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