OpenAI Codex hands-on review
zackproser.com169 points by fragmede 4 days ago
169 points by fragmede 4 days ago
I was a Plus subscriber and upgraded to Pro just to test Codex, and at least in my experience, it’s been pretty underwhelming.
First, I don’t think they got the UX quite right yet. Having to wait for an undefined amount of time before getting a result is definitely not the best, although the async nature of Codex seems to alleviate this issue (that is, being able to run multiple tasks at once).
Another thing that bugs me is having to define an environment for the tool to be useful. This is very problematic because AFAIK, you can’t spin up containers that might be needed in tests, severely limiting its usefulness. I guess this will eventually change, but the fact that it’s also completely isolated from the internet seems limiting, as one of the reasons o3 is so powerful in ChatGPT is because it can autonomously research using the web to find updated information on whatever you need.
For comparison, I also use Claude a lot, and I’ve found it to work really well to find obscure bugs in a somewhat complex React application by creating a project and adding the GitHub repo as a source. What this allows me is to have a very short wait time, and the difference with Codex is just night and day. Gemini also allows you to do this now, and it works very well because of its massive context window.
All that being said, I do understand where OpenAI is going with this. I guess they want to achieve something like a real coworker (they even say that in their promotional videos for Codex) because you are supposed to give tasks to Codex and wait until it’s done, like a real human, but again, IMHO, it’s too “pull-request-focused”
I guess I’ll be downgrading to Plus again and wait a little to see where this ends up.
I agree on the UX. A few basic things seem totally broken.
The flow of connecting a github account works, then disconnects, sometimes doesn't work, sometimes just errors. I can't install things that I could yesterday and my environment is just... broken? I have two versions of a repo and it works in only one.
Speed is a big thing. Not the llm stuff so much, but the setup and everything around it for each step.
Not having search cripples some cases where O3 seems incredible.
but there's a lot of places this feels like it can land tasks that often wouldn't get done. A near infinite army of juniors who can take on the lots of tiny tasks in 15-20 minutes is great. Fix some typos, add a few util functions (a task I have right now running), I even just asked it to add new endpoint to a server and it added it, migrations needed, tests and more and seems alright.
The ideal workflow in a way here is that the people asking for these things get to tag the ticket to codex/whatever, they run off and do the thing, PR lands and discussion and changes happen there, demo envs are setup and then someone can check and approve it.
edit -
To be fair, I also used firebase studio and that was worse. Blank screens, errors in the console, when I refreshed and moved around and got an actual page, it ended up failing to setup firebase. UI for editing and code totally failed after that and the explanations for how to fix it I was linked to I couldn't do.
It's a shame nobody has invented some sort of computerised intelligence that understands code and could fix some of those bugs. Ah well
> AFAIK, you can’t spin up containers that might be needed in tests, severely limiting its usefulness.
This is what's blocking me right now. I couldn't find any documentation on whether they allow Docker-in-Docker which typically means that the answer is "no". Since I'm building an AWS-native app I use LocalStack for end-to-end tests which requires a container engine. Codex not having it is a showstopper.
This might not help you but to the very best of my knowledge localstack can operate over the network just fine and I am pretty sure it has a reset endpoint for zeroing its state (I think it's this https://github.com/localstack/localstack/blob/v4.4.0/localst... )
The other alternative is that I've seen folks mention systemd-nsspawn as a form of isolation if that's what your using docker for (but I've never tried it myself)
I work at OpenAI (not on Codex) and have used it successfully for multiple projects so far. Here's my flow:
- Always run more than one rollout of the same prompt -- they will turn out different
- Look through the parallel implementations, see which is best (even if it's not good enough), then figure out what changes to your prompt would have helped nudge towards the better solution.
- In addition, add new modifications to the prompt to resolve the parts that the model didn't do correctly.
- Repeat loop until the code is good enough.
If you do this and also split your work into smaller parallelizable chunks, you can find yourself spending a few hours only looping between prompt tuning and code review with massive projects implemented in a short period of time.
I've used this for "API munging" but also pretty deep Triton kernel code and it's been massive.
> "Look through the parallel implementations, see which is best (even if it's not good enough), then figure out what changes to your prompt would have helped nudge towards the better solution."
