[tananaev]

I was very skeptical about Codex at the beginning, but now all my coding tasks start with Codex. It's not perfect at everything, but overall it's pretty amazing. Refactoring, building something new, building something I'm not familiar with. It is still not great at debugging things.

One surprising thing that codex helped with is procrastination. I'm sure many people had this feeling when you have some big task and you don't quite know where to start. Just send it to Codex. It might not get it right, but it's almost always good starting point that you can quickly iterate on.

[jackschultz]

Infinitely agree with all. I was skeptical, and then tried Opus 4.5 and was blown away. Codex with 5.0 and 5.1 wasn't great, but 5.2 is big improvement. I can't do code without it because there's no point. Time and quality with the right constraints, you're going to get better code.

And same thought with both procrastination because of not knowing where to start, but also getting stuck in the middle and not knowing where to go. Literally never happens anymore. Having discussions with it for doing the planning and different options for implementations, and you get to the end with a good design description and then, what's the point of writing the code yourself when with that design, it's going to write it quickly and matching the agreements.

[nextaccountic]

You can code without it. Maybe you don't want to, but if you're a programmer, you can

(here I am remembering a time I had no computer and would program data structures in OCaml with pen and paper, then would go to university the next day to try it. Often times it worked the first try)

[7thpower]

What are you talking about? 5.2 literally just came out.

[girvo]

> It is still not great at debugging things.

It's so fascinating to me that the thread above this one on this page says the opposite, and the funniest thing is I'm sure you're both right. What a wild world we live in, I'm not sure how one is supposed to objectively analyse the performance of these things

[AstroBen]

Give them real world problems you're encountering and see which can solve them the best, if at all

A full week of that should give you a pretty good idea

Maybe some models just suit particular styles of prompting that do or don't match what you're doing

[ssl-3]

It's great at some things, and it's awful at other things. And this varies tremendously based on context.

This is very similar to how humans behave. Most people are great a small number of things, and there's always a larger set of things that we may individually be pretty terrible at.

The bots the same way, except: Instead of billions of people who each have their own skillsets and personalities, we've got a small handful of distinct bots from different companies.

And of course: Lies.

When we ask Bob or Lisa help with a thing that they don't understand very well, they usually will try to set reasonable expectations . ("Sorry, ssl-3, I don't really understand ZFS very well. I can try to get the SLOG -- whatever that is -- to work better with this workload, but I can't promise anything.")

Bob or Lisa may figure it out. They'll gather up some background and work on it, bring in outside help if that's useful, and probably tread lightly. This will take time. But they probably won't deliberately lie [much] about what they expect from themselves.

But when the bot is asked to do a thing that it doesn't understand very well, it's chipper as fuck about it. ("Oh yeah! Why sure I can do that! I'm well-versed in -everything-! [Just hold my beer and watch this!]")

The bot will then set forth to do the thing. It might fuck it all up with wild abandon, but it doesn't care: It doesn't feel. It doesn't understand expectations. Or cost. Or art. Or unintended consequences.

Or, it might get it right. Sometimes, amazingly-right.

But it's impossible to tell going in whether it's going to be good, or bad: Unlike Bob or Lisa, the bot always heads into a problem as an overly-ambitious pack of lies.

(But the bot is very inexpensive to employ compared to Bob or Lisa, so we use the bot sometimes.)

[9dev]

I always wonder how people make qualitative statements like this. There are so many variables! Is it my prompt? The task? The specific model version? A good or bad branch out of the non-deterministic solution space?

Like, do you run a proper experiment where you hand the same task to multiple models several times and compare the results? Not snark by the way, I’m asking in earnest how you pick one model over another.

[embedding-shape]

> Like, do you run a proper experiment where you hand the same task to multiple models several times and compare the results?

This is what I do. I have a little TUI that fires off Claude Code, Codex, Gemini, Qwen Coder and AMP in separate containers for most task I do (although I've started to use AMP less and less), and either returns the last message of what they replied and/or a git diff of what exactly they did. Then I compare them side by side. If all of them got something wrong, I update the prompt, fire them off again. Always starting from zero, and always include the full context of what you're doing with the first message, they're all non-interactive sessions.

