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.

> 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.

One issue with junior devs is that because they’re not fully autonomous, you have to spend a non trivial amount of time guiding them and reviewing their code. Even if I had easy access to a lot of them, pretty quickly that overhead would become the bottleneck.

Did you think that managing a lot of these virtual devs could get overwhelming or are they pretty autonomous?

They wrote "You still need to do a lot of work to get it production ready". So I would say it's not much better than real colleagues. Especially since junior devs will improve to a point they don't need your hand holding (remember you also were a junior at some point), which is not proven will happen with AI tools.

Counter-point A: AI coding assistance tools are rapidly advancing at a clip that is inarguably faster than humans.

Counter-point B: AI does not get tired, does not need space, does not need catering to their experience. AI is fine being interrupted and redirected. AI is fine spending two days on something that gets overwritten and thrown away (no morale loss).

Counter-counter-point A: If I work with a human Junior and they make an error or I familiarize them with any quirk of our workflow, and I correct them, they will recall that correction moving forward. An AI assistant either will not remember 5 minutes later (in a different prompt on a related project) and repeat the mistake, or I'll have to take the extra time to code some reminder into the system prompt for every project moving forward.

Advancements in general AI knowledge over time will not correlate to improvements in remembering any matters as colloquial as this.

Counter-counter-point B: AI absolutely needs catering to their experience. Prompter must always learn how to phrase things so that the AI will understand them, adjust things when they get stuck in loops by removing confusing elements from the prompt, etc.