mojo.js VS triton

Have you looked at https://mobx-keystone.js.org/ by any chance?

I've been quite enjoying keystone + lit (or react if required) of late and at the very least it feels like solid prior art towards MVC stuff.

Also https://mojojs.org/ for server side.

(I'm not saying "use these" even if I quite enjoy using them myself, but they're my reference points for comparison here)

Mojo.js

A final interesting note is that the Mojolicious developer has also recently(-ish) created Mojo.js, which is Mojolicious implemented in Typescript.

mojo.js, it's a very old school web framework.

Express middleware support is on the planned feature list for 2.0 and beyond. Could also be implemented as a 3rd party plugin using the existing hook API though. https://github.com/mojolicious/mojo.js/issues/66

Just in case you missed it, Mojolicious is also available for TypeScript now, and the developer experience is pretty close to the Perl original. ;) https://mojojs.org

Aside from NestJS there is also mojo.js still innovating on the old MVC formula with TypeScript.

There's a ton of cool opportunity in the runtime layer. I've been keeping my eye on the compiler-based approaches. From what I've gathered many of the larger "production" inference tools use compilers:

- https://github.com/openai/triton

Does anyone have more inside knowledge from OpenAI or AMD on AMDGPU support for Triton?

I see this:

https://github.com/openai/triton/issues/1073

But it's not clear to me if we will see AMD GPUs as first class citizens for pytorch in the future?

I thought this was a good overview of the idea Triton can circumvent the CUDA moat: https://www.semianalysis.com/p/nvidiaopenaitritonpytorch

It also looks like they added MLIR backend to Triton though I wonder if Mojo has advantages since it was built on MLIR? https://github.com/openai/triton/pull/1004

I'm pretty happy with the setup, because it allows me to keep all the AI stuff and its dozens of conda envs and repos etc. seperate from my normal setup and "portable". It may have some performance impact (although I don't personally notice any significant difference to running it "natively" on windows), and it may enable some extra functionality, such as access to OpenAi's Triton etc., but that's currently neither here nor there.

Very cool development. There is too much busy work going from development to test to production. This will help to unify everything. OpenAI Triton https://github.com/openai/triton/ is going for a similar goal. But this is a more fundamental approach.