The Julia programming language: amazingly nice

Well, at least I am amazed. I took a brief look at Julia a few years ago but since I understood it to be somewhat derivative of GNU/Octave (or Matlab) and R (I sometimes use GNU/Octave, but not often), I only gave Julia a very short look.

Fortunately, a current customer uses Julia so I have been ramping up on the language and I very much like it. A bit off topic but I would like to give a shout-out to the O'Relly Safari Books Online service which I recently joined when they had a $200/year guaranteed for life subscription price (half regular price). I am reading "Getting Started with Julia" by Ivo Balbaert which is fine for now. I have "Julia for Data Science" by Zacharias Voulgaris and "Mastering Julia" by Malcolm Sherrington in my reading queue. When learning a new technology having up to date books available really is better than finding information on the web (or at least augmentation to material on the web).

I very much like the tooling for Julia. Julia is a new language but there are already many useful libraries available. Julia uses github for storing modules in the standard library and the integration works very well, at least on Ubuntu Linux. So far, I have been happy just using GNU/gedit for development. I haven't tried Julia on OS X or Windows 10.

The Julia repl is great! Color coding and auto completion are especially well done.

I like just about everything about the Julia language except for 1-based indexing of matrices. Oh well.

Julia is readable, functions are first class objects and programming in Julia is very "Lisp like." With optional type hints (mostly in defining function arguments) Julia is a very high performance language. I love developing in Ruby but I do dream of much higher performance. Julia does not seem like a complete replacement for Ruby (for me) however. That might change.

In addition to doing work with Julia, I have also been experimenting with lots of little coding projects: the Merly web framework (simple, sort of like Sinatra), using the standard HiddenMarkovModels library, and experimenting with a few of the neural network libraries. All good stuff.


Comments

Post a Comment

Popular posts from this blog

My Dad's work with Robert Oppenheimer and Edward Teller

Robert Oppenheimer and Edward Teller facilitated my Dad getting a professorship at UC Berkeley when I was 3 years old. Oppenheimer left Berkeley but Teller was a good friend of my father and I remember him being in our home. Three weeks ago, my wife and I were just leaving to see the new Oppenheimer movie when my Dad called. He mentioned that when I was in grade school he was invited to give a talk at Princeton. After his talk Oppenheimer talked with my Dad and invited him to have dinner at his house. My Dad said Oppenheimer was not well (I think he died of throat cancer soon afterwards) but his wife Kitty carried the conversation. My Dad, Ken Watson, passed away 10 days ago on August 18, 2023.
Read more

Time and Attention Fragmentation in Our Digital Lives

  As humans we have evolved over a few million years to be both attentive and reactive to danger, live in social communities, and spend much of our time being in the present moment gathering and eating food and socializing. The behavior of rapidly changing short attention to content on social media, too many good short form things to watch on streaming video entertainment platforms, are all rewiring our brains in an unnatural and unhealthy way. I fight back, but in really simple ways that entail little ceremony: Almost every morning I spend 30 minutes scanning Hacker News (about 10 minutes), Apple News (about 5 minutes), and the remaining time on Twitter and Mastodon finding interesting new (mostly tech) things. I make notes in a temporary Apple Note: links of things I may want to research, try, or simply read that day. I like to get this all done at once, and then not feel like I need to interrupt my activities during the day to “catch up” on what is happening in the world. In a w...
Read more

I am moving back to the Google platform, less excited by what Apple is offering

I have been been playing with the Apple Intelligence beta’s in iPadOS and macOS and while I like the direction Apple is heading I am getting more use from Google’s Gemini, both for general analysis of very large input contexts, as well as effective integration my content in Gmail, Google Calendar, and Google Docs. While I find the latest Pixel phone to be compelling, I will stick with Apple hardware since I don’t want to take the time to move my data and general workflow to a Pixel phone. The iPhone is the strongest lock-in that Apple has on me because of the time investment to change. The main reason I am feeling less interested in the Apple ecosystem and platform is that I believe that our present day work flows are intimately wrapped up with the effective use of LLMs, and it is crazy to limit oneself to just one or two vendors. I rely on running local models on Ollama, super fast APIs from Groq (I love Groq for running most of the better open weight models), and other APIs from Mist...
Read more