ChatGPT Codex: The Missing Manual

05/16/25 • 53 min

ChatGPT Codex is here - the first cloud hosted Autonomous Software Engineer (A-SWE) from OpenAI. We sat down for a quick pod with two core devs on the ChatGPT Codex team: Josh Ma and Alexander Embiricos to get the inside scoop on the origin story of Codex, from WHAM to its future roadmap.

Follow them: https://github.com/joshma and https://x.com/embirico

Chapters

00:00 Introduction to the Latent Space Podcast
- 00:59 The Launch of ChatGPT Codex
- 03:08 Personal Journeys into AI Development
- 05:50 The Evolution of Codex and AI Agents
- 08:55 Understanding the Form Factor of Codex
- 11:48 Building a Software Engineering Agent
- 14:53 Best Practices for Using AI Agents
- 17:55 The Importance of Code Structure for AI
- 21:10 Navigating Human and AI Collaboration
- 23:58 Future of AI in Software Development
- 28:18 Planning and Decision-Making in AI Development
- 31:37 User, Developer, and Model Dynamics
- 35:28 Building for the Future: Long-Term Vision
- 39:31 Best Practices for Using AI Tools
- 42:32 Understanding the Compute Platform
- 48:01 Iterative Deployment and Future Improvements

Follow them: https://github.com/joshma and https://x.com/embirico

Chapters

Previous Episode

Claude Code: Anthropic's CLI Agent

More info: https://docs.anthropic.com/en/docs/claude-code/overview

The AI coding wars have now split across four battlegrounds:

1. AI IDEs: with two leading startups in Windsurf ($3B acq. by OpenAI) and Cursor ($9B valuation) and a sea of competition behind them (like Cline, Github Copilot, etc).

2. Vibe coding platforms: Bolt.new, Lovable, v0, etc. all experiencing fast growth and getting to the tens of millions of revenue in months.

3. The teammate agents: Devin, Cosine, etc. Simply give them a task, and they will get back to you with a full PR (with mixed results)

4. The cli-based agents: after Aider’s initial success, we are now seeing many other alternatives including two from the main labs: OpenAI Codex and Claude Code. The main draw is that 1) they are composable 2) they are pay as you go based on tokens used.

Since we covered all three of the first categories, today’s guests are Boris and Cat, the lead engineer and PM for Claude Code. If you only take one thing away from this episode, it’s this piece from Boris: Claude Code is not a product as much as it’s a Unix utility.

This fits very well with Anthropic’s product principle: “do the simple thing first.” Whether it’s the memory implementation (a markdown file that gets auto-loaded) or the approach to prompt summarization (just ask Claude to summarize), they always pick the smallest building blocks that are useful, understandable, and extensible. Even major features like planning (“/think”) and memory (#tags in markdown) fit the same idea of having text I/O as the core interface. This is very similar to the original UNIX design philosophy:

Claude Code is also the most direct way to consume Sonnet for coding, rather than going through all the hidden prompting and optimization than the other products do. You will feel that right away, as the average spend per user is $6/day on Claude Code compared to $20/mo for Cursor, for example. Apparently, there are some engineers inside of Anthropic that have spent >$1,000 in one day!

If you’re building AI developer tools, there’s also a lot of alpha on how to design a cli tool, interactive vs non-interactive modes, and how to balance feature creation. Enjoy!

Timestamps

[00:00:00] Intro

[00:01:59] Origins of Claude Code

[00:04:32] Anthropic’s Product Philosophy

[00:07:38] What should go into Claude Code?

[00:09:26] Claude.md and Memory Simplification

[00:10:07] Claude Code vs Aider

[00:11:23] Parallel Workflows and Unix Utility Philosophy

[00:12:51] Cost considerations and pricing model

[00:14:51] Key Features Shipped Since Launch

[00:16:28] Claude Code writes 80% of Claude Code

[00:18:01] Custom Slash Commands and MCP Integration

[00:21:08] Terminal UX and Technical Stack

[00:27:11] Code Review and Semantic Linting

[00:28:33] Non-Interactive Mode and Automation

[00:36:09] Engineering Productivity Metrics

[00:37:47] Balancing Feature Creation and Maintenance

[00:41:59] Memory and the Future of Context

[00:50:10] Sandboxing, Branching, and Agent Planning

[01:01:43] Future roadmap

[01:11:00] Why Anthropic Excels at Developer Tools

Next Episode

⚡️Multi-Turn RL for Multi-Hour Agents — with Will Brown, Prime Intellect

In an otherwise heavy week packed with Microsoft Build, Google I/O, and OpenAI io, the worst kept secret in biglab land was the launch of Claude 4, particularly the triumphant return of Opus, which many had been clamoring for. We will leave the specific Claude 4 recap to AINews, however we think that both Gemini’s progress on Deep Think this week and Claude 4 represent the next frontier of progress on inference time compute/reasoning (at last until GPT5 ships this summer).

Will Brown’s talk at AIE NYC and open source work on verifiers have made him one of the most prominent voices able to publicly discuss (aka without the vaguepoasting LoRA they put on you when you join a biglab) the current state of the art in reasoning models and where current SOTA research directions lead. We discussed his latest paper on Reinforcing Multi-Turn Reasoning in LLM Agents via Turn-Level Credit Assignment and he has previewed his AIEWF talk on Agentic RL for those with the temerity to power thru bad meetup audio.