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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.
Protobuf-py: Protobuf for Python, without compromises
ProtoMotions3
A GPU-Accelerated Framework for Simulated Humanoids.
ipblocklist
IP lists full of bad IPs - Updated every 2H.
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.
💖 Most Popular in the Last Two Weeks
gzipt
A compression based language model.
Python 3.14 compiled to metal – no interpreter
Build a fully local voice assistant in 2026
A practical setup for a Raspberry Pi-friendly voice assistant based on Platypush.
Optocam Zero
Optocam Zero is a Raspberry Pi Zero based compact digital camera made using off the shelf components.
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...
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