I came across some curious Haskell tweets lately and decided to group them in a unique place.
These made me remember about a curious fact: did you know that there are other types of spaces in Unicode, like U+00A0, the no-break space? What about using it in Ruby? (please don’t)
Whenever I see someone talking about non-ASCII characters in programming languages, I always get back to APL, an old language that used extremely concise notation similar to mathematics itself. Due to most keyboards being horrible, it never caught on. :-(
(Mental note: having some kind of LaTeX math symbols embedded into a language for scientific computing would be… interesting.)
I work for a mobile games company as a data scientist. I use Ruby for data wrangling and some sorts of analysis, Python for more specific things (essentially scikit-learn) and bash scripts for gluing everything together.
The developers use Corona for creating our games, which uses Lua. I decided to give that language a try.
- Lua is tiny. As someone accostumed to Python and Ruby, it is shocking to see such a small standard library. For example, this is the manual – there are only 158 Lua functions listed there.
- The syntax is incredibly simple. Take a look at these diagrams; if you understand the Extended Backus-Naur Form, you can read Lua’s syntax quite easily. For comparison, Ruby’s syntax is complex enough that there are lots (and lots and lots) of small corner cases that I probably never heard about, even after years using it. Ah! And Ruby’s parse.y has 11.3k lines.
- Lua was built with embedding in mind; it is used for interface customization in World of Warcraft, for example.
- It is a Brazilian programming language! :-) Lua was created in 1993 in Rio de Janeiro, according to Wikipedia.
At my last job, I worked with data from the Brazilian educational system in several situations. The details aren’t the important part, but the format – a giant denormalized CSV with an accompanying PDF detailing its fields. It is very nice after you work with it for some time, but there are some things that could be better.
In that format, enumerations (fields with a fixed, finite set of values) are encoded as some arbitrary integer range, boolean values as 0 or 1, and other implementation details that are explained in the PDF. Thus far we have a cute CSV with documented fields. Nice, right?
Programming languages are possibly one of the simplest parts of software engineering. You can know your language from the inside-out and still have problems in a project — knowing the tool doesn’t imply knowing the craft. But learning a new language is really a lot of fun.
Inspired by Avdi Grimm’s roadmap for learning new languages, I decided to give it a try and put my current interests in writing.
- Julia – http://julialang.org/
I have experience writing code in MATLAB, Octave, Python (with Numpy, Scipy and Pandas) and a bit of R, and still I’m excited with Julia.There are at least 3 features of Julia that are powerful and make me wish to work with it: its Just-In-Time compiler, parallel for and the awesome metaprogramming inherited from LISP.
The drawback is… is… well, I didn’t have time to really use it and get comfortable writing Julia programs. Yet.
- Haskell – https://www.haskell.org/
I already tried learning Haskell a couple of times. Maybe 3 or 4 or 5 times. I wrote programs based on mathematics and some simple scripts, most of the syntax isn’t strange anymore, even monads make sense now; however, I still feel a bit stiff when writing Haskell. I don’t know.
Two books I recently bought might help with that – Real World Haskell and Parallel and Concurrent Programming in Haskell. I probably need to motivate myself to write something useful with it.
- Rust – http://www.rust-lang.org/
There is a quote in Rust’s website that sums my expectations of it:
Rust is a systems programming language that runs blazingly fast, prevents nearly all segfaults, and guarantees thread safety.
I know how to read C/C++ and even write a bit of it, but it’s messy and takes more time than I usually have for side projects. Writing code that is safe & fast shouldn’t be so hard. ;)
All-in-all, this is a very brief list. However, I don’t think I should focus on more languages right now. To be honest, I think that my next learning targets are in applied mathematics. I need a stronger foundation in Partial Differential Equations and Probability Theory. There are several topics in optimization that I should take the time to study. Calculus of variations also seems quite cool.
(good thing that I have friends in pure math to help me find references!)