标准电视信号竟然可以成为一种出人意料的"运动探测器"，甚至能透过墙壁捕捉到人的移动。通过使用像 Ettus USRP B200 这类设备，对本地电视台信号的频谱图进行分析，就能看到信号与环境之间的相互作用。直接且无遮挡的信号会呈现为一条强烈、稳定不变的线；而当有人走到信号传播路径附近时，频谱图上就会出现清晰可见的扰动。

这些扰动源于选择性衰减：强反射会把直达信号抵消。由于反射的相位随信号频率变化，这种抵消并不均匀，因而在频谱图上会形成具有辨识度的特征图案。这种状态极为敏感，哪怕是家里很轻微的移动，也会在频谱图中表现得非常明显。

当人开始走动，比如从椅子上起身或在相邻房间里行走时，频谱图会显示出醒目的条纹状图案。这些条纹会以精确的间隔反复出现，因为当传播路径长度每改变一个波长时，反射就会恰好把残余信号再次抵消。通过把这些图案出现的时间与信号"瀑布图"的刷新速率对应起来，甚至可以推算出人的移动速度。

在这个例子里，计算结果显示行走速度约为每秒 1.3 米，也就是每小时 4.6 公里。由于受试者的运动方向与直达信号路径大致垂直，因此不需要进行复杂的几何修正就能得到这个数值。这说明，一套相对简单的无线电接收系统也能以相当高的精度追踪运动。

尽管电视信号能把这一效应直观地呈现出来，但它们并不是唯一可用的媒介。其他宽带无线电信号（例如 WiFi 和 LTE）同样可以被用来提取类似信息。虽然这些信号在频谱图上可能呈现得更"杂乱"，但底层原理依旧相同，使得在不同的无线电频率环境下，都能利用这种方式穿透墙壁探测运动。 A standard television signal can act as a surprising tool for detecting movement, even through walls. By analyzing the spectrogram of a local TV station signal using equipment like an Ettus USRP B200, one can observe how the signal interacts with the environment. While a direct, unobstructed signal appears as a strong and unchanging line, the introduction of a person walking near the signal path creates visible disturbances.

These disturbances are caused by selective fading, a phenomenon where a direct signal is canceled out by a strong reflection. Because the phase of this reflection is dependent on the signal frequency, the cancellation effect is not uniform, resulting in distinctive patterns on a spectrogram. This state is remarkably sensitive, reacting clearly to even minor movements within a home.

When a person moves around, such as getting up from a chair or walking in an adjacent room, the spectrogram displays clear, striped patterns. These stripes appear at precise intervals because the reflection perfectly cancels the residual signal every time the path length changes by one wavelength. By calculating the timing of these patterns against the update rate of the signal waterfall, one can even determine the speed of the person's movement.

In this instance, the math revealed a walking speed of approximately 1.3 meters per second, or 4.6 kilometers per hour. Because the subject was walking perpendicular to the direct signal path, no complex geometric adjustments were required to arrive at this figure. This demonstrates that a relatively simple radio receiver is capable of tracking motion with significant accuracy.

While television signals provide a clear visual demonstration of this effect, they are not the only medium that can be used for such surveillance. Other wideband radio signals, including WiFi and LTE, can also be exploited to extract similar data. While these signals might produce a messier visual output on a spectrogram, the underlying principle remains the same, allowing for the detection of movement through walls across various radio frequency environments.

当一个神秘且断断续续的蜂鸣声开始从一个堆满盒子的房间里传出时，作者转向 Claude Code，创建了一个基于软件的解决方案来对声音进行三角定位。与其手动翻找每一个容器，作者做了 Beep Locator：一套系统，会在房间周围布置多个设备（如手机、平板和笔记本），让它们构成一个麦克风网络。整个方案依赖一个 Python 的 Web 服务器，通过 WebSockets 协调这些设备，使它们能够录制音频、识别特定频率的声音，并把数据回传到中央地图以供分析。

Beep Locator 的架构灵感来自 2007 年一篇名为 BeepBeep 的研究论文，该论文介绍了利用现成硬件实现设备间高精度声学测距的方法。系统通过让各设备录制短音频片段来工作。一旦检测到声音，服务器就会指挥一个流程：设备播放一个短促的 chirp，从而让系统能够精确对齐录制到的音频片段。通过比较声音在不同位置的到达时间，软件就能计算蜂鸣声的坐标。为保证精度，作者还加入了一个手动的 "Rough mode"，允许用户在地图上拖动设备图标来反映它们的真实位置，随后系统再结合声学数据对这些位置进行进一步修正。

