Embeddable office SDK that unifies spreadsheets, docs, slides, canvases and relational tables in one runtime so humans and AI agents can inspect, edit, verify, and collaborate on the same files. Provides programmatic editing APIs, isolated agent worktrees, a formula engine and a headless Node.js runtime for agent workflows.
Build LLM-powered agents and applications from modular components: provider-agnostic model abstractions, tool integrations, retrievers for RAG, and agent orchestration primitives. Suited for prototyping and production agent workflows; requires developer wiring and dependency management.
GPU-accelerated robot-learning framework on NVIDIA Isaac Sim, running thousands of parallel environments on one GPU for reinforcement and imitation learning. Ships 30+ ready-to-train tasks and 16+ robot models wired to RSL RL, SKRL, and RL Games.
Generate a lip-synced talking-head video from a single portrait image and an audio clip using learned 3D motion coefficients for realistic expression and head motion. Offers still/reference modes, Colab/HuggingFace demos, and an Apache-2.0 license.
Bundles ASR, voice activity detection, punctuation, and speaker diarization into one pipeline, with pretrained models like Paraformer and SenseVoice. SenseVoice runs ~17x realtime on CPU; also ships streaming ASR and an OpenAI-compatible API.
Unified Python framework where the same code runs on batch and streaming data, backed by a Rust engine on Differential Dataflow for incremental computation. Aimed at ETL, analytics, and live RAG pipelines over Kafka and 300+ connectors.
Provides reusable computer-vision utilities for dataset loading/conversion, visualization/annotation of detections and segmentation, and connectors to popular detection frameworks—aimed at quick prototyping, dataset work, and visualization.
Curated, community-maintained collection of ready-to-use prompts — roles like interviewer, translator, and code reviewer — that copy directly into ChatGPT, Claude, Gemini, or local models. Available as CSV, markdown, and a Hugging Face dataset.
Provides a searchable, community-curated library of prompts for chat and LLM models, with a browsable site, CSV/Hugging Face dataset, an interactive prompting guide, and self-hosting options. Focused on prompt examples and community contributions for ChatGPT and other LLMs.
Provides human preference comparison pairs and red-team conversation transcripts collected by Anthropic for training preference/reward models and studying harmful model behaviors; intended for RLHF and safety research, not for supervised fine-tuning of dialogue agents.
Community-curated collection of ChatGPT-style prompts mirrored as a Hugging Face dataset; organized by task and model compatibility for quick reuse. Useful for prompt engineering, text-generation prototyping, and building conversational examples across multiple LLMs.