OrangeBot.AI Digest — 2025-09-24

JavaScript/TypeScript-native, low-boilerplate, object-capability RPC system

Free and Open Source, Distributed, RESTful Search Engine

Truly independent web browser

"RAG-Anything: All-in-One RAG Framework"

Ultralytics YOLO 🚀

Connect, secure, control, and observe services.

Gin is a high-performance HTTP web framework written in Go. It provides a Martini-like API but with significantly better performance—up to 40 times faster—thanks to httprouter. Gin is designed for building REST APIs, web applications, and microservices.

Free, open source crypto trading bot

The official repo for “Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting”, ACL, 2025.

Cybersecurity AI (CAI), the framework for AI Security

🥢像老乡鸡🐔那样做饭。主要部分于2024年完工，非老乡鸡官方仓库。文字来自《老乡鸡菜品溯源报告》，并做归纳、编辑与整理。CookLikeHOC.

A collection of (mostly) technical things every software developer should know about

Web-Scale Blockchain for fast, secure, scalable, decentralized apps and marketplaces.

A privacy-first, self-hosted, fully open source personal knowledge management software, written in typescript and golang.

The Web framework for perfectionists with deadlines.

Arabic document OCR remains a challenging task due to the language's cursive script, diverse fonts, diacritics, and right-to-left orientation. While modern Multimodal Large Language Models (MLLMs) have advanced document understanding for high-resource languages, their performance on Arabic remains limited. In this work, we introduce Baseer, a vision-language model fine- tuned specifically for Arabic document OCR. Leveraging a large-scale dataset combining synthetic and real-world documents, Baseer is trained using a decoder-only fine-tuning strategy to adapt a pre-trained MLLM while preserving general visual features. We also present Misraj-DocOCR, a high-quality, expert-verified benchmark designed for rigorous evaluation of Arabic OCR systems. Our experiments show that Baseer significantly outperforms existing open-source and commercial solutions, achieving a WER of 0.25 and establishing a new state-of-the-art in the domain of Arabic document OCR. Our results highlight the benefits of domain-specific adaptation of general-purpose MLLMs and establish a strong baseline for high-accuracy OCR on morphologically rich languages like Arabic.

Imitation-learning-based visuomotor policies have been widely used in robot manipulation, where both visual observations and proprioceptive states are typically adopted together for precise control. However, in this study, we find that this common practice makes the policy overly reliant on the proprioceptive state input, which causes overfitting to the training trajectories and results in poor spatial generalization. On the contrary, we propose the State-free Policy, removing the proprioceptive state input and predicting actions only conditioned on visual observations. The State-free Policy is built in the relative end-effector action space, and should ensure the full task-relevant visual observations, here provided by dual wide-angle wrist cameras. Empirical results demonstrate that the State-free policy achieves significantly stronger spatial generalization than the state-based policy: in real-world tasks such as pick-and-place, challenging shirt-folding, and complex whole-body manipulation, spanning multiple robot embodiments, the average success rate improves from 0\% to 85\% in height generalization and from 6\% to 64\% in horizontal generalization. Furthermore, they also show advantages in data efficiency and cross-embodiment adaptation, enhancing their practicality for real-world deployment.

The growing disparity between the exponential scaling of computational resources and the finite growth of high-quality text data now constrains conventional scaling approaches for large language models (LLMs). To address this challenge, we introduce Reinforcement Learning on Pre-Training data (RLPT), a new training-time scaling paradigm for optimizing LLMs. In contrast to prior approaches that scale training primarily through supervised learning, RLPT enables the policy to autonomously explore meaningful trajectories to learn from pre-training data and improve its capability through reinforcement learning (RL). While existing RL strategies such as reinforcement learning from human feedback (RLHF) and reinforcement learning with verifiable rewards (RLVR) rely on human annotation for reward construction, RLPT eliminates this dependency by deriving reward signals directly from pre-training data. Specifically, it adopts a next-segment reasoning objective, rewarding the policy for accurately predicting subsequent text segments conditioned on the preceding context. This formulation allows RL to be scaled on pre-training data, encouraging the exploration of richer trajectories across broader contexts and thereby fostering more generalizable reasoning skills. Extensive experiments on both general-domain and mathematical reasoning benchmarks across multiple models validate the effectiveness of RLPT. For example, when applied to Qwen3-4B-Base, RLPT yields absolute improvements of 3.0, 5.1, 8.1, 6.0, 6.6, and 5.3 on MMLU, MMLU-Pro, GPQA-Diamond, KOR-Bench, AIME24, and AIME25, respectively. The results further demonstrate favorable scaling behavior, suggesting strong potential for continued gains with more compute. In addition, RLPT provides a solid foundation, extending the reasoning boundaries of LLMs and enhancing RLVR performance.

