Weekly Digest — 2026-W45

Private AI that runs on your device. No account.

Create animations and motion design with your coding agent

Your mind map is now an AI thinking partner

If Grok Bot supported team collaboration and was Open Source

The app designed for targeted and transparent help

Build a website that can do its own SEO & AEO automatically

MCP gateway for production agents that acts on your behalf

Give your coding agents a boss. Answer it from your phone.

Your Mac on your iPad and iPhone with no in-between server

A mailbox that tidies itself as mail arrives

Turn prompts and PDFs into audio, on the web or via API

The dashboard for your team's AI use

Unified conditional image generation requires a single model to preserve different aspects of a visual input while producing a target image that follows textual guidance. Pretrained video models provide a promising foundation by encoding visual transformations across time, yet existing approaches typically assign the condition and target to fixed temporal positions. We observe that condition-target relationships vary substantially across generation tasks, while video models naturally encode visual transformations at different temporal scales. This motivates us to model conditional generation through input-adaptive virtual temporal relations. We propose FrameForge, which captures two complementary aspects of such relations. Mixture of Temporal Experts (MoTE) models how far the target lies from the condition by dynamically selecting temporal RoPE experts with different virtual intervals. Chain of Temporal Reasoning (CoTR) models how the condition is transformed into the target through a latent intermediate state, with a reasoning-bottleneck attention mask encouraging information propagation through this pathway. Experiments on DreamBench, COCO2017, and GEdit-Bench show that FrameForge achieves superior performance in subject-driven generation, canny-to-image generation, and image editing among the compared methods.

In recent years, agentic orchestration has been widely applied to solve complex human tasks. However, existing orchestration learning still faces key challenges, including hand-designed curricula, advantage collapse when a task's rollouts all reach the same outcome, and unreliable evaluation signals that judge each skill by one noisy score. To address these challenges, we propose R^2Orch, a new paradigm of Recursive Self-Play Orchestration: an inner loop of self-play that improves the orchestrator on self-chosen tasks, and an outer loop that improves how the inner loop improves. To support this paradigm, we introduce Recursive Learnability, a training objective that rewards a Proposer for choosing tasks whose rollouts keep reaching different outcomes as skills are withdrawn. Built on this objective, we further propose Skill Withdrawal, in which the outer loop withdraws each skill once a controlled evaluation within self-play shows that the orchestrator performs as well without it. Moreover, the two loops form one cycle: failures are distilled into skills, skills into parameters, and withdrawals lead the Proposer to new failures, so the orchestrator moves toward zero-skill deployment. Experimental results on twelve datasets show that R^2Orch consistently outperforms baselines across question answering, math, code, and interactive tasks. Our code is available at https://github.com/beita6969/R2Orch.

Progress toward artificial general intelligence (AGI) has largely relied on scaling a resource-intensive executive, System-2, through larger models, more data and compute, longer reasoning, and more agents. We ask whether the next stage may come instead from co-designing it with System-1, a lightweight, semantically aware predictor that evaluates candidate interactions before execution, so that System-2 acts only on those worth their cost. Our answer is a qualified yes. We study conditional interaction, a predict--gate--execute--update loop: System-1 estimates each candidate's utility, System-2 executes those admitted by an adaptive threshold, and the outcomes update System-1. The loop spans training, test-time reasoning, adaptive decision-making, and agentic tool use, with utility and cost defined by the application. Under ideal screening and equal execution costs, execution compute falls by a factor approaching 1/ρ_u, where ρ_u is the density of utility-relevant interactions (cost-weighted when costs differ); prediction errors, exploration, and distribution shift reduce the saving. We bound selection regret by predictor error and the utility margin. These results motivate our central hypothesis: ρ_u is a measurable coordinate of scaling efficiency and makes informative compute a better description of scaling than total compute.

