Weekly Digest — 2026-W35

Every local project gets a real address.

Create macOS native automations using plain language

Instruments for composing and experiencing sound as a space

Connect Your AI Spend Directly to Your Roadmap

Drive Antigravity agents from any browser

A shared workspace where people and AI agents get work done

The graph-first IDE and interactive map for agentic coding

Turn form data into insights and action with AI

AI sales agents on any channel that runs from Claude Code

Close the loop between vulnerability discovery and patching.

Real-time AI meeting overlay with no bots, 100% private

Transform Static Images Into Dynamic Particle Experiences

Mixture-of-Experts (MoE) architectures significantly expand model capacity without a proportional increase in computational cost. However, optimizing their hyperparameters---particularly the learning rate---at extreme scales of both model size and token budget via sweeping remains computationally prohibitive. In this paper, we propose a compute-efficient, two-step hyperparameter transfer framework that estimates optimal learning rates for training large MoE models by transferring them across scaling model widths, and subsequently extrapolating to trillion-token horizons. First, we formulate a Maximal Update Parameterization (μP) adaptation for MoE architectures utilizing Multi-head Latent Attention (MLA) and the Muon optimizer, demonstrating that optimal learning rates transfer consistently across width-scaled models. Second, we extend this transferability along the token dimension by establishing a predictive scaling law. By applying linear regression to the optimal values derived from small proxy models on limited budgets, we successfully extrapolate the ideal learning rate to massive training horizons (e.g., 10 trillion tokens) with high fidelity (R^2=0.95). Consequently, this indicates that proxy training on small models is sufficient to determine the optimal learning rate for the extensive training of large-scale MoEs. We apply the proposed methodology to pretrain our foundation model (155B total, 17B active parameters) from scratch, and the stable training and evaluation results validate that optimal configurations for full-scale target models can be accurately predicted with minimal ablation costs.

LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms including Prompt Engineering to elicit model capabilities, Context Engineering to manage information access, Harness Engineering to organize external tools and resources, and Loop Engineering to support continual reflection and self-improvement. Yet as tasks grow more complex, individual intelligence faces a fundamental limit: many tasks require heterogeneous expertise, interdependent subtasks, parallel execution, independent verification, and persistent state, exceeding any single agent's organizational capacity. Augmenting one agent's capabilities or context cannot resolve this architectural mismatch; intelligence must instead be distributed across specialized agents and organized at the system level. We call this System Intelligence: an agent system's ability to organize and coordinate multiple intelligent components into a coherent, adaptive whole pursuing a shared objective. Achieving it requires more than adding agents; it demands explicit structures to organize work, coordinate heterogeneous agents, and maintain evolving execution states. We introduce Graph Engineering, an emerging paradigm for next-generation agent systems. Unlike prior paradigms that mainly optimize individual interactions or agent-level behavior, Graph Engineering constructs explicit, dynamic, evolving graph structures representing tasks, agents, and system states. These abstractions provide a unified foundation for organizing complex objectives, orchestrating heterogeneous agents, modeling system dynamics, and enabling scalable agent evolution. We systematically review the principles, methodologies, and applications of Graph Engineering for LLM agents. Related papers, open-source data, and projects are collected at https://github.com/DEEP-JLU/Awesome-Graph-Engineering.

With large pretrained models, existing methods have effectively improved instruction-based video editing. However, most of them rely on an in-place editing assumption. They align the edited video with the given source clip frame by frame over a fixed time span. This pattern fails for open-ended streams, e.g., restyling a live game or applying a camera move to an ongoing shot. In such cases, edits must extend to future frames as they arrive, rather than be applied to a static input clip. In this paper, we study this setting and name it infinite video editing: given a preceding segment and an edit request, a model must generate the next segment that continues the stream while applying the requested edit. This process repeats as an unbounded sequence of edit instructions arrives. This task brings two challenges: the edit must be a faithful continuation rather than a frame-wise rewrite, and generation quality must remain stable as edits accumulate. To address them, we first design a data-collection pipeline for infinite video editing. Based on the collected data, we propose InfinityEdit, a lightweight edit adapter that equips a streaming video generator with unbounded editing ability. The adapter contains three attention modules. History cross-attention guides the denoising frames using the input frames. Temporal causal self-attention keeps temporal cues flowing only from earlier frames to later ones. Edit cross-attention injects the edit request into generation. During inference, the adapter is activated only in the chunk where an edit request arrives. Subsequent chunks are generated by the original model with a reset anchor frame. This scheme applies the edit while preserving the original model's infinite generation ability. Extensive experiments show that InfinityEdit faithfully continues the stream under each edit, and stays stable over unbounded edit sequences.

