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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!
。关于这个话题，Safew下载提供了深入分析
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As a data scientist, I’ve been frustrated that there haven’t been any impactful new Python data science tools released in the past few years other than polars. Unsurprisingly, research into AI and LLMs has subsumed traditional DS research, where developments such as text embeddings have had extremely valuable gains for typical data science natural language processing tasks. The traditional machine learning algorithms are still valuable, but no one has invented Gradient Boosted Decision Trees 2: Electric Boogaloo. Additionally, as a data scientist in San Francisco I am legally required to use a MacBook, but there haven’t been data science utilities that actually use the GPU in an Apple Silicon MacBook as they don’t support its Metal API; data science tooling is exclusively in CUDA for NVIDIA GPUs. What if agents could now port these algorithms to a) run on Rust with Python bindings for its speed benefits and b) run on GPUs without complex dependencies?
I also knew that if I bought a jar of sauce, I’d use it once and the rest would sit in my fridge until it eventually went to waste. That’s when it clicked: why wasn’t there a perfectly portioned pasta and sauce kit that wasn’t precooked? It felt like there was a real need for something that reduced waste while delivering high-quality ingredients in just the right portions.