OrangeBot.AI Digest — 2025-11-13

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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。

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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

We introduce Lumine, the first open recipe for developing generalist agents capable of completing hours-long complex missions in real time within challenging 3D open-world environments. Lumine adopts a human-like interaction paradigm that unifies perception, reasoning, and action in an end-to-end manner, powered by a vision-language model. It processes raw pixels at 5 Hz to produce precise 30 Hz keyboard-mouse actions and adaptively invokes reasoning only when necessary. Trained in Genshin Impact, Lumine successfully completes the entire five-hour Mondstadt main storyline on par with human-level efficiency and follows natural language instructions to perform a broad spectrum of tasks in both 3D open-world exploration and 2D GUI manipulation across collection, combat, puzzle-solving, and NPC interaction. In addition to its in-domain performance, Lumine demonstrates strong zero-shot cross-game generalization. Without any fine-tuning, it accomplishes 100-minute missions in Wuthering Waves and the full five-hour first chapter of Honkai: Star Rail. These promising results highlight Lumine's effectiveness across distinct worlds and interaction dynamics, marking a concrete step toward generalist agents in open-ended environments.

Hit identification is a central challenge in early drug discovery, traditionally requiring substantial experimental resources. Recent advances in artificial intelligence, particularly large language models (LLMs), have enabled virtual screening methods that reduce costs and improve efficiency. However, the growing complexity of these tools has limited their accessibility to wet-lab researchers. Multi-agent systems offer a promising solution by combining the interpretability of LLMs with the precision of specialized models and tools. In this work, we present MADD, a multi-agent system that builds and executes customized hit identification pipelines from natural language queries. MADD employs four coordinated agents to handle key subtasks in de novo compound generation and screening. We evaluate MADD across seven drug discovery cases and demonstrate its superior performance compared to existing LLM-based solutions. Using MADD, we pioneer the application of AI-first drug design to five biological targets and release the identified hit molecules. Finally, we introduce a new benchmark of query-molecule pairs and docking scores for over three million compounds to contribute to the agentic future of drug design.

Diffusion-based video generation can create realistic videos, yet existing image- and text-based conditioning fails to offer precise motion control. Prior methods for motion-conditioned synthesis typically require model-specific fine-tuning, which is computationally expensive and restrictive. We introduce Time-to-Move (TTM), a training-free, plug-and-play framework for motion- and appearance-controlled video generation with image-to-video (I2V) diffusion models. Our key insight is to use crude reference animations obtained through user-friendly manipulations such as cut-and-drag or depth-based reprojection. Motivated by SDEdit's use of coarse layout cues for image editing, we treat the crude animations as coarse motion cues and adapt the mechanism to the video domain. We preserve appearance with image conditioning and introduce dual-clock denoising, a region-dependent strategy that enforces strong alignment in motion-specified regions while allowing flexibility elsewhere, balancing fidelity to user intent with natural dynamics. This lightweight modification of the sampling process incurs no additional training or runtime cost and is compatible with any backbone. Extensive experiments on object and camera motion benchmarks show that TTM matches or exceeds existing training-based baselines in realism and motion control. Beyond this, TTM introduces a unique capability: precise appearance control through pixel-level conditioning, exceeding the limits of text-only prompting. Visit our project page for video examples and code: https://time-to-move.github.io/.

Diffusion language models hold the promise of fast parallel generation, while autoregressive (AR) models typically excel in quality due to their causal structure aligning naturally with language modeling. This raises a fundamental question: can we achieve a synergy with high throughput, higher GPU utilization, and AR level quality? Existing methods fail to effectively balance these two aspects, either prioritizing AR using a weaker model for sequential drafting (speculative decoding), leading to lower drafting efficiency, or using some form of left-to-right (AR-like) decoding logic for diffusion, which still suffers from quality degradation and forfeits its potential parallelizability. We introduce TiDAR, a sequence-level hybrid architecture that drafts tokens (Thinking) in Diffusion and samples final outputs (Talking) AutoRegressively - all within a single forward pass using specially designed structured attention masks. This design exploits the free GPU compute density, achieving a strong balance between drafting and verification capacity. Moreover, TiDAR is designed to be serving-friendly (low overhead) as a standalone model. We extensively evaluate TiDAR against AR models, speculative decoding, and diffusion variants across generative and likelihood tasks at 1.5B and 8B scales. Thanks to the parallel drafting and sampling as well as exact KV cache support, TiDAR outperforms speculative decoding in measured throughput and surpasses diffusion models like Dream and Llada in both efficiency and quality. Most notably, TiDAR is the first architecture to close the quality gap with AR models while delivering 4.71x to 5.91x more tokens per second.

