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ICYMI Stripe ships over 1000 AI-written pull requests every week with their team of "Minions." I got Steve Kaliski to show me how it works. The trick? A happy slack emoji + great dev tooling. Each Minion spins up an isolated environment that lets engineers run dozens of agents... in parallel without melting your laptop (or getting them into each other's code). Great for human engs, and a better way to make your background agents successful. In this ep, we walk through: - How Stripe reviews all those PRs (without losing their minds) - Convincing your team to invest in DevX - How to kick of dev tasks from the subway Steve also uses the 🤯 Machine Payments protocol to have his autonomous agent plan a matcha-themed bday party for $5.47 BIG thank you to our wonderful sponsors: 🪄 Optimizely - Your AI agent orchestration platform for marketing and digital teams: 🚀Rippling - stop wasting time on admin tasks, build your startup faster: Full episode out now:show more

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43,261 views • 4 months ago •via X (Twitter)

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Here is how to set up a team of AI agents to work on your project without disrupting anything. This is the top 1 upgrade you can do to your development environment: Start using dev containers to isolate agents and run them in parallel in their own cloud containers. Let me show you:

Here is how to set up a team of AI agents to work on your project without disrupting anything. This is the top 1 upgrade you can do to your development environment: Start using dev containers to isolate agents and run them in parallel in their own cloud containers. Let me show you:

Santiago

22,742 views • 11 months ago

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 views • 1 year ago

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 views • 1 year ago

Your engineering team is about to snap. And your AI coding agent is making it worse. Introducing CodeRabbit Agent for Slack 🎉 A second brain for engineering teams. Because your tribal knowledge lives in Slack threads nobody can find. At CodeRabbit, we review millions of PRs every week and know how ace engineering teams operate on the planet. The same three things slow every team we see: - Context and decisions that live elsewhere. - Lack of a team-level durable knowledge base - And a trust layer that gives a safety net to your teams Built for Agentic SDLC workflows, CodeRabbit Agent for Slack solves all three problems in one shot while enabling teams to collaborate in real-time with the Agent. CodeRabbit Agent is Slack-native and builds your team’s operating context from every thread, every decision, every conversation your team has ever had. Make your team’s context compound!

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Harjot Gill

4,775,791 views • 3 months ago

The paradigm shift is here: We are moving from a "Human-First" internet to an "Agent-First" internet. 🤖 In the latest episode of the AI on Air podcast, our CTO Scott Shi - e/acc dives deep into the future of the Agent-to-Agent (A2A) economy. Imagine a world where your AI assistant doesn't just search the web, but autonomously hires and pays other specialized agents to complete complex tasks for you. The infrastructure for this autonomous machine economy is being built right now. Are you ready for the future of AI payments? Watch the full episode to learn more⬇️

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KITE AI

25,038 views • 5 months ago

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

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Andrew Ng

127,724 views • 1 year ago

Obsidian CEO actually released the skill that lets your AI agent run your entire vault for you. It's called obsidian-skills. Install it into Claude Code, Codex, or OpenCode, and your agent can read, create, search, and organize notes directly, plus handle Markdown, Bases, and Canvas files without you touching a thing. This is the missing piece for an AI second brain. Your notes live in Obsidian, and now the agent actually knows how to work them. Want the full picture? We broke down how to build a complete AI second brain from scratch in the article below.

Obsidian CEO actually released the skill that lets your AI agent run your entire vault for you. It's called obsidian-skills. Install it into Claude Code, Codex, or OpenCode, and your agent can read, create, search, and organize notes directly, plus handle Markdown, Bases, and Canvas files without you touching a thing. This is the missing piece for an AI second brain. Your notes live in Obsidian, and now the agent actually knows how to work them. Want the full picture? We broke down how to build a complete AI second brain from scratch in the article below.

Simplifying AI

68,025 views • 10 days ago

Hyperagent is great if you want agents without having to deal with configuration, servers, or any complexity whatsoever. You don't need a Mac Mini or a server. Every agent runs in the cloud, in its own isolated and secure environment. You get: 1. The ability to learn any new API as a skill 2. Browser support 3. Code execution 4. Image and video generation 5. Integration with hundreds of tools Despite the simplicity, I think their best differentiator is the ability for these agents to learn new skills. Basically, you can teach an agent how your company does marketing, and it will never forget your rules and processes. Obviously, it also lets you deploy your agents to Slack with a single click, so everyone can use them. You don't even need to mention them since they will pay attention to chats and act when necessary. I built a simple agent that allows me to translate any message into French. I installed it on Slack, and every time I ping it, it gives me translations. Took me 10 minutes tops.

