一点进去发现，好家伙，小红书这波操作，终于是 把官方AI整上了我的首页 。 是新功能，但也是老面孔。AI助手名叫 点点 ， 用户们应该挺眼熟，就是之前在评论区常会被@的小红书版评论罗伯特。 鱼羊 发自 凹非寺 量子位 | 公众号 QbitAI 事情是这样的。 作为一个小红书重度用户，今天一开软件我天塌了：我的侧边栏呢？？？ 我赶紧一个搜索，原来官方真是更新了玩法。 评论区@不到了，但现在，你可以在小红书里这样玩AI：笔记直接分享给点点，不用手动跳转，即可开启边刷边聊模式。 还真别说，现在的社交媒体上，要没点AI出没，是有那么点不习惯。 像微博，不止有到处串场的评论罗伯特，也把「智搜」功能插进了每一个热门话题里，主打一个让用户吃瓜不迷路；而微信，也把元宝总结的 功能内置进了公众号文章页面。 看上去在AI上一直比较保守的小红书，现在也醒了一「点点」。 AI一点点，体验变好了吗？ AI一点点，有没有让刷社媒的体验变好，还是得实测一波才知底细。 交互体验 先来看看交互方式。 第一种方式，就是在原来首页侧边栏的位置， 点击小气泡进入点点对话框 ： 用法跟别的AI助手没有什么不同，好处就是无需跳转其他App，在小红书本书 ...

Core Viewpoint - Nvidia is aggressively advancing in open-source models with the introduction of the "most efficient open model family" Nemotron 3, utilizing a hybrid Mamba-Transformer MoE architecture and NVFP4 low-precision training [1][22]. Group 1: Model Architecture and Efficiency - Nemotron 3 combines Mamba and Transformer architectures to maximize inference efficiency [7]. - The model architecture features a unique arrangement of Mamba-2 layers and MoE layers, significantly reducing the reliance on self-attention layers [10]. - In typical inference scenarios with 8k input and 16k output, Nemotron 3 Nano 30B-A3B achieves a throughput 3.3 times greater than Qwen3-30B-A3B, with advantages becoming more pronounced as sequence length increases [12]. - The model demonstrates robust performance on long-context tasks, scoring 68.2 on the RULER benchmark with 1 million token input length, compared to only 23.43 for Nemotron 2 Nano 12B [14]. Group 2: LatentMoE Architecture - For larger models, Nvidia introduces the LatentMoE architecture, which performs expert routing in a latent space [15]. - LatentMoE addresses two bottlenecks in MoE layer deployment: low-latency scenarios and high-throughput scenarios, reducing the weight loading and communication costs significantly [16][18]. - LatentMoE utilizes 512 experts with 22 activated, compared to the standard MoE's 128 experts with 6 activated, achieving better performance across various tasks [20]. Group 3: Training Innovations - Nvidia employs NVFP4 format for training, achieving a peak throughput three times that of FP8, and has successfully trained models on up to 250 trillion tokens [22]. - The training process retains high precision for certain layers to maintain model stability, while most layers are quantized to NVFP4 [23]. - Nemotron 3's post-training utilizes multi-environment reinforcement learning, covering a wide range of tasks simultaneously, which enhances stability and avoids common issues associated with phased training [24][26]. Group 4: Performance Metrics and Open Source - The model shows consistent accuracy across various downstream tasks, with NVFP4-trained models closely matching BF16 versions in performance [28]. - The entire post-training software stack is open-sourced under the Apache 2.0 license, including NeMo-RL and NeMo-Gym repositories [32]. - Nemotron 3 allows for cognitive budget control during inference, enabling users to specify the maximum number of tokens for thought chains, thus balancing efficiency and accuracy [34].

衡宇 发自 凹非寺 量子位 | 公众号 QbitAI 好震惊，好意外，现在一份4–6个月的AI相关实习，月薪已经接近14万人民币了！ 而且 这个价格不是个例 —— OpenAI、Anthropic、Meta、Google DeepMind等巨头，都为实习、Fellowship、Residency这类短期岗位，开出足以对标全职研究员的价 格。 Business Insider最新披露的一组数据显示，目前AI相关实习和研究型短期项目的月薪，已经普遍来到7000–18000美元区间，折合人民币约 4.9-12.6万元。 换算成年薪水平，是不是 已经明显超出大多数行业对"实习生"这一角色的传统认知 …… 真·AI人才的生活，我的梦 （没错已经开始白日做梦了） 。 书归正传。 继大厂、巨头为成熟的AI人才大动干戈，甚至扎克伯格为了挖OpenAI的人亲自洗手作羹汤端到想挖的人嘴边过后， 这场纷争终于开始波及那 些还没有正式毕业、甚至刚刚进入研究路径不久的人。 水涨船高的AI实习工资 在薪酬层面，实习生、学生研究员、驻留项目，已经可以和全职研究岗站在同一水平线上。 我们先展开来看看硅谷那边的具体情况。 OpenAI Ope ...

