Собрали в одном месте самые важные ссылкии сделали Тренажер IT-инцидентов для DevOps/SRE
Monte Carlo simulations (or methods) is the technique of applying randomness and the Law of large numbers to the solution of various scientific and engineering problems.
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A written guide to my talk on deploying Django and why 90% of it is the same.
SIMD is Single Instruction, Multiple Data, an approach that does calculations with vectors of data sets. Python doesn’t support it natively, but libraries like NumPy allow you to code this way.
Hardcoded paths and parameters can quickly drift out of sync across a data science project’s scripts. This article shows how to move them into Hydra configuration files, access values with dot notation, override settings from the command line, swap entire configuration groups, and run experiment sweeps with a single multirun flag.
A recap on the work done in NumPy and CPython to make multi-threaded NumPy workloads scale on the free-threaded build of CPython.
Floating point math is often slower than integer math because the compiler is being conservative about how it optimizes your code. While some programming languages already had solutions of a sort, until now Rust did not have a good stable way to deal with this limitation. But now, starting in version 1.98, Rust will allow telling the compiler it can optimize your code further—but with extra control so that you can still write numeric algorithms with minimal rounding errors.