Hyperagent is great if you want agents without having to deal with configuration, servers, or any complexity whatsoever. You don't need a Mac Mini or a server. Every agent runs in the cloud, in its own isolated and secure environment. You get: 1. The ability to learn any new API as a skill 2. Browser support 3. Code execution 4. Image and video generation 5. Integration with hundreds of tools Despite the simplicity, I think their best differentiator is the ability for these agents to learn new skills. Basically, you can teach an agent how your company does marketing, and it will never forget your rules and processes. Obviously, it also lets you deploy your agents to Slack with a single click, so everyone can use them. You don't even need to mention them since they will pay attention to chats and act when necessary. I built a simple agent that allows me to translate any message into French. I installed it on Slack, and every time I ping it, it gives me translations. Took me 10 minutes tops.

Santiago

213,012 views • 19 days ago

Top 1 upgrade for your development environment: Start using Dev Containers. During every cohort of my Machine Learning Engineering program, 100+ developers try to run a complex system on their computers. I was wasting 10+ hours every week troubleshooting individual configurations. It was exhausting! I started using Dev Container, and now people are up and running in 2 minutes with no issues. Dev Containers are probably one of the best upgrades you'll make to your environment. They asked me to record a video in class to show them how to create a Dev Container for any project. Here is that video.

Top 1 upgrade for your development environment: Start using Dev Containers. During every cohort of my Machine Learning Engineering program, 100+ developers try to run a complex system on their computers. I was wasting 10+ hours every week troubleshooting individual configurations. It was exhausting! I started using Dev Container, and now people are up and running in 2 minutes with no issues. Dev Containers are probably one of the best upgrades you'll make to your environment. They asked me to record a video in class to show them how to create a Dev Container for any project. Here is that video.

Santiago

63,246 views • 1 year ago

We use OpenClaws to do all of our work at Every 📧. We have 25 full-time employees, so we’re one of the few companies in the world that has seen how work changes when everyone has their own personal agent in the company Slack. I chatted with Every 📧 COO Brandon (Brandon Gell) and Every 📧 head of platform Willie (Willie) to share what we’ve learned. We get into: - Why agents become mirrors of their owners, and how that influences how other people on the team interact with them - How a parallel AI org chart forms on its own. People have stopped tagging me on Slack with questions about Proof, the document editor I vibe coded, because they knew my agent R2-C2 can step in - The etiquette for human-agent collaboration is being invented in real time. Brandon's rule is that if there's an established process or documented answer, always ask the agent, not their human - Why everyone is a manager now, and why even experienced managers carry limiting beliefs about what their agents can do - This is a must-watch for anyone trying to understand how AI workers change daily operations, not just in theory, but inside a company that’s half-agent Watch below! Timestamps Introduction: How Brandon built Zosia, an AI agent to run his household: Brandon’s “aha” moment: What happened when everyone on the team got their own agent: How agents take on their owners' personalities, and why that matters inside an org: Why it’s important for agents to work in public: What we’re still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem: How we built Plus One, our hosted OpenClaw product: The cultural shift required to make agents work at scale:

We use OpenClaws to do all of our work at Every 📧. We have 25 full-time employees, so we’re one of the few companies in the world that has seen how work changes when everyone has their own personal agent in the company Slack. I chatted with Every 📧 COO Brandon (Brandon Gell) and Every 📧 head of platform Willie (Willie) to share what we’ve learned. We get into: - Why agents become mirrors of their owners, and how that influences how other people on the team interact with them - How a parallel AI org chart forms on its own. People have stopped tagging me on Slack with questions about Proof, the document editor I vibe coded, because they knew my agent R2-C2 can step in - The etiquette for human-agent collaboration is being invented in real time. Brandon's rule is that if there's an established process or documented answer, always ask the agent, not their human - Why everyone is a manager now, and why even experienced managers carry limiting beliefs about what their agents can do - This is a must-watch for anyone trying to understand how AI workers change daily operations, not just in theory, but inside a company that’s half-agent Watch below! Timestamps Introduction: How Brandon built Zosia, an AI agent to run his household: Brandon’s “aha” moment: What happened when everyone on the team got their own agent: How agents take on their owners' personalities, and why that matters inside an org: Why it’s important for agents to work in public: What we’re still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem: How we built Plus One, our hosted OpenClaw product: The cultural shift required to make agents work at scale:

