2026-03-10: Rewrote the NumPy constraints paragraph. The original listed "irregular access patterns, conditionals per element, recursive structures" as things NumPy can't handle. Two of those were wrong: NumPy fancy indexing handles irregular access fine (22x faster than Python on random gather), and np.where handles conditionals (2.8-15.5x faster on 1M elements, even though it computes both branches). Replaced with things NumPy actually can't help with: sequential dependencies (n-body with 5 bodies is 2.3x slower with NumPy), recursive structures, and small arrays (NumPy loses below ~50 elements due to per-call overhead).
В Европе ответили на призыв Трампа по Ормузскому проливу14:49
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Пьяный чиновник из крупного города покусал мужчину в туалете и забыл об этом20:49
coprime_spokes[wheel.n_spokes] = i;