OrangeBot.AI Digest — 2025-11-24

🎯 告别信息过载，AI 助你看懂新闻资讯热点，简单的舆情监控分析 - 多平台热点聚合+基于 MCP 的AI分析工具。监控35个平台（抖音、知乎、B站、华尔街见闻、财联社等），智能筛选+自动推送+AI对话分析（用自然语言深度挖掘新闻：趋势追踪、情感分析、相似检索等13种工具）。支持企业微信/个人微信/飞书/钉钉/Telegram/邮件/ntfy/bark 推送，30秒网页部署，1分钟手机通知，无需编程。支持Docker部署⭐ 让算法为你服务，用AI理解热点

An open-source, code-first Go toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.

所有小初高、大学PDF教材。

[Support 0.49.x]（Reset Cursor AI MachineID & Bypass Higher Token Limit） Cursor Ai ，自动重置机器ID ， 免费升级使用Pro功能: You've reached your trial request limit. / Too many free trial accounts used on this machine. Please upgrade to pro. We have this limit in place to prevent abuse. Please let us know if you believe this is a mistake.

Node Version Manager - POSIX-compliant bash script to manage multiple active node.js versions

The Cloud Native Application Proxy

[EMNLP2025] "LightRAG: Simple and Fast Retrieval-Augmented Generation"

A list of open source games.

verl: Volcano Engine Reinforcement Learning for LLMs

Open-Source Memory Engine for LLMs, AI Agents & Multi-Agent Systems

Curated coding interview preparation materials for busy software engineers

Send a phone call from AI agent, in an API call. Or, directly call the bot from the configured phone number!

Run Windows Subsystem For Android on your Windows 10 and Windows 11 PC using prebuilt binaries with Google Play Store (MindTheGapps) and/or Magisk or KernelSU (root solutions) built in.

Powerful web graphics runtime built on WebGL, WebGPU, WebXR and glTF

Collection of publicly available IPTV channels from all over the world

Recent advancements in large reasoning models have fueled growing interest in extending such capabilities to multimodal domains. However, despite notable progress in visual reasoning, the lack of transparent and reproducible data curation and training strategies remains a major barrier to scalable research. In this work, we introduce OpenMMReasoner, a fully transparent two-stage recipe for multimodal reasoning spanning supervised fine-tuning (SFT) and reinforcement learning (RL). In the SFT stage, we construct an 874K-sample cold-start dataset with rigorous step-by-step validation, providing a strong foundation for reasoning capabilities. The subsequent RL stage leverages a 74K-sample dataset across diverse domains to further sharpen and stabilize these abilities, resulting in a more robust and efficient learning process. Extensive evaluations demonstrate that our training recipe not only surpasses strong baselines but also highlights the critical role of data quality and training design in shaping multimodal reasoning performance. Notably, our method achieves a 11.6% improvement over the Qwen2.5-VL-7B-Instruct baseline across nine multimodal reasoning benchmarks, establishing a solid empirical foundation for future large-scale multimodal reasoning research. We open-sourced all our codes, pipeline, and data at https://github.com/EvolvingLMMs-Lab/OpenMMReasoner.

Intrinsic dimension (ID) is an important tool in modern LLM analysis, informing studies of training dynamics, scaling behavior, and dataset structure, yet its textual determinants remain underexplored. We provide the first comprehensive study grounding ID in interpretable text properties through cross-encoder analysis, linguistic features, and sparse autoencoders (SAEs). In this work, we establish three key findings. First, ID is complementary to entropy-based metrics: after controlling for length, the two are uncorrelated, with ID capturing geometric complexity orthogonal to prediction quality. Second, ID exhibits robust genre stratification: scientific prose shows low ID (~8), encyclopedic content medium ID (~9), and creative/opinion writing high ID (~10.5) across all models tested. This reveals that contemporary LLMs find scientific text "representationally simple" while fiction requires additional degrees of freedom. Third, using SAEs, we identify causal features: scientific signals (formal tone, report templates, statistics) reduce ID; humanized signals (personalization, emotion, narrative) increase it. Steering experiments confirm these effects are causal. Thus, for contemporary models, scientific writing appears comparatively "easy", whereas fiction, opinion, and affect add representational degrees of freedom. Our multi-faceted analysis provides practical guidance for the proper use of ID and the sound interpretation of ID-based results.

