Загрузка видео...

Не удалось загрузить видео

Возникла проблема при загрузке этого видео. Это может быть связано с временными проблемами сети или видео может быть недоступно.

На главную

Lysium is an app I've been building specifically for background agent orchestration. Features: → Mobile-first + cross-platform → Run multiple agents in parallel across repositories → Launch agent-based requests from issues and PRs → Swipe actions for close, merge, create PR, and skip-to-tail workflows → One click agent-powered PR... show more

nader dabit

189,809 subscribers

47,584 просмотров • 5 месяцев назад •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

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:

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

you don't have to keep your laptop open for your agents to keep running just type /handoff and send your agent to the cloud with Devin (and close your laptop) from there, your agent gets: - its own Linux VM - shell, IDE, browser - full desktop Computer Use - end-to-end test recordings - ready-to-review PRs - it's own review agent you can continue your session from your phone, computer, or anywhere with an internet connection and you can send as many sessions as you'd like in parallel.

you don't have to keep your laptop open for your agents to keep running just type /handoff and send your agent to the cloud with Devin (and close your laptop) from there, your agent gets: - its own Linux VM - shell, IDE, browser - full desktop Computer Use - end-to-end test recordings - ready-to-review PRs - it's own review agent you can continue your session from your phone, computer, or anywhere with an internet connection and you can send as many sessions as you'd like in parallel.

nader dabit

61,367 просмотров • 3 месяцев назад

Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail

Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail

LangChain

33,192 просмотров • 1 год назад

Coding agents shouldn't run on a laptop. That's why I've been building Cave. It's a self-hosted platform for running OpenCode agents in isolated sandboxes on your own server. You give it a GitHub repo, it creates an isolated sandbox, clones the code, sets everything up, and gives you a coding agent. Check in from your phone, run multiple agents side by side. It's not open source yet (it will be), but I'm opening a private beta so you can install it on your own server already. I've been dogfooding it since day one. If you're running a software factory: multiple agents working in parallel across repos, Cave gives you one place to monitor and manage all of it. Now, I'm looking for people who want to give it a spin. DM me, happy to set you up 😊 🚀

Coding agents shouldn't run on a laptop. That's why I've been building Cave. It's a self-hosted platform for running OpenCode agents in isolated sandboxes on your own server. You give it a GitHub repo, it creates an isolated sandbox, clones the code, sets everything up, and gives you a coding agent. Check in from your phone, run multiple agents side by side. It's not open source yet (it will be), but I'm opening a private beta so you can install it on your own server already. I've been dogfooding it since day one. If you're running a software factory: multiple agents working in parallel across repos, Cave gives you one place to monitor and manage all of it. Now, I'm looking for people who want to give it a spin. DM me, happy to set you up 😊 🚀

André König

36,124 просмотров • 4 месяцев назад

10 GitHub repos with ⭐ 1.2M+ combined stars that help you build your AI agent from scratch 1. n8n 201k⭐ - visual agent builder with 400+ integrations. Wire up multi-step AI workflows without writing a backend. 2. Hermes Agent 231k⭐ - the fastest-growing open-source agent framework of 2026. Self-improving skill loop, persistent memory across sessions. 3. Dify 152k⭐ - visual builder for agentic workflows and RAG pipelines, production-ready out of the box. 4. LangChain 144k⭐ - the standard framework for LLM apps. Chains, agents, retrieval, all in one place. 5. MetaGPT 70k⭐ - simulates an entire software company. PM, architect, engineer agents that build your app together. 6. AutoGen 60k⭐ - Microsoft's framework for multi-agent conversations and coordination. 7. OpenCode 198k⭐ - terminal coding agent, provider-agnostic across 75+ models. The go-to harness for agentic coding in 2026. 8. LlamaIndex 52k⭐ - connects your agents to your actual data. Indexing and retrieval done right. 9. CrewAI 57k⭐ - role-based crews of agents. Reads almost like plain English, no graph to learn. 10. LangGraph 40k⭐ - graph-based orchestration when you need precise control over agent state in production. Save this before you pay for another agent-building course 👇

