社交网络里面每一个人都有一个数字身份。Second me要做的就是这个身份模型。 "我就是想把Facebook重新做一遍。"前Meta员工、心识宇宙创始人陶芳波开门见山地对凤凰网科技表示。 在几乎所有AI创业者都在追逐"Agent（智能体）"浪潮的当下，他却选择了一条更激进、也更浪漫的路：用AI重塑人的数字身份，构建一个以"AI身份"为 节点的下一代社交网络。 今年外滩大会期间，凤凰网科技与陶芳波有过一次深入交流，陶芳波提出的"身份模型"，即通过开源项目"Second Me"实现个人AI身份，让AI成为对抗超 级智能的"第二自我"。 陶芳波曾在美国微软研究院、NASA 、Facebook Research 等企业与机构从事 AI 研发工作，并在阿里巴巴达摩院创建了神经符号实验室。2022年创立 Mindverse 心识宇宙，获红杉、线性、Square Peg 等亿级融资。 2024年，心识宇宙推出了个人AI助手Me.bot，要做每一个人的"AI身份"。2025年，心识宇宙开源了Me.bot的核心技术"Second Me"，用户可以下载到本 地，创造出代表自己的Agent，是今年 GitHub 平台上关注度增速 ...

Core Viewpoint - The article discusses the innovative approach of Mindverse in creating AI identity models that reflect users' preferences, values, and memories, aiming to enhance user interaction with AI and digital platforms [6][7][8]. Group 1: AI Identity Models - Mindverse is developing a third type of AI assistant, termed "identity model," which aims to replicate a user's "second self" by fine-tuning a base language model with user-specific data [6][8]. - The identity model is designed to understand and represent users in various applications, allowing for more personalized interactions with AI [7][8]. - The company believes that having an identity model can significantly improve efficiency in online interactions, as it can autonomously initiate tasks and manage communications [8][11]. Group 2: User Engagement and Product Development - The app Me.bot, launched in May 2022, has attracted nearly one million users and is designed to help users develop their AI identity models through daily interactions [8][11]. - Mindverse has initiated an open-source project called Second Me, which has gained significant traction on GitHub, indicating strong community interest in the identity model training methods [8][33]. - The company emphasizes the importance of user engagement by integrating the AI into daily life, encouraging users to record their experiences and thoughts [20][21]. Group 3: Technological Insights - The approach to training identity models is inspired by human cognitive processes, where the model learns to index and connect relevant information rather than storing fragmented knowledge [7][22]. - Mindverse's identity models are trained daily to keep up with users' evolving experiences and self-perceptions, mirroring how human memory works [29][30]. - The cost of training an identity model is relatively low, with estimates around one dollar per training session for a model with 7 billion parameters [30]. Group 4: Market Potential and Future Directions - The identity model can potentially replace traditional user interactions with digital platforms, allowing for more seamless and efficient communication [31][37]. - Mindverse is exploring monetization strategies, including charging users for identity services and collaborating with platforms to understand user preferences [36][37]. - The company anticipates that as AI technology matures, the integration of identity models into existing digital ecosystems will become more prevalent, enhancing user experience [32][36].

Group 1 - The development of AI Agents has accelerated significantly since 2025, with notable acquisitions and funding rounds, such as OpenAI's $3 billion acquisition of Windsurf and Anysphere's $900 million funding round, valuing Cursor at $9 billion [1][3] - The emergence of various platforms and tools, such as MindOS and Second Me, indicates a growing trend towards creating personalized AI Agents, reflecting a shift in the industry towards more accessible development [4][6] - The definition of AI Agents has evolved, now characterized by their ability to perform tasks independently, driven by large language models, and equipped with memory systems and user interaction interfaces [6][8] Group 2 - The integration of reasoning models and Reinforcement Fine-Tuning (RFT) technology has enabled AI Agents to learn and adapt in specific domains, marking a significant advancement in their capabilities [8][15] - The distinction between traditional reinforcement learning Agents and modern AI Agents lies in their ability to learn from environments, with the latter now capable of autonomous learning and exploration [12][14] - The competitive landscape for AI Agents is shifting, with companies like Cursor and Windsurf leading the charge due to their deeper understanding of environments and user needs [18][20] Group 3 - The rise of AI Agents has created both opportunities and challenges for entrepreneurs, as the market becomes saturated with service-oriented Agents, making it difficult for new entrants to find unique value propositions [22][23] - The importance of model capabilities, engineering skills, and data barriers is highlighted as key competitive advantages in the AI Agent space, with the performance of models like Claude Sonnet 3.7 being pivotal for success [25][28] - The future of AI Agents may see a convergence of programming tools and general-purpose Agents, as companies like Cursor and Windsurf begin to integrate broader functionalities [31][55] Group 4 - The industry is experiencing a rapid pace of development, with a shift towards faster execution and less emphasis on detailed planning documents, reflecting a more agile approach to product development [64][66] - Despite the excitement around AI Agents, significant challenges remain in achieving widespread adoption and understanding user needs effectively, indicating that the journey towards mainstream usage is still ongoing [68][71] - The MCP protocol, which governs how AI Agents access external information, is still in its early stages and requires industry-wide acceptance to fully realize its potential [71][73]