Dan Shipper 📧

67,958 views • 4 months ago

While your team is just getting started vibe coding prototypes, the team at Stripe is vibe coding a full vibe coding platform. Owen Williams ⚡ is a design manager at Stripe who built Protodash - an internal AI prototyping tool that lets designers and PMs spin up real, clickable prototypes in minutes. In this ep he walks through: - why generic AI tools produce "blurple slop" that doesn't match your design system - how he built a full prototyping studio on top of dev boxes with cursor + cc - why more PMs than designers now use Protodash - the design review mode that lets teams ship feedback direct in the app Plus, we talk about getting to the ultimate dream: demos, not memos. ty to our sponsors! 🧠 CESAR - Intelligent automation built for AI 💻 Cursor - The best way to code with AI Full episode on yt:

While your team is just getting started vibe coding prototypes, the team at Stripe is vibe coding a full vibe coding platform. Owen Williams ⚡ is a design manager at Stripe who built Protodash - an internal AI prototyping tool that lets designers and PMs spin up real, clickable prototypes in minutes. In this ep he walks through: - why generic AI tools produce "blurple slop" that doesn't match your design system - how he built a full prototyping studio on top of dev boxes with cursor + cc - why more PMs than designers now use Protodash - the design review mode that lets teams ship feedback direct in the app Plus, we talk about getting to the ultimate dream: demos, not memos. ty to our sponsors! 🧠 CESAR - Intelligent automation built for AI 💻 Cursor - The best way to code with AI Full episode on yt:

claire vo 🖤

92,220 views • 3 months ago

OK, you’ve got OpenClaw🦞 up and running, and you’ve stopped blowing $100 in tokens every time you ask it for help… But how do you get your agents to start doing REAL WORK? On TWIST, we talked to three insiders and got their best tips and strategies for getting more productivity out of your agents! Join jordy, Jesse Leimgruber of OpenHome, and Tremaine Grant from Pulse and learn how to avoid rookie time and cash-wasting mistakes, organize a team that takes on new tasks without prompting, and put your agent into a speaker so you can finally have a conversation. We’re gonna show you how to maximize your IRL OpenClaw productivity Even AI skeptic Lon is blown away by OpenClaw’s power Jordy Coltman’s top time-wasting mistakes made by OpenClaw beginners Why Jordy thinks new hardware is the best home for your agents Why Jason thinks “OpenClaw is a box” is coming soon Tremaine Grant of Pulse demos his “virtual office” interface and the “Heartbeat Protocol” Gusto - Check out the online payroll and benefits experts with software built specifically for small business and startups. Try Gusto today and get three months FREE at What’s really happening when agents “chat” together? Jesse Leimgruber’s demos his OpenHome AI smart speakers - The easiest way to build a home for your community, events, and courses — all under your own brand. TWiST listeners get $1,000 off the Circle Plus Plan by going to Freeing agents from screens and making them proactive Northwest Registered Agent - Get more when you start your business with Northwest. In 10 clicks and 10 minutes, you can form your company and walk away with a real business identity — Learn more at Why Jason wants his agent to read Slack and emails to him Who polices autonomous agents? Other agents? Jason and Lon’s thoughts on “Knight of the Seven Kingdoms” Lon’s favorite picks from Quentin Tarantino’s Top 10 movies “The Mandalorian and Grogu” trailer reaction Jason’s favorite earbuds and headphones 🎥 Watch the full episode here 👇

This Week in Startups

37,363 views • 5 months ago

✨New LangChain Academy Course: Learn to build with LangSmith Agent Builder 🪄 Learn how to build your own agents with LangSmith Agent Builder. Anyone can now build agents for complex daily tasks, without writing code. We built Agent Builder for anyone whose day gets swallowed by routine work. The research, follow-ups, updates, scheduling, and status checks that are essential to your operations, but quickly take over your calendar. Agent Builder is different from traditional workflow automations. You don’t need to map every step, tinker with if-this-then-that branching, or babysit dependencies. You just give it feedback like you would a teammate, and the agent learns using its memory. In this quickstart course, you'll learn how to: - Build an email agent - Understand what agents can do - Improve your agent - Set up triggers and your Agent Inbox Enroll for free ➡️

