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5月25日周一
  1. Rohan Paul67

    华为提出τ缩放定律,旨在不依赖更先进制程的情况下,通过LogicFolding技术折叠逻辑模块、缩短信号传输距离来提升芯片性能与密度。华为称已量产采用此思路的381颗芯片,并计划于2031年实现等效1.4nm(14Å)密度,该定律以海思负责人何庭波命名。同样,华为在存储领域也展示了类似的“侧向创新”路径,其通过改变封装方式(Die-on-Board)而非追求最先进的NAND层数,推出了容量达122.88TB的AI SSD。

    引用Rohan Paul@rohanpaul_ai

    🇨🇳 🇺🇸 China's Huawei’s new 122TB SSD shows how export controls can move innovation sideways instead of simply stopping it. Huawei just built a 122.88TB AI SSD by changing the package around the memory, not by matching Samsung’s most advanced 400+ layer 3D NAND. And a 245TB version discussed as a future step. High-capacity SSDs usually grow by stacking more NAND layers inside each chip, but Huawei’s access to those chips is blocked because its Entity List status restricts items tied to US technology. So it is not trying to win only by making taller 3D NAND stacks, where Samsung has already shown 400-plus-layer V-NAND work. Instead, Huawei is shifting the contest from the chip itself to the way chips are packed together. Huawei’s workaround is Die-on-Board, which puts NAND dies directly onto the circuit board, cuts out some normal chip packaging, and raises board-level density by packing more lower-density memory into the same device. Direct die placement creates heat and signal problems, but it shows how packaging can recover some of the capacity lost when a company cannot buy the best memory chips.

  2. Greg Brockman63

    GPT-5.5 Pro 用于事实核查: GPT-5.5 Pro 是一个非常可靠的事实核查工具。我可以把整章内容扔给它,它能准确找出每一个关键引用。唯一真正让人烦恼的是它喜欢抠细节,所以会返回很多“大体意思是对的,但你没有考虑到微小细节 X”这样的反馈。

    引用Ethan Mollick@emollick

    GPT-5.5 Pro is a very solid fact checker. I can throw entire chapters at it and it will hunt down every key reference accurately. The only real annoyance is that it loves nuance, so returns a lot of “the general idea is right, but you are not taking into account tiny detail X”

  3. Rohan Paul62

    路透社报道,深度求索宣布其旗舰模型V4-Pro的API价格永久下调75%,但未直接确认这是由于华为昇腾芯片供应改善。报道分析,此举时机恰逢中国AI算力栈从受限的Nvidia芯片向华为昇腾硬件迁移带来的成本下降。据引述分析,DeepSeek的核心战略是通过架构创新(如MoE、DSA,以及V4-Pro的CSA/HCA技术)大幅降低对高端HBM GPU的依赖,其技术指标显示1M-token推理FLOPs和KV cache显著降低。其目标在于优化模型,使更多样的硬件(如LPDDR、NAND、定制ASIC)能够运行前沿AI,以适应不同的工业基础。

    引用Rohan Paul@rohanpaul_ai

    Great article here on DeepSeek. Their real story is not cheaper chatbots, but architecture that turns hardware scarcity into strategy. DeepSeek is not trying to sell coding seats, it is trying to make Chinese memory, accelerators, and systems useful for frontier AI. Every recent DeepSeek move attacks a bottleneck that makes frontier models dependent on elite HBM-heavy GPU stacks: MoE activates only parts of a model, DSA reduces long-context attention cost, and V4-Pro’s official card says CSA/HCA cuts 1M-token single-token inference FLOPs to 27% and KV cache to 10% of V3.2. Engram, a separate research line, pushes the same logic from another side: let static knowledge live in scalable lookup memory, then fetch it predictably from host memory instead of forcing every fact through dense computation. That sounds like engineering detail until you see the business consequence. If models need less HBM and less brute-force compute, then second-best chips, abundant LPDDR, NAND, and customized ASICs become less second-best. Reuters has already reported a permanent 75% DeepSeek V4-Pro price cut, while noting Huawei Ascend supply constraints and expected supernode availability, which is exactly the kind of feedback loop that they wanted. DeepSeek is not only optimizing models for benchmarks, it is optimizing AI for a different industrial base. The prize is not the app layer. The prize is making scarcity programmable.