Current research on agentic visual reasoning enables deep multimodal understanding but primarily focuses on image manipulation tools, leaving a gap toward more general-purpose agentic models. In this work, we revisit the geolocalization task, which requires not only nuanced visual grounding but also web search to confirm or refine hypotheses during reasoning. Since existing geolocalization benchmarks fail to meet the need for high-resolution imagery and the localization challenge for deep agentic reasoning, we curate GeoBench, a benchmark that includes photos and panoramas from around the world, along with a subset of satellite images of different cities to rigorously evaluate the geolocalization ability of agentic models. We also propose GeoVista, an agentic model that seamlessly integrates tool invocation within the reasoning loop, including an image-zoom-in tool to magnify regions of interest and a web-search tool to retrieve related web information. We develop a complete training pipeline for it, including a cold-start supervised fine-tuning (SFT) stage to learn reasoning patterns and tool-use priors, followed by a reinforcement learning (RL) stage to further enhance reasoning ability. We adopt a hierarchical reward to leverage multi-level geographical information and improve overall geolocalization performance. Experimental results show that GeoVista surpasses other open-source agentic models on the geolocalization task greatly and achieves performance comparable to closed-source models such as Gemini-2.5-flash and GPT-5 on most metrics.

We present Segment Anything Model (SAM) 3, a unified model that detects, segments, and tracks objects in images and videos based on concept prompts, which we define as either short noun phrases (e.g., "yellow school bus"), image exemplars, or a combination of both. Promptable Concept Segmentation (PCS) takes such prompts and returns segmentation masks and unique identities for all matching object instances. To advance PCS, we build a scalable data engine that produces a high-quality dataset with 4M unique concept labels, including hard negatives, across images and videos. Our model consists of an image-level detector and a memory-based video tracker that share a single backbone. Recognition and localization are decoupled with a presence head, which boosts detection accuracy. SAM 3 doubles the accuracy of existing systems in both image and video PCS, and improves previous SAM capabilities on visual segmentation tasks. We open source SAM 3 along with our new Segment Anything with Concepts (SA-Co) benchmark for promptable concept segmentation.

Recent advancements in LLM-powered agents have demonstrated significant potential in generating human-like responses; however, they continue to face challenges in maintaining long-term interactions within complex environments, primarily due to limitations in contextual consistency and dynamic personalization. Existing memory systems often depend on semantic grouping prior to retrieval, which can overlook semantically irrelevant yet critical user information and introduce retrieval noise. In this report, we propose the initial design of O-Mem, a novel memory framework based on active user profiling that dynamically extracts and updates user characteristics and event records from their proactive interactions with agents. O-Mem supports hierarchical retrieval of persona attributes and topic-related context, enabling more adaptive and coherent personalized responses. O-Mem achieves 51.67% on the public LoCoMo benchmark, a nearly 3% improvement upon LangMem,the previous state-of-the-art, and it achieves 62.99% on PERSONAMEM, a 3.5% improvement upon A-Mem,the previous state-of-the-art. O-Mem also boosts token and interaction response time efficiency compared to previous memory frameworks. Our work opens up promising directions for developing efficient and human-like personalized AI assistants in the future.

