I built a moderately complex and very good looking website in ~2 hours with the coding agent. Next step would be to write a backend+storage, and given how well the agent performs in these type of tasks, I assume I will be able to do that in the manner of hours too. I have never ever touched any of the technology involving the web development so, in my case, I can say that I no more need a full-stack dev that in normal circumstances I would definitely do. And the cost is ridiculous - few hours invested + $20 subscription.
I agree however on the point that no prior software engineering skills would make this much more difficult.
Yeah, I don't doubt you, it's really effective at knocking out "simple" projects, I've had success vibe-coding for days, but eventually unless you have some reins on the architecture/design, it falls down over it's own slop, and it's very noticeable as the agent spends more and more time trying to work in the changes, but it's unable to.
So the first day or two, each change takes 20-30 minutes. Next day it takes 30-40 minutes per change, next day up to an hour and so on, as the requirements start to interact with each other, together with the ball of spaghetti they've composed and are now trying to change without breaking other parts.
Contrast that with when you really own the code and design, then you can keep going for weeks, all changes take 20-30 minutes, as at day one. But also means I'm paying attention to what's going on, so no vibe-coding, but pair programming with LLMs, and also requires you to understand both the domain, what you're actually aiming for and the basics of design/architecture.
The point was not in simplicity but rather in if AI is replacing some people's jobs. I say that it certainly is, as given by the example, but I also acknowledge that the technology is still not at the point where human engineers are no more required in the loop.
I built other things too which would not be considered trivial or "simple", or as you say they're architecturally complex, and they involve very domain specific knowledge about programming languages, compilers, ASTs, databases, high-performance optimizations, etc. And for a long time, or shall I say never, have I felt this productive tbh. If I were to setup a company around this, which I believe I could, in pre-LLM era I'd quite literally have to hire 3-5 experienced engineers with sufficient domain expertise to build this together with me - and I mean not in every possible potential but the concrete work I've done in ~2 weeks.
> The point was not in simplicity but rather in if AI is replacing some people's jobs. I say that it certainly is, as given by the example, but I also acknowledge that the technology is still not at the point where human engineers are no more required in the loop.
I feel like you have missed emsh's point which is that AI agents significantly become muddled up if your project's complex.
I feel the same way personally. If I don't know how the AI code interacts with each other, I feel a frustration as long as the project continues precisely because of the fact that they mention about first taking less time and then taking longer and longer time having errors which it missed etc.
I personally vibe code projects too but I will admit that there is this error.
I have this feeling that anything really complex will fall heels first if complexity really grows a lot or you don't unclog the slop.
This is also why we are seeing "AI slop janitors" humans whose task is to unsloppify the slop.
Personally I have this intution that AI will create really good small products, there is no denying in that, but those were already un-monetizable or if they were, then even in the past, they were really easy to replicate, this probably just lowered the friction
Now if your project is osmething commercial and large, I don't know how much AI slop can people trust. At some point if people depend on your project which is having these issues because people can understand if the project's AI generated or not, then that would have it issues too.
And I am speaking this from experience after building something like whmcs in golang in AI. At first, I am surprised and I feel as if its good enough for my own personal use case (gvisor) and maybe some really small providers. But when I want it to say hook to proxmox, have the tmate server be connected with an api to allow re-opening easier, have the idea of live migration from one box to another etc., create drivers for the custom firecrackers-ssh idea that I implemented once again using AI.
One can realize how quickly complexity adds in projects and how as emsh's points out that it becomes exponentially harder to use AI.
They are optimizing their organization for throughput by cutting off the fat so they do expect to grow, it's just that they want to be ready for it, and having a loath of managers doesn't help with it. I figure this move must have something to do with the China labs potentially coming out with their own litography system.
WhatsApp was not developed by Meta. They just bought it. That said, I don't think Meta/FB is a net-negative, far from it. They contributed back to the community with high quality infra-level software.
I was thinking about the same paragraph because write-amplification is exactly the problem solved by LSM trees _and_ they already have a solution for that in-house - one of the first acquisitions that OpenAI made is Rockset - a company that actually built the RocksDb at scale.
So, this is the part that actually made me left wondering why.
The comment was not wrong though so I am not sure I understand if flagging it for the sole "it was most likely written by the use of AI" reason is completely valid.
> It's not something you can do if you have a full-time job
> I find it unreasonable to ask a candidate to spend that much time
And same for some reason does not apply to leetcode style interviews?
> It would take something like one week full time to work on this
I am not sure if this is satire or what? You need months of continuous preparation to be ready for the leetcode style interview.
> Optimizing some generated code is certainly fun, but it's as useless as leetcode for your average programmer.
No, it is not. This is specifically the type of job you would be doing tomorrow at Anthropic team if hired. And they are specifically hiring people who are already good enough at that very task. The same cannot be said for the leetcode, not even remotely comparable.
It goes to show how future of SE will look like. Loose reviews (a big win for the industry) with each person capable of doing 10x more work, and basically orchestrating/overlooking the work done by the agents. The bottleneck eventually at some point, if not already, will become a fatigue caused by having to process/acknowledge/understand the sheer volume of code spitted out at speeds that we could never have imagined before.
If you are doing loose reviews I guess you're not strictly more productive, because this would be a different kind of work...a more "loose" work, but a scalar I'm volume.
Loose work? No, quality of work being done does not correlate with reviews in almost any way, unless you're a lousy engineer doing lost work anyways. High quality reviews are exceptionally rare.
This is fantastic, maybe the future/present is in developing more wearables and personalized health AI assistants. Makes me wanna extract my smartwatch data too
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