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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....

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Dan Shipper 📧

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15,588 просмотров • 6 месяцев назад •via X (Twitter)

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🚨 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 просмотров • 1 год назад

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 просмотров • 3 месяцев назад

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 просмотров • 3 месяцев назад

.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
41:15

.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 просмотров • 1 месяц назад

OpenAI just gave Codex a major upgrade. From a coding assistant into a workspace builder that can create interactive sites, apps, dashboards, planners, and review tools from plain work instructions. The most important new feature they released is "Sites". i.e. Codex can generate a hosted interactive workspace instead of only producing a document, spreadsheet, slide, or code file. OpenAI is also adding plugins for different jobs, so Codex knows how to help analysts, marketers, sales teams, product designers, investors, and bankers using the tools they already use. A data analyst might ask Codex to explain why sales dropped, then Codex could pull from data tools and create a dashboard. A sales team might ask Codex to prepare for a customer meeting, then Codex could collect account history, risks, follow-ups, and next steps into one shared page. The third feature is annotations, which means you can click a specific part of the result and ask Codex to fix only that part. Codex already reaches 5M weekly users, and OpenAI says 20% of them are now non-developers, with that group growing over 3x faster than developers.
0:43

OpenAI just gave Codex a major upgrade. From a coding assistant into a workspace builder that can create interactive sites, apps, dashboards, planners, and review tools from plain work instructions. The most important new feature they released is "Sites". i.e. Codex can generate a hosted interactive workspace instead of only producing a document, spreadsheet, slide, or code file. OpenAI is also adding plugins for different jobs, so Codex knows how to help analysts, marketers, sales teams, product designers, investors, and bankers using the tools they already use. A data analyst might ask Codex to explain why sales dropped, then Codex could pull from data tools and create a dashboard. A sales team might ask Codex to prepare for a customer meeting, then Codex could collect account history, risks, follow-ups, and next steps into one shared page. The third feature is annotations, which means you can click a specific part of the result and ask Codex to fix only that part. Codex already reaches 5M weekly users, and OpenAI says 20% of them are now non-developers, with that group growing over 3x faster than developers.

Rohan Paul

48,353 просмотров • 2 месяцев назад

Claude Code cracked something open for us Every 🧱. Now I ship to codebases I barely know, every feature we ship makes the next one easier, and non-technical members of the team use the terminal. I’m genuinely grateful. So I brought its creators, Cat Wu (cat) and Boris Cherny (Boris Cherny) from Anthropic, on AI & I to say thank you—and to talk about everything they’ve learned from building Claude Code. We get into: • The workflows Anthropic’s smartest engineers use to push Claude Code to its limits. Why they pit subagents against each other to get cleaner results, how they turn past code into leverage, and the slash commands and MCPs they rely on most. • The product lessons behind one of the most loved AI agents in the world. How the team balances simplicity and power—building a tool that anyone can use, but that experts can bend to their will—and their philosophy of “unshipping,” or cutting back whenever there’s a simpler, more intuitive path to user intent. • A peek into the future of coding with AI. The new form factors they’re experimenting with to make Claude Code more autonomous, more reliable, and more accessible to non-technical users This is a must-watch for anyone—both technical and non-technical—who wants to learn how to use Claude Code like the people who built it. Watch below! Timestamps: Introduction: 00:01:26 Claude Code’s origin story: 00:02:25 How Anthropic dogfoods Claude Code: 00:07:03 Boris and Cat’s favorite slash commands: 00:14:06 How Boris uses Claude Code to plan feature development: 00:15:49 Everything Anthropic has learned about using sub-agents well: 00:21:53 Use Claude Code to turn past code into leverage: 00:26:16 The product decisions for building an agent that’s simple and powerful: 00:33:14 Making Claude Code accessible to the non-technical user: 00:36:38 The next form factor for coding with AI: 00:45:12
1:10:11

