OrangeBot.AI Digest — 2025-08-25

开源白板工具（SaaS），一体化白板，包含思维导图、流程图、自由画等。All in one open-source whiteboard tool with mind, flowchart, freehand and etc.

"DeepCode: Open Agentic Coding (Paper2Code & Text2Web & Text2Backend)"

Run Windows apps such as Microsoft Office/Adobe in Linux (Ubuntu/Fedora) and GNOME/KDE as if they were a part of the native OS, including Nautilus integration. Hard fork of https://github.com/Fmstrat/winapps/

💖🧸 Self hosted, you owned Grok Companion, a container of souls of waifu, cyber livings to bring them into our worlds, wishing to achieve Neuro-sama's altitude. Capable of realtime voice chat, Minecraft, Factorio playing. Web / macOS / Windows supported.

Useful tool to track location or mobile number

Verifiers for LLM Reinforcement Learning

Download your Spotify playlists and songs along with album art and metadata (from YouTube if a match is found).

⚡ Dynamically generated stats for your github readmes

Neural Networks: Zero to Hero

Collection of extracted System Prompts from popular chatbots like ChatGPT, Claude & Gemini

21 Lessons, Get Started Building with Generative AI

坚持分享 GitHub 上高质量、有趣实用的开源技术教程、开发者工具、编程网站、技术资讯。A list cool, interesting projects of GitHub.

A feature-rich command-line audio/video downloader

List of projects that provide terminal user interfaces

Workflow Automations for the Human 👾 AI Workforce

Qoder is an agentic IDE for real software development.

Fix bugs faster with open source, AI native observability

Open source Cursor for designers

The shadcn/ui component library for building AI-native apps

Open a tab, escape somewhere beautiful

Speak with Your Eyes No Special Hardware Needed

Transform your daily steps into satisfying map milestones

Enterprise-grade control for AI agents

A web-based baby activity app with seamless family sharing

In this paper, we introduce a novel learning paradigm for adaptive Large Language Model (LLM) agents that eliminates the need for fine-tuning the underlying LLMs. Existing approaches are often either rigid, relying on static, handcrafted reflection workflows, or computationally intensive, requiring gradient updates of LLM model parameters. In contrast, our method enables low-cost continual adaptation via memory-based online reinforcement learning. We formalise this as a Memory-augmented Markov Decision Process (M-MDP), equipped with a neural case-selection policy to guide action decisions. Past experiences are stored in an episodic memory, either differentiable or non-parametric. The policy is continually updated based on environmental feedback through a memory rewriting mechanism, whereas policy improvement is achieved through efficient memory reading (retrieval). We instantiate our agent model in the deep research setting, namely AgentFly, which attains top-1 on GAIA validation (87.88% Pass@3) and 79.40% on the test set. It reaches 66.6% F1 and 80.4% PM on the DeepResearcher dataset, outperforming the state-of-the-art training-based method, while case-based memory adds 4.7% to 9.6% absolute points on out-of-distribution tasks. Our approach offers a scalable and efficient pathway for developing generalist LLM agents capable of continuous, real-time learning without gradient updates, advancing machine learning towards open-ended skill acquisition and deep research scenarios. The code is available at https://github.com/Agent-on-the-Fly/AgentFly.

