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by rsav11 hours ago |
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upvoteby aleph_minus_one6 hours ago|
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> >I will, in fact, claim that the difference between a bad programmer and a good one is whether he considers his code or his data structures more important. Bad programmers worry about the code. Good programmers worry about data structures and their relationships.

> -- Linus Torvalds

What about programmers

- for whom the code is a data structure?

- who formulate their data structures in a way (e.g. in a very powerful type system) such that all the data structures are code?

- who invent a completely novel way of thinking about computer programs such that in this paradigm both code and data structures are just trivial special cases of some mind-blowing concept ζ of which there exist other special cases that are useful to write powerful programs, but these special cases are completely alien from anything that could be called "code" or "data (structures)", i.e. these programmers don't think/worry about code or data structures, but about ζ?

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upvoteby sph8 hours ago|
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From what I understand from the vibe coders, they tell a machine what the code should do, but not how it should do it. They leave the important decisions (the shape of data) to an LLM and thus run afoul of this, which is gonna bite them in the ass eventually.
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upvoteby mikepurvis10 hours ago|
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I think this is sometimes a barrier to getting started for me. I know that I need to explore the data structure design in the context of the code that will interact with it and some of that code will be thrown out as the data structure becomes more clear, but still it can be hard to get off the ground when me gut instinct is that the data design isn't right.

This kind of exploration can be a really positive use case for AI I think, like show me a sketch of this design vs that design and let's compare them together.

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upvoteby sph8 hours ago|
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AI is terrible for this.

My recommendation is to truly learn a functional language and apply it to a real world product. Then you’ll learn how to think about data, in its pure state, and how it is transformed to get from point A to point B. These lessons will make for much cleaner design that will be applicable to imperative languages as well.

Or learn C where you do not have the luxury of using high-level crutches.

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upvoteby ignoramous9 hours ago|
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> This kind of exploration can be a really positive use case for AI I think

Not sure if SoTA codegen models are capable of navigating design space and coming up with optimal solutions. Like for cybersecurity, may be specialized models (like DeepMind's Sec-Gemini), if there are any, might?

I reckon, a programmer who already has learnt about / explored the design space, will be able to prompt more pointedly and evaluate the output qualitatively.

> sometimes a barrier to getting started for me

Plenty great books on the topic (:

Algorithms + Data Structures = Programs (1976), https://en.wikipedia.org/wiki/Algorithms_%2B_Data_Structures...

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upvoteby mikepurvis9 hours ago|
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Yeah key word is exploration. It's not "hey Claude write the design doc for me" but rather, here's two possible directions for how to structure my solution, help me sketch each out a bit further so that I can get a better sense what roadblocks I may hit 50-100 hours into implementation when the cost of changing course is far greater.
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upvoteby Zamicol6 hours ago|
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That is excellent. I'm putting that in my notes.
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