The data collected enables strategic decisions about content creation and optimization. If certain queries consistently show competitor sources but never yours, that signals an opportunity to create or improve content addressing that topic. If you're appearing reliably for some questions but not others in the same category, you can analyze what makes your successful content different and apply those lessons to underperforming pieces. If your visibility is declining over time, you know you need to refresh and strengthen your content to maintain AI citation rates.
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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.
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