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"We write very few specs on the Codex team. We're talking 10 bullet points and that's it." Here's my new episode with Alexander Embiricos and Romain Huet where they gave me an inside look at how OpenAI's Codex team operates: → Live demo: Building in seconds with Spark →... How the team built the beautiful Codex app → How they ship without traditional specs and roadmaps Some quotes from Alex and Romain: "The fewer people you need in a room to do anything, the more pure every decision is." "Our designers write more code now than was written by an engineer six months ago." "I'm much less likely to read someone's resume than their ideas and what they've built." Thanks to our sponsors: Granola: The best AI meeting notes app I've ever used Linear: The AI agent platform for modern teams 📌 Watch now:show more

Peter Yang

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290,644 次观看 • 4 个月前 •via X (Twitter)

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OpenAI’s hottest app isn’t ChatGPT—it’s Codex. In the last few weeks alone, the Codex team shipped a desktop app, GPT-5.3 Codex (a new flagship model), and Spark, the fastest coding model I’ve ever used. Usage has grown fivefold since January and over a million people now use Codex weekly. Codex was also the app that OpenAI chose to run an ad for in the Super Bowl. I talked to Thibault (Tibo), head of Codex, and Andrew (Andrew Ambrosino), a member of technical staff who built the Codex app, for Every 📧’s AI & I about what OpenAI is building and how they’re using it internally. We get into: - Why they built a GUI instead of a terminal. Terminals work for quick tasks, they say, but feel limiting when you’re running multiple agents in parallel. The IDE, meanwhile, overwhelms users—and the Codex team wants the AI to dynamically decide which tools to show you for a given task. - How they’re teaching the model to read between the lines. Codex is great at following instructions, but optimize too hard in that direction, and it starts taking you literally—like copying a typo directly into the code. The team obsesses over this tradeoff, and is also introducing “personalities,” modes users can toggle between that control how blunt or supportive the model feels. - How OpenAI uses its own coding agent. Codex lets you schedule prompts to run on a recurring basis, and the team has dozens of automations running at all times. For example, one scans for merge conflicts every couple of hours so code is always ready to ship, and another picks a random file from the codebase multiple times a day and hunts for bugs no one would've gone looking for. - Why speed is a dimension of intelligence. OpenAI’s newest model (Spark) is so fast that they actually slow it down so you can read the output. They see the speed enabling three things: staying super in the flow, replacing brittle developer tools with intelligent ones that can adapt on the fly, and redirecting the model mid-task— especially with voice—so coding starts to feel more and more like a conversation. - Code review is the next bottleneck. Models can generate code faster than ever, but someone still has to verify that it works. The team is exploring a future where the model proves its own fix works—retracing the click path a user would take, screenshotting the results, and attaching the evidence to a pull request. This is a must-watch for anyone who uses AI coding agents—and is curious about the future of programming. Watch below! Timestamps: Introduction: 00:01:27 OpenAI’s evolving bet on its coding agent: 00:05:27 The choice to invest in a GUI (over a terminal): 00:09:42 The AI workflows that the Codex team relies on to ship: 00:20:38 Teaching Codex how to read between the lines: 00:26:45 Building affordances for a lightening fast model: 00:28:45 Why speed is a dimension of intelligence: 00:33:15 Code review is the next bottleneck for coding agents: 00:36:30 How the Codex team positions against the competition: 00:41:24

Dan Shipper 📧

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"I ran a startup for 5 years. I thought we moved fast. Then I joined OpenAI—and realized I had no idea what fast actually meant." Head of Product for OpenAI's Codex Alexander Embiricos and I chat about: 🔸 How OpenAI built the Sora Android app (which hit #1) in just 18 days by using Codex 🔸 Codex growing 20x since August 🔸 How they're thinking about the dev bottleneck shifting from coding to reviewing AI-generated work 🔸 Why coding will be a core competency for every AI agent—because writing code is how agents use computers best 🔸 Why the bottleneck to AGI-level productivity isn’t model capability—it’s human typing speed 🔸 The vision of Codex, and how they plan to win Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 WorkOS — Modern identity platform for B2B SaaS, free up to 1 million MAUs: 🏆 Fin — The #1 AI agent for customer service: 🏆 Atlassian Jira Product Discovery — Confidence to build the right thing:

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334,422 次观看 • 8 个月前

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I have been hyping Brian Scanlan and Intercom to every eng leader I've spoken to since getting a behind the scenes look at how they 2x'd their productivity in their R&D org. In this week's episode of How I AI, Brian pulls up dashboards and tools and shows us how it's done: - explicit targets + reporting on leading indicators of AI adoption - how they measured how quality increased with more AI usage - centralized skills repo for the entire company - telemetry on claude code + evals on internal success - building for an agent-first onboarding flow Sorry to be that bro, but truly: STOP what you are doing and watch this right now if you are looking to inflect your eng + product team with AI. As always, a huge thanks to our sponsors: 🧠Celigo - Intelligent automation built for AI: Cursor - The best (and my fave!) way to code with AI: On YT now 👉

I have been hyping Brian Scanlan and Intercom to every eng leader I've spoken to since getting a behind the scenes look at how they 2x'd their productivity in their R&D org. In this week's episode of How I AI, Brian pulls up dashboards and tools and shows us how it's done: - explicit targets + reporting on leading indicators of AI adoption - how they measured how quality increased with more AI usage - centralized skills repo for the entire company - telemetry on claude code + evals on internal success - building for an agent-first onboarding flow Sorry to be that bro, but truly: STOP what you are doing and watch this right now if you are looking to inflect your eng + product team with AI. As always, a huge thanks to our sponsors: 🧠Celigo - Intelligent automation built for AI: Cursor - The best (and my fave!) way to code with AI: On YT now 👉

claire vo 🖤

12,857 次观看 • 3 个月前

OpenAI member of product staff Alexander Embiricos describes the evolution of "Lord Bottleneck," an internal Codex loop developed by a single staff member that ultimately ended up creating a tight feedback and improvement loop for new user experiences: "This person on the growth team needed to figure out what experiments to run. And they needed to write code to run the experiment. Then they needed to analyze the experiment." "They started using Codex for each separate thing. So they had it run a bunch of analyses, interrogate the data, talk to Codex about the data. Then they would pick an experiment, and ask Codex to write the code. Then they would run the experiment, then ask Codex what the results of the experiment were. Then they would produce a deck." "All steps they were doing individually. They didn't start by saying, 'I'm going to automate this entire thing,' because that's hard and scary. They just started with using Codex to accelerate themselves." "Then, they started connecting all these things together into a giant skill. And one day, they just said [to Codex], 'Why don't you do this every morning?'" "They gave it a name: 'Lord Bottleneck.' Because it's solving the bottlenecks of friction for new users." "Now, every morning, Lord Bottleneck evaluates past experiments, looks at data, proposes some [new] experiments, and offers to the team to run the experiments. The team picks [what experiments to do]. Then Lord Bottleneck is like, 'Ok cool. Here's some code or whatever config that needs to be done,' runs the experiment, and they go and do the same loop the next day." "It's really serious value. I forget the numbers, but it's produced significant company value automatically through Codex."

OpenAI member of product staff Alexander Embiricos describes the evolution of "Lord Bottleneck," an internal Codex loop developed by a single staff member that ultimately ended up creating a tight feedback and improvement loop for new user experiences: "This person on the growth team needed to figure out what experiments to run. And they needed to write code to run the experiment. Then they needed to analyze the experiment." "They started using Codex for each separate thing. So they had it run a bunch of analyses, interrogate the data, talk to Codex about the data. Then they would pick an experiment, and ask Codex to write the code. Then they would run the experiment, then ask Codex what the results of the experiment were. Then they would produce a deck." "All steps they were doing individually. They didn't start by saying, 'I'm going to automate this entire thing,' because that's hard and scary. They just started with using Codex to accelerate themselves." "Then, they started connecting all these things together into a giant skill. And one day, they just said [to Codex], 'Why don't you do this every morning?'" "They gave it a name: 'Lord Bottleneck.' Because it's solving the bottlenecks of friction for new users." "Now, every morning, Lord Bottleneck evaluates past experiments, looks at data, proposes some [new] experiments, and offers to the team to run the experiments. The team picks [what experiments to do]. Then Lord Bottleneck is like, 'Ok cool. Here's some code or whatever config that needs to be done,' runs the experiment, and they go and do the same loop the next day." "It's really serious value. I forget the numbers, but it's produced significant company value automatically through Codex."