Parallel reasoning improves the accuracy and robustness of large reasoning models by exploring multiple solution paths, but its computational cost grows with reasoning depth and branch count. Existing methods for managing these parallel paths typically rely on final-answer consensus, local token confidence, or isolated intermediate probes. However, these signals are often delayed, weakly tied to actual reasoning progress, or too noisy for dynamic, branch-level control. To address these limitations, we introduce ParaTempo, a training-free asynchronous parallel reasoning framework. ParaTempo is driven by temporal confidence, a branch-local measure of answer-space convergence. Each branch is periodically probed for a tentative answer probability distribution, and temporal confidence quantifies how sharply the recent intermediate probes concentrate on a dominant answer. Once sufficient evidence has accumulated, ParaTempo drives its entire control process from this single signal: low-confidence branches are pruned, branches that persistently commit to their dominant answer are retired early, freed computation is reallocated by forking new branches, and generation stops globally once the confidence-weighted vote concentrates. Without requiring synchronization among reasoning trajectories, ParaTempo adaptively allocates computation based on branch-level convergence. Experiments on challenging mathematical and scientific reasoning benchmarks show that ParaTempo reduces average latency by 21.8-32.2% and total token usage by 18.1-30.3% while maintaining competitive accuracy. Moreover, temporal confidence exhibits stronger temporal stability and predictive power for future branch convergence than token-level and instantaneous signals.

Recent omni-modal large language models (Omni-LLMs) show great potential as real-time video assistants, which continuously perceive environments and guide users to achieve specific goals. Unlike traditional passive video understanding, interactive assistants should actively combine visual states, user goals, and prior knowledge to provide effective help. Evaluating this is rather challenging, as the model's unpredictable response dynamically changes the user's subsequent actions, which static offline datasets cannot accommodate. To address this bottleneck, we introduce OmniAssistBench. To solve the issue of diverging interaction paths where the same user goal can be achieved through various methods, we provide models with predefined priors derived from the source video, requiring them to guide users along the exact same routes. Since real interaction videos are rare, we construct the dataset by reverse-engineering existing Internet videos. We deduce logical user goals and segment the videos into multi-turn clips to simulate continuous interactions. This rigorous pipeline required over 1000 expert person-hours to build the dataset. Results show that the proprietary Gemini-3-Pro reaches 66.4 out of the max point of 100, while the open-source Qwen3-Omni-Instruct achieves 51.2. Although current models generally understand user inputs, they frequently provide incorrect or incomplete answers. Specifically, they struggle with visual prompts (e.g., hand gestures), fail to maintain historical context during multi-turn interactions, and fail to delay response until the target event. Results indicate substantial room for improvement before models can become reliable assistants.

Hybrid-thinking multimodal large language models (MLLMs) allow a single model to alternate between deliberative thinking and latency-efficient non-thinking inference. Although these modes differ in reasoning budget, their delivered responses should satisfy the same user-facing standard. Correctness alone may not characterize this response quality; we therefore evaluate task accuracy and response-pattern failures as complementary outcomes. We study this gap through response-pattern alignment: whether thinking and non-thinking interfaces preserve acceptable final-response behavior. We introduce PatternEval, a failure-enriched diagnostic benchmark comprising 2,415 multimodal prompts spanning visual perception and grounding, structured image understanding, and multimodal knowledge reasoning. PatternEval tests four recurrent failures: chain-of-thought leakage, response repetition, logical contradiction, and performative reasoning. Response-pattern failures are widespread across models from different providers, with non-thinking inference exhibiting substantially higher failure rates and thereby creating systematic misalignment between thinking and non-thinking interfaces. Motivated by this diagnosis, we develop PatternRM, a response-level reward model, and PatternRL, which introduces pattern-specific penalties during reinforcement learning. Experiments on Qwen3-VL-4B and Qwen3-VL-8B show that incorporating pattern-specific penalties into reinforcement learning can mitigate cross-mode misalignment while incurring a marginal task performance trade-off. Together, PatternEval and PatternRL provide an evaluation-and-training framework for aligning user-visible response patterns across hybrid-thinking interfaces.

