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Abstractions are ultimately a tool for humans to manage complexity in a particular class of problem, and to reduce cognitive load. Good abstractions come from imaginative human brains with taste and lived experience. Abstractions are also mind constructs that often have analogies in the real world. Even at the level of the CPU circuitry, abstractions serve that role. An LLM can only spit out what was already invented by humans and stitch it together in a tasteless, erroneous manner.

by libertas_quae_s3 hours ago|

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Bruce Lee was ages ahead of its time with the Water phrase.

by FjordWarden9 hours ago|

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That is not what the text is arguing for and it has many good ideas, dropping the abstractions but keeping the test harness is one of them. I've worked on many aspect of programming language implementation through the years and I would struggle with the abstractions of parser-generators and bytecode designed by committee like an enterprise Java developer struggles with the Hibernate ORM. Dunno what people are putting in their prompts but ask LLM to roofline and it will pursue that target like a bloodhound.

by socketcluster8 hours ago|

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Many abstractions we encounter in software industry are poor abstractions which are counter-productive. I agree that we should drop those...

But we can't drop all abstractions. It's not physically possible. The AI agent is going to break logic up into files... The AI will create/impose an abstraction for each piece of logic whether we like it or not.

So saying "drop abstractions" is actually saying "let the AI decide what the abstractions should be" and unfortunately because LLMs are trained on average code, those abstractions are often pretty poor.

Coming up with the right abstractions is actually one of the main skills which AI hasn't automated and where the human brings the most value. It affects maintainability directly; including when using AI.

I've worked on projects with poor abstractions and ones with good abstractions using AI. The ones with poor abstractions require 10x to 100x more tokens and time to solve problems and implement new features. You're constantly fighting it to prevent it from coming up with nasty hacks and workarounds. Poor abstractions create the need for hacks and workarounds.

by AdieuToLogic8 hours ago|

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Software abstractions are a reification of problem domain understanding. They embody a solution within the mathematical constraints of a programming language such that the collaborations, when done properly, appropriately reflect said understanding.

Discarding abstractions is nothing more than rediscovery of a Big Ball of Mud[0].

0 - https://www.laputan.org/mud/mud.html

by vatsachak8 hours ago|

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Abstractions aren't a burden though, they are more like a support. A good abstraction let's you write 10 programs in the time it would have taken to write one. Also, we now have "production" languages with generics and monomorphization so abstraction is easier than ever.

Djikstra has a great article as to why English language programming will always be second to a programming language. A "formal system" (edited from regular language) is the most precise way to specify things. An "abstraction" is basically a domain specific language.

The way I like to use LLMs is to build abstractions. I like to build mathematical code and ask the LLM to use it. I think that LLMs are very mathematical thinkers and they like it when they see Semigroup<T>. And it's not hifalutin either, I'm processing calculations along DAGs with a million+ nodes in milliseconds, thanks to abstractions

by rhdunn5 hours ago|

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LLMs have been trained on a lot of data and have built representations of various concepts and links between them from that training data. This is why they are good when seeing names of things like `Semigroup` as they can draw on all the information about that concept from multiple sources (mathematics papers and discussions, programming articles and code, etc.).

There was an article or comment somewhere that said that when an LLM was prompted in comments/variable names about a complex algorithm or something like that in a very specific domain it was very knowledgeable. However, when given different names/terminology for the same thing it didn't understand the problem it was prompted to solve.

This is also why LLMs are good at generating code using Bootstrap, Tailwind or any other technologies as they can relate terms like `flex` and `grid` to various layout patterns. So using sensible names from the literature/specifications/etc. helps both developers and LLMs.