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Natural neighbour interpolation has a number of strengths over linear barycentric interpolation. Namely, it provides a smooth or continuous slope between samples3, and is always the same for a given point set, unlike the TIN where the quality of the triangulation can produce biases in the outcome, even in the best case.

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1. Weight by max same-font SSIM, not binary membership. If any font produces SSIM = 0.999, the pair is maximum risk regardless of how it scores in other fonts. Users do not control which font their browser chooses. The 82 pixel-identical pairs should be treated as definite blocks. The 49 high-scoring pairs should be treated as likely blocks. The 611 low-scoring pairs can be treated as informational warnings rather than hard rejections.

However, due to modern LLM postraining paradigms, it’s entirely possible that newer LLMs are specifically RLHF-trained to write better code in Rust despite its relative scarcity. I ran more experiments with Opus 4.5 and using LLMs in Rust on some fun pet projects, and my results were far better than I expected. Here are four such projects:

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