General-purpose language models can reason and synthesize knowledge, but complex work also requires sustained interaction with files, information sources, and executable code, together with state maintenance, failure recovery, and verifiable delivery. We call this working capability: sustained, verifiable progress toward a real-world objective. Apodex 1.1 develops this capability along two complementary dimensions. Environment Scaling expands the diversity and verifiability of executable file, search, and code environments, while Agentic Coordination Scaling trains agents to decompose long-horizon tasks, delegate parallel work, integrate asynchronous results, and replan. A shared execution harness and AgentOS maintain task state and provenance across tools and agents, and training turns environment trajectories and coordination traces into reliable behavior. Across complex professional work, finance, scientific research, mathematics, coding, and search, Apodex 1.1 reaches the leading performance band despite using a substantially smaller model than many frontier systems. The 35B-parameter Apodex 1.1 Mini further retains strong working capability in a locally deployable form. These results ground agentic intelligence in useful, verifiable work completed over time and advance our goal of building a Heavy-Duty Solver for ambitious, long-running tasks.

We present EchoWM, an omnimodal world model for enterable generative media that responds to continuous navigation while jointly generating 720p video, environmental sound, music and speech. We organize interaction around camera intent: in first-person scenes, it specifies observer motion, while in third-person scenes, camera--character dynamics are learned from data without view-specific controllers. Discrete commands and continuous poses are mapped to a shared metric-scale relative 6-DoF trajectory, with dataset-level calibration preserving motion magnitude across heterogeneous data. To jointly learn audio-visual generation and trajectory control, we construct a complementary data engine and adopt progressive training followed by autoregressive post-training for long-horizon generation. Extensive evaluations show that \model achieves strong trajectory following and high visual quality on public world-model benchmarks, supporting both first- and third-person interaction across varied subjects, and maintaining synchronized environmental sound and speech over long-horizon generation.

E-commerce live streaming requires omni-modal understanding of noisy, temporally extended streams, where product facts are distributed across speech, video frames, product images, overlaid text, and user queries. We present TLive-Omni, an omni-modal understanding model tailored to live-commerce scenarios. It maps image, video, audio, and text inputs into a unified representation space. For long-form live streaming analysis, we introduce Per-vGrid, a timestamped token organization that groups each video grid with its temporally corresponding audio within explicit boundary tokens to facilitate temporal alignment. We design a three-stage supervised training recipe that progressively develops live-commerce understanding, from omni-modal perception to instruction-following responses. We then propose Faithful-RFT, a reinforcement fine-tuning stage that further improves answer faithfulness and expression quality while meeting real-time demands, scoring final responses directly with task-verifiable feedback rather than optimizing for reasoning-style exploration during rollout. Moreover, TLive-Omni is supported by a scenario-oriented atomic capability taxonomy and a compact data production engine that converts live-commerce audio, image, and video streams into training signals for speech recognition, speaker analysis, product visual grounding, text recognition, temporal grounding, video dense caption, and omni-modal QA, etc. For scalable training, a synchronized length-grouped sampler reduces padding while preserving comparable workloads across workers, while a lightweight dynamic sampling strategy regenerates rollout groups with near-zero reward variance to maintain meaningful relative advantages for GRPO. Experiments on e-commerce live streaming benchmarks demonstrate strong performance across live-commerce domain tasks, together with excellent generalization on general benchmarks.

Existing image editing frameworks predominantly follow the training paradigm of text-to-image diffusion models. However, extending this paradigm to image editing highlights two inherent discrepancies, specifically, the insufficient attention to edit concept granularity and the training inefficiency caused by sparse supervision signals. To address these issues, we establish a comprehensive hierarchical taxonomy featuring over 1,000 fine-grained edit concepts and build ConceptEdit-12M, a massive dataset of 12 million high-quality editing pairs via an improved synthesis framework. This library-driven approach effectively rectifies the distribution collapse of generated data while ensuring high data fidelity. Furthermore, we propose a dense supervision training strategy that synthesizes multiple non-interfering concepts into single image pairs. By providing richer learning signals, this strategy significantly enhances both training efficiency and overall model performance. Training results validate our strategy, significantly outperforming prior works. Finally, we present ConceptEdit-Bench, a granular evaluation suite designed to diagnose model capabilities across a vast array of real-world scenarios.

