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I’ve noticed something similar in my own workflow.
Codex becomes much more useful once the project grows beyond simple prototyping.
For small tasks, many AI tools feel comparable. But in larger repositories, I started valuing:
context recovery multi-file consistency debugging support architecture awareness
more than just raw code generation speed.
I still use different tools for different situations, but Codex has been especially helpful for cleanup and recovery when things become messy.
Curious what workflows other people here are using with Codex.
I’ve been using Codex heavily in real-world workflows lately, and one thing that helped me a lot was treating it less like “magic AI” and more like a collaborative engineering assistant.
Right now my workflow is usually:
Windsurf → fast prototyping Codex → multi-file cleanup, debugging, refactoring ChatGPT → architecture discussions and brainstorming
Using this approach, I’ve already built 5 AI-assisted projects, including:
automation tools content workflow systems web apps AI integrations and even a trading bot project
One thing I learned: Codex performs much better when:
the scope is clear repository structure is organized instructions are specific and tasks are broken into smaller chunks
In small projects, it feels incredibly fast. In larger systems, context management becomes the real challenge.
Still, the productivity boost is very real if you learn how to work with it properly.