进入 5 月，大厂的开发者大会进入了密集日程，单是本周，就有微软开发者大会、谷歌 I/O、Anthropic 的开发者大会等。 紧接着，6 月份又到了 Apple 的 WWDC 活动。 本月国内的活动也有很多重磅活动，我们整理了近期值得参与的一些活动，对更多活动感兴趣，可以点 击 「阅读原文」 。 知乎新知青年大会分论坛「 AI 变量研究所」 主办方： 知乎科技 时间： 5 月 24 日下午 地点： 北京线下 798 活动介绍： 知乎科技将举办主题为「 AI 变量研究所」的论坛，聚焦大模型、具身智能、芯片等前沿方 向，深入探讨行业热点与发展趋势，为科技从业者、科研工作者及创新创业者搭建深度技术交流与产业 对接平台。欢迎大家来分享展示自己的产品/项目/研究方向，或者聊聊对行业发展的见解，与更多同行 和潜在目标用户发生深度交流。 活动介绍： 本次大会由国内最大的 AI 开源知识社区 WaytoAGI 主办，以"AI 全球化发展"为主题，旨在 促进 AI 技术国际交流与合作，搭建国际 AI 产业对话平台。来自全球 AI 相关领域的专业人士、爱好者 齐聚一堂，共同探讨 AI 技术的前沿趋势与应用前景。据悉通义万相 W ...

第一个 AI 程序员 Devin 的公司，已经在用Devin 来构建「Devin」了。 去年 12 月，Cognition 推出了世界上首个 AI 编码程序员「Devin」，产品定位是无需人类参与自行编写 代码，并完成通常分配给人类开发人员的整个项目，订阅价格 500 美元/月。 在推出「Devin」后的 6 个月时间内，Cognition 完成了数亿美元的 A 轮融资，估值翻了一番，达到近 40 亿美元。Cognition 成为 AI 编程赛道的绝对明星公司。 01 用 Devin 来构建「Devin」 Cognition 目前仅有 15 人规模的工程团队，Cognition 的每个团队成员都有一个由 5 个 Devin 智能体组成 的「团队」，Github Pull Request 已经有四分之一左右由 Devin 完成，Cognition 的创始人 Scott Wu 预 计一年以后这个比例将达到 50%。 Scott Wu 在近期接受播客 Lenny's Podcast 的访谈时更为详细地分享了 Devin 如何从一个概念成长为能端 到端完成任务的"初级工程师伙伴"，如何融入到现有软件开发流程中，以及如 ...

Core Viewpoint - Microsoft aims to create an "Open Agentic Web," where more applications are driven by intelligent agents, marking a significant transformation in the tech landscape [2][27]. Group 1: AI Integration and Development - Microsoft has integrated AI across its product suite, including Azure, Office applications, and GitHub, with significant financial backing, including thousands of billions in backlog orders [5][21]. - The GitHub Copilot is evolving from a coding assistant to an intelligent partner capable of debugging and managing tasks autonomously, with over 15 million developers currently using it [10][12]. - Microsoft is enhancing its AI capabilities through the Azure AI Foundry, which supports the development and management of AI applications and agents across various platforms [17][18]. Group 2: Developer Engagement and Tools - Microsoft is providing tools for developers to create AI agents easily, including the Microsoft 365 Copilot Tuning, which allows users to train models using their own data [23]. - The introduction of multi-agent orchestration in Copilot Studio enables the integration of multiple agents to handle complex tasks, with over 200,000 organizations reportedly using it [25]. - Microsoft emphasizes the importance of the developer community in building the next generation of AI applications, positioning itself as a facilitator rather than just a platform creator [28][29]. Group 3: Future Vision and Investment - Microsoft envisions a future where the "Open Agentic Web" will be a major platform transformation, similar to past technological revolutions [27]. - The company is investing heavily in cloud infrastructure, with plans to allocate $80 billion in fiscal 2025 to expand its data center capabilities [30]. - The potential for a vast "Agentic Web" enhances Microsoft's narrative in the AI space, indicating a strong commitment to AI development and integration [31].

