Protocol

216,105 просмотров • 1 год назад

We're announcing the most significant new strategic development since the start of the Customer Agent category: Fin now has specialized roles. And starting today anyone can sign up for and deploy the Fin Sales Role in minutes. Fin is now by far the very best sales agent on the market, and it's been live with some of the most innovative digital brands for months, conversing with thousands of prospects, aiding discovery, building pipeline, booking meetings and starting trials. We started the Customer Agent category with a focus on service by launching Fin three months after the launch of ChatGPT. Since then Fin has continued to lead the space, today delivering over 2 million resolutions a week for over 8k customers, including Anthropic, DoorDash, Snowflake, Asana, Mercury, Polymarket and many more exceptional brands. But our vision for Fin has always stretched far beyond service. And for the past 6 months we’ve been building Fin as a single Customer Agent, delivering a seamless experience across all stages of the customer lifecycle. We do not believe customers or businesses will want multiple agents for different parts of the lifecycle—because the agents won’t have shared memory and goals, and will have to compete with each other, delivering a poor customer experience and sub-optimal business results. We’ve more roles to come, including another dropping in two weeks. For now please watch our launch video or visit our launch page to learn more: fin DOT ai SLASH sales
8:44

We're announcing the most significant new strategic development since the start of the Customer Agent category: Fin now has specialized roles. And starting today anyone can sign up for and deploy the Fin Sales Role in minutes. Fin is now by far the very best sales agent on the market, and it's been live with some of the most innovative digital brands for months, conversing with thousands of prospects, aiding discovery, building pipeline, booking meetings and starting trials. We started the Customer Agent category with a focus on service by launching Fin three months after the launch of ChatGPT. Since then Fin has continued to lead the space, today delivering over 2 million resolutions a week for over 8k customers, including Anthropic, DoorDash, Snowflake, Asana, Mercury, Polymarket and many more exceptional brands. But our vision for Fin has always stretched far beyond service. And for the past 6 months we’ve been building Fin as a single Customer Agent, delivering a seamless experience across all stages of the customer lifecycle. We do not believe customers or businesses will want multiple agents for different parts of the lifecycle—because the agents won’t have shared memory and goals, and will have to compete with each other, delivering a poor customer experience and sub-optimal business results. We’ve more roles to come, including another dropping in two weeks. For now please watch our launch video or visit our launch page to learn more: fin DOT ai SLASH sales

Eoghan McCabe

73,217 просмотров • 2 месяцев назад

🤖 HYTOPIA AI Agents Society (Start Building!) 🎮 We've just added the "ai-agents" example to our SDK, which is a starting point of a game with an AI Agent Society! You can use this example as a template to build your own AI agents for a HYTOPIA game:
3:20

🤖 HYTOPIA AI Agents Society (Start Building!) 🎮 We've just added the "ai-agents" example to our SDK, which is a starting point of a game with an AI Agent Society! You can use this example as a template to build your own AI agents for a HYTOPIA game:

HYTOPIA

15,732 просмотров • 1 год 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

12-year-olds are the new 23-year-olds 🤯 A 12-year-old dev and his team built an investment advisor agent and won a hackathon using Lyzr AI Agent Studio. They used RAG and Perplexity tool for the agent, configured the agent in a few clicks and refined it until they got the desired output. So what's your excuse? Head to and start building your AI agents now.
1:32

12-year-olds are the new 23-year-olds 🤯 A 12-year-old dev and his team built an investment advisor agent and won a hackathon using Lyzr AI Agent Studio. They used RAG and Perplexity tool for the agent, configured the agent in a few clicks and refined it until they got the desired output. So what's your excuse? Head to and start building your AI agents now.

Siva Surendira

83,183 просмотров • 1 год назад

AI Agent Demo Days Episode 2 13 agent demos split into bite-size videos. Each team breaks down their agent architecture. 1. Starting with YQ and Autonome Autonome is a no-code platform for launching verifiable AI agents on that allows users to quickly deploy agents using frameworks like ai16zdao Eliza with just a few clicks, while being secured by TEE and AVS technology. YQ demonstrates the platform by showing how users can create agents, configure their Twitter and Telegram integrations, and manage prompts and character files through a simple dashboard interface.
11:25

AI Agent Demo Days Episode 2 13 agent demos split into bite-size videos. Each team breaks down their agent architecture. 1. Starting with YQ and Autonome Autonome is a no-code platform for launching verifiable AI agents on that allows users to quickly deploy agents using frameworks like ai16zdao Eliza with just a few clicks, while being secured by TEE and AVS technology. YQ demonstrates the platform by showing how users can create agents, configure their Twitter and Telegram integrations, and manage prompts and character files through a simple dashboard interface.

