I found this article informative. I didn’t actually know the details of how the credit score is determined. But I especially found interesting the conclusion that Uncle Sam is (or should be) a sub-prime borrower.

Update (January, 2014). I’ve been asked to remove the link because it’s causing the originator’s spam rating to go up. Sounds like a bug in the algorithm. Here’s the equivalent link, in case it’s helpful: http://goo.gl/swFwgW.

We’ve been pretty happy customers of Netflix for 2+ years now, but I never knew what it cost us, compared with a brick-and-mortar DVD rental place. Now, courtesy of Feedflix the answer is as clear as this graph:

I’m working on migrating myself to the mutt email client. If you’re at all curious about why, and how I’m doing, read on.

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We just installed a MacSpeech Dictate on the iMac. I’m using it right now to type this review. I have to say, having used it just a bit, it is an awesome program.

When I first installed the software, I had to train it by reading a few sentences. That only took perhaps five minutes. Then I was ready to go. I goofed around for a few moments, trying to think of sentences to throw at it. I was curious how well it understood church jargon, so I made up a few sentences about the Trinitarian controversy and about Tertullian. It was funny that the speech software knew how to spell Tertullian, which the Mac’s spellcheck service underlined in red.

I was trying to think of a real world test. Here’s what I decided to do.

I recently finished a book called Visioneering, by Andy Stanley. Whenever I read a book, I underline things that catch my interest as I go along. That way, the next time I read the book, the highlights will jump out at me. What occurred to me was that I could read those highlights into the computer and then I would be able to grep on them.

So I did it. I flipped through the whole book, reading everything I’d underlined. There were 97 quotes totaling 1808 words. Reading them in took me about 30 minutes. (Mind you, I’d never used this software before, nor any other voice recognition software.)

But the truly amazing thing is that one of my children was in the next room playing “Lego Star Wars” the whole time, with the volume turned up to 11.

Maybe the reason I’ve had such good luck is because of my Princeton trained voice. After all, I’m a preacher: I use my voice every Sunday. Maybe that’s why the software was able to understand me so well. But considering the technical vocabulary I was using and the noisy environment, I’m still impressed with how good this software is. I look forward to getting a copy to use at work.