Tootfinder

Excelente resumen sobre el cambio de líder en el gobierno y partido laborista del Reino Unido de Gran Bretaña y Irlanda del Norte (para darlo su nombre oficial).
Burnham (vivo en la ciudad donde es alcalde) serš, sin duda, un mejoramiento, pero no un cambio de rumbo fundamental.
Keir Starmer da un paso al lado y el alcalde de Manchester toma el relevo al frente del gobierno britšnico

Keir Starmer da un paso al lado y el alcalde de Manchester toma el relevo al frente del gobierno británico
El primer ministro británico cede después de años de presión y anuncia su salida del número 10 de Downing Street junto a su dimisión como líder del partido laborista.

The Weapon Who Will Truly Define the Las Vegas Raiders’ Season https://heavy.com/sports/nfl/las-vegas-raiders/raiders-linderbaum-trench-warfare-impact/

Dependencies and Dataflow in Seed-Filter-Extend Pipelines
Shiv Sundram
https://arxiv.org/abs/2606.06811 https://arxiv.org/pdf/2606.06811 https://arxiv.org/html/2606.06811
arXiv:2606.06811v1 Announce Type: new
Abstract: Comparing genomes is critical for discovering mutations, tracking evolutionary lineages, and advancing cross-species genomics. Fundamentally, this reduces to an O(n^2) string-matching dynamic programming (DP) problem, a challenge that has driven decades of performance research. However, executing a strict O(n^2) DP algorithm is computationally intractable for genomes spanning millions to billions of base pairs. Consequently, modern aligners rely on global heuristics to identify thousands of candidate similarity regions between species. Unfortunately, these methods are burdened by complex serial dependencies. Once candidate regions are identified, the pipeline executes localized DP alignments, which introduce their own non-trivial heuristics and irregular data dependencies. While parallelizing dense, two-dimensional DP is a well-studied problem, accelerating this end-to-end pipeline is significantly more challenging. Parallelizing across candidate regions and offloading irregular, heuristic-laden local alignments to modern hardware (such as GPUs) remains a major hurdle. In this work, we address the challenge of overcoming these serial bottlenecks by optimizing the global pipeline across regions. We take inspiration from four papers: LASTZ, SegAlign, Darwin-WGA, and SNAP, synthesizing findings across each to inform optimizations, which we either prototype or implement directly in LASTZ.
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