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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,... show more

𝕱𝖔𝖗𝕷𝖔𝖔𝖕

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10,867 次观看 • 5 个月前 •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.

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.

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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:

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

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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!

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Morlex

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6,990,606 次观看 • 3 个月前

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HYTOPIA

15,732 次观看 • 1 年前

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Andrew Ng

161,260 次观看 • 5 个月前

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.

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 次观看 • 1 年前

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.

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

185,401 次观看 • 7 个月前

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.

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

249,829 次观看 • 1 年前

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):

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

18,934 次观看 • 6 个月前