Weekly Digest — 2026-W24

Run your Claude AI artifacts natively, No browser. No cloud.

Relive vintage operating systems right on your desktop

Lip-synced AI dubbing for creators, brands and studios

Give your agents muscle memory for automating the web

A living journal for your pet's life, with AI insights

Free, open-source, cross-platform, modern file manager app

The compute efficient layer for AI inference

Real-time speech-to-speech translation API

Manage AI agents, tasks, workspaces from one control layer

Interactive diagrams with AI, and your code always in sync

A fitness coach with personalized plans

AI-powered city discovery built on taste, not reviews

Large language models exhibit impressive zero-shot capabilities across a wide range of downstream tasks. However, they struggle to function as off-the-shelf embedding models, leading to suboptimal performance on massive text embedding benchmarks. In this paper, we identify a potential cause underlying this deficiency. Our motivation stems from an unexpected observation: text embeddings tend to align with frequent but uninformative tokens when projected onto the vocabulary space. We argue that this excessive expression of high-frequency tokens suppresses the model's ability to capture nuanced semantics. To address this, we introduce EmbedFilter, a simple linear transformation designed to refine text embeddings derived from LLMs directly. Specifically, we uncover that the unembedding matrix within LLMs encodes a latent space that is actively writing these frequent tokens into embedding space. By filtering out this subspace, EmbedFilter suppress the influence of high-frequency tokens, thereby enhancing semantic representations. As a compelling byproduct, this enables an inherent dimensionality reduction, lowering index storage and speedup retrieval while fully preserving the refined embedding quality. Our experiments across multiple LLM backbones demonstrate that LLMs equipped with EmbedFilter achieve superior zero-shot downstream performance even with significantly reduced embedding dimensions. We hope our findings provide deeper insights into the mechanisms of LLM-based representations and inspire more principled designs to improve text embeddings training. Our code is available at https://github.com/CentreChen/EmbFilter.

Progress in genomic foundation models is difficult to assess due to fragmented benchmarks, incompatible evaluation protocols, and task-specific reporting. As a result, claims of superiority or generality across models are often not directly comparable. We introduce GENEB, a large-scale diagnostic benchmark that evaluates frozen representations from 40 genomic foundation models across 100 tasks spanning 13 functional categories under a unified probing-based protocol, including few-shot regimes. GENEB enables controlled comparison across model scale, architecture, tokenization, and pretraining data while explicitly exposing task-level trade-offs. Our analysis shows that aggregate leaderboards are unstable: model rankings vary sharply across task categories, scale provides only modest and inconsistent gains, and architectural and pretraining alignment frequently outweigh parameter count. These results highlight limitations of current evaluation practices and position GENEB as a reference framework for principled comparison and category-aware model selection in genomic machine learning.

Evaluating LLM mediators remains challenging, as mediation unfolds as a real-time trajectory shaped by disputants' shifting emotions, intentions, and context. Existing testbeds rely on a few expert-authored domains, vary mainly strategic posture, and score every turn against every topic, introducing off-topic noise. We introduce SoCRATES, a benchmark for evaluating proactive LLM mediators in realistic, multi-domain testbeds. It constructs scenarios from real conflicts through an agentic pipeline across eight domains, probes five socio-cognitive adaptation axes (strategic posture, party composition, history length, emotional reactivity, and cultural identity), and scores each topic only on the turns that advance it via a topic-localized evaluator. The evaluator reaches 0.82 alignment with human experts, more than doubling a per-turn baseline. Benchmarking eight frontier LLMs, we find that even the strongest mediator closes only about a third of the unmediated consensus gap under diverse and realistic testbeds, with performance varying sharply by socio-cognitive axis, highlighting that progress lies in social adaptation to diverse conditions.

