OrangeBot.AI Digest — 2025-07-09

MCP Toolbox for Databases is an open source MCP server for databases.

🚀 High-performance distributed object storage for MinIO alternative.

Anthropic's Interactive Prompt Engineering Tutorial

🌐 WebAgent for Information Seeking bulit by Tongyi Lab: WebWalker & WebDancer & WebSailor https://arxiv.org/pdf/2507.02592

视频号、小程序、抖音、快手、小红书、直播流、m3u8、酷狗、QQ音乐等常见网络资源下载!

Repo for the Complete Agentic AI Engineering Course

Open source free capture HTTP(S) traffic software ProxyPin, supporting full platform systems

11 Lessons to Get Started Building AI Agents

A collection of MCP clients.

🚀 Strapi is the leading open-source headless CMS. It’s 100% JavaScript/TypeScript, fully customizable, and developer-first.

[CVPR'25 Oral] MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervision

MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba. Full multimodal LLM Android App:[MNN-LLM-Android](./apps/Android/MnnLlmChat/README.md). MNN TaoAvatar Android - Local 3D Avatar Intelligence: apps/Android/Mnn3dAvatar/README.md

Repo to accompany my mastering LLM engineering course

🌸 A command-line fuzzy finder

Animate your Figma designs in seconds with AI

Engineering manager’s copilot in Slack

A more visual way to experience Product Hunt

The most affordable docsend alternative

Classify, extract, enrich, and validate any file

Mood boards for photographers and visual creators

Learn a language by speaking

AI-powered design workflows, professional-grade control

Has AI made you smoothbrained? Test yo' self!

Let readers chat with your archive in their ChatGPT app

AI Voice notes made easy

Pepper runs your day. Better than you could

A smarter and visual way to clean your iCloud Photo Library

Design your financial flow

Shell history & file monitoring sync

Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, especially when guided by explicit chain-of-thought (CoT) reasoning that verbalizes intermediate steps. While CoT improves both interpretability and accuracy, its dependence on natural language reasoning limits the model's expressive bandwidth. Latent reasoning tackles this bottleneck by performing multi-step inference entirely in the model's continuous hidden state, eliminating token-level supervision. To advance latent reasoning research, this survey provides a comprehensive overview of the emerging field of latent reasoning. We begin by examining the foundational role of neural network layers as the computational substrate for reasoning, highlighting how hierarchical representations support complex transformations. Next, we explore diverse latent reasoning methodologies, including activation-based recurrence, hidden state propagation, and fine-tuning strategies that compress or internalize explicit reasoning traces. Finally, we discuss advanced paradigms such as infinite-depth latent reasoning via masked diffusion models, which enable globally consistent and reversible reasoning processes. By unifying these perspectives, we aim to clarify the conceptual landscape of latent reasoning and chart future directions for research at the frontier of LLM cognition. An associated GitHub repository collecting the latest papers and repos is available at: https://github.com/multimodal-art-projection/LatentCoT-Horizon/.

Low-Rank Adaptation (LoRA) has significantly advanced parameter-efficient fine-tuning of large pretrained models. LoRA augments the pre-trained weights of a model by adding the product of two smaller matrices that together form a low-rank matrix update. Recent research has shown that scale disparities between these two matrices often cause unstable training dynamics, leading to suboptimal performance. In this paper, we propose SingLoRA, which reformulates low-rank adaptation by learning the weights update as a decomposition of a single low-rank matrix multiplied by its transpose. This simple design inherently removes inter-matrix scale conflicts, ensuring stable optimization, and roughly halves the parameter count. We analyze SingLoRA within the infinite-width neural network framework, showing that it guarantees stable feature learning by construction. Extensive experiments on multiple tasks validate these benefits. In common sense reasoning, fine-tuning LLama 7B on MNLI with SingLoRA achieves 91.3% accuracy - surpassing LoRA (89.1%) and LoRA+ (90.2%) - while using only 60% of their parameter budget. In image generation, fine-tuning Stable Diffusion with SingLoRA significantly improves image fidelity on DreamBooth, achieving a DINO similarity score of 0.151, compared to scores of 0.148 and 0.143 for DoRA and LoRA, respectively.

