Nim vs Standard ML
When comparing Nim vs Standard ML, the Slant community recommends Standard ML for most people. In the question“What are the best languages for learning functional programming?” Standard ML is ranked 15th while Nim is ranked 20th. The most important reason people chose Standard ML is:
The module system that Standard ML uses gives the programmer the power to define custom data types whose internal implementation is invisible to other programmers using the module.
Specs
Ranked in these QuestionsQuestion Ranking
Pros
Pro Great metaprogramming features
There are generics, templates, macros in Nim. They can allow you to write new DSL for your application, or avoid all boilerplate stuff.
Pro Strict typing
Checks your code at compile time.
Pro Has built-in unittest module
With built-in "unittest" module you can create test with a very readable code.
Pro Has built-in async support
Nim has "asyncdispatch" module, which allows you to write async applications.
Pro Compile-time execution
Nim has a built-in VM, which executes macros and some other code at compile time. For example, you can check if you're on Windows, and Nim will generate code only for it.
Pro Really cross-platform
The same code can be used for web, server, desktop and mobile.
Pro Easy to read
Nim has a lot of common with Python in terms of syntax. Indentation-based syntax, for/while loops.
Pro Multi paradigm
Imperative, OOP, functional programming in one language.
Pro Easy to integrate with another languages
You can use Nim with any language that can be interfaced with C. There's a tool which helps you to create new C and C++ bindings for Nim - c2nim.
Also, you can use Nim with Objective C or even JavaScript (if you're compiling for these backends).
Pro Garbage-collected
You don't need to deal with all those manual memory allocations, Nim can take care of it. But also you can use another GC, or tweak it for your real-time application or a game.
Pro Type interferencing
You only need to specify types in your procedures and objects - you don't need to specify type when you're creating a new variable (unless you're creating it without initialization).
Pro Built-in Unicode support
You can use unicode names for variables, there is "unicode" module for operations with unicode.
Pro Supports UFCS (Unified Function Call Syntax)
writeLine(stdout, "hello") can be written as stdout.writeLine("hello")
proc add(a: int): int = a + 5 can be used like 6.add.echo or 6.add().echo()
Pro Powerful module system
The module system that Standard ML uses gives the programmer the power to define custom data types whose internal implementation is invisible to other programmers using the module.
Pro Implementing laziness is trivial
Since mutability is only confined to a special type of reference cells, implementing laziness in SML can be done in only 20 lines of code.
Pro Enforces distinction between data and computations
Since it uses strict evaluation, it enforces distinction between data and computations which in turn enables you to use induction on algebraic data types as a reasoning principle.
Pro Great exception system
Secret messages can be sent across distant parts of a program without possibility of being intercepted by unintended recipients in the middle.
Cons
Con Not very popular outside academia
SML is mostly used in academia and doesn't have many uses in industry. While it's a good language for learning functional programming concepts, the language itself won't be very useful.
