编辑部 发自 凹非寺 量子位 | 公众号 QbitAI AI产业方向 岗位职责： AI热潮还在汹涌，但如果你还不知道如何参与……那为什么不来 量子位 呢？ 我们是一家以 追踪AI新进展 为核心的内容平台，经过8年积累，目前拥有顶流影响力，广泛且备受认可的产业资源，以及时代风口的最佳观 测和学习生态位。 目前，我们有 三大方向 岗位招聘，希望你是 （或者能成为） 这三个方向的内容专家： 岗位均为全职，工作地点：北京中关村。 岗位面向： 加入我们，你可以获得： 以下是岗位详情： 所有岗位不同能力层级职位均在开放，欢迎结合个人履历和经验申请。 AI财经商业方向 岗位职责： AI产业方向 ：关注基建层创新，包含芯片、AI Infra、云计算； AI财经方向 ：关注AI领域创投和财报，跟踪产业链资本动向； AI产品方向 ：关注AI在应用和硬件终端方向的进展。 社招：覆盖编辑、主笔、主编各个层级，按能力匹配岗位； 校招：应届毕业生，接受实习且可转正。 站在AI浪潮之巅 ：第一时间接触和了解AI领域最新技术和产品，构建完整的AI认知体系。 玩转AI新工具 ：将各种AI新技术、新工具应用于工作，提升工作效率和创造力。 打造个 ...

极客公园 . 用极客视角，追踪你最不可错过的科技圈。欢迎同步关注极客公园视频号 作者｜张鹏 编辑｜ 连冉 我最近意识到，自己刷小红书的时间越来越多了，而且，原因很奇特： 以下文章来源于极客公园 ，作者张鹏 我竟然是去刷科技动态和找创新产品的！ 为什么大家开始在小红书上聊科技和做产品了？ 没错，虽然我有很多内容渠道和自己写的Agent可以做这些事，包括极客公园整个编辑部、社区团队、投资团队都在帮我做这件事，甚至连我 的抖音都被自己「调教」为科技频道了，但是我统计了下我的小红书使用时长，今年上升最明显。 我仔细想了想，可能是因为小红书上有种比较独特的「人间视角」，看着许多真实的人在科技话题上「自然涌现」的讨论，和彼此间的「吵吵 闹闹」，似乎能更好的支持自己一些更具体的「直觉感知」。 我甚至觉得，对于科技圈来说，过去大家觉得主要是为了「吃喝玩乐」的小红书，如今正在有一种「新绿洲」的即视感。 这个感觉不好一句说清，不妨写一篇出来看看大家是不是也有所共鸣吧。 从科技内容的「夜市烟火气」开始 不知道大家是否和我一样，在过去几年的AI浪潮里，因为要疯狂学习和跟上时代，阅读了大量科技内容，但逐渐也时不时会有一个厌倦感，有 时 ...

Core Viewpoint - Meta has acquired Manus to enhance its capabilities in developing general AI agents, marking a significant investment in the AI sector [3][5]. Group 1: Acquisition Details - Meta's acquisition of Manus is reported to be in the range of several billion dollars, making it the third-largest acquisition in Meta's history [8][9]. - The acquisition follows Meta's previous significant purchase of Scale AI, indicating a strategic focus on AI development [5][6]. - Manus will continue to operate in Singapore and provide its products and subscription services through its app and website [4]. Group 2: Financial Performance and Projections - Manus achieved an annual revenue of $125 million earlier this year, which Bloomberg speculates will help Meta recover its investment more quickly [15]. - The specific financial terms of the acquisition have not been disclosed as of the article's publication [16]. Group 3: Team and Leadership - Manus founder, Xiao Hong, will become the Vice President at Meta following the acquisition [7]. - The core team of Manus includes key figures such as co-founder and chief scientist Ji Yichao, and partner Zhang Tao, who have extensive backgrounds in technology and product development [21][22][25]. Group 4: Product and Market Strategy - Manus is recognized for its product narrative as the "first general agent," capable of autonomously breaking down tasks and delivering results based on user requests [21]. - The strategic focus of Manus is on creating a "general-purpose platform + high-frequency scenario optimization" to drive its development [32]. Group 5: Historical Context and Development - Manus was launched in March 2023 and quickly gained traction, leading to significant discussions in the tech community [34]. - The company has undergone rapid growth, including a $75 million investment led by Benchmark and previous funding from Tencent and Sequoia China, raising its valuation to $500 million [43][45]. Group 6: Future Prospects - Manus has plans for further development and expansion, including a focus on international markets and a significant presence in Singapore [49][56]. - The company has established a typical overseas structure to facilitate global operations and financing, indicating a long-term strategy for international growth [58].

