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og:description: A podcast conversation with Aidan Fitzpatrick, CEO of Reincubate, maker of Camo Studio and Camo Streamlight, covers building native camera and lighting...
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# On Your Desktop: Aidan Fitzpatrick (CEO of Camo) on Building Award-Winning Native Apps

**[Windows Developer](https://daily.dev/sources/windowsdevelopers)** · 37 min read · 0 upvotes · 0 comments

## Summary

A podcast conversation with Aidan Fitzpatrick, CEO of Reincubate, maker of Camo Studio and Camo Streamlight, covers building native camera and lighting software across six platforms, the company's journey through on-device ML on Windows (DirectML, QNN, OpenVINO, ONNX Runtime, and now Windows ML), engineering constraints like fitting ML processing into 16ms per frame for 60fps video, why NPUs offer better efficiency than GPUs for sustained workloads, the shift to using AI coding assistants like GitHub Copilot for internal development, business strategy around premium pricing, grandfathering, MSIX/Microsoft Store distribution, and partnerships with HP and Razer.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.youtube.com/watch?v=FtTSD1F_dIc>

## Questions this post answers

### How much time do you have to process each video frame for real-time camera effects at 60fps?

You have about 16 milliseconds per frame to process the ML effect while also handling everything else the video pipeline needs, such as work being done by Windows, Teams, or Zoom simultaneously. This tight budget is why on-device processing must be extremely efficient, and why NPUs are often favored over GPUs since they deliver the needed performance without the same battery and thermal cost.

_Developers optimizing real-time video pipelines can track NPU and on-device ML developments on daily.dev._

### What is Windows ML and how does it differ from using DirectML or vendor-specific ML runtimes like QNN or OpenVINO?

Windows ML is Microsoft's current on-device ML framework that dynamically pulls the execution providers needed for whatever hardware is present, letting a single model run without heavy re-quantization work. It succeeded an evolution that started with DirectML, then moved to vendor-specific stacks like QNN for Qualcomm and OpenVINO for Intel, then to Microsoft's ONNX Runtime (ORT), and now Windows ML, which developers describe as far more straightforward than earlier approaches.

_Teams choosing an on-device ML stack for Windows can follow these framework shifts on daily.dev._

## Similar posts on daily.dev

- [Accelerate Showcase: Built with AI, Designed with Stitch](https://daily.dev/posts/accelerate-showcase-built-with-ai-designed-with-stitch-bmzxkgvaw) · Avalonia UI Blog · 11 upvotes · 0 comments

---

Tags: [#windows](https://daily.dev/tags/windows), [#npu](https://daily.dev/tags/npu)

[View this post on daily.dev](https://daily.dev/posts/on-your-desktop-aidan-fitzpatrick-ceo-of-camo-on-building-award-winning-native-apps-gwm8zczzw)

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