We introduce MMAE, a Massive Multitask Audio Editing benchmark, serving as the first comprehensive evaluation testbed designed for general-purpose instruction-based audio editing. Spurred by the shift toward intelligent creation, interactive editing has rapidly expanded from visual domains, pioneered by models like Nano-banana 2 for images and Gemini-Omni for video, into audio. However, the current evaluation infrastructure lags severely, remaining highly fragmented and restricted to specific subdomains or basic operations. Unlike existing benchmarks that are limited in scope, MMAE extends to a broad spectrum of real-world scenarios, encompassing 7 distinct audio modalities, including sound, speech, music, and their mixtures. Furthermore, we establish a comprehensive taxonomy spanning 6 levels of task complexity, from basic modifications to multi-hop reasoning and multi-round editing, 2 levels of granularity, and 8 distinct operation types. Meticulously curated through human-agent collaboration, MMAE comprises 2,000 high-fidelity samples paired with a pioneering rubric-based evaluation framework. By decomposing free-form tasks into 17,741 verifiable criteria, this robust rubric-based paradigm enables a precise, multi-dimensional assessment of both instruction following and context consistency. Our extensive evaluation of leading models reveals that current systems remain far from achieving reliable edits. Strikingly, the Exact Match Rate (EMR) consistently falls below 5% and plummets to an absolute 0% in complex, mixed-modality tasks, exposing critical bottlenecks in precise execution and structural robustness. We hope MMAE will serve as a catalyst for future advances in the intelligent creation community, providing a clear diagnostic roadmap and establishing a standardized, long-lasting evaluation paradigm for next-generation audio editing systems.

Despite being a pivotal frontier, interactive world modeling remains underexplored in terms of the versatile controllability required by practical scenarios. To bridge this gap, we present AnchorWorld, a framework that advances egocentric simulation through enhanced interaction integrity and a flexible mechanism for world customization. First, we utilize 3D human motion as the primary interaction modality. To complement the out-of-view or truncated body parts in egocentric views, we introduce an auxiliary training supervision that incorporates exogenous viewpoints decoupled from the agent's first-person sensorium. It allows the model to observe the agent's full-body positioning relative to the environment, facilitating a more robust spatial grounding of human-world interactions. Furthermore, we propose a simple yet effective mechanism for customizing self-evolving worlds. This is achieved by defining anchor views within a unified world coordinate system, coupled with textual descriptions dictating the dynamic evolution of local scenes. Experimental results show that AnchorWorld significantly outperforms state-of-the-art baselines, while ablation studies validate the effectiveness of our key designs. Notably, our customization scheme exhibits promising spatio-temporal geometric consistency and adheres strictly to the prescribed evolutionary dynamics.

Object insertion aims to seamlessly composite a reference object into a specified region of a background image. Recent diffusion-based methods achieve high visual quality but formulate insertion as a simple 2D inpainting task, providing no explicit control over the object's 3D pose and limiting their practical applicability. We propose DIRECT (Decomposed Injection for Reference Composition and Target-integration), a novel framework that integrates interactive pose manipulation with high-fidelity 2D image synthesis to enable pose-controllable object insertion. Our method decomposes the insertion conditions into three complementary components: appearance guidance capturing visual details from the reference object, geometry guidance derived from the user-adjusted 3D proxy, and context guidance from the target background. By injecting them through separate pathways, DIRECT avoids feature entanglement and simultaneously preserves reference appearance, follows the user-specified pose, and adapts the object to the target scene. We also introduce an automated data construction pipeline to improve the diversity and quality of training data. Experiments show that DIRECT outperforms previous methods in both geometric controllability and visual quality.

On-policy distillation (OPD) is increasingly used to improve large language model reasoning, but its training dynamics remain poorly understood. We characterize the trajectory of OPD updates in parameter space and compare it with supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR). A suite of parameter-space diagnostics consistently places OPD in a relaxed off-principal regime: compared with SFT, its updates affect fewer weights and avoid principal directions more strongly, while compared with RLVR, they remain less tightly constrained. Beyond this static localization, OPD exhibits subspace locking: its cumulative updates rapidly enter a narrow low-dimensional channel. Constraining training to the update subspace formed early in training preserves OPD performance but substantially degrades SFT, indicating that the locked subspace is functionally sufficient for OPD. Control experiments further show that sparsifying the update tokens and shifting rollout generation off-policy preserve the rank dynamics, whereas mixing the OPD objective with RLVR changes them. Overall, these results suggest that OPD is not merely an intermediate point between SFT and RLVR, but induces its own update geometry in parameter space.

Agent systems increasingly use textual skills to encode reusable task procedures, but injecting these skills into the prompt at every step incurs substantial context overhead and exposes skill content as plaintext. We present LatentSkill, a framework that converts textual skills into plug-and-play LoRA adapters through a pretrained hypernetwork. LatentSkill stores skill knowledge in weight space rather than context space, removing per-step skill tokens while preserving modular loading, scaling, and composition. On ALFWorld and Search-QA, LatentSkill outperforms the corresponding in-context skill baseline while using substantially fewer prefill tokens: it improves ALFWorld success by 21.4 and 13.4 points on the seen and unseen splits with 64.1% fewer prefill tokens, and improves Search-QA exact match by 3.0 points with 72.2% lower skill-token overhead. Further analysis shows that generated skill LoRAs form a structured semantic geometry, can be precisely controlled via the LoRA scaling coefficient, and can be composed through parameter-space arithmetic when skill components are aligned. These findings suggest that weight-space skills provide an efficient, modular, and less exposed substrate for extending LLM agents.

