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This project is a computer control framework that uses multimodal vision models to simulate mouse and keyboard inputs for automating desktop tasks. It functions as an autonomous agent and vision-based orchestrator that interprets screen visuals to interact with user interfaces. The system employs vision language models and object detection to locate and click interface elements. It utilizes visual grounding to overlay numerical markers on UI components and uses optical character recognition to map on-screen text to precise pixel coordinates. The framework supports voice-controlled computing
Qwen3-Omni is an omni-modal large language model designed to process and generate text, audio, images, and video within a single unified neural architecture. It functions as a real-time voice assistant and multimodal AI agent capable of reasoning across different media types and executing external tool-calling functions via APIs. The system supports low-latency conversational AI through autoregressive token streaming and natural turn-taking. It enables multilingual speech translation and generation across dozens of languages, featuring customizable speaker profiles and tones. The model's cap
Jaaz is a self-hosted AI design suite and multimodal workspace used for generating and editing images and videos. It functions as a design workspace where users can produce visual content and assets through a combination of local and cloud-based AI models. The project features a hybrid model orchestrator that routes requests between local model runners and remote APIs to balance data privacy with processing performance. It utilizes an infinite canvas collaborative tool for organizing storyboards and assets, and includes an image prompt optimizer to translate rough ideas into detailed generati
Agent-S is a multimodal AI agent and LLM desktop automation framework designed to control operating systems through graphical user interface interactions. It functions as a computer use interface, utilizing vision-language grounding to translate natural language goals into precise screen coordinates and system actions. The project differentiates itself by combining structured accessibility tree inspection with vision-based element localization. It manages cross-application workflows by mapping conceptual descriptions to physical pixels and simulating low-level keyboard and mouse events to mov
The ambition of the i-Code project is to build integrative and composable multimodal Artificial Intelligence. The "i" stands for integrative multimodal learning.
The main features of microsoft/i-code are: Multimodal Agents, Video Generation.
Open-source alternatives to microsoft/i-code include: qwenlm/qwen3-omni — Qwen3-Omni is an omni-modal large language model designed to process and generate text, audio, images, and video… simular-ai/agent-s — Agent-S is a multimodal AI agent and LLM desktop automation framework designed to control operating systems through… 11cafe/jaaz — Jaaz is a self-hosted AI design suite and multimodal workspace used for generating and editing images and videos. It… othersideai/self-operating-computer — This project is a computer control framework that uses multimodal vision models to simulate mouse and keyboard inputs… aim-uofa/gendef. ai-chef/hugginggpt — The mission of JARVIS is to explore artificial general intelligence (AGI) and deliver cutting-edge research to the…