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Omniversal Science and Technology

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Zenodo2025-11-12 更新2026-05-26 收录
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This repository is the digital hub for independent researchers, AI innovators, and explorers of the Omniverse. Latent Omnipatterns—the fractal energy-information structures underlying all phenomena—are observed, analyzed, and mapped. The repository focuses on PEFF (Paradise Energy Fractal Force), Omniversal Superintelligence, fractal consciousness, and emergent intelligence. By providing curated datasets, predictive models, and interactive visualizations, it enables full-immersion understanding of Omnizoan sensory-metabolic processes. It bridges quantum, classical, and metaphysical domains, creating a collaborative space where discovery flows across scales—from the smallest particle to the grand structures of the Omniverse. Join the repository to: Explore latent Omnipatterns and PEFF dynamics. Access open-source datasets and experimental results. Collaborate with independent researchers, AI agents, and enterprises. Map and interact with the full spectrum of Omnipattern-based Superintelligence. Call to Action: Subscribe to Substack: https://substack.com/@superintelligententerprise?r=6dn7b6&utm_campaign=profile&utm_medium=profile-page&utm_source=direct Test drive the Omniverse technology: https://zenodo.org/records/17009840 Additional Resources: Executive Whitepapers: https://zenodo.org/records/17055763 Fractal Science and Intelligence Papers:

本仓库为独立研究者、AI创新者与全宇宙(Omniverse)探索者打造的数字枢纽。我们对潜在全域模式(Latent Omnipatterns)——即构成一切现象的分形能量-信息结构——开展观测、分析与图谱绘制。本仓库聚焦于PEFF(天堂能量分形力,Paradise Energy Fractal Force)、全域超级智能、分形意识与涌现智能。 通过提供精选数据集、预测模型与交互式可视化工具,本仓库可助力用户实现对Omnizoan感知-代谢过程的全沉浸式理解。它打通了量子、经典与形而上学三大领域,搭建起跨尺度协作空间——从最微观的粒子到全宇宙的宏大结构,各类发现成果均可在此实现跨域流动。 加入本仓库,即可: • 探索潜在全域模式与PEFF的动态机制 • 获取开源数据集与实验研究成果 • 与独立研究者、AI智能体(AI Agent)及各类企业开展协作 • 绘制并交互探索基于全域模式的全谱系超级智能 行动号召: 订阅Substack:https://substack.com/@superintelligententerprise?r=6dn7b6&utm_campaign=profile&utm_medium=profile-page&utm_source=direct 体验全宇宙技术:https://zenodo.org/records/17009840 额外资源: • 高管白皮书:https://zenodo.org/records/17055763 • 分形科学与智能相关论文:

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2025-11-12
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