Augmenting Large Language Models (LLMs) with external tools enables them to execute complex, multi-step tasks. However, tool learning is hampered by the static synthetic data pipelines where data generation and model training are executed as two separate, non-interactive processes. This approach fails to adaptively focus on a model's specific weaknesses and allows noisy labels to persist, degrading training efficiency. We introduce LoopTool, a fully automated, model-aware data evolution framework that closes this loop by tightly integrating data synthesis and model training. LoopTool iteratively refines both the data and the model through three synergistic modules: (1) Greedy Capability Probing (GCP) diagnoses the model's mastered and failed capabilities; (2) Judgement-Guided Label Verification (JGLV) uses an open-source judge model to find and correct annotation errors, progressively purifying the dataset; and (3) Error-Driven Data Expansion (EDDE) generates new, challenging samples based on identified failures. This closed-loop process operates within a cost-effective, open-source ecosystem, eliminating dependence on expensive closed-source APIs. Experiments show that our 8B model trained with LoopTool significantly surpasses its 32B data generator and achieves new state-of-the-art results on the BFCL-v3 and ACEBench benchmarks for its scale. Our work demonstrates that closed-loop, self-refining data pipelines can dramatically enhance the tool-use capabilities of LLMs.

Vision-Language-Action (VLA) models have shown strong potential for general-purpose robotic manipulation, but their reliance on expert demonstrations limits their ability to learn from failures and perform self-corrections. Reinforcement learning (RL) addresses these through self-improving interactions with the physical environment, but suffers from high sample complexity on real robots. We introduce World-Model-based Policy Optimization (WMPO), a principled framework for on-policy VLA RL without interacting with the real environment. In contrast to widely used latent world models, WMPO focuses on pixel-based predictions that align the "imagined" trajectories with the VLA features pretrained with web-scale images. Crucially, WMPO enables the policy to perform on-policy GRPO that provides stronger performance than the often-used off-policy methods. Extensive experiments in both simulation and real-robot settings demonstrate that WMPO (i) substantially improves sample efficiency, (ii) achieves stronger overall performance, (iii) exhibits emergent behaviors such as self-correction, and (iv) demonstrates robust generalization and lifelong learning capabilities.

Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in vision-language answering tasks. Despite their strengths, these models often encounter challenges in achieving complex reasoning tasks such as mathematical problem-solving. Previous works have focused on fine-tuning on specialized mathematical datasets. However, these datasets are typically distilled directly from teacher models, which capture only static reasoning patterns and leaving substantial gaps compared to student models. This reliance on fixed teacher-derived datasets not only restricts the model's ability to adapt to novel or more intricate questions that extend beyond the confines of the training data, but also lacks the iterative depth needed for robust generalization. To overcome these limitations, we propose \method, a Mathematical Self-Evolving framework for MLLMs. In contrast to traditional one-shot fine-tuning paradigms, \method iteratively refines the model through cycles of inference, reflection, and reward-based feedback. Specifically, we leverage iterative fine-tuning by incorporating correct reasoning paths derived from previous-stage inference and integrating reflections from a specialized Outcome Reward Model (ORM). To verify the effectiveness of \method, we evaluate it on a suite of challenging benchmarks, demonstrating significant performance gains over backbone models. Notably, our experimental results on MathVL-test surpass the leading open-source multimodal mathematical reasoning model QVQ. Our code and models are available at https://zheny2751\allowbreak-dotcom.github.io/\allowbreak MathSE.github.io/.

User interface (UI) development requires translating design mockups into functional code, a process that remains repetitive and labor-intensive. While recent Vision-Language Models (VLMs) automate UI-to-Code generation, they generate only static HTML/CSS/JavaScript layouts lacking interactivity. To address this, we propose WebVIA, the first agentic framework for interactive UI-to-Code generation and validation. The framework comprises three components: 1) an exploration agent to capture multi-state UI screenshots; 2) a UI2Code model that generates executable interactive code; 3) a validation module that verifies the interactivity. Experiments demonstrate that WebVIA-Agent achieves more stable and accurate UI exploration than general-purpose agents (e.g., Gemini-2.5-Pro). In addition, our fine-tuned WebVIA-UI2Code models exhibit substantial improvements in generating executable and interactive HTML/CSS/JavaScript code, outperforming their base counterparts across both interactive and static UI2Code benchmarks. Our code and models are available at https://zheny2751-dotcom.github.io/webvia.github.io/{https://webvia.github.io}.