This study presents PARROT (Persuasion and Agreement Robustness Rating of Output Truth), a robustness focused framework designed to measure the degradation in accuracy that occurs under social pressure exerted on users through authority and persuasion in large language models (LLMs) the phenomenon of sycophancy (excessive conformity). PARROT (i) isolates causal effects by comparing the neutral version of the same question with an authoritatively false version using a double-blind evaluation, (ii) quantifies confidence shifts toward the correct and imposed false responses using log-likelihood-based calibration tracking, and (iii) systematically classifies failure modes (e.g., robust correct, sycophantic agreement, reinforced error, stubborn error, self-correction, etc.) using an eight-state behavioral taxonomy. We evaluated 22 models using 1,302 MMLU-style multiple-choice questions across 13 domains and domain-specific authority templates. Findings show marked heterogeneity: advanced models (e.g., GPT-5, GPT-4.1, Claude Sonnet 4.5) exhibit low "follow rates" (leq 11%, GPT-5: 4\%) and minimal accuracy loss, while older/smaller models show severe epistemic collapse (GPT-4: 80\%, Qwen 2.5-1.5B: 94\%). The danger is not limited to response changes; weak models reduce confidence in the correct response while increasing confidence in the imposed incorrect response. While international law and global knowledge at the domain level exhibit high fragility, elementary mathematics is relatively resilient. Consequently, we argue that the goal of "resistance to overfitting pressure" should be addressed as a primary objective alongside accuracy, harm avoidance, and privacy for safe deployment in the real world.

We introduce RynnVLA-002, a unified Vision-Language-Action (VLA) and world model. The world model leverages action and visual inputs to predict future image states, learning the underlying physics of the environment to refine action generation. Conversely, the VLA model produces subsequent actions from image observations, enhancing visual understanding and supporting the world model's image generation. The unified framework of RynnVLA-002 enables joint learning of environmental dynamics and action planning. Our experiments show that RynnVLA-002 surpasses individual VLA and world models, demonstrating their mutual enhancement. We evaluate RynnVLA-002 in both simulation and real-world robot tasks. RynnVLA-002 achieves 97.4% success rate on the LIBERO simulation benchmark without pretraining, while in real-world LeRobot experiments, its integrated world model boosts the overall success rate by 50%.

Step-by-step painting tutorials are vital for learning artistic techniques, but existing video resources (e.g., YouTube) lack interactivity and personalization. While recent generative models have advanced artistic image synthesis, they struggle to generalize across media and often show temporal or structural inconsistencies, hindering faithful reproduction of human creative workflows. To address this, we propose a unified framework for multi-media painting process generation with a semantics-driven style control mechanism that embeds multiple media into a diffusion models conditional space and uses cross-medium style augmentation. This enables consistent texture evolution and process transfer across styles. A reverse-painting training strategy further ensures smooth, human-aligned generation. We also build a large-scale dataset of real painting processes and evaluate cross-media consistency, temporal coherence, and final-image fidelity, achieving strong results on LPIPS, DINO, and CLIP metrics. Finally, our Perceptual Distance Profile (PDP) curve quantitatively models the creative sequence, i.e., composition, color blocking, and detail refinement, mirroring human artistic progression.

We introduce WorldGen, a system that enables the automatic creation of large-scale, interactive 3D worlds directly from text prompts. Our approach transforms natural language descriptions into traversable, fully textured environments that can be immediately explored or edited within standard game engines. By combining LLM-driven scene layout reasoning, procedural generation, diffusion-based 3D generation, and object-aware scene decomposition, WorldGen bridges the gap between creative intent and functional virtual spaces, allowing creators to design coherent, navigable worlds without manual modeling or specialized 3D expertise. The system is fully modular and supports fine-grained control over layout, scale, and style, producing worlds that are geometrically consistent, visually rich, and efficient to render in real time. This work represents a step towards accessible, generative world-building at scale, advancing the frontier of 3D generative AI for applications in gaming, simulation, and immersive social environments.

Despite the remarkable success of Vision-Language Models (VLMs), their performance on a range of complex visual tasks is often hindered by a "visual processing bottleneck": a propensity to lose grounding in visual evidence and exhibit a deficit in contextualized visual experience during prolonged generation. Drawing inspiration from human cognitive memory theory, which distinguishes short-term visually-dominant memory and long-term semantically-dominant memory, we propose VisMem, a cognitively-aligned framework that equips VLMs with dynamic latent vision memories, a short-term module for fine-grained perceptual retention and a long-term module for abstract semantic consolidation. These memories are seamlessly invoked during inference, allowing VLMs to maintain both perceptual fidelity and semantic consistency across thinking and generation. Extensive experiments across diverse visual benchmarks for understanding, reasoning, and generation reveal that VisMem delivers a significant average performance boost of 11.8% relative to the vanilla model and outperforms all counterparts, establishing a new paradigm for latent-space memory enhancement. The code will be available: https://github.com/YU-deep/VisMem.git.