Core Viewpoint - The article introduces Yulan-FinSight, a multi-modal report generation system developed by Renmin University of China, designed to meet real financial research and investment needs, showcasing advanced capabilities in data analysis and report writing [1][3]. Group 1: Challenges of General AI in Financial Research - General AI struggles with financial reports due to their highly structured, logical, and visual nature, which involves multiple processes [5]. - Financial research demands higher data integration, analytical depth, and expression forms compared to general AI tasks [6]. - Three main challenges faced by existing general AI systems include: 1. Fragmentation of domain knowledge and data, making it difficult to integrate structured financial data with unstructured information [7]. 2. Lack of professional-level visualization capabilities, as current models can only produce basic visualizations without ensuring data consistency [8]. 3. Absence of iterative research capabilities, where existing systems follow a fixed process that limits dynamic adjustments based on intermediate findings [9]. Group 2: FinSight's Innovations - FinSight aims to emulate human financial analysts by focusing on cognitive processes and introducing three key technological innovations [10]. - The core architecture is based on a Code-Driven Variable-Memory (CAVM) multi-agent framework, allowing for collaborative reasoning through a unified variable space instead of traditional message-based communication [14][16]. - An iterative vision-enhanced mechanism is employed for generating financial charts, combining the strengths of language models for coding and visual models for feedback [20][21]. - The writing framework is restructured into a two-phase process: analysis followed by integration, ensuring clarity and depth in long reports [24][25]. Group 3: Performance and Evaluation - FinSight significantly outperformed existing deep research systems in factual accuracy, analytical depth, and presentation quality, achieving an average score of 8.09 [34]. - The system's visualization capabilities received a score of 9.00, indicating a substantial improvement in generating professional financial charts [35]. - In practical applications, FinSight produced reports averaging over 20,000 words with more than 50 charts, maintaining quality as report length increased [38]. - FinSight ranked first in the AFAC 2025 Financial Intelligence Innovation Competition, demonstrating its robustness and practical utility [39]. Group 4: Broader Implications - FinSight represents a significant advancement in AI capabilities within expert-intensive fields, suggesting that AI can now perform tasks traditionally reserved for human experts, such as problem decomposition and hypothesis validation [40][41]. - This paradigm shift indicates potential applications in various complex domains, including research analysis, legal assessment, and medical decision-making, paving the way for a new generation of productivity centered around expert-level AI agents [43].

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。 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。 编辑部 发自 凹非寺 量子位 | 公众号 QbitAI 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？ 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。 打造个人影响力 ：通过撰写独家原创内容，建立个人知名度，成为AI领域的意见领袖。 拓展行业人脉 ：与AI领域大咖零距离接触，参与重要科技活动和发布会，拓展行业视野。 获得专业指导 ...

