Tag
Explore by tags
Converts microphone or streamed audio to text with sub-second latency, pairing WebRTC/Silero voice-activity detection and wake-word activation with swappable local backends — faster-whisper by default, plus whisper.cpp, Moonshine, and sherpa-onnx.
Provides a memory-first library and managed service that stores, reasons about, and serves long-term state for agents and users — offering continual representations, session context, vector search, and a chat-style API for personalized behavior.
Routes LLM and agent decisions through semantic similarity instead of waiting for full generations, useful for intent routing, tool selection, guardrails, and multimodal handling.
Provides a scalable physics-and-rendering simulation interface for robotics and embodied-AI research — unified multi-physics solvers, the Nyx renderer, and the Quadrants compiler. Runs from laptop to datacenter GPUs; suited for sensor-rich data generation and RL/robotics prototyping.
Performs speaker diarization (who spoke when) with pyannote-audio: combines voice-activity detection, speaker-change and overlapped-speech detection to produce time-stamped speaker segments; compatible with Hugging Face Endpoints and ASR pipelines.
Provides a PyTorch-native platform for experimenting with and scaling generative AI training, including composable parallelism, checkpointing, float8, logging, and Llama recipes.
Triton kernels and PyTorch layers for linear-attention, state-space, and sparse-attention token mixers (GLA, RWKV, Mamba2, GSA) as drop-in replacements for multihead attention. Runs on NVIDIA, AMD, and Intel GPUs with Hugging Face support.
A PyTorch-native, hardware-agnostic stack for robot learning: data collection, training, and deployment across 11+ robots, from SO100 to Unitree G1. Includes imitation, RL, and vision-language-action policies (ACT, Diffusion, Pi0, SmolVLA).
GPU kernel library for LLM inference attention, sampling, and KV-cache, built on block-sparse formats with JIT-compiled customizable templates. Reports 29-69% inter-token-latency cuts vs compiler backends; powers SGLang, vLLM, and MLC-Engine.
Chains pre-trained AI weather and climate models like GraphCast, Pangu, and FourCastNet into composable inference pipelines. Swap prognostic or diagnostic components, plug in reanalysis sources, and add ensemble perturbations or in-loop metrics.
React components for building LLM chat and agent interfaces: message bubbles, prompt sets, conversation lists, and sender inputs under a RICH interaction paradigm, plus a streaming Markdown renderer and hooks for wiring UI to model data streams.