Foundation models are becoming remarkably reliable at high-level reasoning, yet they remain too slow and costly to serve as fine-grained controllers. Conversely, fast policies like VLAs and whole-body controllers offer more efficient reflexes, but struggle to generalize. While pairing a deliberative brain (foundation models) with a reactive cerebellum (fast policies) seems natural for embodied systems, two obstacles remain: methods are built and scored on incompatible stacks, and their asynchronous coordination is difficult. For example, an agent that calls the VLA in the agentic loop can stall the fast execution, while a VLA that calls the agent on errors tends to re-trigger fallbacks repeatedly or completely miss silent failures. To address this, we propose REA (Routed Embodied Agent), a unified runtime where a deliberative System 2, an embodied router System 1 and reactive System 0 policies cooperate under a single golden rule - no running policy waits on the brain. Instead of querying the brain at each failure, REA employs a route memory that stores each policy's measured success ahead of time, a boundary-triggered judge gate that consults the brain on policy execution, and a shared-budget escalation ladder for bounded recovery. Evaluated on 3,000 test episodes across 60 LIBERO-Pro tasks, REA reaches 95.9% success and beats the best single-agent baseline in its own policy pool (94.7%) while reducing control steps by 25% and control time by 17%. In a separate 100-episode RoboTwin 2.0 study, REA achieves 84% success with VLA policies and 86% with VLA+code, compared with 80% for the strongest single VLA baseline MEDex. These results demonstrate learned and programmatic policies operating within the same runtime. REA also transfers to BEHAVIOR-1K and a real Unitree G1 humanoid, achieving the highest observed success among the compared methods.

A capable voice assistant must do more than recognize words; it must understand requests, reason over context, sustain conversations, and act through tools. We present Apriel Voice 27B Preview, an open speech-language model that unifies these capabilities within a shared reasoning backbone while retaining text and image input modalities. Built on the post-trained Gemma 4 26B-A4B checkpoint, Apriel Voice directly processes speech and generates both responses and tool calls without requiring a separate dialogue model. We introduce a two-stage training procedure to adapt the model to speech. The first stage establishes the speech interface, while the second jointly adapts the projector and backbone through supervised fine-tuning, text replay, and source-dependent cross-entropy and forward Kullback--Leibler divergence supervision. Automatically constructed speech-conditioned dialogues provide conversational training data, while local teacher serving, overlapping teacher and student computation, and fused full-vocabulary losses enable efficient online distillation. In thinking mode, Apriel Voice achieves a score of 98.90 on Big Bench Audio, 81.13 on Speech-BFCL, and a task success rate of 65.51% on τ-Voice, with a mean latency of 1.91 seconds. It also achieves 51.27% English EVA accuracy and 74.13% experience pass@1. Finally, we deploy Apriel Voice 27B Preview on a custom serving platform to support interactive voice interactions.

Short-video search needs compact query rewriters that recover users' personalized intent from ambiguous queries under strict latency constraints. Existing LLM-based methods have improved rewriting by using synthetic reasoning and subsequent consumption-behavioral feedback. However, overemphasizing subsequent consumption can favor engaging content over the user's immediate need, causing intent drift even when rewritten queries are grounded in behavioral history. We propose WeFair, Fidelity-Aware Alignment from Human Feedback for Personalized Query Rewriting in WeChat short-video search. WeFair uses human feedback according to how directly it reflects personalized intent, assigning feedback fidelity to a progression from demonstration to preference to verification. We first mine actual queries and user reformulations from search sessions, and then screen them for contextual support. In the demonstration stage, a supervised fine-tuning curriculum progresses from large-scale context-to-query learning to reasoning and direct prediction on human reformulation and preservation. In the preference stage, direct preference optimization learns relative preferences from subsequent consumption feedback. In the verification stage, group relative policy optimization rewards exact agreement with reformulation or preservation targets to constrain intent drift. Further analysis shows that broad supervision benefits the full curriculum supervised training, while strict verification achieves better performance than auxiliary rewards. In offline experiments, WeFair-2B outperforms strong rewriting baselines in overall accuracy and intent preservation. An online A/B test shows relative increases of 2.593% in effective video-view rate and a 2.350% relative reduction in query reformulation rate. WeFair-2B has been deployed in the WeChat short-video search system, which serves over 100 million queries per day.