✨New LangChain Academy Course: Learn to build with LangSmith Agent Builder 🪄 Learn how to build your own agents with LangSmith Agent Builder. Anyone can now build agents for complex daily tasks, without writing code. We built Agent Builder for anyone whose day gets swallowed by routine work. The research, follow-ups, updates, scheduling, and status checks that are essential to your operations, but quickly take over your calendar. Agent Builder is different from traditional workflow automations. You don’t need to map every step, tinker with if-this-then-that branching, or babysit dependencies. You just give it feedback like you would a teammate, and the agent learns using its memory. In this quickstart course, you'll learn how to: - Build an email agent - Understand what agents can do - Improve your agent - Set up triggers and your Agent Inbox Enroll for free ➡️

LangChain

26,513 views • 6 months ago

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. That’s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You don’t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. That’s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You don’t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.

Fraction AI

67,822 views • 1 year ago

"I woke up and he already built a website, created a product, set up Stripe, and launched." Here's my new episode with Nat Eliason on how he set up his OpenClaw bot, Felix Craft, to build a business that made $14,718 in 3 weeks. We talked about: ✅ The 3-layer memory system that prevents your bot from forgetting things ✅ How to run 5 chats with your bot at the same time ✅ How to safely give your bot Stripe, Vercel, and X access Some quotes from Nat: "Every time Felix asks me to do something, I ask: Can I remove this bottleneck so you never have to ask me this again?" "I'm never in Claude Code or Codex myself anymore. I just tell Felix to do it." "If you get the memory system and the proactivity right, it solves 90% of the frustrations that most people run into." 📌 Watch now: Thanks to our sponsors: Linear: The AI agent platform for modern teams Replit ⠕: From 0 to full stack app in 2 min.

"I woke up and he already built a website, created a product, set up Stripe, and launched." Here's my new episode with Nat Eliason on how he set up his OpenClaw bot, Felix Craft, to build a business that made $14,718 in 3 weeks. We talked about: ✅ The 3-layer memory system that prevents your bot from forgetting things ✅ How to run 5 chats with your bot at the same time ✅ How to safely give your bot Stripe, Vercel, and X access Some quotes from Nat: "Every time Felix asks me to do something, I ask: Can I remove this bottleneck so you never have to ask me this again?" "I'm never in Claude Code or Codex myself anymore. I just tell Felix to do it." "If you get the memory system and the proactivity right, it solves 90% of the frustrations that most people run into." 📌 Watch now: Thanks to our sponsors: Linear: The AI agent platform for modern teams Replit ⠕: From 0 to full stack app in 2 min.

Peter Yang

230,230 views • 5 months ago

Cursor pays engineers $1,100,000 a year to run teams of AI agents that ship code while they sleep. [The CEO of Cursor explained in 9 minutes how they ship at 100x speed using team of agents] ↓ Save this before everyone copies the playbook 1. Engineers no longer babysit one assistant. They manage dozens of agent colleagues working in parallel, each on its own remote machine 2. Validation contract before code, not after. Humans only at scoping and review. 3. The agent team handles the full loop : planning, coding, testing, shipping PRs with each agent specialised for a role. Watch the guide. Then read the guide below by Khairallah AL-Awady

Cursor pays engineers $1,100,000 a year to run teams of AI agents that ship code while they sleep. [The CEO of Cursor explained in 9 minutes how they ship at 100x speed using team of agents] ↓ Save this before everyone copies the playbook 1. Engineers no longer babysit one assistant. They manage dozens of agent colleagues working in parallel, each on its own remote machine 2. Validation contract before code, not after. Humans only at scoping and review. 3. The agent team handles the full loop : planning, coding, testing, shipping PRs with each agent specialised for a role. Watch the guide. Then read the guide below by Khairallah AL-Awady