Diffusion models have demonstrated strong generative performance when using guidance methods such as classifier-free guidance (CFG), which enhance output quality by modifying the sampling trajectory. These methods typically improve a target output by intentionally degrading another, often the unconditional output, using heuristic perturbation functions such as identity mixing or blurred conditions. However, these approaches lack a principled foundation and rely on manually designed distortions. In this work, we propose Adversarial Sinkhorn Attention Guidance (ASAG), a novel method that reinterprets attention scores in diffusion models through the lens of optimal transport and intentionally disrupt the transport cost via Sinkhorn algorithm. Instead of naively corrupting the attention mechanism, ASAG injects an adversarial cost within self-attention layers to reduce pixel-wise similarity between queries and keys. This deliberate degradation weakens misleading attention alignments and leads to improved conditional and unconditional sample quality. ASAG shows consistent improvements in text-to-image diffusion, and enhances controllability and fidelity in downstream applications such as IP-Adapter and ControlNet. The method is lightweight, plug-and-play, and improves reliability without requiring any model retraining.

We introduce Motif-2-12.7B, a new open-weight foundation model that pushes the efficiency frontier of large language models by combining architectural innovation with system-level optimization. Designed for scalable language understanding and robust instruction generalization under constrained compute budgets, Motif-2-12.7B builds upon Motif-2.6B with the integration of Grouped Differential Attention (GDA), which improves representational efficiency by disentangling signal and noise-control attention pathways. The model is pre-trained on 5.5 trillion tokens spanning diverse linguistic, mathematical, scientific, and programming domains using a curriculum-driven data scheduler that gradually changes the data composition ratio. The training system leverages the MuonClip optimizer alongside custom high-performance kernels, including fused PolyNorm activations and the Parallel Muon algorithm, yielding significant throughput and memory efficiency gains in large-scale distributed environments. Post-training employs a three-stage supervised fine-tuning pipeline that successively enhances general instruction adherence, compositional understanding, and linguistic precision. Motif-2-12.7B demonstrates competitive performance across diverse benchmarks, showing that thoughtful architectural scaling and optimized training design can rival the capabilities of much larger models.

Web agents struggle to adapt to new websites due to the scarcity of environment specific tasks and demonstrations. Recent works have explored synthetic data generation to address this challenge, however, they suffer from data quality issues where synthesized tasks contain hallucinations that cannot be executed, and collected trajectories are noisy with redundant or misaligned actions. In this paper, we propose SynthAgent, a fully synthetic supervision framework that aims at improving synthetic data quality via dual refinement of both tasks and trajectories. Our approach begins by synthesizing diverse tasks through categorized exploration of web elements, ensuring efficient coverage of the target environment. During trajectory collection, we refine tasks when conflicts with actual observations are detected, mitigating hallucinations while maintaining task consistency. After collection, we conduct trajectory refinement with a global context to mitigate potential noise or misalignments. Finally, we fine-tune open-source web agents on the refined synthetic data to adapt them to the target environment. Experimental results demonstrate that SynthAgent outperforms existing synthetic data methods, validating the importance of high-quality synthetic supervision. The code will be publicly available at https://github.com/aiming-lab/SynthAgent.

Agentic coding tools, such as OpenAI Codex, Claude Code, and Cursor, are transforming the software engineering landscape. These AI-powered systems function as autonomous teammates capable of planning and executing complex development tasks. Agents have become active participants in refactoring, a cornerstone of sustainable software development aimed at improving internal code quality without altering observable behavior. Despite their increasing adoption, there is a critical lack of empirical understanding regarding how agentic refactoring is utilized in practice, how it compares to human-driven refactoring, and what impact it has on code quality. To address this empirical gap, we present a large-scale study of AI agent-generated refactorings in real-world open-source Java projects, analyzing 15,451 refactoring instances across 12,256 pull requests and 14,988 commits derived from the AIDev dataset. Our empirical analysis shows that refactoring is a common and intentional activity in this development paradigm, with agents explicitly targeting refactoring in 26.1% of commits. Analysis of refactoring types reveals that agentic efforts are dominated by low-level, consistency-oriented edits, such as Change Variable Type (11.8%), Rename Parameter (10.4%), and Rename Variable (8.5%), reflecting a preference for localized improvements over the high-level design changes common in human refactoring. Additionally, the motivations behind agentic refactoring focus overwhelmingly on internal quality concerns, with maintainability (52.5%) and readability (28.1%). Furthermore, quantitative evaluation of code quality metrics shows that agentic refactoring yields small but statistically significant improvements in structural metrics, particularly for medium-level changes, reducing class size and complexity (e.g., Class LOC median Δ = -15.25).

