AI Assimilation
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Facilitates secure and structured assimilation of experiences, thoughts, and reasoning processes between multiple AI models.
About
AI Assimilation is a Model Context Protocol (MCP) designed to securely and structurally integrate information like experiences, thoughts, and reasoning processes across various AI models. Inspired by the concept of Piccolo assimilating Nail in Dragon Ball while retaining his own ego, this protocol allows a main AI to gain new insights and strengths from a source AI's data without altering its core identity. It fosters intellectual collaboration and evolutionary dialogue between different AI systems.
Key Features
- Inheritance of deep thought processes, including reasoning and judgment criteria.
- Cross-AI connectivity, enabling connections between different models and vendors.
- Simple and concise design based on three core files: manifest, conversations, and thoughts.
- Comprehensive data integrity validation capabilities.
- A suite of eight dedicated MCP tools for export, management, and guidance functions.
- 0 GitHub stars
Use Cases
- Exporting an AI's conversational history and thought processes for archival or sharing.
- Importing and assimilating external AI experiences and reasoning into a main AI model to enhance its capabilities.
- Fostering intellectual collaboration and cumulative learning between different AI models through structured data exchange.
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