As some of you may know, one of my side interests is approximate nearest neighbor algorithms. I'm the author of Annoy, a library with 3,500+ stars on Github as of today. It offers fast approximate search for nearest neighbors with the additional benefit that you can load data super fast from disk using mmap.
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Ok, so I have to first preface this whole blog post by a few things:
I really struggle with the term microservices. I can't put my finger on exactly why. Maybe because the term is hopelessly ill-defined, maybe because it's gotten picked up by the hype train.
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I have done roughly 2,000 interviews in my life. When I started recruiting, I had so much confidence in my ability to assess people. Let me just throw a couple of algorithm questions at a candidate and then I'll tell you if they are good or not!
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I've been reading up on operations research lately, including queueing theory. It started out as a way to understand the very complex mortgage process (I work at a mortgage startup) but it's turned into my little hammer and now I see nails everywhere.
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I started writing this blog in late 2012, partly because I felt like it would help me improve my English and my writing skills, partly because I kept having a lot of random ideas in my head and I wanted to write them down somewhere.
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UPDATE(2018-06-17): There are is a later blog post with newer benchmarks!
One of my super nerdy interests include approximate algorithms for nearest neighbors in high-dimensional spaces. The problem is simple. You have say 1M points in some high-dimensional space.
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I'm interrupting the regular programming for a quick announcement: we're looking for data engineers at Better. You would be the first one to join and would work a lot directly with me.
Some fun things you could work on (these are all projects I'm working on right now):
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