mojo VS rust

Until that video comes out, you can read the proposal[1]. This appears to be quite recent (the timestamp says 4 days ago).

I personally found the writing style of the proposal to be clearer than the tutorial-flavor of the original blog post (I haven't watched the video). But that's probably because I have a programming language background and am intimately familiar with Rust.

[1]: https://github.com/modularml/mojo/blob/main/proposals/lifeti...

https://github.com/modularml/mojo/blob/main/LICENSE Is this not it?

Interesting, but the intent seems similar to Chris Lattner's new Mojo language which arguably has similar characteristics and is further along in its development.

https://docs.modular.com/mojo/

Mojo is a programming language that combines the performance and control inherent in systems languages like C++ and Rust with the flexibility and simplicity of use typical of dynamic languages like Python. Because of its combination of performance, extensibility, and usability, its design makes it possible to construct high-performance systems, which makes it a good option for AI development.

If you take a look at the optimized Mojo code doing the matrix multiply [1], it takes an expert to understand. It’s not just some simple for-loops in Mojo they’re comparing against.

[1] https://github.com/modularml/mojo/blob/5ce18c47a27c0c4123de1...

Mojo truly unlocks a world of possibilities in high-performance computing.

For a concrete example of Liquid Haskell, see how Gabriella Gonzalez safely removed bound checks of high-performance protocol parsing, in "Scrap your Bounds Checks with Liquid Haskell" [1].

With Liquid Haskell, the bound checks are moved from runtime to compile time, semi-automatically handled by SMT-solvers. With static types, programmers can write correct programs faster, and the programs also run faster.

As an aside, speeding up programs with static analysis (constrained dynamism) are also present in Mojo (a variant of Python) or Swift [2].

[1]: https://github.com/Gabriella439/slides/blob/main/liquidhaske... "Scrap your Bounds Checks with Liquid Haskell"

[2]: https://github.com/modularml/mojo/discussions/466 "Mojo and Dynamism"

It seems to be more fussy about compiler optimizations, though: https://github.com/rust-lang/rust/issues/125543

apimock-rs is one of my projects on API mock Server generating HTTP/JSON responses to help to develop microservices and APIs, written in Rust.

Rust, the language itself depends on 220 packages: https://github.com/rust-lang/rust/blob/e8753914580fb42554a79...

If you trust nobody, it is hard to use anything.

But about your second note, (environment, mismatched dependencies), I would argue that Rust provides the best tooling to solve or identify issues on that area.

rustc source code

First look is into The Unstable Book. Well, it does not look informative but gives us some background from the rust-lang Github project-const-generics. It says:

Rust has been around for several years and works well as a system and general programming language. There are many fine introductions to the language, a good place to start is here: https://www.rust-lang.org/

‍For the rest of this post I’ll list off some more tactical examples of things that you can do towards this goal. Savvy readers will note that these are not novel ideas of my own, and in fact a lot of the things on this list are popular core features in modern languages such as Kotlin, Rust, and Clojure. Kotlin, in particular, has done an amazing job of emphasizing these best practices while still being an extremely practical and approachable language.

> There are online Rust compilers and interpreters already if you just want to rapid prototype and develop ideas in Rust

You are responding to one of the key developers of Rust early on[1], who's been working with the language for 14 years at that point.

[1] https://github.com/rust-lang/rust/graphs/contributors?from=2... and he's still #16 in commits overall today, despite almost no activity on the rust compiler since 2014.

If you haven't dipped your touch-typing fingers into Rust yet, you really owe it to yourself. Rust is a modern programming language with features that make it suitable not only for systems programming -- its original purpose, but just about any other environment, too; there are frameworks that let your build web services, web applications including user interfaces, software for embedded devices, machine learning solutions, and of course, command-line tools. Since a custom GitHub Action is essentially a command-line tool that interacts with the system through files and environment variables, Rust is perfectly suited for that as well.

Here's an example of someone citing a disagreement between CRT and shell32:

https://github.com/rust-lang/rust/issues/44650

This in addition to the Rust CVE mentioned elsewhere in the thread which was rooted in this issue:

https://blog.rust-lang.org/2024/04/09/cve-2024-24576.html

Here are some quick programs to test contrasting approaches. I don't have examples of inputs where they parse differently on hand right now, but I know they exist. This was also a problem that was frequently discussed internally when I worked at MSFT.

    #include