Core Viewpoint - The global AI industry has transitioned from a model performance competition to a "life-and-death race" for the large-scale implementation of intelligent agents, where cost reduction is no longer optional but a critical factor for profitability and industry breakthroughs [1] Group 1: Cost Reduction Breakthrough - Inspur Information has launched the Yuan Brain HC1000 ultra-scalable AI server, achieving a breakthrough in inference cost to 1 yuan per million tokens for the first time [2][3] - This breakthrough is expected to eliminate the cost barriers for the industrialization of intelligent agents and reshape the underlying logic of competition in the AI industry [3] Group 2: Future Cost Dynamics - Liu Jun, Chief AI Strategist at Inspur, emphasized that the current cost of 1 yuan per million tokens is only a temporary victory, as the future will see an exponential increase in token consumption and demand for complex tasks, making current cost levels insufficient for widespread AI deployment [4][5] - For AI to become a fundamental resource like water and electricity, token costs must achieve a significant reduction, evolving from a "core competitiveness" to a "ticket for survival" in the intelligent agent era [5] Group 3: Historical Context and Current Trends - The current AI era is at a critical point similar to the history of the internet, where significant reductions in communication costs have driven the emergence of new application ecosystems [7] - As technology advances and token prices decrease, companies can apply AI on more complex and energy-intensive tasks, leading to an exponential increase in token demand [8] Group 4: Token Consumption Data - Data from various sources indicates a significant increase in token consumption, with ByteDance's Doubao model reaching a daily token usage of over 50 trillion, a tenfold increase from the previous year [13] - Google's platforms are processing 1.3 trillion tokens monthly, equivalent to a daily average of 43.3 trillion, up from 9.7 trillion a year ago [13] Group 5: Cost Structure Challenges - Over 80% of current token costs stem from computing expenses, with the core issue being the mismatch between inference and training loads, leading to inefficient resource utilization [12] - The architecture must be fundamentally restructured to enhance the output efficiency of unit computing power, addressing issues such as low utilization rates during inference and the "storage wall" bottleneck [14][16] Group 6: Innovations in Architecture - The Yuan Brain HC1000 employs a new DirectCom architecture that allows for efficient aggregation of massive local AI chips, achieving a breakthrough in inference cost [23] - This architecture supports ultra-large-scale lossless expansion and enhances inference performance by 1.75 times, with single card utilization efficiency (MFU) potentially increasing by 5.7 times [27] Group 7: Future Directions - Liu Jun stated that achieving a sustainable and significant reduction in token costs requires a fundamental innovation in computing architecture, shifting the focus from scale to efficiency [29] - The AI industry must innovate product technologies, develop dedicated computing architectures for AI, and explore specialized computing chips to optimize both software and hardware [29]

JarvisEvo团队 投稿 量子位 | 公众号 QbitAI 下面就来了解一下详细情况吧～ 自我评估和修正 研究背景与动机 近年来，基于指令的图像编辑模型虽然取得了显著进展，但在追求"专业级"修图体验时，仍面临两大核心挑战： 1. 指令幻觉 (Instruction Hallucination)： 现有的文本思维链 (Text-only CoT) 存在信息瓶颈。模型在推理过程中"看不见"中间的修图结果，仅凭文本"脑补"假设进行下一步操作的 视觉结果，容易导致事实性错误，无法确保每一步都符合用户意图。 一句话让照片变大片，比专业软件简单、比AI修图更可控！ 腾讯混元携手厦门大学推出 JarvisEvo ——一个统一的图像编辑智能体模拟人类专家设计师，通过 迭代编辑、视觉感知、自我评估和自我反 思 来"p图"。 "像专家一样思考，像工匠一样打磨" 。JarvisEvo不仅能用Lightroom修图，更能"看见"修图后的变化，并自我评判好坏，从而实现无需外部 奖励的自我进化 。 2. 奖励黑客 (Reward Hacking)： 在强化学习进行偏好对齐的过程中，策略模型（Policy）是动态更新的，而奖励模型（R ...

Core Viewpoint - Tesla's FSD v14 has been recognized as the first AI to pass the "physical Turing test," showcasing significant advancements in autonomous driving technology [1][7]. Group 1: User Experience and Feedback - Jim Fan, NVIDIA's robotics head, expressed astonishment at the FSD v14 experience, stating it felt indistinguishable from a human driver [3][4]. - User feedback on FSD v14 has been overwhelmingly positive, with many Tesla owners reporting an addictive quality to the technology [6][10]. - Specific user experiences highlight FSD's improved decision-making, such as effectively reading parking signs and executing lane changes decisively [11][12][26]. Group 2: Technical Enhancements - The FSD v14.2.2 update includes significant upgrades to the neural network's visual encoder, enhancing perception and understanding capabilities [32]. - New features allow for better recognition of emergency vehicles and dynamic navigation adjustments in response to real-time traffic conditions [35][37]. - The update introduces two new driving modes, SLOTH and MADMAX, which cater to different driving styles and preferences [44]. Group 3: Competitive Landscape - Tesla's Robotaxi service is still in its early stages, with approximately 30 vehicles deployed in Austin, compared to Waymo's nearly 200 vehicles in the same area [42]. - Waymo leads in market presence and operational scale, with over 2,500 vehicles across multiple cities and a significant number of weekly paid rides [43][47]. - Despite the current gap, Tesla's FSD improvements and growing user interest indicate a potential for accelerated growth in the Robotaxi market [53][54]. Group 4: Future Outlook - Elon Musk has set ambitious goals for Tesla's Robotaxi service, aiming for full autonomy without safety monitors, which appears to be progressing with the latest FSD updates [29][30]. - The ongoing competition between Tesla and Waymo highlights differing technological approaches, with Tesla focusing on a neural network model while Waymo relies on a modular system [63]. - The future of autonomous driving technology will likely influence consumer purchasing decisions, making it a critical area for both companies [69].

