In a world filled with great free IDEs, JetBrains keeps being competitive with commercial IDE offerings. I think that their RubyMine product is hands down the best Ruby development environment (although I do sometimes use GEdit on Linux and TextMate on OS X). Offering the 2.0 upgrade for free (to be released in a few weeks) is a nice way to say thanks to their customers. A beta 2.0 download is available here . I'm running the beta right now, and it looks like a good upgrade.

My article is a quick introduction to Scheme, and then some examples building small compiled applications in Scheme. Gambit-C Scheme compiles to C, and the generated C code is then compiled and linked. When I need to use Lisp, I tend to use Common Lisp for large applications and Gambit-C Scheme for small utilities. For me, being able to use a high level and expressive language like Scheme to build efficient and compact applications is a big win :-) I find the development environment of Gambit-C Scheme with Gambit-C's Emacs support to be very productive. Marc Feeley, the developer of Gambit-C, mentioned to me that several companies are doing product develop in Gambit-C Scheme. I have a NLP toolkit that I have ported to Gambit-C and I hope to get the time to finish and "ship" it sometime this year.

We live in the mountains of Central Arizona (Sedona) - great area with mountains, trees, water for kayaking, etc. We do have our problems though. I was hiking with friends this morning, beautiful day. Fast forward to this afternoon: very heavy monsoon rains with lots of flooding: wash below our house overflowed into yards below us. We had water flowing through our yard, but it did not make it into our house house. We also ended up with several inches of accumulated hail on our deck and parts of our yard: the white ice looks like snow if you don't look too carefully.

This is the text of the speech he is scheduled to give in a few days. Well worth reading since some crazy right wingers have been telling lies and sowing so much disinformation. I have very much appreciated some conservative pundits who have publicly called this disinformation "stupid." Not all conservatives put the well being of their political party above the well being of our country. Good for them for speaking up! When I was in grade school, a friend's father, who was conservative, helped arrange for our whole class to get to see President John F. Kennedy speak. I think that my friend's Dad disagreed with President Kennedy on things political, but he wanted his son and his son's classmates to hear a President speak. There is a lot of anti-American rhetoric coming from some conservatives - it is up to the rest us, the majority I think, to speak up and point out stupidity when we hear it.

I remain very enthusiastic about Google's AppEngine (and also I am very much enjoying my developer's Wave account). That said, Amazon's AWS services are having a much larger effect on my work for customers and my own work and research. AppEngine is great for some types of projects, but EC2 can be used for anything. I have a text mining experiment that have been planning for a while, and today I have some free time to start setting it up. I have 3 old desktop computers (with a reasonable amount of memory and disk) that I usually haul out of my closet, run "headless," and set up for text mining and machine learning projects. Although I own these boxes, there is a drawback to leaving them running for several weeks in my home office: noise, heat generation, messing up my work environment, etc. I did a quick calculation and estimated that if I instead use one EC2 instance, a reasonably large ESB disk volume, and Elastic MapReduce when I need it to make Hadoop Map Reduc

Kai-Fu Lee is leaving Google (he managed their China operations) to form an angel investing fund for young Chinese entrepreneurs. I have had some interest in Kai-Fu Lee's career since purchasing a copy of his doctoral thesis in the late 1980s on the Sphinx real time speech recognition system. I was looking at using time delayed neural networks for speech recognition, and Kai-Fu Lee's thesis was both interesting and inspiring.

Peter Norvig's recent talk at UC Berkeley discussed how the effects of large data sets and increasing computer resources make it possible to achieve increasingly better modeling and predictive results. Well worth an hour to listen to. There were a lot of gems in this talk, but one that I may put to immediate use is using non-text data in map reduce, specifically using the protocol buffer tools. I have been using Hadoop more frequently and it is worth looking the effects of binary data for intermediate results. His comment that using map reduce is not necessarily incompatible with indexing data was also interesting. There is an overhead for creating indices, but it seems like there are opportunities to use indices for access to global information in a data set while making a complete sweep through the input data set during the map phase.