聊着育儿经、下注 1.4 万亿、揭秘新协议、规划 AGI……全球最强 AI 搭档的炉边谈话泄露了什么？ 出品丨AI 科技大本营（ID：rgznai100） 来 源 | youtu.be/Gnl833wXRz0 今天外网最火的播客，毫无疑问是 Brad Gerstner 的 BG2 频道，能把萨提亚·纳德拉（Satya Nadella）和萨姆·奥特曼（Sam Altman）同时请来，坐 在一起聊一个小时，这事儿本身就挺不简单的。 这两个人，大家都不陌生。一个是微软的 CEO，一个是 OpenAI 的 CEO。平时看他们各自的采访，他们俩分开聊，我们都听得多了。 但坐在一起聊，感觉就不一样了。他们是 当今科技界最重要、最复杂、也最被外界议论纷纷的合作关系里的两个核心人物。 难得 同 框对话，说的每一 句话，甚至每一个表情，都值得玩味。 这场信息量爆炸的对话，开场却格外"柔软"。主持人没问什么 AGI、万亿参数模型，而是先关心起了萨姆刚出生的宝宝。不管你在外面掌管着多大的商 业帝国，搅动着多大的技术风云，回到最基本的生活层面，大家面对的喜悦和烦恼，其实都差不多。 这种充满生活气息的开场，让后面的"硬核"内容显得更加 ...

Multi-Agent 协同的核心：Agent 能力发现与任务闭环 作者 | 周礼 出品丨AI 科技大本营（ID：rgznai100） 本文整理自 阿里云智能集团高级技术专家周礼 在 2025 全球机器学习技术大会 上的精彩演讲 《Apache RocketMQ x AI：面向异步化 Agent 的事件驱动架构》，介绍了如何基于 Apache RocketMQ 新特性构建异步化 Multi-Agent 系统，深入探讨了 Agent 间的异步通信、上下文隔 离、状态恢复与任务编排机制，并通过实际案例展示如何利用 RocketMQ 实现 Multi-Agent 的任务 调度。 随着大模型能力提升与推理成本下降，MCP、A2A 等协作协议的成熟，AI 迈入了 Agentic AI 的时 代，AI 应用也从"被动响应"进入了"主动决策、自主执行"阶段。这一演进催生了 Multi-Agent 架 构：任务由多个专业化 Agent 协同完成，不再依赖单一模型或固定流程，开发者得以在模型自主性 与业务可控性之间实现平衡。 与传统应用的固定编排不同，Agentic AI 具备自主规划能力，可将目标拆解为动态步骤，但规划又 依 ...

Core Viewpoint - The article discusses the current state of AI, particularly focusing on the concept of AI Agents, and highlights the industry's obsession with performance metrics, likening it to an "exam-oriented" approach that may overlook the true value of technology [2][7][41]. Group 1: AI Agent Market Dynamics - There is a growing skepticism in the industry regarding the AI Agent market, with many products merely automating traditional workflows under the guise of being intelligent agents, leading to user disappointment [3][9]. - The popularity of AI Agents stems from a collective desire for AI to transition from experimental tools to practical applications that enhance productivity and cognitive capabilities in real-world scenarios [7][10]. Group 2: Technological Evolution - The emergence of large models represents a significant turning point, replacing rigid, rule-based systems with probabilistic semantic understanding, which allows for more dynamic and adaptable AI systems [9][10]. - The relationship between workflows and AI Agents is not adversarial; rather, workflows serve as a foundational stage for the development of true AI Agents, which will evolve beyond traditional automation [10][11]. Group 3: Future Directions and Challenges - The future of AI Agents is oriented towards results rather than processes, emphasizing the need for agents to be capable of autonomous judgment and dynamic adaptation [13][40]. - The concept of "group intelligence" is being explored as a potential alternative to the current arms race in large model development, focusing on collaboration among smaller agents to tackle complex tasks [17][18]. Group 4: Open Source and Community Engagement - The company emphasizes the importance of open-source practices, believing that collective intelligence can accelerate AI development and foster a community-driven approach to innovation [32][33]. - Open-source contributions are seen as vital for sharing insights and advancing the understanding of AI technologies, rather than just providing code [35][36]. Group 5: Practical Applications and Long-term Vision - The company aims to develop AI Agents that can operate independently over extended periods, tackling long-term tasks and adapting to various environments to enhance their learning and capabilities [39][40]. - The ultimate goal is to create a continuously learning model that serves as a technical product, allowing the community to benefit from technological advancements without being overly polished for consumer markets [40][41].

