skiko VS mgl

All I know is that they use skiko.

It built on a render framework named skia, JetBrains create a kotlin mapping which named skiko for it.

I was curious to give it a go but unusable on Gnome 42.5; the window lags so much that simply trying to drag to reposition it on the screen is impossible seems related an issue with Skiko and OpenGL reported here: https://github.com/JetBrains/skiko/issues/494

Fleet does not use Compose, but it does use Skiko[1], which also provides binding for Skia[2] (the native graphics library also used by Chrome & Flutter).

The main difference between the libraries is that Skija provides Java/JVM bindings for Skia, whereas Skiko provides Kotlin bindings for Kotlin/JVM, Kotlin/JS, and Kotlin/Native targets. Of course Skiko's Kotlin/JVM bindings can be used with other JVM languages, not just with Kotlin.

[1] https://github.com/JetBrains/skiko

[2] https://skia.org/

How about skiko?

JetBrains is moving from skija to skiko, but it could be interesting either way!

They Merged today the functions that will be usaged on Compose for opening, closing and handling text inputs https://github.com/JetBrains/skiko/pull/455 (if I understand correctly). Currently, the examples rely on xcodegen file, based on the currently plugin that Compose has for Desktop, it provide alot of things including packaging for desktop, I think they will provide a xcodegen file automatically and use xcodegen behind the scenes to generate everything needed.

https://github.com/JetBrains/skiko (a Kotlin MPP binding for Skia). Skiko Does not contain build for iOS. Now Skiko contains build for iOS, MAC arm processor via Kotlin/Native. So we can use Skiko for pure canvas rendering in all platform. It is matter of time they release compose for iOS. So UI also goes multiplatform.

But it does seem like the solution they're using for Fleet is different. Skia is the same graphics library used by Chromium and Flutter. I'll remain cautiously optimistic for now, but the quality of the text rendering and customization options will make or break it for me personally.

If you are interested in machine learning, check out Gabor Melis's library: https://github.com/melisgl/mgl. It's not an area I'm super familiar with, so I can't speak to it's feature set, but I believe he used it to win a machine learning competition some years ago.

I don't think anyone's written a transformer or diffusion model with it, could be a fun challenge.

although not necessarily bert or resnet the following probably has all the ingredients for what you are looking for. the author of this library is a research scientist at deepmind since 2015

https://github.com/melisgl/mgl#x-28MGL-BP-3A-40MGL-BP-20MGL-...