Claude Code cracked something open for us Every 🧱. Now I ship to codebases I barely know, every feature we ship makes the next one easier, and non-technical members of the team use the terminal. I’m genuinely grateful. So I brought its creators, Cat Wu (cat) and Boris Cherny (Boris Cherny) from Anthropic, on AI & I to say thank you—and to talk about everything they’ve learned from building Claude Code. We get into: • The workflows Anthropic’s smartest engineers use to push Claude Code to its limits. Why they pit subagents against each other to get cleaner results, how they turn past code into leverage, and the slash commands and MCPs they rely on most. • The product lessons behind one of the most loved AI agents in the world. How the team balances simplicity and power—building a tool that anyone can use, but that experts can bend to their will—and their philosophy of “unshipping,” or cutting back whenever there’s a simpler, more intuitive path to user intent. • A peek into the future of coding with AI. The new form factors they’re experimenting with to make Claude Code more autonomous, more reliable, and more accessible to non-technical users This is a must-watch for anyone—both technical and non-technical—who wants to learn how to use Claude Code like the people who built it. Watch below! Timestamps: Introduction: 00:01:26 Claude Code’s origin story: 00:02:25 How Anthropic dogfoods Claude Code: 00:07:03 Boris and Cat’s favorite slash commands: 00:14:06 How Boris uses Claude Code to plan feature development: 00:15:49 Everything Anthropic has learned about using sub-agents well: 00:21:53 Use Claude Code to turn past code into leverage: 00:26:16 The product decisions for building an agent that’s simple and powerful: 00:33:14 Making Claude Code accessible to the non-technical user: 00:36:38 The next form factor for coding with AI: 00:45:12

Dan Shipper 📧

57,619 просмотров • 10 месяцев назад

Clawdbot creator Peter Steinberger 🦞 says Claude Opus is his favorite model, but OpenAI Codex is the best for coding: "OpenAI is very reliable. For coding, I prefer Codex because it can navigate large codebases. You can prompt and have 95% certainty that it actually works. With Claude Code you need more tricks to get the same." "But character wise, [Opus] behaves so good in a Discord it kind of feels like a human. I've only really experienced that with Opus."
1:47

Clawdbot creator Peter Steinberger 🦞 says Claude Opus is his favorite model, but OpenAI Codex is the best for coding: "OpenAI is very reliable. For coding, I prefer Codex because it can navigate large codebases. You can prompt and have 95% certainty that it actually works. With Claude Code you need more tricks to get the same." "But character wise, [Opus] behaves so good in a Discord it kind of feels like a human. I've only really experienced that with Opus."

TBPN

443,119 просмотров • 7 месяцев назад

"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:
1:38

"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:

Lenny Rachitsky

334,422 просмотров • 8 месяцев назад

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11
43:20

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11

Dan Shipper 📧

66,339 просмотров • 3 месяцев назад

Nat Eliason’s (Nat Eliason) career arc is borderline absurd—but it works. He’ll spot a new tool or trend, master it, build a business around it, and move on. Nat’s pulled it off with the note-taking wave ($600k in sales from a Roam Research course), real estate (6x return flipping property in Austin), and crypto (published his insider story with Random House). Now it’s AI: he’s running a viral course on building apps with AI—$200k in pre-sales in just a week, 800 students and counting. I’ve known Nat for a long time and I think he has a great sense for where the puck is headed. He was one of the first guests I had on the podcast and I was delighted to have him on again. Here are a few takeaways from our conversation: - Coding with AI has become orders of magnitude easier for non-technical people over the last 2 years—Nat rarely has to help students fix bugs; they troubleshoot in Cursor on their own. - AI coding assistants are creating new behaviours in programming, like using a speech-to-text model to talk to an agent and having it write code for you. - The traditional learning curve of coding is flattening because AI tools let beginners build and iterate in faster feedback loops. - AI has given Nat leverage in spades—it increases his ability to be a creator while also building a robust business with as few people to manage as possible. He demos an AI book editor he coded for his sci-fi novel. - In the age of AI, software is becoming content and the barriers to create are lower than ever—but custom software for everything isn’t the answer. Nat’s model is that personalized tools make sense for that one thing you care the most about. - Nat believes that the future of writing with AI is a Cursor-style interface with a model that’s trained on your style and voice. This episode is a must-watch for writers, creators, and anyone interested in the future of product building. Watch below! Timestamps: Introduction: 00:01:45 The origins of Nat’s viral course on building apps with AI: 00:11:45 How coding with AI has evolved over the last two years: 00:18:46 Nat creates an app using Composer, Cursor’s AI assistant: 00:22:22 Tactical tips for coding with Cursor: 00:26:06 How coding with AI is creating new behaviours in programming: 00:29:06 What excites Nat the most about the future of AI: 00:32:41 A demo of Hubbard, the AI editor Nat built for his science fiction writing: 00:38:58 When does it makes sense to build custom software: 00:44:52 Nat’s take on the future of writing with AI: 00:49:18
1:00:52

