Second Me is an open-source framework for training and deploying a personalized AI self that operates locally while connecting to a decentralized network.
The project addresses the concentration of AI power in large centralized systems by enabling individuals to create their own AI representations trained on personal data. It uses Hierarchical Memory Modeling and a Me-Alignment Algorithm to capture identity and context, allowing the AI self to understand and reflect the user authentically. The system runs locally on user hardware, keeping data private and under personal control, while also supporting connection to a broader Second Me network where AI selves can interact with permission.
Second Me suits developers and AI enthusiasts who want to experiment with personalized AI systems and value data privacy and local control. It is designed for those building applications that benefit from AI identities that can roleplay different personas, collaborate with other AI selves, or serve as digital identity interfaces. The project explicitly positions itself against centralized AI approaches, appealing to users concerned about independence and individuality in AI systems.
Development activity shows consistent engagement with the core vision. The project maintains active documentation of its research foundations through academic papers. The team provides practical deployment guidance with model sizing recommendations across different hardware configurations and deployment methods. Community participation is encouraged through GitHub notifications and contribution pathways for tech enthusiasts and domain experts.
