magrittr VS prusti-dev

R (yes, the statistics language) has exactly this.

You can literally extract the body of a function as a list of "call" objects (which are themselves just dressed-up lists of symbols), inject/delete/modify individual statements, and then re-cast your new list to a new function object.

I don't know why the original devs thought this was necessary or even desirable in a statistics package, but it turns out to be a lot of fun to program with. It has also made possible a wide variety of clever and elegant custom syntaxes, such as a pipe infix operator implemented as a 3rd-party library without any custom language extensions [0]. The pipe infix operator got so popular that it was eventually made part of the language core syntax in version 4.1 [1].

[0]: https://magrittr.tidyverse.org/

[1]: https://www.r-bloggers.com/2021/05/the-new-r-pipe/

In R (thanks to magrittr) you can now perform operations with a more functional piping syntax via %>%. This means that instead of coding this:

Try reading the documentation for magrittr.

Related note: the statistical programming language R has a library named magrittr to support the pipe operator.

About the pipe (%>%) symbol, it's provided by the magrittr package. The package documentation details how to use the pipe operator.

I was wondering what some good ways are to handle nested function calls without chaining them in long, ugly nested statements. I am looking for functionality similar to the pipe forward operator %>% in magrittr/R or |> in F#.

You may like R: https://magrittr.tidyverse.org

Agreed on everything else you said (especially the type safety stuff, it massively helps in production), but one correction: magrittr is absolutely in the tidyverse suite. It's not considered one of its "core" packages that it visibly tells you it loads, but magrittr is loaded when calling library(tidyverse) and development of the package is handled by the tidyverse team under their Github account: https://github.com/tidyverse/magrittr

> The overall goal would be to figure out classical error conditions like nill pointers deference.

> If I can figure out if a pointer will be nil in some execution branch, there is no reason why a computer cannot do the same.

Note, this is called flow-sensitive typing (also called type narrowing) and I think that typescript does it.

https://en.wikipedia.org/wiki/Flow-sensitive_typing

> I personally would see this as an human race level upgrades. Imagine feeding your code to a CI that spit back something like: "you will have a panic at line 156 when your input is > 4"

A model checker can do that!

See this

https://model-checking.github.io/kani/tutorial-kinds-of-fail...

Other techniques are also possible

https://github.com/viperproject/prusti-dev#quick-example

(Here I could link a lot of things, I just selected two Rust projects to illustrate)

This works better if you are able to provide contracts in your API that says which guarantees you provide. Alternatively, asserts are useful too.

You might be interested in the Prusti project, which statically checks for absence of reachable panics, overflows etc. It also allows user-defined specifications such as pre and post-conditions, loop body invariants, termination checking and so on.

https://github.com/viperproject/prusti-dev

This is called refinement types and prusti might be the project you saw.

But there's also a lot of exciting work around formal verification like Prusti.

prusti

Efforts: seL4, Project Everest, the Prossimo project of the ISRG, Let's Encrypt, and Prusti for the Rust language

Wow that sounds really cool! I'm not an expert but does that mean that one day you could implement dependend types or refinement types in Rust as a crate ? I currently only know of tools like: Flux Creusot Kani Prusti