Comment by lechatonnoir
6 days ago
I somehow can't reply to your child comment.
It depends on whether the cost of search or of verification dominates. When searching for common consumer products, yeah, this isn't likely to help much, and in a sense the scales are tipped against the AI for this application.
But if search is hard and verification is easy, even a faulty faster search is great.
I've run into a lot of instances with Linux where some minor, low level thing has broken and all of the stackexchange suggestions you can find in two hours don't work and you don't have seven hours to learn about the Linux kernel and its various services and their various conventions in order to get your screen resolutions correct, so you just give up.
Being in a debug loop in the most naive way with Claude, where it just tells you what to try and you report the feedback and direct it when it tunnel visions on irrelevant things, has solved many such instances of this hopelessness for me in the last few years.
So instead of spending seven hours to get at least an understanding how the Linux kernel work and the interaction of various user-land programs, you've decided to spend years fumbling in the dark and trying stuff every time an issue arises?
I would like to understand how you ideally imagine a person solving issues of this type. I'm for understanding things instead of hacking at them in general, and this tendency increases the more central the things to understand are to the things you like to do. However, it's a point of common agreement that just in the domain of computer-related tech, there is far more to learn than a person can possibly know in a lifetime, and so we all have to make choices about which ones we want to dive into.
I do not expect to go through the process I just described for more than a few hours a year, so I don't think the net loss to my time is huge. I think that the most relevant counterfactual scenario is that I don't learn anything about how these things work at all, and I cope with my problem being unfixed. I don't think this is unusual behavior, to the degree that it's I think a common point of humor among Linux users: https://xkcd.com/963/ https://xkcd.com/456/
This is not to mention issues that are structurally similar (in the sense that search is expensive but verification is cheap, and the issue is generally esoteric so there are reduced returns to learning) but don't necessarily have anything to do with the Linux kernel: https://github.com/electron/electron/issues/42611
I wonder if you're arguing against a strawman that thinks that it's not necessary to learn anything about the basic design/concepts of operating systems at all. I think knowledge of it is fractally deep and you could run into esoterica you don't care about at any level, and as others in the thread have noted, at the very least when you are in the weeds with a problem the LLM can often (not always) be better documentation than the documentation. (Also, I actually think that some engineers do on a practical level need to know extremely little about these things and more power to them, the abstraction is working for them.)
Holding what you learn constant, it's nice to have control about in what order things force you to learn them. Yak-shaving is a phenomenon common enough that we have a term for it, and I don't know that it's virtuous to know how to shave a yak in-depth (or to the extent that it is, some days you are just trying to do something else).
More often than not, the actual implementation is more complex than the theory that outlines it (think Turing Machine and today's computer). Mostly because the implementation is often the intersection of several theories spanning multiple domain. Going at a problem at a whole is trying to solve multiple equations with a lot of variables and it's an impossible task for most. Learning about all the domains is also a daunting tasks (and probably fruitless as you've explained it).
But knowing the involved domain and some basic knowledge is easy to do and more than enough to quickly know where to do a deep dive. Instead of relying on LLMs that are just giving plausible mashup on what was on their training data (which is not always truthful).