实现这款应用是一个由 Claude Code 和 Fable 5.2 model 驱动的迭代过程。作者使用了一种 "hot-swap" 机制，让服务器能够在实时监听的设备上推送新的 JavaScript 代码，从而免去了开发期间与设备进行物理交互。过程中，作者陆续加入了多项功能，比如用于测试的 clap-detection mode 、带时间戳的日志（logs），以及回放（replay）功能——作者可以逐一收听每个设备录制的片段，确认它们是否与那个神秘蜂鸣声一致。最终，系统把噪声源（一个旧的 carbon monoxide detector）定位到了大约 40 厘米的范围内。

这个项目不只是为了立刻解决问题，也展示了把编码代理（例如 Claude）赋予感知能力的可能性。尽管 Claude 没有原生的音频输入，但它依然可以通过让外部设备借助浏览器去采集并分析音频数据，从而有效地"听见"外界。通过让编码代理能够感知并回应现实世界的刺激，作者展示了一种富有创意的代理工作流。能够与物理环境互动并进行观察，也意味着 AI 代理在协助处理复杂的非数字任务方面迈出了令人期待的一步。 When a mysterious, intermittent beeping began originating from a room filled with packed boxes, the author turned to Claude Code to create a software-based solution for triangulating the sound. Rather than manually searching through every container, the author built Beep Locator, a system that utilizes multiple devices—such as phones, tablets, and laptops—positioned around the room as a network of microphones. The setup relies on a Python web server that coordinates these devices via WebSockets, allowing them to record audio, detect specific sound frequencies, and feed data back to a central map for analysis.

The architecture of Beep Locator was inspired by a 2007 research paper titled BeepBeep, which outlines techniques for high-accuracy, device-to-device acoustic ranging using off-the-shelf hardware. The system functions by having devices record short audio buffers. When a sound is detected, the server orchestrates a process where devices play a short chirp, allowing the system to align the recorded audio clips precisely. By comparing the arrival times of the sounds at different locations, the software can triangulate the coordinates of the beep. To ensure accuracy, the author included a manual "Rough mode," allowing users to drag device icons on the map to reflect their actual positions, which the system then refines using acoustic data.

Building the application was an iterative process driven by Claude Code and the Fable 5.2 model. The author utilized a "hot-swap" mechanism that enabled the server to push new JavaScript code to the listening devices in real-time, removing the need for physical interaction with the devices during development. Throughout the process, the author added several functional features, such as a clap-detection mode for testing, timestamped logs, and a replay feature that allowed the author to listen to the specific clips recorded by each device to confirm they matched the mystery beep. Eventually, the system identified the source of the noise—an old carbon monoxide detector—to within approximately 40 centimeters.

Beyond simply solving the immediate problem, this project highlights the potential for giving coding agents, like Claude, sensory capabilities. Although Claude lacks native audio input, it can effectively "hear" the world by commanding external devices to capture and analyze audio data through the browser. By enabling a coding agent to perceive and respond to real-world stimuli, the author demonstrated a creative approach to agentic workflows. This capability to interact with and observe the physical environment marks an exciting step in the evolution of how AI agents can assist with complex, non-digital tasks.

通过 ACME 协议获取并维护 SSL/TLS 证书的过程，常常被看作一项令人烦不胜烦的琐事，需要持续盯着系统，才能确保自动化流程始终正常运转。除了管理上的负担，人们还对那套依赖明文连接的 certificate authority 体系抱有某种疑虑：它在历史上甚至曾接受过极其薄弱的 keys 。

为了解决这些挫败感，有人提出了一种别出心裁的办法，用来生成 vanity certificates 。它们既能当作一种"抗议"，又仍然能够提供必需的网页加密。

该方法的关键在于改造 RSA 公钥，尤其是其中的模数（modulus），把一段自定义信息嵌进去。这样，当用户用浏览器查看这份证书时，就能看到那段信息。由于 certificate authority 在校验 key 时不会进一步审查模数的具体内容，因而可以直接把 ASCII 文本塞进 key 里。由于证书中的 key 采用 base64 编码展示，作者会反复调整模数，直到期望的字符在编码结果中以正确的形式出现。通常只需对用于生成 RSA key 的质数做几处很小的改动。

从数学角度看，这种做法是先固定模数的上半部分以承载特定字符串，然后再重新计算其余的质因数，确保仍然得到一个可用的 RSA key 。具体而言，选择一个质数作为其中一项，再让第二个质数把模数"凑"到目标数值。接着再进行少量微调，使得最终乘积依然是两个质数的乘积。虽然这种改动会略微降低密钥的整体熵，但对公共网站的场景来说，这种降低被认为几乎可以忽略。现代 TLS 协议引入了前向安全性（forward secrecy），即使 private key 发生泄露，被动窃听依旧会变得异常困难。

尽管这种方式用来个性化公开证书既独特又带点幽默，但前提是：一旦被修改后的两个质数过于接近，就确实存在潜在风险。理论上，这种配置可能让攻击者更容易对模数进行因式分解，从而找回 private key 。不过由于这些 vanity keys 主要面向普通的对外公开网站，而非高安全环境，所以这种取舍被视为一次可以接受的尝试，用来测试我们在与现有网页安全基础设施互动时，究竟能把边界推到多远。 The process of obtaining and maintaining SSL/TLS certificates through the ACME protocol is often viewed as a cumbersome chore, requiring constant attention to ensure that automated systems remain functional. Beyond the administrative burden, there is a underlying skepticism regarding the efficacy of a certificate authority system that relies on plaintext connections and has historically accepted incredibly weak keys. In response to these frustrations, there is a creative approach to generating vanity certificates that serve as a form of protest while still providing necessary web encryption.