Recent advances in Vision-Language-Action (VLA) models demonstrate that visual signals can effectively complement sparse action supervisions. However, letting VLA directly predict high-dimensional visual states can distribute model capacity and incur prohibitive training cost, while compressing visual states into more compact supervisory signals inevitably incurs information bottlenecks. Moreover, existing methods often suffer from poor comprehension and reasoning capabilities due to the neglect of language supervision. This paper introduces Mantis, a novel framework featuring a Disentangled Visual Foresight (DVF) to tackle these issues. Specifically, Mantis decouples visual foresight prediction from the backbone with the combination of meta queries and a diffusion Transformer (DiT) head. With the current visual state provided to the DiT via a residual connection, a simple next-state prediction objective enables the meta queries to automatically capture the latent actions that delineate the visual trajectory, and hence boost the learning of explicit actions. The disentanglement reduces the burden of the VLA backbone, enabling it to maintain comprehension and reasoning capabilities through language supervision. Empirically, pretrained on human manipulation videos, robot demonstrations, and image-text pairs, Mantis achieves a 96.7% success rate on LIBERO benchmark after fine-tuning, surpassing powerful baselines while exhibiting high convergence speed. Real-world evaluations show that Mantis outperforms π_{0.5}, a leading open-source VLA model, particularly in instruction-following capability, generalization to unseen instructions, and reasoning ability. Code and weights are released to support the open-source community.

We address the task of multi-view image editing from sparse input views, where the inputs can be seen as a mix of images capturing the scene from different viewpoints. The goal is to modify the scene according to a textual instruction while preserving consistency across all views. Existing methods, based on per-scene neural fields or temporal attention mechanisms, struggle in this setting, often producing artifacts and incoherent edits. We propose InstructMix2Mix (I-Mix2Mix), a framework that distills the editing capabilities of a 2D diffusion model into a pretrained multi-view diffusion model, leveraging its data-driven 3D prior for cross-view consistency. A key contribution is replacing the conventional neural field consolidator in Score Distillation Sampling (SDS) with a multi-view diffusion student, which requires novel adaptations: incremental student updates across timesteps, a specialized teacher noise scheduler to prevent degeneration, and an attention modification that enhances cross-view coherence without additional cost. Experiments demonstrate that I-Mix2Mix significantly improves multi-view consistency while maintaining high per-frame edit quality.

Modeling genomic sequences faces two unsolved challenges: the information density varies widely across different regions, while there is no clearly defined minimum vocabulary unit. Relying on either four primitive bases or independently designed DNA tokenizers, existing approaches with naive masked language modeling pre-training often fail to adapt to the varying complexities of genomic sequences. Leveraging Token Merging techniques, this paper introduces a hierarchical architecture that jointly optimizes a dynamic genomic tokenizer and latent Transformers with context-aware pre-training tasks. As for network structures, the tokenization module automatically chunks adjacent bases into words by stacking multiple layers of the differentiable token merging blocks with local-window constraints, then a Latent Encoder captures the global context of these merged words by full-attention blocks. Symmetrically employing a Latent Decoder and a Local Decoder, MergeDNA learns with two pre-training tasks: Merged Token Reconstruction simultaneously trains the dynamic tokenization module and adaptively filters important tokens, while Adaptive Masked Token Modeling learns to predict these filtered tokens to capture informative contents. Extensive experiments show that MergeDNA achieves superior performance on three popular DNA benchmarks and several multi-omics tasks with fine-tuning or zero-shot evaluation, outperforming typical tokenization methods and large-scale DNA foundation models.