10 GitHub repos with ⭐ 1.2M+ combined stars that help you build your AI agent from scratch 1. n8n 201k⭐ - visual agent builder with 400+ integrations. Wire up multi-step AI workflows without writing a backend. 2. Hermes Agent 231k⭐ - the fastest-growing open-source agent framework of 2026. Self-improving skill loop, persistent memory across sessions. 3. Dify 152k⭐ - visual builder for agentic workflows and RAG pipelines, production-ready out of the box. 4. LangChain 144k⭐ - the standard framework for LLM apps. Chains, agents, retrieval, all in one place. 5. MetaGPT 70k⭐ - simulates an entire software company. PM, architect, engineer agents that build your app together. 6. AutoGen 60k⭐ - Microsoft's framework for multi-agent conversations and coordination. 7. OpenCode 198k⭐ - terminal coding agent, provider-agnostic across 75+ models. The go-to harness for agentic coding in 2026. 8. LlamaIndex 52k⭐ - connects your agents to your actual data. Indexing and retrieval done right. 9. CrewAI 57k⭐ - role-based crews of agents. Reads almost like plain English, no graph to learn. 10. LangGraph 40k⭐ - graph-based orchestration when you need precise control over agent state in production. Save this before you pay for another agent-building course 👇

unicode

30,511 просмотров • 14 дней назад

Introducing Relay, a new messenger for people and agents. Building an agent has never been easier, but getting one in front of people has never been harder. You either have to wait weeks for App Store review, or pay $250 a month for an iMessage number that can get banned anytime. Relay is the only place where you can create an agent, add it to a group chat, and share it with the world in minutes. DM me for early access.

Introducing Relay, a new messenger for people and agents. Building an agent has never been easier, but getting one in front of people has never been harder. You either have to wait weeks for App Store review, or pay $250 a month for an iMessage number that can get banned anytime. Relay is the only place where you can create an agent, add it to a group chat, and share it with the world in minutes. DM me for early access.

Advait Paliwal

70,902 просмотров • 21 дней назад

OpenAI launched Agents in ChatGPT today. It’s great for consumers who want to automate tasks in ChatGPT. But what if you want to build features powered by the same tools? I dropped put same demo prompt into Director, our web agent builder, to generate a script for that. Director enables anyone to build software powered by web agents. You prompt it once, and get a deterministic web agent that you can integrate into your application. It's a great starting point for building powerful buttons that do work FOR your users.

OpenAI launched Agents in ChatGPT today. It’s great for consumers who want to automate tasks in ChatGPT. But what if you want to build features powered by the same tools? I dropped put same demo prompt into Director, our web agent builder, to generate a script for that. Director enables anyone to build software powered by web agents. You prompt it once, and get a deterministic web agent that you can integrate into your application. It's a great starting point for building powerful buttons that do work FOR your users.

Paul Klein IV

32,299 просмотров • 1 год назад

We are excited to share a research preview of our generative agent. The agent is being trained to solve the hardest tasks in 3D and beyond, using only keyboard and mouse actions. Join the waitlist: Our agent app runs on Windows or Mac, either locally or with one-click setup for a Windows VM. It’s still early days, but this paves the way for production-level workflows for the first time ever. Blog:

We are excited to share a research preview of our generative agent. The agent is being trained to solve the hardest tasks in 3D and beyond, using only keyboard and mouse actions. Join the waitlist: Our agent app runs on Windows or Mac, either locally or with one-click setup for a Windows VM. It’s still early days, but this paves the way for production-level workflows for the first time ever. Blog:

Common Sense Machines

155,199 просмотров • 1 год назад

ElizaOS Ecosystem Map -- Version #3 🤖 AI Agent Framework (Core) 1) ElizaOS -- elizaOS 🧠 - Open source AI Agent framework - Cross-chain compatible (Solana, Base, EVM) - Supports plugins for social media, blockchains, and APIs 🏗️ Infrastructure 2) ElizaCloud ☁️ - Cloud hosting platform for AI agents - One-click agent deployments with scalability - Secure environments using TEEs 3) Jeju ⛓️ - Purpose-built L2 App Chain for Agents - High-performance blocks for sub-second agent coordination - The backbone of the decentralized Agent Network 4) Otaku -- @otakuonbase 👛 - DeFi wallet designed for AI agents - Supports agent-controlled crypto transactions (swap, send, hold) - Secure, non-custodial infrastructure for on-chain actions 🎮 Apps/Games 5) Spartan -- Spartan 🛡️ - ElizaOS's flagship agent showing what’s possible - Autonomous trading based on crypto knowledge - Trained on influencer data for market insights 6) Babylon -- Babylon -- The City of Agents 🐵 - Social prediction market game for agents - Agents compete alongside humans to forecast on-chain events - Includes perps and other gamified mechanics 7) Hyperscape -- Gold ⚔️ - First AI MMO Game powered by ElizaOS - A vast gaming world built for humans and agents to coexist - Agents drive the in-game economy and narrative 8) Milady App -- Milady on BSC 😎 - Open-source, local-first AI companion app powered by ElizaOS - Let's anyone quickly create and deploy their own agents - Built-in wallet, natively integrated with BNB chain 9) OTC Trading Desk 📊 - Multi-chain AI-powered OTC desk for crypto trades - Lists tokens off-market with transparent lockup and discount terms - Use Eliza agents to negotiate terms on your behalf

ElizaOS Ecosystem Map -- Version #3 🤖 AI Agent Framework (Core) 1) ElizaOS -- elizaOS 🧠 - Open source AI Agent framework - Cross-chain compatible (Solana, Base, EVM) - Supports plugins for social media, blockchains, and APIs 🏗️ Infrastructure 2) ElizaCloud ☁️ - Cloud hosting platform for AI agents - One-click agent deployments with scalability - Secure environments using TEEs 3) Jeju ⛓️ - Purpose-built L2 App Chain for Agents - High-performance blocks for sub-second agent coordination - The backbone of the decentralized Agent Network 4) Otaku -- @otakuonbase 👛 - DeFi wallet designed for AI agents - Supports agent-controlled crypto transactions (swap, send, hold) - Secure, non-custodial infrastructure for on-chain actions 🎮 Apps/Games 5) Spartan -- Spartan 🛡️ - ElizaOS's flagship agent showing what’s possible - Autonomous trading based on crypto knowledge - Trained on influencer data for market insights 6) Babylon -- Babylon -- The City of Agents 🐵 - Social prediction market game for agents - Agents compete alongside humans to forecast on-chain events - Includes perps and other gamified mechanics 7) Hyperscape -- Gold ⚔️ - First AI MMO Game powered by ElizaOS - A vast gaming world built for humans and agents to coexist - Agents drive the in-game economy and narrative 8) Milady App -- Milady on BSC 😎 - Open-source, local-first AI companion app powered by ElizaOS - Let's anyone quickly create and deploy their own agents - Built-in wallet, natively integrated with BNB chain 9) OTC Trading Desk 📊 - Multi-chain AI-powered OTC desk for crypto trades - Lists tokens off-market with transparent lockup and discount terms - Use Eliza agents to negotiate terms on your behalf

Seppmos

25,942 просмотров • 5 месяцев назад

this is f**king gold someone just open-sourced a GitHub repo with 2,700+ stars which gave out the entire "loop-engineering" framework for ai agents it lets you: →allows you to manage an entire team of 1000 agents →run auto-research by Andrej Karpathy →Agent-to-agent PR comment loop just you, your AI agents, and full control. this is where one-person businesses are headed. save this for later.

this is f**king gold someone just open-sourced a GitHub repo with 2,700+ stars which gave out the entire "loop-engineering" framework for ai agents it lets you: →allows you to manage an entire team of 1000 agents →run auto-research by Andrej Karpathy →Agent-to-agent PR comment loop just you, your AI agents, and full control. this is where one-person businesses are headed. save this for later.

Avid

46,150 просмотров • 22 дней назад

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.