Multimodal Large Language Models (MLLMs) are undergoing rapid progress and represent the frontier of AI development. However, their training and inference efficiency have emerged as a core bottleneck in making MLLMs more accessible and scalable. To address the challenges, we present MiniCPM-V 4.5, an 8B parameter model designed for high efficiency and strong performance. We introduce three core improvements in model architecture, data strategy and training method: a unified 3D-Resampler model architecture for highly compact encoding over images and videos, a unified learning paradigm for document knowledge and text recognition without heavy data engineering, and a hybrid reinforcement learning strategy for proficiency in both short and long reasoning modes. Comprehensive experimental results in OpenCompass evaluation show that MiniCPM-V 4.5 surpasses widely used proprietary models such as GPT-4o-latest, and significantly larger open-source models such as Qwen2.5-VL 72B. Notably, the strong performance is achieved with remarkable efficiency. For example, on the widely adopted VideoMME benchmark, MiniCPM-V 4.5 achieves state-of-the-art performance among models under 30B size, using just 46.7\% GPU memory cost and 8.7\% inference time of Qwen2.5-VL 7B.

Recent advances in reinforcement learning for foundation models, such as Group Relative Policy Optimization (GRPO), have significantly improved the performance of foundation models on reasoning tasks. Notably, the advantage function serves as a central mechanism in GRPO for ranking the trajectory importance. However, existing explorations encounter both advantage reversion and advantage mirror problems, which hinder the reasonable advantage allocation across different query samples. In this work, we propose an easy but effective GRPO strategy, Mixed Advantage Policy Optimization (MAPO). We reveal that the trajectory appears with different certainty and propose the advantage percent deviation for samples with high-certainty trajectories. Furthermore, we dynamically reweight the advantage function for samples with varying trajectory certainty, thereby adaptively configuring the advantage function to account for sample-specific characteristics. Comparison with related state-of-the-art methods, along with ablation studies on different advantage variants, validates the effectiveness of our approach.

Unified multimodal models have recently attracted considerable attention for their remarkable abilities in jointly understanding and generating diverse content. However, as contexts integrate increasingly numerous interleaved multimodal tokens, the iterative processes of diffusion denoising and autoregressive decoding impose significant computational overhead. To address this, we propose Hyper-Bagel, a unified acceleration framework designed to simultaneously speed up both multimodal understanding and generation tasks. Our approach uses a divide-and-conquer strategy, employing speculative decoding for next-token prediction and a multi-stage distillation process for diffusion denoising. The framework delivers substantial performance gains, achieving over a 2x speedup in multimodal understanding. For generative tasks, our resulting lossless 6-NFE model yields a 16.67x speedup in text-to-image generation and a 22x speedup in image editing, all while preserving the high-quality output of the original model. We further develop a highly efficient 1-NFE model that enables near real-time interactive editing and generation. By combining advanced adversarial distillation with human feedback learning, this model achieves ultimate cost-effectiveness and responsiveness, making complex multimodal interactions seamless and instantaneous.

