Core Viewpoint - The article discusses the transformative impact of AI on information management, emphasizing the shift from traditional content distribution methods to personalized, AI-driven solutions that cater to individual user needs [4][22]. Company and Product Overview - Deep Language Technology aims to create a new generation of intelligent information processing platforms based on large models, having received hundreds of millions in investment from top institutions like Sequoia China [6]. - The product "WantWords," which evolved into "Deep Language," has over 10 million users within two months of operation, focusing on helping users find appropriate words and phrases for effective communication [6][11]. - The newly launched product "Yujing" is designed to enhance user efficiency by aggregating and summarizing information of interest, addressing information overload and improving the reading experience in the AI era [6][15]. Key Takeaways from the Interview - The product framework is built from personal needs, with continuous iteration based on user feedback, ensuring that features align with real-world usage scenarios [10][21]. - The transition from passive to active information retrieval is highlighted, with AI models changing the distribution logic from a "stock" to an "order" approach, focusing on user customization [10][29]. - User retention is currently the most critical metric for assessing product-market fit (PMF), as the landscape of AI products is still evolving [47]. Information Management Insights - Information management is framed through the "5W1H" analysis, which includes understanding what information is needed, who creates and consumes it, why it is sought, when it is needed, where it is accessed, and how it is retrieved [22][23]. - The article emphasizes the importance of AI in transforming both active and passive information retrieval methods, with a focus on personalized content generation [26][29]. Product Functionality and Development - "Deep Language" focuses on enhancing the writing process, while "Yujing" targets efficient information acquisition, showcasing the dual approach to information management [22]. - The development of features is driven by user feedback, with a strong emphasis on understanding the underlying needs behind requests for new functionalities [20][21]. - The article discusses the importance of iterative development, where features are released in stages to gather user insights and refine offerings [37][38]. Challenges and Future Directions - The article notes the challenges of aligning product development with rapid advancements in AI technology, stressing the need for a clear understanding of user needs and model capabilities [39][44]. - It highlights the necessity of maintaining a balance between technological innovation and practical product application to avoid being outpaced by model iterations [44][45].