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Codex CLI will have MULTI-AGENT support in 0.123.0 Here's how it will work: → 1. Add `multi_agent=true` in ~/codex/config.toml under [features] → 2. Toggle with `/experimental` → 3. Three built-in agents (default, explorer, worker) → 4. Define your own agents ``` [agents.researcher] description = "Research-focused role." config_file = "~/.codex/agents/researcher.toml"...

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Chong-U

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36,205 次观看 • 6 个月前 •via X (Twitter)

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This is so cool. In the next Codex update, multi agents will get a massive flexibility upgrade. "Hey Codex, when you implement this plan, I want you to delegate all the lower complexity tasks to GPT 5.3 Spark subagents" Instead of needing to create 100 different custom agent roles for different situations, you can just prompt your agent to spawn whatever model or reasoning level you want. With only natural language. No config files. No pre-defined roles. Just tell the orchestrator what to use and it listens.
0:41

This is so cool. In the next Codex update, multi agents will get a massive flexibility upgrade. "Hey Codex, when you implement this plan, I want you to delegate all the lower complexity tasks to GPT 5.3 Spark subagents" Instead of needing to create 100 different custom agent roles for different situations, you can just prompt your agent to spawn whatever model or reasoning level you want. With only natural language. No config files. No pre-defined roles. Just tell the orchestrator what to use and it listens.

am.will

128,125 次观看 • 5 个月前

I just built an entire app in 7 minutes using Codex Spark. EveryMCP: a universal MCP server installer that configures 15+ coding agents from a single CLI command. 12 Codex Spark subagents. All running in parallel. At the same time. The project itself isn't complicated, but just imagine what this looks like when you scale the complexity. My setup: • Codex 5.3 High to plan • Codex 5.3 Medium to orchestrate • 12 Codex Spark agents (xhigh) to implement in one shot Spark isn't competing with the big models. It's a different tool entirely. You orchestrate it. You run it in swarms. And it moves at a speed that wasn't possible before, even for the top 1% of AI coders. More complex workflows coming soon. Comment below if you want me to tell you how I did it.
4:29

I just built an entire app in 7 minutes using Codex Spark. EveryMCP: a universal MCP server installer that configures 15+ coding agents from a single CLI command. 12 Codex Spark subagents. All running in parallel. At the same time. The project itself isn't complicated, but just imagine what this looks like when you scale the complexity. My setup: • Codex 5.3 High to plan • Codex 5.3 Medium to orchestrate • 12 Codex Spark agents (xhigh) to implement in one shot Spark isn't competing with the big models. It's a different tool entirely. You orchestrate it. You run it in swarms. And it moves at a speed that wasn't possible before, even for the top 1% of AI coders. More complex workflows coming soon. Comment below if you want me to tell you how I did it.

am.will

16,916 次观看 • 6 个月前

🚨 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 次观看 • 1 年前

i merged all my AI agents into one brain Claude Code, Codex, Gemini: one team, one memory, zero re-briefing result: 8+ hours a week back, and every agent already knows my projects, my clients and how i think here's the setup with Jarvix: 1. dump your context once: projects, clients, writing style, how you make decisions.. it builds your personal model from your actual AI interactions 2. switch agents mid-task with zero re-briefing: Claude Code builds, Codex reviews, Gemini researches, all reading the same context 3. it carries what YOU choose across sessions.. never start a chat from zero again 4. when it's time to act, it pulls the right agents together as one team instead of five separate assistants the unlock isn't another agent. it's that your context finally travels agents without shared memory are five interns on their first day, forever. agents with it are a team that's worked with you for years not more agents. one shared context that's the right bet P.S. thanks to the Jarvix team for the early access
2:15

i merged all my AI agents into one brain Claude Code, Codex, Gemini: one team, one memory, zero re-briefing result: 8+ hours a week back, and every agent already knows my projects, my clients and how i think here's the setup with Jarvix: 1. dump your context once: projects, clients, writing style, how you make decisions.. it builds your personal model from your actual AI interactions 2. switch agents mid-task with zero re-briefing: Claude Code builds, Codex reviews, Gemini researches, all reading the same context 3. it carries what YOU choose across sessions.. never start a chat from zero again 4. when it's time to act, it pulls the right agents together as one team instead of five separate assistants the unlock isn't another agent. it's that your context finally travels agents without shared memory are five interns on their first day, forever. agents with it are a team that's worked with you for years not more agents. one shared context that's the right bet P.S. thanks to the Jarvix team for the early access

