Depending on what you're trying to do with the data, you may find http://diffbot.com/products/automatic/ helpful for getting the clean article text and categorization in JSON format. It can be used as a complement/augmentation to the great suggestions here for getting the links.
A lot of the commentary here is just shifting around the definition of networking--anywhere from attending public meetup events to "people skills".
The useful realization for an entrepreneur in a place like Silicon Valley is that networking is increasingly a commodity. The social graphs are so much more open and connected than before that anyone you'd want to meet is just one or two hops away. Thus, time and attention is better spent not on networking, but on the harder task of being a person of value to the network, so that other people will want to network with you.
The recent "duon" hype around there being a "2nd meaning of genetic sequences" isn't something scientists haven't know for a while. To take the analogy even further, there are regions of the genetic code for control flow (if/else/while/mutexes) and other regions with data (constants/numbers/strings), that make it into the final product.
Ok, NOW imagine what 10,000 3.84GHz cores would cost (100 times more aggregate cycles per second than 1x 384GHz core). What's that, you figure a measly $10-30M instead of more money than exists on earth?
Any research simulation is going to want to be parallelized anyway. You'll bump into the limits of the 384GHz core, no doubt about that, at which point you are back to distributed computing. For limitless complexity and limitless appetite for computing power, distributed computing will always be the answer.
I'm afraid the machine you'd get would be a furnace or a torch... AFAIK power that you'd need to dissipate is linear on frequency, so unless we will discover technology to radically lower the voltage used in CPUs or some other parameter, dissipating 100x heat would be kind of a big problem for such CPU.
In one clock cycle, light would be able to travel about 780 micrometers. The real propagation speed of signals on a chip will be substantially less than the speed of light, and it gets slower at smaller feature sizes. This could be tricky.
Unfortunately, many machine learning algorithms aren't easily parallelizable. Consider any boosting ensemble learner, such as the popular random forest, which optimizes the result from the previous iteration. (I run the machine learning startup diffbot.com and we could really use faster cycles.)
The machine learning I did was never limited by CPU power, but rather my algorithm and especially the states. Asking for better performance without better states is garbage-in-glory-out.
you can essentially do this (post a twitter message from the address bar) in Chrome or Firefox without installing anything if you just configure a custom search engine with the path http://twitter.com/home?status=%s
1. In Chrome, go to Options > Default Search engine > Manage > Add
3. Now from the address bar, you can type: tweet [msg]
4. Forwards you to twitter. Although you still have to click a button on twitter, you could easily write your own serverside twitter API client that could post your tweet without the click.
Disclosure: Founder of Diffbot here.