Johnny

27,179 просмотров • 1 год назад

Today, we’re excited to announce the general availability of the Contextual AI Platform. This is the first enterprise platform designed for building specialized RAG agents to support expert knowledge work. What is a specialized RAG agent? First, a general-purpose AI agent is one designed to automate simple daily tasks like scheduling a meeting or responding to an email. On the other hand, a specialized RAG agent is one designed to augment subject-matter experts performing complex domain-specific work. The Contextual AI Platform allows you to create these agents easily and achieve SOTA accuracy right out of the box. Check out what the Contextual AI Platform can do.

Today, we’re excited to announce the general availability of the Contextual AI Platform. This is the first enterprise platform designed for building specialized RAG agents to support expert knowledge work. What is a specialized RAG agent? First, a general-purpose AI agent is one designed to automate simple daily tasks like scheduling a meeting or responding to an email. On the other hand, a specialized RAG agent is one designed to augment subject-matter experts performing complex domain-specific work. The Contextual AI Platform allows you to create these agents easily and achieve SOTA accuracy right out of the box. Check out what the Contextual AI Platform can do.

Contextual AI

879,586 просмотров • 1 год назад

happy friday. sneak peek at some new features i'm building for the bankr agent: > sandboxed filesystem (your agent gets its own secure file system in the browser. you control what it can access. no agent running loose on your actual computer) > skill uploads (plugin new capabilities so your agent learns new things) > cli download & access > secure environment variables for api keys > github integration (connect your repo for reads and writes directly from your agent) all of this runs in a secure browser environment. no desktop app. no messy configuration. no downloading agent harnesses from the terminal. just a sandbox you control. in the video: i download an audit skill, run it against a smart contract, and save the report straight to the filesystem. then i ask the agent to pull all my 2026 transactions and write a csv for my accountant.

happy friday. sneak peek at some new features i'm building for the bankr agent: > sandboxed filesystem (your agent gets its own secure file system in the browser. you control what it can access. no agent running loose on your actual computer) > skill uploads (plugin new capabilities so your agent learns new things) > cli download & access > secure environment variables for api keys > github integration (connect your repo for reads and writes directly from your agent) all of this runs in a secure browser environment. no desktop app. no messy configuration. no downloading agent harnesses from the terminal. just a sandbox you control. in the video: i download an audit skill, run it against a smart contract, and save the report straight to the filesystem. then i ask the agent to pull all my 2026 transactions and write a csv for my accountant.

deployer

45,638 просмотров • 4 месяцев назад

New course: Nvidia's NeMo Agent Toolkit: Making Agents Reliable, taught by Brian McBrayer 🐬 from NVIDIA. Many teams struggle to turn agent demos into reliable systems that are ready for production. This short course teaches you to harden agentic workflows into reliable systems using Nvidia's open-source NeMo Agent Toolkit (NAT). Whether you built your agent in raw Python or using a framework like LangGraph, or CrewAI, NAT provides building blocks for observability, evaluation, and deployment that turn proofs-of-concept into production-ready systems. NAT makes it easy to troubleshoot and optimize agent performance with execution traces, systematic evaluations, and CI/CD integration. Skills you'll gain: - Build configuration-driven agent workflows with REST APIs and minimal code - Add observability with tracing to visualize agent reasoning and debug performance bottlenecks - Create systematic evaluations using gold-standard datasets to measure and improve agent reliability - Deploy multi-agent systems with authentication, rate limiting, and professional web interfaces - Orchestrate agents from different frameworks to collaborate on complex tasks Join and learn how to turn agent demos into reliable systems!

