How good is your model at picking good data structures?
There’s several orders of magnitude less available discussion of selecting data structures for problem domains than there is code.
If the underlying information is implicit in high volume of code available then maybe the models are good at it, especially when driven by devs who can/will prompt in that direction. And that assumption seems likely related to how much code was written by devs who focus on data.
> There’s several orders of magnitude less available discussion of selecting data structures for problem domains than there is code.
I believe that’s what most algorithms books are about. And most OS book talks more about data than algorithms. And if you watch livestream or read books on practical projects, you’ll see that a lot of refactor is first selecting a data structure, then adapt the code around it. DDD is about data structure.
- ideologically, he's spent his career chasing complexity reduction, adovcating for code sobriety, resource efficiency, and clarity of thought. Large, opaque, energy-intensive LLMs represent the antithesis.
Oh, my bad! I forgot to update the documentation: Actually, in union by name mode, all worksheets are now analyzed; otherwise, it remains limited to the first sheet only.