It's funny how they specifically use the phrase "output that follows real-world physics" to describe the marble rolling video. At the end of the zigzag track, the marble jumps up for no reason. In a couple of other places it speeds up with no apparent energy source. It's still an amazing result, but they could have picked a better example for this claim!
White peaches in California in stone fruit season are delicious. Pluots also pretty darn good. Freestone peaches in Niagara are the best on the continent bar none. Georgia has pretty good peaches probably on par or maybe a bit better than california, but smaller and mixed bag whenever I have had them in June.
I'm not a Haskell programmer, but from my limited awareness: Wouldn't they want to encode the restriction that April 31 doesn't exist directly in the type system instead of using raw integers for the underlying struct?
A very specific shortcoming of this implementation is indeed "Day of Month" and "Month of Year" aren't given their own types! The type specification should likely be applied all the way down! I felt the examples conveyed the point well enough and it was shorter in many cases.
Routing games like Mini Metro/Motorways, Freeways, and Fly Corp are not quite puzzle games in the traditional sense, but I'd definitely encourage any puzzle fans to try them.
I knew that adding sriracha and hoisin was considered somewhat gauche / insulting to the chef. But I was surprised to see no mention of Basil, since every pho shop in the USA provides it. Is that also nontraditional? I really like it...
Surprised Python is only 21x slower than C for tree traversal stuff. In my experience that's one of the most painful places to use Python. But maybe that's because I use numpy automatically when simple arrays are involved, and there's no easy path for trees.
Be careful with that, numpy arrays can be slower than Python tuples for some operations. The creation is always slower and the overhead has to be worth it.
Yeah. Many seem to forget it. For one-off computation tasks, NumPy, PyTorch, JAX have non-trivial overhead, and might even be slower than vanilla Python. Only when repetition, loops, etc. come into the picture, which is recurring in many people’s workflow - JAX or NumPy is worth it.
You can turn trees into numpy-style matrix operations because graphs and matrices are two sides of the same coin. I don't see the code for the binary-tree benchmark in the repo to see how it's written, but there are libraries like graphblas that use the equivalence for optimization.
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