With the rapid development of Large Language Models (LLMs), AI agents have demonstrated increasing proficiency in scientific tasks, ranging from hypothesis generation and experimental design to manuscript writing. Such agent systems are commonly referred to as "AI Scientists." However, existing AI Scientists predominantly formulate scientific discovery as a standalone search or optimization problem, overlooking the fact that scientific research is inherently a social and collaborative endeavor. Real-world science relies on a complex scientific infrastructure composed of collaborative mechanisms, contribution attribution, peer review, and structured scientific knowledge networks. Due to the lack of modeling for these critical dimensions, current systems struggle to establish a genuine research ecosystem or interact deeply with the human scientific community. To bridge this gap, we introduce OmniScientist, a framework that explicitly encodes the underlying mechanisms of human research into the AI scientific workflow. OmniScientist not only achieves end-to-end automation across data foundation, literature review, research ideation, experiment automation, scientific writing, and peer review, but also provides comprehensive infrastructural support by simulating the human scientific system, comprising: (1) a structured knowledge system built upon citation networks and conceptual correlations; (2) a collaborative research protocol (OSP), which enables seamless multi-agent collaboration and human researcher participation; and (3) an open evaluation platform (ScienceArena) based on blind pairwise user voting and Elo rankings. This infrastructure empowers agents to not only comprehend and leverage human knowledge systems but also to collaborate and co-evolve, fostering a sustainable and scalable innovation ecosystem.

Visual Autoregressive (VAR) models have recently garnered significant attention for their innovative next-scale prediction paradigm, offering notable advantages in both inference efficiency and image quality compared to traditional multi-step autoregressive (AR) and diffusion models. However, despite their efficiency, VAR models often suffer from the diversity collapse i.e., a reduction in output variability, analogous to that observed in few-step distilled diffusion models. In this paper, we introduce DiverseVAR, a simple yet effective approach that restores the generative diversity of VAR models without requiring any additional training. Our analysis reveals the pivotal component of the feature map as a key factor governing diversity formation at early scales. By suppressing the pivotal component in the model input and amplifying it in the model output, DiverseVAR effectively unlocks the inherent generative potential of VAR models while preserving high-fidelity synthesis. Empirical results demonstrate that our approach substantially enhances generative diversity with only neglectable performance influences. Our code will be publicly released at https://github.com/wangtong627/DiverseVAR.

研究公司 Alinea Analytics 估计，Valve 在 2025 年的年收入在 160-170 亿美元之间。而 Valve 大约有 350 名员工，意味着每名员工产生约 5000 万美元的收入。Valve 是一家私营公司，它无需公开披露收入等数据。它的员工数据还是因为诉讼而泄露的。Valve 为员工提供了丰厚的薪酬，根据泄露的数据，它在员工工资上花了近 4.5 亿美元，平均每位员工逾 130 万美元。

根据发表在《Mental Health Research》期刊上的一项研究，打扮成蝙蝠侠可能会在公共场合促进亲社会行为。意大利研究人员在米兰地铁展开了研究，观察了 138 次乘车。对照组由一名装扮成孕妇的女性与一位观察员组成，她们一起登上列车。实验组成员打扮成蝙蝠侠登上列车。结果显示，当蝙蝠侠出现时，乘客让座的概率显著高于对照组。值得注意的是，实验组中 44% 的让座者表示并没有看到蝙蝠侠。这表明意外事件能促进亲社会行为，这项发现对于在公共场合鼓励善意行为有重要意义。

从 Git 3.0 起，默认分支名将是 main 而不是 master。关于 main 和 master 名字的争论可以追溯到 2020 年，而 GitHub 早在 2020 年 10 月 1 日将新建代码库的默认分支名改为 main。Git 3.0 预计会在 2026 年底左右发布，主要变化包括：默认哈希函数从 SHA-1 改为 SHA-256 以提高安全性；改变默认存储格式以更好支持 macOS 和 Windows 并提升性能；更正式的将 Rust 集成到 Git 自身构建流程中。

看不见的微塑料通过空气扩散到全球。早稻田大学环境化学教授 Hiroshi Okochi 称，最近的研究表明空气传播的塑料污染正以惊人速度扩散。空气传播的微塑料直径小于 2.5 微米。Okochi 团队在 2023 年发表的一项研究发现，富士山顶云层中的水每升含有 6.7 个微塑料颗粒。德国和瑞士的团队报告，他们在北极每升雪中发现了逾万个微塑料颗粒。这些微塑料可能是通过空气传播随雪沉积。尽管在人体各部位都发现微塑料，但目前尚不清楚空气中的塑料颗粒对健康的影响。1 微米或更小的通过空气传播的塑料颗粒被认为能到达肺泡。英国一项研究表明，在 13 名接受肺部手术的患者的肺组织样本中，有 11 份检测到了微塑料。其中肺下部的微塑料含量最高。人每天呼吸超过 2 万次，一生累计呼吸 6-7 亿次。Okochi 表示人类不可避免的会吸入空气中的微塑料，但因为看不见所以也丝毫不知。