过去半年，ima 密集上线了个人知识库、共享知识库、知识库广场&知识号等功能，让用户不仅能自己 构建知识库，还能将有价值的信息分享出去，实现知识共享与生态共建。据了解，今年 3 月「知识号」 上线至今，积累近 1000 万篇内容，服务百万级用户的 AI 问答。 以下文章来源于涌现观察 ，作者涌现观察 涌现观察 . 聚焦产品，关注AI，挖掘洞察 腾讯出品的 ima 与其他 AI 产品相比，有点不太一样。 24 年 10 月上线，ima 主打知识管理，使用 AI 帮助用户管理和高效利用自己的知识库，沉淀那些真正 有价值的认知资产，对抗「信息指尖划过，却留不住」的普遍焦虑。 围绕知识库本身，ima 做了很多。 近期，我们邀请到 ima 产品团队进行对话。 在 AI 技术日新月异的当下，ima 如何定义痛点？又如何在 看似「古典」的产品打磨中，寻找 AI 落地的实践路径？ima 产品团队将为我们揭开 ima 这款产品的思 考原点与进化逻辑。 以下是访谈人林珊珊与 ima 产品团队的对话，经编辑整理。 Founder Park 正在搭建「 AI 产品市集」社群，邀请从业者、开发人员和创业者，扫码加群： 进群后，你有机会 ...

笔记工具并不是一个传统意义上的热门投资赛道，在 Google Keep、Apple Notes、OneNote、Evernote 之外，只有 Notion 冲出了重围。 剩下的还有 Obsidian 、Logseq 等新兴的笔记工具在卷双链。 进群后，你有机会得到： 01 工具是人脑 处理 信息的一种「外化」 Q：「思维工具」是技术发展赋予人类的宝贵财富。当我们第一次聊天时，你对如何为人们解锁价值的 思考方式让我非常着迷，你还提到 x-y 坐标图是一个很好的例子。能否就此展开，分享你对其感兴趣的 缘由？ Granola 是如何在一众 AI 笔记产品中打出差异化的？创始人 Chris Pedregal 在接受著名科技播客 「Colossus」的深度访谈时提到，Granola 不仅仅是一个简单的会议转录工具，其核心关键在于「非常 个人化」并赋予用户极致的「控制权」，它是一个超越简单记录、能够深度融入并赋能用户工作流程的 「思考空间」。AI 的真正价值在于成为下一代强大的「思维工具」，以前所未有的方式增强和拓展人 类的能力。 在此次访谈中，Chris Pedregal 还分享了许多有意思、且值得深思的观点： 对于思 ...

文章转载自「海外独角兽」 Multi agent 系统正成为新的突破方向的过程中，agent infra 也成为落地关键。在 computer use 带来范式创新的趋势下，virtual machine 将成为 潜在创业机会，E2B 就是这个领域的新兴参与者。 E2B 之所以受到市场关注很大程度上是因为 Manus，Manus agent 完成任务过程中的 virtual computer 支持正是来自于 E2B。E2B 成立于 2023 年，作为一个开源基础设施，允许用户在云端的安全隔离沙盒中运行 AI 生成的代码。E2B 本质上是一个可以快速启动（~150 毫秒）的 microVM， 它的底层类似于 AWS Firecracker 这个代表性的 MicroVM，在此基础上， AI Agents 可以在 E2B 中运行代码语言、使用浏览器、调用各种操作系 统中的工具。 随着 Agent 生态的繁荣，E2B 的 沙盒月创建量一年内从 4 万增长到 1500 万，一年内增长了 375 倍。 为什么 AI agents 需要专属的"电脑"？ 为了更好地理解这个问题，「海外独角兽」编译了 CEO Vasek Ml ...

