One of the biggest advantages of SLR cameras over camera phones is the ability to achieve shallow depth of field and bokeh effects. Shallow depth of field makes the object of interest “pop” by bringing the foreground into focus and de-emphasizing the background. Achieving this optical effect has traditionally required a big lens and aperture, and therefore hasn’t been possible using the camera on your mobile phone or tablet.

That all changes with Lens Blur, a new mode in the Google Camera app. It lets you take a photo with a shallow depth of field using just your Android phone or tablet. Unlike a regular photo, Lens Blur lets you change the point or level of focus after the photo is taken.

Here’s an example in Apple’s calendar app. It uses a red tint color for buttons, but it also highlights the current day with a round circle using the tint color. It looks tappable, but it’s not.

And here’s an even worse example, from the App Store app. “Categories” in this screenshot is a button, but “Paid” directly underneath it — same blue, same font and style — is just highlighted to show that you are viewing paid apps. It’s actually “Top Grossing” that is the button.

So, we’ve got inline tag data that is simple to display, but is virtually impossible to query. Regular indexing doesn’t really work well at finding matches in the middle of character data. Enter (trumpets) SQL Server Full-Text Search. This is inbuilt to SQL Server (which we were already using), and allows all kinds of complex matching to be done using CONTAINS, FREETEXT, CONTAINSTABLE and FREETEXTTABLE. But there were some problems: stop words and non-word characters (think “c#”, “c++”, etc). For the tags that weren’t stop words and didn’t involve symbols, it worked great. So how to convince Full Text Search to work with these others? Answer: cheat, lie and fake it. At the time, we only allowed ASCII alpha-numerics and a few reserved special characters (+, –, ., #), so it was possible to hijack some non-English characters to replace these 4, and the problem of stop words could be solved by wrapping each tag in a pair of other characters. It looked like gibberish, but we were asking Full Text Search for exact matches only, so frankly it didn’t matter. A set of tags like “.net c#” thus became “éûnetà écñà”.

[…]

We finally had a reason to remove this legacy from the past. […] After some thought and consideration, we settled on a pipe (bar) delimited natural representation, with leading/trailing pipes, so “.net c#” becomes simply “|.net|c#|”.