How can non-technical people tell what's "best"? You need to know what you're doing at this point, look for the right pitfalls, inspect everything in detail... this right here is the entire counter-argument for LLMs eliminating SWE jobs...
> How can non-technical people tell what's "best"? You need to know what you're doing at this point, look for the right pitfalls, inspect everything in detail... this right here is the entire counter-argument for LLMs eliminating SWE jobs...
I'm not sure a tool that positions itself as a "programmer co-worker" is aiming to be useful to non-technical people. I've said it before, but I don't think LLMs currently are at the stage where they enable you to do things you have 0 experience in, but rather can help you speed up working through things you are familiar with. I think people who claim LLMs will completely replace jobs are hyping the technology without really understanding it.
For example, I'm a programmer, but never done any firmware flashing with UART before via a USB flasher. Today I managed to do that in 1-2 hours thanks to ChatGPT helping me out understanding how to do it. If I'd do it completely on my own, I'm sure it would have taken me at least the full day to do so, instead of the time it took. I was able to see when it got mislead, and could rewrite/redirect from there on, but someone with 0 programming experience, probably wouldn't have been able to.
It depends on their setup and where they or the LLM gets stuck. If an experienced programmer is there to back them up, then a total beginner could totally make something. That is, given some familiarity with the terminal, specifically the know-how to setup a git repo on GitHub and clone it locally, and then setting up env keys and Aider, and the know-how to run npm I and npm run dev, a non programmer with some terminal skills someone is able to make simple games, purely by talking to Aider using the /voice command. When the LLM or they get stuck is when they'll need some backup from somebody with a decent amount of programming experience to get unstuck. Depending on what their doing though, it's entirely possible they won't get stuck until much further along in the dev process.
I don’t think anyone expects software engineers will disappear and get replaced by janitors trained to proompt. I’m sure experts will stick around until the singularity curve starts looking funny. It’s probably gonna suck to enter the industry from now on, though.
Well, right, how does one become a senior engineer in a world where no one needs to hire a junior? I'm sure many other industries have experiences this already, where the only people who know anything retire and the people are left maintaining a system they could not rebuild such that when something goes wrong the only practicable choice is to replace it with new equipment.
That's where I see AI-written software going, write-once. Some talented engineer gets an AI system to create a whole k8s cluster to run an application and if any changes need to be made, bugs fixed, it will take another talented engineer to come in and have an AI write a replacement and throw out the old one.
Reminds me of this blog, The real value isn’t in the code [0], we're heading for a world that is only code and no one who knows what it does. But maybe it won't matter.
[0] https://jonayre.uk/blog/2022/10/30/the-real-value-isnt-in-th...
> Well, right, how does one become a senior engineer in a world where no one needs to hire a junior?
You don't. Unless the person is super brilliant I just don't think the industry needs many more new people, there are enough for the next 1-2 decades and after that humans will probably not be needed at all.
People should go where the demand is - medicine, education, policing or whatever it may be.
> People should go where the demand is - medicine, education, policing or whatever it may be.
'Where' is becoming an increasingly small niche with ever higher educational requirements.
One could put a lot of time into open source or run your own side hustle to build up experience to a senior engineer level.
I don't see the corporate path being the best way given the circumstances.
> I don’t think anyone expects software engineers will disappear
holy gaslighting Christ have some links, lots of people think that
https://www.reddit.com/r/ITCareerQuestions/comments/126v3pm/...
https://medium.com/technology-hits/the-death-of-coding-why-c...
https://medium.com/@TheRobertKiyosaki/are-programmers-obsole...
https://www.forbes.com/sites/hessiejones/2024/09/21/the-auto...
and on and on, endless thinkpieces about this. Certainly SOMEONE, someone with a lot of money, thinks software engineers are imminently replaceable.
> until the singularity curve starts looking funny.
well there's absolutely no evidence whatsoever that we've made any progress to bringing about Kurzweil's God so I think regardless of what Sam Altman wants you to believe about "general AI" or those thinkpieces, experts are probably okay.
I think you are correct that people say this, but its absurd that they are saying it in the first place.
