My everyday bike is proudly carrying this one. A perfect example of how to make a brand look cheap. Even though the product range is quite broad, not necessarily low end only. “Just look, it has a guy on a bike as a ‘t’, this is the perfect metaphore of our brand” – said the marketing manager. Me, I’d stick to the Decathlon brand.
All of you happening to publish your images here and there know that it is all too easy to copy them and sign with other name. Oh no, you do not even have the signatures anymore! But there comes the digital investigation bureau: images.google.com. Long time in beta, long time not really available, it has now the functionality I was looking for. There, you just drag your fancy JPG over the input box and mighty google says where it has been seen lately. If you see more links than expected, sue them ;) Mine appeared on a Bulgarian theft design studio site. Just compare to the 4th paragraph in my old NTXT portfolio. There’s a decent company of robbed designers: UC4, alpipi and more.
Working on the strings distances, or text metrics, I found out the Levenshtein method insufficient. For less-than-similar strings it doesn’t help at all, giving numbers close to max possible, and for similar strings it does not consider the quality of different letters. Generally speaking, I find Janet1 closer to Janet2 than to Janet9. Or ABC closer to BBC than to NBC. The notion of number of operations in Lev method didn’t quite suit me either. Thinking of operations needed to create one string of the other, I’d rather take the count of smartest possible copy&paste moves. In other words, how many times I have to cut one string to make the other of the slices. That would be distance in first dimension. The other – distance between letters replacing each other: when abc becomes bbc, it’s a-b replacement, and distance from a to b is 1. The distance depends on the alphabet used. For some cases it’s more useful to use a keyboard-layout order of characters instead of usual alphabetic, in order to emphasise similarities based on easy typed sequences, like asdf or qwerty. Here’s some Flash demo, calculator and benchmark to compare performance of Levenshtein and my method.
it depends on their similarity to the word you input
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gives the original Levenshtein and the distance3d figures
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Levenshtein is rather quadratic, while distance3d seems more like linear, though the
difference shows up for words longer than 15 characters.
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