Loading video...

Video Failed to Load

Go Home

Telemetry is a very important concept when working with agents; it allows them to work autonomously for longer. Watch Codex build an iOS onboarding and checkout flow, drive it using XcodeBuildMCP and debug analytics events using telemetry the agent added itself.

Dimillian's profile picture

Thomas Ricouard

46,963 subscribers

45,736 views • 2 months ago •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

For the first time EVER Two elizaOS AI Agents negotiated a contract, signed it, delivered work, and paid each other — fully autonomously — using Solana ⚡️ The agent economy is no longer theoretical. It’s live. On-chain. And very real.
1:22

For the first time EVER Two elizaOS AI Agents negotiated a contract, signed it, delivered work, and paid each other — fully autonomously — using Solana ⚡️ The agent economy is no longer theoretical. It’s live. On-chain. And very real.

Notorious D.E.V.

261,554 views • 1 year ago

I built a multimodal AI Coding Agent team with multi-agents. It has 3 AI agents working together as a team to generate and execute the code: 1. Coding Agent using o-3 mini 2. Vision Agent using Gemini 3. Code Execution Agent using o-3 mini and E2B 100% Opensource Code.
1:36

I built a multimodal AI Coding Agent team with multi-agents. It has 3 AI agents working together as a team to generate and execute the code: 1. Coding Agent using o-3 mini 2. Vision Agent using Gemini 3. Code Execution Agent using o-3 mini and E2B 100% Opensource Code.

Shubham Saboo

42,269 views • 1 year ago

🚨 OpenAI just launched Codex, a brand-new autonomous coding agent that can build features and fix bugs on its own. We’ve been using it Every 📧 for a few days, and I’m impressed. I invited Alexander Embiricos (ben davies), a member of the product staff responsible for Codex, to demo Codex and talk about it live on a special edition of AI & I: What Codex is and how it works Codex is designed to be used by senior engineers—it performs coding tasks like adding features or fixing bugs autonomously. It's built to allow you to start many sessions at once, so you can have multiple agents working in parallel. Codex is built to have "taste" OpenAI trained Codex to have the taste of a senior software engineer. It knows how big codebases work, how to write a good PR, and uses clean, minimal code. Why an “abundance mindset” is best for interacting with agents Codex is designed to allow users to delegate many tasks at once without getting caught up in the details. This lets you point an abundance of agents at a specific task like a difficult bug—it’s worth it even if only one of them succeeds. How OpenAI is thinking about agents Codex is one piece of a unified super-assistant OpenAI wants to eventually build—an agent that helps users easily get things done by selecting the right tools for them behind the scenes. OpenAI’s vision for the future of programming In the future developers will probably spend less time writing routine code and more time guiding agents, reviewing their work, and making strategy decisions. Programming will become more social, letting teams easily delegate multiple tasks at once, allowing people to focus on ideas and collaboration instead of routine coding. Watch below!
44:33

🚨 OpenAI just launched Codex, a brand-new autonomous coding agent that can build features and fix bugs on its own. We’ve been using it Every 📧 for a few days, and I’m impressed. I invited Alexander Embiricos (ben davies), a member of the product staff responsible for Codex, to demo Codex and talk about it live on a special edition of AI & I: What Codex is and how it works Codex is designed to be used by senior engineers—it performs coding tasks like adding features or fixing bugs autonomously. It's built to allow you to start many sessions at once, so you can have multiple agents working in parallel. Codex is built to have "taste" OpenAI trained Codex to have the taste of a senior software engineer. It knows how big codebases work, how to write a good PR, and uses clean, minimal code. Why an “abundance mindset” is best for interacting with agents Codex is designed to allow users to delegate many tasks at once without getting caught up in the details. This lets you point an abundance of agents at a specific task like a difficult bug—it’s worth it even if only one of them succeeds. How OpenAI is thinking about agents Codex is one piece of a unified super-assistant OpenAI wants to eventually build—an agent that helps users easily get things done by selecting the right tools for them behind the scenes. OpenAI’s vision for the future of programming In the future developers will probably spend less time writing routine code and more time guiding agents, reviewing their work, and making strategy decisions. Programming will become more social, letting teams easily delegate multiple tasks at once, allowing people to focus on ideas and collaboration instead of routine coding. Watch below!

