jlrs VS evcxr

And in the rare case that Julia isn't enough, Julia-Rust interop is pretty good! Julia can call Rust using ccall and Rust can call Julia using the jlrs crate.

Github

While there is Jlrs , there is no seamless and easy way to call Rust. We need 'rustcall' on a level of 'pycall'.

I have a roadmap issue with some ideas/designs/arguments https://github.com/Taaitaaiger/jlrs/issues/66

An option for Julia/Rust interop is jlrs

jlrs automates some of this, using stack frames to garantee that Julia won't gc the references you're holding in Rust. It also provides ways to coerce Rust structs into Julia structs, and it does a bunch of other stuff I'm probably forgetting.

Github

Emacs didn't invent REPL, and it's common everywhere. For Rust: https://github.com/evcxr/evcxr/blob/main/evcxr_repl/README.m.... But heck, the compiler is reasonably fast enough that any IDE can REPL by compiling the code.

The value here is more in being able to read a script before you run it, then have it run fast, maybe tweaking something here and there. And a compiled script will run 10,000 times faster than LISP, which can be important.

https://github.com/evcxr/evcxr can run Rust in a Jupyter notebook. It's not Golang but close enough.

The engine uses rust_decimal::Decimal to represent high precision decimal numbers, like the weight property. Serialization of RocksDB keys is done by the storekey crate. To know how Yumi's machine stores diffs, we can now ask- How does storekey serialize rust_decimal? Well, using evcxr to run Rust in Jupyter, the answer is as a null-terminated string:

In theory you should be able to create Rust notebooks (Jupyter notebook) using evcxr so maybe some AI, data analysis, prototyping make sense if you aim for good performance in final application (protype in evcxr and use notebook as reference to implement final application in Rust for speed and safety).

You should check out evcxr

There is also implementations of rust REPLs, like the beautifully named evcxr.