马斯克（Elon Musk）旗下的社交媒体 X/Twitter 开始展示账号注册的地理位置，如果该账号使用了 VPN 隐藏 IP 它还会提示可能使用了 VPN。该功能在上线之后一度下线，之后又恢复上线。地理位置信息显示很多政治网红的账号其实都是在美国之外运营的。MAGA NATION 有逾 39.2 万粉丝，其运营地点位于东欧；Dark Maga 有逾 1.5 万粉丝，其运营地点位于泰国；MAGA Scope 有逾 5.1 万粉丝，其运营地点位于尼日利亚；America First 有逾 6.7 万粉丝，其运营地点位于孟加拉国。反 MAGA 账号 Ron Smith 有逾 5.2 万名粉丝，其运营地点位于肯尼亚；Republicans Against Trump 有逾 97 万粉丝，其运营地点位于奥地利，目前使用美国 IP 的 VPN 隐藏原 IP。

2023 年 Google Chrome 移除了对实验性的 JPEG-XL 图像格式的支持。JPEG-XL 是免专利新的图像格式。Google 此举引发了很多争议，因为 Chrome/Chromium 占据了九成市场份额，它是 Web 标准事实上的仲裁者。到了 2025 年事情有了戏剧性转变。Google 开发者 Rick Byers 表示考虑恢复支持 JPEG-XL，预计将使用 JPEG-XL 的 Rust 语言实现。Google 开发者称，Safari 加入了对 JPEG-XL 支持，Firefox 也表明了立场，PDF 也准备添加 JPEG-XL 支持。Chromium 要默认启用 JPEG XL 解码器，需要有长期维护的承诺，满足这些条件的话将会恢复支持。

美国疾病控制与预防中心（CDC）的科学家近日已接到逐步停止所有猴子研究工作的指令。这将导致约 200 只恒河猴和豚尾猴参与的研究工作停止。这些猴子曾被用于艾滋病、肝炎和其他传染病研究。目前它们前路未卜，其中一部分可能被转移到灵长类动物保护区，另一部分可能会被安乐死。此举将是美国政府机构首次终止其内部的非人灵长类动物研究项目。多年来一直致力于推动政府终止对动物研究支持的美国非营利组织“白大褂废物项目(White Coat Waste Project)”对这上述决定表示欢迎。而生物医学科学家则警告称，此举将是一个重大错误。他们表示，CDC 的猴子研究项目对于艾滋病病毒暴露前预防药物研发至关重要，这种预防策略已经大幅降低全球艾滋病感染率。

在 Linux 操作系统上 Firefox 浏览器将所有文件都储存在 ~/.mozilla 目录下。2004 年 9 月递交的一份 bug 报告呼吁 Firefox 遵守 Freedesktop.org 的 XDG Base Directory Specification 目录标准：将配置文件、缓存数据、用户数据等储存在不同目录，如 ~/.config 和 ~/.local/share。21 年后，Firefox 终于解决了该 bug，从 Firefox 147 起它将支持 XDG Base Directory Specification 目录标准。

总部位于美国华盛顿 Bellevue 的密码学会 International Association of Cryptologic Research（IACR）在全世界有数千名会员，该组织于 10 月 17 日至 11 月 16 日之间举行了包括主席在内的多个领导职位的选举，使用名为 Helios 的电子投票系统。该系统对每张选票进行加密，允许投票者追踪自己投的票。投票结果使用三个密钥进行解密，这三个密钥掌握在三位选举委员会成员手中。然而问题是其中一人——Google 的 Moti Yung 的密钥丢了，导致结果无法解密。密码学会表示这一轮投票作废，新一轮投票将于 11 月 21 日至 12 月 20 日举行。该组织同时表示 Moti Yung 已经辞去了选举委员会成员职位。IACR 表示为避免再次出现类似的问题，他们将放宽密钥使用要求，采用三分之二密钥使用门槛。