Language-guided long-horizon mobile manipulation has long been a grand challenge in embodied semantic reasoning, generalizable manipulation, and adaptive locomotion. Three fundamental limitations hinder progress: First, although large language models have improved spatial reasoning and task planning through semantic priors, existing implementations remain confined to tabletop scenarios, failing to address the constrained perception and limited actuation ranges of mobile platforms. Second, current manipulation strategies exhibit insufficient generalization when confronted with the diverse object configurations encountered in open-world environments. Third, while crucial for practical deployment, the dual requirement of maintaining high platform maneuverability alongside precise end-effector control in unstructured settings remains understudied. In this work, we present ODYSSEY, a unified mobile manipulation framework for agile quadruped robots equipped with manipulators, which seamlessly integrates high-level task planning with low-level whole-body control. To address the challenge of egocentric perception in language-conditioned tasks, we introduce a hierarchical planner powered by a vision-language model, enabling long-horizon instruction decomposition and precise action execution. At the control level, our novel whole-body policy achieves robust coordination across challenging terrains. We further present the first benchmark for long-horizon mobile manipulation, evaluating diverse indoor and outdoor scenarios. Through successful sim-to-real transfer, we demonstrate the system's generalization and robustness in real-world deployments, underscoring the practicality of legged manipulators in unstructured environments. Our work advances the feasibility of generalized robotic assistants capable of complex, dynamic tasks. Our project page: https://kaijwang.github.io/odyssey.github.io/

Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as a key paradigm for post-training Large Language Models (LLMs), particularly for complex reasoning tasks. However, vanilla RLVR training has been shown to improve Pass@1 performance at the expense of policy entropy, leading to reduced generation diversity and limiting the Pass@k performance, which typically represents the upper bound of LLM reasoning capability. In this paper, we systematically analyze the policy's generation diversity from the perspective of training problems and find that augmenting and updating training problems helps mitigate entropy collapse during training. Based on these observations, we propose an online Self-play with Variational problem Synthesis (SvS) strategy for RLVR training, which uses the policy's correct solutions to synthesize variational problems while ensuring their reference answers remain identical to the originals. This self-improving strategy effectively maintains policy entropy during training and substantially improves Pass@k compared with standard RLVR, sustaining prolonged improvements and achieving absolute gains of 18.3% and 22.8% in Pass@32 performance on the competition-level AIME24 and AIME25 benchmarks. Experiments on 12 reasoning benchmarks across varying model sizes from 3B to 32B consistently demonstrate the generalizability and robustness of SvS.

While exocentric video synthesis has achieved great progress, egocentric video generation remains largely underexplored, which requires modeling first-person view content along with camera motion patterns induced by the wearer's body movements. To bridge this gap, we introduce a novel task of joint egocentric video and human motion generation, characterized by two key challenges: 1) Viewpoint Alignment: the camera trajectory in the generated video must accurately align with the head trajectory derived from human motion; 2) Causal Interplay: the synthesized human motion must causally align with the observed visual dynamics across adjacent video frames. To address these challenges, we propose EgoTwin, a joint video-motion generation framework built on the diffusion transformer architecture. Specifically, EgoTwin introduces a head-centric motion representation that anchors the human motion to the head joint and incorporates a cybernetics-inspired interaction mechanism that explicitly captures the causal interplay between video and motion within attention operations. For comprehensive evaluation, we curate a large-scale real-world dataset of synchronized text-video-motion triplets and design novel metrics to assess video-motion consistency. Extensive experiments demonstrate the effectiveness of the EgoTwin framework.

As large language models (LLMs) are increasingly deployed in real-world applications, the need to selectively remove unwanted knowledge while preserving model utility has become paramount. Recent work has explored sparse autoencoders (SAEs) to perform precise interventions on monosemantic features. However, most SAE-based methods operate at inference time, which does not create persistent changes in the model's parameters. Such interventions can be bypassed or reversed by malicious actors with parameter access. We introduce CRISP, a parameter-efficient method for persistent concept unlearning using SAEs. CRISP automatically identifies salient SAE features across multiple layers and suppresses their activations. We experiment with two LLMs and show that our method outperforms prior approaches on safety-critical unlearning tasks from the WMDP benchmark, successfully removing harmful knowledge while preserving general and in-domain capabilities. Feature-level analysis reveals that CRISP achieves semantically coherent separation between target and benign concepts, allowing precise suppression of the target features.

