How to publish to PyPI using GitHub Actions securely

The post gives three practical steps for publishing Python packages to PyPI more securely from GitHub Actions: run zizmor, use Trusted Publishing, and require manual approval through GitHub environments. It focuses on reducing credential risk by removing long-lived PyPI tokens, pinning actions, limiting workflow permissions, disabling persisted checkout credentials, and adding a human ap...

https://snarky.ca/how-to-publish-to-pypi-using-github-actions-securely/

cupy / cupy

NumPy & SciPy for GPU

https://github.com/cupy/cupy

fortress

Stealth Chromium engine that stops scrapers and browser agents from getting blocked, with one line of code change.

https://github.com/tiliondev/fortress

bradautomates / claude-video

Give Claude the ability to watch any video. /watch downloads, extracts frames, transcribes, hands it all to Claude.

https://github.com/bradautomates/claude-video

You shouldn't trust Trusted Publishing

The post explains that PyPI Trusted Publishing is an authentication mechanism for machine-to-machine trust between CI/CD workflows and package registries, not a signal that a package is safe or high quality. It shows how Trusted Publishing reduces long-lived credential risk, while warning that users should not treat it like a “green checkmark” because malicious or low-quality packages ca...

https://blog.yossarian.net/2026/07/07/You-shouldnt-trust-trusted-publishing

Write a coding agent from first principles: better tools

This tutorial builds on the coding agent you implemented in the tutorial “Write a coding agent from first principles”. In this tutorial, you'll take your agent and improve its capabilities by implementing the text edit and bash command tools that Anthropic provides.

https://mathspp.com/blog/write-a-coding-agent-from-first-principles-better-tools

ipblocklist

IP lists full of bad IPs - Updated every 2H.

https://github.com/bitwire-it/ipblocklist

ProtoMotions3

A GPU-Accelerated Framework for Simulated Humanoids.

https://github.com/NVlabs/ProtoMotions

Hamiltonian Neural Networks from a Differential Geometry Perspective

The post explains Hamiltonian Neural Networks through differential geometry, showing why a normal MLP can fit motion data but still invent or lose energy over long rollouts. It shows that by learning a scalar Hamiltonian and deriving motion through the symplectic gradient, HNNs make energy conservation structurally unavoidable instead of hoping the network learns it from data.

https://abscondita.com/blog/symplectic-sledgehammer-for-a-spring

harvard-edge / cs249r_book

Machine Learning Systems

https://github.com/harvard-edge/cs249r_book