Coding/engineering/etc is all problem solving in a strucutred manner.
That skill is not going anywhere
oh I agree but the last three years has felt like an endless chorus of people telling me SWE was going to be obsolete very soon so I had to push back against the idea that "nobody" thinks that.
I wouldn't have to listen to people talk about it all the time if nobody thought it was true
(not GP) To be fair, just because someone says something doesn't mean they believe it. Most of those folks have to know they're being absurd. But I agree saying "nobody" thinks something is over the top. People on the internet can be quite looney tunes.
A lot of people believe that programming is the typing of odd sequences of characters into a computer.
To them, it seems LLMs are also perfectly capable of typing odd sequences of characters.
The idea that SWEs do actual structured problem solving is mostly native to industry insiders.
Thank you for this. A very well stated explanation of a major reason the hype is soo off base from the people doing the work every day.
> proompt
The verb you use when you only need to produce boilerplate.
> Prompt™
The verb you use when it's time to innovate.
How much faster is this than simply writing the code yourself?
I end up asking the same question when experimenting with tools like Cursor. When it can one-shot a small feature, it works like magic. When it struggles, and the context gets poisoned and I have to roll back commits and retry part of the way through something, it hits a point where it was probably easier for me to just write it. Or maybe template it and have it finish it. Or vice versa. I guess the point being that best practices have yet to truly be established, but totally hands-off uses have not worked well for me so far.
Why commit halfway through implementing something with Cursor? Can you not wait until it’s created a feature or task that has been validated and tests written for it?
Why wait until everything is finalized before committing? Git is distributed/local, so while one philosophy is to interact with it as little as possible, the other one is to commit early and commit often, and easily be able to rollback to a previous (working) state, with the caveat that you clean-up history before firing off a PR.
Why not create a branch and rollback only what needs to be rolled back? Branches are O(1) with git, right?
OP was insinuating that rolling back commits is a pain point.
Well, same statement applies. Rolling back commits is also O(1) and just as easy. And if you branch to start with it's not even a "rollback" through the commit history, it's just a branch switch. Feel like OP has never used git before or something.
Easily 5-10x or even more in certain special cases (when it'd take me a lot of upfront effort to get context on some problem domain). And it can do all the "P2"s that I'd realistically never get to. There was a day where I landed 7 small-to-medium-size pull requests before lunch.
There are also cases where it fails to do what I wanted, and then I just stop trying after a few iterations. But I've learned what to expect it to do well in and I am mostly calibrated now.
The biggest difference is that I can have agents working on 3-4 parallel tasks at any given point.
This has been my experience too. Certain tickets that would’ve taken me hours (and in one case, days), I’ve been able to finish in minutes.
Other tasks take maybe the same amount of time.
But just autocomplete saves micro-effort all day long.
For me, it’s not that the actual coding is faster. It’s that you can do other things at the same time.
If I’m writing an integration, I can be researching the docs while the agent is coding something up. Worst case, I throw all of the agents work away while now having done research. Best case, it gets a good enough implementation that I can run with.
Totally. I feel like it’s akin to jamming with someone. We both go down our own paths for a bit, then I have a next step for it, and I can review what it last came up with and iterate while it does more of its own thing. Rinse, repeat. This is more fun and less energy consuming than “do it all yourself”, which certainly means a lot.
This way works for me. Any time I tried to treat it as a colleague that I can just assign tasks to, it’s failed miserably.
> Worst case, I throw all of the agents work away while now having done research
The worst case is you take the agent's work without really understanding it, continue doing it indefinitely and at some point get a buggy repo you have no idea how to handle - at the exact same moment some critical issue pops up and your agent has no clue how to help you anymore.
I don't think GP said they couldn't do their job, but you instantly jumped to incompetence. That seems little uncharitable to me.
At the current capabilities of most LLMs + my personal tolerance for slop, the most productive workflow seems to be: spin up multiple agents in the background to work on small scope, straightforward tasks while I work on something bigger that requires more exploration, requirements gathering, or just plain more complex/broad changes to the code. Review the output of the agents or unstick them when there is downtime.
