monadless VS rust

Well `RecordWildcards` has been around for 14 years... but even without it instead of `{..}` you'd just have `_`s. The main thing that is different is that your Kind example had nested case statements while your Haskell example tried to match everything on one shot, which makes for a non-equivalent comparison.

> Not sure how that could work, though. Idris had an interesting syntax, but IIRC it wasn't general.

I assume you're talking about idiom brackets for applicatives? The general syntax is given in something like https://github.com/monadless/monadless. The idea is to basically take async-await syntax and generalize it to any monad.

So e.g. your `Maybe` example (using `!` for the equivalent of `await` for concision) would look like

  Maybe {

> If anything, async-await feels like an extremely non-functional thing to begin with

It, like many other things, forms a monad. In fact async-await is a specialization of various monad syntactic sugars that try to eliminate long callback chains.

Hence things like Haskell's do-notation are direct precursors to async-await (some libraries such as Scala's monadless https://github.com/monadless/monadless make it even more explicit, there lift and unlift are exactly generalized versions of async and await).

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.