  4. Simon Willison 博客58

    引用阿尔明·罗纳彻的话

    当前最令人沮丧的问题是,人们提交的 issue 报告并非本人原话,而是经过 AI 重写。这种“AI 垃圾”导致描述混乱,AI 生成的结论往往不准确却充满自信,造成对根本原因的完全猜测、虚假的最小复现步骤、错误的代码类比以及不相关的错误列表。作者因此希望 issue 报告能浓缩为人类实际观察到的四个要素:运行的命令、预期结果、实际结果以及具体的错误或日志。

5月24日周日
  1. Rohan Paul47

    李飞飞重新定义机器人学,强调其核心是“空间智能”——即机器在三维物理空间中感知、理解与行动的能力。这一能力使机器人能执行任务并实现人机协作。3D生成与重建技术正打破人类仅能体验单一物理世界的局限,创造出用于训练、创造、旅行与社交的无限数字多元宇宙。未来,人们将以“多元宇宙”的方式生活,极大拓展人类想象与交互的边界。

    引用a16z@a16z

    For all of history, humanity shared one 3D world. @theworldlabs co-founder @drfeifei says spatial intelligence now lets us generate and reconstruct infinite universes for creativity, travel, storytelling, even socialization. “It suddenly will enable us to live in a multiverse way.”

  2. IT之家·人工智能56

    小鹏副总裁:小鹏可能是唯一一个欢迎特斯拉 FSD 入华的车企

    小鹏副总裁公开表示,小鹏可能是唯一欢迎特斯拉FSD入华的车企。该高管指出,特斯拉FSD与小鹏VLA辅助驾驶系统目前均采用纯视觉技术路线。小鹏集团通用智能中心负责人刘先明进一步说明,FSD入华将形成良性竞争,推动行业进步。他强调,相比特斯拉,小鹏在中国本土化场景适配、数据积累和算力优化方面具备优势,能更好地解决中国路况问题,双方将在竞争中共同促进自动驾驶技术发展。

5月23日周六
  1. IT之家·人工智能66

    Linus Torvalds 论 AI 与 Linux 开发:效率提升,但核心仍是协作

    Linus Torvalds 在开源峰会上指出,AI 工具已实质性改变 Linux 内核开发节奏,近两个版本提交量增长约 20%。他肯定 AI 降低了参与门槛并提升效率,但也指出核心挑战在于协作与沟通等“社会性瓶颈”,而非纯技术问题。谈及未来,他反驳了“99% 代码由 AI 编写”的说法,认为 AI 像编译器一样能提升约10倍生产率,但开发者必须深入理解代码与系统,才能维护复杂软件。

  2. IT之家·人工智能55

    龙虾 OpenClaw 工程师示警:AI 正批量制造低质量危险代码

    据华尔街日报5月22日报道,参与开发“龙虾”OpenClaw的两名工程师发出警告,指出AI在加速代码编写的同时,正将大量低质量代码批量扩散到真实产品与服务中,导致软件漏洞增多、安全隐患、技术债务累积及基础设施崩溃风险。工程师将此现象称为“vibe slop”,并强调AI编程工具更适合辅助生成草稿和初步验证,在正式业务系统中仍需严格的人工审查、测试、重构与安全评估,以避免将开发成本转移至后续修复与治理环节。

  3. IT之家·人工智能79

    黄仁勋:AI 基建年度开支要冲到 4 万亿美元!

    英伟达发布2027财年Q1财报,营收816亿美元,同比增长85%,净利润583亿美元,翻两倍多,市值达5.7万亿美元,已超德国2026年GDP预测。黄仁勋预测,超大规模云厂商的AI基建年度开支将从当前的1万亿美元,增长至3-4万亿美元,远超华尔街预期。财报同时显示,数据中心业务营收752亿美元,占比超九成。值得注意的是,AI基建的高能耗正推高居民电费,数据中心用电成本转嫁效应已初步显现。

