evcxr VS rust

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.

Almost fixed the compiler: https://github.com/rust-lang/rust/pull/123325

Rust: A secure, efficient, and modern programming language (omitting ten thousand words). You can simply follow the installation instructions provided on the official website.

Recently did something similar in Rust but for generating SVGs. We've adopted it for snapshot testing of cargo and rustc's output. Don't have a good PR handy for showing Github's rendering of changes in the SVG (text, side-by-side, swiping) but https://github.com/rust-lang/rust/pull/121877/files has newly added SVGs.

To see what is supported, see the screenshot in the docs: https://docs.rs/anstyle-svg/latest/anstyle_svg/

We strongly believe in Rust as a powerful language for building production-grade software, especially for systems like ours that run alongside Kubernetes.

The above Assert<{N % 2 == 1}> requires #![feature(generic_const_exprs)] and the nightly toolchain. See https://github.com/rust-lang/rust/issues/76560 for more info.

Another week, another dive into Rust. This time, we're delving into structs. Structs bear resemblance to interfaces in TypeScript, enabling the grouping of intricate data sets within an object, much like TypeScript/JavaScript. Rust also accommodates functions within these structs, offering a semblance of classes, albeit with distinctions. Let's delve into this topic.

There’s also other reasons. For example, take binary search:

* prefetch + cmov. These should be part of the STL but languages and compilers struggle to emit the cmov properly (Rust’s been broken for 6 years: https://github.com/rust-lang/rust/issues/53823). Prefetch is an interesting one because while you do optimize the binary search in a micro benchmark, you’re potentially putting extra pressure on the cache with “garbage” data which means it’s a greedy optimization that might hurt surrounding code. Probably should have separate implementations as binary search isn’t necessarily always in the hot path.

* Eytzinger layout has additional limitations that are often not discussed when pointing out “hey this is faster”. Adding elements is non-trivial since you first have to add + sort (as you would for binary search) and then rebuild a new parallel eytzinger layout from scratch (i.e. you’d have it be an index of pointers rather than the values themselves which adds memory overhead + indirection for the comparisons). You can’t find the “insertion” position for non-existent elements which means it can’t be used for std::lower_bound (i.e. if the element doesn’t exist, you just get None back instead of Err(position where it can be slotted in to maintain order).

Basically, optimizations can sometimes rely on changing the problem domain so that you can trade off features of the algorithm against the runtime. These kinds of algorithms can be a bad fit for a standard library which aims to be a toolbox of “good enough” algorithms and data structures for problems that appear very very frequently. Or they could be part of the standard library toolkit just under a different name but you also have to balance that against maintenance concerns.