I have been working on a web app using Compojure, Noir, and Hiccup. I usually run both lein repl and lein run concurrently and use either IntelliJ or Aquamacs (Mac Emacs) as a code editor. If I am working on a small bit of new code and experimenting a lot, then Aquamacs + nrepl works well for me. If I have my MacBook Air plugged into a huge monitor sometimes I run lein run and a LeClojure repl inside IntelliJ and detach a few edit panes as separate windows - so many nice choices for development! I don't really like debuggers because getting into the habit of using them can end up wasting a lot of time that could be spent writing unit tests, etc. That said, I was looking at some Ruby code I wrote for a customer but have not deployed and it occurred to me to try the RubyMine debugger which worked very well and generally didn't get in my way or waste too much time manually stepping through some code. So, I decided to spend a little time trying IntelliJ LeClojure's debug

I am not actually using functional programming in the kitchen :-) I am re-writing my old cookingspace.com web app in Clojure with a new twist: the new web app will be an AI agent for planning recipes based on the food you have on-hand and based on a user's history, preferences, and the general mood they are in (food-wise). I have written some tools to convert my old relational database code for (more or less) static data (USDA nutrition information, recipes) to Clojure literal data. Based on the type of food a user feels like cooking and what food they have on-hand, I need to transform recipes in an intelligent way to (hopefully!) also taste great after ingredients have substituted or morphed because a user would prefer a different type of sauce over a base recipe, etc., etc. Anyway, working in Lisp and experimenting in a repl is making the entire process easier. My wife and I are both very good cooks and we can make up super-tasty recipes on the fly with whatever ingredie

Faceted search is search where the domain being search is filtered by categories or some taxonomy. Individuals become first class objects in Facebook's new Graph Search and (apparently) search is relative to a node in their social graph that represents the Facebook user, other users they are connected with, and data for connected users. I don't yet have access to Facebook's new Graph Search but I have no reason to doubt that as it evolves both Facebook users and Facebook's customers (i.e., advertisers and other organizations that make money from user data) should be happy with the service. Google's Knowledge Graph and their search in general are also personalized per user. Once again this is made possible by collecting data on users and monetizing by providing this information to their customers (i.e., once more, advertisers, etc.) Pardon a plug for the Evernote service (I am a happy paying customer): Evernote serves as private search. Throw everything relavan

Since I (mostly) shut down my consulting business (which I have enjoyed for 15 years) in favor of developing my own business ideas I find that I am working more hours a week now. When consulting the breakdown of my time spent per week was roughly 15 hours consulting, 8 hours writing, 5 hours reading technical material (blogs, books, and papers), 5 hours programming on side projects, and many hours of "me time" (hiking, cooking, reading fiction, meditation, and hanging out with my wife and our friends). Wondering what "The Three Things" are? Hang on, I will get to that. I now find myself spending 40+ hours a week on business development. Funny thing is, I still am spending a lot of time on the same activities like hiking, hanging out, reading, and side projects. I feel like a magician who has created extra time. Now for The Three Things: I have always known this but things have become more clear to me: Spend time on what energizes you. If work drags you down an

Here is a good 11 minute interview with Ray Kurzweil. In the past Google has been fairly open with publishing details of how their infrastructure works (e.g., map reduce, google file system, etc.) so I am hopeful that the work of Ray Kurzweil, Peter Norvig, and their colleagues will be published, sooner rather than later. Kurzweil talks in the video about how the neocortex builds hierarchical models of the world through experience and he pioneered the use of Hierarchical hidden Markov Models. It is beyond my own ability to judge if HHMMs are better than the type of hierarchical models formed in deep neural networks, as discussed a lot by Geoffrey Hinton in his class "Neural Networks for Machine Learning." In this video and in Kurzweil's recent Authors at Google talk he also discusses IBM's Watson project and how it is capable of capturing semantic information from articles it reads; humans do a better job at getting information from a single article, but as Kurzw