The core of this method involves manipulating the RSA public key, specifically the modulus, to embed a custom message that becomes visible when viewing the certificate in a browser. Since the certificate authority validates the key without scrutinizing the specific contents of the modulus, it is possible to embed ASCII text directly into the key. Because base64 encoding is used to represent the key in the certificate, the author adjusts the modulus until the desired characters appear correctly in the encoded output, a process that typically requires only a few minor adjustments to the prime numbers used to generate the RSA key.

Mathematically, the technique relies on fixing the upper half of the modulus to represent a specific string, then recalculating the remaining prime factors to maintain the integrity of a functional RSA key. By choosing one prime and setting the second to reach the target modulus, one can perform minor adjustments to ensure the product remains a product of two primes. While this modification slightly reduces the overall entropy of the key, the reduction is considered negligible for the purpose of a public website. Modern TLS protocols, which utilize forward secrecy, ensure that even if the private key were compromised, passive eavesdropping would remain difficult.

Although this approach creates a unique and humorous way to personalize a public certificate, it does come with potential risks if the modified prime numbers end up being too close to one another. Such a configuration could theoretically make it easier for an attacker to factor the modulus and recover the private key. Nevertheless, as these vanity keys are primarily intended for standard public-facing websites rather than high-security environments, the trade-off is viewed as an acceptable experiment in stretching the boundaries of how we interact with the web's existing security infrastructure.

基于儿童富有想象力的绘画，构建一款完全可玩的多关卡 beat 'em up 游戏，曾是个人难以逾越的任务，但近期 AI agent 技术的进步从根本上改变了这一现实。先前一次尝试只做出了平平无奇的原型，而 Claude Fable 5.2 的发布则提供了完成该项目所需的关键能力：通过向 AI 提供详细提示词、可访问的设计扫描数据，以及针对图像与音频生成的特定集成，实现把高质量游戏体验自动化制作出来。

开发过程高效得惊人——AI 基本接管了世界级游戏开发者的工作。它舍弃了先前那些受限的代码库，重新搭建出一款完整的 12 关游戏，生成了约 175 张图像和 140 段音频片段，让构想真正落地。这种 agentic workflow 的成熟度极高：它甚至编写了一个机器人，在无头浏览器中把所有关卡通关一遍，自动修正错误，并确保游戏循环始终保持足够吸引力。

在技术之外，这款游戏还通过把家庭成员变成可扮演的英雄，呈现出一种更具个人与协作色彩的温度。从狗开始，扩展到各类亲戚，甚至加入了对孩子们已故曾祖母的动人天使主题致敬——游戏里的英雄数量也从最初的五名增长到十八个各具特色的角色。每个角色都拥有基于家庭反馈定制的特殊招式和主题动画，展现了这些工具在高度个性化数字创意项目上的潜力。

项目最终也通过在同一台机器上连接四个控制器进行 playtesting 得到了验证。快速迭代的能力尤为关键——根据反馈调整招式组合，或新增家庭成员——体现出当代 AI 辅助开发的灵活与顺畅。只要提供几张参考照片和一个概念，AI 就能持续更新过场动画与游戏平衡，并在整个开发周期中保持高质量水准。

归根结底，这个项目体现了 coding agents 能力的一次重要跨越：处理大约 3.4 亿个 tokens 来生成一个具备原创美术与音频、可运行的多人完整体验，说明 AI 驱动的软件创作已经远远超出了简单脚本的范畴。只要工具到位、愿景清晰，单个开发者完全有可能在一次尝试里从概念直接做出一款完整且好玩的游戏。 Building a fully playable, multi-level beat 'em up game based on a child's imaginative drawings was once an insurmountable task for an individual, but recent advancements in AI agent technology have fundamentally shifted that reality. After a previous attempt resulted in a lackluster prototype, the release of Claude Fable 5.2 provided the necessary power to tackle the project in a single, ambitious effort. By providing the AI with detailed prompts, access to design scans, and specific integrations for image and sound generation, it became possible to automate the creation of a polished gaming experience.