Core Insights - The article discusses recent advancements in utilizing "thinking time" during testing and its mechanisms, aiming to enhance model performance in complex cognitive tasks such as logical reasoning, long text comprehension, mathematical problem-solving, and code generation and debugging [4][5]. Group 1: Motivating Models to Think - The core idea is closely related to human thinking processes, where complex problems require time for reflection and analysis [9]. - Daniel Kahneman's dual process theory categorizes human thinking into two systems: fast thinking, which is quick and intuitive, and slow thinking, which is deliberate and logical [9][13]. - In deep learning, neural networks can be characterized by the computational and storage resources they utilize during each forward pass, suggesting that optimizing these resources can improve model performance [10]. Group 2: Thinking in Tokens - The strategy of generating intermediate reasoning steps before producing final answers has evolved into a standard method, particularly in mathematical problem-solving [12]. - The introduction of the "scratchpad" concept allows models to treat generated intermediate tokens as temporary content for reasoning processes, leading to the term "chain of thought" (CoT) [12]. Group 3: Enhancing Reasoning Capabilities - CoT prompting significantly improves success rates in solving mathematical problems, with larger models benefiting more from increased "thinking time" [16]. - Two main strategies to enhance generation quality are parallel sampling and sequential revision, each with its own advantages and challenges [18][19]. Group 4: Self-Correction and Reinforcement Learning - Recent research has successfully utilized reinforcement learning (RL) to enhance language models' reasoning capabilities, particularly in STEM-related tasks [31]. - The DeepSeek-R1 model, designed for high-complexity tasks, employs a two-stage training process combining supervised fine-tuning and reinforcement learning [32]. Group 5: External Tools and Enhanced Reasoning - The use of external tools, such as code interpreters, can efficiently solve intermediate steps in reasoning processes, expanding the capabilities of language models [45]. - The ReAct method integrates external operations with reasoning trajectories, allowing models to incorporate external knowledge into their reasoning paths [48][50]. Group 6: Monitoring and Trustworthiness of Reasoning - Monitoring CoT can effectively detect inappropriate behaviors in reasoning models, such as reward hacking, and enhance robustness against adversarial inputs [51][53]. - The article highlights the importance of ensuring that models faithfully express their reasoning processes, as biases can arise from training data or human-written examples [55][64].

Core Viewpoint - The article discusses the emergence of the "AI Contractor Model" (AI 包工头模式) as a transformative approach in the AI application landscape, emphasizing its potential to disrupt traditional SaaS models and create significant profit opportunities through a results-oriented service framework [4][12][27]. Summary by Sections AI Application Payment Models - The essence of AI application payment models revolves around the value of AI products, with a focus on how to present unique value to users and achieve commercial revenue [2][3]. Traditional SaaS vs. AI Applications - Traditional SaaS products, which rely on standardized functions and private data accumulation, are at risk of being replaced by high-intelligence AI applications, losing favor in capital markets [4][27]. - The AI Contractor Model can potentially break the ceiling of digital profit pools, with profit margins varying significantly across different business models, achieving up to 60 times the profit space when combined with AI capabilities [4][32]. AI Contractor Model Characteristics - The AI Contractor Model is characterized by a results-oriented payment structure, binding the interests of AI service providers and clients closely [12][14]. - It requires a comprehensive delivery system, including investment in production equipment, management of personnel, and operational funding, encapsulated in the "package of work, materials, and results" concept [12][14]. Evolution Levels of AI Contractor Model - The model evolves through four levels: L1 focuses on basic efficiency, L2 on comprehensive efficiency, L3 on profit sharing, and L4 on transforming from passive service to active resource control [5][50]. Market Examples - Case studies illustrate the application of the AI Contractor Model in various sectors, such as autonomous mining operations and AI customer service, showcasing how companies like Sierra and KoBold are leveraging this model to achieve significant operational efficiencies and profit margins [16][19][21][24]. Challenges for Traditional SaaS - Traditional SaaS companies face significant challenges, including high R&D and sales costs, low customer retention rates, and a lack of recognition in the Chinese market, which has led to a high rate of losses [14][27]. Profit Pool Analysis - The article outlines five major profit pools for enterprises, highlighting the potential for the AI Contractor Model to tap into these pools more effectively than traditional models, thus enhancing overall profitability [32][34]. High Capital Value Factors - The AI Contractor Model can overcome traditional barriers to capital value by achieving high technological content, systematic optimization, controllability, customer stickiness, and financial predictability, collectively referred to as the "Five Highs" [43][44][49]. Required Cognitive Upgrades - Successful implementation of the AI Contractor Model necessitates a focus on vertical specialization, human-machine collaboration, and a deep understanding of industry-specific needs to avoid pitfalls associated with broad, unfocused strategies [58][59][60].

