First I mapped all that I could recall from memory, pancakes, crepes, waffles, scrambled eggs, popovers, omelettes, and on and on, scouring my brain for every fast I had ever broken. The beginnings of the contours of breakfast began to reveal themselves. A gaping hole stared back at me, but I couldn’t yet be sure. I had to search the dark corners of the world to see if somewhere in far off lands that abyss had yet been filled. I called upon friendly ghosts. I paged through ancient tomes. I added kaiserschmarrn, swedish pancakes, dan bing, madeleines, crumpets, clafoutis, blinis, pannu kakku, parathas, nalesniki. The map filled in bit by bit, but it was no use. The gap in the fabric of breakfast remained.
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.
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