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The second approach offers broader feature support, seen in projects like Cloud Hypervisor or QEMU microvm. Built for heavier and more dynamic workloads, it supports hot-plugging memory and CPUs, which is useful for dynamic build runners that need to scale up during compilation. It also supports GPU passthrough, which is essential for AI workloads, while still maintaining the fast boot times of a microVM.

To this end, I have written a small C library for the sole purpose of generating the Delaunay triangulation of a colour palette and using the resulting structure to perform barycentric interpolation as well as natural neighbour interpolation for use in colour image dithering. The library and source code is available for free on Github. I’ve included an additional write up that goes into a bit more detail on the implementation and also provides some performance and quality comparisons against other algorithms. Support would be greatly appreciated!

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Developers using the streams API are expected to remember to use options like highWaterMark when creating their sources, transforms, and writable destinations but often they either forget or simply choose to ignore it.

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