Video world models that maintain 3D spatial consistency across generated frames typically rely on explicit point cloud memory constructed in RGB space. This design is both computationally expensive, requiring repeated rendering and VAE encoding, and inherently lossy, as the round trip through pixel space discards rich features of the learned latent representation. In this paper, we introduce latent spatial memory for video world models, a persistent 3D cache that stores scene information directly in the diffusion latent space, avoiding pixel-space reconstruction. Building on this, we propose Mirage, a latent-space spatial memory framework that constructs the memory by lifting latent tokens into 3D via depth-guided back-projection and queries it by synthesizing novel views through direct latent-space warping. This unified formulation eliminates both the information loss of pixel-space reconstruction and the computational burden of repeated encoding and rendering. Experiments show that latent spatial memory achieves up to 10.57times faster end-to-end video generation and 55times reduction in memory footprint relative to explicit 3D baselines. Leveraging the geometric prior of the diffusion model, Mirage attains state-of-the-art performance on WorldScore and strong reconstruction quality on RealEstate10K.

Conventional LLMs keep the full KV cache loaded during decoding, causing a severe GPU memory bottleneck for ultra-long context serving. In this report, we propose Lookahead Sparse Attention (LSA), a novel inference paradigm powered by a Neural Memory Indexer built upon the DeepSeek-V4 architecture. Rather than passively attending to all historical tokens, LSA proactively predicts future context demands and preserves only the query-critical KV chunks in the GPU memory. Crucially, we instantiate this architecture via a backbone-free decoupled training strategy. By formulating the indexer as a standard dual-encoder architecture, we train it independently using standard retrieval training frameworks without ever loading the massive backbone model into GPU memory. We demonstrate that this "less is more" paradigm significantly maximizes serving efficiency while acting as an effective attention denoiser in tasks that rely on long-term global memory. Across primary long-context evaluation suites (e.g., LongBench-v2, LongMemEval, and RULER), FM-DS-V4 compresses the average physical KV cache footprint down to merely 13.5% of the full-context baseline, while consistently preserving or slightly elevating downstream accuracy (+0.6% absolute margin on average). Crucially, at extreme 500K scales, FlashMemory suppresses the physical KV cache overhead by over 90% without destabilizing the backbone's core reasoning capacities.

Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional domains. We argue that this gap is largely an evaluation problem: widely used benchmarks lack sustained performance measurement on real and economically valuable workflows. This paper introduces Agents' Last Exam (ALE), a benchmark designed to evaluate AI agents on long-horizon, economically valuable, real-world tasks with verifiable outcomes. Developed in collaboration with 250+ industry experts, ALE covers non-physical industries defined with reference to O*NET / SOC 2018 (the U.S. federal occupational taxonomy). It is organized around a task taxonomy with 55 subfields grouped into 13 industry clusters covering 1K+ tasks. Current results show that the hardest tier remains far from saturated: across mainstream harness and backbone configurations, the average full pass rate is 2.6%. ALE is designed as a living benchmark: its task pool grows continuously as new workflows and industries are onboarded. More broadly, ALE is intended not merely as another leaderboard, but as an instrument for closing the gap between benchmark success and GDP-relevant impact.

We present Echo-Memory, a controlled study of memory mechanisms in action-conditioned world models. These models generate multi-segment videos from a first frame, text prompt, and camera-action sequence, but their central failure is often memory rather than local image synthesis: after the camera leaves and returns, the scene or salient object may silently change. Existing memory designs are hard to compare because gains are entangled with backbone, training, retrieval, and evaluation differences. Echo-Memory fixes the action-to-video interface and varies only how history is stored and read by the generator. Under a shared video diffusion backbone, optimizer, camera-action representation, sampler, and evaluation pipeline, we compare raw context, compression-based memory, spatial summaries with different read-out paths, and state-space recurrence. This matched matrix separates four otherwise conflated axes: capacity, compression, read-out, and recurrence. We also evaluate memory through a three-branch protocol: replay quality, in-domain loop revisit, and open-domain return probes. The branches routinely disagree, showing that replay fidelity is not a sufficient proxy for remembering a world. Three findings follow. Raw context is a strong capacity baseline and improves open-domain return far more than it improves replay metrics. Compactness is not a free substitute for capacity: aggressive spatial and hybrid-compression memories lose the salient evidence needed for return. Finally, block-wise state-space recurrence is the strongest open-domain return mechanism in our matrix, showing that the structure of implicit memory matters as much as the decision to use it. These results provide a compact protocol for studying memory in action world models beyond isolated replay metrics.