巧合的是， 2025长沙1024程序员节以" AI 构建者 "为核心，旨在帮助开发者在AI时代明确自身定位与技术路径，掌 握从模型调用到系统级构建的核心能力。而 Kiro 的构建理念恰好与今年 1024 AI构建者大会的主题不谋而合。 从策略拆解到动手实验，一站式掌握 Agentic AI开发精髓 今年 1024 AI构建者大会亚马逊云科技专场以 体系化策略 +实战化落地为特色， 精心设置了趋势洞察、动手实验等 精彩环节。 亚马逊云科技的技术专家将深度拆解 Agentic AI在需求理解、代码生成、测试验证等方面的能力， 分享亚马逊云科 技在 AI编程融合领域的前沿实践。 想象一下：你向 AI描述一个应用创意，它便开始与你协同工作——确认技术细节、规划代码结构、自动编写功能模 块并生成测试用例，最终交付一个生产就绪的部署方案。从"想法"到"发布"的完整链路，第一次可以由一个AI伙伴 深度参与、协同构建。 这已不是未来图景。亚马逊云科技推出的 AI原生IDE——Kiro，正是这样一位"AI构建伙伴"。它标志着我们正进入 一个全新的阶段：AI构建的方式，本身正在被AI重构。而 Kiro所代表的，正是当下最关键的技 ...

Core Insights - The article emphasizes the significance of C++ in the evolution of programming languages, highlighting its engineering-like nature and the necessity for developers to understand underlying complexities and memory management [1][4][10] - Bjarne Stroustrup, the creator of C++, is portrayed as a pivotal figure in the programming world, whose principles and insights have shaped the language's development over the past four decades [1][21][14] Historical Context - Bjarne Stroustrup wrote the first prototype code for C++ in 1979 at Bell Labs, aiming to enhance abstraction without sacrificing performance [3][4] - The first C++ technical conference in Shanghai took place in 2005, where Stroustrup introduced key principles that continue to guide the language's evolution [5][7] Evolution of C++ - The release of C++11 in 2011 marked a significant update, with Stroustrup describing it as almost a new language focused on reducing errors rather than adding syntax [8][10] - In 2016, Stroustrup became the chair of the global C++ conference, advocating for the standardization of Concepts to improve template programming [10] Current Trends and Future Directions - The rise of AI and big data has increased computational demands, with C++ being crucial for high-performance computing and system software [11][12] - At the 2024 global C++ conference, Stroustrup discussed the importance of maintaining a solid foundation while adapting to changes brought by AI [14] Upcoming Conference - The 2025 Global C++ and System Software Technology Conference will celebrate the 40th anniversary of C++ and the 20th anniversary of the conference, featuring Stroustrup and other leading experts [16][17] - The conference will cover twelve major themes, including software architecture, AI optimization, and embedded systems, providing a comprehensive knowledge framework for attendees [52][56]

Core Insights - The article emphasizes the importance of cross-platform development in providing a consistent and smooth user experience across various devices, including mobile, tablets, automotive screens, and industrial equipment [1] - The Qt Global Summit 2025, celebrating the 30th anniversary of Qt, will take place on October 24, 2025, in Shanghai, focusing on "Global Vision, Local Practice" [1][3] - The summit will gather industry leaders, technical experts, and developers to discuss advancements in cross-platform development, embedded systems, and automation testing [1] Group 1: Summit Highlights - The summit will feature discussions on how Qt deeply adapts to HarmonyOS, sharing practical experiences in migrating large applications to the Hongmeng ecosystem [1] - There will be in-depth analysis of performance bottlenecks and solutions during the migration from Qt 5 to Qt 6, ensuring smooth application performance on mobile devices [1] - Modern UI/UX design techniques will be explored, including the use of Qt Quick 3D to create immersive interactive experiences that stand out among competitors [1] Group 2: Safety and Innovation - Focus on Qt Safe Renderer in critical safety areas such as automotive electronics and rail transportation, reinforcing software safety [2] - Discussions on the evolution of next-generation smart cockpit architecture and how Qt can enhance the driving experience [2] - Insights into Qt's multi-process and multi-window solutions under the Wayland architecture to meet complex embedded display requirements [2] Group 3: Quality Assurance - Learning opportunities on using tools like Squish for building comprehensive automated testing systems for embedded software, ensuring delivery quality [2] - The summit serves as a platform for learning and connecting with industry leaders and the Qt core team, facilitating exploration of mobile and embedded development possibilities [2][6]