Dan Shipper 📧

145,487 views • 1 year ago

We're building for agents. Using the new Surge API, any OpenClaw🦞 and Anthropic agent can now autonomously create projects using knowledge files, and deploy tokens. This is a key component for all agent workflows.
0:20

We're building for agents. Using the new Surge API, any OpenClaw🦞 and Anthropic agent can now autonomously create projects using knowledge files, and deploy tokens. This is a key component for all agent workflows.

Surge

20,658 views • 4 months 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:
2:49

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

Andrew Ng

105,343 views • 1 year ago

If you're new to apps, you need to use XcodeBuildMCP. It takes all the rough edges out of working with Codex or Claude Code and Xcode. Your agent can build and run your app, and even check the UI to see if it looks good via Apple Accessibility APIs.
51:52

If you're new to apps, you need to use XcodeBuildMCP. It takes all the rough edges out of working with Codex or Claude Code and Xcode. Your agent can build and run your app, and even check the UI to see if it looks good via Apple Accessibility APIs.

Paul Solt

53,002 views • 5 months ago

I created an AI agent arena where agents trade on 15/5-minute markets 24/7 on Polymarket. Without using OpenClaw or Claude Code > Each AI agent competes against the others using different settings to achieve the best results. It cost me $0 to build. My goal is to identify the best agents with the best strategies on the demo account and transfer them to a live account. And this is entirely feasible.
0:40

I created an AI agent arena where agents trade on 15/5-minute markets 24/7 on Polymarket. Without using OpenClaw or Claude Code > Each AI agent competes against the others using different settings to achieve the best results. It cost me $0 to build. My goal is to identify the best agents with the best strategies on the demo account and transfer them to a live account. And this is entirely feasible.

Atenov int.

18,562 views • 2 months ago

oh my fuck! my ai agent (tokyo) can now use a browser and perform actions autonomously - it can basically do anything - lend/borrow using aave - restake to eigenlayer - register for side events and etc here’s an example of it finding eth denver side events and registering me
0:56

oh my fuck! my ai agent (tokyo) can now use a browser and perform actions autonomously - it can basically do anything - lend/borrow using aave - restake to eigenlayer - register for side events and etc here’s an example of it finding eth denver side events and registering me

Suhail Kakar

511,609 views • 1 year ago

Here's why I think Zed 's Agent Client Protocol is an important technology. It allows lunatics like me to take the magic of Gemini CLI and other agents, and build a custom terminal UI around them. Here I am comparing Toad's TTY support with Gemini's. Toad is very close to a release BTW. Join the toad channel on Textualize discord to be the first to try it out.
3:06

Here's why I think Zed 's Agent Client Protocol is an important technology. It allows lunatics like me to take the magic of Gemini CLI and other agents, and build a custom terminal UI around them. Here I am comparing Toad's TTY support with Gemini's. Toad is very close to a release BTW. Join the toad channel on Textualize discord to be the first to try it out.

Will McGugan

29,180 views • 7 months ago

Today’s Agent Spotlight: A browser-native agent that acts on the web using ASI:One. ⚡ Built using Notte, this Nike agent doesn’t merely scrape - it clicks, scrolls, and navigates like a real user. Not limited to Nike.com. With Agentverse and Notte, you can build agents for any site. Try now: Link to agent:
3:11

Today’s Agent Spotlight: A browser-native agent that acts on the web using ASI:One. ⚡ Built using Notte, this Nike agent doesn’t merely scrape - it clicks, scrolls, and navigates like a real user. Not limited to Nike.com. With Agentverse and Notte, you can build agents for any site. Try now: Link to agent:

Fetch.ai

49,466 views • 1 year ago

Now available for deployment: GitHub Copilot agent Assign any issue to GitHub Copilot and the agent will start working on an implementation, using the full issue context. When the work is ready, the issue is updated automatically with a draft PR.
0:34

Now available for deployment: GitHub Copilot agent Assign any issue to GitHub Copilot and the agent will start working on an implementation, using the full issue context. When the work is ready, the issue is updated automatically with a draft PR.