The creation of 3D assets with explicit, editable part structures is crucial for advancing interactive applications, yet most generative methods produce only monolithic shapes, limiting their utility. We introduce OmniPart, a novel framework for part-aware 3D object generation designed to achieve high semantic decoupling among components while maintaining robust structural cohesion. OmniPart uniquely decouples this complex task into two synergistic stages: (1) an autoregressive structure planning module generates a controllable, variable-length sequence of 3D part bounding boxes, critically guided by flexible 2D part masks that allow for intuitive control over part decomposition without requiring direct correspondences or semantic labels; and (2) a spatially-conditioned rectified flow model, efficiently adapted from a pre-trained holistic 3D generator, synthesizes all 3D parts simultaneously and consistently within the planned layout. Our approach supports user-defined part granularity, precise localization, and enables diverse downstream applications. Extensive experiments demonstrate that OmniPart achieves state-of-the-art performance, paving the way for more interpretable, editable, and versatile 3D content.

Translating natural language mathematical statements into formal, executable code is a fundamental challenge in automated theorem proving. While prior work has focused on generation and compilation success, little attention has been paid to the critic phase-the evaluation of whether generated formalizations truly capture the semantic intent of the original problem. In this paper, we introduce CriticLean, a novel critic-guided reinforcement learning framework that elevates the role of the critic from a passive validator to an active learning component. Specifically, first, we propose the CriticLeanGPT, trained via supervised fine-tuning and reinforcement learning, to rigorously assess the semantic fidelity of Lean 4 formalizations. Then, we introduce CriticLeanBench, a benchmark designed to measure models' ability to distinguish semantically correct from incorrect formalizations, and demonstrate that our trained CriticLeanGPT models can significantly outperform strong open- and closed-source baselines. Building on the CriticLean framework, we construct FineLeanCorpus, a dataset comprising over 285K problems that exhibits rich domain diversity, broad difficulty coverage, and high correctness based on human evaluation. Overall, our findings highlight that optimizing the critic phase is essential for producing reliable formalizations, and we hope our CriticLean will provide valuable insights for future advances in formal mathematical reasoning.

Vision-and-Language Navigation (VLN) in real-world settings requires agents to process continuous visual streams and generate actions with low latency grounded in language instructions. While Video-based Large Language Models (Video-LLMs) have driven recent progress, current VLN methods based on Video-LLM often face trade-offs among fine-grained visual understanding, long-term context modeling and computational efficiency. We introduce StreamVLN, a streaming VLN framework that employs a hybrid slow-fast context modeling strategy to support multi-modal reasoning over interleaved vision, language and action inputs. The fast-streaming dialogue context facilitates responsive action generation through a sliding-window of active dialogues, while the slow-updating memory context compresses historical visual states using a 3D-aware token pruning strategy. With this slow-fast design, StreamVLN achieves coherent multi-turn dialogue through efficient KV cache reuse, supporting long video streams with bounded context size and inference cost. Experiments on VLN-CE benchmarks demonstrate state-of-the-art performance with stable low latency, ensuring robustness and efficiency in real-world deployment. The project page is: https://streamvln.github.io/{https://streamvln.github.io/}.

Large language models (LLMs) excel at logical and algorithmic reasoning, yet their emotional intelligence (EQ) still lags far behind their cognitive prowess. While reinforcement learning from verifiable rewards (RLVR) has advanced in other domains, its application to dialogue-especially for emotional intelligence-remains underexplored. In this work, we introduce RLVER, the first end-to-end reinforcement learning framework that leverages verifiable emotion rewards from simulated users to cultivate higher-order empathetic abilities in LLMs. Within this framework, self-consistent affective simulated users engage in dialogue rollouts and produce deterministic emotion scores during conversations, serving as reward signals to guide the LLM's learning. Fine-tuning publicly available Qwen2.5-7B-Instruct model with PPO boosts its Sentient-Benchmark score from 13.3 to 79.2 while largely preserving mathematical and coding competence. Extensive experiments reveal that: (i) RLVER consistently improves multiple dialogue capabilities; (ii) Thinking and non-thinking models show distinct trends--thinking models excel in empathy and insight, while non-thinking models favor action; (iii) GRPO often yields stable gains, while PPO can push certain capabilities to a higher ceiling; (iv) More challenging environments are not always better-moderate ones can yield stronger outcomes. Our results show that RLVER is a practical route toward emotionally intelligent and broadly capable language agents.

