Off topic, but I remember fondly the pre-LLM days when I used to love reading about programming languages. I never got a chance to professionally work with Rust, but made some cool hobby projects with it. Would have eventually tried out zig too.
Now it all feels so pointless though. Like memorizing rules to do mental arithmetic. Sure, there is still use for language expertise, but not enough to get excited over new concepts and ideas.
I was thinking about that too recently. We have a new service that we’re trying to publish an SDK for. I don’t like the SDK that was created and kept nitpicking about how verbose certain things are and how “unergonomic” it feels (long tedious type names, annoying redundant constructs, etc) But then I was wondering if for a brand new service/SDK if anyone cares anymore and how much fuss i should be making about that.
This is a bit nuanced, because if there’s redundant constructs then that does impact maintainability and efficiency with both runtime and LLMs working with the code.
I’d push this more towards personal preference of how code is expressed matters much less now than how maintainable it is.
There is the aspect of long type names, they often don’t have much impact when tokenized. The character count of words is nearly negligible - they often become one or two tokens anyways. But, the choice of words may have a greater impact on how the word choice weights an LLMs contextual processing of that word (a human may be able to ignore an inaccuracy in naming a bit more flexibly than some LLMs).
karmakurtisaani · · focus · HN ↗
Now it all feels so pointless though. Like memorizing rules to do mental arithmetic. Sure, there is still use for language expertise, but not enough to get excited over new concepts and ideas.
eddythompson80 · · focus · HN ↗
karmakurtisaani · · focus · HN ↗
skhameneh · · focus · HN ↗
I’d push this more towards personal preference of how code is expressed matters much less now than how maintainable it is.
There is the aspect of long type names, they often don’t have much impact when tokenized. The character count of words is nearly negligible - they often become one or two tokens anyways. But, the choice of words may have a greater impact on how the word choice weights an LLMs contextual processing of that word (a human may be able to ignore an inaccuracy in naming a bit more flexibly than some LLMs).