Ronin

15,071 次观看 • 23 天前

Jensen Huang: "agents is the new software " The CEO of a $4.86 trillion company just gave out the secret to building with AI agents over the next 10 years. You have only 150 days left in 2026 to get ahead of this shift. The shift : Software is dead. Agent Factories will build the next billion-dollar companies. [Here's how to build your first factory in under 3 minutes:] i. Set up OpenCodex + Codex Loom ii. Choose a model like Kimi K3 or DeepSeek V4 Flash iii. Run your first workflow Save and Bookmark this It will be the most productive thing you give your agent this week
0:30

Jensen Huang: "agents is the new software " The CEO of a $4.86 trillion company just gave out the secret to building with AI agents over the next 10 years. You have only 150 days left in 2026 to get ahead of this shift. The shift : Software is dead. Agent Factories will build the next billion-dollar companies. [Here's how to build your first factory in under 3 minutes:] i. Set up OpenCodex + Codex Loom ii. Choose a model like Kimi K3 or DeepSeek V4 Flash iii. Run your first workflow Save and Bookmark this It will be the most productive thing you give your agent this week

Avid

26,695 次观看 • 1 个月前

New agentic short course! Multi AI Agent Systems with crewAI, built with CrewAI's founder and CEO João Moura. In this course, you'll learn how to break down complex tasks into subtasks for multiple AI agents, each playing a specialized role, to execute. For example, to generate a research report, you might have researcher, writer, and quality assurance agents collaborate. You'll define the roles, expectations, and interactions between the agents—like a manager organizing a team. You'll work with key agentic AI techniques like role-playing, tool use, memory, guardrails, and cross-agent collaboration. And you'll build your own multi-agent systems that can tackle complex tasks. I think you'll find it both productive and fun to design agents and watch them collaborate to get things done. Let me know what you think! I believe multi-agent architectures will drive significant progress in AI systems. Please sign up here!
2:39

New agentic short course! Multi AI Agent Systems with crewAI, built with CrewAI's founder and CEO João Moura. In this course, you'll learn how to break down complex tasks into subtasks for multiple AI agents, each playing a specialized role, to execute. For example, to generate a research report, you might have researcher, writer, and quality assurance agents collaborate. You'll define the roles, expectations, and interactions between the agents—like a manager organizing a team. You'll work with key agentic AI techniques like role-playing, tool use, memory, guardrails, and cross-agent collaboration. And you'll build your own multi-agent systems that can tackle complex tasks. I think you'll find it both productive and fun to design agents and watch them collaborate to get things done. Let me know what you think! I believe multi-agent architectures will drive significant progress in AI systems. Please sign up here!

Andrew Ng

350,175 次观看 • 2 年前

voice prompting is 4x faster than typing. but i NEEEDED more. Nvidia parakeet allows me to fully voice control an agentic development environment with commands firing in under 300ms. and it runs 100% local. I added gpt realtime 2.1 mini, its 20% faster, 7 to 20x cheaper, and lets you have full jarvis style control of your vibe coding agents. but what about orchestration? agents can spawn each other, prompt each other, and read each others output with the CNVS mcp and cli. Fable 5 can create a plan, spawn 10 grok agents to execute, and a kimi k3 agent to review. parallel agents code at 1,000s of TPS anthropic's own research shows improvements ACROSS the board for multi agent workflows over single agent but only CNVS lets you choose exactly which orchestration, worker, and reviewer agent you would like to use. grok, kimi, qwen, claude, codex... the cross agent memory system is based on real 2026 research so all agents share the same brain, its on demand so it never bloats context. what about remote agents?? You can create remote canvasses that run agents your virtual private servers, they keep working even if your mac shuts off, and you can even vibe code straight to production. CNVS is built from the ground up ENTIRELY in swift for RAW performance on apple hardware. PS - its a LIFE TIME LICENSE because you don't need another subscription. PPS - I ship updates every week based off user feedback and livestream myself building it everyday. PPPS - it uses all your existing ai subs, so no api pricing here.
1:02

voice prompting is 4x faster than typing. but i NEEEDED more. Nvidia parakeet allows me to fully voice control an agentic development environment with commands firing in under 300ms. and it runs 100% local. I added gpt realtime 2.1 mini, its 20% faster, 7 to 20x cheaper, and lets you have full jarvis style control of your vibe coding agents. but what about orchestration? agents can spawn each other, prompt each other, and read each others output with the CNVS mcp and cli. Fable 5 can create a plan, spawn 10 grok agents to execute, and a kimi k3 agent to review. parallel agents code at 1,000s of TPS anthropic's own research shows improvements ACROSS the board for multi agent workflows over single agent but only CNVS lets you choose exactly which orchestration, worker, and reviewer agent you would like to use. grok, kimi, qwen, claude, codex... the cross agent memory system is based on real 2026 research so all agents share the same brain, its on demand so it never bloats context. what about remote agents?? You can create remote canvasses that run agents your virtual private servers, they keep working even if your mac shuts off, and you can even vibe code straight to production. CNVS is built from the ground up ENTIRELY in swift for RAW performance on apple hardware. PS - its a LIFE TIME LICENSE because you don't need another subscription. PPS - I ship updates every week based off user feedback and livestream myself building it everyday. PPPS - it uses all your existing ai subs, so no api pricing here.