IMO just keeping an IDE window open and babysitting an agent while it works is less productive than just writing the code mostly yourself with AI assistance in the form of autocomplete and maybe highly targeted oneshots using manual context provided "Edit" mode or inline prompting.
My company is dragging their feet on AI governance and let the OpenAI key I was using expire, and what I noticed was that my output of small QoL PRs and bugfixes dropped drastically because my attention remains focused on higher impact work.
Do you find yourself ditching on the things when they change something important with the new prompt? I don't get how people aren't absolutely exhausted by actually implementing this prompt messing advice when I thought there were studies saying small seemingly insignificant changes greatly change the result, hide blind spots, and even having a prompt for engineering a better prompt has knock on increases in instability. Do people just have a higher tolerance for doing work that is not related to the problem than I do? Perhaps I only work on stuff there is no prior example for, but every few days I read someone's anecdote on here and get discouraged in all new ways.
Not to downplay the issue you raise but I haven't noticed this.
Every iteration I make on the prompts only make the request more specified and narrow and it's always gotten me closer to my desired goal for the PR. (But I do just ditch the worse attempts at each iteration cycle)
Is it possible that reasoning models combined with the actual interaction with the real codebase makes this "prompt fragility" issue you speak of less common?
No, I've played with all the reasoning models and they just make the noise and weirdness even worse. When I dig into every little issue, it's always something incredibly bespoke. Like the actual documentation that's on the internet is out of date for the library that was installed and the API changed, the way the one library works in one language is not how it works in the other language, just all manner of surprising things. I really learned a lot about the limits of digital representation of information.
Can it be used to fix bugs? Because the ChatGPT web app is full of them and I don't think they are getting fixed. Pasting big amounts of text freezing the tab is one of them.
Bugs? Those are grubby human work.
Seriously, everyone should get good at fixing bugs. LLMs are terrible at it when it’s slightly non-obvious and since everyone is focusing on vibe coding, I doubt they’ll get any better.
The Android app is even worse.
If that is what the best unlimited AI can deliver we are safe for at least 10 years more.
Sounds like you're manually doing something that could form the basis of further reinforcement learning.
Nudging the UI slightly for this exact flow could generate good training data.
how much would this cost you if you didn't work at OpenAI?
I think the Pro plan is $200/mo for everyone? (But honestly I don't know the GPU cost and I'm interested in this question)
“As I wrote about in Walking and talking with AI in the woods, ideally I'd like to start my morning in an office, launch a bunch of tasks, get some planning out of the way, and then step out for a long walk in nature.”
Wouldn’t we all want that, but it sounds like you can leave task launching and planning to an AI and go find another career.
If you're building a React app using a popular UI framework, AI will seem like magic at how well it one-shots things.
To the author's point about one-shotting. I think it will be a real challenge pushing an AI coding workflow forward because of this problem. In my experience, AI seems to fall off a cliff when you ask it to write code using more obscure libraries and frameworks. It will always hallucinate something rather than admitting it has no knowledge of how something works.
I've had better success with things like o3 with search, because it can actually go and read docs to help fix problems. It helped me dig through matrix specs, proposals and PRs and while the first suggestion didn't work (I thought it would have done) it ended up finding proof that only part of that got merged and found how to enable the experimental side that allowed the other. The iteration of searching and going through things was incredibly helpful. Probably saved me a good few hours or meant I was able to do this at all.
> Codex will support me and others in performing our work effectively away from our desks.
This feels so hopelessly optimistic to me, because "effectively away from our desks" for most people will mean "in the unemployment line"
Think we've got a long time yet for that. We're going to be writing code a lot faster but getting these things to 90-95% on such a wide variety of tasks is going to be a monumental effort, the first 60-70% on anything is always much easier than the last 5-10%.
Also there's a matter of taste, as commented above, the best way to use these is going to be running multiple runs at once (that's going to be super expensive right now so we'll need inference improvements on today's SOTA models to make this something we can reasonably do on every task). Then somebody needs to pick which run made the best code, and even then you're going to want code review probably from a human if it's written by machine.
Trusting the machine and just vibe coding stuff is fine for small projects or maybe even smaller features, but for a codebase that's going to be around for a while I expect we're going to want a lot of human involvement in the architecture. AI can help us explore different paths faster, but humans need to be driving it still for quite some time - whether that's by encoding their taste into other models or by manually reviewing stuff, either way it's going to take maintenance work.