Feed-forward 3D Gaussian Splatting (3DGS) has emerged as a highly effective solution for novel view synthesis. Existing methods predominantly rely on a pixel-aligned Gaussian prediction paradigm, where each 2D pixel is mapped to a 3D Gaussian. We rethink this widely adopted formulation and identify several inherent limitations: it renders the reconstructed 3D models heavily dependent on the number of input views, leads to view-biased density distributions, and introduces alignment errors, particularly when source views contain occlusions or low texture. To address these challenges, we introduce VolSplat, a new multi-view feed-forward paradigm that replaces pixel alignment with voxel-aligned Gaussians. By directly predicting Gaussians from a predicted 3D voxel grid, it overcomes pixel alignment's reliance on error-prone 2D feature matching, ensuring robust multi-view consistency. Furthermore, it enables adaptive control over Gaussian density based on 3D scene complexity, yielding more faithful Gaussian point clouds, improved geometric consistency, and enhanced novel-view rendering quality. Experiments on widely used benchmarks including RealEstate10K and ScanNet demonstrate that VolSplat achieves state-of-the-art performance while producing more plausible and view-consistent Gaussian reconstructions. In addition to superior results, our approach establishes a more scalable framework for feed-forward 3D reconstruction with denser and more robust representations, paving the way for further research in wider communities. The video results, code and trained models are available on our project page: https://lhmd.top/volsplat.

Large reasoning models (LRMs) spend substantial test-time compute on long chain-of-thought (CoT) traces, but what *characterizes* an effective CoT remains unclear. While prior work reports gains from lengthening CoTs and increasing review (revisiting earlier steps) via appended *wait* tokens, recent studies suggest that shorter thinking can outperform longer traces. We therefore conduct a systematic evaluation across ten LRMs on math and scientific reasoning. Contrary to the "longer-is-better" narrative, we find that both naive CoT lengthening and increased review are associated with *lower* accuracy. As CoT unfolds step by step, token-level metrics can conflate verbosity with process quality. We introduce a graph view of CoT to extract structure and identify a single statistic-the *Failed-Step Fraction (FSF)*, the fraction of steps in abandoned branches-that consistently outpredicts length and review ratio for correctness across models. To probe causality, we design two interventions. First, we rank candidate CoTs by each metric at test time, where FSF yields the largest pass@1 gains; second, we edit CoTs to remove failed branches, which significantly improves accuracy, indicating that failed branches bias subsequent reasoning. Taken together, these results characterize effective CoTs as those that *fail less* and support *structure-aware* test-time scaling over indiscriminately generating long CoT.

The ability to generate virtual environments is crucial for applications ranging from gaming to physical AI domains such as robotics, autonomous driving, and industrial AI. Current learning-based 3D reconstruction methods rely on the availability of captured real-world multi-view data, which is not always readily available. Recent advancements in video diffusion models have shown remarkable imagination capabilities, yet their 2D nature limits the applications to simulation where a robot needs to navigate and interact with the environment. In this paper, we propose a self-distillation framework that aims to distill the implicit 3D knowledge in the video diffusion models into an explicit 3D Gaussian Splatting (3DGS) representation, eliminating the need for multi-view training data. Specifically, we augment the typical RGB decoder with a 3DGS decoder, which is supervised by the output of the RGB decoder. In this approach, the 3DGS decoder can be purely trained with synthetic data generated by video diffusion models. At inference time, our model can synthesize 3D scenes from either a text prompt or a single image for real-time rendering. Our framework further extends to dynamic 3D scene generation from a monocular input video. Experimental results show that our framework achieves state-of-the-art performance in static and dynamic 3D scene generation.

Dialects represent a significant component of human culture and are found across all regions of the world. In Germany, more than 40% of the population speaks a regional dialect (Adler and Hansen, 2022). However, despite cultural importance, individuals speaking dialects often face negative societal stereotypes. We examine whether such stereotypes are mirrored by large language models (LLMs). We draw on the sociolinguistic literature on dialect perception to analyze traits commonly associated with dialect speakers. Based on these traits, we assess the dialect naming bias and dialect usage bias expressed by LLMs in two tasks: an association task and a decision task. To assess a model's dialect usage bias, we construct a novel evaluation corpus that pairs sentences from seven regional German dialects (e.g., Alemannic and Bavarian) with their standard German counterparts. We find that: (1) in the association task, all evaluated LLMs exhibit significant dialect naming and dialect usage bias against German dialect speakers, reflected in negative adjective associations; (2) all models reproduce these dialect naming and dialect usage biases in their decision making; and (3) contrary to prior work showing minimal bias with explicit demographic mentions, we find that explicitly labeling linguistic demographics--German dialect speakers--amplifies bias more than implicit cues like dialect usage.