    推荐理由:黄仁勋的4万亿美元AI基建预测比华尔街共识高四倍,不是吹牛,是给AGI时代的入场券标价,所有云厂商已经用脚投票了。

  4. Rohan Paul59

    媒体指出,SpaceX涉足AI并非简单追逐技术热点,而是源于埃隆·马斯克世界观下的“防御性必要”。其核心逻辑是,若AI未来成为主导全球协调、预测与决策的关键层,那么火星环境也将被其建模与优化。若决策层被其他智能体掌控,马斯克构建的“文明运输层”便是不完整的。结合SpaceX近期以“SPCX”代码申请纳斯达克IPO的动向,其定位已从纯粹的发射服务商,转变为涵盖发射、Starlink卫星网络与AI基础设施的综合平台公司。此次IPO反映出投资者正将其重新估值为拥有运输、通信乃至地外算力潜力的平台级公司,而不仅是传统航天承包商。

    引用Rohan Paul@rohanpaul_ai

    SpaceX just filed for a Nasdaq IPO under SPCX that could turn Elon Musk’s SpaceX stake into the first $1T personal fortune. Major underwriters include Goldman Sachs (lead), Morgan Stanley, Bank of America, Citigroup, and J.P. Morgan, among others. The IPO is widely expected to be one of the largest in history. The bet is not only rockets, because SpaceX now looks like a combined launch company, Starlink internet network, and AI infrastructure firm. Starlink is the cash engine because thousands of low-orbit satellites can sell internet directly to homes, ships, aircraft, militaries, and remote regions without waiting for fiber cables. Investors are being asked to price SpaceX less like an aerospace contractor and more like a platform company that owns transport, communications, and possibly off-planet computing capacity. The risk is that this still needs huge spending, flawless execution, regulatory approval, and public-market patience with losses.

5月22日周五
  1. Gary Marcus:The Road to AI We Can Trust65

    这个奇怪操作可能让你的养老基金损失数十亿美元

    国会当前推进的某项政策调整,可能导致美国养老基金遭受数十亿美元的巨额损失。该政策变更被指存在重大漏洞,将直接影响数百万退休人员的资产安全。有专家发出警告,呼吁民众立即联系所在选区的国会议员,要求其重新评估并阻止这一潜在损害养老金储备的决策。

    推荐理由:Gary Marcus 把 S&P 500 规则变更和你的退休金直接挂钩,逻辑链清晰——这可能是普通人第一次切身感受 AI 泡沫的传导渠道,值得警惕。

  2. IT之家·人工智能57

    英伟达黄仁勋:LPX 等 SRAM AI 芯片将长期属于利基市场

    英伟达CEO黄仁勋在FY2027Q1财报电话会议上表示,基于SRAM的AI推理解码芯片(如LPX)将长期处于利基市场,GPU仍为主流。他指出,LPX专为低延迟和高词元速率设计,但吞吐量和容量较低,适合软件编程中的上下文处理,却在代理式任务中表现有限。其最佳应用场景是高定价、高词元速率的AI服务,而这类服务目前在整体AI市场中占比不足20%,未来可能增至20%,因此潜在客户群体相对较少。

  3. IT之家·人工智能59

    联想集团发布2026财年财报:营收增长20%

    联想集团发布2026财年年报,营业总收入830.75亿美元,同比增长20%;净利润19.12亿美元,同比增长38%。其中人工智能相关收入同比增长105%,占总营收比例达33%。董事长杨元庆在业绩沟通会上指出,AI基础设施正从训练向推理快速转移,预计未来GPU服务器将从目前约70%-80%用于训练,反转为70%用于推理、30%用于训练,同时训练需求仍将持续增长,仅靠大型云厂商已不足以满足。

  4. Simon Willison 博客55

    FTC要求三家公司支付近百万美元和解AI营销服务欺诈指控

    美国联邦贸易委员会(FTC)宣布,Cox Media Group、MindSift和1010 Digital Works三家公司因虚假宣传其“Active Listening” AI营销服务,将支付近100万美元罚款以和解指控。这些公司声称该服务能通过智能设备实时监听用户对话,并结合行为数据进行精准广告投放。然而,FTC调查发现,该服务实际上从未监听对话或使用语音数据,也未能准确投放广告。其真实操作仅为加价转售从其他数据经纪商处购买的电子邮件列表。