Nim VS advent_of_code

22. Nim - $80,000

I think the right answer to your question would be NimLang[0]. In reality, if you're seeking to use this in any enterprise context, you'd most likely want to select the subset of C++ that makes sense for you or just use C#.

[0]https://nim-lang.org/

I don't think it's a rust-inspired language, but since it has strong typing and compiles to javascript, did you give a look at nim [0] ?

For what it takes, I find the language very expressive without the verbosity in rust that reminds me java. And it is also very flexible.

[0] : https://nim-lang.org/

I see a valid cert for https://nim-lang.org/

FYI, on the front page, https://nim-lang.org, in large type you have this:

> Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula.

You better off with using a compiled language.

If you interested in a language that's compiled, fast, but as easy and pleasant as Python - I'd recommend you take a look at [Nim](https://nim-lang.org).

And to prove what Nim's capable of - here's a cool repo with 100+ cli apps someone wrote in Nim: [c-blake/bu](https://github.com/c-blake/bu)

Chapel has at least several full-time developers at Cray/HPE and (I think) the US national labs, and has had some for almost two decades. That's much more than $100k.

Chapel is also just one of many other projects broadly interested in developing new programming languages for "high performance" programming. Out of that large field, Chapel is not especially related to the specific ideas or design goals of Mojo. Much more related are things like Codon (https://exaloop.io), and the metaprogramming models in Terra (https://terralang.org), Nim (https://nim-lang.org), and Zig (https://ziglang.org).

But Chapel is great! It has a lot of good ideas, especially for distributed-memory programming, which is its historical focus. It is more related to Legion (https://legion.stanford.edu, https://regent-lang.org), parallel & distributed Fortran, ZPL, etc.

Here's the relevant extract from my (recursive) solution. Full code on GitHub.

Like others, I used lcm. Here's an extract of the solution, omitting parsing and main(). Full solution on GitHub.

Solutions to parts 1 and 2 nearly identical and pretty much worked first time. Nothing clever here. This was far simpler than I thought it would be. I probably spent most time trying to think of a mathematical way of getting all points between p1 and p2. In the end, I just constructed two ranges. Full code here.

Full code on github.

The core of my solution below using numpy (because I'm learning it!). See github for full code.

Python 3 solution using numpy. I got held up because I assume (but know better) that numpy arrays are [x,y] instead of [row, col], which is [y,x].

Below is the new solution, which works for part 1 and 2. The full code is on GitHub.

These are the key functions. The whole code is on GitHub

Part 2 (extract shown below) was a matter of starting with the low points found in part 1 then recursively looking around for relevant points. Full code on GitHub

Code is on GitHub.