As on-device LLM agents evolve into personal copilots, the mobile operating system has become a key testbed for this paradigm, making rigorous capability evaluation essential. Yet existing benchmarks fall into two camps, each with a critical blind spot: GUI-centric benchmarks test surface-level screen manipulation while overlooking background tool use and long-horizon planning, whereas static function-calling benchmarks rely on offline API matching that is detached from real runtime constraints. To close this gap, we present MobilePA-Bench, an interactive, stateful, and tool-centric benchmark for evaluating the tool-calling and planning abilities of mobile planning agents. MobilePA-Bench runs on an executable sandbox that maintains live application databases and returns structured feedback, spanning 13 functional domains and 212 realistic mobile tools. Beyond basic tool use, it evaluates a central planning agent along three advanced dimensions: (1)~Sub-agent Collaboration---decomposing a complex task and delegating specialized work to capable sub-agents; (2)~Memory Usage---recalling stored memories, user profiles, and past preferences to resolve implicit requests; and (3)~Skill Usage---invoking pre-packaged composite skills instead of planning every step from scratch. Extensive experiments show that current frontier LLMs remain unreliable in mobile settings: performance drops sharply under strict tool ordering, permission limits, and unexpected runtime errors. By pairing an interactive function-calling sandbox with evidence-based verification, MobilePA-Bench serves as both a practical diagnostic benchmark and an interactive foundation for agentic reinforcement learning---accelerating the development of dependable mobile agents.

Language models are sequential processors, but long-horizon agency requires external information and computation beyond model weights and active context. Prime Agent is an open-source harness for long-horizon evaluation and coding-agent workflows. A persistent IPython REPL follows the Recursive Language Model abstraction for programmatic context processing and test-time compute, while Continual Harness preserves histories, memories, skills, prompts, and subagent specifications across trajectories. Recursive subagents coordinate through direct agent-to-agent communication, and the Agents View lets humans inspect and manage daemon-backed sessions. Prime Agent standardizes execution, recovery, verification, and resource accounting while leaving strategy construction to the model. This low-friction, expressive membrane prevents harness failures from becoming model failures and pushes measurement toward the model's true maximal underlying capability. Prime Agent raises ARC-AGI-3 RHAE Best@1 from 30% to 95.5% and matches or exceeds native and popular harnesses across long-context coding, GPU-kernel generation, emulator construction, and autonomous nanoGPT speedruns. On Factorio, we find refinement allows for continuous technology progression and dedicated subagents enable parallelized work. Code is available at https://github.com/PrimeIntellect-ai/prime-agent.

Bloomberg : Sources: smart ring maker Oura and some of its backers seek to raise up to $3B in a US IPO that could take place as soon as September and value it at over $16B —  Oura Health Oy, a maker of smart rings that track health, fitness and sleep, and some of its backers are seeking to raise …

Dan Primack / Axios : Source: robotics startup Generalist, which released its GEN-1 model to complete physical tasks in April, raised ~$200M led by 8VC, after raising $400M in June —  Generalist has quietly raised around $200 million in new funding, just two months after the robotics AI startup raised $400 million.

Colin Campbell / Axios : Canada-based logistics software company Descartes acquires Tai, a California-based transportation management system provider, for $100M in cash —  Logistics software giant Descartes has acquired transportation management system provider Tai for $100 million in cash, the companies announced today.

