Project for the Blockspace Symmetry Hackathon in Berlin. Individual learning paths for people curious about blockchain & web3.

Empathy Technologies is a project aimed at optimizing knowledge transfer and collaboration across diverse fields by mapping and translating complex information into user-specific contexts. Leveraging advanced cognitive science, semantics-based algorithms, and mathematical frameworks, the project seeks to minimize cognitive load and enhance learning efficiency within decentralized ecosystems.

The core idea behind Empathy Technologies is to create personalized learning paths—coined as the "Path of Tao"—which identify the optimal route for learners based on their existing knowledge and familiarity with the subject matter. This approach not only improves understanding and retention but also facilitates cross-disciplinary collaboration by making domain-specific knowledge more accessible.

• NFT App
• Presentation
• Figma
• Foundations Paper
• Empathy Technologies Overview and Scientific Validation]

• Python: For developing and implementing the semantics-based algorithms and mathematical models.
• Graphviz: For visualizing knowledge graphs and mapping cognitive distances.
• Figma: Front-end mockup
• J3D.AI Platform: Integrating the Empathy Technologies framework for real-time usage.

Empathy Technologies is designed with the broader goal of enhancing public goods by fostering more inclusive and effective learning environments. By breaking down complex knowledge silos and making information more accessible, the project contributes to the democratization of knowledge, enabling more individuals and organizations to participate in and contribute to decentralized ecosystems like Polkadot.

• Building out the front-end and integrating the chatbot
• Integration of Compassion Technologies and Peace Technologies to further enhance collaborative efforts.
• Expansion of the knowledge mapping framework to include more diverse fields and user profiles.
• Development of a decentralized application (dApp) for wider community engagement and contribution.
• Continuous improvement of the semantic algorithms and mathematical models to increase the personalization and effectiveness of learning paths.