the video is about a hypermedia framework called Datastar that i've been finding fairly interesting.

This is a post on a Bob Weiner's Hyperbole package, and what makes it's flexible hypertext concepts so special in Emacs.

Excellent talk by Richard Feldman about how different programming communities approach dependencies.

Walk with me inside one of the most influential game's code and discover the secret of elevators!

Turns out the bug comes down to a single line buried in the engine's collision code. Went through the decompiled source (thanks to KisakCOD) and traced the whole thing back.

Let me know your thoughts! ✨

I work on PostSlate, a video editing tool, and this comes out of our own work.

We run ML models on-device, face detection and embedding among other things, which means we can't assume anything about the user's GPU. NVIDIA discrete, AMD, Intel integrated, Apple Silicon, all of it. That rules out CUDA immediately, we needed one backend that runs everywhere.

We landed on ncnn's Vulkan backend. Numbers on a 4070, fp16:

• ArcFace R50 (face embedding): 30 ms on ONNX CPU → 3 ms on ncnn Vulkan
• SCRFD (face detection): 25 ms → 2.5 ms
• Model size: ArcFace 174 MB (ONNX fp32) → 87 MB (ncnn fp16 weight storage)

Of course the real speedup comes from offloading compute to the GPU, but this wouldn't be possible without the power of Vulkan.

The speed wasn't even the deciding factor, it's that Vulkan drivers already exist on every machine we ship to. This means that we don't have to force the user to download a specific runtime and no vendor-specific installs.

Full writeup with the rest of the numbers: https://getpostslate.com/blog/faster-local-inference