Grace Kay / The Information : At his first Cursor all-hands, Musk said Grok needs to catch up, AI will become impossible for humans to control, Anthropic is leading the AI race, and more —  Earlier this month, on the day SpaceX announced it had completed its $60 billion acquisition of Cursor, Elon Musk called …

Michael Kan / PCMag : Mercury Research: AMD's share of x86 client CPU shipments tops 30% for the first time, hitting 30.3% in Q2, up from 21.1% two years earlier, vs. Intel's 69.7% —  For the first time, AMD's share of the x86 client CPU market, covering both mobile and desktop, has crossed 30% …

Raphael Boyd / The Guardian : The UK backs Ofcom to take “tough action” under the OSA against social media sites failing to remove dangerous driving videos, after a crash killed seven people —  Downing Street condemns ‘utterly disgraceful’ clips and says sites are legally obliged to take them down

Wall Street Journal : Intuit reports Q4 revenue up 14% YoY to $4.35B, vs. $4.27B est., and forecasts FY 2027 revenue growth of 9% to 10%, below ~11% est.; INTU drops 8%+ after hours —  The company expects revenue to increase 9% to 10% for fiscal 2027, down from 14% this year  —  Intuit forecast slower sales growth …

Brody Ford / Bloomberg : Zoom reports Q2 revenue up 4.9% YoY to $1.28B, vs. $1.27B est., enterprise revenue up 7.8% to $787.5M, and forecasts Q3 adjusted EPS below estimates —  Zoom Communications Inc. gave a sales outlook for the current quarter that was about in line with analysts' estimates …

Skild AI : Skild AI unveils S1, a robotics foundation model that it says can learn tasks never seen during pretraining, using a single video demo, without fine-tuning —  UNSEEN TASKS10-MINUTE HORIZONSONE VIDEO PROMPTNO POST-TRAINING  —  The evolution of language modeling provides a blueprint …

Anissa Gardizy / Wall Street Journal : Sources: Chris Malone, who joined OpenAI as head of data centers in March 2025, left the company last week amid a broader exodus —  Chris Malone joins a string of recent high-level executive departures as the AI giant heads toward an IPO and ramps up its spending on computing power

Anna Heim / TechCrunch : Keenable, which is building a web search index for AI agents and says several AI labs are using its API, raised a $26M seed led by Accel —  Search engines were built and optimized for people, who can't spare the time or attention required to scan entire webpages.

Madlin Mekelburg / Bloomberg : An ex-Meta staffer testified that less than 1% of teens used Instagram's opt-in “Take a Break” tool when it launched; the feature is now on by default for teens —  A former data scientist at Meta Platforms Inc. testified that less than 1% of teenagers were early adopters …

微软曾从 2013 年起向全世界的小型非营利组织免费提供 Microsoft 365 Business Premium，但在 2025 年初它宣布将从 2025 年 7 月起停止提供免费授权，转为提供折扣价付费订阅。6 月 11 日前如果没有转为付费的账号内相关数据将被删除。根据 Slate 的报道，没有注意到微软邮件通知的非营利组织遭到重创，17.1 万非营利组织储存在 OneDrive 中的数据被删除，全部丢失。一名经营着一家儿童医保组织的人士称，他翻遍了该组织的邮件存档和垃圾邮件，没有看到任何授权终止的通知。在 Reddit 和微软官方的技术社区论坛，很多非营利组织都表示他们没有收到提前通知就被删除了数据。还有很多人报告在数据删除后才注意到微软的警告邮件。微软没有解释为什么客户的数据无法恢复。

从 2009 年的 Wi-Fi 4 起，每一代 Wi-Fi 的一大卖点都是相比前一代数据速率提升多少。Wi-Fi 5 的最大数据速率十倍于 Wi-Fi 4，到 Wi-Fi 7 每频段理论最大吞吐量达到了 23Gbps，对于大部分用户的网速是绰绰有余了。也许是时候放慢速度了。正在开发中的 Wi-Fi 8 将与 Wi-Fi 7 维持基本相同的最大数据速率、支持相同数量的空间流，使用相同的 4096-QAM（4K-QAM）调制，工作在相同的频段，支持相同的 320MHz 信道带宽。Wi-Fi 8 将专注于提升可靠性，目标是在不同的信噪比 (SINR) 水平下吞吐量提升 25%；95% 的请求延迟降低 25%；MAC 协议数据单元丢失率 (MPDU) 降低 25%。Wi-Fi 8 标准预计将于 2028 年定稿。

