Compact deep neural network models of the visual cortex

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score-all-pairs loads the render index and computes SSIM for every valid source/target combination. 235,625 comparisons, two modes: same-font (both characters in the same font) and cross-font (source in a supplemental font, target in a standard font).

Historically, LLMs have been poor at generating Rust code due to its nicheness relative to Python and JavaScript. Over the years, one of my test cases for evaluating new LLMs was to ask it to write a relatively simple application such as Create a Rust app that can create "word cloud" data visualizations given a long input text. but even without expert Rust knowledge I could tell the outputs were too simple and half-implemented to ever be functional even with additional prompting.

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Transforms don't execute until the consumer pulls. There's no eager evaluation, no hidden buffering. Data flows on-demand from source, through transforms, to the consumer. If you stop iterating, processing stops.