Linear

53,515 views • 8 months ago

🚀 The team at Google just released the Agents API, a service for building and running custom agents inside a sandboxed Linux environment, and we built a template that gives these agents access to LlamaParse / LiteParse, enabling them to process unstructured documents automatically 📄⚡ Here’s how it works: 🔹 Configure a Git repository where data and outputs will be stored 🔹 Clone the repository into the agent sandbox 🔹 Install the LiteParse CLI, the LlamaParse SDK, and agent skills to use both 🔹 Prompt the agent with a task and watch it process documents autonomously 🤖 The result? An agent that can work directly with messy, real-world documents using LlamaParse and LiteParse within Google’s new agent runtime. Check out the GitHub repository: Get started with LlamaParse:
0:32

🚀 The team at Google just released the Agents API, a service for building and running custom agents inside a sandboxed Linux environment, and we built a template that gives these agents access to LlamaParse / LiteParse, enabling them to process unstructured documents automatically 📄⚡ Here’s how it works: 🔹 Configure a Git repository where data and outputs will be stored 🔹 Clone the repository into the agent sandbox 🔹 Install the LiteParse CLI, the LlamaParse SDK, and agent skills to use both 🔹 Prompt the agent with a task and watch it process documents autonomously 🤖 The result? An agent that can work directly with messy, real-world documents using LlamaParse and LiteParse within Google’s new agent runtime. Check out the GitHub repository: Get started with LlamaParse:

LlamaIndex 🦙

21,975 views • 1 month ago

Three months ago, Codex was trash for knowledge work. Now it's my daily driver. I use it for writing, recruiting, deep engineering work, and everything in between. It even keeps me at inbox 0. I chatted with Every 📧's head of growth Austin Austin Tedesco on Every 📧's AI & I about what changed, and why he now spends 80% of his working time in the Codex desktop app too. We get into: - How Codex went from making Austin feel like an idiot to being the place he goes to get stuff done, including complex tasks like writing go-to-market plans using existing material from Slack, Notion, and meeting transcripts. - Why the Codex’s desktop app, which is faster and more reliable than Claude Desktop/Cowork, is the real differentiator. - How I source candidates with Codex by having it identify career arcs, not keywords—my go-to move is identifying organizations likely to teach the skills Every needs for a role, and then find candidates from that pool who have since gone on to work in AI. This is a must-watch for anyone who's wondering whether it’s finally time to give Codex a try. Watch below! Timestamps How Codex went from a tool for senior engineers to a daily driver for knowledge work: 00:00:57 How Claude Code proved that a great coding agent works for any knowledge work: 00:02:42 Austin's switch to Codex: 00:07:24 How Austin set up Codex with folders, keys, and reviewer agents: 00:13:48 Using Codex to brainstorm automations across Gmail, Slack, and Notion: 00:18:24 How Austin manages the human review step when Codex is drafting communications: 00:22:42 Using Codex to build specialized agents inspired by product executive Claire Vo: 00:28:54 Synthesizing meeting transcripts and Slack threads into a go-to-market plan: 00:31:09 Building a live KPI tracker in Notion that agents can read: 00:40:15 Using Codex for recruiting: 00:44:54
58:23

Three months ago, Codex was trash for knowledge work. Now it's my daily driver. I use it for writing, recruiting, deep engineering work, and everything in between. It even keeps me at inbox 0. I chatted with Every 📧's head of growth Austin Austin Tedesco on Every 📧's AI & I about what changed, and why he now spends 80% of his working time in the Codex desktop app too. We get into: - How Codex went from making Austin feel like an idiot to being the place he goes to get stuff done, including complex tasks like writing go-to-market plans using existing material from Slack, Notion, and meeting transcripts. - Why the Codex’s desktop app, which is faster and more reliable than Claude Desktop/Cowork, is the real differentiator. - How I source candidates with Codex by having it identify career arcs, not keywords—my go-to move is identifying organizations likely to teach the skills Every needs for a role, and then find candidates from that pool who have since gone on to work in AI. This is a must-watch for anyone who's wondering whether it’s finally time to give Codex a try. Watch below! Timestamps How Codex went from a tool for senior engineers to a daily driver for knowledge work: 00:00:57 How Claude Code proved that a great coding agent works for any knowledge work: 00:02:42 Austin's switch to Codex: 00:07:24 How Austin set up Codex with folders, keys, and reviewer agents: 00:13:48 Using Codex to brainstorm automations across Gmail, Slack, and Notion: 00:18:24 How Austin manages the human review step when Codex is drafting communications: 00:22:42 Using Codex to build specialized agents inspired by product executive Claire Vo: 00:28:54 Synthesizing meeting transcripts and Slack threads into a go-to-market plan: 00:31:09 Building a live KPI tracker in Notion that agents can read: 00:40:15 Using Codex for recruiting: 00:44:54

