I don’t think that when people say “it’s a documentary” they mean that it’s literally a “documentary”, more like “this satire is so close to reality, that you can call it documentary”.
I recently spoke to a very junior developer (he's still in school) about his hobby projects.
He doesn't have our bagage. He doesn't feel the anxiety the purists feel.
He just pipes all errors right back in his task flow. He does period refactoring. He tests everything and also refactors the tests. He does automated penetration testing.
There are great tools for everything he does and they are improving at breakneck speeds.
He creates stuff that is levels above what I ever made and I spent years building it.
How is that measured? Is his stuff maintainable? Is it fast? Are good architectural decisions baked in that won't prevent him from adding a critical new feature?
I don't understand where this masochism comes from. I'm a software developer, I'm an intelligent and flexible person. The LLM jockey might be the same kind of person, but I have years of actual development experience and NOTHING preventing me from stepping down to that level and doing the same thing, starting tomorrow. I've built some nice and complicated stuff in my life, I'm perfectly capable of running a LLM in a loop. Most of the stuff that people like to call prompt/agentic/frontier or whatever engineering is ridiculously simple, and the only reason I'm not spending much time on it is that I don't think it leads to the kind of results my employer expects from me.
You can still survive without using generative tools. Just not writing crud apps .
There is plenty of code that require proof of correctnesss and solid guarantees like in aviation or space and so on. Torvalds in a recent interview mentioned how little code he gets is generated despite kernel code being available to train easily .
Your experience may be valuable, and in fact made me think, but I also think the brashness of framing everything in the "adapt or die" ultimatum is unnecessary and off-putting.
The way I see it, the kid has a dangerous dependency on at least one expensive service, cannot solve problems by himself and highly likely doesn't understand core concepts of programming and computers in general.
Yeah I dread the software landscape in 10 years, when people will have generated terabytes of unmaintainable slop code that I need to fix.
I always wonder how much smaller and faster models could be if they were only trained on the latest versions of the languages I use, so for me that is PHP, SQL, HTML, JS, CSS, Dutch, English, plus tool use for my OS of choice (MacOS).
Right now it feels like hammering a house onto a nail instead of the other way around.
Not very. LLMs derive a lot of their capability profile from the sheer scale.
LLMs have something that's not entirely unlike the "g factor" in humans - a broad "capability base" that spans domains. The best of the best "coding LLMs" need both good "in-domain training" for coding specifically and a high "capability base". And a lot of where that "base" comes from is: model size and the scale of data and compute used in pre-training.
Reducing the model scale and pruning the training data would result in a model with a lower "base". It would also hurt in-domain performance - because capabilities generalize and transfer, and pruning C code from the training data would "unteach" the model things that also apply to code in PHP.
Thus, the pursuit of "narrow specialist LLMs" is misguided, as a rule.
Unless you have a well defined set bar that, once cleared, makes the task solved, and there is no risk of scope adjustment, no benefit from any future capability improvements above that bar, and enough load to justify the engineering costs of training a purpose-specific model? A "strong generalist" LLM is typically a better bet than a "narrow specialist".
In practice, this is an incredibly rare set of conditions to be met.
It's more complicated than that. Small specialized LLMS are IMO better framed as "talking tools" than generalized intelligence. With that in mind, it's clear why something that can eg look at an image and describe things about it or accurately predict weather, then converse about it, is valuable.
There are hardware-based limitations in the size of LLMs you can feasibly train and serve, which imposes a limit in the amount of information you can pack into a single model's weights, and the amount of compute per second you can get out of that model at inference-time.
My company has been working on this specifically because even now most researchers don't seem to really understand that this is just as much an economics and knowledge problem (cf Hayek) as it is "intelligence"
It is much more efficient to strategically delegate specialized tasks, or ones that require a lot of tokens but not a lot of intelligence, to models that can be served more cheap. This is one of the things that Claude Code does very well. It's also the basis for MOE and some similar architectures with a smarter router model serving as a common base between the experts.
I seem to remember that's one of the first things they tried, but the general models tended to win out. Turns out there's more to learn from all code/discussions than from just JS.
From my own empirical research, the generalized models acting as specialists outperform both the tiny models acting as specialists and the generalist models acting as generalists. It seems that if peak performance is what you're after, then having a broad model act as several specialized models is the most impactful.
Wouldn't that mean they're bad at migration tasks? I feel like for most languages, going from [old] to [current] is a fairly to very common usage scenario.
Try asking Gemini information from workshop manuals that are not publicly available. It will pretty much tell you everything you want to know, but it will refuse to tell where it got the information.
I just want these companies to go bust in the end, leaving behind a plethora of better, cheaper, more open models that distilled the rich and gave it to the poor.
Comments like these remind me of the football spectators that shout "Even I could have scored that one" when they see a failed attempt.
Sure. You could have. But you're not the one playing football in the Champions League.
There were many roads that could have gotten you to the Champions League. But now you're in no position to judge the people who got there in the end and how they did it.
I don't think this is warranted given that the comment you're criticising is simply expressing an opinion explicitly solicited by the comment it's responding to.
It’s more like “Player A is better than Player B” coming from a professional player in a smaller league who is certainly qualified to have that opinion.
> Sure. You could have. But you're not the one playing football in the Champions League.
The only reason people are using Claude Code is because it's the only way to use their (heavily subsidized) subscription plans. People who are okay with using and paying for their APIs often opt out for other, better, tools.
Also, analogies don't work. As we know for a fact that Claude Code is a bloated mess that these "champions league-level engineers" can't fix. They literally talk about it themselves: https://news.ycombinator.com/item?id=47598488 (they had to bring in actual Champions League engineers from bun to fix some of their mess).
"Even I would have scored that goal"
==
"I would never ever have created a bloated mess like Anthropic"
You just repeat the same statement.
That bloated mess is what got them to the Champions League. They did what was necessary to get them here. And they succeeded so far.
But hey, according to some it can be replicated in 50k lines of wrapper code around a terminal command, so for Anthropic it's just one afternoon of vibe coding to get rid of this mess. So what's the problem? /s
Yes, exactly. I like this analogy. I am surprised the level of pearl clutching in these discussions on Hacker News. Everybody wants to be an attention sharecropper, lol.
Yup, strange to see people still don’t understand LLMs massively speed up coding greenfield pet projects. Anytime you see a bee web app it’s better to assume AI use rather than not anymore.
I'm not familiar enough with this animation library to answer that. Someone could be very used to this type of website and just copy paste things they've done before.
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