Tootfinder

“Apple is only continuing to patch iOS 18 on devices that can’t run iOS 26.”
https://arstechnica.com/gadgets/2026/01/are-people-avoiding-ios-26-because-of-liquid-glass-its-complicated/
Wow. So not only has Apple los…

Are people avoiding iOS 26 because of Liquid Glass? It’s complicated.
Liquid Glass is controversial, but adoption rates aren't as low as they seem.

from my link log —
Avoiding fallback and cold paths.
https://timkellogg.me/blog/2021/01/29/cold-paths
saved 2023-11-03 https://…

At 5:49 p.m., air traffic controllers told pilots over the Caribbean that a SpaceX Starship rocket had exploded.
All planes were ordered to avoid an area where the Federal Aviation Administration estimated debris would fall
launches exploded at unexpected points on their flight paths,
Last year, three of Starship’s five launches exploded at unexpected points on their flight paths,
twice raining flaming debris over congested commercial airways and disrupting flights. …

“We’re Too Close to the Debris”
The FAA predicted Elon Musk’s Starship would cause “minor or minimal” disruption. Then the rockets exploded twice in three months over busy airspace. Flight data reveals how many planes scrambled to protect passengers and avoid burning debris.

Fast Sparse Matrix Permutation for Mesh-Based Direct Solvers
Behrooz Zarebavami, Ahmed H. Mahmoud, Ana Dodik, Changcheng Yuan, Serban D. Porumbescu, John D. Owens, Maryam Mehri Dehnavi, Justin Solomon
https://arxiv.org/abs/2602.00898 https://arxiv.org/pdf/2602.00898 https://arxiv.org/html/2602.00898
arXiv:2602.00898v1 Announce Type: new
Abstract: We present a fast sparse matrix permutation algorithm tailored to linear systems arising from triangle meshes. Our approach produces nested-dissection-style permutations while significantly reducing permutation runtime overhead. Rather than enforcing strict balance and separator optimality, the algorithm deliberately relaxes these design decisions to favor fast partitioning and efficient elimination-tree construction. Our method decomposes permutation into patch-level local orderings and a compact quotient-graph ordering of separators, preserving the essential structure required by sparse Cholesky factorization while avoiding its most expensive components. We integrate our algorithm into vendor-maintained sparse Cholesky solvers on both CPUs and GPUs. Across a range of graphics applications, including single factorizations, repeated factorizations, our method reduces permutation time and improves the sparse Cholesky solve performance by up to 6.27x.
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