Despite it's age, SML feels very young in many ways; SML had features that mainstream languages would not pick up for decades and are still being experimented with today.
Here are some of ML's "cutting-edge" features:
- strong static typing
- automatic type inference
- exception handling
- pattern matching
- parametric polymorphism
- first class functions
SML was originally designed for developping proofs about first-order predicate calculus (read: computer programs) and it can have a distinct academic feel about it.
However it's emphasis on immutability and strong typing has led SML to be used in many fields where program correctness is paramount (compiler design, code analysis, financial systems, medical systems, etc...).
Learning SML makes you a better programmer, because it forces you to write code that is stateless and to use closures effectively.
It's also many programmers first introduction to pattern matching and (truely) strong typing. And because SML's type system is so strong and well-thought out, it often feels like you are working in a dynamically typed language instead.
There are several popular implementations:
You can find information on the language on each implementation's sites.
Help us explain this better! File a GitHub issue at https://github.com/exercism/sml/issues if you have suggestions, or submit a patch with improvements to the https://github.com/exercism/sml/blob/master/docs/ABOUT.md file.
If you've downloaded the command-line client and have Standard ML installed on your machine, then go ahead and fetch the first problem.
exercism fetch sml
In order to be able to submit your solution, you'll need to configure the client with your Exercism API key.
exercism configure --key=YOUR_EXERCISM_KEY
When you've written a solution, submit it to the site. You'll have to configure the command-line client with your exercism API key before you can submit.
exercism submit PATH_TO_FILE