Recent advances in video generation have shown remarkable progress in open-domain settings, yet medical video generation remains largely underexplored. Medical videos are critical for applications such as clinical training, education, and simulation, requiring not only high visual fidelity but also strict medical accuracy. However, current models often produce unrealistic or erroneous content when applied to medical prompts, largely due to the lack of large-scale, high-quality datasets tailored to the medical domain. To address this gap, we introduce MedVideoCap-55K, the first large-scale, diverse, and caption-rich dataset for medical video generation. It comprises over 55,000 curated clips spanning real-world medical scenarios, providing a strong foundation for training generalist medical video generation models. Built upon this dataset, we develop MedGen, which achieves leading performance among open-source models and rivals commercial systems across multiple benchmarks in both visual quality and medical accuracy. We hope our dataset and model can serve as a valuable resource and help catalyze further research in medical video generation. Our code and data is available at https://github.com/FreedomIntelligence/MedGen

Data scaling has driven remarkable success in foundation models for Natural Language Processing (NLP) and Computer Vision (CV), yet the principles of effective data scaling in robotic manipulation remain insufficiently understood. In this work, we investigate the nuanced role of data diversity in robot learning by examining three critical dimensions-task (what to do), embodiment (which robot to use), and expert (who demonstrates)-challenging the conventional intuition of "more diverse is better". Throughout extensive experiments on various robot platforms, we reveal that (1) task diversity proves more critical than per-task demonstration quantity, benefiting transfer from diverse pre-training tasks to novel downstream scenarios; (2) multi-embodiment pre-training data is optional for cross-embodiment transfer-models trained on high-quality single-embodiment data can efficiently transfer to different platforms, showing more desirable scaling property during fine-tuning than multi-embodiment pre-trained models; and (3) expert diversity, arising from individual operational preferences and stochastic variations in human demonstrations, can be confounding to policy learning, with velocity multimodality emerging as a key contributing factor. Based on this insight, we propose a distribution debiasing method to mitigate velocity ambiguity, the yielding GO-1-Pro achieves substantial performance gains of 15%, equivalent to using 2.5 times pre-training data. Collectively, these findings provide new perspectives and offer practical guidance on how to scale robotic manipulation datasets effectively.

Large language models (LLMs) have achieved remarkable progress in code generation, yet their true programming competence remains underexplored. We introduce the Code Triangle framework, which systematically evaluates LLMs across three fundamental dimensions: editorial analysis, code implementation, and test case generation. Through extensive experiments on competitive programming benchmarks, we reveal that while LLMs can form a self-consistent system across these dimensions, their solutions often lack the diversity and robustness of human programmers. We identify a significant distribution shift between model cognition and human expertise, with model errors tending to cluster due to training data biases and limited reasoning transfer. Our study demonstrates that incorporating human-generated editorials, solutions, and diverse test cases, as well as leveraging model mixtures, can substantially enhance both the performance and robustness of LLMs. Furthermore, we reveal both the consistency and inconsistency in the cognition of LLMs that may facilitate self-reflection and self-improvement, providing a potential direction for developing more powerful coding models.

Graphical user interface (GUI) agents autonomously operate across platforms (e.g., Linux) to complete tasks by interacting with visual elements. Specifically, a user instruction is decomposed into a sequence of action proposals, each corresponding to an interaction with the GUI. After each action, the agent observes the updated GUI environment to plan the next step. However, two main challenges arise: i) resolving ambiguity in task planning (i.e., the action proposal sequence), where selecting an appropriate plan is non-trivial, as many valid ones may exist; ii) accurately grounding actions in complex and high-resolution interfaces, i.e., precisely interacting with visual targets. This paper investigates the two aforementioned challenges with our GUI Test-time Scaling Agent, namely GTA1. First, to select the most appropriate action proposal, we introduce a test-time scaling method. At each step, we sample multiple candidate action proposals and leverage a judge model to evaluate and select the most suitable one. It trades off computation for better decision quality by concurrent sampling, shortening task execution steps, and improving overall performance. Second, we propose a model that achieves improved accuracy when grounding the selected action proposal to its corresponding visual elements. Our key insight is that reinforcement learning (RL) facilitates visual grounding through inherent objective alignments, rewarding successful clicks on interface elements. Experimentally, our method establishes state-of-the-art performance across diverse benchmarks. For example, GTA1-7B achieves 50.1%, 92.4%, and 67.7% accuracies on Screenspot-Pro, Screenspot-V2, and OSWorld-G, respectively. When paired with a planner applying our test-time scaling strategy, it exhibits state-of-the-art agentic performance (e.g., 45.2% task success rate on OSWorld). We open-source our code and models here.

