OpenAI Codex hands-on review
zackproser.com
zackproser.com
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.
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.
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.
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)
- 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.
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.
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?
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...
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.
Coding/engineering/etc is all problem solving in a strucutred manner.
That skill is not going anywhere
I wouldn't have to listen to people talk about it all the time if nobody thought it was true
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.
The verb you use when you only need to produce boilerplate.
> Prompt™
The verb you use when it's time to innovate.
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...
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.
'Where' is becoming an increasingly small niche with ever higher educational requirements.
I don't see the corporate path being the best way given the circumstances.
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.
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.
Other tasks take maybe the same amount of time.
But just autocomplete saves micro-effort all day long.
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.
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.
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.
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.
Nudging the UI slightly for this exact flow could generate good training data.
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.
This feels so hopelessly optimistic to me, because "effectively away from our desks" for most people will mean "in the unemployment line"
Are you pretending that automation doesn’t take away human jobs?
We should welcome automation and efficiency, but also address the situation of the "losers" of the development and not just expect the invisible hand will sort everything out.
automation was good for farming, but the consolidation into corporate megafarms probably not so much
I would argue that automating labor isn't bad, but it's being used to take labor away without a solution
If I try to approximate the same today, the median US family can be fed for something around 1/10th of their labor.
That seems like a fantastic improvement, unless you really, really like farming.
I'd like you to be right, but I live in society where joy at work is often considered antithetical to productivity. No matter how much more productive I get, that space is used to fill in more productivity. We'll need more than tooling to stop this.
With Codex and Claude Code, these model agents are cars.
Some of horses will become drivers of cars and some of us will no longer be needed to pull wagons and will be out of a job.
Is that the proper framing?
An amusing image, but your analogy lost me here.
I think CEOs or PMs or Founders are like horse jockeys. Devs are like horses. (Some of them are both the jockey and the horse).
AI is a car. CEO or PM or Founder might smoothly swap out the horse for a car and continue on with little change.
For the horse to become a driver of a car is a more difficult challenge, but not impossible. It needs to evolve.
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.
Now you could argue that any non technical person could just oversee the agents instead. Possibly. Though in my experience, humans like to have other humans they trust oversee and understand important stuff for them.
If you’ve seen the work hours and work ethic of farmers, it’s safe to say that most of those people got other jobs that take far less work than farmers did/do.
Closer to our field, I think we’d have far worse work lives (fewer of us employed and much lower pay) if we had to code everything in assembler still. The creation of more powerful abstractions and languages allowed more of us to become software devs and make a living this way than if all we had were the less productive tools of the early days of computing.
If we can find a way to support those displaced even that would be a much better start. e.g. re-education funds, training schools/government run apprenticeships on projects, etc. Especially if the scale of the displacement is large. We all only have the one life IMV - its about giving those lives the opportunities to pivot. Ageism, and gatekeeping will stop many from changing careers.
We've used our software development skills to automate other people out of work for what can be argued to be literally decades. Each time we did it, we certainly expected that the people affected would find other work. New jobs were created. The world didn't end. I honestly don't think it would be that much worse this time.
Uh.. I'm having trouble considering this as a serious question. It's objectively going to lead to them being in a worse situation. Mostly irrelevant resume and needing to re-skill into something and start from the bottom.. out of a well paid career that many enjoy and find fulfilling
My question wasn't an ethical one. It's why are the people that are the target of this automation happy about the progress, to the point of trying to push it forward faster, cheering it on
But the automation is not the problem, it's the economic structure in which increased efficiency makes a lot of people worse off.
And that's the shitty part of the job, and everyone should be uncomfortable with it. I haven't literally automated anyone out of a job (that I know), but I definitely did not like finding out (after the fact) that one project was meant to enable a large offshoring effort.
> Each time we did it, we certainly expected that the people affected would find other work.
I do not expect that. That's a comforting lie people tell themselves.
> New jobs were created. The world didn't end. I honestly don't think it would be that much worse this time.
It didn't end, but it often got significantly worse for some. If the AI hype pans out, it's going to get significantly worse for software engineers. Your "newly created job," if it exists, will likely pay out a lot less that you're used to. At best, you'll get knocked down to the bottom of the career ladder.
