"十一月初，我买了一套 32GB 的 DDR5 内存，准备用在 Minisforum BD790i X3D 主板上。 结果三周后，同样那款 DDR5 内存现在居然标价 330 美元——不到一个月涨价 156%！ 按照这个趋势，到圣诞节时，这套 DDR5 的价格可能会比我整个平台（Zen 4 X3D）还贵！"Moore's Law Is Dead 的作者 Tom 写到。 他写了一篇文章，深入讲解了 Sam Altman 的"肮脏 DRAM 交易"。他强调，他自己那套内存涨价 156% 并不是个例，也不是外界情况的极端样本，现在 甚至业内人士买到内存都很难。 一位在美国零售商工作的渠道告诉 Tom：某家内存厂商竟然打电话来询问他们能不能"倒买内存"给他们，用于供给其他客户。"这就像 Corsair 打电话问 Best Buy 有没有 RAM 可以卖给他们一样离谱。"Tom 评价。另一位在整机公司工作的消息源表示：他们询问现在下单 RAM 的交付时间，对方给出的预 计是 2026 年 12 月。 实际上，TrendForce 估计 DRAM 价格可能会上涨 8% 到 13%，而 Counterpoint 则预测涨幅 ...

Core Insights - The article discusses the pivotal moments in NVIDIA's history, highlighting the company's early struggles, strategic pivots, and the introduction of groundbreaking technologies like the CUDA Toolkit 13.1 and the CUDA Tile programming model [1][2][4][5]. Group 1: NVIDIA's Historical Context - NVIDIA faced significant challenges in its early days, including near bankruptcy and strategic missteps, which led to a critical reassessment of its technology and direction [8][9]. - The company’s turnaround involved a focus on 3D graphics technology, leveraging insights from Silicon Graphics to innovate and compress workstation performance into PC graphics cards [8][9][74]. Group 2: Technological Advancements - The launch of CUDA Toolkit 13.1 is described as the most comprehensive update in 20 years, introducing the CUDA Tile programming model, which simplifies GPU programming and enhances compatibility across generations [2][4][5]. - Key features of the new toolkit include improved resource management, enhanced precision simulation in cuBLAS, and a complete overhaul of documentation and tools, aimed at increasing usability for developers [7][8]. Group 3: CEO's Vision and Philosophy - CEO Jensen Huang emphasizes a continuous sense of urgency and fear of failure as driving forces behind NVIDIA's innovation and resilience [8][9]. - Huang's perspective on technology competition highlights the ongoing race in AI development, asserting that technological leadership is crucial for gaining advantages in various fields [13][14][20]. Group 4: Future of AI and Workforce Implications - Huang discusses the transformative potential of AI, predicting that its capabilities have improved by 100 times in the past two years, and emphasizes the importance of guiding AI development towards safety and accuracy [12][16][50]. - The conversation touches on the implications of AI on jobs, suggesting that while some roles may be automated, new opportunities will emerge, and the essence of work will shift towards more meaningful contributions beyond mere task execution [38][45][48].

整理 | 褚杏娟 "谷歌刚把免费版 Gemini API 的每日请求次数从 250 降到了 20，我的 n8n 自动化脚本现在基本都用 不了了。这对任何开发小型项目的人来说都是个打击。"网友 Nilvarcus 表示。 近日，有网友曝出 Google 收紧了 Gemini API 免费层级的限制：Pro 系列已经取消，Flash 系列每天 仅 20 次。这对开发者来说远远不够用。 | Category | RPM J | | TPM | | | --- | --- | --- | --- | --- | | Text-out models | | 4/5 | C | 39.17K / 250K | | Text-out models | | 6 / 10 | | 17.24K / 250K | 还有网友发现，谷歌已经从其"批量 API 速率限制"列表中删除了 Gemini 免费 API 项。"它彻底结束 了。" | Tier 1 Tier 2 | Tier 3 | | | --- | --- | --- | | Model | | Batch Enqueued Tokens | | Text-out mode ...

