Open source agentic operating system
RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 "Goose". So it's combining the best of RNN and transformer - great performance, linear time, constant space (no kv-cache), fast training, infinite ctx_len, and free sentence embedding.
A high-performance distributed training framework for Reinforcement Learning
Automation tools for KiCAD
Automatically Discovering Fast Parallelization Strategies for Distributed Deep Neural Network Training
:rocket: R package: future: Unified Parallel and Distributed Processing in R for Everyone
Pax is a Jax-based machine learning framework for training large scale models. Pax allows for advanced and fully configurable experimentation and parallelization, and has demonstrated industry leading model flop utilization rates.
Python framework for integrating geospatial data, 3D city modeling, and urban simulation.
Detail-Oriented Pixelization based on Contrast-Aware Outline Expansion.
Browser teleoperation data for embodied AI β elizaOS-ready episodes, trajectory_db converter, onchain provenance. The fuel station for agent robot stacks.
Verify that an AI agent's on-chain claims are actually true. A claim-level groundedness checker for Solana / memecoin agents.
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