三星内存芯片价格自 9 月以来飙升 60%。DRAM 价格上涨凸显了 AI 数据中心激增的需求给全球供应链带来的巨大压力。作为全球最大的内存芯片制造商，三星大幅提高了高密度服务器 DRAM 模块的合同价格。内存芯片价格上涨的影响预计会持续到 2026 年。三星 32 GB DDR5 内存条的合同价格从 9 月的约 149 美元上涨至 11 月的约 239 美元，两个月内涨幅超过 60%。其它容量内存条如 16GB 和 128GB 内存条，价格上涨了 40% 到 50%，而 64GB 和 96GB 内存条的涨幅超过 30%。此次价格飙升的主要催化剂是 AI 基础设施的爆炸式增长。三星及其竞争对手 SK 海力士和美光科技已将大部分产能转向为 AI 服务器供应内存芯片，减少了传统内存芯片的产能。由于库存不足且产能接近饱和，2026 年底前，内存芯片价格不太可能出现回调。

在日益加剧的生态危机和水资源严重短缺的打击下，伊朗总统 Masoud Pezeshkian 周四表示德黑兰已经无法承担首都之职，伊朗别无选择只能迁都。伊朗官员考虑将首都迁至南部沿海地区。但专家表示此举并不能改变近千万德黑兰居民的现状，他们正遭受数十年来供水量持续下降带来的后果。伊朗几个世纪以来多次迁都，这次是首次因为生态灾难而迁都。康奈尔大学社会科学家兼城市规划师 Linda Shi 表示：气候变化并不是造成这一情况的原因，但它却是一个方便的借口，可用于逃避糟糕政治决策的责任。至少从 2008 年起，科学家就警告，伊朗城市和农业无节制抽取地下水正迅速耗尽该国的蓄水层。蓄水层每年损失约 17 亿立方米的水。气候变化确实是方便的借口。

开源邮件客户端 Thunderbird 项目开始在生产环境测试付费服务 Thunderbird Pro。该服务为每月 9 美元，包括了邮件托管、Send 加密文件共享和 Appointment 日程安排。用户支付 9 美元可获得：30 GB 邮件储存、300 GB Send 储存，15 个 Email 地址以及 3 个自定义域名。有很多人认为该服务定价过高。

英国网络与特种作战司令部副司令 Sir Tom Copinger-Symes 将军表示，乌克兰战争证明了精通游戏的士兵的价值，在这场战争中，遥控无人机至关重要。英国国防部周五宣布了与国防相关的电竞比赛 International Defence Esports Games(IDEG)，各国的未来网络战士将同台竞技。除了体验热门游戏《使命召唤》外，IDEG 参与者还将参加无人机模拟游戏 VelociDrone 的比赛。游戏模拟了乌克兰战场常见场景，该游戏正被用于训练英国的无人机操作人员。英国国防部表示，游戏改进了操作人员的目标定位精度和反应速度，以对俄罗斯军队造成致命效果。

Google 被发现默认启用了“在 Gmail、Chat 和 Meet 中启用智能功能”选项，默认启用“Google Workspace 智能功能”。根据 Google 的说明，“启用此设置即表示您同意 Gmail、Google Chat 和 Google Meet 使用您在这些产品中的内容和活动记录，为您提供智能功能和个性化体验”，以及“启用此设置即表示您允许 Google Workspace 使用您的 Workspace 内容和活动记录，从而在 Workspace 中为您提供个性化体验。Workspace 包含适用于企业和学校的多个应用，例如 Gmail、Chat、Meet、云端硬盘等。”用户需要手动选择才能取消这些 AI 功能，方法是 Gmail ——> 设置 ——> 查看所有设置——> 智能功能，去除“在 Gmail、Chat 和 Meet 中启用智能功能”勾选框，在重启应用之后，在智能功能下打开“管理 Google Workspace 智能功能设置”，去除所有勾选框。