The use of continuous instead of discrete tokens during the Chain-of-Thought (CoT) phase of reasoning LLMs has garnered attention recently, based on the intuition that a continuous mixture of discrete tokens could simulate a superposition of several reasoning paths simultaneously. Theoretical results have formally proven that continuous tokens have much greater expressivity and can solve specific problems more efficiently. However, practical use of continuous tokens has been limited by strong training difficulties: previous works either just use continuous tokens at inference time on a pre-trained discrete-token model, or must distill the continuous CoT from ground-truth discrete CoTs and face computational costs that limit the CoT to very few tokens. This is the first work introducing a scalable method to learn continuous CoTs via reinforcement learning (RL), without distilling from reference discrete CoTs. We use "soft" tokens: mixtures of tokens together with noise on the input embedding to provide RL exploration. Computational overhead is minimal, enabling us to learn continuous CoTs with hundreds of tokens. On math reasoning benchmarks with Llama and Qwen models up to 8B, training with continuous CoTs match discrete-token CoTs for pass@1 and surpass them for pass@32, showing greater CoT diversity. In systematic comparisons, the best-performing scenario is to train with continuous CoT tokens then use discrete tokens for inference, meaning the "soft" models can be deployed in a standard way. Finally, we show continuous CoT RL training better preserves the predictions of the base model on out-of-domain tasks, thus providing a softer touch to the base model.

Data scarcity remains one of the most limiting factors in driving progress in robotics. However, the amount of available robotics data in the wild is growing exponentially, creating new opportunities for large-scale data utilization. Reliable temporal task completion prediction could help automatically annotate and curate this data at scale. The Generative Value Learning (GVL) approach was recently proposed, leveraging the knowledge embedded in vision-language models (VLMs) to predict task progress from visual observations. Building upon GVL, we propose OpenGVL, a comprehensive benchmark for estimating task progress across diverse challenging manipulation tasks involving both robotic and human embodiments. We evaluate the capabilities of publicly available open-source foundation models, showing that open-source model families significantly underperform closed-source counterparts, achieving only approximately 70% of their performance on temporal progress prediction tasks. Furthermore, we demonstrate how OpenGVL can serve as a practical tool for automated data curation and filtering, enabling efficient quality assessment of large-scale robotics datasets. We release the benchmark along with the complete codebase at github.com/budzianowski/opengvl{OpenGVL}.

Conditional generative modeling aims to learn a conditional data distribution from samples containing data-condition pairs. For this, diffusion and flow-based methods have attained compelling results. These methods use a learned (flow) model to transport an initial standard Gaussian noise that ignores the condition to the conditional data distribution. The model is hence required to learn both mass transport and conditional injection. To ease the demand on the model, we propose Condition-Aware Reparameterization for Flow Matching (CAR-Flow) -- a lightweight, learned shift that conditions the source, the target, or both distributions. By relocating these distributions, CAR-Flow shortens the probability path the model must learn, leading to faster training in practice. On low-dimensional synthetic data, we visualize and quantify the effects of CAR. On higher-dimensional natural image data (ImageNet-256), equipping SiT-XL/2 with CAR-Flow reduces FID from 2.07 to 1.68, while introducing less than 0.6% additional parameters.

Recently, 3D Gaussian Splatting (3DGS) has emerged as a powerful alternative to NeRF-based approaches, enabling real-time, high-quality novel view synthesis through explicit, optimizable 3D Gaussians. However, 3DGS suffers from significant memory overhead due to its reliance on per-Gaussian parameters to model view-dependent effects and anisotropic shapes. While recent works propose compressing 3DGS with neural fields, these methods struggle to capture high-frequency spatial variations in Gaussian properties, leading to degraded reconstruction of fine details. We present Hybrid Radiance Fields (HyRF), a novel scene representation that combines the strengths of explicit Gaussians and neural fields. HyRF decomposes the scene into (1) a compact set of explicit Gaussians storing only critical high-frequency parameters and (2) grid-based neural fields that predict remaining properties. To enhance representational capacity, we introduce a decoupled neural field architecture, separately modeling geometry (scale, opacity, rotation) and view-dependent color. Additionally, we propose a hybrid rendering scheme that composites Gaussian splatting with a neural field-predicted background, addressing limitations in distant scene representation. Experiments demonstrate that HyRF achieves state-of-the-art rendering quality while reducing model size by over 20 times compared to 3DGS and maintaining real-time performance. Our project page is available at https://wzpscott.github.io/hyrf/.