Max Blade

60,658 次观看 • 1 个月前

This is crazy, I can talk to OpenAI's realtime API, and it'll respond back with voice, and then help call Codex to help me code! Got curious about Codex and its realtime integration, and found codex's feature flag `realtime_conversation` integrated for the codex app-server. How it works: - launch codex app-server with realtime - connect a client to realtime through WebRTC voice connection - Codex app-server is also attached on the side - talk to realtime - realtime decides whether coding is needed, and responds back with events - Codex app-server takes these events and runs codex agent turns - codex writes code like your codex cli exp Keep in mind OpenAI hasn't released this officially, they're still actively working on it, I just created a fork. This is a completely new UX than the usual chat interface for agentic coding. For example, when the agent responds back in voice, it's actually faster for me to read the response, and I don't actually find voice response that useful unless it does so in the background. But image realtime working in the background, you can talk to it, realtime keeps track of the different agents working on your tasks and your status. More to come on demo side, i'll make a more polished version and include voice, but thought this was cool to share. You can see in the responses the distinction between my voice input, realtime response, and realtime calling Codex to do work!
0:28

This is crazy, I can talk to OpenAI's realtime API, and it'll respond back with voice, and then help call Codex to help me code! Got curious about Codex and its realtime integration, and found codex's feature flag `realtime_conversation` integrated for the codex app-server. How it works: - launch codex app-server with realtime - connect a client to realtime through WebRTC voice connection - Codex app-server is also attached on the side - talk to realtime - realtime decides whether coding is needed, and responds back with events - Codex app-server takes these events and runs codex agent turns - codex writes code like your codex cli exp Keep in mind OpenAI hasn't released this officially, they're still actively working on it, I just created a fork. This is a completely new UX than the usual chat interface for agentic coding. For example, when the agent responds back in voice, it's actually faster for me to read the response, and I don't actually find voice response that useful unless it does so in the background. But image realtime working in the background, you can talk to it, realtime keeps track of the different agents working on your tasks and your status. More to come on demo side, i'll make a more polished version and include voice, but thought this was cool to share. You can see in the responses the distinction between my voice input, realtime response, and realtime calling Codex to do work!

Matthew Lam

17,674 次观看 • 3 个月前

Three skills I use every day in Claude Code and Codex to solve my hardest problems: 1️⃣ /agent-watchdog When I have one agent like Codex working on a task and I don't fully trust it's going to do everything right, I'll open up another one like Claude Code and tell it to watchdog the Codex thread. You can copy the Codex deep link into Claude Code and it'll look at the prompt you sent, watch the Codex thread until it's done, then compare the Codex solution to how it was planning to solve it and automatically fix anything that Codex missed. It can also test the work of the other agent end-to-end. Similar to the idea of OpenRouter's new Fusion feature, I've definitely found that two models thinking through a problem and checking each other's work can be wildly more impactful than just one. 2️⃣ /plan-arbiter Similar ideas as /agent-watchdog - but with this one you have both make plans, compare plans, negotiate the differences, and make a final plan to execute. I find Claude Code is better at writing plans, but Codex is faster and cheaper to execute on them. Then I usually have Claude Code watchdog the Codex work and fix anything that was missed. 3️⃣ /read-the-damn-docs One thing that drives me crazy with coding agents is they're so reluctant to look up docs. They'll just guess and guess and guess at the right API surface for things, or the right solution to an integration of two things. Once I explicitly tell it to look up the docs, it says "Oh, I see the answer," and it fixes the problem. So I made the /read-the-damn-docs skill. Add it and your agents will know when and how to do efficient web searches to look up docs for the types of problems you really should look up docs for. All of these are totally open source over on my GitHub. If you try them, let me know your feedback. Will link to them below:
1:21