We introduce Nile-Chat-4B, 3x4B-A6B, and 12B, a collection of LLMs for Egyptian dialect, uniquely designed to understand and generate texts written in both Arabic and Latin scripts. Specifically, with Nile-Chat-3x4B-A6B, we introduce a novel language adaptation approach by leveraging the Branch-Train-MiX strategy to merge script-specialized experts, into a single MoE model. Our Nile-Chat models significantly outperform leading multilingual and Arabic LLMs, such as LLaMa, Jais, and ALLaM, on our newly introduced Egyptian evaluation benchmarks, which span both understanding and generative tasks. Notably, our 12B model yields a 14.4% performance gain over Qwen2.5-14B-Instruct on Latin-script benchmarks. All our resources are publicly available. We believe this work presents a comprehensive methodology for adapting LLMs to dual-script languages, addressing an often overlooked aspect in modern LLM development.

Large Language Models (LLMs) have recently been applied to reranking tasks in information retrieval, achieving strong performance. However, their high computational demands often hinder practical deployment. Existing studies evaluate the efficiency of LLM-based rerankers using proxy metrics such as latency, the number of forward passes, input tokens, and output tokens. However, these metrics depend on hardware and running-time choices (\eg parallel or not, batch size, etc), and often fail to account for model size, making it difficult to interpret and obscuring the evaluation of the efficiency-effectiveness tradeoff. To address this issue, we propose E2R-FLOPs, for LLM-based rerankers: ranking metrics per PetaFLOP (RPP) for relevance per compute and queries per PetaFLOP (QPP) for hardware-agnostic throughput. Companied with the new metrics, an interpretable FLOPs estimator is built to estimate the FLOPs of an LLM-based reranker even without running any experiments. Based on the proposed metrics, we conduct comprehensive experiments to evaluate a wide range of LLM-based rerankers with different architecture, studying the efficiency-effectiveness trade-off and bringing this issue to the attention of the research community.

Recent "segment anything" efforts show promise by learning from large-scale data, but adapting such models directly to medical images remains challenging due to the complexity of medical data, noisy annotations, and continual learning requirements across diverse modalities and anatomical structures. In this work, we propose SAMed-2, a new foundation model for medical image segmentation built upon the SAM-2 architecture. Specifically, we introduce a temporal adapter into the image encoder to capture image correlations and a confidence-driven memory mechanism to store high-certainty features for later retrieval. This memory-based strategy counters the pervasive noise in large-scale medical datasets and mitigates catastrophic forgetting when encountering new tasks or modalities. To train and evaluate SAMed-2, we curate MedBank-100k, a comprehensive dataset spanning seven imaging modalities and 21 medical segmentation tasks. Our experiments on both internal benchmarks and 10 external datasets demonstrate superior performance over state-of-the-art baselines in multi-task scenarios. The code is available at: https://github.com/ZhilingYan/Medical-SAM-Bench.

Deep learning-based computational methods have achieved promising results in predicting protein-protein interactions (PPIs). However, existing benchmarks predominantly focus on isolated pairwise evaluations, overlooking a model's capability to reconstruct biologically meaningful PPI networks, which is crucial for biology research. To address this gap, we introduce PRING, the first comprehensive benchmark that evaluates protein-protein interaction prediction from a graph-level perspective. PRING curates a high-quality, multi-species PPI network dataset comprising 21,484 proteins and 186,818 interactions, with well-designed strategies to address both data redundancy and leakage. Building on this golden-standard dataset, we establish two complementary evaluation paradigms: (1) topology-oriented tasks, which assess intra and cross-species PPI network construction, and (2) function-oriented tasks, including protein complex pathway prediction, GO module analysis, and essential protein justification. These evaluations not only reflect the model's capability to understand the network topology but also facilitate protein function annotation, biological module detection, and even disease mechanism analysis. Extensive experiments on four representative model categories, consisting of sequence similarity-based, naive sequence-based, protein language model-based, and structure-based approaches, demonstrate that current PPI models have potential limitations in recovering both structural and functional properties of PPI networks, highlighting the gap in supporting real-world biological applications. We believe PRING provides a reliable platform to guide the development of more effective PPI prediction models for the community. The dataset and source code of PRING are available at https://github.com/SophieSarceau/PRING.