Richard Nieva / Forbes : Source: Cursor, which is prepping for an expected acquisition by SpaceX, passed $4B in annualized revenue in the last week, up from $3B in April and $2B in Feb. —  Elon Musk's rocket and AI company is expected to acquire the buzzy AI coding startup shortly after it goes public.

Ashley Capoot / CNBC : OpenAI confidentially files for an IPO, says it has “not decided on timing yet”, as “there are things we want to do that are likely easier as a private company” —  OpenAI has confidentially filed for an IPO with the Securities and Exchange Commission …

Abner Li / 9to5Google : Google lowers the price of its Google AI Plus plan to $4.99 per month, down from $7.99, and doubles the included storage to 400GB —  Google announced today that its AI Plus subscription is getting a price drop to $4.99 per month and now includes 400 GB of storage.

Ivan Mehta / TechCrunch : Google upgrades NotebookLM, which now runs on Gemini 3.5 and Antigravity, to deliver new agentic capabilities and more advanced reasoning for AI Ultra users —  Google on Monday announced an update to its NotebookLM research tool, which includes new features and the shift to Gemini 3.5 as the default model.

Todd Spangler / Variety : Apple's Craig Federighi says some companies “appear to be racing forward” to develop “AI for the sake of AI” without regard for the humans using the technology —  Apple said it has rebuilt the Siri personal assistant from the ground up with artificial intelligence at its core …

Hartley Charlton / MacRumors : Apple announces new features for the Home app, including AI-generated descriptions of HomeKit Secure Video camera clips and smarter grouping of notifications —  Apple today announced new Apple Intelligence features for the Home app, including AI-generated descriptions of HomeKit Secure Video camera clips …

Harshita Mary Varghese / Reuters : Super Micro Computer aims to raise $7B in a series of equity and equity-linked financing transactions to fund its component purchases to satisfy AI orders —  Super Micro Computer (SMCI.O) on Tuesday said it plans to raise $7 billion in a series of equity and equity-linked financing transactions …

Reuters : Analysis: Trump and his sons profited $2.3B+ from four crypto ventures including $TRUMP since January 2025, while other investors in those projects lost ~$2.3B —  Risking little of their own money, the US president and his sons have added at least $2.3 billion to the family fortune …

New York Times : Docs: ~34K Instagram accounts, including Obama's White House account, were affected in the breach tied to Meta's AI chatbot; attackers changed 3,500+ usernames —  The flaw, which Meta said it had fixed, allowed anyone to take over Instagram accounts using a bug in the company's new artificial intelligence software.

Matthias Bastian / The Decoder : Anthropic says Fable 5 has invisible safeguards that use prompt modification, steering vectors, or PEFT to limit its effectiveness for building frontier LLMs —  Key Points … Ask about this article...  Both models share the same base model.  Fable 5 ships with conservative safety guardrails for general use.

Ethan Mollick / One Useful Thing : Hands-on with Claude Fable 5: impressive results with complex projects, including a data analysis tool built in just 9.5 hours and an interactive isochrone map —  Claude Fable represents another big jump in AI  —  I had early access to the first Mythos-class AI model being released to the public, Claude 5 Fable.

Wall Street Journal : Kalshi plans to require users seeking to make bets in some markets linked to material nonpublic information to submit an online form disclosing where they work —  The popular predictions market is putting new guardrails in place for certain bets as concerns about suspicious activity rise

根据发表在《Current Obesity Reports》期刊上的一项研究，肥胖并非只是个人选择的结果，肥胖风险的遗传率高达 40%-70%，能通过复杂的生物和环境因素代代相传。最新证据表明，肥胖会影响精子质量，改变表观遗传标记。这些变化可能会影响儿童的食欲调节、新陈代谢和长期患病风险。好消息是这些变化是可逆转的。生活方式改变以及减肥可改善精子健康，改变与肥胖相关的表观遗传模式。