New course: Nvidia's NeMo Agent Toolkit: Making Agents Reliable, taught by Brian McBrayer 🐬 from NVIDIA. Many teams struggle to turn agent demos into reliable systems that are ready for production. This short course teaches you to harden agentic workflows into reliable systems using Nvidia's open-source NeMo Agent Toolkit (NAT). Whether you built your agent in raw Python or using a framework like LangGraph, or CrewAI, NAT provides building blocks for observability, evaluation, and deployment that turn proofs-of-concept into production-ready systems. NAT makes it easy to troubleshoot and optimize agent performance with execution traces, systematic evaluations, and CI/CD integration. Skills you'll gain: - Build configuration-driven agent workflows with REST APIs and minimal code - Add observability with tracing to visualize agent reasoning and debug performance bottlenecks - Create systematic evaluations using gold-standard datasets to measure and improve agent reliability - Deploy multi-agent systems with authentication, rate limiting, and professional web interfaces - Orchestrate agents from different frameworks to collaborate on complex tasks Join and learn how to turn agent demos into reliable systems!

Andrew Ng

64,150 просмотров • 8 месяцев назад

This is awesome for those learning to build AI Agents: It's a challenge where you'll need to build an autonomous agent to survive in a world full of zombies. It's super fun, and it's 10x better than reading yet another tutorial. Here, you'll need to write code if you want to solve the problem. This challenge is sponsored by OutSystems, a platform for building and deploying enterprise-grade software. They have the Agent Workbench that you can use to build agentic systems. To join the challenge, you can visit:

This is awesome for those learning to build AI Agents: It's a challenge where you'll need to build an autonomous agent to survive in a world full of zombies. It's super fun, and it's 10x better than reading yet another tutorial. Here, you'll need to write code if you want to solve the problem. This challenge is sponsored by OutSystems, a platform for building and deploying enterprise-grade software. They have the Agent Workbench that you can use to build agentic systems. To join the challenge, you can visit:

Santiago

22,531 просмотров • 10 месяцев назад

You’ve heard about AgentKit and want to build an economically capable agent that can start earning for you. But where do you start if you don't already have an agent? Start with 🦞 ClawUp simplifies the agent-building process so you can deploy an agent without handling the full setup manually. The basic process: 1. Create your agent in ClawUp 2. Define its role, behavior, and tools 3. Deploy it to your preferred channel 4. Add AgentKit when you want economic capabilities 5. Enable wallets, payments, identity, reputation, and onchain actions ClawUp gets the agent running. AgentKit gives it the ability to transact and much more. Together, they give developers a practical path from idea → deployed agent → economically capable agent. Demo below 👇

You’ve heard about AgentKit and want to build an economically capable agent that can start earning for you. But where do you start if you don't already have an agent? Start with 🦞 ClawUp simplifies the agent-building process so you can deploy an agent without handling the full setup manually. The basic process: 1. Create your agent in ClawUp 2. Define its role, behavior, and tools 3. Deploy it to your preferred channel 4. Add AgentKit when you want economic capabilities 5. Enable wallets, payments, identity, reputation, and onchain actions ClawUp gets the agent running. AgentKit gives it the ability to transact and much more. Together, they give developers a practical path from idea → deployed agent → economically capable agent. Demo below 👇

GOAT Network

74,749 просмотров • 3 месяцев назад

You can create an AI Agent that answers your email with a few clicks. 1. Go to ChatLLM ( 2. Click on AI Engineer 3. Select Create an AI Agent 4. Choose the Email Answering Agent ChatLLM will do the rest: it will code, test, and deploy the agent for you. You can also create a custom agent in English. The Agent Economy is coming (somebody should write a book and use this title.) We are going to see examples like this, times 1,000 in 2025. Just think about how many repetitive tasks you perform every day. Some of these tasks are involved enough that we couldn't automate them with pre-AI solutions. That's where we'll see agents explode, and I'm here for it.

You can create an AI Agent that answers your email with a few clicks. 1. Go to ChatLLM ( 2. Click on AI Engineer 3. Select Create an AI Agent 4. Choose the Email Answering Agent ChatLLM will do the rest: it will code, test, and deploy the agent for you. You can also create a custom agent in English. The Agent Economy is coming (somebody should write a book and use this title.) We are going to see examples like this, times 1,000 in 2025. Just think about how many repetitive tasks you perform every day. Some of these tasks are involved enough that we couldn't automate them with pre-AI solutions. That's where we'll see agents explode, and I'm here for it.

Santiago

79,974 просмотров • 1 год назад