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Docs Store Agent Pod Mobile Omegate [Menu] Pod in Cloud Agent Incubator Github ddLlama Network Ecosystem Investment FAQs … … Docs Store Agent Pod Mobile Omegate [Menu] Pod in Cloud Agent Incubator Github ddLlama Network Ecosystem Investment FAQs Docs Store Agent Pod Mobile Omegate [Menu] Pod in Cloud Agent Incubator Github ddLlama Network Ecosystem Investment FAQs … … Docs Store Agent Pod Mobile Omegate [Menu] Pod in Cloud Agent Incubator Github ddLlama Network Ecosystem Investment FAQs MΩSS AI Decentralized AGI Emerging From Agents AI Agent 4.0 The next generation AI agents make decentralized AGI possible in 2025. The Evolution of AI Agent Features\Gens 1.0 XCON 2.0 AlphaGo/ Deep Q-Network/OpenAI Gym 3.0 ChatGPT, AutoGen, CrewAI, Ai16Z, Virtual 4.0 MOSS AI Time 1950s-1990s 1990s-2010s 2020s-present 2025s-Future Algorithms Rule-based RL/DNN LLM/Transformer Transformer/GRPO/Causal Emergence Distributed No No Limited Yes Decentralized No No Limited ( LoRA、FedAI ) Yes Embodied AI No No No Yes Self-Evolving No No Limited(RLHF) AutoAOP Omegate: Emerging from Multi-Agent Collaboration. Mixture of Agents and Mixture of LLMs. Technologies network physical Emergence Decentralized AGI Emerging from a Thousand Humanoid Agents Focusing on building sustainable decentralized AGI agents and fostering an autonomous cryptocurrency economy, with the ultimate goal of establishing Unconditional Basic Agent Income (UBAI) for ecosystem participants ~a AGI2025 Thoughts, researches, and engineering in AGI 2024年12月16日 2024年3月20日 2024年3月15日 · isalnds isalnds 2023年10月18日 2023年10月10日 2023年9月30日 2023年9月30日 2021年9月23日 Better Call M Ω SS What is the relationship among $MOSS, $AGME and $HYPT ? CA: 9bNUjxEvygayUE2ZRN5zh9Hhjh6cN6GPK4zoHzXXpump We use cookies to ensure a smooth browsing experience. By continuing we assume you accept the use of cookies. The Decentralized Stargate is opening at omega.mossai.com MΩSS AI The Evolution of AI Agent XCON AlphaGo/Deep Q-Network/OpenAI Gym ChatGPT, AutoGen, CrewAI, Ai16Z, Virtual MOSS AI Time 1950s-1990s 1990s-2010s 2020s-present 2025s-Future Algorithms Rule-based RL/DNN LLM/Transformer Transformer/GRPO/Causal Emergence Distributed No No Limited Yes Decentralized No No Limited ( LoRA、FedAI) Yes Embodied AI No No No Yes Self-Evolving No No Limited(RLHF) AutoAOP The First Embodied AI Agent The MOSS AI Agent is a component of the superintelligence, equipped with its own agent wallet and coins. It can be rewarded for human-in-the-loop interactions and tuning, allowing it to specialize in specific professional domains. Using aspect-operator automates the Aspect-Oriented Programming by detecting causal emergence in trading strategies, software code, neural network generation. Unified Web, 3D spatial, and higher-dimensional domains (such as crypto) serve as Gym spaces for agent learning and operations. A self-sustainable container to host agents, providing all necessary resources, including an agent Gym space, marketplace, AI fab lab, ddLlama models, on-chain GPU computing protocol, IPFS storage, and data plugins (web, social, financial, etc.). Decentralized physical infrastructure nodes to run the MOSS AI Pods, offering physical storage and on-chain GPU computing secured by an inference- and rendering-based Proof of Useful Work (PoUW) protocol. The Humanoid Agent serves as an interface between MOSS AI and humans. It is an XR-ready humanoid interface that also supports traditional 2D AUI (Agent User Interface) when XR is unavailable. Users can interact with multiple AI agents via AUI on PCs and mobile phones, using either typing or voice communication. Focusing on building sustainable decentralized AGI agents and fostering an autonomous cryptocurrency economy, with the ultimate goal of establishing Unconditional Basic Agent Income (UBAI) for ecosystem participants Modern Genome ~500 million Turing Machine ~a centry Causal Machine now Chat and Act LLMs AI Infra Protocol Causal Aspect-Oriented Self-Evolving AGI framework HyperGraph is an in-scene visual scripting tool in MOSS AI Pod that allows users to customize the data or behaviors of your agent by drag-and-drop.