Recent advances in diffusion transformer models for motion-guided video generation, such as Tora, have shown significant progress. In this paper, we present Tora2, an enhanced version of Tora, which introduces several design improvements to expand its capabilities in both appearance and motion customization. Specifically, we introduce a decoupled personalization extractor that generates comprehensive personalization embeddings for multiple open-set entities, better preserving fine-grained visual details compared to previous methods. Building on this, we design a gated self-attention mechanism to integrate trajectory, textual description, and visual information for each entity. This innovation significantly reduces misalignment in multimodal conditioning during training. Moreover, we introduce a contrastive loss that jointly optimizes trajectory dynamics and entity consistency through explicit mapping between motion and personalization embeddings. Tora2 is, to our best knowledge, the first method to achieve simultaneous multi-entity customization of appearance and motion for video generation. Experimental results demonstrate that Tora2 achieves competitive performance with state-of-the-art customization methods while providing advanced motion control capabilities, which marks a critical advancement in multi-condition video generation. Project page: https://github.com/alibaba/Tora .

中国研究人员首次直接观测到“反Klein隧穿”（AKT）现象——这一量子悖论描述的是手性粒子在遇到势垒时并非穿越，而是被完全反射。“Klein隧穿”是量子物理中的一个著名悖论：质量为零的相对论粒子可以无视能垒的存在，自由穿越而不发生反射。与之相对的“反Klein隧穿”则预言：对于具有“手性”特征的有质量粒子，能垒会导致完全反射。这种奇特的传播行为长期以来仅存在于理论推演和间接证据中，始终缺乏实验验证。该研究中，团队设计了一种结构可调的双层声子晶体，在其声学色散关系中引入了手性与质量，使系统中的声子类比于双层石墨烯中的手性准粒子。当声波遇到由此构成的势垒结构时，传播行为取决于结构参数的调控：在特定配置下，声波被完全反射，即出现反Klein隧穿；而在另一配置下，则可以实现完全穿透，即Klein隧穿。

上个月美国总统特朗普第三次给予 TikTok 90 天宽限期，TikTok 必须在 9 月 17 日之前将其美国业务出售给美国财团，否则将会面临被禁。The Information 报道，如果出售给美国财团的交易获得批准，TikTok 已开发了一个美国专用版本，计划于 9 月 5 日推出。所有美国 TikTok 用户将被提示在 2026 年 3 月之前切换到新版应用，届时原版应用将停止运行。目前不清楚 TikTok 的美国版本与全球版本有什么区别。

AI 爬虫早已超过搜索引擎爬虫，成为众多网站服务器的压力来源，原因是 AI 爬虫抓取频率更高，对内容有着无止境的需求，而且通常无视 robots.txt 规则。维基基金会今年早些时候表示 AI 爬虫导致其带宽消耗增加了五成。加拿大开发者 Xe Iaso 今年 1 月发布的工作量证明开源工具 Anubis 帮助网站抵御来自 AI 爬虫的无止境请求，至今它已被下载了近 20 万次，被桌面环境项目 GNOME、Linux 内核邮件列表存档和 Git 服务器、FFmpeg、Wine 和 FreeCAD 等知名开源项目以及 UNESCO（联合国教科文组织）等联合国组织使用。Anubis 会验证访客是人类还是机器人，方法是要求浏览器使用 JavaScript 执行加密数学运算，浏览器能自动完成，但 AI 爬虫除非模仿使用浏览器的用户，否则将会被挡住。而 AI 如果要模拟浏览器用户则将会大幅增加计算开销，导致其无法承受。鉴于部分用户的浏览器出于隐私等方面的考虑会禁用 JavaScript，Xe Iaso 正在开发一种不使用 JavaScript 的验证方法。