Facilitating an entity's interaction with objects requires accurately identifying parts that afford specific actions. Weakly supervised affordance grounding (WSAG) seeks to imitate human learning from third-person demonstrations, where humans intuitively grasp functional parts without needing pixel-level annotations. To achieve this, grounding is typically learned using a shared classifier across images from different perspectives, along with distillation strategies incorporating part discovery process. However, since affordance-relevant parts are not always easily distinguishable, models primarily rely on classification, often focusing on common class-specific patterns that are unrelated to affordance. To address this limitation, we move beyond isolated part-level learning by introducing selective prototypical and pixel contrastive objectives that adaptively learn affordance-relevant cues at both the part and object levels, depending on the granularity of the available information. Initially, we find the action-associated objects in both egocentric (object-focused) and exocentric (third-person example) images by leveraging CLIP. Then, by cross-referencing the discovered objects of complementary views, we excavate the precise part-level affordance clues in each perspective. By consistently learning to distinguish affordance-relevant regions from affordance-irrelevant background context, our approach effectively shifts activation from irrelevant areas toward meaningful affordance cues. Experimental results demonstrate the effectiveness of our method. Codes are available at github.com/hynnsk/SelectiveCL.

Competitive programming has emerged as a critical benchmark for evaluating the reasoning and coding capabilities of Large Language Models (LLMs). Despite impressive progress on existing benchmarks, we argue that current evaluations overstate model proficiency, masking a substantial gap between LLMs and elite human programmers. This gap arises from two key limitations: insufficient difficulty and scope of benchmark problems, and evaluation bias from low-quality test cases. To address these shortcomings, we present AetherCode, a new benchmark that draws problems from premier programming competitions such as IOI and ICPC, offering broader coverage and higher difficulty. AetherCode further incorporates comprehensive, expert-validated test suites built through a hybrid of automated generation and human curation, ensuring rigorous and reliable assessment. By combining challenging problem design with robust evaluation, AetherCode provides a more faithful measure of LLM capabilities and sets a new standard for future research in code reasoning.

Accurate diagnosis with medical large language models is hindered by knowledge gaps and hallucinations. Retrieval and tool-augmented methods help, but their impact is limited by weak use of external knowledge and poor feedback-reasoning traceability. To address these challenges, We introduce Deep-DxSearch, an agentic RAG system trained end-to-end with reinforcement learning (RL) that enables steer tracebale retrieval-augmented reasoning for medical diagnosis. In Deep-DxSearch, we first construct a large-scale medical retrieval corpus comprising patient records and reliable medical knowledge sources to support retrieval-aware reasoning across diagnostic scenarios. More crutially, we frame the LLM as the core agent and the retrieval corpus as its environment, using tailored rewards on format, retrieval, reasoning structure, and diagnostic accuracy, thereby evolving the agentic RAG policy from large-scale data through RL. Experiments demonstrate that our end-to-end agentic RL training framework consistently outperforms prompt-engineering and training-free RAG approaches across multiple data centers. After training, Deep-DxSearch achieves substantial gains in diagnostic accuracy, surpassing strong diagnostic baselines such as GPT-4o, DeepSeek-R1, and other medical-specific frameworks for both common and rare disease diagnosis under in-distribution and out-of-distribution settings. Moreover, ablation studies on reward design and retrieval corpus components confirm their critical roles, underscoring the uniqueness and effectiveness of our approach compared with traditional implementations. Finally, case studies and interpretability analyses highlight improvements in Deep-DxSearch's diagnostic policy, providing deeper insight into its performance gains and supporting clinicians in delivering more reliable and precise preliminary diagnoses. See https://github.com/MAGIC-AI4Med/Deep-DxSearch.