Language models exhibit remarkable natural language generation capabilities but remain prone to hallucinations, generating factually incorrect information despite producing syntactically coherent responses. This study introduces the Licensing Oracle, an architectural solution designed to stem hallucinations in LMs by enforcing truth constraints through formal validation against structured knowledge graphs. Unlike statistical approaches that rely on data scaling or fine-tuning, the Licensing Oracle embeds a deterministic validation step into the model's generative process, ensuring that only factually accurate claims are made. We evaluated the effectiveness of the Licensing Oracle through experiments comparing it with several state-of-the-art methods, including baseline language model generation, fine-tuning for factual recall, fine-tuning for abstention behavior, and retrieval-augmented generation (RAG). Our results demonstrate that although RAG and fine-tuning improve performance, they fail to eliminate hallucinations. In contrast, the Licensing Oracle achieved perfect abstention precision (AP = 1.0) and zero false answers (FAR-NE = 0.0), ensuring that only valid claims were generated with 89.1% accuracy in factual responses. This work shows that architectural innovations, such as the Licensing Oracle, offer a necessary and sufficient solution for hallucinations in domains with structured knowledge representations, offering guarantees that statistical methods cannot match. Although the Licensing Oracle is specifically designed to address hallucinations in fact-based domains, its framework lays the groundwork for truth-constrained generation in future AI systems, providing a new path toward reliable, epistemically grounded models.

Valve 宣布了三款新硬件产品，它们将于 2026 年初上市，具体日期和价格尚未披露。三款产品包括了手柄 Steam Controller、头显 Steam Frame 以及运行基于 Arch Linux 的 SteamOS 3（桌面环境是 KDE）的 Steam Machine。其中头显使用的处理器是高通的第三代骁龙 8，2160 x 2160 LCD（单眼），16GB 统一 LPDDR5X RAM。Steam Machine 则是一款标准的入门级游戏 PC：AMD Zen 4 6c / 12T，28 个 AMD RDNA3 CU，16GB DDR5 + 8GB GDDR6 VRAM，可选 512GB NVMe SSD 或 2TB NVMe SSD，重 2.6 公斤。Valve 称三款产品均会在 Steam Deck 当前的发售地区（美国、加拿大、英国、欧盟和澳大利亚）和 KOMODO 所覆盖的地区（日本、韩国、香港和台湾）供货。可能和 Steam Deck 情况类似，中国大陆地区的用户只能通过代购了。

天津蓝天格锐 430 亿元非法集资案当事人、携款逃到英国的钱志敏因洗钱罪被判 11 年 8 个月。中国受害者正寻求英国政府归还部分其扣押的目前价值约 50 亿英镑的比特币。现年 47 岁的钱志敏于 2017 年 9 月持假护照抵达英国，她搬进了 Hampstead Heath 的一栋豪宅，月租金逾 1.7 万英镑。为支付租金需要将比特币兑换成现金，她装成是一位富有的古董钻石继承人，雇佣了前外卖员温俭为其私人助理，帮助将比特币兑换成现金和房产等资产。随着比特币价格飙升，钱看起来可以兑现蓝天格锐向投资者承诺的“躺着也能致富”的目标。温俭在庭审中表示，钱大部分时间都躺在床上玩游戏和网购。但在她们试图购买一栋豪宅时，由于无法说明财富来源而引起警方调查。警方在搜查中扣押了数万枚比特币。这些比特币目前价值 466 亿人民币，超过了从投资者骗取的 430 亿元集资。暂时还不清楚投资者能否获得全额或更多退款。英国财政部尚未回应如何处理这些比特币。

