ComputeSharp VS go

A couple other options than what has been suggested so far: - TerraFX.Interop.Windows. Raw, blittable, 1:1 bindings for all Win32, D2D/D3D11/D3D12 APIs (there's also a version with Vulkan bindings). As close to doing #include as you can get in C#. This is my personal favorite, I use it in my own ComputeSharp library, and transitively we use it in the Microsoft Store too 🙂 - Silk.NET another version of high-performance bindings, more opinionated than TerraFX and with some additional helpers to make it a bit easier to use.

A hello world .NET console app with NativeAOT is 1.08 MB. Goes down to 1.08 MB with some tweaking. Goes below 1MB with more tweaking. I have a fully self-contained NativeAOT sample app that renders some fancy animated shaders with DX12 that's about 2MB in size.

I dont think it's exactly what you're looking for, but I've found ComputeSharp to be a great C# GPU tool

Here's some: - MVVM Toolkit - PolySharp - ComputeSharp

If you don't mind being Windows only, I would also recommend trying out (shameless plug) ComputeSharp. You can find it on NuGet and it's available for UWP and WinUI 3 as well with ready to use XAML controls. The point is that it would allow you do your entire rendering via pixel/compute shaders, so it could basically run entirely on the GPU. That'd allow you to render even millions of particles with pretty good performance 🙂

Create a UWP or WinUI 3 applicaation and use the AnimatedComputeShaderPanel from ComputeSharp. The library lets you write shaders (ie. code to run on the GPU) entirely in C#, and handles all the logic to setup a DX12 animated swapchain, similar to Win2D. If you look at the repo you can also see a whole bunch of examples, which you can also try out for yourself. There's also several wiki pages with lots of info.

Now, I’m not going to use C++ again; I left that chapter years ago, and it’s not going to happen. C++ isn’t memory safe and easy to use and would require extended time for developers to adapt. Rust is the new kid on the block, but I’ve heard mixed opinions about its developer experience, and there aren’t many libraries around it yet. LLRD is too new for my taste, but **Go** caught my attention.

Generative AI development has been democratised, thanks to powerful Machine Learning models (specifically Large Language Models such as Claude, Meta's LLama 2, etc.) being exposed by managed platforms/services as API calls. This frees developers from the infrastructure concerns and lets them focus on the core business problems. This also means that developers are free to use the programming language best suited for their solution. Python has typically been the go-to language when it comes to AI/ML solutions, but there is more flexibility in this area. In this post you will see how to leverage the Go programming language to use Vector Databases and techniques such as Retrieval Augmented Generation (RAG) with langchaingo. If you are a Go developer who wants to how to build learn generative AI applications, you are in the right place!

net/http: add methods and path variables to ServeMux patterns Discussion about ServeMux enhancements

Make sure you have Go installed https://go.dev/.

The Go Programming Language

When Go first shipped, it was already well-documented that the only stable ABI on some platforms was via dynamic libraries (such as libc) provided by said platforms. Go knowingly and deliberately ignored this on the assumption that they can get away with it. And then this happened:

https://github.com/golang/go/issues/16606

If that's not "getting burned", I don't know what is. "Trying to provide a nice feature" is an excuse, and it can be argued that it is a valid one, but nevertheless they knew that they were using an unstable ABI that could be pulled out from under them at any moment, and decided that it's worth the risk. I don't see what that has to do with "not being as broadly compatible as they had hoped", since it was all known well in advance.

Sadly, I think that is indeed radically different from Go’s design. Go lacks anything like sum types, and proposals to add them to the language have revealed deep issues that have stalled any development. See https://github.com/golang/go/issues/57644

I've been writing a lot about Go and gRPC lately: