I have super mixed feelings on this.
Minimalism makes for a clean, easy to understand codebase, that avoids some of the performance pitfalls associated with bigness.
But sometimes it’s easy to build 80% of what you need, and then massively difficult to get to 100%, and the ease of getting to 80% can lead you astray.
For instance, I can write a pretty fast matrix multiplication algorithm in maybe a few dozen lines of code. But then you look at OpenBLAS, Eigen, MKL, FBGEMM, etc. and you see thousands of lines of code. And it’s not because I’m 100x better at programming than the developers of these libraries, it’s that they’ve really put in effort to getting the best performance, in all corner cases, on all platforms.
You could argue that matrix multiplication is an extreme case, and it is, but I think it’s still valuable to critically think about what you give up by opting for minimalism before you write off other people’s software as aimless bloatware.
Minimalism makes for a clean, easy to understand codebase, that avoids some of the performance pitfalls associated with bigness.
But sometimes it’s easy to build 80% of what you need, and then massively difficult to get to 100%, and the ease of getting to 80% can lead you astray.
For instance, I can write a pretty fast matrix multiplication algorithm in maybe a few dozen lines of code. But then you look at OpenBLAS, Eigen, MKL, FBGEMM, etc. and you see thousands of lines of code. And it’s not because I’m 100x better at programming than the developers of these libraries, it’s that they’ve really put in effort to getting the best performance, in all corner cases, on all platforms.
You could argue that matrix multiplication is an extreme case, and it is, but I think it’s still valuable to critically think about what you give up by opting for minimalism before you write off other people’s software as aimless bloatware.