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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...

48,246 次观看 • 16 天前 •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!

Dan Shipper 📧

145,487 次观看 • 1 年前

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?

GREG ISENBERG

62,374 次观看 • 14 天前

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 📧

15,588 次观看 • 4 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."

TBPN

80,403 次观看 • 1 个月前