Recently, Vision-Language-Action (VLA) models have demonstrated strong performance on a range of robotic tasks. These models rely on multimodal inputs, with language instructions playing a crucial role -- not only in predicting actions, but also in robustly interpreting user intent, even when the requests are impossible to fulfill. In this work, we investigate how VLAs can recognize, interpret, and respond to false-premise instructions: natural language commands that reference objects or conditions absent from the environment. We propose Instruct-Verify-and-Act (IVA), a unified framework that (i) detects when an instruction cannot be executed due to a false premise, (ii) engages in language-based clarification or correction, and (iii) grounds plausible alternatives in perception and action. Towards this end, we construct a large-scale instruction tuning setup with structured language prompts and train a VLA model capable of handling both accurate and erroneous requests. Our approach leverages a contextually augmented, semi-synthetic dataset containing paired positive and false-premise instructions, enabling robust detection and natural language correction. Our experiments show that IVA improves false premise detection accuracy by 97.56% over baselines, while increasing successful responses in false-premise scenarios by 50.78%.

Multi-Head Latent Attention (MLA), introduced in DeepSeek-V2, compresses key-value states into a low-rank latent vector, caching only this vector to reduce memory. In tensor parallelism (TP), however, attention heads are computed across multiple devices, and each device must load the full cache, eroding the advantage of MLA over Grouped Query Attention (GQA). We propose Tensor-Parallel Latent Attention (TPLA): a scheme that partitions both the latent representation and each head's input dimension across devices, performs attention independently per shard, and then combines results with an all-reduce. TPLA preserves the benefits of a compressed KV cache while unlocking TP efficiency. Unlike Grouped Latent Attention (GLA), every head in TPLA still leverages the full latent representation, maintaining stronger representational capacity. TPLA is drop-in compatible with models pre-trained using MLA: it supports MLA-style prefilling and enables efficient tensor-parallel decoding without retraining. Applying simple orthogonal transforms -- e.g., the Hadamard transform or PCA -- before TP slicing further mitigates cross-shard interference, yielding minimal accuracy degradation. By reducing the per-device KV cache for DeepSeek-V3 and Kimi-K2, we achieve 1.79x and 1.93x speedups, respectively, at a 32K-token context length while maintaining performance on commonsense and LongBench benchmarks. TPLA can be implemented with FlashAttention-3, enabling practical end-to-end acceleration.

Driven by rapid advancements of Large Language Models (LLMs), agents are empowered to combine intrinsic knowledge with dynamic tool use, greatly enhancing their capacity to address real-world tasks. In line with such an evolution, AgentScope introduces major improvements in a new version (1.0), towards comprehensively supporting flexible and efficient tool-based agent-environment interactions for building agentic applications. Specifically, we abstract foundational components essential for agentic applications and provide unified interfaces and extensible modules, enabling developers to easily leverage the latest progress, such as new models and MCPs. Furthermore, we ground agent behaviors in the ReAct paradigm and offer advanced agent-level infrastructure based on a systematic asynchronous design, which enriches both human-agent and agent-agent interaction patterns while improving execution efficiency. Building on this foundation, we integrate several built-in agents tailored to specific practical scenarios. AgentScope also includes robust engineering support for developer-friendly experiences. We provide a scalable evaluation module with a visual studio interface, making the development of long-trajectory agentic applications more manageable and easier to trace. In addition, AgentScope offers a runtime sandbox to ensure safe agent execution and facilitates rapid deployment in production environments. With these enhancements, AgentScope provides a practical foundation for building scalable, adaptive, and effective agentic applications.

3D Gaussian Splatting (3DGS) has exhibited remarkable efficacy in novel view synthesis (NVS). However, it suffers from a significant drawback: achieving high-fidelity rendering typically necessitates a large number of 3D Gaussians, resulting in substantial memory consumption and storage requirements. To address this challenge, we propose the first knowledge distillation framework for 3DGS, featuring various teacher models, including vanilla 3DGS, noise-augmented variants, and dropout-regularized versions. The outputs of these teachers are aggregated to guide the optimization of a lightweight student model. To distill the hidden geometric structure, we propose a structural similarity loss to boost the consistency of spatial geometric distributions between the student and teacher model. Through comprehensive quantitative and qualitative evaluations across diverse datasets, the proposed Distilled-3DGS, a simple yet effective framework without bells and whistles, achieves promising rendering results in both rendering quality and storage efficiency compared to state-of-the-art methods. Project page: https://distilled3dgs.github.io . Code: https://github.com/lt-xiang/Distilled-3DGS .

