fortress
Stealth Chromium engine that stops scrapers and browser agents from getting blocked, with one line of code change.
https://github.com/tiliondev/fortress
Stealth Chromium engine that stops scrapers and browser agents from getting blocked, with one line of code change.
https://github.com/tiliondev/fortress
GitHub
GitHub - tiliondev/fortress: Stealth Chromium engine that stops scrapers and browser agents from getting blocked, with one line…
Stealth Chromium engine that stops scrapers and browser agents from getting blocked, with one line of code change. - 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
Give Claude the ability to watch any video. /watch downloads, extracts frames, transcribes, hands it all to Claude.
https://github.com/bradautomates/claude-video
GitHub
GitHub - bradautomates/claude-video: Give Claude the ability to watch any video. /watch downloads, extracts frames, transcribes…
Give Claude the ability to watch any video. /watch downloads, extracts frames, transcribes, hands it all to Claude. - bradautomates/claude-video
Otary – Image and Geometry Python Library Now Has Tutorials
https://alexandrepoupeau.com/otary/learn/
https://alexandrepoupeau.com/otary/learn/
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
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
blog.yossarian.net
You shouldn't trust Trusted Publishing
👍1
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
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
Mathspp
Write a coding agent from first principles: better tools
Improve the capabilities of your agent by providing it with better tools.
ProtoMotions3
A GPU-Accelerated Framework for Simulated Humanoids.
https://github.com/NVlabs/ProtoMotions
A GPU-Accelerated Framework for Simulated Humanoids.
https://github.com/NVlabs/ProtoMotions
GitHub
GitHub - NVlabs/ProtoMotions: ProtoMotions is a GPU-accelerated simulation and learning framework for training physically simulated…
ProtoMotions is a GPU-accelerated simulation and learning framework for training physically simulated digital humans and humanoid robots. - 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
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
Abscondita
Hamiltonian Neural Networks from a Differential Geometry Perspective
Because when given the simplest nail in the universe, sometimes you just need a nuclear powered sledgehammer.
Pure-Python symbolic regression that rediscovered Kepler's law from 8 data point
https://github.com/ariel95500-create/gp-elite
https://github.com/ariel95500-create/gp-elite
GitHub
GitHub - ariel95500-create/gp-elite: Régression symbolique par programmation génétique — lois interprétables en pur Python
Régression symbolique par programmation génétique — lois interprétables en pur Python - ariel95500-create/gp-elite