The development process was remarkably efficient, with the AI essentially taking over the role of a world-class game developer. It discarded the previous, limited codebase and architected a complete 12-level game, generating approximately 175 images and 140 audio clips to bring the vision to life. The agentic workflow was so advanced that it even programmed a bot to play through all stages in a headless browser, self-correcting errors and ensuring the gameplay loop was consistently engaging.

Beyond the technical implementation, the game captures a personal, collaborative spirit by transforming family members into playable heroes. From the dog to various relatives and even a poignant, angel-themed tribute to the children's late great-grandmother, the game grew from five initial heroes to eighteen unique characters. Each character features custom special moves and thematic animations derived from family input, showcasing how these tools can personalize digital creative projects to a high degree.

The success of the project was ultimately confirmed through playtesting with four controllers connected to a single machine. The ability to iterate quickly—adjusting move sets or adding new family members based on feedback—highlighted the fluid nature of modern AI-assisted development. By simply providing a few reference photos and a concept, the AI was able to consistently update cutscenes and game balance, maintaining a high level of quality throughout the development cycle.

Ultimately, this project stands as a milestone in the capabilities of coding agents. Managing roughly 340 million tokens to produce a fully functional, multi-player experience with original art and audio demonstrates that AI-driven software creation has moved well beyond simple scripts. It is now entirely possible for a single developer to go from concept to a complete, fun game in one shot, provided they have the right tools and a clear vision.

Claude Fable 5.2，由 Anthropic 于 2026 年 11 月发布，是一款能力出众的专有推理模型，尽管它的性能表现让人明显感受到与行业常见水平的差异。在 Artificial Analysis Intelligence Index 上它得分为 65，高于 40 分的中位数，表明其在处理复杂任务时具备很强的智能。该模型可同时接收文本和图像输入，并输出文本结果，配备了 1 million token 的上下文窗口。

不过，尽管智力水平很高，这个模型在资金和时间上也要付出不小的代价。它被归为高成本选项：输入费用为每 million tokens 8.00 美元，输出费用为每 million tokens 40.00 美元。在评估其 Intelligence Index 表现时，单个任务的平均成本达到 26.54 美元，使其在同类模型的定价区间中处于偏高的位置。

运行速度也是 Claude Fable 5.2 区别于同业者的关键点之一。该模型刻意采用更为严谨的流程，在 Intelligence Index 上平均每个任务需要 44.1 分钟。相比类似模型观察到的 12.9 分钟中位数，它要慢得多，反映出其推理过程对时间的消耗更高。同时，它在"首个回答 token"上的耗时也较长，达到 231.55 秒，这通常意味着系统在正式生成答案前会进行大量的内部推理。

此外，它的输出也格外冗长。在智能基准测试中，该模型共产生了 275 million 个输出 token 。这个数量远高于 72.8 million 的中位数，显示出它更倾向于给出内容非常详尽、篇幅很长的回答。综合这些因素，Claude Fable 5.2 更像是一款专门工具：它把深度与分析的严谨性放在首位，而不是优先考虑速度或成本效益。 Claude Fable 5.2, released by Anthropic in November 2026, stands out as a highly capable proprietary reasoning model, though its performance characteristics set it apart from typical industry averages. Scoring a 65 on the Artificial Analysis Intelligence Index, it ranks well above the median score of 40, demonstrating significant intelligence across complex tasks. It is designed to handle both text and image inputs while providing text outputs, supported by a substantial 1 million token context window.

Despite this high level of intelligence, the model comes with a notable cost in both capital and time. It is categorized as an expensive option, with input costs set at $8.00 per million tokens and output costs at $40.00 per million tokens. When evaluating its performance on the Intelligence Index, the average cost per task reaches $26.54, which places it at the higher end of the pricing spectrum compared to similar models.

Operational speed is another area where Claude Fable 5.2 differs from its peers. The model is intentionally methodical, requiring an average of 44.1 minutes per task on the Intelligence Index. This is considerably slower than the 12.9-minute median observed in similar models, reflecting the time-intensive nature of its reasoning processes. Furthermore, it exhibits a high time to first answer token at 231.55 seconds, which is characteristic of a system that performs extensive internal reasoning before generating a response.

The model is also exceptionally verbose, generating 275 million output tokens during its intelligence benchmarking. This output volume far exceeds the median of 72.8 million, highlighting its tendency to produce very detailed and extensive responses. These factors combine to position Claude Fable 5.2 as a specialized tool that prioritizes depth and analytical rigor over speed or cost-efficiency.

