PythonHub

Building a small Django app with help from AI This tutorial demonstrates how to build a basic Django idea-voting application using AI as an interactive assistant rather than an autonomous developer. It emphasizes using targeted prompts to generate boilerplate, explain error messages, write tests, and catch edge cases like database race conditions while keeping architectural decisions in the developer’s hands. https://codewithstein.com/building-a-small-django-app-with-help-from-ai/

Python Hub Weekly Digest for 2026-10-25 https://pythonhub.dev/digest/2026-10-25/

How I Found a $90,000 Remote Code Execution Vulnerability in Google A leaked Flask session signing secret let an attacker forge session cookies containing malicious pickled data, turning OAuth state handling into unauthenticated remote code execution on several Google developer sites. The writeup shows how combiningpicklewith client-controlled session data can turn a secret leak into code execution, and why untrusted data should never cross a pickle dese... https://simonkoeck.com/writeups/google-devsite-flask-session-pickle-rce

Bridging between Scala & Python with GraalVM This talk explores using GraalVM to enable seamless interoperability between Scala 3 and Python 3 code, focusing on generating idiomatic Scala facades for Python libraries. It details strategies for handling dynamic Python concepts, such as type hints, structural typing, and decorators, within the static and nominal type system of Scala. https://www.youtube.com/watch?v=EnEVtmbr-uM

How to Build a Decision Model Like Jev Learn how to build AI decision models that return calibrated probabilities for classification, scoring, and routing tasks without generating text, using Hugging Face Transformers and the /v1/systemone API. The guide covers custom option tokens, model fine-tuning, probability calibration, and tree batching to improve accuracy, reduce latency, and lower inference costs. https://www.fratepietro.com/2026/how-to-build-a-decision-model-like-jev/

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/

Introducing TorchTPU TorchTPU is an open source collaboration between Google and Meta that introduces a native and principled PyTorch backend for Google TPUs. It enables seamless out-of-the-box model execution and TPU migration while delivering industry-competitive inference and training performance through eager execution optimizations, torch.compile integration, and native hardware acceleration. https://pytorch.org/blog/introducing-torchtpu/