Core Insights - The article discusses the unconventional journey of Gabriel Petersson, a self-taught AI researcher at OpenAI, who transitioned from being a high school dropout to a member of the Sora team, focusing on video generation models [3][4][33] - It emphasizes the potential for individuals to leverage AI tools like ChatGPT to accelerate their learning and achieve advanced knowledge without formal education [4][19][32] Group 1: Gabriel's Journey - Gabriel Petersson, a high school dropout from a small town in Sweden, utilized project-driven learning and AI to self-educate in mathematics and machine learning, ultimately joining OpenAI [3][4][8] - His initial foray into entrepreneurship involved creating a recommendation system, where he learned coding and sales through hands-on experience rather than formal education [11][12][15] - Gabriel's approach to learning was driven by real-world projects, which forced him to acquire necessary skills quickly, demonstrating that practical experience can be more effective than traditional education [16][18][24] Group 2: Learning Methodology - The article outlines a "recursive learning" method where individuals can identify gaps in their knowledge and use AI to fill those gaps by asking targeted questions [27][28] - Gabriel advocates for a top-down learning approach, starting with real tasks and drilling down into the foundational concepts as needed, contrasting with traditional bottom-up educational methods [20][21][22] - The use of AI, particularly ChatGPT, is highlighted as a transformative tool for learning, allowing users to interactively explore concepts and receive immediate feedback [30][31][32] Group 3: Industry Implications - The narrative suggests that the traditional barriers to entry in high-tech fields, such as the necessity of advanced degrees, are diminishing due to the availability of AI tools that facilitate self-learning [33][34] - Gabriel's experience illustrates a shift in the industry where practical skills and the ability to leverage AI for problem-solving are becoming more valuable than formal educational credentials [46][48] - The article posits that the integration of AI in learning could lead to significant productivity gains across various sectors, potentially contributing to substantial GDP growth [33][34]

Group 1 - Doubao mobile assistant faces login restrictions from Alibaba apps, including Taobao and Xianyu, due to security measures [3][4] - Doubao assistant claims it does not bypass authentication for sensitive operations and plans to adjust AI capabilities in certain scenarios [4][5] - The initial release of Doubao mobile assistant sold out quickly, with second-hand prices significantly higher than the official price, indicating strong market interest [5] Group 2 - The T800 humanoid robot from Zhongqing Robotics gained attention after a video showed it kicking the CEO, highlighting the robot's capabilities [6][9] - T800 is priced starting at 180,000 yuan and features advanced joint modules and sensory technology for various tasks [9] Group 3 - Jiuyue Automotive is undergoing bankruptcy restructuring, with plans to introduce new investors while Baidu seeks to exit its investment [10][11] - The restructuring faces challenges due to significant debt, estimated at 7 billion yuan, with major stakeholders like Geely and Baidu involved [11] Group 4 - Apple CEO Tim Cook reportedly experiences hand tremors, raising concerns among employees amid significant executive turnover at the company [12][13] Group 5 - New Oriental employee expresses dissatisfaction with the company's overtime culture, leading to internal repercussions [14][15] - The employee's complaints highlight issues with work-life balance and management practices within the company [14][15] Group 6 - Canon's Zhongshan factory announced generous severance packages for laid-off employees, with compensation reaching up to 400,000 yuan [16] - The factory's closure is part of a broader trend of production capacity shifting to Southeast Asia [16] Group 7 - A controversy arose when the chairman of Aibisen rejected the position due to dissatisfaction with a salary of 4.35 million yuan, which was later attributed to a clerical error [17] Group 8 - Meta's CEO Mark Zuckerberg plans to shift focus away from the metaverse, with the division having incurred losses exceeding 70 billion dollars [18][19] Group 9 - Microsoft denies reports of lowering AI sales targets, clarifying the distinction between growth goals and sales quotas [20][21] Group 10 - Nvidia launched the Alpamayo-R1 model, aimed at advancing autonomous driving technology through a new visual language model [28][29] Group 11 - Li Auto introduced its first AI smart glasses, Livis, with a starting price of 1,699 yuan after subsidies, aiming to integrate AI capabilities into daily life [30][31] Group 12 - MiHoYo's co-founder launched an AI chat model, AnuNeko, which aims to create interactive NPCs for gaming, reflecting a unique approach to AI integration in games [33][34] Group 13 - SenseTime released the NEO architecture for multimodal models, marking a significant advancement in AI capabilities [35]

编译 | Tina Gemini 在数据上第一次"反超"了 ChatGPT。 根据《金融时报》披露的最新统计，截至 2025 年底，用户在桌面端和移动网页端单次使用 Gemini 的平均停留时长已经达到约 7.2 分钟，首次超过 ChatGPT 的约 6 分钟，也略高于 Anthropic Claude 大约 6 分钟的水平。 更长的停留时间，意味着这已经不是"下个 App 玩两下"的新鲜感，而是用户真的愿意在 Gemini 里待 更久、反复用它解决问题。 与此同时，在 App 下载量 上，虽然 ChatGPT 依然以约 8700 万的月度下载量领先，但 Gemini 的追 赶速度非常惊人——从 2025 年年中每月约 1500 万下载，一路飙升到年底的约 6600 万。 这说明，谷歌把 Gemini 深度嵌入自家生态——从 Android 设备到 Google Workspace——的策略， 正在用户获取上逐步见效。 停留时长尤其值得关注，因为它反映的是真实使用，而不是"下载即走"。用户主动选择待得更久，说 明 Gemini 的回答、功能或整体体验确实发生了可感知的提升。 对谷歌来说，这次"逆袭"格外解气： ...