USPS ZIP Code Boundaries 资源提供了一张覆盖全国的地图。该地图基于官方投递地址和位置信息来确定高度准确的地理参数。由于投递分布会随着新增地点的出现或既有路线的调整而发生变化，数据设计时已考虑这些随时间变化的情况。因此，对于需要准确且最新了解全国邮政区域如何划分与组织的机构而言，它是一种可靠的来源。

众多行业依赖这份数据来优化运营。测绘、物流、导航与运输公司使用这些边界进行路线规划与优化，确保投递地点分组能够以尽可能高效的方式被处理。同时，房地产、保险与营销等领域则把这些多边形作为组织与展示人口统计信息的基础框架。作为 ZIP Code 区域的"事实底座"，该数据使用户能够将相关功能集成到自身系统中，或校正现有数据集，使其反映当前的运营现状。

邮政署（Postal Service）为该数据提供三种不同的访问级别，所有级别都包含四次季度更新，以保证信息全年保持有效性。 Internal 选项面向内部使用；Enterprise 级别允许在子公司与合作伙伴之间进行有限分发。对使用范围要求更严格的实体，还提供 Publication 级别。

为帮助用户落地应用这些数据，网站提供一整套配套资源，包括简报资料和技术指南，并提供供开发人员与分析师使用的示例数据文件，如 GeoJSON 等格式。需要获取数据的用户可通过门户直接获取所需的许可协议与订单表格，并可同时使用其他管理工具，例如 Electronic Product Fulfillment Web Access Request Form 。 The USPS ZIP Code Boundaries resource provides a national map that relies on official delivery address and location information to establish highly accurate geographic parameters. Because delivery distribution is subject to change as new locations are added or existing routes are adjusted, the data is designed to account for these temporal fluctuations. This makes it a reliable source for organizations that require precise, up-to-date information regarding how postal areas are organized across the country.

A wide range of industries rely on this data to improve their operations. Mapping, logistics, navigation, and transportation companies use these boundaries for optimized routing, ensuring that delivery location groupings are handled as efficiently as possible. Meanwhile, sectors like real estate, insurance, and marketing leverage the polygons as a foundational structure for organizing and displaying demographic information. By serving as a ground truth for ZIP Code areas, the data allows users to integrate these features into their own systems or correct existing datasets to reflect current operational realities.

The Postal Service offers three distinct tiers of access for this data, all of which include four quarterly updates to ensure the information remains relevant throughout the year. The Internal option is designed for in-house use, while the Enterprise tier allows for limited distribution to subsidiaries and partner companies. A Publication tier is also available for entities requiring more restricted usage.

To assist users in implementing this data, the site provides a comprehensive suite of resources. This includes briefing sheets and technical guides, as well as sample data files in formats such as GeoJSON for developers and analysts. Those looking to acquire the data can access the necessary license agreements and order forms directly through the portal, alongside other administrative tools like the Electronic Product Fulfillment Web Access Request Form.

Anthropic 已经推出 Claude Fable 5.2 和 Claude Mythos 5.2，这两款是其为应对高度复杂技术挑战而打造的最新模型。这些模型在编码、推理和视觉能力方面都有显著提升，已经能够处理大规模软件迁移、设计可用的电子设备，并攻克物理学领域中的开放性问题。尽管两种版本共享相同的底层架构，但 Claude Fable 5.2 面向通用用途配备了更完善的网络安全与生物学防护；而 Claude Mythos 5.2 则只对参与经验证计划、用于专业研究的组织开放。

从性能指标来看，这些模型相较于前代产品以及 Claude Opus 5.5 系列都有明显进步。它们实现了更快的 token 生成速度，并新增一种可选的快速模式，速度最高可提升 2.5 倍。在内部测试中，这些模型在代理式编码、科学研究以及硬件工程相关的基准任务上表现更优。值得注意的是，它们的效率尤为突出：往往能在显著更少的资源消耗下达到高精度结果，为雄心勃勃、开放式的项目提供了更具成本效益的选择。

在软件与工程方向，这些模型展现出惊人的自主性。比如，Claude Fable 5.2 的早期版本能够在八天内协调超过 1,000 个子代理，从而设计出一个可运行的操作系统。同样，在理论物理领域，该模型成功完成了关于黑洞与全息对偶的复杂计算。此外，在生物科学方面，它提出了一种预测病毒衣壳结构的新方法，可能对基因疗法的研发产生重要影响。

安全与对齐仍是这些模型部署的重点。公司在发布前进行了大量测试，包括一系列用于识别沙箱逃逸等风险行为的新评估；结果显示，这些模型在此类行为上相比以往迭代呈现更低的倾向。公司还部署了针对非法蒸馏的先进防护措施——这是一种在缺少内置保障的情况下提取模型能力的手段。相关防御已被证明非常有效，显著减少了未经授权方所能获取的数据量。