Computer graphics is built on code. Large language model (LLM) agents can now generate graphics code and iteratively refine it through various feedback. This capability opens the door to a potentially new paradigm of computer graphics, with graphics algorithms increasingly built around agentic systems to automate progressively complex tasks. Over the past years, research on agentic graphics has expanded rapidly across a diverse range of tasks. This rapid growth makes it increasingly difficult for manual literature reviews to keep pace with new developments. At the same time, similar ideas are often described using different terminology across these tasks, further complicating efforts to develop a systematic understanding of the field. In this report, we address these challenges through two contributions. First, we introduce a unified taxonomy for characterizing agentic systems across diverse computer graphics tasks. Second, we develop an agentic literature system that can assist human researchers in analyzing existing and newly published papers at scale. Building on these contributions, we analyze emerging research trends and discuss open challenges in coordinating agents and tools across graphics tasks, designing interfaces and representations for human-agent collaboration, sustaining long-horizon planning and refinement, and reliably evaluating interactive graphics systems.

Real-time long-video generation requires continuous output with consistent appearance and natural motion. Existing chunk-wise autoregressive methods face challenges in both generation efficiency and long-video quality: serialized denoising and history-cache refresh require two forward passes even with one-step denoising, and longer rollouts can degrade quality and cause camera drift. We propose Wave Forcing (WF), which co-designs generation, training, and execution for continuous video output with multi-step denoising. WF uses chunk-wise causal attention to access earlier, cleaner chunks before their denoising is complete, and distributes staggered denoising and cache-refresh stages across full-model replicas to achieve 1F1C: one output chunk per concurrent forward round after pipeline fill. To adapt the model to this continuous generation process, we develop a three-stage training procedure that successively introduces mixed-noise denoising, motion supervision, and causal adaptation using self-generated history. To execute this process efficiently, Wave Runtime (WaveRT) uses Wave Parallelism (WP) for early K/V delivery and communication-computation overlap, together with optimized kernels and pipelined VAE decoding. Experiments on Wan2.1-T2V-1.3B show that 5-step WF maintains overall quality comparable to Rolling Forcing while achieving higher Dynamic Degree on both short and long videos. Our system efficiency experiments show that 4-step WF achieves 147.6 DiT FPS and 134.8 end-to-end FPS on eight H200 GPUs, yielding 7.07x the single-GPU end-to-end throughput. In DiT-only tests with matched SageAttention+W8A8 backends, denoising steps, and GPU counts, WP delivers up to 2.53x the throughput of the faster of the Ulysses and Ring context-parallel baselines across six model scales (1.3B-14B).

Despite recent advances in CUAs, their development remains constrained by the limited diversity, controllability, and reliability of interaction data. Moreover, conventional optimization pipelines lack iterative feedback, while existing recursive improvement approaches often rely on self-generated experiences, limiting ability expansion and yielding diminishing returns. To address these challenges, we introduce CUA-Master, a nested double-loop recursive improvement framework. At the system level, an outer world-data-model flywheel continuously incorporates open-world corpus and leverages evolving model weaknesses to guide subsequent data construction and optimization iterations. At the data level, an inner flywheel couples open-world seeding and autocatalytic propagation. The former mines authentic workflows as seed tasks from instructional videos, and employs visual-semantic dual-path trajectory reconstruction; The latter transforms these seeds into reusable primitives for controllable synthesis of diverse, complex, and verifiable tasks. To enable efficient and reliable trajectory collection, we further introduce a feedback-guided snapshot-rollback retry mechanism in various real environments. Extensive experiments demonstrate that CUA-Master-27B achieves competitive performance across GUI-only and GUI-CLI hybrid benchmarks, scoring 80.2 on OSWorld Verified, 36.9 on OSWorld 2.0, 43.9 on WeaveBench, and 25.0 on Agents' Last Exam. Our framework also substantially improves the quality and efficiency of data synthesis, and exhibits continuous gains across recursive iterations.