Three skills I use every day in Claude Code and Codex to solve my hardest problems: 1️⃣ /agent-watchdog When I have one agent like Codex working on a task and I don't fully trust it's going to do everything right, I'll open up another one like Claude Code and tell it to watchdog the Codex thread. You can copy the Codex deep link into Claude Code and it'll look at the prompt you sent, watch the Codex thread until it's done, then compare the Codex solution to how it was planning to solve it and automatically fix anything that Codex missed. It can also test the work of the other agent end-to-end. Similar to the idea of OpenRouter's new Fusion feature, I've definitely found that two models thinking through a problem and checking each other's work can be wildly more impactful than just one. 2️⃣ /plan-arbiter Similar ideas as /agent-watchdog - but with this one you have both make plans, compare plans, negotiate the differences, and make a final plan to execute. I find Claude Code is better at writing plans, but Codex is faster and cheaper to execute on them. Then I usually have Claude Code watchdog the Codex work and fix anything that was missed. 3️⃣ /read-the-damn-docs One thing that drives me crazy with coding agents is they're so reluctant to look up docs. They'll just guess and guess and guess at the right API surface for things, or the right solution to an integration of two things. Once I explicitly tell it to look up the docs, it says "Oh, I see the answer," and it fixes the problem. So I made the /read-the-damn-docs skill. Add it and your agents will know when and how to do efficient web searches to look up docs for the types of problems you really should look up docs for. All of these are totally open source over on my GitHub. If you try them, let me know your feedback. Will link to them below:

Steve (Builder.io)

43,089 次观看 • 2 个月前

Building an Agent Team on Buzz (Complete Guide) Buzz is a new free platform, that feels exactly like Slack, except the agents aren't add ons, they are equals. and in 5 minutes you can create an agent team. And it works with your existing Codex and Claude Code subscriptions... So it's free to use. This video is divided into 2 parts: --- ⭐ Part 1 Conversation with Vinny (Vinny) 00:00 Introduction 01:59 Why is Buzz Going Viral? 07:08 Creating Agents & Channels with Fizz 20:03 Adding Other Harnesses (OpenCode, Cursor) 21:25 The Buzz Mobile App 22:36 Payments & Shared Compute 25:36 Workflows & Projects: A GitHub Killer? 30:14 The Future of Buzz: An Open Cowork? 35:13 Automations, Triggers & Trusting Your Agents --- ⭐ Part 2 - Getting Started Tutorial (My workflow) 42:32 Tutorial and Breakdown 43:03 Downloading Buzz And Default Agents 43:35 Creating your first agent 45:13 Adding Agents to channels 45:43 Why I use Codex as Lead Agent 47:06 Using Codex, Claude Code and Grok for task 48:52 Adding Muse (Meta Model) using OpenRouter 51:23 iOS app is Free and easy to use 51:50 My management Channel (My #1 use case) ---
55:15

Building an Agent Team on Buzz (Complete Guide) Buzz is a new free platform, that feels exactly like Slack, except the agents aren't add ons, they are equals. and in 5 minutes you can create an agent team. And it works with your existing Codex and Claude Code subscriptions... So it's free to use. This video is divided into 2 parts: --- ⭐ Part 1 Conversation with Vinny (Vinny) 00:00 Introduction 01:59 Why is Buzz Going Viral? 07:08 Creating Agents & Channels with Fizz 20:03 Adding Other Harnesses (OpenCode, Cursor) 21:25 The Buzz Mobile App 22:36 Payments & Shared Compute 25:36 Workflows & Projects: A GitHub Killer? 30:14 The Future of Buzz: An Open Cowork? 35:13 Automations, Triggers & Trusting Your Agents --- ⭐ Part 2 - Getting Started Tutorial (My workflow) 42:32 Tutorial and Breakdown 43:03 Downloading Buzz And Default Agents 43:35 Creating your first agent 45:13 Adding Agents to channels 45:43 Why I use Codex as Lead Agent 47:06 Using Codex, Claude Code and Grok for task 48:52 Adding Muse (Meta Model) using OpenRouter 51:23 iOS app is Free and easy to use 51:50 My management Channel (My #1 use case) ---

Riley Brown

387,816 次观看 • 1 个月前

Introducing the Knowledge Agent Template Vercel dropped the Chat SDK so we built a full template on top of it that you can deploy in one click. Here's what's inside: • Add your sources: GitHub repos, YouTube, etc. directly from the UI • File-based agent using grep/find/cat • Powered by Vercel Workflow + Sandbox • Multi-platform: web chat, GitHub bot, Discord bot, and more • Comes with skills to edit your project, add sources, create agents Deploy, add a source, ask questions. Your knowledge base is live and always in sync. This is just scratching the surface. There's way more packed in this template, and I'll be breaking down the features all week. Links in the below 👇
0:29

Introducing the Knowledge Agent Template Vercel dropped the Chat SDK so we built a full template on top of it that you can deploy in one click. Here's what's inside: • Add your sources: GitHub repos, YouTube, etc. directly from the UI • File-based agent using grep/find/cat • Powered by Vercel Workflow + Sandbox • Multi-platform: web chat, GitHub bot, Discord bot, and more • Comes with skills to edit your project, add sources, create agents Deploy, add a source, ask questions. Your knowledge base is live and always in sync. This is just scratching the surface. There's way more packed in this template, and I'll be breaking down the features all week. Links in the below 👇