为应对现代 AI 使用过程中的复杂性，Anthropic 也在推出 Enterprise Frontier Safeguards 。该方案支持在不对参与客户保留数据的前提下进行自动化安全监控。这些模型现已覆盖主要云平台，公司也将提供推广性使用额度，帮助用户把新能力更快地融入既有工作流程。通过在更强性能与定向安全措施之间取得平衡，并对高风险领域提供专业化访问权限，公司希望在推动更高水平技术工作的同时，尽量降低潜在风险。 Anthropic has introduced Claude Fable 5.2 and Claude Mythos 5.2, its latest models engineered to address highly complex technical challenges. These models demonstrate significant advancements in coding, reasoning, and vision, having already proven capable of managing extensive software migrations, designing functional electronic devices, and solving open problems in physics. While both versions share the same underlying architecture, Claude Fable 5.2 includes robust cybersecurity and biology safeguards for general use, whereas Claude Mythos 5.2 is accessible to organizations participating in verified programs for specialized research.

Performance metrics indicate that these models offer substantial improvements over their predecessors and the Claude Opus 5.5 line. They provide faster token generation and a new optional fast mode that increases speed by up to 2.5 times. In internal testing, the models showed superior results on benchmarks related to agentic coding, scientific research, and hardware engineering. The efficiency of these models is particularly notable, as they often achieve high-accuracy results with significantly fewer resources compared to previous versions, offering a more cost-effective solution for ambitious, open-ended projects.

In the realm of software and engineering, the models have exhibited remarkable autonomy. For instance, an early iteration of Fable 5.2 was able to design a functional operating system by coordinating over 1,000 subagents over the course of eight days. Similarly, in the field of theoretical physics, the model successfully completed complex calculations regarding black holes and holographic duality. Furthermore, in the biological sciences, it demonstrated a novel method for predicting the structure of viral capsids, which could have significant implications for the development of gene therapies.

Safety and alignment remain central to the deployment of these models. Extensive prerelease testing was conducted, including novel evaluations designed to detect concerning behaviors like sandbox escapes, where the models showed a reduced propensity for such actions compared to previous iterations. The company has also implemented advanced protections against illicit distillation, a process used to extract model capabilities without built-in safeguards. These defenses have proven highly effective, significantly reducing the amount of data harvested by unauthorized entities.

To manage the complexities of modern AI usage, Anthropic is also rolling out Enterprise Frontier Safeguards, which allow for automated safety monitoring while maintaining zero data retention for participating customers. These models are now available across major cloud platforms, and the company is providing promotional usage credits to help users integrate the new capabilities into their existing workflows. By balancing increased power with targeted safety measures and specialized access for high-stakes fields, the company aims to facilitate sophisticated technical work while mitigating potential risks.

e-means 算法代表了聚类领域的一项重大进展，由 Meta FAIR 开发，旨在弥补传统 Lloyd's 算法的不足。尽管 Lloyd's 仍是全批次 k-means 的标准，但它往往运行缓慢，而且容易陷入次优的局部极小值。 e-means 通过采用基于指数移动平均的现代小批次方案，实现了在 epoch 计数和挂钟时间上的约 10 倍加速，同时还能提升最终的聚类质量。

e-means 的核心更新规则使用指数移动平均来跟踪各个聚类质心。对于静态数据集而言，这种方法比标准的梯度下降更稳定、更高效。通过调整每个聚类的总和与计数统计量的更新方式，算法能够"忘记"更早期、可能较差的分配，使模型把注意力放在更近期的数据模式上。该方法高度可并行，并通过专门的 GPU kernels 进一步优化，从而把理论效率真正转化为实际的性能收益。

e-means 的一个突出优势是不需要手动调参。借助推导出的特定缩放定律，算法会根据数据集特征自动确定最优的批次大小和步长，减轻了使用者的负担，并确保模型在不同规模的数据上都能保持高水平性能。此外，算法还加入了一套可靠的 dead-cluster 复活策略：当某些聚类数据不足时，会通过对大型活跃聚类进行拆分来替换它们，从而避免计算资源被浪费，并提升整体覆盖度。

该算法在逃离局部极小值方面也展现出更强能力，这是 Lloyd's 常见的问题。由于 e-means 采用小批次，其更新天然更具噪声，从而促进对优化空间的更充分探索。对包括合成任务与复杂嵌入任务在内的数十个数据集进行实验验证表明：e-means 在仅 1.2 到 3 个 epoch 的训练内，就能达到 Lloyd's 经过 25 个 epoch 后取得的测试损失。与此同时，算法还配有一个最终的质心微调阶段，在几乎不增加额外运行时间的情况下，进一步保证结果质量。

总的来说，e-means 为传统聚类方法提供了一种现代、稳健且更易用的替代方案。它现已集成到 Faiss 库中，便于用户将该技术轻松融入现有工作流程。通过用经过智能调优的小批次更新替代全批次优化，e-means 给出了一个切实可行的方案，能够在机器学习应用中同时兼顾通常相互矛盾的速度与聚类精度需求。 The e-means algorithm represents a significant advancement in clustering, developed at Meta FAIR to address the limitations of the traditional Lloyd's algorithm. While Lloyd's remains a standard for full-batch k-means, it is often slow and prone to getting stuck in suboptimal local minima. By transitioning to a modern, mini-batch approach that utilizes exponential moving averages, e-means achieves a speedup of roughly 10 times in both epoch count and wall-clock time, while simultaneously improving the final clustering quality.