天文学家利用韦伯太空望远镜以及引力透镜效应首次测量了一个早期宇宙休眠黑洞质量。该黑洞是 MRG-M0138 星系的中心，星系已经不再形成恒星，而黑洞也不再吞噬周围的物质而处于休眠状态。MRG-M0138 位于一个巨大星系团的背后，被引力透镜效应放大了约 30 倍。黑洞距离地球大约 100 亿光年，其质量为太阳的 60 亿倍。天文学家组合了引力透镜以及黑洞引力对恒星运动的影响确定了其质量。

越来越多的证据表明社媒平台算法给民主带来了风险。由于算法的不透明性以及以最大化用户参与度和平台停留时间为导向，完全不在乎推送内容的质量，算法被认为是造成政治极化的罪魁祸首。以 X 平台为例，在马斯克（Elon Musk）在 2024 年宣布支持特朗普之后，倾向共和党的账号曝光度显著提升。马斯克本人在 2024 年 7 月至 11 月间所发布推文的累计浏览量高达 171 亿次，超过了该平台所有政治竞选广告的总和。2025 年德国联邦选举期间，各大社交平台算法推荐给年轻用户的政党相关内容中半数涉及极右翼政党。一项分析发现，X 平台算法不成比例的放大了政治极端政党（尤其是极右翼政党）的内容，系统性压制中间政党。另一项研究发现，相比按时间排序的内容，用户接触 X 平台算法推送内容七周后，政治态度会向更保守的方向转变。禁用算法后这种转变并未逆转。这些研究显示平台算法目前的运作方式不利于民主。社媒平台算法放大极端声音导致的一个结果是扭曲对观点分布的感知，发表边缘观点的人会认为自己是主流，这种网络同质性被称为“虚假共识效应（false consensus effect）”。如果不能采取强有力的保护措施，我们会进入到一个日益极化和分裂的威权社会。

根据发表在《JCO Oncology Practice》期刊上的一项研究，服用 GLP-1 减肥药与女性更低的乳腺癌风险相关。对逾 11 万名年龄在 45 岁至 80 岁之间的回顾性分析发现，服用 GLP-1 药物的女性患乳腺癌的风险比未服用的女性低约 30%。这是一项观察性研究，GLP-1 减肥药与降低乳腺癌发病率之间是否存在关联还有待进一步研究。GLP-1 药物模拟了人体天然激素 glucagon‑like peptide‑1，该激素有助于调节血糖和食欲。GLP-1 药物最初被用于减肥，如今被发现还可能有助于预防癌症。研究人员指出，GLP-1 药物会影响许多与癌症发展相关的靶点和通路，因此值得进一步展开研究。

在索尼之后，微软重新加强游戏独占策略。索尼停止将其第一方 3A 游戏移植到 PC 平台，而微软的 Xbox 平台此前开始将其 3A 游戏移植到索尼的 PS 平台，但新 CEO Asha Sharma 上任之后，她改变了这一做法，强调 Xbox 平台“必须有独占内容和服务”。在周日的 XBOX Games Showcase 上，微软宣布其《Gears of War: E-Day》和《Clockwork Revolution》将是 Xbox 独占，并且不是限时独占。微软表示，此前宣布支持 PS5 的游戏如《Halo: Campaign Evolved》和《Forza Horizon 6》仍然会按计划推出。

领取规则：未注册过开发者的用户可以通过如下链接免费选择一门 DLI 在线自主培训的付费课程，配套云端实验环境和可获得 NVIDIA 培训证书。每位用户（每个邮箱账号）仅可选择一门。 https://developer.nvidia.cn/login?ncid=ref-dev-557858&sfdcid=Zhiding 目前可选课程包括 7 门英文课，5 门中文课，目前课程列表如下，随时下架，免费名额有限，先到先得：

在 CES 2026 上芬兰初创企业 Donut Lab 宣称其研发出一款能量密度达 400Wh/kg、循环寿命 10 万次、5 分钟即可充满电，并且在 -30℃-100℃ 的温度范围内，仍能保持 99% 以上容量的固态电池。由 20 多位业内独立专家开展的调查证实，全固态电池系造假，实为普通锂离子电池。证据包括：其电压曲线与现有液态高镍三元锂离子电池特征完全吻合；电池充电时离子会嵌入负极材料，使电池产生规律性膨胀，采用石墨负极的电池，在电量充至 50% 至 70% 区间时，膨胀曲线会出现一处明显拐点，这是离子在石墨层状结构中重新排布所形成的独有特征，Donut Lab 的这款电池，曲线中恰好出现了这一标志性拐点。电池的实际能量密度约为 298Wh/kg，属于当前三元锂电池的正常水平。调查团队发现，Donut Lab 之所以如此欺诈宣传，核心是为了从资本市场获利，在该公司 1300 余名股东中，逾 900 人持股不超过 50 股，单笔投入估计在 3000 至 23000 美元之间。

