A Research Preview of Codex

Some engineers on my team at Assembled and I have been a part of the alpha test of Codex, and I'll say it's been quite impressive.

We’ve long used local agents like Cursor and Claude Code, so we didn’t expect too much. But Codex shines in a few areas:

Parallel task execution: You can batch dozens of small edits (refactors, tests, boilerplate) and run them concurrently without context juggling. It's super nice to run a bunch of tasks at the same time (something that's really hard to do in Cursor, Cline, etc.)

It kind of feels like a junior engineer on steroids, you just need to point it at a file or function, specify the change, and it scaffolds out most of a PR. You still need to do a lot of work to get it production ready, but it's as if you have an infinite number of junior engineers at your disposal now all working on different things.

Model quality is good, but hard to say it's that much better than other models. In side-by-side tests with Cursor + Gemini 2.5-pro, naming, style and logic are relatively indistinguishable, so quality meets our bar but doesn’t yet exceed it.

> Parallel task execution: You can batch dozens of small edits (refactors, tests, boilerplate) and run them concurrently without context juggling. It's super nice to run a bunch of tasks at the same time (something that's really hard to do in Cursor, Cline, etc.)

> It kind of feels like a junior engineer on steroids, you just need to point it at a file or function, specify the change, and it scaffolds out most of a PR. You still need to do a lot of work to get it production ready, but it's as if you have an infinite number of junior engineers at your disposal now all working on different things.

What's the benefit of this? It sounds like it's just a gimmick for the "AI will replace programmers" headlines. In reality, LLMs complete their tasks within seconds, and the time consuming part is specifying the tasks and then reviewing and correcting them. What is the point of parallelizing the fastest part of the process?

In my experience, it still does take quite a bit of time (minutes) to run a task on these agentic LLMs (especially with the latest reasoning models), and in Cursor / Cline / other code editor versions of AI, it's enough time for you to get distracted, lose context, and start working on another task.

So the benefit is really that during this "down" time, you can do multiple useful things in parallel. Previously, our engineers were waiting on the Cursor agent to finish, but the parallelization means you're explicitly turning your brain off of one task and moving on to a different task.

In my experience in Cursor with Claude 3.5 and Gemini 2.5, if an agent has run for more than a minute it has usually lost the plot. Maybe model use in Codex is a new breed?