It's a mistake to think about things in aggregate like you're doing. It's easy to hide inconvenient truths.
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.
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?
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.
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.
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.
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.
employing humans is something that couldn't be affordably addressed by software, and is what they're trying to now address with software
this is good for owners, and bad for workers
thusly AI is bad for workers as a class, and you're betting that you're one of the workers they decide to keep
all I have left to say on the matter is, good luck
Software engineers are dumb. Really dumb.
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.
- it's a GREAT oneshot coding model (in the pod we find out that they specifically finetuned for oneshotting OAI SWE tasks, eg prioritized over being multiturn)
- however comparatively let down by poorer integrations (eg no built in browser, not great github integration - as TFA notes "The current workflow wants to open a fresh pull request for every iteration, which means pushing follow-up commits to an existing branch is awkward at best." - yeah this sucks ass)
fortunately the integrations will only improve over time. i think the finding that you can do 60 concurrent Codex instances per hour is qualitatively different than Devin (5 concurrent) and Cursor (1 before the new "background agents").
btw
> I haven't yet noticed a marked difference in the performance of the Codex model, which OpenAI explains is a descendant of GPT-3 and is proficient in more than 12 programming languages.
incorrect, its an o3 finetune.
1. Cannot git fetch and sync with upstream, fixing any integration bugs; 2. Cannot pull in new library as dependency and do integration evaluations.
Besides that, cannot apt install in the setup script is annoying (they blocked the domain to prevent apt install I believe).
The agent itself is a bit meh, often opt-to git grep rather than reading all the source code to get contextual understanding (from what the UI has shown).
This is Open AI's fault (and literally every AI company is guilty of the same horrid naming schemes). Codex was an old model based on GPT-3, but then they reused the same name for both their Codex CLI and this Codex tool...
I mean, just look at the updates to their own blog post, I can see why people are confused.
https://openai.com/index/openai-codex/
Edit:
Google just did it too. "Gemini Ultra" is both a model (https://deepmind.google/models/gemini/ultra/) and their new top-tier subscription plan (a la Open AI's Pro plan). Why is this so difficult?
I feel it will get there in short order..but for the time being I feel that we'll be doing some combination of scattershot smaller & maintenance tasks across Codex while continuing to build and do serious refactoring in an IDE...
There are so many content changes or small CSS fixes (anyway you would verify that it was fixed by looking at it visually) where I really don't want to be bothered being involved in the writing of it, but I'm happy to do a code review.
Letting a non-dev see the ticket, start off a coding thing, test if it was fixed, and then just say "yea this looks good" and then I look at the code, seems like good workflow for most of the minor bugs/enhancements in our backlog.
This almost seems like this is a funnel to force people to become software engineers.
Like:
- When making CSS changes, make sure that the code is responsive. Add WCAG 2.0 attributes to any HTML markup.
- When making changes, run <some accessibility linter command> to verify that the changes are valid.
etc.
The non-dev doesn't need to know/care.
It'll probably get there eventually, but today these are not things solvable with context.
Yes, they do. Context can't be all-compassing of everything that must be done.
For instance I ask it to make a change and as part of the output it makes a bunch of value on the class nullable to get rid of compiler warnings.
This technically "works" in the sense that it made the change I asked for and the code compiles but it's clearly incorrect in the sense that we've lost data integrity. And there's a bunch of other examples like that I could give.
If you just let it run loose on a codebase without close supervision you'll devolve into a mess of technical debt pretty quickly.
"Because we’re doing a fair amount of dynamic/Reflect.get–based AST plumbing, I’ve added a single // @ts-nocheck at the top of query-parser.ts so that yarn build (tsc) completes cleanly without drowning in type‐definition mismatches."
Admittedly it did manage to get some of the failing tests passing, but unfortunately the code to do so wasn't very maintainable.
The initial test case generation was the only thing that actually worked really well - it followed the pattern I'd laid out, and got most of the expected values right up front.
The application of patches is hit and miss. If there changes are across multiple files, I find it gets stuck going in circles.
But still been a definite net positive in terms of productivity.
Slurping up trade secrets
but maybe I'll sound like the people that are afraid of using github and other cloud git protocols
interesting crossroads