The core of the e-means update rule relies on exponential moving averages to track cluster centroids, a technique that proves more stable and efficient for static datasets than standard gradient descent methods. By adjusting how sum and count statistics are updated for each cluster, the algorithm ensures that older, potentially poor assignments are forgotten, allowing the model to focus on recent data patterns. This approach is highly parallelizable and further optimized through specialized GPU kernels, which effectively translate the theoretical efficiency into real-world performance gains.

A standout feature of e-means is its ability to operate without the need for manual hyperparameter tuning. Through the derivation of specific scaling laws, the algorithm automatically determines the optimal batch size and step size based on the dataset's characteristics. This removes the burden from the practitioner, ensuring that the model maintains peak performance across various scales of data. Additionally, the algorithm incorporates a robust dead-cluster revival strategy, where clusters with insufficient data are replaced by splitting large, active clusters, thereby preventing the waste of computational capacity and improving overall coverage.

The algorithm also demonstrates a superior ability to escape local minima, a common pitfall for Lloyd's. Because e-means uses mini-batches, the updates are inherently noisier, which encourages better exploration of the optimization landscape. Experimental validation across dozens of datasets, including synthetic and complex embedding tasks, confirms that e-means consistently reaches the test loss achieved by Lloyd's after 25 epochs in just 1.2 to 3 epochs. This efficiency is complemented by a finalization stage that fine-tunes the centroids, ensuring high-quality results with minimal extra runtime.

Ultimately, e-means provides a modern, robust, and user-friendly alternative to legacy clustering methods. It is now available as part of the Faiss library, allowing users to easily integrate the technique into existing workflows. By replacing full-batch optimization with intelligently tuned mini-batch updates, e-means offers a practical solution that reconciles the usually opposing requirements of speed and clustering accuracy in machine learning applications.

最近一项全国性咨询显示，相当多的加拿大男性和男孩正面临心理健康问题和社会孤立，且往往得不到他们所需的支持。政府于 11 月 2 日发布的《 What We Heard Report 》汇总了近 6,000 名加拿大人的反馈，指出现有体系常常无法在个人陷入危机之前提供足够的照护。

这项咨询于 2026 年 2 月启动，纳入了来自社区组织、医疗专业人士、原住民伙伴以及全国各地个人的意见。参与者明确了若干亟待改进的关键方向，尤其是需要建立更紧密的社会联系、提供更完善的心理健康服务，以及消除阻碍男性寻求帮助的现有障碍。报告同时强调，这些支持结构的成效会受到文化、收入和地理位置等多重因素影响，指出农村和偏远地区的人们在获取资源方面往往面临更独特的困难。

倡导者呼吁将重点转向更早期的干预。加拿大男性健康基金会总裁兼首席执行官 Kenton Boston 强调，等到危机发生才采取行动不再是一种可接受的策略。相反，需要通过培育积极的指导与基于社区的支持，更早地接触到男孩和年轻男性。与此同时，Movember 全球首席执行官 Andrew Little 也指出，要为更健康的生活创造正确条件，就必须采取一致且以预防为导向的做法，在严重问题出现很久之前就加强社会联系。

除了承受的个人代价之外，优先推进这些改进还有充分的经济理由。 Movember Canada 在 2025 年的一项研究认为，如果能让男性健康获得更好的结果，国家每年或可节省约 124 亿元，同时提升全国生产力。联邦政府计划借助这份报告的洞见，补充现有的女性健康相关举措，并承认不同人群需要有针对性的预防与治疗策略。

展望未来，这些发现将构成加拿大首个《联邦男性与男孩健康及福祉战略》的基础，预计将于 2027 年初启动。尽管具体资金和项目细节尚未确定，但政府与 Movember 等组织之间的合作仍在持续，双方共同目标是为全国的男性和男孩建设更坚实的支持网络。 A recent nationwide consultation has revealed that a significant number of Canadian men and boys are struggling with mental health issues and social isolation, often without access to the support they need. Released on November 2, the government's What We Heard Report highlights feedback from nearly 6,000 Canadians, emphasizing that current systems frequently fail to provide adequate care before individuals reach a point of crisis.

The consultation, which began in February 2026, incorporated insights from community organizations, health professionals, Indigenous partners, and individuals across the country. Participants identified several critical areas for improvement, specifically the need for stronger social connections, better mental health services, and the removal of existing barriers that prevent men from seeking help. The report also highlights that the efficacy of these support structures is influenced by diverse factors, such as culture, income, and geographic location, noting that those in rural and remote regions often face distinct challenges in accessing resources.

