This month, OpenAI announced their Codex app and my coworkers were asking questions. So I downloaded it, and as a test case for the GPT-5.2-Codex (high) model, I asked it to reimplement the UMAP algorithm in Rust. UMAP is a dimensionality reduction technique that can take in a high-dimensional matrix of data and simultaneously cluster and visualize data in lower dimensions. However, it is a very computationally-intensive algorithm and the only tool that can do it quickly is NVIDIA’s cuML which requires CUDA dependency hell. If I can create a UMAP package in Rust that’s superfast with minimal dependencies, that is an massive productivity gain for the type of work I do and can enable fun applications if fast enough.
更关键的是，它们通过“资本闭环”深度绑定下游订单：英伟达投资OpenAI并获得长期采购承诺，台积电则凭借先进制程的垄断优势，锁定了所有主流AI芯片客户。。关于这个话题，搜狗输入法2026提供了深入分析
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