Maze团队 投稿 量子位 | 公众号 QbitAI 在大模型智能体 （LLM Agent） 落地过程中，复杂工作流的 高效执行、资源冲突、跨框架兼容、分布式部署 等问题一直困扰着开发者。 而一款名为Maze的分布式智能体工作流框架，正以任务级精细化管理、智能资源调度、多场景部署支持等核心优势，为这些痛点提供一站 式解决方案。 以报告生成场景为例，「添加分析章节」与「数据预处理」等无依赖关系的任务可同时执行，相比串行执行效率提升显著。这种设计不仅让 工作流更灵活可组合，更能充分发挥硬件资源潜力，尤其适合复杂多步骤的Agent应用。 无论是开发者想要快速构建高并发Agent工作流，还是非技术人员需要零代码搭建场景化应用，Maze都给出了兼顾灵活性与易用性的答 案。 不止是工作流框架，更是分布式执行引擎 Maze的核心定位是任务级分布式智能体工作流框架，但它并非传统意义上的单纯工作流管理工具，而是集成了 「分布式执行引擎」 的全 能型平台。 其设计初衷是解决LLM Agent在大规模部署时的效率瓶颈——通过任务级拆分与并行执行，让智能体工作流的端到端处理速度实现质的飞 跃，同时保障系统在高并发场景下的稳定性。 简 ...

Core Viewpoint - The robotics field is still in its early stages, with significant advancements in hardware but limitations in software reliability and performance [1][12]. Group 1: Hardware and Software Dynamics - Current hardware advancements outpace software development, leading to reliability issues that hinder software iteration speed [11][14]. - Many demonstrations of robotic capabilities are often the result of selecting the best performance from numerous attempts, rather than consistent reliability [7][22]. - The need for extensive operational teams to manage robots highlights the challenges in hardware reliability, including overheating and motor failures [18][19]. Group 2: Benchmarking Challenges - The robotics sector lacks standardized benchmarks, making it difficult to assess performance consistently across different hardware platforms and tasks [21][22]. - The absence of consensus on evaluation criteria leads to a situation where every new demonstration can be considered state-of-the-art, complicating progress in the field [22][23]. Group 3: VLA Model Limitations - The Vision-Language-Action (VLA) model, currently a dominant paradigm, faces structural issues as it is primarily optimized for visual question answering rather than physical task execution [24][50]. - The performance of VLA models does not improve linearly with the increase in VLM parameters due to misalignment in pre-training objectives [26][52]. - A shift towards video world models is suggested as a more suitable pre-training target for robotics, as they inherently encode physical dynamics [27][53]. Group 4: Importance of Data - Data plays a crucial role in shaping model capabilities, and the integration of hardware and data is essential for effective robotic performance [31][32]. - Recent advancements in hardware, such as Figure03 and others, demonstrate improved motion capabilities, but challenges remain in enhancing hardware reliability [35][37]. - The Generalist model illustrates the scaling law in embodied intelligence, where larger datasets lead to better task performance [38][41]. Group 5: Future Trends and Market Potential - The robotics industry is projected to grow from $91 billion to $25 trillion by 2050, indicating significant investment potential [60]. - Major tech companies are increasingly investing in robotics software and hardware, reflecting the sector's attractiveness despite current challenges [62].