Rapid advances in cowork agents are shifting human-AI collaboration toward real-world automation, delivering software, designs, and other artifacts. However, such productivity work demands both high-quality delivery on complex productivity tasks and continued monitoring and decision-making over long horizons, a combination that remains an open challenge. We introduce CoworkOS, a general-purpose harness for long-horizon work that separates task strategy from execution management: the model decides actions, delegation, and acceptance, while the runtime maintains state and feedback. Its three-layer architecture enables direct execution or delegation to Subagents, synchronously or asynchronously, under stable responsibilities. Through a shared application layer, humans and agents inspect and revise the same evolving documents, presentations, CAD models, and Blender scenes, so human-in-the-loop participation can enter both through conversation and through direct modification of the work itself. To sustain continuity across agents and sessions, CoworkOS further introduces hierarchical workspaces and persistent semantic memory, so working files, key decisions, and long-running Job logs remain shareable, reusable, and resumable after interruption. Across 12 benchmarks and 6 categories of real-world applications, CoworkOS proves highly competitive with frontier agent applications such as Codex and DeepSeek Harness. As a sustained-work case study, CoworkOS uses GLM-5.3 as its backbone to conduct RSI-driven automated post-training on Qwen3.6-35B-A3B. Over five rounds, the model's average score across eight benchmarks rose from 33.19 to 42.95 (+9.76). Looking ahead, we believe CoworkOS opens a meaningful path toward general cowork-agent intelligence.

Video generation is shifting from end-to-end synthesis toward agentic generation, where planning, tool use, and feedback enable coherent, controllable multi-shot videos. In parallel, video world models provide action-conditioned observations and predicted futures, serving as interactive environments for agents. We study their intersection through the agent harness: an executable runtime that constructs context, exposes tools, maintains state, routes feedback, and manages budgets around video models. This report examines three directions. First, agentic video generation, where harnesses coordinate scripting, shot planning, asset management, production, and editing, alongside the evaluation challenges of these systems. Second, agentic world modeling, where harnesses mediate observation, control, execution, and state. We discuss hybrid designs in which game engines or agent-written programs maintain world state while video models render observations, and how agents and world models can mutually evaluate and improve one another. Third, recursive self-improvement (RSI) in video model development, distinguishing output revisions, updates to harness state, generator parameters, and evaluators, and changes to the improvement procedure itself. We identify how these improvements feed back into subsequent development. Across these directions, we analyze essential agent capabilities, including audiovisual perception, long-horizon planning, memory, code-based interaction, feedback-guided control, skill reuse, and multi-agent coordination. We conclude with challenges in autonomous video production, lifelong memory, programmable hybrid control, autonomous model development, provenance, cost accounting, and evaluation linking video quality and task performance to sustained improvements in models and harnesses.

Robot-use Agents (RUAs) have shown promising zero-shot manipulation capabilities, but reliance on frontier, proprietary models limits research into further training. To democratize RUA research, we introduce uni-robot-agent, a unified harness for competitive open-weight robot use that supports further training. Without privileged inputs, an out-of-the-box Qwen3.8-Flash-Next achieves a RoboDojo Score of 31.39, surpassing the published GPT-6 Astra-RoboProbe result of 28.97 and many VLA/WAM baselines, and performs competitively on 19 real-world tasks. Our analysis across model generations, families, and sizes links effective robot use to four general capabilities, suggesting that future RUA training should develop them jointly.

Julie Bort / TechCrunch : Anthropic S-1 claims it may “imminently meet” its RSI threshold and may fail to identify “significant acceleration” in its development until after it's occurred —  While much attention has focused on the updated financials Anthropic revealed in its revised S-1A on Monday …

Jason Shubnell / The Block : Brazil's largest crypto exchange Mercado Bitcoin halts withdrawals after a breach involving $46M+ in assets, per Arkham; Tether froze 4.5M+ USDT in response —  The exchange paused deposits and withdrawals after the incident, while Tether froze more than 4.5 million USDT linked to the attacker.

Wall Street Journal : Sources: institutional investors told Anthropic that its IPO valuation should be around $1.5T to $1.6T, down from estimates of $2T, citing a tech stock selloff —  Institutional investors who plan to buy shares in IPO show concern following a selloff in tech stocks

Loree Seitz / The Wrap : Apple reports Q4 revenue from Services, which includes the App Store, Apple TV, Apple Music, iCloud, and more, grew 10% YoY to $31.57B, vs. $31.58B est. —  The tech conglomerate beat Wall Street expectations with revenue hitting $114.48 billion  —  Getting your Trinity Audio player ready...

