Core Insights - The article highlights the development and capabilities of the domestic AI search product, Mita AI Search, which is one of the earliest AI search products launched by a startup team [1] - The collaboration with MiniMax Speech enhances the user experience by providing a natural and pleasant voice for knowledge explanations through the "Tazi Teacher" feature [2] - The M2 model from MiniMax is utilized for deep research, offering reasoning capabilities and a strong interlinked thinking chain [3] Group 1 - Mita AI Search is recognized for its excellent experience in knowledge indexing, organization, and output due to the team's background in computer science and law [1] - The "Deep Research" mode aims to demystify the search planning algorithm by presenting a dynamic "problem chain" during the analysis process, making complex research clearer [4] - MiniMax M2 is designed for top-tier coding and agentic capabilities, focusing on delivering excellent performance at optimal pricing [4]

Core Insights - The article discusses how to maximize the value of AI tools, emphasizing the importance of understanding user patterns and selecting the right AI model based on specific needs [1][3]. Group 1: AI Model Selection - Users have approximately nine choices for advanced AI systems, including Claude by Anthropic, Gemini by Google, ChatGPT by OpenAI, and Grok by xAI, with several free usage options available [3][4]. - For those considering paid accounts, starting with free versions of Anthropic, Google, or OpenAI is recommended before upgrading [4][6]. - The article highlights the differences in capabilities among AI models, such as web search efficiency, image creation, and handling complex tasks, which should guide user selection [4][7]. Group 2: Advanced AI Features - Advanced AI systems require monthly fees ranging from $20 to $200, depending on user needs, with the $20 tier suitable for most users [6][7]. - The article outlines the distinctions between chat models, agent models, and wizard models, recommending agent models for complex tasks due to their stability and performance [9][10]. - Users can choose specific models within systems like ChatGPT, Gemini, and Claude, with options for deeper thinking and extended capabilities [11][13][14]. Group 3: Enhancing AI Output - The article emphasizes the importance of "deep research" mode, which allows AI to conduct extensive web research before answering, significantly improving output quality [16][18]. - Connecting AI to personal data sources, such as emails and calendars, enhances its utility, particularly noted in Claude's capabilities [18]. - Multi-modal input options, including voice and image uploads, are available across various AI platforms, enhancing user interaction [19][20]. Group 4: Future Trends and User Engagement - The article predicts an increase in AI usage, with 10% of the global population currently using AI weekly, suggesting that user familiarity will evolve alongside model improvements [24]. - Users are encouraged to experiment with AI capabilities to develop an intuitive understanding of what these systems can achieve [24]. - The article warns against over-reliance on AI outputs, as even advanced models can produce errors, highlighting the need for critical engagement with AI responses [26].

Core Viewpoint - The article challenges the perception that brokerage asset management firms lack equity investment capabilities, highlighting that some firms, like Caitong Asset Management, have successfully established themselves in this area through active management and a focus on deep research and value investment [1][3]. Group 1: Company Overview - Caitong Asset Management has a total management scale exceeding 300 billion yuan, with nearly 110 billion yuan in public fund management, maintaining a leading position in the brokerage asset management industry [3]. - The firm has achieved a 156.69% absolute return rate for its equity funds over the past seven years, ranking in the top 20% among fund managers [3]. Group 2: Investment Philosophy and Team Structure - The investment philosophy of Caitong Asset Management is centered around "deep research, value investment, absolute returns, and long-term assessment," which has guided its equity investment strategy since its inception [4]. - The equity research team consists of approximately 40 members, with over 20 dedicated equity researchers, and has grown the scale of its equity public funds from 700 million yuan to over 17 billion yuan [4][5]. Group 3: Research and Investment Strategy - The firm has established a structured approach to integrate research and investment, with clear departmental divisions focusing on public and private equity investments, each led by experienced fund managers [8]. - Caitong Asset Management emphasizes a long-term investment strategy, focusing on fundamental research to uncover intrinsic value, regardless of market fluctuations [13][15]. Group 4: Team Development and Culture - The average experience of equity fund managers and investment managers at Caitong Asset Management exceeds 14 years, with many having backgrounds in absolute return investments [5]. - The firm fosters a culture of openness and shared values, encouraging diverse investment styles while ensuring that all team members receive adequate research support [12].

Core Viewpoint - The article emphasizes the resurgence of technological innovation in various sectors, highlighting the importance of deep industry research and understanding for successful investment in technology stocks [1][2]. Group 1: Importance of Deep Research - Continuous monitoring and deep immersion in the industry are essential for capturing opportunities behind changes in technology and market dynamics [2][3]. - The success of investment products from firms like Caitong Asset Management demonstrates the effectiveness of deep research and industry understanding [3][4]. Group 2: Investment Strategies and Performance - Caitong Asset Management's technology-focused funds have shown impressive performance, with specific funds ranking in the top 1% and 5% of their categories over various time frames [3][4]. - The investment strategies employed by fund managers like Bao Laiwen and Li Jing focus on understanding macro trends and industry dynamics, which have led to significant returns [7][10]. Group 3: Team Dynamics and Research Integration - The integration of research and investment processes is crucial, with a focus on building trust and collaboration between researchers and fund managers [15][18]. - The culture of sharing and collaboration within the team enhances the overall investment decision-making process, allowing for a more comprehensive understanding of market opportunities [22][23]. Group 4: Future Trends and Market Opportunities - The article discusses the potential of AI and other emerging technologies as key investment areas, with a focus on understanding real user needs and market demands [11][12]. - Caitong Asset Management's proactive approach to investing in AI-related sectors reflects a commitment to identifying and capitalizing on long-term industry trends [12][29]. Group 5: Performance Metrics - The performance metrics of Caitong Asset Management's funds indicate a strong track record, with specific annual growth rates and comparisons to benchmarks demonstrating effective management [30][31].

