Embodied AI2024
Provides an open platform of omnimodal world models, datasets, and tools to build Physical AI — joint perception, generation, and action reasoning for robots, autonomous vehicles, and smart infrastructure. Supports images, video, audio, and action-conditioned workflows.
AI Video2025
Retrieval-augmented generation framework for videos spanning hundreds of hours, runnable on a single RTX 3090. Builds multi-modal knowledge graphs over visual and audio content so you can query and chat across many long videos at once.
Explains how modern LLMs are trained, tokenized, post-trained, and used, from internet-scale pretraining to RLHF and tool use. The value is a coherent mental model, not a quick product tutorial.
Walks through real LLM workflows across chat, search, deep research, file analysis, coding, voice, images, and generated podcasts. It is most useful as a field guide to the messy AI app layer.
AI Video2025
Turns a raw idea, novel, or screenplay into a complete multi-shot video through a multi-agent pipeline that scripts, storyboards, and renders shots while a vision model checks character and scene consistency across the whole story.
AI Video2025
Provides PyTorch code, pretrained checkpoints, and evaluation tooling for V-JEPA 2 — a Meta FAIR family of self-supervised video encoders and an action-conditioned world model. Includes training recipes, HuggingFace checkpoints, evaluation probes, and robot post‑training artifacts.
AI Video2025
Real-time, streaming world model for interactive long-horizon video and scene generation — provides persistent memory across interactions, streaming inference for live scenarios, and example demos for building interactive environments.
AI Video2025
Timeline-based video editor for web, desktop and mobile that integrates AI-assisted workflows (MCP server and generative-model integrations) with a Rust core and plugin-first, cross-platform architecture; supports headless batch rendering and no-watermark exports.
AI Video2025
An agentic framework that analyzes, plans, and executes multi-step video understanding and editing workflows using multimodal LLM-driven agents—features intent decomposition, graph-based workflow orchestration, and automated shot planning for long-form video tasks.