美国总生育率自 2007 年以来下降了 22%，这一下降趋势难以用经济状况、避孕、住房或托儿成本等进行解释，智能手机的普及被认为与生育率下降相关，2007 年就是第一代 iPhone 发布之年。在美国，从 2007 年 6 月到 2011 年 2 月，iPhone 仅在 AT&T 网络销售。这就是为研究智能手机对生育影响提供了一个天然的实验环境。研究人员利用 AT&T 移动网络覆盖范围的差异去识别 iPhone 对生育的影响。结果显示，iPhone 的普及使 15-19 岁女性的生育率下降了 4.5%-8.0%，20-24 岁女性的生育率下降了 3.2%-6.6%。iPhone 的普及加速了 30 岁以下女性生育率的下降，抑制了 30 岁以上女性生育率的上升。研究人员称，iPhone 的普及能解释 15-44 岁女性总体生育率下降的 33%-52%。原因被认为是智能手机减少了线下的面对面人际交往，增加了色情内容的使用，降低了性生活频率。

本周一编号为 B 1067 的 Falcon 9 火箭第一级完成了第 35 次发射任务，在将 29 颗 Starlink 卫星送入轨道之后成功着陆在无人驳船 A Shortfall of Gravitas 上。B 1067 是 SpaceX 重复使用次数最多的火箭第一级，服役了五年多时间，曾在一个月内执行了两次发射，SpaceX 的目标是火箭第一级能重复使用 40 次，B 1067 正接近这一目标。B 1067 发射次数比竞争对手联合发射联盟（ULA）过去五年的总发射次数还要多（ULA 完成了 29 次发射）。

最新发布的《世界海洋评估》报告警告，气候变化、污染、过度开发等多重压力正在持续削弱海洋健康，而海洋的未来与人类的未来紧密相连。报告指出，即便远离海岸，海洋依然深刻影响着每个人的生活。海洋吸收了地球大部分额外热量和温室气体，在减缓气候变化方面发挥关键作用。海洋还为全球数十亿人口提供食物、氧气和药物资源，并支撑着全球贸易、旅游业和大量就业岗位。报告强调，海洋环境恶化不仅会影响沿海地区，还将波及粮食安全、供应链稳定以及全球经济发展。评估显示，海洋变暖和海平面上升正在加速。由于冰盖融化和海水热膨胀，全球海平面上升速度已从 2015 年前每年最高 1.9 毫米增加到 2023 年的 4.3 毫米。北极升温速度达到全球平均水平的四倍。与此同时，海洋缺氧区面积已扩大至约 450 万平方公里，大量海洋生物生存空间受到挤压。自 1970 年代以来，加勒比地区约 80% 的珊瑚礁已经消失。如果全球升温超过工业化前水平 1.5 摄氏度，全球 90% 的珊瑚礁可能面临消失风险。报告显示，每年约有 5200 万吨塑料垃圾进入海洋，形成约 24 万亿个微塑料颗粒，已影响 4000 多种海洋生物。

微软下线了数十个托管在 GitHub 上的开源项目，原因是安全公司发现这些项目被入侵植入了窃取密码等敏感凭证的恶意代码。微软在一份声明中表示，它正对此展开调查，部分下线的项目在审核之后已恢复上线，作为调查的一部分，它通知了下载受影响项目的一小部分用户。调查显示，至少 73 个项目受到影响。这是过去一个月微软第二次开源项目库遭到入侵。

气候中心（Climate Central）发布分析结果称，美加墨世界杯比赛将遭遇全球变暖带来的高温天气，球员表现受到负面影响的可能性升高。此次世界杯将在 16 个场馆共举行 104 场比赛，其中 97 场比赛可能出现导致恢复能力等下降的炎热天气。不仅球员的健康风险上升，比赛的质量也可能受到影响。本届世界杯由美国、墨西哥、加拿大共同主办，赛程为当地时间 6 月 11 日至 7 月 19 日。基于以往数据对赛事期间气温的预测显示，有较高概率在 97 场比赛中出现超过 28 度的气温。此前研究指出，超过 28 度会对球员的跑动速度、距离与恢复时间产生影响，也会影响到战术和比赛风格。