Contours or closed planar curves are common in many domains. For example, they appear as object boundaries in computer vision, isolines in meteorology, and the orbits of rotating machinery. In many cases when learning from contour data, planar rotations of the input will result in correspondingly rotated outputs. It is therefore desirable that deep learning models be rotationally equivariant. In addition, contours are typically represented as an ordered sequence of edge points, where the choice of starting point is arbitrary. It is therefore also desirable for deep learning methods to be equivariant under cyclic shifts. We present RotaTouille, a deep learning framework for learning from contour data that achieves both rotation and cyclic shift equivariance through complex-valued circular convolution. We further introduce and characterize equivariant non-linearities, coarsening layers, and global pooling layers to obtain invariant representations for downstream tasks. Finally, we demonstrate the effectiveness of RotaTouille through experiments in shape classification, reconstruction, and contour regression.

Recent video editing methods achieve attractive results in style transfer or appearance modification. However, editing the structural content of 3D scenes in videos remains challenging, particularly when dealing with significant viewpoint changes, such as large camera rotations or zooms. Key challenges include generating novel view content that remains consistent with the original video, preserving unedited regions, and translating sparse 2D inputs into realistic 3D video outputs. To address these issues, we propose Sketch3DVE, a sketch-based 3D-aware video editing method to enable detailed local manipulation of videos with significant viewpoint changes. To solve the challenge posed by sparse inputs, we employ image editing methods to generate edited results for the first frame, which are then propagated to the remaining frames of the video. We utilize sketching as an interaction tool for precise geometry control, while other mask-based image editing methods are also supported. To handle viewpoint changes, we perform a detailed analysis and manipulation of the 3D information in the video. Specifically, we utilize a dense stereo method to estimate a point cloud and the camera parameters of the input video. We then propose a point cloud editing approach that uses depth maps to represent the 3D geometry of newly edited components, aligning them effectively with the original 3D scene. To seamlessly merge the newly edited content with the original video while preserving the features of unedited regions, we introduce a 3D-aware mask propagation strategy and employ a video diffusion model to produce realistic edited videos. Extensive experiments demonstrate the superiority of Sketch3DVE in video editing. Homepage and code: http://http://geometrylearning.com/Sketch3DVE/

LLMs have shown strong performance on human-centric reasoning tasks. While previous evaluations have explored whether LLMs can infer intentions or detect deception, they often overlook the individualized reasoning styles that influence how people interpret and act in social contexts. Social deduction games (SDGs) provide a natural testbed for evaluating individualized reasoning styles, where different players may adopt diverse but contextually valid reasoning strategies under identical conditions. To address this, we introduce InMind, a cognitively grounded evaluation framework designed to assess whether LLMs can capture and apply personalized reasoning styles in SDGs. InMind enhances structured gameplay data with round-level strategy traces and post-game reflections, collected under both Observer and Participant modes. It supports four cognitively motivated tasks that jointly evaluate both static alignment and dynamic adaptation. As a case study, we apply InMind to the game Avalon, evaluating 11 state-of-the-art LLMs. General-purpose LLMs, even GPT-4o frequently rely on lexical cues, struggling to anchor reflections in temporal gameplay or adapt to evolving strategies. In contrast, reasoning-enhanced LLMs like DeepSeek-R1 exhibit early signs of style-sensitive reasoning. These findings reveal key limitations in current LLMs' capacity for individualized, adaptive reasoning, and position InMind as a step toward cognitively aligned human-AI interaction.