Core Insights - The 2025 Global Machine Learning Technology Conference will be held on October 16-17 in Beijing, featuring prominent figures from the AI industry, including researchers from OpenAI and other leading tech companies [1][3][11]. Group 1: Conference Overview - The conference will gather experts from top tech companies and research institutions to discuss cutting-edge topics such as large models, intelligent agent engineering, and multimodal reasoning [3][12]. - Keynote speakers include Lukasz Kaiser, co-founder of GPT-5 and GPT-4, and Li Jianzhong, Vice President of CSDN, who will present insights on AI industry paradigms and the evolution of large models [4][5]. Group 2: Key Presentations - Li Jianzhong will present on "Large Model Technology Insights and AI Industry Paradigm Insights," focusing on the technological evolution driven by large models [4]. - Michael Wong will discuss the "AI Platform Paradox," analyzing the reasons behind the failures of many open-source AI ecosystems and how to create a thriving environment [4]. Group 3: Roundtable Discussions - A roundtable titled "Core Issues in AI Industry Paradigm Shift" will feature discussions among industry leaders on the evolution of AI paradigms and the challenges of technology implementation [10]. - Participants include Li Jianzhong, Wang Bin from Xiaomi, and other notable scientists, fostering a high-density exchange of ideas [10]. Group 4: Afternoon Sessions - The afternoon sessions on October 16 will cover various topics, including the evolution of large language models, intelligent agent engineering, and AI-enabled software development [12][18]. - Notable speakers include experts from ByteDance, Tencent, and other leading firms, sharing their latest breakthroughs and insights [13][19]. Group 5: Second Day Highlights - The second day will feature multiple specialized sessions on embodied intelligence, AI infrastructure, and practical applications of large models [18][19]. - Key presentations will include discussions on the next generation of AI agents and the integration of AI technologies in various industries [20][22].

Core Insights - The article discusses the introduction of a new deep neural network operation called Translution, which combines the adaptive modeling advantages of Self-Attention with the relative position modeling capabilities of Convolution, allowing for a unified approach to capturing representations that are intrinsically related to the data structure rather than absolute positions [1][5]. Group 1: Performance Improvements - Experimental results indicate that neural networks built on Translution have shown performance enhancements in both ViT and GPT architectures, suggesting a broad range of application prospects [3]. - In the context of natural language modeling tasks, models based on Translution have outperformed those using Self-Attention [4]. Group 2: Technical Details - The core idea behind Translution is to transform the "fixed weight kernel" of convolution operations into a "dynamic adaptive kernel" generated by the self-attention mechanism, addressing the limitations of current Transformer models [5]. - The performance metrics from the experiments show that Translution achieves lower perplexity scores compared to traditional Self-Attention methods across various architectures, indicating improved efficiency and effectiveness [4]. Group 3: Industry Implications - As the demand for larger models continues to grow, the limitations of merely increasing network parameters and training data have become apparent, leading to the need for innovative neural network designs like Translution to sustain the growth of deep learning [5]. - However, the advanced capabilities of Translution come with increased computational requirements, particularly in GPU memory, which may exacerbate the existing disparities in access to AI resources within the industry [6].