Core Viewpoint - Microsoft has denied plans to rewrite Windows 11 using AI, contradicting earlier statements from an internal engineer about eliminating C/C++ code by 2030 through AI and Rust integration [2][3][9]. Group 1: Microsoft’s AI Strategy - The initial claim by a Microsoft engineer suggested that one engineer could rewrite one million lines of code in a month, which sparked significant online debate and concern about the feasibility and risks of such an approach [4][5][10]. - Many users expressed admiration for Microsoft's ambition but also raised alarms about the potential risks associated with aggressively pushing AI into critical codebases [6][10]. - The engineer later clarified that the post was intended to attract like-minded engineers and not to announce a new strategy for Windows 11, emphasizing that the project was more about exploring technology for language migration rather than a definitive plan [16][17]. Group 2: Concerns Over Code Quality and Legacy Issues - The transition from C/C++ to Rust raises concerns about the quality of AI-generated code, with estimates suggesting that current AI technology could produce a bug for every ten lines of code, leading to significant potential issues in a large codebase [13][25]. - Microsoft's historical reliance on C/C++ has resulted in approximately 70% of Windows security vulnerabilities being attributed to these languages, highlighting the need for a more secure alternative like Rust [25][26]. - The complexity and legacy of Windows code, accumulated over decades, pose significant challenges for any large-scale rewrite, as many existing implementations may be critical to system stability [38][40]. Group 3: Rust as a Potential Solution - Rust is viewed as a promising alternative due to its design focus on memory safety, which could help mitigate long-standing security issues in Windows [27][34]. - However, Rust's ecosystem is still maturing, and the transition would require substantial investment in developer training and adaptation, which could hinder immediate implementation [43][44]. - Despite the challenges, Microsoft has begun experimenting with Rust in rewriting parts of the Windows kernel, although this effort remains limited to a few modules [36]. Group 4: The Role of AI in Development - The rapid advancement of AI programming capabilities presents an opportunity for Microsoft to leverage AI as a bridge in transitioning to Rust, potentially reducing the barriers associated with the switch [45]. - However, the effectiveness of AI as a reliable tool for such critical tasks remains uncertain, and current AI technologies may not yet be capable of handling the complexities involved in core system engineering [46][48]. - Microsoft's CEO has emphasized the importance of AI in the company's future, indicating a strong internal push towards integrating AI into development processes, but the recent backlash suggests a need for a more measured approach [50][53][56].

Core Viewpoint - Xiaomi has launched its new flagship imaging smartphone, the 17 Ultra, which emphasizes optical photography enhancements and features significant upgrades over its predecessor, the 15 Ultra [2][3]. Pricing and Variants - The starting price for the 17 Ultra is 6,999 yuan for the 12GB+512GB version, with additional configurations of 16GB+512GB priced at 7,499 yuan and 16GB+1TB at 8,499 yuan [7]. - A special edition, "Xiaomi 17 Ultra by Leica," is available, with prices starting at 7,999 yuan for the 16GB+512GB version and 8,999 yuan for the 1TB version, both 500 yuan more than their standard counterparts [9]. Imaging Technology - The 17 Ultra features a 1-inch sensor with a 3.2-micron pixel size and an f/1.67 aperture, allowing for double the light intake compared to the iPhone 17 Pro Max [16][17]. - The LOFIC technology enhances dynamic range, with the new pixel structure offering 6.3 times the electronic capacity of the previous generation, improving performance in high-contrast scenes [19][20][21]. - The device includes a "fireworks capture" mode, designed for challenging lighting conditions, showcasing its advanced imaging capabilities [29]. Optical Zoom and Performance - The 17 Ultra incorporates a 200-megapixel continuous optical zoom, utilizing a 28nm process that reduces power consumption by 40% [35]. - It achieves high-quality imaging across various focal lengths without relying on digital cropping, maintaining full resolution [46][49]. - The optical architecture includes eight elements in three groups, with special glass lenses that enhance light transmission and color accuracy [50][54]. Memory and Market Trends - The 17 Ultra starts with a minimum storage of 512GB, reflecting a shift in consumer demand towards higher memory capacities due to the rising need for AI applications [60][64]. - The overall memory supply chain is experiencing price increases, impacting smartphone pricing strategies [65].