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 submitted by /u/ppchaos [link] [留言]

Your mom never gets you a thoughtless gift, so you shouldn’t get her one, either. Here’s every cool gift WIRED writers would give their mothers.

Grafana Labs has expanded the capabilities of Grafana Assistant, enabling its AI-powered observability assistant to query and correlate data across more than 30 different data sources through natural language. By Craig Risi

Over the last few months, I've spent a lot of time talking to my computer. One underrated feature of the LLM revolution has been a remarkable leap in all kinds of dictation technology - even the fastest, cheapest models are getting very good at understanding and processing speech. I've tested lots of these apps, from […]

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Article URL: https://windowsforum.com/windows-news.4/asrock-4u16x-gnr2-packs-8-b300-gpus-demands-liquid-cooling.440701/ Comments URL: https://news.ycombinator.com/item?id=49082366 Points: 4 # Comments: 1

Should you splurge on the new Series 11, or will the SE 3 do? Let us help you figure out which version to get (and which to avoid).

Let them pick out their own hiking boots. Instead, try gifting a useful blade or a nature journal to delight your outdoorsy friend.

A few months ago, my AI coding workflow looked something like...

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? submitted by /u/DotDeveloper [link] [留言]

Researchers investigating Hugging Face found the majority of its models would generate non-consensual deepfakes.

Ben Greene discusses how software engineers can adapt and thrive in an era of rapid AI code automation. Drawing on his startup experience, he explains key mindsets like starting simple, maintaining code comprehension, attacking hard problems first, and focusing on customer impact. He shares why human empathy, agency, and practical problem-solving remain irreplaceable when code is automated. By Ben Greene

submitted by /u/arauhala [link] [留言]