Archive for May, 2014

Moving the caret to the end of text in an <input> element

Very simple, and the following will work in all modern browsers.


<input name="url" type="text" value="http://" />


var inputElem = document.getElementsByName("url")[0];
var valLen = inputElem.value.length;

inputElem.selectionStart = valLen;
inputElem.selectionEnd = valLen;


The same technique will work for <textarea> elements as well.

Identifying the operating system with XPCOM

The following shows how to get a string identifying the current operating system from an instance of nsIXULRuntime:

var getOS = function() {
var env = Components.classes[";1"].getService(Components.interfaces.nsIXULRuntime);
return env.OS;

The nsIXULRuntime.OS string is one of the OS_TARGET values.

Ideally, I’d prefer XUL and XPCOM code to remain platform-agnostic, but I’ve used OS detection as a cheap way (versus jumping through 3 objects) to determine what path separator to use when referencing files and directories (backslash for “WINNT”, forward-slash for everything else). XPCOM is sensitive to the path separator; on Windows, it will not reference a file or directory if you use the forward slash. This is actually bizarre because Win32 API functions will accept paths with the forward slash as a separator. Even more bizarre is that we have a layer of abstraction that actually makes it harder to write platform-independent code.

Data driven

The Economist recently wrote a bit about how speech recognition got so good:

… words do not appear in random order, so the computer does not have to guess from (say) a vocabulary of 20,000 words for each word you speak. Instead, the software assesses how likely you are to have said a given word based on the surrounding words, drawing on statistical models derived from vast repositories of digitised documents and the previous utterances of other users.

This reminded me of a talk by Peter Norvig: The Unreasonable Effectiveness of Data, where he discusses utilizing such large repositories of data in order to develop effective algorithms for a number of problems; there is a heavy focus on natural language processing problems but the concept can, of course, be applied in other areas.

(If the name Peter Norvig sounds familiar, he’s the co-author of Artificial Intelligence: A Modern Approach which you might have used if you ever took an AI class.)

As a programmer, this is exciting stuff and certainly changed my thinking in regards to how I would approach similar problems in the future. Whereas before I would look at sample data sets and try to derive an algorithm, I’d now attempt to mine as much data as I could, build a statistical model, and use that as the basis of the algorithm. Of course mining a massive data set is sometimes easier said than done; especially in regards to data, much of the web is still a walled garden.