The efficiency depends on the query size relative to the data distribution. A small query in a sparse region prunes almost everything. A query that covers the whole space prunes nothing (because every node overlaps), degenerating to a brute-force scan. The quadtree gives you the most benefit when your queries are spatially local, which is exactly the common case for map applications, game physics, and spatial databases.
return urls, next_url
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As for Feldberg, during his 14 years on the platform he says he's noticed a number of changes including a more modern interface, more targeted ads, and the move away from seeing how many downvotes a post would attract.