Nat Eliason’s (Nat Eliason) career arc is borderline absurd—but it works. He’ll spot a new tool or trend, master it, build a business around it, and move on. Nat’s pulled it off with the note-taking wave ($600k in sales from a Roam Research course), real estate (6x return flipping property in Austin), and crypto (published his insider story with Random House). Now it’s AI: he’s running a viral course on building apps with AI—$200k in pre-sales in just a week, 800 students and counting. I’ve known Nat for a long time and I think he has a great sense for where the puck is headed. He was one of the first guests I had on the podcast and I was delighted to have him on again. Here are a few takeaways from our conversation: - Coding with AI has become orders of magnitude easier for non-technical people over the last 2 years—Nat rarely has to help students fix bugs; they troubleshoot in Cursor on their own. - AI coding assistants are creating new behaviours in programming, like using a speech-to-text model to talk to an agent and having it write code for you. - The traditional learning curve of coding is flattening because AI tools let beginners build and iterate in faster feedback loops. - AI has given Nat leverage in spades—it increases his ability to be a creator while also building a robust business with as few people to manage as possible. He demos an AI book editor he coded for his sci-fi novel. - In the age of AI, software is becoming content and the barriers to create are lower than ever—but custom software for everything isn’t the answer. Nat’s model is that personalized tools make sense for that one thing you care the most about. - Nat believes that the future of writing with AI is a Cursor-style interface with a model that’s trained on your style and voice. This episode is a must-watch for writers, creators, and anyone interested in the future of product building. Watch below! Timestamps: Introduction: 00:01:45 The origins of Nat’s viral course on building apps with AI: 00:11:45 How coding with AI has evolved over the last two years: 00:18:46 Nat creates an app using Composer, Cursor’s AI assistant: 00:22:22 Tactical tips for coding with Cursor: 00:26:06 How coding with AI is creating new behaviours in programming: 00:29:06 What excites Nat the most about the future of AI: 00:32:41 A demo of Hubbard, the AI editor Nat built for his science fiction writing: 00:38:58 When does it makes sense to build custom software: 00:44:52 Nat’s take on the future of writing with AI: 00:49:18

Dan Shipper 📧

27,207 просмотров • 1 год назад

When I talk to serious software engineers, they tell me everyone is sleeping on Codex + GPT 5.2 from OpenAI Which is why I was hyped to get an insiders "how to" from Alexander Embiricos product lead for Codex, showing us how he and the team use Codex to ship almost everything at oai. He'll show us how to: - jump into a new repo and start contributing, even as a non-technical PM - run git worktrees to parallelize agent tasks across codex instances - use best practices and to spec out your work and let codex rip We also talk model vs. harness and how to spy on codex development (which is all OSS!) to understand how to optimize your own implementations. This episode is brought to you by our amazing sponsors 🧠🤑 Brex - The intelligent finance platform built for founders 🦎🐛 Greptile - Your AI code review platform Watch it here:
0:47

When I talk to serious software engineers, they tell me everyone is sleeping on Codex + GPT 5.2 from OpenAI Which is why I was hyped to get an insiders "how to" from Alexander Embiricos product lead for Codex, showing us how he and the team use Codex to ship almost everything at oai. He'll show us how to: - jump into a new repo and start contributing, even as a non-technical PM - run git worktrees to parallelize agent tasks across codex instances - use best practices and to spec out your work and let codex rip We also talk model vs. harness and how to spy on codex development (which is all OSS!) to understand how to optimize your own implementations. This episode is brought to you by our amazing sponsors 🧠🤑 Brex - The intelligent finance platform built for founders 🦎🐛 Greptile - Your AI code review platform Watch it here:

claire vo 🖤

48,102 просмотров • 7 месяцев назад

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 просмотров • 6 месяцев назад

