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these last few weeks, @lynn and me have been working on a c compiler for uxn, based on rui ueyama's “chibicc”. it's been a lot of fun! it's such a cute little vm… assembly for this tiny stack machine involved so many little puzzles.

i made a demo https://github.com/lynn/chibicc/blob/uxn/examples/star.c :3

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i am pleased to announce oneko-uxn: a port of oneko-sakura to #uxn. this is a version of the classic software ”Neko”! 🐈🖱

https://github.com/hikari-no-yume/oneko-uxn

日本語版も有ります。

this all was made possible with chibicc-uxn, the c compiler for uxn that @lynn and i have worked on together.

have fun!

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i found a bug in oneko-uxn: the 16-bit Euclidean distance calculation can overflow even for 8-bit inputs :(

but @lynn found a great alternative: the average of the L1 norm and L∞ norms is within ~6% of the Euclidean distance, and way easier to compute!

https://github.com/hikari-no-yume/oneko-uxn/commit/060bc664e647e3ea6d1362fa5d882c1d1b95a87c

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@cr1901 @lynn

0 ≤ x ≤ 2π

cx = cos(x)
sx = sin(x)

i plot a point at (cx, sx)

interpreting (cx, sx) as a vector from (0, 0), the euclidean distance is always going to be 1, right

i then calculate the same distance with our approximation, and plot a point at (cx * distance, sx * distance)

so the red line shows how far it strays from euclidean distance at various angles

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@cr1901 @lynn you can see that at 0°, 90°, 180° and 270°, there's no difference, which should be intuitive considering that these are exactly the directions for which euclidean distance, L1/manhattan distance and L∞/chebyshev distance are identical, so an average of these last two should be the same as the first of these

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