oxipng VS rust

I have had good experiences with https://github.com/shssoichiro/oxipng . Although, I suspect this wouldn't give you nearly enough space savings as jpg.

If you do want the file as a PNG (for transparency and a common format that's well supported), but don't want it so huge, consider something like oxipng. https://github.com/shssoichiro/oxipng

oxipng, pngquant and svgcleaner — optimizing images

I wonder how `pngcrush` compares to `oxipng` (https://github.com/shssoichiro/oxipng).

Personally, I use `oxipng` if I want lossless compression. However, most of the time, I use `pngquant` instead, since it gives significant size reduction even at `99%` (I can't even distinguish between the original and reduced image).

    pngquant --quality=99 --ext=.png --force file.png

Depending on your workflow it might make sense to export PNGs directly from Affinity and then reduce their size with a utility like Oxipng, which uses all your cores to find the best algorithm for each particular image.

Assuming your frames are PNG files, you could use a lossless optimizer like optipng to try if their size can be reduced. I prefer oxipng, which is faster and multithreaded, and seems to have more active development.

Not sure what extensions of images you use, but if they’re PNG you could use oxipng: https://github.com/shssoichiro/oxipng

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.