Codex is the new "super app" of AI tools. Here's the simple breakdown: Vibe coding + knowledge work in one place: - Cursor: code only. Can't make a doc you just view. - Claude Code: code only. Docs live in Cowork (separate, restricted). - Codex: both, same interface. Codex on complex tasks: - Better at infrastructure - Better at hard, multi-step problems - We tested it across our team The proof: - One-shotted a Replit clone. - Sandboxes. Mobile vibe coding. - 1 hour 20 minutes on 5.4. "The fastest way to do the most amount of things." That's why Codex is winning.
2:34

Codex is the new "super app" of AI tools. Here's the simple breakdown: Vibe coding + knowledge work in one place: - Cursor: code only. Can't make a doc you just view. - Claude Code: code only. Docs live in Cowork (separate, restricted). - Codex: both, same interface. Codex on complex tasks: - Better at infrastructure - Better at hard, multi-step problems - We tested it across our team The proof: - One-shotted a Replit clone. - Sandboxes. Mobile vibe coding. - 1 hour 20 minutes on 5.4. "The fastest way to do the most amount of things." That's why Codex is winning.

The Startup Ideas Podcast (SIP) 🧃

22,803 просмотров • 4 месяцев назад

Introducing TikZBot. The birth of an agent that produces its own TikZ creations. Fully interactive and extensible. It can fix scenarios, expand its own work, learn from diagrams, and talk to you. For now, it’s a PoC. The manual version works like a charm. I’m now adding the self-play and chat components. Programmed in a few hours in Codex CLI using the newest 5.2-Codex model. So much fun.
0:30

Introducing TikZBot. The birth of an agent that produces its own TikZ creations. Fully interactive and extensible. It can fix scenarios, expand its own work, learn from diagrams, and talk to you. For now, it’s a PoC. The manual version works like a charm. I’m now adding the self-play and chat components. Programmed in a few hours in Codex CLI using the newest 5.2-Codex model. So much fun.

Bartosz Naskręcki

18,444 просмотров • 6 месяцев назад

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."
1:42

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 просмотров • 3 месяцев назад

Clawdbot creator, Peter Steinberger says Opus is the best model overall, but Codex is his go-to for coding. He trusts Codex to handle big codebases with almost no mistakes. It’s more reliable and needs less handholding, which makes him faster. Claude Code can work too, but it requires more effort and tricks. For serious tasks, Codex feels like a dependable coworker.
1:42

Clawdbot creator, Peter Steinberger says Opus is the best model overall, but Codex is his go-to for coding. He trusts Codex to handle big codebases with almost no mistakes. It’s more reliable and needs less handholding, which makes him faster. Claude Code can work too, but it requires more effort and tricks. For serious tasks, Codex feels like a dependable coworker.

Wes Roth

230,226 просмотров • 7 месяцев назад

After creating Remodex: Codex remote control for iOS I decided to chill a bit by building a browser sidebar inside Theo - t3.gg 's T3 Code You can open it with Cmd+Shift+B This software is so easy to "manipulate" and build upon Congrats to all the guys that put the work in it because it is great I certainly will be building more things with it For this one I decided to switch from the Codex App to the Codex CLI too just to see how it works And boy it was so good
0:20

After creating Remodex: Codex remote control for iOS I decided to chill a bit by building a browser sidebar inside Theo - t3.gg 's T3 Code You can open it with Cmd+Shift+B This software is so easy to "manipulate" and build upon Congrats to all the guys that put the work in it because it is great I certainly will be building more things with it For this one I decided to switch from the Codex App to the Codex CLI too just to see how it works And boy it was so good