This paper introduces CommonForms, a web-scale dataset for form field detection. It casts the problem of form field detection as object detection: given an image of a page, predict the location and type (Text Input, Choice Button, Signature) of form fields. The dataset is constructed by filtering Common Crawl to find PDFs that have fillable elements. Starting with 8 million documents, the filtering process is used to arrive at a final dataset of roughly 55k documents that have over 450k pages. Analysis shows that the dataset contains a diverse mixture of languages and domains; one third of the pages are non-English, and among the 14 classified domains, no domain makes up more than 25% of the dataset. In addition, this paper presents a family of form field detectors, FFDNet-Small and FFDNet-Large, which attain a very high average precision on the CommonForms test set. Each model cost less than $500 to train. Ablation results show that high-resolution inputs are crucial for high-quality form field detection, and that the cleaning process improves data efficiency over using all PDFs that have fillable fields in Common Crawl. A qualitative analysis shows that they outperform a popular, commercially available PDF reader that can prepare forms. Unlike the most popular commercially available solutions, FFDNet can predict checkboxes in addition to text and signature fields. This is, to our knowledge, the first large scale dataset released for form field detection, as well as the first open source models. The dataset, models, and code will be released at https://github.com/jbarrow/commonforms

YouTube 成为最新一个放弃事实核查政策，拥抱保守派的社交媒体平台。YouTube 母公司 Alphabet 宣布，将恢复在疫情期间因散播 COVID-19 虚假信息以及传播美国大选虚假信息而封禁的用户账号，谴责了疫情期间拜登政府向该公司持续施压限制和删除虚假 COVID-19 信息的做法，称拜登政府的决定是不可接受的、是错误的。Alphabet 同时表示将采用类似 X/Twitter 等平台的政策，让其用户去验证信息，允许用户在内容中添加上下文注释。

自 2025 年 9 月 1 日起，越南银行将关闭被认为不活跃或不符合新生物识别规则的账户。政府估计，如果用户未能更新身份识别信息，2 亿个账户中逾 8600 万账户面临风险。越南国家银行还同时实施了更严格的交易门槛：超过 1000 万越南盾（约 379 美元）的在线转账必须进行面部认证；每日累计超过 2000 万越南盾（约 758 美元）的转账也需要生物识别认证。最新政策旨在打击欺诈、身份盗窃和深度伪造诈骗。受新政策影响最大的可能是外国居民和外籍的越南银行客户。

加州律师 Amir Mostafavi 因其诉状包含了 ChatGPT 生成的虚假案例而被罚款 1 万美元，这可能是加州法院就 AI 造假开出的金额最高的罚单。这名律师被发现引用的 23 个案例有 21 个是 AI 虚构的。律师告诉法庭，上诉书是他自己写的，然后尝试使用 AI 进行改进，他不知道 AI 会在修饰文本时会引用虚构的案例或捏造内容。他承认自己没有在 AI 修饰后阅读其生成的文本。三名法官组成的审判团队以递交无理诉讼、违反法庭规则、引用虚假案例以及浪费法庭时间和纳税​​人金钱为由对他处以罚款。