作者 | 褚杏娟 这波快速发展的 AI 浪潮，吸引了很多人投身创业。根据 Thunderbit 数据，2025 年全球有超过 1 万家获得融资的 AI 创业公司，如果把所有"沾 AI 边"的公司都算上，甚至高达 5 万多家。 在谷歌、YouTube 等大厂做过技术、管理，同时还有投资经验的 Bryan Zhou 就是这次 AI 创业浪潮中的一员。 Bryan 前后毕业于上海交通大学、芝加哥大学，他曾是谷歌 AI 系统团队的创始工程师之一，从 0 到 1 见证了 Google Assistant 用户量增长到五 亿的过程，也曾担任 YouTube 的技术负责人。此外，他还在 AI 和加密领域的有过投资经历。作为一名连续创业者，他已有一次成功退出经历， 因此，创业对他来说不算陌生。 这一次，以大模型代表的 AI 发展比以往任何技术都要快，这让程序员出身的 Bryan 敏锐地发觉到了新的契机，而且不同的职业经验也让他视角 更多元。因此，他决定再次创业。 创始团队组建得非常快，从决定创业到公司正式运作大概就用了三个月的时间。 这主要得益于创始团队成员早就熟知彼此：有的是校友，有的之前就一起创过业，而且大家之前都在湾区 ...

作者 ｜ 木子、高允毅 刚刚， Gemini 3 的 Deep Think 模式 终于 正式上线 了。 顾名思义，这是 Gemini 3 的深度思考模式， 推理能力显著加强 ， 能处理复杂、多步骤，以及更多 创新的问题， 还可以搞定超难的科学问题和数学题！ ARC-AGI-2 则将任务升级为多步骤、递归、隐藏规则等，是 更接近"类人智慧"的高阶推理场景 。 其中，Gemini 3 Deep Think 正确率达 45.1% ，比非深度思考模式的 Gemini 3 Pro（正确率 31.1%）高出了 14%。而在这项测试中，GPT-5 Pro 的正确率仅有 18.3% 。 是 ARC-AGI、HLE 等 多项权威测评中的第一名 先来看看 Gemini 3 Deep Think 是怎么一回事。 在公认的大模型最难测试之一、全球 最接近"通用智能（AGI）核心能力"验证 的基准测试 ARC-AGI 中，Gemini 3 Deep Think 在 2 个榜单中均 拔得头筹 。 其中， ARC-AGI-1 主要测 模型的基础抽象推理 。在这项测试中，Gemini 3 Deep Think 的答题正确 率排第一，达到了 ...

作者｜冬梅 今天早上，国产 GPU 企业摩尔线程智能科技（北京）股份有限公司（以下简称"摩尔线程"）正式在科创板挂牌上市，发行价为 114.28 元，股票代码为 688795，截至发稿前，摩尔线程股价已经涨至620元/股，总股本 47002.8217 万股，市值已经破 2800 亿元。 | ( | | | N摩尔-UO | | | | □ 电脑版 Q | | --- | --- | --- | --- | --- | --- | --- | --- | | | | | 688795 科创板 融 注 成 | | | | | | 620.00 | | 今开 | - 最高 | | ー 最低 | | | | 442.53% | | 505.72 次子 | 0.00% 总手 | | - 金额 | | | | 总值 | | 2914.17亿 流值 | 动 182.17亿 市盈 | 0 | -302.08 | | 更多 | | 分时 | | 王日 日K | 围K | | 月K | | 分钟 ▲ | | 1028.52 | | | 800.00% | 菱5 委书 | | | l | | | | | | 英3 | | - | ...

作者 ｜陈勇 过去几年，基于哈希中心的深度哈希方法逐渐成为大规模图像检索的主流。这类方法通过为每个类别预设固定的哈希中心作为学习目标，避免了成对或 三元组方法的高计算复杂度。然而，随机初始化的哈希中心往往忽略了类间语义关系。现有两阶段方法 SHC 通过引入与哈希函数训练分离的中心生成 阶段，为哈希中心注入了语义。但其引入的额外复杂性、计算开销和阶段间偏差导致了次优性能。 近日，北京邮电大学、北京航空航天大学和中国电信等机构联合提出了一种新颖的端到端框架——中心重分配哈希（Center-Reassigned Hashing, CRH），它在训练哈希函数的同时，通过重分配动态地更新哈希中心。CRH 无需复杂的预训练或离线优化阶段，就能将语义关系无缝集成到中心学习 过程中，显著提升了检索精度和语义一致性。该创新工作论文已被 AAAI 2026 收录，代码已开源，为哈希学习领域提供了新的思路和可复现基础。 论文题目： Codebook-Centric Deep Hashing: End-to-End Joint Learning of Semantic Hash Centers and Neural Hash Fun ...