I believe KAN hype died off due to practical reasons (e.g. FLOPs from implementation) and empirical results, i.e. people reproduced KANs and they found the claims/results made in the original paper were misleading.
Here's a paper showing KANs are no better than MLPs, if anything they are typically worse when comparing fairly. https://arxiv.org/pdf/2407.16674
The last time I used MacPorts it fell out of popularity and did not have nearly as many packages as Homebrew had. It's unfortunate. Homebrew is dogshit slow.
How do you measure beauty? You can't: "beauty" is subjective. And even if you try e.g. count the times you use recursion vs. iteration: that metric is subjective and not grounded in reality.
Sometimes recursion does allow you to reason about code more easily or come to a working solution faster, sometimes it does not.
Measure the concrete: CPU time and memory consumed. Iteration will likely trump recursive methods w.r.t both these metrics. If it doesn't, you can likely transform your iterative algorithm to one that utilizes SIMD (not always).
How is applying DRY entering premature optimization territory (maybe relative to LOC?)? I argue it is instead: premature abstraction.
Optimization is specialization (which is the opposite of DRY): to enable DRY you likely need to generalize the problem (i.e. abstract) such that you remove duplication.
What should be evaluated is removing indirection and tightly packing your data. I'm sure you'll gain a better performance improvement. virtual calls and shared_ptr are littered in the codebase.
In this way: you can avoid the need for the `final` keyword and do the optimization the keyword enables (de-virtualize calls).
>Yes, it is very hacky and I am disgusted by this myself. I would never do this in an actual product
Why? What's with the C++ community and their disgust for macros without any underlying reasoning? It reminds me of everyone blindly saying "Don't use goto; it creates spaghetti code".
Sure, if macros are overly used: it can be hard to read and maintain. But, for something simple like this, you shouldn't be thinking "I would never do this in an actual product".
Mojo is a programming language, this is a library for Python that provides an abstraction over three Python-specific ML frameworks. Apples and oranges.
You could use Keras inside of Mojo since Mojo is Python-compatible/embeds a Python interpreter.
I would be interested in how CLIP performs post quantization. This should be relatively simple to test via ImageNet zero-shot top-1 acc as one example metric.