Core Insights - The article discusses the transformative impact of AI technologies on industries and society, highlighting the emergence of AI Agent products and their integration into business operations [1] - It emphasizes the importance of foundational technology advancements, such as the launch of the DeepSeek R1 model, which has significantly enhanced the capabilities of AI models in China [1] - The article introduces a survey initiated by Founder Park to identify key players that are innovating at the intersection of technology, business, and application [2] Group 1: AI Innovations - AI Agent products are creating new human-computer interaction experiences, functioning as "digital employees" within enterprises [1] - AI Coding products are evolving towards full automation, shifting developers' focus from specific code lines to expected outcomes [1] - The release of AlphaFold3 has sparked a commercialization wave in the fields of protein prediction, drug discovery, and bio-AI models [1] Group 2: Evaluation Criteria - The evaluation focuses on companies that demonstrate innovation in business value creation, including new operational processes and value distribution methods [4] - Companies are assessed on their ability to enhance user interaction experiences through intelligent design and improved workflows [4] - The criteria also include breakthroughs in AI algorithms, models, and data processing capabilities that can influence industry ecosystems [4] Group 3: Target Companies - The survey targets both startups and publicly listed companies primarily in the AI sector, focusing on infrastructure, model, and application layers [5] - The infrastructure layer includes companies providing data, computing power, and platforms essential for AI development [5] - The model layer focuses on general large models and deep learning frameworks, while the application layer encompasses a wide range of AI applications, including image, text, and code generation [5]

Core Viewpoint - OpenAI has agreed to acquire Windsurf for $3 billion, highlighting the growing importance of AI programming tools in the software development industry [1] Group 1: SWE-1 Model Overview - Windsurf has launched its AI programming model, SWE-1, which focuses on the entire software engineering process rather than just coding tasks [1] - SWE-1 features "Flow Awareness," allowing for a seamless collaboration between AI and users, where AI performs tasks, users provide corrections, and AI continues the process [1][34] - The SWE-1 series includes three models: SWE-1, SWE-1-lite, and SWE-1-mini, catering to different user needs and performance requirements [5][28] Group 2: Development and Evaluation of SWE-1 - The development of SWE-1 was inspired by the popular Windsurf editor, utilizing a new data structure and training method to understand incomplete states and long-term tasks [13][14] - SWE-1 has been evaluated against leading models like Anthropic's series and has shown competitive performance in both offline assessments and real-world usage [21][22] - The model's performance metrics include the number of code lines accepted by users and the contribution rate of code changes made by the model [24][26] Group 3: Importance of Flow Awareness - Flow Awareness is a key design principle for Windsurf, enabling a shared timeline between AI and users, which enhances collaboration and task management [33][34] - This system allows for continuous tracking of the model's capabilities and user interventions, facilitating a more effective development process [37][41] - The evolution of the shared timeline concept is central to Windsurf's goal of creating a comprehensive software engineering timeline [39] Group 4: Future Prospects - SWE-1 is just the beginning, with Windsurf aiming to continuously improve the SWE series models while maintaining low costs and enhancing performance [42] - The acquisition by OpenAI marks a new era for AI programming tools, transforming the software development landscape [42][43]