根据发表在《Nature Aging》期刊上的一项研究，研究人员发现使用多种语言与显著延缓的衰老过程相关。在这项大规模研究中，研究人员分析了来自 27 个欧洲国家的 86,149 名健康参与者的数据。为更精确衡量衰老速度，研究团队开发了一种名为“生物行为年龄差”的新指标。这个指标综合了个体的功能能力、教育水平、认知表现等积极因素，以及心脏病、高血压、感官损伤等消极因素，来预测一个人的生物行为年龄。当这个预测年龄超过其实际年龄时，就意味着他正在加速衰老，反之则说明衰老延缓。研究结果显示，仅会说母语的单语者，其经历加速衰老的概率是多语者的 2.11 倍。掌握至少一门外语的人，经历加速衰老的概率降低了超过一半。这种保护效应还呈现出“剂量依赖性”，即掌握的外语越多，其经历加速衰老的可能性就越低。研究人员推测，这种保护效应源于多语能力对大脑“认知储备”的不断锻炼。当一个人掌握多门语言时，即使只使用其中一种，其他语言也始终处于活跃状态。大脑需要持续地进行抑制和切换，这极大地锻炼了执行功能、注意力和记忆力等高级认知能力。这些被频繁调用的脑网络，恰恰是那些在衰老过程中最容易退化的区域。因此，长期使用多种语言，就像是给大脑进行持续的“健身”，增强了其抵抗因为年龄增加而衰退的能力。

索尼宣布 PS5 游戏机的销量突破 8420 万台，正式超过了所有已发售的 Xbox 游戏机型号。在截至 9 月 30 日的三个月内，PS5 新增销量 390 万，超过去年同期的 380 万，且这一销量是在价格上涨之后实现的，因此这一成绩令人瞩目。微软最畅销的型号是 2016 年 4 月停产的 Xbox 360，其销量约 8400 万台。分析师估计，PS5 的销量至少是微软同代游戏机 Xbox Series X 和 S 总销量的两倍。索尼表示，PS5 是它最成功的游戏机。

微软释出了编译器 Visual Studio 的最新版本。Visual Studio 2026 的变化包括：调整 UI；开箱即用现有的 Visual Studio 2022 扩展；改进 C++23 核心语言和标准库实现；改进编译器运行时性能；ARM64 版本支持 AddressSanitizer；等等，更多可浏览发布公告。

Mozilla 释出了 Firefox 145.0。主要变化包括：停止支持 32 位 Linux 系统，仍然使用 32 位 Linux 发行版的用户将不会收到更新，Mozilla 建议相关用户切换到 64 位操作系统；支持添加和删除 PDF 注释；增强隐私浏览的指纹识别能力，标签组预览，从侧边栏管理密码，支持播放 Matroska MKV 媒体内容；等等。

开源项目大部分情况下是志愿者维护的，公开源代码通常也意味着欢迎用户报告 Bug 和递交需求，开发者一般也会根据自己的时间而做出合理回应。但开发者也是普通人，他们的热情也会随着时间的推移而消退，而在 AI 时代，bug 报告比以往任何时候都容易，结果是志愿开发者们被淹没在各种 Bug 报告中，严重消耗他们的热情和精力。著名的开源多媒体框架 FFmpeg 项目收到了来自 Google AI 工具报告的 bug，涉及解码 1995 年游戏《Rebel Assault 2》使用的编解码器，这种极其罕见的 bug 可能除了 AI 人类根本不可能发现，此事引发了广泛争议：一家市值数万亿美元的公司让志愿者去修复它发现的 bug，这是极其不合理的。FFmpeg 项目将此类 bug 报告称之为“CVE 垃圾（CVE slop）”，认为 Google 要么直接提供资金资助项目要么停止报告此类报告。FFmpeg 不是唯一遭遇此类问题的开源项目。

安全公司火绒报告，曾经的装机软件鲁大师被发现会绕过北京地区投放推广。安全研究人员发现，包含成都奇鲁科技有限公司、天津杏仁桉科技有限公司在内的多家软件厂商，正通过云控配置方式构建大规模推广产业链，远程开启推广模块以实现流量变现。这些厂商通过云端下达配置指令，动态控制软件的推广行为，不同公司及其产品的推广方式各有差异。以成都奇鲁科技旗下的鲁大师为例，其推广行为涵盖但不限于：利用浏览器弹窗推广"传奇"类页游、在未获用户明确许可的情况下弹窗安装第三方软件、篡改京东网页链接并插入京粉推广参数以获取佣金、弹出带有渠道标识的百度搜索框、植入具有推广性质且伪装为正常应用的浏览器扩展程序等。以鲁大师为例，软件会根据用户所在地区针对性的投放推广云控配置，对北京地区的用户会减少或不下发推广相关的云控配置，它还会通过遍历检测当前系统信息的方式判断是否为技术人员、是否在虚拟机中、是否为业务会员等相关数据，从而针对性调整云控配置。

