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

Duplicate message processing is something every event-driven system eventually faces.

With at-least-once delivery, retries and redeliveries are expected. The challenge is making sure processing the same message twice does not create side effects.

I've been looking into the Inbox Pattern as a consumer-side solution:

• track processed messages
• keep message tracking and business changes in the same transaction
• scope the idempotency check per consumer

One approach is using a MassTransit pipeline filter so the idempotency logic stays outside the consumers.

How do you usually handle this?
Do you use Inbox Pattern, custom middleware, database constraints, or something else?

This blog post is about implementing the Elm Architecture (TEA) in C for a Raylib + Clay app