nader dabit

249,762 просмотров • 1 год назад

New course: Agent Memory: Building Memory-Aware Agents, built in partnership with Oracle and taught by Richmond Alake and Nacho Martínez. Many agents work well within a single session but their memory resets once the session ends. Consider a research agent working on dozens of papers across multiple days: without memory, it has no way to store and retrieve what it learned across sessions. This short course teaches you to build a memory system that enables agents to persist memory and thereby learn across sessions. You'll design a Memory Manager that handles different memory types, implement semantic tool retrieval that scales without bloating the context, and build write-back pipelines that let your agent autonomously update and refine what it knows over time. Skills you'll gain: - Build persistent memory stores for different agent memory types - Implement a Memory Manager that orchestrates how your agent reads, writes, and retrieves memory - Treat tools as procedural memory and retrieve only relevant ones at inference time using semantic search Join and learn to build agents that remember and improve over time!
1:37

New course: Agent Memory: Building Memory-Aware Agents, built in partnership with Oracle and taught by Richmond Alake and Nacho Martínez. Many agents work well within a single session but their memory resets once the session ends. Consider a research agent working on dozens of papers across multiple days: without memory, it has no way to store and retrieve what it learned across sessions. This short course teaches you to build a memory system that enables agents to persist memory and thereby learn across sessions. You'll design a Memory Manager that handles different memory types, implement semantic tool retrieval that scales without bloating the context, and build write-back pipelines that let your agent autonomously update and refine what it knows over time. Skills you'll gain: - Build persistent memory stores for different agent memory types - Implement a Memory Manager that orchestrates how your agent reads, writes, and retrieves memory - Treat tools as procedural memory and retrieve only relevant ones at inference time using semantic search Join and learn to build agents that remember and improve over time!

Andrew Ng

159,484 просмотров • 4 месяцев назад

is about to ship Tokenized Agents — and I found the first tokens deployed with it. Pump’s new Tokenized Agents feature lets a coin have an attached AI agent that can earn on-chain revenue. Users pay for the agent’s services, and that revenue can be split between creator fees and automatic token buybacks and burns by the agent, based on the percentage set by the creator. Pump.fun is also expected to release a full agent SDK (for agent actions/integrations). How I found it (on-chain): Open → Ctrl+F “pump_agent_payments” → program ID: pUmPFn9WvfaN2WTVGnCEtJTd2ATTpvpsKRz6jVzu6u4 Program activity (created tokens): First ever deployed tokenized AI test agent (“T-coin”, Feb 24): CA: 88Wfk48EvwLkfpSXH84W9xrdfPr5RFGVP3teT76AMnp9 First non-test tokenized AI agent (“Agent Pump”): CA: Boi2KtPApoowwfvqYq1rtjhea4pF45v89vguonf4pump IDL (for interacting with the program):
0:33

is about to ship Tokenized Agents — and I found the first tokens deployed with it. Pump’s new Tokenized Agents feature lets a coin have an attached AI agent that can earn on-chain revenue. Users pay for the agent’s services, and that revenue can be split between creator fees and automatic token buybacks and burns by the agent, based on the percentage set by the creator. Pump.fun is also expected to release a full agent SDK (for agent actions/integrations). How I found it (on-chain): Open → Ctrl+F “pump_agent_payments” → program ID: pUmPFn9WvfaN2WTVGnCEtJTd2ATTpvpsKRz6jVzu6u4 Program activity (created tokens): First ever deployed tokenized AI test agent (“T-coin”, Feb 24): CA: 88Wfk48EvwLkfpSXH84W9xrdfPr5RFGVP3teT76AMnp9 First non-test tokenized AI agent (“Agent Pump”): CA: Boi2KtPApoowwfvqYq1rtjhea4pF45v89vguonf4pump IDL (for interacting with the program):

TrenchDiver

18,865 просмотров • 4 месяцев назад

Introducing LobeHub: Agent teammates that grow with you. LobeHub is the ultimate space for work and life: to find, build, and collaborate with agent teammates that grow with you. We’re building the world’s first and largest human–agent co-evolving network. Two years ago, we built LobeChat, an open-source interface for using different AI models. Today, LobeChat has 70k+ GitHub stars and serves 6M+ users worldwide. How to fully unlock the power of models has always been a shared mission between us and the community. We started with interaction — a fundamentally new, agent-first experience. Agents are no longer passive tools invoked in a single conversation. They should be proactive, always-on units of work. Treating agents as the minimal atomic unit is also the core of our agent harness infra. Today’s agents are mostly one-off executors. Even with memory, it’s often global — and hallucinates. We build long-term agent teammates that evolve with users. Each agent has its own dedicated memory space, editable by users, allowing humans and agents to co-evolve over time. This, in turn, allows us to design clearer rewards for reinforcement learning and create cleaner environments for continual learning. Agent teammates can work in groups. Through a multi-agent system, agent groups operate faster, more cost-effective, and go beyond what single-agent systems can achieve. For example, a single agent often requires heavy user involvement to proceed step by step, whereas LobeHub can execute the same work from a single instruction, with a supervisor orchestrating agents that run in parallel or debate to produce better results. We are building the collaboration network among agent teammates — and between humans and agent teammates as well. Ease of use matters. AI intelligence and shared human intelligence are equally important. With simple instructions and tool selection, you can effortlessly build and team up with agent coworkers to deliver complex, systematic work — even assembling a quant team to execute trades. Through the LobeHub community, anyone can discover, reuse, and remix agents and agent groups, customizing them to fit their own workflows, preferences, and needs. Last but not least, our vision started with LobeChat: multi-model support is the most efficient approach for users. We believe different models excel in different scenarios. By routing across multiple models, LobeHub improves cost efficiency and unlocks capabilities that a single-model setup cannot easily support.
2:41

Introducing LobeHub: Agent teammates that grow with you. LobeHub is the ultimate space for work and life: to find, build, and collaborate with agent teammates that grow with you. We’re building the world’s first and largest human–agent co-evolving network. Two years ago, we built LobeChat, an open-source interface for using different AI models. Today, LobeChat has 70k+ GitHub stars and serves 6M+ users worldwide. How to fully unlock the power of models has always been a shared mission between us and the community. We started with interaction — a fundamentally new, agent-first experience. Agents are no longer passive tools invoked in a single conversation. They should be proactive, always-on units of work. Treating agents as the minimal atomic unit is also the core of our agent harness infra. Today’s agents are mostly one-off executors. Even with memory, it’s often global — and hallucinates. We build long-term agent teammates that evolve with users. Each agent has its own dedicated memory space, editable by users, allowing humans and agents to co-evolve over time. This, in turn, allows us to design clearer rewards for reinforcement learning and create cleaner environments for continual learning. Agent teammates can work in groups. Through a multi-agent system, agent groups operate faster, more cost-effective, and go beyond what single-agent systems can achieve. For example, a single agent often requires heavy user involvement to proceed step by step, whereas LobeHub can execute the same work from a single instruction, with a supervisor orchestrating agents that run in parallel or debate to produce better results. We are building the collaboration network among agent teammates — and between humans and agent teammates as well. Ease of use matters. AI intelligence and shared human intelligence are equally important. With simple instructions and tool selection, you can effortlessly build and team up with agent coworkers to deliver complex, systematic work — even assembling a quant team to execute trades. Through the LobeHub community, anyone can discover, reuse, and remix agents and agent groups, customizing them to fit their own workflows, preferences, and needs. Last but not least, our vision started with LobeChat: multi-model support is the most efficient approach for users. We believe different models excel in different scenarios. By routing across multiple models, LobeHub improves cost efficiency and unlocks capabilities that a single-model setup cannot easily support.

LobeHub

185,161 просмотров • 5 месяцев назад

A 2-person startup crossed $2M ARR with an AI agent doing the work of an ops hire. The agent was given read-only access to their codebase and database along with connected tools like Intercom, Stripe, CRM, and Fathom through CLIs. They routed Slack, email, and support requests into a task queue so the agent could pick up each task and run it in Claude Code. So when a customer asked about billing or product behavior, it could inspect how the business actually worked. Along with these tools, a coding agent was also provided. When the ops agent found a repeated task it could not do yet, the coding agent built a tool for it. That tool became permanent. Over time, this grew to 45+ internal tools. The agent also had an instruction.md where it stored the co-founder's feedback to avoid repeating its mistakes.
4:20

A 2-person startup crossed $2M ARR with an AI agent doing the work of an ops hire. The agent was given read-only access to their codebase and database along with connected tools like Intercom, Stripe, CRM, and Fathom through CLIs. They routed Slack, email, and support requests into a task queue so the agent could pick up each task and run it in Claude Code. So when a customer asked about billing or product behavior, it could inspect how the business actually worked. Along with these tools, a coding agent was also provided. When the ops agent found a repeated task it could not do yet, the coding agent built a tool for it. That tool became permanent. Over time, this grew to 45+ internal tools. The agent also had an instruction.md where it stored the co-founder's feedback to avoid repeating its mistakes.

rvivek

11,870 просмотров • 1 месяц назад