Beyond engineering itself, strictly computer engineering? How many coders have no idea what goes on behind an IDE. Have not even the slightest notion how a computer works. Who thinks building a computer means watching a Youtube video and buying ready made parts, putting them together, and then think they should be employed by NASA.

To begin: Math, Linux, Devops, C, and Assembly. Not a youtube video. Not arithmetic. Learn to the point that you could be employed by any of the above as a senior. And don't fear failure. Keep doing it until you understand it.

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pgwhalen 11 hours ago | parent | next [-]

I agree with your original post that the need for hard skills will persist, but I see it in the other direction: software engineers are going to have to get better at thinking in larger abstractions, not deeper understanding of the stack. Those who can only solve problems locally and repeat the patterns they've seen before rather than create new patterns from building blocks are the ones who are going to struggle.

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CrulesAll 11 hours ago | parent [-]

"software engineers are going to have to get better at thinking in larger abstractions" ........Math was first on my list. I don't know how else to say that.

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ben_w 11 hours ago | parent | next [-]

Computer science is indistinguishable from sufficiently advanced maths.

The AI can already do that part.

The abstraction that matters going forward, is understanding why the abstraction chosen by the AI does or doesn't match the one needed by the customer's "big picture".

The AI is a bit too self-congratulatory in that regard, even if it can sometimes spot its own mistakes.

	▲	ndriscoll 10 hours ago \| parent [-]
		A lot of studying math is just learning jargon and applications for what are actually pretty straightforward concepts, which lets you better communicate with the computer. You get higher bandwidth communication and better ability to know all of the nuances in things it might propose. You can propose things and understand when it replies with nuances you missed. Like intro differential geometry is basically a deep dive into what one actually does when reading a paper map. Something everyone (over 30?) is familiar with. But it turns out there's plenty to fill a graduate level tome on that topic. Linear algebra is basically studying easy problems: y=ax. Plenty to write about how to make your problem (or at least parts of it) fit that mould. I suspect and think I've seen others say that you get better outputs from LLMs when using jargon. Essentialy, its pattern matching tells it to say what an expert would say when using the terminology experts use.

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codingdave 11 hours ago | parent | prev | next [-]

> I don't know how else to say that.

Yep, exactly. The failure to realize that you mean different things when talking about "larger abstractions" is exactly the kind of miscommunication that software people will need to navigate better in the future.

	▲	CrulesAll 11 hours ago \| parent [-]
		If you need to have that explained to you, you are the problem.

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pgwhalen 11 hours ago | parent | prev [-]

Ah, I think “Math” as a single word on its means many different things to many different people, I didn’t interpret in quite the same way. But I see what you mean.

I’m not sure that my colleagues who I think of as “good at math” and “good at thinking in larger abstractions” are necessarily the same ones, but there’s definitely a lot of overlap.

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cindyllm 10 hours ago | parent | prev [-]

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