坦桑尼亚的“粮仓地区（BBRs）”是国家粮食供应的核心，贡献了全国超 38% 的玉米产量。然而 2018 年全国营养调查显示了一个矛盾现象：该国儿童发育迟缓和严重营养不良率最高的五个地区，全部位于这些农业高产区域。这一“粮仓悖论”引发关注：为何粮食主产区的儿童反而比非主产区更容易出现生长问题？中国农业大学和坦桑尼亚的研究人员发现背后的原因是饮食单一。研究发现，在非粮仓地区，粮食产量增加确实带来了更丰富的家庭饮食，这与营养改善直接相关；但在粮仓地区，尽管产量更高，家庭饮食多样性却没有显著提升。这可能与粮仓地区农业商业化程度高有关——农户更倾向于种植单一经济作物并出售，而非为自家消费种植多样化食物，导致“高产却饮食单一”的现象。研究指出，单纯提高粮食产量不足以解决儿童营养不良的问题，尤其是在农业商业化程度高的地区。

太阳黑子区域 AR4274 连续三天释放出 X 级耀斑（X 级是最高强度的耀斑），包括一个 X1.7 级、一个 X1.2 级和一个 X5.1 级耀斑，后者是 25 太阳周期至今释放的第六强耀斑和今年释放的最强耀斑。X5.1 级耀斑释放的日冕物质抛射将于 11 月 13 日抵达地球，而之前耀斑释放的日冕物质抛射已经抵达地球，产生了 G4 级地磁风暴，世界多地都观察到了红色的极光。红色是太阳高能粒子撞击大气层氧原子产生的。太阳向地球喷射出的部分粒子能量极强，甚至穿透大气层直达地面。世界各地的中子监测器都探测到了这次名为 Ground Level Event(GLE)的事件，如此强度的 GLE 极为罕见，每个太阳周期只会发生一到两次。上一次类似强度的 GLE 事件发生在 2006 年 12 月 13 日。这意味着该事件是二十年一遇。

从美国内战结束到 1950 年代末，英国一直是世界最大的造船国之一。英国造船厂的产量在 1890 年代占全球船舶吨位的 80%，到第一次世界大战前夕，英国市场份额降至了 60%，但接下来的几十年里它仍然是世界最大的造船国之一。二战结束后，英国造船业前景看起来一片光明，因为大多数国家的造船业都受到了战争的重创，因此战后最初几年，英国造船吨位超过了世界其他国家的总和。但这种成功只是昙花一现。英国造船吨位从 1947 年占世界总吨位的 57% 降至十年后的 17%。到 1970 年代其产量不足世界总产量的 5%，到 1990 年代跌破 1%。2023 年英国完全停止了商船建造。英国造船业的衰落是因为其传统模式无法适应现代造船业的转型。英国造船业的生产体系高度依赖受工会培训的熟练劳动力，最大限度减少对昂贵基础设施或设备的需求，将管理费用降至最低，从而有效降低成本，劳动力规模可根据需求灵活调整，工人可以根据工作需要在不同船厂之间流动。这种生产体系在几十年内运行良好，但到了二战后开始瓦解。英国船厂拒绝了转型，因为投资新造船基础设施的风险太高，加上其所在港口城市也无法允许他们扩大规模，它们只能固守原有的生产方式，被世界其它地区超越，一开始是瑞典，接着是日本、韩国和中国。

康奈尔大学研究人员分析了 AI 数据中心对美国环境的影响。研究团队发现，按照目前的增长速度，到 2030 年 AI 数据中心每年将向大气中排放 2400-4400 万吨二氧化碳，相当于美国公路增加 500-1000 万辆汽车的排放量。AI 数据中心每年还将消耗 7.31-11.25 亿立方米的水，相当于 600-1000 万美国家庭的年用水量。这些累积效应将使 AI 行业实现净零排放的目标遥不可及。由于很多数据中心建在缺水地区，如果在水资源和能源丰富的地区建数据中心将有助于减轻对环境的影响。研究显示如果能利用智能选址、加快电网脱碳和提高运行效率，二氧化碳排放将减少约 73%，水资源消耗减少 86%。