Marcus Mendes / 9to5Mac : Apple Q4: iPhone up 17% YoY to $57.58B, vs. $56.54B est., Mac up 24% to $10.8B, iPad down 10% to $6.2B, and Wearables, Home, and Accessories down 8% to $8.3B —  Apple sees Q4 revenue climb 12% year over year  —  Last year, for its fourth fiscal quarter, Apple reported $102.47 billion in revenue …

Apple : Apple reports Q4 revenue up 12% YoY to $114.48B, vs. $113.39B est., China revenue up 21% to $17.5B, vs. $18.9B est., in John Ternus' first earnings report —  September quarter records for total company revenue, iPhone revenue, and EPS Services revenue reaches new all-time high CUPERTINO …

Rashi Shrivastava / Forbes : Traversal, which provides AI agents for site reliability engineering, raised $120M co-led by Kleiner Perkins, Redpoint, and Khosla at a $1B valuation —  Traversal has raised $120 million in funding to help companies like American Express and Pepsi troubleshoot site outages.  —  for The Prompt

Reuters : Block reports Q3 revenue up 10% YoY to $6.7B, above est., Cash App gross profit up 27% to $2.07B, raises its full-year profit outlook; XYZ jumps 5%+ after hours —  Jack Dorsey-led Block (XYZ.N) raised its full-year gross profit outlook on Tuesday after posting a rise in third-quarter earnings …

David Swan / The Age : Canva says it is restructuring, which will impact 1,271 workers, or 22% of its workforce, as it merges its product, growth, and platform teams —  Canva will cut more than 1000 jobs in a major restructure in response to slowing growth at the design giant.  —  Co-founder and chief executive …

Tobias Mann / The Register : Revolut says it cut AI coding inference costs by ~90% in less than a year by making self-hosted GLM models its default over commercial tools like Claude Code —  Revolut says that in less than a year it's managed to slash its AI coding inference costs by roughly 90 percent by making self-hosted models …

Reuters : Super Micro reports Q1 revenue up 298% YoY to $19.95B, vs. $16.39B est., and forecasts Q2 revenue above estimates; SMCI jumps 7%+ after hours —  Super Micro Computer (SMCI.O) forecast second-quarter revenue above Wall Street estimates on Tuesday as strong demand for its AI-optimized servers boosted sales …

Katie Tarasov / CNBC : AMD reports Q3 revenue up 45% YoY to $13.38B, vs. $13.09B est., Data Center revenue up 97% to $8.5B, forecasts Q4 revenue above est.; AMD jumps 7%+ after hours —  Advanced Micro Devices reported third-quarter earnings on Tuesday that beat Wall Street's expectations, with revenue jump 45% …

小行星灭绝了恐龙，鳄鱼为何幸存？科学家或许解开了这个困扰6600万年的谜团。一项大规模化石与古气候分析表明，鳄鱼偏爱温暖环境的习性，让它们在小行星撞击后仍能栖身于条件尚可的热带“避难所”。研究团队发现，鳄鱼及其远古近亲在长达 2.5 亿年的演化史中，始终“追着温暖气候走”，而非适应剧烈变化的温度。地球变暖时，它们扩散到新区域，最远抵达南极洲；气温回落时，它们再退回热带。研究分析了 7000 多块化石、400 多种现存与灭绝物种的记录，并结合电脑模型，重建了 2.45 亿年间地球气候与地理的变迁。结果显示，小行星撞击后，热带和亚热带地区对鳄鱼近亲依然宜居，而许多恐龙的生存环境却在不断恶化。只有在极端气候模拟条件下，这些潜在的避难所才会消失。研究人员还模拟了 6600 万年前撞击发生后的地球状况，发现赤道附近地区可能仍足够温暖，足以庇护鳄鱼近亲的幸存种群，让它们得以存续并逐步恢复。

