Frank Krueger writes about his C# and F# tools, iOS apps like iCircuit, and experiments in AI and GPU computing.
HN commenters mention it for his iPad IDE, cuneiform translation with ML, and webgpu-torch, and call his work magnificent.
That's actually not what we see with our customers and their apps.
Here is a real-world example of code sharing percentages for an iOS, Android, and Mac app written in C#:
http://lipsky.me/blog/2012/9/11/touchdraw-code-reuse-updated
And here is another one across iOS, Android, Mac, and Windows, also in C#:
http://praeclarum.org/post/31799384896/icircuit-code-reuse-the-fourth-edition
Both of these apps are using 100% _native widgets_ for their user interface, and I think it's fair to say that both of them are fairly UI-heavy. And yet they average >70% code sharing.
It should also be pointed out that these apps were written from scratch. In fact, Jon Lipsky didn't even know C# before he started writing TouchDraw (the first app I linked).
Yes, I actually liked VS Code a lot, and I was an emacs user before that. MSVS is pretty good too. I am still using F# here and there for small, personal projects but I use my iPad Pro (2015) with Frank Kruger's [1] Continuous app[2], an F# and C# IDE with a built-in compiler, syntax highlighting, code completion, and live debugging. Frank is a juggernaut in .NET and many other eclectic things within the tech domain! Check him out. Continuous is the best way to play with code outside of sitting at your desktop, and even then too!
[1] https://praeclarum.org/
[2] http://continuous.codes/
https://praeclarum.org/webgpu-torch/tests/
This is a dumb question but... are GPUs really that much faster than CPUs specifically at the math functions tested on this page?
xlogy trunc tan/tanh sub square sqrt sin/sinc/silu/sinh sign sigmoid sqrt/rsqrt round relu reciprocal rad2deg pow positive neg mul logaddexp/logaddexp2 log/log1p/log10/log2 ldexp hypot frac floor expm1 exp2 exp div deg2rad cos/cosh copysign ceil atan/atan2 asinh/asin add acosh/acos abs
Those are the types of math GPUs are good at? I thought they were better at a different kind of math, like matrices or something?
There are a few cases where people have used ChatGPT to generate SVG[0], with mostly unimpressive results. I'm sure sooner or later models will be developed specifically for creating vector drawings instead of raster, including with the ability to apply textures and gradients.
Also, the resolution of Stable Diffusion's output isn't much of a limitation if you're willing to use other tools to massage the output into something professional-quality. See [1]
[0] For example: https://praeclarum.org/2023/04/03/chatsvg.html
[1] https://news.ycombinator.com/item?id=35592847
I see your point. Closures also don't affect structure on conceptual level, but I think they're pretty darn useful. By the way, async can affect things on conceptual level if you embrace[1] it (I posted this link somewhere below as well, but just in case you haven't seen it).
[1]: http://praeclarum.org/post/45277337108/await-in-the-land-of-ios-scripting-users
Not my app, but you could check out iCircuit's experience:
http://praeclarum.org/post/31799384896/icircuit-code-reuse-the-fourth-edition
He achieves anywhere from 69-87% code reuse depending on the platform. They did a bit of a case study a while ago where Frank talks about it - http://blog.xamarin.com/2011/10/26/java-to-ipad-in-2-months/