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Cover Image for Codex: The Autonomous SWE
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Codex: The Autonomous SWE

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ChatGPT Codex just landed— a cloud-hosted Autonomous Software Engineer (A-SWE) from OpenAI.

In this session, we’ll discover Codex, powered by codex-1

That’s right, Codex.

But this one is different than the Codex CLI Coding Agent OpenAI recently released and we covered in April.

In fact, during this most recent Codex release they also announced additional Codex CLI support.

Last month, we launched Codex CLI, a lightweight open-source coding agent that runs in your terminal. It brings the power of models like o3 and o4-mini into your local workflow, making it easy to pair with them to complete tasks faster.

Today, we’re also releasing a smaller version of codex-1, a version of o4-mini designed specifically for use in Codex CLI. This new model supports faster workflows in the CLI and is optimized for low-latency code Q&A and editing, while retaining the same strengths in instruction following and style. It’s available now as the default model in Codex CLI and in the API as codex-mini-latest.

~ Introducing Codex, May 16, 2025

So the evolution of the name “Codex” continues at OpenAI, where we’ve gone from CLI command line agent to a fully autonomous software engineer.

Here’s what we know:

  • “Technical teams at OpenAI have started using Codex as part of their daily toolkit.”

  • “Codex can read and edit files, as well as run commands including test harnesses, linters, and type checkers.”

  • “Once Codex completes a task, it commits its changes in its environment.”

In this hands-on session, we discuss exactly how powerful this tool really is, and how we as aspiring AI Engineers should think about leveraging tools like this in our workflows.

Additionally, we’ll discuss use cases for A-SWEs like Codex, and gain some insight into why people are saying

“Codex isn’t just a better code model. It’s an agent with its own computer, a sandboxed terminal, and a playbook for writing, testing, and landing PR-ready code—all while you sleep.” — ChatGPT Codex: The Missing Manual

Traditional AI coding tools autocomplete your thoughts; Codex executes them. By giving the model a dedicated cloud environment, persistent filesystem, and sandboxed shell, OpenAI unlocks hours-long reasoning loops, automated test runs, and self-generated pull-requests that arrive ready to merge.

Consider an example. Your next sprint could look like this:

  1. Kick off up to 60 parallel Codex instances per hour to explore ideas or tackle flaky tests.

  2. Let Codex draft and refine an Agents.md hierarchy that captures architecture guidelines, code style, and repo-specific conventions.

  3. Watch it author concise PR descriptions, link to deterministic test logs, and cite every file it touched—no bespoke prompt engineering required.

  4. Approve or tweak; Codex rebases, retests, and ships.

Join us in live to learn how to set up workflows like this one.

🧭 What you’ll learn

  • The architecture behind Codex’s cloud “personal computer”

  • Best-practice playbook: linters, commit hooks, modular design & abundance mindset prompting

  • How Agents.md and smart repo naming super-charge agent discoverability

  • Scaling tips for concurrent agents and long-running jobs

  • Where Codex sits in the broader agentic tooling landscape (Devin, Claude Code, Cursor, etc.)

🤖 Who should attend

  • AI/ML engineers building agent-oriented dev workflows

  • Platform teams evaluating automated PR pipelines

  • Tech leads hunting for 10× engineer leverage without 10× headcount

  • Anyone curious about the future of autonomous software engineering

Speakers:

  • Dr. Greg” Loughnane is the Co-Founder & CEO of AI Makerspace, where he is an instructor for their AI Engineering Bootcamp. Since 2021, he has built and led industry-leading Machine Learning education programs.  Previously, he worked as an AI product manager, a university professor teaching AI, an AI consultant and startup advisor, and an ML researcher.  He loves trail running and is based in Dayton, Ohio.

  • Chris “The Wiz” Alexiuk is the Co-Founder & CTO at AI Makerspace, where he is an instructor for their AI Engineering Bootcamp. During the day, he is also a Developer Advocate at NVIDIA. Previously, he was a Founding Machine Learning Engineer, Data Scientist, and ML curriculum developer and instructor. He’s a YouTube content creator YouTube who’s motto is “Build, build, build!” He loves Dungeons & Dragons and is based in Toronto, Canada.

Follow AI Makerspace on LinkedIn and YouTube to stay updated about workshops, new courses, and corporate training opportunities.

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58 Went