TBPN

80,403 次观看 • 3 个月前

Databricks CEO and co-founder Ali Ghodsi joined Bloomberg Technology to discuss the launch of Genie Code, an autonomous AI agent built specifically for data teams. "Just six months ago, we were talking about autocompleting code — now agents automate the code. The new question is: how do we take the code that’s been written into production and make sure we're monitoring it?" Watch the full conversation

Databricks CEO and co-founder Ali Ghodsi joined Bloomberg Technology to discuss the launch of Genie Code, an autonomous AI agent built specifically for data teams. "Just six months ago, we were talking about autocompleting code — now agents automate the code. The new question is: how do we take the code that’s been written into production and make sure we're monitoring it?" Watch the full conversation

Databricks

19,463 次观看 • 5 个月前

.Natalia rode so hard for Claude Code we devoted an episode to how she was using it to automate her job running Every 📧’s consulting practice. Fast forward to five months later, and she rides just as hard for Codex. I had her back on AI & I to talk about what caused her to make the switch, including how she ran a prompt in Codex before bed and woke up to a finished, custom CRM tool. We get into: - Why she finds Codex easier to use than Claude Code - How she’s using loops in Codex to create customized tools that work exactly how she needs them to - Why the consulting team still pays for SaaS products like Attio and Asana even though they could vibe code their own versions - How she built an app to manage her father’s medical care in Codex - How knowledge work is evolving from sculpting to gardening, in which you develop the context and logic you need for an agent to execute for you This is a must-watch for anyone trying to figure out whether to build their own tools or buy real software—and what it takes to get an AI agent to run unsupervised for hours and nail the output. Watch below! Timestamps 1. Introduction: 00:01:05 2. How Natalia manages Claudie, the consulting team’s AI project manager: 00:02:35 3. Why the consulting team still pays for SaaS products: 00:04:55 4. Codex as a game changer : 00:11:47 5. Building personalized learning guides and illustrated explainers with AI: 00:14:55 6. Inside Natalia's AI-powered email triage system: 00:21:40 7. The shift from knowledge work as sculpting to knowledge work as gardening: 00:26:44 8. Using Codex to on-shot a custom CRM: 00:28:57 9. Using Codex to build an app that coordinates her father’s medical care: 00:33:16

.Natalia rode so hard for Claude Code we devoted an episode to how she was using it to automate her job running Every 📧’s consulting practice. Fast forward to five months later, and she rides just as hard for Codex. I had her back on AI & I to talk about what caused her to make the switch, including how she ran a prompt in Codex before bed and woke up to a finished, custom CRM tool. We get into: - Why she finds Codex easier to use than Claude Code - How she’s using loops in Codex to create customized tools that work exactly how she needs them to - Why the consulting team still pays for SaaS products like Attio and Asana even though they could vibe code their own versions - How she built an app to manage her father’s medical care in Codex - How knowledge work is evolving from sculpting to gardening, in which you develop the context and logic you need for an agent to execute for you This is a must-watch for anyone trying to figure out whether to build their own tools or buy real software—and what it takes to get an AI agent to run unsupervised for hours and nail the output. Watch below! Timestamps 1. Introduction: 00:01:05 2. How Natalia manages Claudie, the consulting team’s AI project manager: 00:02:35 3. Why the consulting team still pays for SaaS products: 00:04:55 4. Codex as a game changer : 00:11:47 5. Building personalized learning guides and illustrated explainers with AI: 00:14:55 6. Inside Natalia's AI-powered email triage system: 00:21:40 7. The shift from knowledge work as sculpting to knowledge work as gardening: 00:26:44 8. Using Codex to on-shot a custom CRM: 00:28:57 9. Using Codex to build an app that coordinates her father’s medical care: 00:33:16