根据发表在《Psychology & Health》期刊上的一项研究，驱动人的大部分行为是习惯而不是有意识的选择。习惯就是根据我们所学以及通常反应在日常环境中被自动提示去做的行为。研究发现，人的日常行为有 65% 是出于习惯而自动进行的。研究还发现，46% 的行为是由习惯以及与相一致的有意识意图触发的，表明人们养成了有助于实现个人目标的习惯，且经常会打破与之冲突的习惯。在这项研究中，研究人员在一周内每天向 105 名参与者的手机发送六个随机提示，要求他们描述目前正在做的事，以及是出于习惯还是有意为之。研究人员称，人们喜欢认为自己是理性的决策者，会在行动前仔细思考。但事实上许多重复行为极少是预先思考的，而是由习惯自动产生的。锻炼也能靠习惯推动，但相比其它行为，它无法完全靠自动实现。

一位伊朗程序员在 GitHub 上记录了他使用西方 IT 服务的经历。美国对伊朗实施了制裁，禁止本国的个人和企业与伊朗打交道，违反者将面临惩罚，GitHub 一度禁止伊朗开发者访问其服务，2021 年它从美国政府获得许可，允许为伊朗开发者提供服务。这位伊朗程序员称，他曾在微软应用商店 Microsoft Store 发布了一款开源程序 EyesGuard，但有一天微软删除了他的账号移除了 EyesGuard；笔记服务 Notion 删除了所有居住在伊朗的用户的数据，即使用户以后离开伊朗，数据也不会恢复；VPN 服务 Grepular 屏蔽了伊朗 IP，理由是伊朗向俄罗斯出售无人机；除了 GitHub，另一个代码托管服务 GitLab 也屏蔽了伊朗用户账号，至今没有解禁。作者表示他记录这些经历并不是请求解除对伊朗的制裁，而是强调伊朗人民也是受害者。

内蒙古自治区锡林浩特市公安局发布关于锡林浩特市集中采集男性居民血样并录入本地 DN A数据库的通告，引发网友关注。在锡林浩特市之前，多地都曾开展集中采集男性居民血样的工作，此举是为了推进“Y库”建设。“Y库”的全称为“Y库家系工匠系统”，此前白银连环杀人案、南医大女生被害案等案件告破，“Y库”都立了功。据锡林浩特市公安局通告，为进一步夯实公安基础工作，完善该市居民基础信息库数据，健全居民个人信息管理，提升重大风险防范与应对能力，精准落实相关工作举措，根据上级部门统一部署，锡林浩特市公安局各派出所将开展男性居民血样集中采集工作。采集时间为2025年9月5日起，采集对象为锡林浩特市辖区内男性居民，采集地点为居民户籍所在地派出所。通告还称，本次血样采集的作用是完善公民身份信息，直接关联到个人身份证、护照等证件的办理。并且对于防范老人儿童走失、人员身份信息确认等方面，具有重大作用。请广大男性居民积极支持配合此项工作，携带本人有效身份证件（身份证、户口本等）前往指定采集点完成信息登记与血样采集。采集过程严格遵循相关规范，居民个人信息及生物样本将依法严格保密，确保信息安全。此项工作功在当代、利在千秋，望全体市民理解支持，共同推动工作顺利开展。

一项研究表明，“肥胖但健康”是有科学依据的。基于数万丹麦人数据的研究发现，在 5 年随访期间，体重指数（BMI）为超重，甚至部分肥胖的人群，其死亡风险并不比 BMI 处于正常范围上限（22.5~25）的人群高。研究人员对 85761 人（女性占 81.4%，基线中位年龄 66.4 岁）的 BMI 和死亡率之间的关系进行了研究。随访期间，共有 7555 人（占比 8%）死亡。分析发现，低体重人群的死亡风险几乎是接近健康上限的参照组（22.5~25）的 3 倍。BMI ≥40 的严重肥胖人群的死亡风险则是参照组的 2.1 倍。BMI 在 35 以下并未显示出与较高的死亡风险相关，即使在 35~40 区间也仅与轻微增加的风险有关。

