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The exact sequence of API calls to use is arcane, and there are multiple ways to perform this process, each of which has different tradeoffs that are not clear to most developers. This process generally just needs to be memorized or generated by a tool for you.

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You immediately must scale “the wall” of wrangling the many different pieces to work together. The end result is often only worth it for large projects.

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After OpenAI released GPT-5.3-Codex (high) which performed substantially better and faster at these types of tasks than GPT-5.2-Codex, I asked Codex to write a UMAP implementation from scratch in Rust, which at a glance seemed to work and gave reasonable results. I also instructed it to create benchmarks that test a wide variety of representative input matrix sizes. Rust has a popular benchmarking crate in criterion, which outputs the benchmark results in an easy-to-read format, which, most importantly, agents can easily parse.