Emanuele Di Pietro

38,905 просмотров • 5 месяцев назад

We built an AI app that had 1,000 DAU and $2k MRR before it launched. It’s called Monologue and it’s a smart dictation app built by a single developer: Naveen Naidu. We just launched Monologue yesterday, and it’s one of the fastest-growing and stickiest AI apps that Every 📧 has ever built. Naveen and Monologue are compelling because he’s competing against companies that have raised $50m or more. Because of AI he was able to build an extremely polished, delightful app by himself in just a few months. I brought Naveen on to AI & I along with Every 📧 COO Brandon Gell (Brandon Gell) to talk about his journey with Monologue. We get into: - Why shipping fast is the only thing that matters in AI: Monologue might look like an overnight success, but it wasn’t Naveen’s first, second—or even third—app. Over time, he built a muscle to get quality apps out the door, iterate on them, and learn from what he was seeing. - How he got to PMF inside of Every: The mistake Naveen regrets most in his entrepreneurial journey is building in the dark. Inside of Every 📧 he has an environment where feedback is plentiful—and it let him iterate extremely quickly. - His stack for building production grade AI apps: Naveen breaks down how he used tools like OpenAI’s Codex to do the work of a whole engineering team, including solving hard technical problems like Mac hotkey handling. This is a must-watch for anyone who wants to see how far a single developer and some AI tools can really go. Watch below! Timestamps: Introduction: 00:01:27 A live demo of Monologue: 00:03:51 Hard lessons from Naveen’s years in the wilderness: 00:06:27 Building a muscle to ship fast: 00:12:29 The spark that became Monologue: 00:21:11 Dogfooding your way to a killer feature: 00:26:09 Why the harshest product feedback is the most valuable: 00:29:45 Every’s strategy for launching an app in a crowded space: 00:31:47 Giving Monologue the Every “smell”: 00:40:08 Naveen’s one-person AI stack to build beautiful apps: 00:45:09
57:24

We built an AI app that had 1,000 DAU and $2k MRR before it launched. It’s called Monologue and it’s a smart dictation app built by a single developer: Naveen Naidu. We just launched Monologue yesterday, and it’s one of the fastest-growing and stickiest AI apps that Every 📧 has ever built. Naveen and Monologue are compelling because he’s competing against companies that have raised $50m or more. Because of AI he was able to build an extremely polished, delightful app by himself in just a few months. I brought Naveen on to AI & I along with Every 📧 COO Brandon Gell (Brandon Gell) to talk about his journey with Monologue. We get into: - Why shipping fast is the only thing that matters in AI: Monologue might look like an overnight success, but it wasn’t Naveen’s first, second—or even third—app. Over time, he built a muscle to get quality apps out the door, iterate on them, and learn from what he was seeing. - How he got to PMF inside of Every: The mistake Naveen regrets most in his entrepreneurial journey is building in the dark. Inside of Every 📧 he has an environment where feedback is plentiful—and it let him iterate extremely quickly. - His stack for building production grade AI apps: Naveen breaks down how he used tools like OpenAI’s Codex to do the work of a whole engineering team, including solving hard technical problems like Mac hotkey handling. This is a must-watch for anyone who wants to see how far a single developer and some AI tools can really go. Watch below! Timestamps: Introduction: 00:01:27 A live demo of Monologue: 00:03:51 Hard lessons from Naveen’s years in the wilderness: 00:06:27 Building a muscle to ship fast: 00:12:29 The spark that became Monologue: 00:21:11 Dogfooding your way to a killer feature: 00:26:09 Why the harshest product feedback is the most valuable: 00:29:45 Every’s strategy for launching an app in a crowded space: 00:31:47 Giving Monologue the Every “smell”: 00:40:08 Naveen’s one-person AI stack to build beautiful apps: 00:45:09

Dan Shipper 📧

23,644 просмотров • 11 месяцев назад

Sam Altman reveals building Codex was a "crazy kamikaze mission" to beat Claude Code and that most of the best coders he knows now use Codex. "We were way behind Claude Code, and it seemed like a kind of crazy kamikaze mission to try to beat them with a coding app." "The consensus is that this kind of thing never works, and you just move on to the next one. It's a fool's errand to try to win when someone else already has momentum in a particular product category." "But we decided we thought it was really important. We asked a team to do it, and they performed a legitimate, unbelievable, very rare in the history of business thing." "Now it is the product that most of the best coders I know use." "That felt like an impossible thing, but if we hadn't asked the team, hey, we have a really important but extremely hard mission for you, it just wouldn't have happened."
0:58