男性想要长寿的话可随时尝试阉割。对包括人类在内的哺乳动物，切除睾丸有助于延长雄性的寿命。一项新研究发现，与此前的认知不同，阉割手术并不一定要在生命早期进行。对年长雄鼠进行去势处理，可使其寿命延长逾 30%。这一发现颠覆了以往对于睾丸如何影响衰老的既有观念。在人类等物种中，雌性的寿命显著长于雄性。以美国为例，男女之间的平均寿命差距为 6 年。男性也更容易罹患与衰老相关的疾病如癌症和心脏病。这一现象背后原因尚不明确，有证据表明雄性的短寿与睾丸相关。多项研究表明去势能普遍带来寿命延长。在最新研究中，研究人员对不同年龄同一品系的雄鼠进行去势处理，时间点分别是在青春期前、青春期刚过，以及成年期。结果显示这三组小鼠的寿命均长于对照组。出人意料的是成年期接受去势手术的小鼠获益最大。手术使其寿命延长了约 33%；相比之下在青春期前或青春期后接受手术的小鼠，寿命分别延长了 19% 和 26%。成年雄鼠受益于去势的原因尚不清楚，但研究结果促使研究人员给四岁雄狗做绝育手术。

在法国之后、比利时、瑞典、卢森堡和爱尔兰多家超市的冷冻和冷藏设备遭遇类似的网络攻击。比利时和卢森堡有约 40 家 Delhaize 超市受影响，瑞典 ICA 和 Coop 的多家门店受影响，爱尔兰有 13 家 SuperValu 门店受影响。瑞典的一家 ICA 门店称，其冷冻设备的温度被篡改设定到 50 摄氏度。一家 Delhaize 门店称它损失了价值 10 万欧元的商品。法资超市家乐福表示位于比利时的门店有 15 家受到影响，法国受影响超市都使用了丹麦公司 Danfoss 的制冷控制器，运行的是已停止支持的型号。目前不清楚最新受影响的超市是否使用了 Danfoss 控制器。

昆明动物所的研究人员对高黎贡山 62 种鸣禽的历史标本与当代样本进行全基因组比较发现，过去半个世纪内其遗传多样性平均下降 43%。高黎贡山面积仅占我国陆地面积的 0.36%，却孕育了占中国鸟类物种总数 1/3 以上的鸟类，且位于喜马拉雅山地、印缅地区和中国西南山地三大生物多样性热点交汇地带，保护基础较好。研究团队进一步分析发现，扩散能力是造成物种间遗传多样性下降差异的重要因素：扩散能力强的物种遗传多样性降幅相对较小，扩散能力弱的降幅更大。研究同时发现，种群数量越多的物种遗传多样性下降情况越严重。

以提供隐私服务著称的 Proton 公司正全力推动工程师转向采用 AI，CEO Andy Yen 还在内部 Slack 频道训斥了提出安全顾虑的员工，称是“失败者的思维”。Proton 甚至计划对员工进行重新面试，要求员工说明他们如何使用 AI。如果员工没有充分使用 AI 他们可能会面临被解雇。在引发公众抗议之后，Proton 取消了重新面试。尽管如此，Proton 公布了一份 AI 宣言：“首先工程文化和工程师心态需要发生转变。你们的工作性质已经变了。你们的任务不再是写代码，LLM 也不是为了帮助你们更快、更好地写代码。你们的工作，是帮助 LLM 更快更好地写代码。”

Valve 公布了 2026 年 10 月的 Steam 硬件和软件调查，结果显示 Steam 用户中使用 Linux 比例占 3.95%，过去几个月它的份额一直在 3% 以上，最高超过 5%，这一波动可能与简体中文用户增加或减少相关。凡是简体中文用户数增长的月份 Linux 份额就会下降 Windows 份额会上升。2026 年 10 月简体中文用户减少 9.41% 至 21.98%，英文用户增长 4.84% 至 39.04%。其它数字包括：Windows 10 仍然有 21.11%，玩家使用英特尔 CPU 的比例占 52.63%，AMD 占 47.37%。

《纽约时报》、《芝加哥论坛报》和《奥兰多哨兵报》等美国出版商起诉 Brave Software，指控其未经许可抓取网站内容，并帮助用户绕过付费墙。Brave Software 被控将其抓取内容的索引数据库 Brave Index 提供给 Anthropic、Perplexity、Mistral、xAI 和 Cohere 等 AI 公司，通过 Paywall blocker 功能帮助用户绕过数百家新闻媒体的付费墙。《纽约时报》发言人 Graham James 在一份声明中表示：“我们支持合乎道德且负责任的使用与开发 AI，但 Brave 公然蔑视现行版权法，对《纽约时报》多次要求其停止侵权行为的诉求置之不理。我们提起此项诉讼，旨在追究 Brave 的责任——它窃取了我们的内容，不仅用于支撑其自身产品，还将其提供给部分全球最知名、资源最雄厚的生成式 AI 公司。”