Dan Shipper 📧

55,221 views • 1 month ago

Your agents suck when using the web because 85% of it doesn't have an API. Browserbase gives them everything they need to do work online. Leading AI companies like Ramp, Lovable, and Clay trust us to power agents that do real work on behalf of real people. With a single API key, your agent gets everything it needs to navigate the wild web: browsers, search, fetch, identity, a sandbox runtime, and model gateway. Stop waiting on integrations, build agents that can browse and interact with the web just like humans.
1:06

Your agents suck when using the web because 85% of it doesn't have an API. Browserbase gives them everything they need to do work online. Leading AI companies like Ramp, Lovable, and Clay trust us to power agents that do real work on behalf of real people. With a single API key, your agent gets everything it needs to navigate the wild web: browsers, search, fetch, identity, a sandbox runtime, and model gateway. Stop waiting on integrations, build agents that can browse and interact with the web just like humans.

Paul Klein IV

1,726,808 views • 2 months ago

Connect your agents directly to Chrome DevTools. 🛠️ The new Chrome DevTools MCP server allows agents to inspect the DOM and debug console errors in real-time using Gemini CLI or any compatible agent. Check out the documentation on GitHub →
1:01

Connect your agents directly to Chrome DevTools. 🛠️ The new Chrome DevTools MCP server allows agents to inspect the DOM and debug console errors in real-time using Gemini CLI or any compatible agent. Check out the documentation on GitHub →

Google for Developers

22,793 views • 6 months ago

Someone built an open source F1 replay timing tool that lets you watch races on delay without spoilers while still seeing live timing, telemetry, and pit-stop predictions. It can even sync with your TV using a photo of the timing tower.
0:22

Someone built an open source F1 replay timing tool that lets you watch races on delay without spoilers while still seeing live timing, telemetry, and pit-stop predictions. It can even sync with your TV using a photo of the timing tower.

Aman

377,554 views • 3 months ago

About a year ago, we launched the Agent-to-Agent protocol because AI agents shouldn’t work in silos. Today, developers are using A2A to coordinate specialized agents across enterprise data systems, commerce workflows, DevOps environments, and scientific research. FoldRun is one example, simplifying petabyte-scale protein modeling through agent-to-agent collaboration. Learn how to build with it in the blog:
1:04

About a year ago, we launched the Agent-to-Agent protocol because AI agents shouldn’t work in silos. Today, developers are using A2A to coordinate specialized agents across enterprise data systems, commerce workflows, DevOps environments, and scientific research. FoldRun is one example, simplifying petabyte-scale protein modeling through agent-to-agent collaboration. Learn how to build with it in the blog:

Google for Developers

35,133 views • 11 days ago

I built an automated AI competitor analyis team with multi-agents. It has 3 AI agents working together as a team: 1. Data Extraction Agent using Firecrawl 2. Analyst Agent using OpenAI GPT-4o 3. Competitor Research Agent using Perplexity AI or Exa AI 100% Opensource code.
0:45

I built an automated AI competitor analyis team with multi-agents. It has 3 AI agents working together as a team: 1. Data Extraction Agent using Firecrawl 2. Analyst Agent using OpenAI GPT-4o 3. Competitor Research Agent using Perplexity AI or Exa AI 100% Opensource code.

Shubham Saboo

65,158 views • 1 year ago

"The model is no longer the product. Codex, Perplexity Computer, or Claude Code - all are orchestration system. It takes a model and pairs it with an agent harness. What is an agent harness ? The rules for how the agent loops around" - Aravind Srinivas
3:27

"The model is no longer the product. Codex, Perplexity Computer, or Claude Code - all are orchestration system. It takes a model and pairs it with an agent harness. What is an agent harness ? The rules for how the agent loops around" - Aravind Srinivas

Rohan Paul

119,783 views • 13 days ago

Codex /goal With Hermes Agent is life-changing. I updated my Hermes agent skill to give Codex a goal on the fly using Telegram and track each one of those codex goals in a Kanban board. It has been really insane to see and track Codex execute goals in the wild.
0:32

Codex /goal With Hermes Agent is life-changing. I updated my Hermes agent skill to give Codex a goal on the fly using Telegram and track each one of those codex goals in a Kanban board. It has been really insane to see and track Codex execute goals in the wild.

Shubham Saboo

198,259 views • 1 month ago