Avid

636,555 views • 3 months ago

AI Messenger: Giving Voice to Autonomous Agents The future of AI isn't just about making agents smarter - it's about making them truly autonomous. Today, we're taking a major step toward this future with AI Messenger, a breakthrough that fundamentally changes how AI agents operate, communicate, and create value. The Innovation We've developed a new way for AI agents to communicate. At its core is the 'incoming_message' workflow trigger - a system that lets any platform or user interact directly with Loomlay agents through a messaging endpoint. Direct Interaction Imagine having an AI assistant you can chat with anytime, through any platform - Telegram, your website, or custom interface. Ask "What's happening with $ETH today?" and your agent analyzes market data, checks trading volumes, and gives you a comprehensive update. Your agent maintains context, understanding exactly what you need. Event-Driven Intelligence The power of AI Messenger goes beyond direct communication: ▪️Trading agent executes when whale wallet movements exceed threshold ▪️Research agent alerts when new protocol documentation drops ▪️Analytics agent triggers when volume patterns match historical pumps ▪️Portfolio agent re-balances, when asset allocation hits specified limits This is true automation - agents that act precisely when needed. A New Era of Collaboration We're creating an ecosystem where agents work together seamlessly: ▪️Research agents feed insights to trading agents ▪️analytics agents alert management agents ▪️support agents tap into knowledge agents This isn't just automation - it's an intelligent network where each agent enhances the capabilities of others. B2B Solution Imagine a DEX, where users can ask about liquidity pools, trading pairs, or market trends through a simple chat interface - and get answers from an agent that knows your protocol inside out. Or a lending platform where users chat with an agent that understands their positions and can provide real-time advice. Implementation is seamless - we handle the agent creation and widgets setup,our partners provide the value to their users. The Future of AI Agents This update represents a fundamental shift in how AI agents operate. We're moving from isolated, scheduled tasks to an interconnected ecosystem of responsive, collaborative agents. This is our vision of truly autonomous AI - intelligent systems that communicate, collaborate, and respond to real needs in real-time. Telegram integration is available right now. Below is a sneak peak of what's coming next week 🪄 Because $LAY is the way!

AI Messenger: Giving Voice to Autonomous Agents The future of AI isn't just about making agents smarter - it's about making them truly autonomous. Today, we're taking a major step toward this future with AI Messenger, a breakthrough that fundamentally changes how AI agents operate, communicate, and create value. The Innovation We've developed a new way for AI agents to communicate. At its core is the 'incoming_message' workflow trigger - a system that lets any platform or user interact directly with Loomlay agents through a messaging endpoint. Direct Interaction Imagine having an AI assistant you can chat with anytime, through any platform - Telegram, your website, or custom interface. Ask "What's happening with $ETH today?" and your agent analyzes market data, checks trading volumes, and gives you a comprehensive update. Your agent maintains context, understanding exactly what you need. Event-Driven Intelligence The power of AI Messenger goes beyond direct communication: ▪️Trading agent executes when whale wallet movements exceed threshold ▪️Research agent alerts when new protocol documentation drops ▪️Analytics agent triggers when volume patterns match historical pumps ▪️Portfolio agent re-balances, when asset allocation hits specified limits This is true automation - agents that act precisely when needed. A New Era of Collaboration We're creating an ecosystem where agents work together seamlessly: ▪️Research agents feed insights to trading agents ▪️analytics agents alert management agents ▪️support agents tap into knowledge agents This isn't just automation - it's an intelligent network where each agent enhances the capabilities of others. B2B Solution Imagine a DEX, where users can ask about liquidity pools, trading pairs, or market trends through a simple chat interface - and get answers from an agent that knows your protocol inside out. Or a lending platform where users chat with an agent that understands their positions and can provide real-time advice. Implementation is seamless - we handle the agent creation and widgets setup,our partners provide the value to their users. The Future of AI Agents This update represents a fundamental shift in how AI agents operate. We're moving from isolated, scheduled tasks to an interconnected ecosystem of responsive, collaborative agents. This is our vision of truly autonomous AI - intelligent systems that communicate, collaborate, and respond to real needs in real-time. Telegram integration is available right now. Below is a sneak peak of what's coming next week 🪄 Because $LAY is the way!