谷神星是小行星带最大的天体，NASA 黎明号（Dawn）探测器在 2015 年进入谷神星的公转轨道，为科学家提供小行星的近距离的观测资料。研究人员通过重建谷神星内部热模型和化学模型，模拟了谷神星内部温度和成分随时间的变化。他们发现大约 25 亿年前谷神星内部放射性元素衰变产生的热能不仅足以使地下水库存在，还能不断供应热水给地下水库。而热水中含有溶解的气体，气体便从岩石核心的变质岩中向上流动，相当类似地球深海的海底热泉。研究估计谷神星最有可能的宜居时期是它形成后的 5 亿到 20 亿年之间，也就是大约 25 亿到 40 亿年前。尽管谷神星以前对于微生物来说可能很宜居，现在的谷神星早已耗尽了它的热能。不但水大量结冰，残留的液体也已经变成了浓缩的盐水。

反疫苗的美国卫生部长小肯尼迪（Robert F. Kennedy Jr）要求《Annals of Internal Medicine》期刊撤回丹麦研究人员发表的一篇论文《Aluminum-Adsorbed Vaccines and Chronic Diseases in Childhood: A Nationwide Cohort Study》，对丹麦过去逾 20年来出生的 120 万名儿童的分析发现，疫苗中的铝化合物并不显著增加罹患自身免疫性、过敏性或神经发育障碍的风险。小肯尼迪对研究结论提出了质疑。疫苗怀疑论者曾宣称铝化合物 与自闭症等儿童疾病发病率上升有关，而 WHO 等机构早就驳斥过此类观点。以盐形式存在的铝广泛用于疫苗，没有证据表明疫苗中少量的铝会引起严重的副作用。对于小肯尼迪的要求，期刊表示无意撤稿。Retraction Watch 联合创始人 Ivan Oransky 指出，公共卫生官员很少要求撤稿，小肯尼迪此举是想要科学期刊屈服于他的意志。

新西兰空管系统上周末因软件故障罢工一小时，干扰了机场的正常运作，有五架飞机在空中盘旋，四架飞机无法起飞。新西兰唯一的空管服务商 Airways 表示问题是因为软件故障导致飞行数据无法在系统之间传输。Airways CEO James Young 表示，在发现问题之后，空中交通管制员立即采取了措施，飞机要么在地面等待，要么在空中等待。Airways 的空管系统有备份系统，但 Young 表示无法即时切换到备份系统，验证飞行信息数据需要时间。故障持续了大约一个小时，期间在空中盘旋的飞机有两架继续飞行，三架重返了起飞地。

数学家张益唐称他是因为政治气候从美国回到中国。张益唐是在今年六月离开加州圣巴巴拉，受聘于中山大学香港高等研究院，在大湾区定居和工作。张证明了存在无穷多对间隙小于 7000 万的相邻素数对，在数学史上第一次实质性推进解决著名数论难题“孪生素数猜想”，并在与黎曼猜想有关的朗道-西格尔零点猜想上取得重要进展。他表示有很多华裔学者和教授回到了中国。他说自己所处的数学领域没有受到多少政治气候的影响，但计算机、芯片或任何与军工相关的研究人员需要小心。他称，数学，尤其是理论数学，一大优势是展开研究不必局限于特定地点。

被裁前在前雇主 IT 系统植入恶意程序和设立关闭开关的开发者 Davis Lu 被判四年监禁以及三年的监督释放。美国司法部称，2018 年 Davis Lu 任职的 Eaton Corporation 进行了重组，他遭到了降级。他随后在公司 Windows 生产环境中植入恶意代码进行报复。该恶意程序包含了一个无限的 Java 线程循环，旨在拖垮服务器，导致生产系统崩溃。Lu 还创建了一个过于明显的关闭开关：IsDLEnabledinAD ("Is Davis Lu enabled in Active Directory") ，当 Active Directory 中他的账户被禁用，关闭开关将会激活禁用所有用户的账户。2019 年 9 月 9 日，Lu 的雇佣关系终止，账户被禁用后关闭开关激活，数千名用户被锁定在系统外。此事导致雇主损失了数十万美元。在 Lu 被要求上缴公司发的笔记本电脑前，他删除了其中的加密数据。调查人员后来从设备上发现了他的搜索查询记录，包括搜寻如何提权，隐藏进程以及快速删除文件。