然而，一个问题逐渐显现： 视觉工具用得越多，模型真的更聪明吗？ 大量实验发现，许多模型正在陷入"盲目用工具"的状态——即便任务并不需要，也会条件反射式地调用裁剪、抽帧、区域放大等工具。 结果却是：推理路径更长了，算力消耗更高了，准确率却没有同步提升，甚至在部分任务中出现下降。 这并不是工具不够强，而是模型从来没有学会一件事：什么时候真的值得用工具。 来自港中文MMLab等的研究团队，针对这一核心问题提出了 AdaTooler-V ——一个具备 自适应工具使用能力 的多模态推理模型，让模型 学会判断"该不该用工具"，而不只是"怎么用工具"。 AdaTooler-V团队 投稿 量子位 | 公众号 QbitAI 近期，以DeepEyes、Thymes为代表的类o3模型通过调用视觉工具，突破了传统纯文本CoT的限制，在视觉推理任务中取得了优异表现。 在12个主流图像和视频推理基准上，AdaTooler-V展现出了显著优势。例如，在高分辨率视觉推理任务V 上，AdaTooler-V-7B的准确率达 到 *89.8% 工具使用的有效性探究 研究团队引入了一个关键指标—— Tool Benefit Score （工具有益分 ...

Core Insights - The article discusses the emergence of a "GPT moment" in the computer vision (CV) field, similar to what has been seen in natural language processing (NLP) with the introduction of large language models (LLMs) [3][16]. - It highlights the potential of Google's DeepMind's video model, Veo 3, which can perform various visual tasks using a single model, thus addressing the fragmentation issue in CV [12][24]. Group 1: Video Model Breakthrough - The paper titled "Video models are zero-shot learners and reasoners" presents a significant advancement in video models, indicating that video is not just an output format but also a medium for reasoning [17][18]. - The model utilizes a "Chain-of-Frames" (CoF) approach, allowing it to demonstrate reasoning through the generation of video frames, making the inference process visible [18][22]. - Veo 3 exhibits zero-shot capabilities, meaning it can handle 62 different visual tasks without specific training for each task, showcasing its versatility [25][26]. Group 2: Transition from NLP to CV - The transition from NLP to CV is marked by the ability of a single model to handle multiple tasks, which was previously achieved through specialized models for each task in CV [7][10]. - The article emphasizes that the fragmentation in CV has limited its advancement, as different tasks required different models, leading to high development costs and restricted generalization capabilities [10][11]. - By leveraging large-scale video and text data for generative training, Veo 3 bridges the gap between visual perception and language understanding, enabling cross-task generalization [13][15]. Group 3: Implications for Future Development - The ability of video models to perform reasoning through continuous visual changes rather than static outputs represents a paradigm shift in how visual tasks can be approached [24][25]. - This unified generative mechanism allows for the integration of various visual tasks, such as segmentation, detection, and path planning, into a single framework [24]. - The advancements in video models signal a potential revolution in the CV field, akin to the disruption caused by LLMs in NLP, suggesting a transformative impact on AI applications [28].

百万年薪急招一名高管！ 在一连接到多起安全指控后，OpenAI终于坐不住了。 于是在最近，这家公司豪掷 55.5万美元 （约合人民币389万元） +股权 ，原地开招一名安全防范负责人 （Head of Preparedness） —— 其核心职责是，制定并执行OpenAI的安全防范框架。 一水 发自 凹非寺 量子位 | 公众号 QbitAI 而且CEO奥特曼还特意强调： 这将是一份压力很大的工作，你几乎会立即面临严峻的挑战。 以上种种不难看出，OpenAI在安全方面确实态势严峻。 而且有一说一，OpenAI的安全团队似乎一直命途多舛，印象中光是负责人就换了一茬又一茬—— Ilya领导的超级对齐团队一度解散、北大校友翁荔也曾短暂担任过Preparedness团队负责人…… 直到现在，OpenAI又想起了它的安全团队。 所以，到底发生了什么让OpenAI又开始把目光转向安全了？ 一切还要从彭博社最近提到的一起安全事件说起—— ChatGPT被指间接导致一位青少年离世 但不久之后，孩子就被发现离开人世了。 据彭博社消息，有一对夫妇最近指控ChatGPT间接导致了其儿子自杀。 其儿子从去年秋天开始使用ChatGPT， ...