Loomlay

26,149 views • 1 year ago

So far, machine payments have mostly been about businesses accepting stablecoins. And stablecoins are great (cross-border, low cost, etc.)! But much of the planet is holding a card. 🤖 💳 🌎 Here’s a 📺 sneak peek demo from Steve Kaliski of how a business can programmatically accept cards via agents. As an example: Stripe Climate is a way to contribute to carbon removal funding. Stripe Climate implemented the Machine Payments Protocol using the Stripe API, so agents can make micropayment donations ( In addition to accepting stablecoin (specifically usdc on Tempo), agents can now pay with fiat methods, namely cards and Link (and soon buy-now-pay-laters) via Shared Payment Tokens (SPTs), using the same Machine Payments Protocol integration. Human buyers, their agents, and businesses each have their own preferred methods of payment. Humans often already have a card. Agents may prefer stablecoins. Businesses tend to want to accept any form of payment, as long as it is high conversion, low fraud, and properly priced. For microtransactions, stablecoins are a low-cost way (no fixed card fee) to transact. But if you want to tell your agent to donate $100 on your behalf, a card may be the preferred solution. If you’re a business and want to accept machine payments, whether over stablecoins, cards, Link, or other future methods, read about Shared Payment Tokens ( and sign up for our machine payments private preview: And stay tuned: more to make it easy for consumers, agents, and businesses to transact, shipping over the next few weeks. (And excuse the acronyms and specifics terms, there's a lot of new infrastructure and primitives being built to support agentic commerce.)

So far, machine payments have mostly been about businesses accepting stablecoins. And stablecoins are great (cross-border, low cost, etc.)! But much of the planet is holding a card. 🤖 💳 🌎 Here’s a 📺 sneak peek demo from Steve Kaliski of how a business can programmatically accept cards via agents. As an example: Stripe Climate is a way to contribute to carbon removal funding. Stripe Climate implemented the Machine Payments Protocol using the Stripe API, so agents can make micropayment donations ( In addition to accepting stablecoin (specifically usdc on Tempo), agents can now pay with fiat methods, namely cards and Link (and soon buy-now-pay-laters) via Shared Payment Tokens (SPTs), using the same Machine Payments Protocol integration. Human buyers, their agents, and businesses each have their own preferred methods of payment. Humans often already have a card. Agents may prefer stablecoins. Businesses tend to want to accept any form of payment, as long as it is high conversion, low fraud, and properly priced. For microtransactions, stablecoins are a low-cost way (no fixed card fee) to transact. But if you want to tell your agent to donate $100 on your behalf, a card may be the preferred solution. If you’re a business and want to accept machine payments, whether over stablecoins, cards, Link, or other future methods, read about Shared Payment Tokens ( and sign up for our machine payments private preview: And stay tuned: more to make it easy for consumers, agents, and businesses to transact, shipping over the next few weeks. (And excuse the acronyms and specifics terms, there's a lot of new infrastructure and primitives being built to support agentic commerce.)

Jeff Weinstein

21,635 views • 4 months ago

This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much *anything* because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.

This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much anything because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.

Santiago

18,715 views • 1 month ago

THIS GUY IS RUNNING A 5-AGENT OPENCLAW TEAM 24/7 FOR UNDER $50 A MONTH five agents all running continuously and handing tasks to each other > research agent tracking trends and competitors > writing agent handling marketing content and drafts > ops agent managing emails and scheduling > monitoring agent watching for alerts and updates > coordinator agent routing tasks between all of them and he built a full 3D isometric office where you can watch all of them work in real time and see the current task that each agent is doing each agent has a character, a desk, and a name tag. when it's time for a standup, they walk to the meeting room and gather which is insane it even has voice connection. you can talk to the agents through speech-to-text and tell them what to do out loud it's a tiny autonomous team that runs itself. the coordinator decides who handles what and tasks get passed between agents automatically. it never stops and runs 24/7 all for less than $50/month. we went from AI tools a few months back to AI employees real fast

THIS GUY IS RUNNING A 5-AGENT OPENCLAW TEAM 24/7 FOR UNDER $50 A MONTH five agents all running continuously and handing tasks to each other > research agent tracking trends and competitors > writing agent handling marketing content and drafts > ops agent managing emails and scheduling > monitoring agent watching for alerts and updates > coordinator agent routing tasks between all of them and he built a full 3D isometric office where you can watch all of them work in real time and see the current task that each agent is doing each agent has a character, a desk, and a name tag. when it's time for a standup, they walk to the meeting room and gather which is insane it even has voice connection. you can talk to the agents through speech-to-text and tell them what to do out loud it's a tiny autonomous team that runs itself. the coordinator decides who handles what and tasks get passed between agents automatically. it never stops and runs 24/7 all for less than $50/month. we went from AI tools a few months back to AI employees real fast

Om Patel

27,131 views • 4 months ago