开源邮件客户端 Thunderbird 释出了 v140 ESR 版(长期支持版本)，将提供一年的安全更新，相比非 ESR 版本，它主要针对企业级和教育市场，这些领域不需要频繁更新以免引入破坏兼容性的功能。v140 ESR 的主要变化也与此相关：实现企业策略允许细粒度应用内通知控制；新邮件提醒包含在消息处理按钮内；邮件通知添加 Mark as Read（标记已读）和 Delete（删除）操作；邮件通知添加“Mark as Spam（标记为垃圾邮件）”和“加星”操作；等等。

Legacy Update 是帮助 Windows 7 用户继续从微软获取更新的工具，上周用户开始报告检查更新时报错。开发者在调查之后发现，原来是 Microsoft Update 使用的一个文件的数字签名于 2025 年 7 月 1 日过期。该签名的签发日期是 2017 年 12 月 1 日，也就是说 Microsoft Update 不是第一次因为签名过期而罢工。最新的问题直到数天后由微软员工通过更新签名解决。新的签名过期时间是 2033 年 7 月 1 日。

中国科学院近代物理研究所科研人员等依托兰州重离子加速器冷却储存环，首次精确测量了极缺中子原子核硅-22的质量，发现在硅-22中质子数14是一个新幻数。原子核由质子和中子构成。当原子核的质子数或中子数为2、8、20、28、50、82、126时，原子核表现出相对稳定的性质。这些数字被称为幻数。此前，科学家发现在氧-22中，中子数14具有“幻数”特征。根据核结构的镜像对称性，理论预言，在氧-22的镜像核硅-22中，质子数14应是一个幻数。硅-22是目前已知的最缺中子的硅同位素，因其产生截面小、寿命短，在实验中产生和测量均面临较大挑战，该理论预言此前未被实验证实。

虽然太空船能藉由恒星辨识方位，但要准确掌握其离开地球多远、行经何处，通常仍需仰赖地面以电波进行精密追踪。NASA 新视野号（New Horizo​​ns）任务团队的成员利用这艘目前已距地球超过 88 亿公里的飞船，成功示范仅透过星野影像即可判定方向与位置的导航方法。随着太空船深入太空，从其所在位置所见的恒星位置会开始偏离地球所见的位置。一艘航行至银河系深处的太空船可藉由这种因视差效应产生的偏移，来定位自己相对于邻近恒星的位置。而新视野号已飞行至足够遥远的距离，得以首次真实示范星际导航的可行性。自 2006 年发射以来，新视野号飞越冥王星与柯伊伯带天体 Arrokoth，并将在未来十年间逐步脱离太阳系，进入星际空间。2020 年新视野号科学团队同时从地球与太空中观测并拍摄了邻近恒星比邻星（距离地球4.2光年）与沃夫359（距离7.86光年）周围的星野。这项实验生动呈现出新视野号从内太阳系飞往外太阳系时的视角变化。而针对 2020 年影像中两颗恒星精确位置的更进一步分析，新视野号团队成员及成功推算出新视野号相对于邻近恒星的三维空间位置，精度达约 660 万公里。

日本总务省公布的 2025 年《信息通信白皮书》中发布调查结果称，使用生成式 AI 的个人仅占 26.7%。与上次调查相比增加至约 3 倍，但与进行对比调查的中国（81.2%）、美国（68.8%）和德国（59.2%）仍存在较大差距。关于不使用的理由，比例最高的是“生活和业务上没有需要”，超过 4 成，“不知道使用方法”也接近 4 成。使用率存在明显的年龄差异。使用率最高的 20~29岁人群为 44.7%，其次是 40~49 岁（29.6%）、30~39 岁（23.8%）、50~59 岁（19.9%）。最低的 60~69 岁仅为15.5%。日本国内企业的利用率为 55.2%，而中国（95.8%）、美国（90.6%）和德国（90.3%）均超过 9 成。

Netflix 加大了对动漫的投资，公布了其全球订户的动漫观看数据，凸显了日本动漫从小众市场成长为全球主流内容市场的过程。Netflix 称，其全球订户——逾 1.5 亿家庭约 3 亿用户——在观看动漫。过去五年，该平台动漫收视率增长了两倍，2024 年有 33 部动漫作品登上了它的 Global Top 10 (Non-English)排行榜，是 2021 年的两倍多。2024 年全球动漫内容的观看次数逾 10 亿次，其中 80% 至 90% 的用户选择观看配音版。为满足这一需求，Netflix 开始为动漫作品提供最多 33 种语言的配音和解说。