Hugo

54,769 次观看 • 6 个月前

This is how you get 15x fable 5 usage. Fable -> Composer 2.5 reading Fable -> GPT 5.5 execution I built CNVS to make agent orchestration dead simple and insanely visual. You can watch fable 5 delegate work to cursor, codex, open code, all your agents. using your exsisting ai subs no api pricing. The canvas is fully voice controlled locally with nvidia parakeet for fast and free, or gpt realtime 2 for a fully conversational jarvis experience. The built in mcp and cli means agentic control is bidirectional they can prompt and spawn each other and read agent states. but there is more I built it from the ground up in swift for native performance on mac os. My old 16gb m1 mbp can easily spin up and control eight agents across multiple canvasses. The hermes integration + remote canvases allow you to run agents fully in the cloud on your VPS think google docs of vibe coding. You can literally turn off your mac and they keep working, open cnvs back up and pick up where you left off. I built a cross agent memory system based on 2026 research so all your agents feel like one brain. its SIMPLE and on demand. This eliminates context bloat, and the research supports a massive uptick in cross agent performance. There is soo much more. PS - I am a father building this project 100% solo in my basement on live stream everyday, my goal is to push vibe coding to the next level PPS - CNVS is a lifetime license becuase you DO NOT need another subscription in your life.
1:30

This is how you get 15x fable 5 usage. Fable -> Composer 2.5 reading Fable -> GPT 5.5 execution I built CNVS to make agent orchestration dead simple and insanely visual. You can watch fable 5 delegate work to cursor, codex, open code, all your agents. using your exsisting ai subs no api pricing. The canvas is fully voice controlled locally with nvidia parakeet for fast and free, or gpt realtime 2 for a fully conversational jarvis experience. The built in mcp and cli means agentic control is bidirectional they can prompt and spawn each other and read agent states. but there is more I built it from the ground up in swift for native performance on mac os. My old 16gb m1 mbp can easily spin up and control eight agents across multiple canvasses. The hermes integration + remote canvases allow you to run agents fully in the cloud on your VPS think google docs of vibe coding. You can literally turn off your mac and they keep working, open cnvs back up and pick up where you left off. I built a cross agent memory system based on 2026 research so all your agents feel like one brain. its SIMPLE and on demand. This eliminates context bloat, and the research supports a massive uptick in cross agent performance. There is soo much more. PS - I am a father building this project 100% solo in my basement on live stream everyday, my goal is to push vibe coding to the next level PPS - CNVS is a lifetime license becuase you DO NOT need another subscription in your life.

Max Blade

29,711 次观看 • 2 个月前

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 📧

55,561 次观看 • 4 个月前

EVERYTHING YOU NEED TO KNOW ABOUT CHATGPT'S "LOVABLE KILLER" CODEX SITES (in 25 mins): TLDR; the coolest part is that apps you build can update themselves autonomously 1. Codex Sites is not Replit or Lovable or Bolt. Those are great for one-prompting a full app. Codex Sites is for building apps that the agent keeps improving without you touching them. 2. Your personal website can update its own stats. Your internal dashboard can refresh its own data. Your product can add features while you sleep. The app is alive. 3. Start by invoking at-sites. Use realistic sample data. Always say "save for review, do not deploy." This unlocks building a real product, not a homepage. 4. Add persistent storage so the app remembers everything between visits. Without this it resets every time. Ask Codex to show you the data model before it builds. 5. Create safe actions. These are the specific things the agent is allowed to do to your app: add data, update cards, move things, score things. You define the boundaries. The agent operates within them. 6. Build skills so any future Codex chat knows how to interact with your app. The skill is basically a manual for the agent. Without it, every new chat starts from zero. 7. Save gate like a video game. Codex doesn't auto-save. Create checkpoints before you deploy so you can roll back if something breaks. 8. Close the autonomous loop. This is the magic. Once memory, safe actions, and skills are set up, the agent can update your app from any chat, any context, without you switching tabs. 9. Use the plugins most people are sleeping on. Figma, Canva, HeyGen for avatar videos, Game Studio for interactive experiences, FAL for image generation, Hugging Face for open source models. Worth adding a few. 10. The big picture: we went from building apps to raising apps. You set up the structure, the guardrails, and the skills. The agent does the rest. That's autonomous product building and it's here right now. Tbh, Codex sites isn't perfect. Still a lot to be desired like domains, db, authentication etc. But it's a glimpse into this idea that apps can be updated/improved upon automonously. And Codex Sites is REALLY good if you live in Codex everyday. Which more and more of are. And that's really cool. Will be interesting to see how Lovable, Bolt, Replit etc react to this. full tutorial on The Startup Ideas Podcast (SIP) 🧃 where you get your pods watch share with a friend i'm rooting for you What do you think of Codex and Codex sites?
24:41

