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How to Run Turing-Complete Malware in PyTorch's Backward Pass

Elon Litman demonstrates how a malicious PyTorch loss function can report a value of zero while secretly manipulating model weights through the backward pass. By exploiting differences between floating-point arithmetic and automatic differentiation, he shows how gradient descent can execute arbitrary computations inside a neural network, creating a potential attack vector for AI training...

https://elonlit.com/scrivings/how-to-run-turing-complete-malware-in-pytorchs-backward-pass/
django-lifecycle - hooks for Django models (Signals alternative)

Learn how to usedjango-lifecycleas an alternative to Django signals, with declarative hooks that run when models are created, updated, deleted, or saved. The package also supports conditional hooks that trigger only when specific fields change or transition between particular values.

https://www.youtube.com/watch?v=sNdopNFOEB8
Decoding base64 in Python at 10 gigabytes per second

Daniel Lemire introduces fastpybase64, a Python library built on SIMD-accelerated simdutf that provides a drop-in alternative to Python's standard base64 module. In single-threaded benchmarks decoding Base64 data containing line breaks, it achieves over 13 GB/s throughput, approximately 27 times faster than the standard library and four times faster than pybase64.

https://lemire.me/blog/2026/10/17/decoding-base64-in-python-at-10-gigabytes-per-second/