Core Insights - TRAE has emerged as a leader in the AI IDE sector, showcasing significant advancements in AI coding capabilities and user engagement metrics [7][48]. Group 1: Key Metrics and User Engagement - TRAE wrote 100 billion lines of code in a year, equivalent to the output of 3 million programmers working continuously [2][4]. - Over 50% of users utilize the Tab key daily, indicating high engagement with the Cue feature [5]. - Global user base exceeds 6 million, with monthly active users surpassing 1.6 million across nearly 200 countries [5]. - Token consumption surged by 700% in just six months, highlighting increased user activity [5]. - There are 6,000 "hardcore" users who wrote code for over 200 days in a year, demonstrating deep engagement [21]. Group 2: AI Integration and User Behavior - The Cue feature has become a critical part of programmers' muscle memory, with over 50% of users actively using it [11][15]. - The SOLO mode has seen a 7,300% increase in question volume since its launch, indicating a shift towards more complex AI-assisted programming tasks [18]. - Users are evolving from mere coders to commanders, managing AI to handle intricate programming tasks [19]. Group 3: Technological Evolution - TRAE's evolution can be categorized into three phases: 1. TRAE 1.0 focused on basic AI integration as a plugin [26]. 2. TRAE 2.0 introduced the SOLO mode, enhancing user interaction with AI [28]. 3. TRAE 3.0 represents a fully responsive coding agent capable of independent task execution [30][32]. Group 4: Performance Metrics - TRAE achieved the top position in the SWE-bench Verified AI programming capability rankings [34]. - Key performance indicators include a 60% reduction in completion latency, an 86% decrease in initial token processing time, and a 43% reduction in memory usage [52]. - The platform has maintained a 99.93% success rate in code completion, emphasizing reliability [52]. Group 5: Market Position and Future Outlook - TRAE is positioned as the leading AI IDE in China, with a clear strategy to build a comprehensive AI development ecosystem [48][56]. - The company aims to redefine the developer ecosystem by integrating open-source contributions, community engagement, and academic collaboration [56]. - As AI transitions from a tool to a collaborator, TRAE's advancements signify a pivotal moment in the AI coding landscape [49][60].

Core Viewpoint - The article emphasizes the ongoing AI boom and invites individuals to join the company "Quantum Bit," which focuses on tracking AI advancements and has established itself as a leading content platform in the industry [1]. Group 1: Job Opportunities - The company is hiring for three main directions: AI Industry, AI Finance, and AI Product, with positions available for both experienced professionals and fresh graduates [2][4]. - Positions are full-time and based in Beijing, with various levels of roles open for application [2][4]. Group 2: Job Responsibilities - **AI Industry Direction**: Focuses on innovations in infrastructure, including chips, AI infrastructure, and cloud computing [6]. - **AI Finance Direction**: Involves tracking venture capital and financial reports in the AI sector, monitoring capital movements within the industry [6]. - **AI Product Direction**: Concentrates on the application and hardware advancements in AI, including software applications and product evaluations [6]. Group 3: Benefits and Growth Opportunities - Employees will have the chance to engage with the latest AI technologies, enhance their work efficiency through new AI tools, and build personal influence by creating original content [6]. - The company offers competitive salaries, comprehensive benefits including social insurance, meal allowances, project performance bonuses, and a supportive team environment [6]. Group 4: Company Reach and Impact - As of 2025, Quantum Bit has over 2.4 million subscribers on WeChat and more than 7 million users across platforms, with an average daily readership exceeding 2 million [12]. - The company is recognized as the top new media outlet in the AI and frontier technology sectors according to third-party data platforms [12].