this limitation has a purpose - it allows us to have a more efficient matching algorithm implementation, which i don’t think is immediately obvious and i don’t think even experts in the field fundamentally “get” this. before you “fix” the problem and turn my linear matching algorithm into a quadratic one, make sure you thoroughly understand why we have this limitation. the reason is that we have only one “kind” of match, and marking the end of the position is always correct. with arbitrary lookarounds, instead of marking the match ended here, you would have to mark the match for lookaround #7 ended here, and the match for lookaround #3 ended 5 characters ago, and the match for lookaround #12 ended 100 characters ago. i had a lot of temptation to support arbitrary lookarounds, and it really does seem like an easy fix at first glance, but the complexities arise much later in the match loop, and this is a good example of how some theory just does not hold up when you implement it. as a side note, we can actually loosen this restriction on R3 (above) which is still work in progress, but not R1, because R1 is the one that determines the start position of the match.
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.
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