Sam Altman reveals building Codex was a "crazy kamikaze mission" to beat Claude Code and that most of the best coders he knows now use Codex. "We were way behind Claude Code, and it seemed like a kind of crazy kamikaze mission to try to beat them with a coding app." "The consensus is that this kind of thing never works, and you just move on to the next one. It's a fool's errand to try to win when someone else already has momentum in a particular product category." "But we decided we thought it was really important. We asked a team to do it, and they performed a legitimate, unbelievable, very rare in the history of business thing." "Now it is the product that most of the best coders I know use." "That felt like an impossible thing, but if we hadn't asked the team, hey, we have a really important but extremely hard mission for you, it just wouldn't have happened."

Fireside Alpha

1,261,432 просмотров • 1 месяц назад

Even when things are going great, running a $1.5 billion AI startup is a knife fight. Granola was one of the first AI apps of this generation to achieve near-ubiquitous adoption. But meeting notes are not the company’s be-all and end-all. The real battle is over owning the interface that everyone uses to get their work done in an AI-native world. I had Chris Pedregal (Chris Pedregal), cofounder and CEO of Granola, back on Every 📧’s AI & I to talk about the current state of the application layer, AI’s frontier, and the future of work. We get into: - Why meeting notes clones don’t matter. Three big companies cloned Granola’s core feature. To him, meeting notes were never the real prize. “Easy come, easy go” is his view of anyone’s lead, including his own. - How he thinks about building proactive features in AI. Granola pre-generates millions of pre-meeting briefs, which include context on the nature of the meeting and people participating, that most people never open. But when they do, they have a magical experience. - Why Granola is betting on “bring your own agent.” Chris says the API and MCP will get “a lot better” over the next few months, and we talk about their agent-native strategy and why they’ve pushed the product that way. This is a must-watch for anyone building at the application layer. Watch the episode! Timestamps Introduction: 00:00:59 Why running a company is a knife fight even when it’s working: 00:01:57 Granola’s counterintuitive view on competition: 00:04:33 Dan’s “pirate and architect” model for early-stage product teams: 00:10:44 Granola’s “shaping” and “validation” phases for building features: 00:13:09 Why Dan lives almost entirely inside Codex: 00:18:17 The case for “Codex-native apps”: 00:24:40 Granola’s “handrail” philosophy: 00:35:37 Why Granola is going all in on winning meeting-adjacent context: 00:38:12 What a transcript alone can never capture: 00:44:19
59:38

Even when things are going great, running a $1.5 billion AI startup is a knife fight. Granola was one of the first AI apps of this generation to achieve near-ubiquitous adoption. But meeting notes are not the company’s be-all and end-all. The real battle is over owning the interface that everyone uses to get their work done in an AI-native world. I had Chris Pedregal (Chris Pedregal), cofounder and CEO of Granola, back on Every 📧’s AI & I to talk about the current state of the application layer, AI’s frontier, and the future of work. We get into: - Why meeting notes clones don’t matter. Three big companies cloned Granola’s core feature. To him, meeting notes were never the real prize. “Easy come, easy go” is his view of anyone’s lead, including his own. - How he thinks about building proactive features in AI. Granola pre-generates millions of pre-meeting briefs, which include context on the nature of the meeting and people participating, that most people never open. But when they do, they have a magical experience. - Why Granola is betting on “bring your own agent.” Chris says the API and MCP will get “a lot better” over the next few months, and we talk about their agent-native strategy and why they’ve pushed the product that way. This is a must-watch for anyone building at the application layer. Watch the episode! Timestamps Introduction: 00:00:59 Why running a company is a knife fight even when it’s working: 00:01:57 Granola’s counterintuitive view on competition: 00:04:33 Dan’s “pirate and architect” model for early-stage product teams: 00:10:44 Granola’s “shaping” and “validation” phases for building features: 00:13:09 Why Dan lives almost entirely inside Codex: 00:18:17 The case for “Codex-native apps”: 00:24:40 Granola’s “handrail” philosophy: 00:35:37 Why Granola is going all in on winning meeting-adjacent context: 00:38:12 What a transcript alone can never capture: 00:44:19

Dan Shipper 📧

13,146 просмотров • 1 месяц назад