Advocates are now calling for a shift toward earlier intervention. Kenton Boston, president and CEO of the Canadian Men's Health Foundation, stressed that waiting until a crisis occurs is no longer an acceptable strategy. Instead, there is an urgent need to reach boys and young men earlier by fostering positive mentorship and community-based support. Similarly, Movember's global chief executive officer, Andrew Little, noted that creating the right conditions for healthier lives requires consistent, preventative approaches that strengthen social ties long before serious problems develop.

Beyond the personal toll, there is a clear economic argument for prioritizing these improvements. A 2025 study by Movember Canada suggested that better outcomes in men's health could save the country approximately $12.4 billion annually while simultaneously boosting national productivity. The federal government intends to use the insights from this report to complement existing women's health initiatives, acknowledging that different populations require tailored prevention and treatment strategies.

Looking ahead, these findings will serve as the foundation for Canada's first Federal Men and Boys' Health and Well-being Strategy, which is anticipated to launch in early 2027. While specific funding and program details are yet to be determined, the collaboration between the government and organizations like Movember continues, with a shared goal of building more robust support networks for men and boys across the country.

当 Google 在 2023 年首次推出 Gemini Workspace extensions 时，公司明确承诺：从 Gmail 、 Docs 和 Drive 检索到的个人内容不会被人工审核员查看。然而，Google 已悄悄改变了这一立场。近期对官方支持文档的更新，以及对其 2023 年原始公告的细微修改，已证实：通过这些 extensions 连接的数据确实会被路由到人工审核流程中。 Google 还明确告诫用户，如果他们的应用包含机密信息，而这些信息是他们希望避免被人眼看到的，那么就不要连接这些应用。

这种数据摄取方式尤其令人担忧，因为它会在日常的后台对话中自动发生，而不需要像 @Drive 或 @Gmail 这类明确的用户提示。由于底层的大语言模型工具调用和上下文注入本身具有概率性，单次测试未必能暴露真实行为。具体取决于系统如何解析请求，它可能选择零检索、只给出通用的元数据摘要，提取包含敏感个人身份信息的未脱敏部分引文，甚至直接执行全文件摄取——把完整文档喂入会话上下文。

想要验证这一行为的用户，不能只做一次测试。因为系统是非确定性的，用户需要删除对话历史并反复提交相同问题，才能观察到不同的结果。研究人员通过要求模型打印其检索工具返回的精确文本和元数据，发现过模型会暴露完整的账号、确认细节以及个人交易数据，甚至可能把一封电子邮件的全文内容连同标头一起转储出来。

这些结果凸显了数据保护上的巨大漏洞。 Google 的自动清理机制往往难以有效对叙述性数据进行去标识化，例如姓名、地点和事件细节的组合——这些信息叠加起来足以指向某个人。归根结底，这种变化意味着对最初"严格隔离"的承诺的背离。用户如今需要正视这样的现实：他们的私人文件和电子邮件可能会因为随口一问就被拉入会话，随后又被交由人工承包商进行审查。 When Google first introduced Gemini Workspace extensions in 2023, the company explicitly promised that personal content retrieved from Gmail, Docs, and Drive would not be seen by human reviewers. However, Google has quietly reversed this stance. Recent updates to official support documentation and a subtle edit to their original 2023 announcement now confirm that data connected through these extensions is indeed routed into human review pipelines. Google explicitly warns users against connecting their apps if they contain confidential information that they would prefer to keep hidden from human eyes.

The ingestion process is particularly concerning because it occurs autonomously during casual background conversations, rather than requiring explicit user prompts like @Drive or @Gmail tags. Because the underlying large language model tool-calling and context injection are probabilistic, a single test might not reveal the behavior. Depending on how the system resolves a query, it may opt for zero retrieval, provide a generic metadata summary, extract partial unredacted quotations containing sensitive personally identifiable information, or perform a full-file ingestion where the complete document is fed into the session context.

For users attempting to verify this behavior, simply testing once is insufficient. Because the system is nondeterministic, one must delete their conversation history and repeat queries to observe the varying outcomes. By asking the model to print the exact text and metadata returned by its retrieval tool, researchers have observed instances where the model exposes full account numbers, confirmation details, and personal transaction data, or even the entire text dump of an email including its headers.

These findings highlight a significant gap in data protection. Google's automated scrubbers often struggle to effectively deidentify narrative data, such as combinations of names, locations, and event details that collectively identify an individual. Ultimately, the shift represents a departure from the original guarantee of strict isolation. Users now face a reality where their private files and emails can be pulled into a session through casual interaction, subsequently exposing that data to human contractors for review.