EVERYTHING YOU NEED TO KNOW ABOUT CHATGPT'S "LOVABLE KILLER" CODEX SITES (in 25 mins): TLDR; the coolest part is that apps you build can update themselves autonomously 1. Codex Sites is not Replit or Lovable or Bolt. Those are great for one-prompting a full app. Codex Sites is for building apps that the agent keeps improving without you touching them. 2. Your personal website can update its own stats. Your internal dashboard can refresh its own data. Your product can add features while you sleep. The app is alive. 3. Start by invoking at-sites. Use realistic sample data. Always say "save for review, do not deploy." This unlocks building a real product, not a homepage. 4. Add persistent storage so the app remembers everything between visits. Without this it resets every time. Ask Codex to show you the data model before it builds. 5. Create safe actions. These are the specific things the agent is allowed to do to your app: add data, update cards, move things, score things. You define the boundaries. The agent operates within them. 6. Build skills so any future Codex chat knows how to interact with your app. The skill is basically a manual for the agent. Without it, every new chat starts from zero. 7. Save gate like a video game. Codex doesn't auto-save. Create checkpoints before you deploy so you can roll back if something breaks. 8. Close the autonomous loop. This is the magic. Once memory, safe actions, and skills are set up, the agent can update your app from any chat, any context, without you switching tabs. 9. Use the plugins most people are sleeping on. Figma, Canva, HeyGen for avatar videos, Game Studio for interactive experiences, FAL for image generation, Hugging Face for open source models. Worth adding a few. 10. The big picture: we went from building apps to raising apps. You set up the structure, the guardrails, and the skills. The agent does the rest. That's autonomous product building and it's here right now. Tbh, Codex sites isn't perfect. Still a lot to be desired like domains, db, authentication etc. But it's a glimpse into this idea that apps can be updated/improved upon automonously. And Codex Sites is REALLY good if you live in Codex everyday. Which more and more of are. And that's really cool. Will be interesting to see how Lovable, Bolt, Replit etc react to this. full tutorial on The Startup Ideas Podcast (SIP) 🧃 where you get your pods watch share with a friend i'm rooting for you What do you think of Codex and Codex sites?

GREG ISENBERG

68,972 次观看 • 3 个月前

There's probably $100+ billion up for grabs for people who build startup for AI agents Over the next 10 years you're going to have a market of billions of customers (agents) with millions of wallets that want to use your services. TLDR; The internet was built for people: 1. Search google 2. Read landing page 3. Book demo 4. Talk to sales 5. Buy Agents don’t do that. Agents will: 1. Ask which product to use 2. Read your docs/pricing/security pages 3. Compare you to competitors 4. Check if you have an MCP/API/tool layer 5. Buy or recommend you without ever “visiting” your site like a person Everyone is going to have personal agents and business agents. This feels inevitable at this point. OpenClaw, Hermes, Claude Code, Codex, Google Spark. The tools are here. Which means there will be more agents on the internet than humans. So, where's the opportunity?? Go look at every SaaS tool you use. Notion. Slack. Jira. Google Analytics. Now ask: what is the version of this built purely for agents? Agent-native payments. Agent-native communication. Agent-native memory. Every category gets rebuilt. I clearly break down this shift and explain you everything on today's ep of The Startup Ideas Podcast (SIP) 🧃. Over the next 10 years you're going to have a market of billions of customers (agents) with millions of wallets that want to use your services. The founders who build for them now are going to look like the people who built websites in 1995. Might feel janky at the moment, but also obvious in hindsight. This is the next shift. Link over here: Watch
13:59

There's probably $100+ billion up for grabs for people who build startup for AI agents Over the next 10 years you're going to have a market of billions of customers (agents) with millions of wallets that want to use your services. TLDR; The internet was built for people: 1. Search google 2. Read landing page 3. Book demo 4. Talk to sales 5. Buy Agents don’t do that. Agents will: 1. Ask which product to use 2. Read your docs/pricing/security pages 3. Compare you to competitors 4. Check if you have an MCP/API/tool layer 5. Buy or recommend you without ever “visiting” your site like a person Everyone is going to have personal agents and business agents. This feels inevitable at this point. OpenClaw, Hermes, Claude Code, Codex, Google Spark. The tools are here. Which means there will be more agents on the internet than humans. So, where's the opportunity?? Go look at every SaaS tool you use. Notion. Slack. Jira. Google Analytics. Now ask: what is the version of this built purely for agents? Agent-native payments. Agent-native communication. Agent-native memory. Every category gets rebuilt. I clearly break down this shift and explain you everything on today's ep of The Startup Ideas Podcast (SIP) 🧃. Over the next 10 years you're going to have a market of billions of customers (agents) with millions of wallets that want to use your services. The founders who build for them now are going to look like the people who built websites in 1995. Might feel janky at the moment, but also obvious in hindsight. This is the next shift. Link over here: Watch