Sometimes I do 3x Codex instead of different agents, just to double-check that all of them would do the same thing. If they go off and do different things from each other, I know the initial prompt isn't specific/strict enough, and again iterate.

[energy123]

I have sent the same prompt to GPT-5.2 Thinking and Gemini 3.0 Pro many times because I subscribe to both.

GPT-5.2 Thinking (with extended thinking selected) is significantly better in my testing on software problems with 40k context.

I attribute this to thinking time, with GPT-5.2 Thinking I can coax 5 minutes+ of thinking time but with Gemini 3.0 Pro it only gives me about 30 seconds.

The main problem with the Plus sub in ChatGPT is you can't send more than 46k tokens in a single prompt, and attaching files doesn't help either because the VM blocks the model from accessing the attachments if there's ~46k tokens already in the context.

[enraged_camel]

Last night I gave one of the flaky tests in our test suite to three different models, using the exact same prompt.

Gemini 3 and Gemini 3 Flash identified the root cause and nailed the fix. GPT 5.1 Codex misdiagnosed the issue and attempted a weird fix despite my prompt saying “don’t write code, simply investigate.”

I run these tests regularly, and Codex has not impressed me. Not even once. At best it’s on par, but most of the time it just fails miserably.

Languages: JavaScript, Elixir, Python

[thek3nger]

This works for me in general. If I am procrastinating, I ask a coding agent for a small task. If it works, I have something to improve upon. If it doesn’t work, my OCD forces me to “fix it.” :D

[freedomben]

Same actually. Though, for some reasons Codex utterly falls down with podman, especially rootless podman. No matter how many explicit instructions I give it in the prompt and AGENTS.md, it will try to set a ton of variables and break podman. It will then try use docker (again despite explicit instructions not too) and eventually will try to sudo podman. One time I actually let it, and it reused its sudo perms to reconfigure selinux on my system, which completely broke it so that I could no longer get root on my own machine and the machine never booted again (because selinux was blocking everything). It has tried to do the same thing three times now on different projects.

So yeah, I use codex a lot and like it, but it has some really bad blind spots.

[jillesvangurp]

> One surprising thing that codex helped with is procrastination.

Heh. It's about the same as an efficient compilation or integration testing process that is long enough to let it do it's thing while you go and browse Hacker News.

IMHO, making feedback loops faster is going to be key to improving success rates with agentic coding tools. They work best if the feedback loop is fast and thorough. So compilers, good tests, etc. are important. But it's also important that that all runs quickly. It's almost an even split between reasoning and tool invocations for me. And it is rather trigger happy with the tool invocations. Wasting a lot of time to find out that a naive approach was indeed naive before fixing it in several iterations. Good instructions help (Agents.md).

Focusing attention on just making builds fast and solid is a good investment in any case. Doubly so if you plan on using agentic coding tools.

[wahnfrieden]

On the contrary, I will always use longer feedback cycle agents if the quality is better (including consulting 5.2 Pro as oracle or for spec work).

The key is to adapt to this by learning how to parallelize your work, instead of the old way of doings things where devs are expected to focus on and finish one task at a time (per lean manufacturing principles).

I find now that painfully slow builds are no longer a serious issue for me. Because I'm rotating through 15-20 agents across 4-6 projects so I always have something valuable to progress on. One of these projects and a few of these agents are clear priorities I return to sooner than the others.

[anabis]

> One surprising thing that codex helped with is procrastination.

The Roomba effect is real. The AI models do all the heavy implementation work, and when it asks me to setup an execute tests, I feel obliged to get to it ASAP.

[cmrdporcupine]

I think Opus + Claude Code is the more competent overall general "making things" system, while it makes sense to have a $20 Codex subscription to find bugs and review the things that Claude Code makes.

On its own, as sole author, I find Codex overcomplicates things. It will riddle your code with unnecessary helper functions and objects and pointless abstractions.

It is however useful for doing a once over for code review and finding the things that Claude rushed through.

[BinaryIgor]

I have similar experiences with Claude Code ;) Have you used it as well? How does it compare?