Dan Shipper 📧

25,701 次观看 • 1 个月前

"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 次观看 • 5 个月前

"Designers will realize—all this back and forth, screenshots, redlines, 50 pixels off—they could just send it to Cursor and it's fixed." As the head of design at Cursor, Ryo Lu is at the cutting edge of designers who ship code instead of mocks. In our new episode, he showed me: ✅ His retro OS (the best vibe app I've seen) ✅ Demo: How he designs & builds with Cursor ✅ How the Cursor team ships products Some quotes from Ryo: "We prototype in Cursor after initial exploration. It lets us interact with real states. It feels a lot more real than static mocks." "Start with one simple app, then keep building. I asked the agent 'what if we make it more retro?' and just kept going from there." "At Cursor, roles between designers, PMs, engineers are muddy. We use our unique strengths and let AI agents tie everything together." Watch now: Also available on: Spotify: Apple: Newsletter:

"Designers will realize—all this back and forth, screenshots, redlines, 50 pixels off—they could just send it to Cursor and it's fixed." As the head of design at Cursor, Ryo Lu is at the cutting edge of designers who ship code instead of mocks. In our new episode, he showed me: ✅ His retro OS (the best vibe app I've seen) ✅ Demo: How he designs & builds with Cursor ✅ How the Cursor team ships products Some quotes from Ryo: "We prototype in Cursor after initial exploration. It lets us interact with real states. It feels a lot more real than static mocks." "Start with one simple app, then keep building. I asked the agent 'what if we make it more retro?' and just kept going from there." "At Cursor, roles between designers, PMs, engineers are muddy. We use our unique strengths and let AI agents tie everything together." Watch now: Also available on: Spotify: Apple: Newsletter:

Peter Yang

108,220 次观看 • 9 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!

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 • 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

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 • 3 个月前

A designer who can ship code is one of the most valuable hires in tech today. Here's my new episode with Felix Lee (CEO of ADPList) where he showed me how to use Claude Code and Figma MCP to go from: ✅ Figma → Code: Design to website in 15 min ✅ Code → Figma: App back to editable design Felix also demoed a few apps he's designed and built in Claude Code, including a 3D globe website and a landing page analyzer. Some takeaways from Felix: 1. Stop pasting screenshots into Claude Code. Instead, use Figma MCP to pull in all your assets, icons, and images automatically. 2. You can use the same MCP to convert code back into editable Figma layers to explore more variations. 3. An underrated way to build: Use FigJam to plan a flowchart of your app first, then paste the link into Claude Code with the MCP. 📌 Watch now: Thanks to our sponsors: Replit ⠕: Plan, design, and build with AI agents Linear: The AI agent platform for modern teams

A designer who can ship code is one of the most valuable hires in tech today. Here's my new episode with Felix Lee (CEO of ADPList) where he showed me how to use Claude Code and Figma MCP to go from: ✅ Figma → Code: Design to website in 15 min ✅ Code → Figma: App back to editable design Felix also demoed a few apps he's designed and built in Claude Code, including a 3D globe website and a landing page analyzer. Some takeaways from Felix: 1. Stop pasting screenshots into Claude Code. Instead, use Figma MCP to pull in all your assets, icons, and images automatically. 2. You can use the same MCP to convert code back into editable Figma layers to explore more variations. 3. An underrated way to build: Use FigJam to plan a flowchart of your app first, then paste the link into Claude Code with the MCP. 📌 Watch now: Thanks to our sponsors: Replit ⠕: Plan, design, and build with AI agents Linear: The AI agent platform for modern teams

Peter Yang

77,115 次观看 • 4 个月前