I previously mentioned the new news site Thoof, and I know several folks checked it out. So I just had to share these new videos done in the Apple vs. PC style, but this time comparing Digg and Thoof.

The degree of flexibility and customization - not to mention simplicity of use - between traditional sites like Digg, Reddit and others as compared to Thoof is astonishing. Thoof is just plain simple to use while the others require you to dig through a bunch of crap to find things that interest you.
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By Guest Star (and responsible Power-Digger): SuxMonkey of AllSux.com

Have you submitted great content to Digg and wondered why it didn’t make the Digg homepage? Are you just curious about how Digg, the most popular of social media sites, works? Did some content of yours make it to the front page, but you don’t know why it did or why other content of yours didn’t?

This article will start to address these questions, and refer you to further resources to help you on your way to understanding the social news powerhouse that is Digg.com. It will also provide advice for sharing news and information on Digg as well as how to promote your own content responsibly. Keep in mind that this article is about understanding how to build a better Digg community for everyone - not about gaming Digg for personal gain.
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Most people surf the Web unaware that an army of Artificial Intelligence engines are watching your every move in an effort to match interests with commercial messaging. Well, the bad news is… they are. The good news is that a brand new Web site called Thoof is going to use an even better version of this technology to entertain you instead!

Thoof’s founder and CEO, Ian Clarke, arrived at the conclusion that news aggregation sites such as Digg, Google News, Reddit and others provided volumes of articles, but left it to the readers to search endlessly for ones that appeal specifically to you. Being the uber-genius he is, he decided that someone needed to apply some Artificial Intelligence to the process and allow the system to learn and predict user preferences in order to present only articles with the highest probability of appealing to each individual.
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