Anthropic 的美国客户正使用其最强大 AI 工具的更廉价替代品，引发了外界对其高投入商业模式的质疑，而市场普遍预计 Anthropic 即将进行史上规模最大的 IPO。根据支付服务集团 Ramp 收集的 7 万家公司支出数据，Anthropic 规模最大、成本最高的模型 Fable 5 发布两个多月后，其支出占该公司各类工具总支出的比例仍仅约为 11%，目前已趋于稳定。这打破了企业用户默认选择最强大模型的惯例。分析师和投资者表示，这一变化主要是由于 Fable 5 价格高昂，以及旧款模型能满足大部分企业需求。如果这一转变持续下去，可能会彻底改变前沿 AI 实验室的商业模式。前沿 AI 实验室一直将数十亿美元的研发经费投入到训练规模越来越大、技术越来越复杂的模型上。Fable 的低需求和低市场接受度，加剧了Anthropic 在 IPO 前的不确定性。投资者预计 Anthropic 的 IPO 估值将达到 2 万亿美元或更高，它最早可能会在下个月上市。

Valve 由前微软员工 Gabe Newell 和 Mike Harrington 创办于 1996 年 8 月 24 日，该公司的第一款游戏是备受好评的《半条命》，它被认为对 FPS 这一游戏类别产生了深远影响。Harrington 于 2000 年离开 Valve。Valve 在 2003 年开发了 Steam，之后随《半条命2》的发布强制捆绑推送给玩家，此举曾引起广泛批评，但它最终成为 Valve 最主要的收入来源，它过于成功以至于部分导致 Valve 大幅减少了游戏新作的开发速度。Valve 的游戏开发主要集中在 2013 年前，它发布了一系列享有盛誉的游戏作品，包括《反恐精英》系列、《传送门》系列，《求生之路》系列和《Dota 2》。2020 年代之后的新作包括 VR 游戏《Half-Life: Alyx》，以及仍然在封闭测试的多人游戏《Deadlock》。Valve 还开发了一系列硬件产品，包括 Linux 游戏机 Steam Machine、掌机 Steam Deck 和 VR 头显 Valve Index。

今年 10 月在法国举行的国际计量大会将讨论用闰时取代闰秒。闰秒需要经常性修改，会导致混乱，而引入闰时后数百年内无需修改。协调世界时（UTC）基于用原子振动实现的高精度原子钟来确定，成为 1 秒长度的标准。不过由地球自转时间决定的 1 天长度会发生不规则变化，因此会产生偏差。地球的自转速度根据潮汐涨落以及地球内部地核运动而波动。地球的一天即地球自转一圈，大约需要 86,400 秒。闰秒的作用就是调整与原子钟的偏差，当偏差累积即将超过 0.9 秒时，就会在一天中增加1秒。1972 年引入闰秒以来已修改了 27 次。然而对于需要同步时间进行自动控制的金融交易、交通、电力等高端通信系统而言，哪怕是微小的误差也可能导致整个网络陷入混乱。2012 年增加闰秒时大型网络服务等发生故障。因此美国 IT 巨头 Google 和微软等方面为避免影响而引入各自的调整方法。各公司采用的“时间系统”各不相同，导致闰秒作为标准时间基准的地位动摇。

TikTok 与字节跳动同意支付 4 亿美元与美国司法部就儿童网络隐私争议达成和解。美国司法部 2024 年代表联邦贸易委员会（FTC）起诉 TikTok，指控 TikTok 允许数百万名 13 岁以下儿童在家长不知情或未同意的情况下创建账户，并设置障碍，让家长难以要求删除这些账户。诉讼指 TikTok 违反儿童网络隐私法，在未取得家长同意的情况下收集儿童个人资料。TikTok 与字节跳动同意达成一项 4 亿美元的和解协议，以解决美国司法部对 TikTok 的指控。

因保安可能罢工 Anthropic 通知旧金山办公室的员工本周在家远程办公。Anthropic 上周收到了提供安保服务的 Allied Universal 公司的通知，称该公司的保安可能罢工。Anthropic 随后通知其员工，为以防万一周一和周二在家办公。代表保安的工会 Service Employees International Union (SEIU)表示正与 Allied 等安保服务公司进行合同谈判，但工会没有发起罢工授权投票，也没有发出任何罢工威胁。SEIU 代表了加州数以千计的保安，自 4 月以来一直在进行谈判，以争取更高的工资、更好的医保和更全面的职业培训。