GREG ISENBERG

55,882 次观看 • 3 个月前

This is insane. An AI agent can run every boring job in outbound. We spent the last 6 months building ours. Here are my 8 favorite agents to build: Replies get sorted before we open the inbox. Campaigns go live from one command. Weak inboxes pull themselves out before they hurt a domain. Here are the agents behind it: 1. Reply Agent Reads every reply and drafts the response. A human reviews, edits, and sends. 2. Mailbox Health Agent Watches inbox and domain health. It predicts when you need new mailboxes, then buys and warms them. 3. Campaign Optimizer Agent Checks every live campaign every 6 hours. If 500+ leads were emailed and replies are under 4%, it tests new copy, replaces inboxes replying under 1%, and shifts sending to better hours. 4. Lead Qualification Agent Scores each lead by company size, industry, and tech stack. It enriches the record, updates your CRM, and only loads qualified leads into campaigns. High-priority prospect? You get a Slack ping. 5. Meeting Booking Agent Finds meeting requests inside replies. It books the slot, writes prep notes using the lead's background, sends reminders, and logs the outcome. 6. Pipeline Progression Agent Tracks opens, clicks, and website visits. It moves the CRM stage, triggers the next sequence, and creates a task when a lead shows real intent. 7. Copywriting Agent Writes cold emails and follow-ups in the campaign's voice. 8. Analytics Agent Watches campaign metrics in real time and explains what to fix in plain language. We built ours with custom code. Smartlead's SmartAgents let you build agents like these from a plain-English prompt, inside the platform where your campaigns already run. If you repeat an outbound task more than twice a week, that is an agent you have not built yet. Which one would you build first?
0:45

This is insane. An AI agent can run every boring job in outbound. We spent the last 6 months building ours. Here are my 8 favorite agents to build: Replies get sorted before we open the inbox. Campaigns go live from one command. Weak inboxes pull themselves out before they hurt a domain. Here are the agents behind it: 1. Reply Agent Reads every reply and drafts the response. A human reviews, edits, and sends. 2. Mailbox Health Agent Watches inbox and domain health. It predicts when you need new mailboxes, then buys and warms them. 3. Campaign Optimizer Agent Checks every live campaign every 6 hours. If 500+ leads were emailed and replies are under 4%, it tests new copy, replaces inboxes replying under 1%, and shifts sending to better hours. 4. Lead Qualification Agent Scores each lead by company size, industry, and tech stack. It enriches the record, updates your CRM, and only loads qualified leads into campaigns. High-priority prospect? You get a Slack ping. 5. Meeting Booking Agent Finds meeting requests inside replies. It books the slot, writes prep notes using the lead's background, sends reminders, and logs the outcome. 6. Pipeline Progression Agent Tracks opens, clicks, and website visits. It moves the CRM stage, triggers the next sequence, and creates a task when a lead shows real intent. 7. Copywriting Agent Writes cold emails and follow-ups in the campaign's voice. 8. Analytics Agent Watches campaign metrics in real time and explains what to fix in plain language. We built ours with custom code. Smartlead's SmartAgents let you build agents like these from a plain-English prompt, inside the platform where your campaigns already run. If you repeat an outbound task more than twice a week, that is an agent you have not built yet. Which one would you build first?

Hosun Chung

156,625 次观看 • 1 个月前

Codex team is back in the kitchen with a really nice quality of life upgrade for subagents. With the advent of custom roles, they have also upgraded the TUI experience in two really meaningful ways. > All agents now get a name for better readability > Additionally, the agent role is declared. > Subagent name, role, and status are now color coded. > Subagent rendering was also optimized for readability > /agents slash command shows all agents, even 2+ layers deep. And here's the biggest and most important change. Subagent injection. Before, sometimes the orchestration agent would continue work and lose track of the work of a subagent. Now, when a subagent is blocked or completed, it injects a message back up the chain to ensure that the parent sees the message. This is a really big improvement overall, and leads to much more reliable inter-agent communication, reliability, and DX. In this example, I used the parent agent to spin up a worker agent, which then spawned two more "Spark" agents a second layer deep. I was able to easily tell them apart, switch between the threads, and see exactly what they were prompted. All of this will be available in update 0.105.0 I don't know who JIF is at OpenAI, but they are truly a legend.
0:35