施乐发表新闻稿，宣布完成了对美国打印机制造商利盟（Lexmark）的收购。利盟最初是 IBM 的打印机部门，1991 年独立成立利盟国际，它一度是财富 500 强之一，2016 年珠海艾派克科技(现纳思达）、香港太盟投资（PAG）及君联资本组成的财团以每股 40.5 美元的现金斥资 25.4 亿美元收购利盟。2023 年美国因利盟在产品生产中使用强迫劳动而对其实施制裁。此举意味着利盟在美国市场的销售面临困境。施乐是在去年 12 月宣布以 15 亿美元从中国财团手中收购利盟，它表示这笔交易有助于增强其产品组合。

印度外包巨头 Infosys 通知员工，警告他们每天工作时间不得超过 9 小时 15 分钟。该公司将监控员工工作时间，此举旨在防止员工工作倦怠，但这与公司联合创始人、英国前首相 Rishi Sunak 的岳父 Narayana Murthy 呼吁印度人每周工作 70 小时的立场相悖。

中国电影基金会等组织计划利用AI技术，对包括《警察故事》《黄飞鸿》和《精武门》等在内的 100 部经典功夫影片进行“重焕”。该基金会表示，将与上海灿星文化传媒股份有限公司等企业合作，向 AI 公司授权调用电影素材，以在全球范围内重新推出这些电影，吸引年轻观众。参与功夫片“重焕”项目的官员表示，AI 将用于为电影添加“令人惊叹的真实感”。他们正计划打造“身临其境的观看体验”，例如在竹林决斗，“感受动与静的哲学”。功夫电影的“重焕”将扩展到其他领域，包括创建武术视频游戏。行业观察人士表示，中国重新挖掘经典功夫电影作品的举措是明智的，这些作品多年来一直是美国动作电影的灵感来源。

南极洲的变暖速度是世界其他地区的两倍，但过去十年南极洲周围海冰面积缩小的程度超过了气候模型的估计。发表在 PNAS 期刊上的一项研究给出了一种解释，认为可能发生了危险的反馈循环。海水因密度不同而分成不同层的现象被称为分层，其中冷淡水层位于较深较温暖和较咸的水层之上，将热量困在海洋深处，使表层海水保持较凉的状态，有助于海冰的形成。海水密度越大重量也越大。当表层海水盐度升高时，它们更容易下沉，搅动了海洋的不同层，使深层的热量上升。这种热涌在冬季也会融化海冰，使得海冰更难形成，从而形成了一个反馈循环：高盐海水将更多热量带到海洋表面，融化了更多冰，吸收了更多热，循环加剧。

欧盟的新能效标签要求厂商标明电池的额定充电次数。那么根据充电次数，今天哪些手机品牌的电池更耐用？数据显示， 三星手机电池遥遥领先。Google Pixel 系列手机电池充电次数基本上是一千次；三星基本上是 2000 次（少数几款 1200 次）；Fairphone 5 1200 次，Fairphone 6 降至 1000 次；摩托罗拉 Edge 50 系列为 1200 次，G55 800 次，其它型号基本上是 1000 次；Nothing 系列手机为 1400 次；OnePlus OnePlus 13R 1200 次，OnePlus 13 1000 次；索尼 Xperia 1 VII 为 1400 次，苹果 iPhone 16 系列都是 1000 次。

根据 Internet Society 的统计数字，自 2018 年以来它记录到了 863 次断网事件，其中印度一国就占了近半多达 411 次，其次是伊拉克的 140 次，叙利亚的 66 次，苏丹的 33 次，巴基斯坦和阿尔及利亚的 17 次，伊朗的 16 次。印度频繁断网的一个原因是法律授予官员以维护公共次序的名义切断互联网，地方官员有法定权力能命令电信公司手动关闭网络服务。要断网时，官员只需写信和发邮件给所有在当地有办事处的 ISP，ISP 随后屏蔽所有进出数据。 伊拉克断网则主要是因为考试。