以 TikTok 为代表社媒通过无限滚动的信息流让用户无法对其浏览的内容进行深度思考。这种设计在世界各地引发激烈的讨论，欧洲正考虑限制此类设计。伯明翰大学的研究人员通过访谈和小组讨论的方式，收集了英格兰各地中小学和大学 16-25 岁年轻人的意见。年轻人并非社交媒体的被动用户，他们会思考，且常对所看到的内容持怀疑态度。但研究表明，TikTok 的设计使得批判性思维难以发挥作用。参与者一致认为 TikTok 是一个娱乐平台，而非严肃的学习场所，他们用“有趣但虚假”来形容它。研究还发现，TikTok 的算法驱动设计鼓励用户快速被动地消费内容。严肃的心理健康视频之后可能紧接着无关的娱乐内容，几乎没有给用户留下反思或批判性评估的机会。尽管参与者表示他们试图通过点赞或跳过内容去训练算法，但很多人发现算法的反馈不稳定，有害内容仍然会不断涌现。

Windows 画图（Paint）和照片（Photos）应用都集成了微软的 AI 工具 Copilot，支持通过本地模型和云端生成图像。AI 生成的图像会嵌入两个水印，其一是可见的 Copilot logo，其二是不可见的能跟踪到用户身份的唯一识别码 GUID。画图和照片使用的本地模型（仅限于 Copilot+ PC）共四个文件，容量不到 400MB，无论本地还是云端用户输入的提示词都会发送到微软服务器进行内容审核，服务器会返回 GUID 以及审核后的提示词，GUID 随后就嵌入在 AI 生成的图像之中。

1991 年 8 月 25 日，Linus Torvalds 在新闻组 comp.os.minix 宣布了他正在开发的操作系统内核：“我正在为386（486）AT clones写一个（自由的）操作系统（只是爱好而已，不会和 GNU 一样成为广泛且专业的操作系统）。这个计划从 4 月开始酝酿，现在已做好准备。我希望得到人们关于 minix 优缺点的任何反馈意见，因为我的操作系统和它有类似的方面（因为可行性方面的原因，两者的文件系统物理布局相同）。我刚刚把 bash(1.08) 和 gcc(1.40) 移植到了系统上，而且看来运行得很好。这意味着我可以在几个月内我就可以把它变得有实用性了。我想知道大家想要些什么特色。欢迎提任何的建议，但是我不保证我会实现你的建议 :-)”Torvalds 原计划将项目名字命名为 “fread”——free和 x（即 Unix）的合成词，然而文件上传的 FTP 服务器管理员认为该名字不好听，因此改名为 Linux。如今这个原本是爱好的项目已经走过了 35 年，成为了世界上最流行的操作系统，被无数人使用，虽然桌面是一个例外。

天文学家通过超深成像观测确认已知最大星系边界。名为 IC1101 的星系直径达到约 520 千秒差距（星系尺度单位），接近 170 万光年，宽度相当于 17 个银河系并排排列，而这个星系目前还在成长。IC1101 的恒星物质总质量约为 3.4 万亿个太阳质量。IC1101 坐落在距地球超过 10 亿光年的 Abell2029 星系团中心，是该星系团中光度最高的星系。这类星系位居宇宙中最庞大星系之列，其成长方式是通过反复吞并附近较小的星系不断扩张，这一过程被称作星系同类相食。作为已知最大的星系，它的存在也揭示了在数十亿年的时间尺度上，宇宙中的最大尺度星系结构可以通过持续吞并周围星系而发展到何等极端的规模。

三个狩猎采集者社会——坦桑尼亚的哈扎人、纳米比亚的桑人、玻利维亚的齐曼内人——他们的睡眠时间并不比生活在工业社会的现代人长。他们每夜睡 6.4-7.1小时。但生活在工业社会的现代人推迟了睡眠时间，不再日复一日的规律性睡眠，与自然光的同步减弱了，可诊断的睡眠障碍也变多了。睡眠的总时长恰恰是变动最小的。研究人员认为，区分现代睡眠与祖先睡眠的关键在于时相与规律性，而非睡眠时长。