The project was migrated from https://code.google.com/archive/p/visual-lambda/ and from https://bitbucket.org/bntr/visual-lambda.
we assign a minterm id to each of these classes (e.g., 1 for letters, 0 for non-letters), and then compute derivatives based on these ids instead of characters. this is a huge win for performance and results in an absolutely enormous compression of memory, especially with large character classes like \w for word-characters in unicode, which would otherwise require tens of thousands of transitions alone (there’s a LOT of dotted umlauted squiggly characters in unicode). we show this in numbers as well, on the word counting \b\w{12,}\b benchmark, RE# is over 7x faster than the second-best engine thanks to minterm compressionremark here i’d like to correct, the second place already uses minterm compression, the rest are far behind. the reason we’re 7x faster than the second place is in the \b lookarounds :^).
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It's tricky territory for music streamers and listeners feeling caught unawares with AI-made music, as Mashable's Rachel Thompson asks, "How should we feel about AI-generated songs finding their way into our listener library? Some people aren't necessarily opposed to giving AI music a try, but their open-mindedness begins to shift once they feel deceived."