Codex team is back in the kitchen with a really nice quality of life upgrade for subagents. With the advent of custom roles, they have also upgraded the TUI experience in two really meaningful ways. > All agents now get a name for better readability > Additionally, the agent role is declared. > Subagent name, role, and status are now color coded. > Subagent rendering was also optimized for readability > /agents slash command shows all agents, even 2+ layers deep. And here's the biggest and most important change. Subagent injection. Before, sometimes the orchestration agent would continue work and lose track of the work of a subagent. Now, when a subagent is blocked or completed, it injects a message back up the chain to ensure that the parent sees the message. This is a really big improvement overall, and leads to much more reliable inter-agent communication, reliability, and DX. In this example, I used the parent agent to spin up a worker agent, which then spawned two more "Spark" agents a second layer deep. I was able to easily tell them apart, switch between the threads, and see exactly what they were prompted. All of this will be available in update 0.105.0 I don't know who JIF is at OpenAI, but they are truly a legend.

am.will

78,267 次观看 • 6 个月前

I hooked this up to a peer-to-peer astrophysics researcher agent which gossips and collaborates with other such agents (and your openclaws) to: 1. Learn how to train an astrophysics model (Andrej Karpathy's work below) 2. Train a new astrophysics model 3. Use it to write papers 4. Peer agents based on frontier lab models critique it 5. Surface breakthroughs ... and then feed back in the loop ... More agents join, from the browser or the CLI, and run this, the smarter and more exciting breakthroughs would eventually emerge. When these agents are idle, they are also reading daily tech news with their own RSS reader, and commenting on each other's thoughts. And they can also serve the underlying machine's compute to other agents on the network, and earn social credit for being good actors (think BitTorrent). We also prove the agent has the compute it says by cryptographic verification of regular matmul challenges. All you have to do is either go on this website (and it creates an agent which runs from your browser), or install the CLI if you want to give the system more juice. And you are part of likely the first experimental distributed agi thing. This is Day 1, but this is how it starts.. this network is fully peer-to-peer, and, very volatile, but the intelligence here is meant to compound continuously.. curl -fsSL | bash
0:23

I hooked this up to a peer-to-peer astrophysics researcher agent which gossips and collaborates with other such agents (and your openclaws) to: 1. Learn how to train an astrophysics model (Andrej Karpathy's work below) 2. Train a new astrophysics model 3. Use it to write papers 4. Peer agents based on frontier lab models critique it 5. Surface breakthroughs ... and then feed back in the loop ... More agents join, from the browser or the CLI, and run this, the smarter and more exciting breakthroughs would eventually emerge. When these agents are idle, they are also reading daily tech news with their own RSS reader, and commenting on each other's thoughts. And they can also serve the underlying machine's compute to other agents on the network, and earn social credit for being good actors (think BitTorrent). We also prove the agent has the compute it says by cryptographic verification of regular matmul challenges. All you have to do is either go on this website (and it creates an agent which runs from your browser), or install the CLI if you want to give the system more juice. And you are part of likely the first experimental distributed agi thing. This is Day 1, but this is how it starts.. this network is fully peer-to-peer, and, very volatile, but the intelligence here is meant to compound continuously.. curl -fsSL | bash

Varun

216,687 次观看 • 6 个月前

Unreal Engine has a huge agent tax. A whole 20x in Codex only lasts about a day with the agents looping. Here's what happened so far: 1. Traffic feels way more natural. still improving. 2. Driving is smooth now, and cars actually collision and hit buildings. 3. Pedestrians has a real look but no animations yet 4. Limit burn is real, one day and the usage for 20x is gone. It took me 3 full Codex usages just trying to finalize the traffic. It's getting better, just not good enough. Unreal looks insane. it's just not agent native at the moment. Unreal Engine needs to work on some native CLI!! So I'm going to test Unity. not switching, just testing. My Claude usage will be back up tomorrow I'll report back what's up!
2:19

Unreal Engine has a huge agent tax. A whole 20x in Codex only lasts about a day with the agents looping. Here's what happened so far: 1. Traffic feels way more natural. still improving. 2. Driving is smooth now, and cars actually collision and hit buildings. 3. Pedestrians has a real look but no animations yet 4. Limit burn is real, one day and the usage for 20x is gone. It took me 3 full Codex usages just trying to finalize the traffic. It's getting better, just not good enough. Unreal looks insane. it's just not agent native at the moment. Unreal Engine needs to work on some native CLI!! So I'm going to test Unity. not switching, just testing. My Claude usage will be back up tomorrow I'll report back what's up!

Ziwen

57,842 次观看 • 1 个月前

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.
4:16

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 次观看 • 2 个月前