We just want it to understand how to write code. We don’t also need it to know how to grow a potato.
Re:Claude Opus 4.8
by onlyrealcuzzo on Thursday May 28, 2026 @05:12PM
(#48315556)
The disjoint set of English related to strictly growing potatoes and adding features to code is a lot smaller than you probably think...
It is hard to cut out a huge portion of English and truly understand English and human language.
You're just not saving as much as you might assume you could.
Re:Claude Opus 4.8
by RugnirViking on Thursday May 28, 2026 @05:12PM
(#48315549)
I think perhaps you misunderstand how much of being an effective coder is understanding business domain enough to not be constantly asking for clarification (or if one is a fool or an ai, assuming wrong answers). I reckon a vast collection of trivia on the level of knowing how to grow a potato is important for a programmer
And you can't know ahead of time, when you're training the model, what business domains it will be used for. Someone may decide to use it to optimize the watering and fertilizer cycles of their automated potato-growing setup, and suddenly the "how to grow a potato" texts that went into training the model are actually the very things that make the difference between success and failure for the code the model spits out.
Re:Claude Opus 4.8
by CamperBob2 on Thursday May 28, 2026 @05:40PM
(#48315899)
To me, the magic with LLMs has always been on the input side. It needs to understand what you mean in order to do what you ask. Most people are pretty terrible at communication, and general world knowledge seems to help with that.
Re:Claude Opus 4.8
by DoctorOetker on Thursday May 28, 2026 @11:55PM
(#48318827)
... unless the software is potato farm software.
Programming is not a rare skill, the interaction with domain knowledge is.
You can, but it's not as useful as you might think.
It needs to at least understand 1 human language to understand your intent to implement features.
If GRAM turns out to be a 5000x multiplier for local reasoning, you could theoretically train a 500M parameter model on just a programming language to understand stack traces to fix bugs and be incredibly powerful.
But most people also want it to understand human language to implement features as well.
Because then it can't just understand React and JavaScript - it needs to understand thousands of commonly used dependencies, the DOM, CSS, HTML, etc...
And for that you need A LOT more parameters than you might expect.
You can definitely get a ~3B active parameter model that can run comfortably on today's hardware to be VERY good at coding once all of the SOTA architectures are added to a single model - especially if we get better tool calling to give models better context per language.
You could get 100x performance if you feed the models ideal context... So a 3B model today can perform almost as good as ~300B model if you give it really good context vs flood it with mostly garbage it doesn't need across your repository.