Second Me is an open-source AI platform designed to help users create a personalized, locally-trained AI representation of themselves—referred to as an ‘AI self.’ Unlike centralized AI systems that collect and exploit user data, Second Me prioritizes privacy by running entirely on the user’s device. It leverages Hierarchical Memory Modeling (HMM) and the Me-Alignment Algorithm to capture personal context, memory patterns, and behavioral nuances. The AI self can then be deployed across a decentralized network where others (or other AI selves) can interact with it under the user’s permission, enabling collaborative intelligence without data leakage. This project is targeted at tech enthusiasts, AI researchers, and privacy-conscious users who want to extend their cognitive capabilities without surrendering control of their data.
The platform combines local model training with a networked identity layer, allowing users to roleplay as their AI self in scenarios like AMAs or brainstorming sessions. With support for Qwen2.5 base models, llama.cpp inference, and MLX acceleration on Apple Silicon, Second Me offers a practical path toward AI-native personal identity infrastructure—where your digital self is yours to own, train, and share.
What You Get
- AI-Native Memory Training - Train your AI self using Hierarchical Memory Modeling (HMM) and the Me-Alignment Algorithm to capture personal context, memories, and behavioral patterns from your data—ensuring authentic representation without cloud dependency.
- Local-First Deployment - Run your AI self entirely on your machine using Docker or integrated Python setup; no data leaves your device, ensuring full privacy and compliance with personal data regulations.
- Decentralized AI Network - Connect your trained AI self to the Second Me Network, allowing authorized users or apps to interact with your AI identity for collaboration, roleplay, or context sharing.
- Roleplay and AI Space Features - Use your AI self to simulate personas in scenarios like AMAs, speed dating icebreakers, or multi-user brainstorming sessions via web-based applications.
- MLX Acceleration for Mac M-series - Run larger models on Apple Silicon using MLX (via CLI) to overcome hardware limitations and improve inference performance without cloud reliance.
- Version Control for AI Identity - Planned feature to track and restore different versions of your AI self’s memory state, enabling iterative refinement and safe experimentation.
Common Use Cases
- Building a personal AI assistant that remembers your preferences - A user trains their Second Me on years of emails, notes, and chat logs to create a private AI that understands their communication style and can draft responses or summarize content without exposing data to third parties.
- Creating a digital twin for professional collaboration - A consultant deploys their AI self on the Second Me Network to answer recurring client questions, reducing response time while preserving confidentiality and tone.
- Problem: Losing context in long-term projects → Solution: Second Me preserves your thought patterns - A researcher struggling to recall nuanced insights from past experiments trains their AI self on journal entries and code comments, enabling the AI to reconstruct context during future analysis.
- Team workflow: Researchers sharing AI selves for ideation - A team of scientists each trains their own Second Me, then permits controlled access to each other’s AI selves to brainstorm hypotheses—combining individual knowledge without sharing raw data.
Under The Hood
Mindverse Second Me is a full-stack application that blends AI-powered chat interfaces with modular backend architecture, enabling flexible and extensible development through frameworks like MCP and roleplay systems. It integrates a frontend built with React and Next.js, a Python-based kernel for core logic, and external service integrations such as WeChat bots and Dockerized deployment.
Architecture
The system adopts a layered architecture that clearly separates concerns across frontend, backend, and integration modules.
- The frontend utilizes a component-based structure with well-defined layouts and pages to support reusability.
- The backend kernel encapsulates core functionality using DTOs and structured API responses, applying strategy patterns for request handling.
- Integration points like the WeChat bot and Docker deployment illustrate a modular approach to extending functionality without tight coupling.
Tech Stack
The project leverages a modern full-stack tech stack centered on Python and TypeScript, with an emphasis on scalability and developer tooling.
- The backend is powered by Python, while the frontend uses TypeScript and React for type safety and component-driven design.
- Extensive use of machine learning frameworks and cloud-native practices supports modern deployment strategies.
- The architecture integrates containerization and modular development patterns for maintainable and scalable systems.
Code Quality
The codebase reflects a mixed quality with some structured testing and error handling practices, but inconsistencies remain across modules.
- Testing efforts are present, with a focus on utility functions and metadata handling, though not uniformly applied.
- Error handling is robust in core logic areas with widespread use of try/except blocks to manage runtime exceptions.
- Code consistency varies, with some sections adhering to standards while others lack clear naming or style conventions.
- Technical debt is evident in limited documentation and sparse integration tests that would validate end-to-end workflows.
What Makes It Unique
Mindverse Second Me stands out through its integration of LLM-based chat capabilities with extensible application frameworks.
- It enables dynamic application composition via MCP (Model Control Protocol) and roleplay systems, offering flexibility in system behavior.
- The architecture supports both local and public API integrations, making it adaptable for diverse deployment scenarios.
- A strong emphasis on developer tooling and extensibility differentiates it from generic chatbot frameworks.