In the near-term, I expect engineering teams to start looking for how to leverage background agents more. New engineering flows need to be built around these and I am bearish on the current status quo of just outsource everything to the beefiest models and hope they can one-shot it. Reviewing a bunch of AI code is also terrible and we have to find a better way of doing that.
I expect since we're going to be stuck on figuring out background agents for a while that teams will start to get in the weeds and view these agents as critical infra that needs to be designed and maintained in-house. For most companies, foundation labs will just be an API call, not hosting the agents themselves. There's a lot that can be done with agents that hasn't been explored much at all yet, we're still super early here and that's going to be where a lot of new engineering infra work comes from in the next 3-5 years.
It's mind blowing to me how many developers are happy about the developments here.. as if they're going to eventually be paid to just sit there while agents do everything. Ah, work is now so easy!
I think in the success case (still TBD), that it will increase productivity to the point where things that can’t be affordably addressed by software will now be able to be addressed with software.
I expect that anyone who is a skilled dev today will be fine. Expectations and competition might be higher, but so will production and value creation.
I think the demand will come, just as Excel didn’t put finance people out of jobs in aggregate.
when in history have workers ever been the primary benefactors of productivity gains
Why would "primary benefactor" be the most relevant question rather than mere "benefactor"? If my life is improved by something, I don't care that someone else's life is improved by more; I don't want to reject that improvement out of spite, jealousy, or envy.
Bankers (and customers) benefited from ATMs as far more bank locations became economically sustainable and bank tellers could do higher value work (and do so more safely).
Millions of software developers continue to benefit from improvements in productivity, the resulting value creation, and the resulting high pay in our sector from ever more productive languages and frameworks. Can you imagine how little pay you'd make trying to sling websites in assembly language at less than 1% of the pace of today?
> Millions of software developers continue to benefit from improvements in productivity
You're absurdly naive if you think developers will see the most benefit. We will have fewer developers just as we have fewer farmers and factory workers. When labor is automated it becomes owned by fewer people, this is historically consistent for over a hundred years across every sector. Thousands of towns have collapsed under this sort of change and effects are felt for generations.
> Can you imagine how little pay you'd make trying to sling websites in assembly language at less than 1% of the pace of today
Productivity gains do not align with income gains, this is a complete strawman. Developers today may be 100x more productive, but they do not have a 100x higher income.
Ask yourself, where did that value go — and is that fair? We're creating the automation and someone else is taking the lions share of the benefit. We're being conned.
You seem hyper-focused on the share of benefit going to others and I am much more focused on the share of benefit going to my family. My family benefits enormously from the value created through technology development and I have benefited enormously from being able to work in a field where I am generously rewarded for doing things that I happily do free in my spare time. If I work on someone else's technology puzzles instead of my own, they are able and willing to pay me a well-above median salary in exchange.
I genuinely hope that they think they're getting rich as part of that exchange (and work to ensure that outcome happens), because that's the very best way that I know how to make the overall situation, including the benefits for me and my family, continue.
If you think I'm being conned in this exchange, thanks for the concern, but I'll tell you that I'm working hard to ensure that it keeps happening.
> they are able and willing to pay me a well-above median salary in exchange.
AI is what they're doing to try to stop this, when we work on AI we're enabling it.
They are making much more money for themselves than they are for you. Your salary is overhead. They will stop paying you if they can and they are trying to use AI to do it.
In a fair agreement you would have more time to spend with your family because you would earn a higher share of the profit and need to work less for it.
If I wanted to keep all of the value I created for myself, I'd start my own business and own all of it.
I don't, because I highly value the structure and capital that others have put up to create the company I work for. They offload an enormous amount of risk and overhead and, so long as they pay me what we've agreed, I'm happy for them to keep the portion of value that is above what they pay out to me and my colleagues.
The agreement is fair to my eyes, because I've agreed to it and both sides have kept up their ends. If yours is not fair to your eyes, perhaps you should change it, possibly up to striking out on your own and keeping 100% of the surplus value you create.