Unfortunately, the tenting doesn’t work for me. Because of the extra keys at the outer edges, raising the middle edges upwards lifts the center keys considerably, which brings my wrists and forearms off the desk instead of letting them rest. Holding them like that created extra neck and shoulder strain on my part, which is sort of the opposite of the goal. But if you’re not into tenting anyway and want a flat, Alice-split board with an adjustable splay, this works quite well.
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It meant Collins and her crew would make it safely home.
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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.