This article surface every once in a while, and I love it.
What the author suggests is very clever.
I have implemented an extended version of that in Go as an experiment.
Instead of using a trie, I used a radix tree.
Functions the same, but it's much more compressed (and faster).
I needed a fuzzy string matching algorithm for finding best name matches among a candidate list. Considered Normalized Levenshtein Distance but ended up using Jaro-Winkler. I'm curious if anybody has good resources on when to use each fuzzy string matching algorithm and when.
I made myself plugin that shows new news in wikipedia's current event page and I was using levenshtein originally (they often edit couple of words in article over span of few days so I compare each new article with previous ones for similarity) but after few days it became too slow (~20s) because O(m*n), so I switched to sorensen-dice instead which is O(m+n) and it's much faster and works very similar way, even tho they do slightly different thing.
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