Core Viewpoint - The article discusses the new feature of Metaso AI's DeepResearch, highlighting its advanced capabilities in conducting in-depth research and analysis, particularly in the context of the competitive landscape of food delivery services in China. Group 1: Introduction of DeepResearch - Metaso AI has introduced a new feature called DeepResearch, which enhances its research capabilities beyond previous modes [5][6][7] - The author expresses a strong preference for using Metaso AI for research tasks, indicating a shift from other AI search products [3][4] Group 2: Functionality and User Experience - The new DeepResearch feature offers a game-like experience, making the research process engaging and intuitive [10] - The interface provides visual representations of the research process, including token usage, sources found, and time spent, enhancing user interaction [25][43] - The system allows for a comprehensive analysis of competitive dynamics, integrating both vertical and horizontal analyses of companies like JD, Meituan, and Taobao [18][19][20] Group 3: Research Output and Quality - The reports generated by DeepResearch are extensive, often exceeding 10,000 words, and are structured into clear chapters, providing detailed insights [52][60] - The analysis of the food delivery market reveals that the underlying cause of competition is "high frequency attacking low frequency," with Meituan being the primary aggressor [54][55][59] - The quality of the reports is noted to be comparable to that of OpenAI's DeepResearch, with precise and relevant findings [48][60] Group 4: Additional Features and User Control - Users can generate interactive visual reports, catering to preferences for visual data representation [66] - The platform allows users to manage source preferences, enhancing the customization of research outputs [67] - Metaso AI offers a generous daily search quota, making it accessible for frequent use compared to other paid services [69]

Core Insights - The article discusses the emergence and evolution of "Deep Research" systems in the AI Agent exploration wave, highlighting the rapid development and competition among major players like Google, OpenAI, and Anthropic since late 2024 [1][2] - A comprehensive survey from Zhejiang University provides a framework for understanding and evaluating the current landscape of deep research systems, emphasizing the shift from model capability to system architecture and application adaptability as the main competitive focus [1][2] Group 1: Current Landscape and System Comparisons - The ecosystem of deep research systems is characterized by significant diversity, with different systems focusing on various technical implementations, design philosophies, and target applications [3] - Key differences among systems are evident in their foundational models and reasoning efficiency, with commercial giants leveraging proprietary models for superior performance in handling complex reasoning tasks [4] - Systems also differ in tool integration and environmental adaptability, showcasing a spectrum from comprehensive platforms to specialized tools [5] Group 2: Application Scenarios and Performance Metrics - In academic research, systems like OpenAI/DeepResearch excel due to their rigorous citation and methodology analysis capabilities, while in enterprise decision-making, systems like Gemini/DeepResearch thrive on data integration and actionable insights [8] - Performance metrics reveal that leading commercial systems maintain an edge in complex cognitive ability benchmarks, although specialized evaluations highlight the strengths of various systems in specific tasks [9][10] Group 3: Implementation Challenges and Technical Solutions - The implementation of deep research systems involves strategic trade-offs across architecture design, operational efficiency, and functional integration [12] - Core challenges include managing hallucination control, privacy protection, and ensuring interpretability, with solutions focusing on source grounding, data isolation, and transparent reasoning processes [15] Group 4: Evaluation Frameworks - The evaluation of deep research systems is evolving from single metrics to a multi-dimensional framework that assesses functionality, performance, and contextual applicability [16] - Functional evaluations focus on task completion capabilities and information retrieval quality, while non-functional assessments consider performance efficiency and user experience [17][18] Group 5: Future Directions in Reasoning Architecture - Future advancements in deep research systems are expected to address limitations in context window size, enabling more comprehensive analysis of large-scale research materials [22][23] - The integration of causal reasoning capabilities and advanced uncertainty modeling will enhance the systems' applicability in complex fields like medicine and social sciences [27][30] - The development of hybrid architectures that combine neural networks with symbolic reasoning is anticipated to improve reliability and interpretability [25][26]

大家好，很久没有和大家见面了，最近几个月我们 补充了 沪深 、港股 和美股 上市公司近 5 年的 财报 、电话会议 文档数据以及相当一部分的结构 化数据，同时 对 Reportify 的整个技术架构、内容解析引擎和产品交互做了一轮比较彻底重构。 有些改进是直观可见的，有些改进是为我们未来产品发展做好了扎实的准备，希望我们持续的改进可以让大家的工作和投资变得更有效率，让我们 一起来体验一下产品上的改进有哪些： - Reportify 功能更新 - 定量分析+可视化，根据用户的问题查询系统的结构化数据，并且进行图表组件的渲染。 | 學度 | 总收入（亿美元） | 净利润(亿美元) | | --- | --- | --- | | 2024Q1 | 213.01 | 11.29 | | 2024Q2 | 255.00 | 14.78 | | 2024Q3 | 251.82 | 21.70 | | | 日期 | 收入(美元) | 净利润(美元) | | --- | --- | --- | --- | | 1 | 2024-09-30 | 25,182,000,000 | 2,167,000,000 | | 2 | 20 ...