Google 宣布美国 K-12 学校学生使用的软件将默认关闭所有 AI 功能，除非学校管理人员自己选择启用。此举旨在回应教育工作者和家长的诉求，他们希望在孩子与 AI 互动上有更多的掌控权。今天 AI 功能已成为 Google 商用搜索引擎的标配，Google 负责教育的副总裁 Chris Phillips 表示，搜索与 AI 已深度融合，如果学校禁用了 AI，那么学生将无法使用搜索。美国 K-12 学校广泛使用 Google 的软件，包括 Gmail、Docs 以及作业平台 Classroom。Google 的 Chromebook 是美国学校最广泛使用的笔记本电脑。

Fedora Linux 项目释出了 v45。Fedora Linux 项目同时释出了 Fedora Workstation、Fedora KDE Plasma Desktop、Fedora Server、Fedora Cloud、Fedora CoreOS 和 Fedora IoT，以及 Atomic Desktops 等众多衍生版本。Fedora Linux 45 的主要变化包括：支持通过 Anaconda 或 Kickstart 在 Stratis 存储上进行安装，移除 NetworkManager 不再维护的 VPN 插件，GNOME 和 KDE ​​Plasma 桌面环境的最新版本，完善 Wayland 支持，改进分数缩放，gcc 16.2、binutils 2.47、glibc 2.44、gdb 17.2、Fontconfig 2.18、LLVM 23 等开发工具的新版本。更多变化可浏览 ChangeSet。

血细胞并不容易形成化石。大多数血细胞在死亡后不久便会分解，这使得找到令人信服的、非常古老的血液痕迹非常困难。然而澳大利亚科廷大学的研究团队在 PNAS 上发表论文称，凭借多条证据，他们在巴西一只 1.15 亿年前的恐龙遗骸中发现了最古老的红细胞化石。这项研究的对象是巴西恐龙“阿拉塔龙”的骨骼化石。研究人员估计，这只恐龙体长约 3 米、重约 34 公斤，死亡后遗体沉入水底，微生物生长并将其掩埋，从而保存了下来。在显微镜下，研究人员在这只恐龙的骨骼中发现化石细胞的迹象：这些结构呈圆形，中心凹陷，每个细胞平均长 23 微米、宽 20 微米，嵌在曾经让血管穿过骨骼的通道中。下一步则是寻找化学线索，以证明这些结构确实含有血细胞，而不仅仅是形似血细胞的矿物质。在他们进行的近 30 项试验中，有一项是将骨样本研磨成粉后用激光照射，从样本散射回来的光显示出血红素的存在。血红素是一种环状化合物，其中的铁元素有助于红细胞运输氧气。研究人员还发现了大量降解形式的血红素。

轻量级桌面环境 Enlightenment 项目释出了 v0.28.0。主要变化包括：初步支持 NetBSD，支持 Network Manager，原生支持 PipeWire，新的剪贴板管理器，电池管理、AC 轮询等相关代码进行重写，新增设置电池阈值和支持 ACPI 电源配置等等，以及大量 bug 修复和更改。

全球在建核电站已增至 79 个机组，达到 40 年来的高水平。据世界核协会统计，2026 年全球有 11 个新核电机组开工建设，截至 10 月 16 日，在建核电机组数量达到 79 个。这是 1986 年（91 个）以来的高位。1986 年是切尔诺贝利核电站事故发生的年份，此后全球新建核电机组数量有所减少。如果将预计未来 15 年内投入运营的正在规划的核电机组也计算在内，数量将达到 201 个。目前全球在运核电机组共有 443 个。即使考虑到因老化而退役的核电机组，到 2040 年前后，全球在运核电机组数量仍有可能超过 600 个。目前全球在建的79个核电机组中，仅中国就有38个，约占总数的一半，其次是印度（8个）和俄罗斯（7个）。从处于规划阶段的核电机组来看，中国同样遥遥领先，达到 42 个，其次是俄罗斯（23个）和印度（14个）。