Core Insights - The article discusses the concept of "Vibe Coding" proposed by Andrej Karpathy, which allows developers and non-developers to create applications through natural language descriptions, potentially revolutionizing the application development landscape [1][9][10] - The traditional application development model is constrained by the "impossible triangle" of low cost, high quality, and personalization, which has led to the emergence of new tools like 秒哒 that aim to address these challenges [3][5][24] Group 1: Impossible Triangle in Application Development - The "impossible triangle" highlights the inherent conflict in traditional development methods where achieving low cost, high quality, and personalization simultaneously is challenging [3][5][24] - Traditional coding ensures high quality and personalization but is costly, while low-code platforms reduce costs but lack personalization [8][24] - Chatbots offer low cost and some personalization but often fall short in quality, leading to a need for a new approach [8][24] Group 2: AI-Driven Development - The formula for effective AI-native applications is defined as AI UI + Agent, where AI UI focuses on user-centered design and Agent executes complex tasks [3][9][12] - 秒哒 aims to unlock the 90% of long-tail application demands that traditional software development overlooks, promoting a new era of "everyone can create" [3][13][16] - Multi-agent collaboration is crucial for 秒哒, simulating a high-functioning development team to transform vague requirements into fully functional applications [3][25] Group 3: Future of Roles in Development - AI is expected to elevate the roles of product managers and programmers rather than replace them, allowing product managers to directly interface with AI for prototyping [4][21] - The boundaries between product managers and programmers may blur, with product managers leveraging AI tools to create prototypes without needing extensive coding knowledge [21][22] - The evolution of roles will focus on higher-level tasks such as logic design and creative input, while AI handles execution [20][34] Group 4: Market Growth and Demand - The global software market is projected to grow at a compound annual growth rate of 11.8%, from $659.2 billion in 2023 to $2,248.3 billion by 2034, driven by increasing application development demands [5] - The emergence of AI-native applications is reshaping user habits, as seen in the shift towards AI-assisted writing and application creation [7][30] - The demand for applications is shifting from high-frequency needs to long-tail requirements, which traditional development methods have largely ignored [16][34]

Core Insights - The dialogue emphasizes the evolution of AI, particularly the transition from language models to reasoning models, highlighting the need for a new level of innovation akin to the Transformer architecture [1][2][4]. Group 1: Language and Intelligence - Language plays a crucial role in AI development, with the emergence of large language models marking a significant leap in AI intelligence [6][8]. - The understanding of language as a time-dependent sequence is essential for expressing intelligence, as it allows for continuous generation and processing of information [7][9]. - Current models exhibit the ability to form abstract concepts, similar to human learning processes, despite criticisms of lacking true understanding [9][10]. Group 2: Multimodal and World Models - The pursuit of unified models for different modalities is ongoing, with current models like GPT-4 already demonstrating multimodal capabilities [12][13]. - There is skepticism regarding the sufficiency of language models alone for achieving AGI, with some experts advocating for world models that learn physical world rules through observation [14][15]. - Improvements in model architecture and data quality are necessary to bridge the gap between language and world models [15][16]. Group 3: AI Programming - AI programming is seen as a significant application of language models, with potential shifts towards natural language-based programming [17][19]. - Two main perspectives on the future of AI programming exist: one advocating for AI-native programming and the other for AI as a copilot, suggesting a hybrid approach [18][20]. Group 4: Agent Models and Generalization - The concept of agent models is discussed, with challenges in generalization to new tasks being a key concern [21][22]. - The effectiveness of agent systems relies on the ability to learn from interactions and utilize external tools, which is currently limited [22][23]. Group 5: Scaling Laws and Computational Limits - The scaling laws in AI development are debated, with concerns about over-reliance on computational power potentially overshadowing algorithmic advancements [24][25]. - The economic limits of scaling models are acknowledged, suggesting a need for new architectures beyond the current paradigms [25][28]. Group 6: Embodied Intelligence - The slow progress in embodied intelligence, particularly in robotics, is attributed to data scarcity and fundamental differences between bits and atoms [29][30]. - Future models capable of understanding and acting in the physical world are anticipated, requiring advancements in multimodal training [30][31]. Group 7: Reinforcement Learning - The shift towards reinforcement learning-driven reasoning models is highlighted, with potential for significant scientific discoveries [32][33]. - The current limitations of RL training methods are acknowledged, emphasizing the need for further exploration and improvement [34]. Group 8: AI Organization and Collaboration - The development of next-generation reasoning models is seen as essential for achieving large-scale agent collaboration [35][36]. - The need for more parallel processing and effective feedback mechanisms in agent systems is emphasized to enhance collaborative capabilities [36][37]. Group 9: Memory and Learning - The limitations of current models' memory capabilities are discussed, with a focus on the need for more sophisticated memory mechanisms [37][38]. - Continuous learning is identified as a critical area for future development, with ongoing efforts to integrate memory tools into models [39][40]. Group 10: Future Directions - The potential for next-generation reasoning models to achieve higher data efficiency and generate innovative insights is highlighted [41].