New Release Of Nim Borrows From Python, Rust, Go, and Lisp (fossbytes.com)

An anonymous reader writes: "Nim compiles and runs fast, delivers tiny executables on several platforms, and borrows great ideas from numerous other languages," according to InfoWorld. After six years, they write, Nim is finally "making a case as a mix of the best of many worlds: The compilation speed and cross-platform targeting of Go, the safe-by-default behaviors of Rust, the readability and ease of development of Python, and even the metaprogramming facilities of the Lisp family..."

Fossbytes adds that Nim's syntax "might remind you of Python as it uses indented code blocks and similar syntax at some occasions. Just like Rust and Go, it uses strong types and first class functions... Talking about the benchmarks, it's comparable to C. Nim compiler produces C code by default. With the help of different compiler back-ends, one can also get JavaScript, C++, or Objective-C.
There's an improved output system in the newest release, and both its compiler and library are MIT licensed. Share your thoughts and opinions in the comments. Is anybody excited about writing code in Nim?

Comments

Re:Whitespace takes the most space

[johannesg]

There's nothing to discuss since any algorithm can be written in any turing complete language.

There's plenty to discuss, since the ease with which you can express yourself matters greatly in any practical sense. However, the progress we have actually made in this area is not nearly as impressive as one might hope - new languages mostly bring us the same thing, but with slightly different syntax. Real breakthroughs are very, very rare. Remember the 4GL initiative from Japan in the nineties? Still waiting for that killer language... The closest I've seen is the Wolfram language. Maybe that's the way forward: a massive support library and huge, online databases.

As for Turing machines... On a machine with finite memory, all states the machine can be in can be enumerated, and each state always leads deterministically to a single next state. Since the total number of states is finite (very large, but finite), this means that at some point it must either return to a previous state, or halt. If it returns to a previous state, it will then continue to loop forever (since each state deterministically leads to a single next state). Thus, if you have the capacity to track state changes for long enough, you will be able to determine if a program will halt or not.

And yet, there's Turing's proof. Why the discrepancy? Well, simple: a Turing machine has an infinite tape, and can therefore produce not a finite, but an infinite number of states. Any computer we have in the real world does not have infinite memory, and is therefore not a Turing machine. To be considered Turing-complete, a language must be able to simulate a Turing machine - and that's actually impossible, since it can never meet the "infinite tape" requirement. You might claim that "any algorithm can be expressed in any Turing complete language", but since we don't have any, that's really a moot point, and we would perhaps be wiser to focus on other aspects of the language rather than a theoretically impossible, and perhaps even undesirable feature.

Your move, AC ;-)