美国白宫公布了字节跳动剥离 TikTok 美国业务的细节，美国总统特朗普预计将在本周晚些时候批准这笔交易。TikTok 美国业务将移交给以甲骨文和银湖资本（Silver Lake）为中心的企业联合体，并由该联合体负责运营。但作为核心技术的算法仍由中国以授权方式提供，并未完全切割。合资公司中美国董事会成员占半数以上，美国政府没有计划派人员进入董事会，也不会获取在重大事项上行使否决权的“黄金股”或实施资本注入。字节跳动对合资公司的持股将控制在 20% 以下。因为相关法规定义，中国资本若在美企持股比例超过 20%，该企业将被视为“在中国的管理之下”。合资公司预计还有多家企业和投资者参与，但“资本构成尚未最终确定”。甲骨文将在美国境内利用用户数据来运行算法的复制版本，并负责安全措施。甲骨文接收的算法副本包括“源代码”，将被纳入甲骨文管理的系统中，以便该公司实施验证。

Google 开始将其 Gemini AI 助手推送给 Google TV 设备。用户将能向 Gemini 寻求电视推荐、节目回顾、评论，甚至执行家庭作业辅导、假期计划或学习新技能等任务的帮助。Gemini AI 将首先推送给 TCL 的 QM9K 系列智能电视，晚些时候推送给 Google TV Streamer、Walmart onn 4K Pro、2025 Hisense U7、U8 和 UX 型号，以及 2025 款 TCL QM7K、QM8K 和 X11K 系列型号。

微软正在为 Windows 11 加入将视频设为桌面墙纸的功能。最新的 Windows 11 预览版包含了该功能，允许用户将 MP4、MOV、AVI、WMV、M4V 或 MKV 文件设置为墙纸，用户查看桌面时视频就会播放。视频墙纸并非是新特性，Windows 操作系统早就支持该功能。Windows Vista 的终极版通过 DreamScene 功能支持视频墙纸，很多 Linux 发行版都支持，macOS 也支持将移动背景设为锁屏墙纸。

AI 芯片最大的供应商宣布与 AI 行业估值最高的公司展开合作，投资建造用于训练 AI 的数据中心。英伟达宣布将向 OpenAI 投资千亿美元，OpenAI 的估值达到了 5000 亿美元，但英伟达手中并没有这么多现金，它的投资承诺更像是助长 AI 泡沫的意向书。

埃及总统赦免了已被关押了六年的活动人士 Alaa Abdel Fattah。Alaa Abd El Fattah 是一位活跃的民主人士，同时也是埃及开源运动的积极倡导者。2014 年，他因未经授权组织政治抗议活动被捕，之后获准保释，但重审后被判处五年监禁。他于 2019 年 3 月获释，但 9 月再次被国家安全局以传播虚假新闻的罪名逮捕，2021 年被判入狱五年。他本应该于 2024 年 9 月 29 日重获自由，但埃及政府拒绝将审前拘留的两年时间计算在服刑时间内。他的家人通过社交媒体证实了赦免的消息。

Multikernel Technologies 公司的 Cong Wang 公布了代码递交了 RFC。代码为 Linux 内核加入多内核架构支持，让多个独立内核实例能在一台物理机器上共存并通信，每个内核实例能在专用 CPU 核心上运行，共享底层硬件资源。Multikernel Technologies 公司承诺将采用社区优先的开发方法。

能通过 U 盘运行的便捷式匿名发行版 Tails 释出了 7.0 版本。Tails 7.0 是基于 Linux 6.12.43、Debian 13 ("trixie") 和 GNOME 48 的首个版本，使用 z std 而不是 xz 压缩 USB 和 ISO 镜像，实现了更快的启动速度。开发者将 Tails 7.0 献给 Tails、Tor 和 Debian 项目的资深开发者 Jérémy Bobbio aka Lunar——他于 2024 年 11 月 8 日去世。

Valve 从 Steam 商店下架了 2D 平台游戏《BlockBlasters》，原因是该游戏最近释出的一个补丁被发现含有恶意程序。《BlockBlasters》于 7 月 31 日发布，8 月 30 日释出了补丁 Build 19799326，其中的文件 game2.bat 表现出了恶意行为，它会收集用户的 IP 和位置信息，检测安装的杀毒软件；收集用户的登录信息，上传收集的信息，执行 VBS 启动器脚本。它最终会安装一个后门和一个窃取程序，从 Google Chrome、Brave Browser 和 Microsoft Edge 窃取信息，它主要窃取加密货币。可能有数百名玩家受到这次攻击的影响。