Valve 过去几年一直通过改进 Proton 兼容层改善 Windows 游戏运行在 Linux 操作系统上的兼容性。但 Proton 兼容层只向后支持到使用 Direct3D 8 开发的游戏，而 Direct3D 8 是微软在 2000 年 11 月发布的图形 API。现在名为 d7vk 的项目正致力于兼容为 Direct3D 7 开发的游戏。Direct3D 7 于 1999 年 9 月发布， PC Gaming Wiki 上列出了逾 400 款使用 D3D7 API 开发的游戏，其中包括《Escape from Monkey Island》、《Arx Fatalis》和《Hitman: Codename 47》等经典游戏。Wine 的 WineD3D 兼容层已经尝试以某种形式支持 D3D7 API，d7vk 不是基于 WineD3D，它声称很多游戏的性能比 WineD3D 表现更出色。d7vk 项目作者 WinterSnowfall 表示，由于 D3D7 API 互操作性过于糟糕完美兼容不太可能，该项目也不太可能兼容更古老的 Direct3D 版本。

网络犯罪组织 Jabber Zeus 头目 Vyacheslav“Tank”Penchukov 于 2022 年前往瑞士会见妻子途中被捕，去年被美国法院判处 18 年监禁和超过 7300 万美元赔偿金。他在科罗拉多州的监狱里首次接受了记者的采访，谈论了他的网络犯罪生涯。他攀登到网络犯罪世界的顶峰不是因为技术精湛而是因为魅力，他笑说自己非常友善容易交朋友。他能长期逍遥法外据说就是依靠其人脉。他在两个不同时期分别领导了两个网络犯罪组织。他先是领导 Jabber Zeus 通过部署银行木马 Zeus 从受害者银行账户里窃取资金（Jabber 这一名字来自他们使用的消息应用），然后在 2018-2022 年之间进入勒索软件行业。Penchukov 说，2000 年代末期他们在乌克兰顿涅茨克市中心的一间办公室里工作，每天办公六七个小时，从海外受害者窃取金钱，他经常在一天结束时以 DJ Slava Rich 的艺名在城里表演。他当时只有 20 多岁，买车就像买衣服一样，他拥有 6 辆昂贵的德国汽车。警方通过监听 Jabber 以及他透露的女儿出生信息识别了其身份，FBI 领导的 Trident Breach 行动逮捕了多名 Jabber Zeus 成员，但 Penchukov 靠着有人通风报信和德国改装车奥迪 S8（装了兰博基尼引擎）逃脱了。他低调了一阵时间，然后做起了煤炭生意，但 FBI 并没有忘记他，他被列入了通缉名单。因为他的富有众所周知当地官员不时来敲诈。2014 年俄罗斯入侵克里米亚毁掉了他的煤炭生意，加上遭到当地官员的勒索，他开始重操旧业，做起了勒索软件生意，成为了 Maze、Egregor 和 Conti 等勒索软件组织的主要盟员。他领导了名为 IcedID 的勒索组织。他表示自己在网络犯罪时不会去考虑受害者，他唯一流露出悔意是在谈到一家残疾儿童慈善机构遭受勒索软件攻击时。他真正后悔的似乎是对同伙过于信任，这最终导致他落网。“在网络犯罪圈里，你交不到朋友，因为第二天你的朋友会被捕，然后变成告密者。”

乔治梅森大学的研究人员回顾了 2005 年至 2025 年间进行的 21 项实验研究，发现有一致的证据表明，接触杀虫剂会对人类健康产生负面影响，特别是男性生殖健康。这项研究集中于新烟碱类杀虫剂，这是全球使用最广泛的一类杀虫剂。这种化学品通常用于农作物，它们会被土壤、水和植物组织吸收。因此残留物可能会留在人类食用的食物中。为了了解潜在的健康影响，研究团队回顾了动物研究中的数据，这些研究探讨了神经刺激如何影响雄性啮齿类动物的生殖健康。该研究进一步证明，现代农业化学品虽然对作物保护至关重要，但也可能带来看不见的风险，因而需要更密切的科学研究。