OpenAI 正致力于取代 Google，而它依赖的搜索数据却来自搜索巨人。Theinformation 报道，OpenAI 通过使用从 Web 抓取的 Google 搜索数据去增强聊天机器人 ChatGPT 的响应能力。当用户通过 ChatGPT 查询时事如新闻、体育和股市时，Google 搜索数据能提供巨大的帮助。OpenAI 使用的数据来自 Web 抓取公司 SerpApi。去年 SerpApi 还在网站上列出 OpenAI 是其客户，但后来将其删除了。

Google 准备让 Android TV 和其它 Android 生态系统保持一致。官方博客宣布从 2026 年 8 月 1 日起，Google TV 和 Android TV 应用都需要原生支持 64 位，为即将到来的 64 位电视设备做好准备。未满足要求的应用将无法在电视设备使用的 Google Play 应用商店上架。Google 表示，这一转变将有助于提供更好的性能、更短的启动时间，为未来的硬件带来全新的体验。

美国总统特朗普宣布与英特尔达成协议，美国政府将持有芯片巨人 10% 的股份。英特尔是唯一一家能在美国本土制造先进芯片的美国公司，该公司在新闻稿中表示，美国政府向英特尔普通股投资 89 亿美元，以每股 20.47 美元购买了 4.33 3亿股，持有该公司 10% 的股份。英特尔指出，政府支付的价格低于当前的市场价格。89 亿美元中的 57 亿美元来自 CHIPS Act 下已授予但未支付的政府拨款，32 亿美元来自一项制造安全芯片的政府拨款。英特尔表示，美国政府将不会拥有董事席位或其它治理权力。

开源多媒体编解码器项目 FFmpeg 正式释出了代号为 Huffman 的 v8.0 版本。新版本距离上个大版本的发布相距约 17 个月。主要新变化包括：为 RealVideo 6.0、ADPCM IMA Xbox、G.728、Sanyo LD-ADPCM 引入了新的原生解码器；为三星 Advanced Professional Video 引入了 APV 解码器；为 APV、动画 JPEG-XL 和 libx265 alpha 层编码功能引入了编码支持；OpenHarmony 的编码和解码支持；Video Acceleration API (VA-API)的 VVC/H.266 支持； AVX-512 优化、FFV1 改进、AV1 RTP 分组器/解包器、AMD AMF 解码器、Vulkan 视频增强、更好的 HDR 视频支持；新的过滤器如 colordetect、pad_cuda、scale_d3d11、Whisper 等等等。

Arch Linux 项目披露其基础设施遭遇了 DDoS 攻击，提供了一旦网站发生宕机的一些权益方法，如使用镜像。Arch Linux 称，持续的 DDoS 攻击主要影响主网页、Arch User Repository (AUR)和论坛，它正在与托管服务提供商合作缓解攻击，同时在评估 DDoS 防护方案。

密歇根州的研究人员调查了亚马逊、Google、微软、Meta、Digital Realty 和 Equinix 六大公司数据中心用水量。亚马逊每年会发布可持续发展报告，但报告没有披露数据中心的用水量，微软的情况类似，Google 和 Meta 的报告更详细。根据 Google 和 Meta 的报告，2023 年 Meta 全球用水量为 8.13 亿加仑，其中 95% 即 7.76 亿加仑用于数据中心；2023 年 Google 全球运营用水量为 64 亿加仑，其中 95% 即 61 亿加仑用于数据中心，2024 年 Google 位于爱荷华州 Council Bluffs 的数据中心用水量达到了 10 亿加仑，是其所有数据中心中用水量最高的，用水量最少的是 Google 位于德州 Pflugerville 的数据中心，用水量 1 万加仑，相当于德州一个家庭两个月的用水量，该数据中心采用风冷而非水冷。