OrangeBot.AI Digest — 2025-12-09

General purpose 3D and 2D game engine using Go (golang) and Vulkan with built in editor

A Claude Code plugin that automatically captures everything Claude does during your coding sessions, compresses it with AI (using Claude's agent-sdk), and injects relevant context back into future sessions.

Free, local, open-source AI app builder ✨ v0 / lovable / Bolt alternative 🌟 Star if you like it!

Open-Source Frontier Voice AI

cuTile is a programming model for writing parallel kernels for NVIDIA GPUs

A collection of sample agents built with Agent Development Kit (ADK)

In-depth tutorials on LLMs, RAGs and real-world AI agent applications.

微舆：人人可用的多Agent舆情分析助手，打破信息茧房，还原舆情原貌，预测未来走向，辅助决策！从0实现，不依赖任何框架。

21 Lessons, Get Started Building with Generative AI

The open source coding agent.

Improve your resumes with Resume Matcher. Get insights, keyword suggestions and tune your resumes to job descriptions.

提供多款 Shadowrocket 规则，拥有强劲的广告过滤功能。每日 8 时重新构建规则。

We introduce Native Parallel Reasoner (NPR), a teacher-free framework that enables Large Language Models (LLMs) to self-evolve genuine parallel reasoning capabilities. NPR transforms the model from sequential emulation to native parallel cognition through three key innovations: 1) a self-distilled progressive training paradigm that transitions from ``cold-start'' format discovery to strict topological constraints without external supervision; 2) a novel Parallel-Aware Policy Optimization (PAPO) algorithm that optimizes branching policies directly within the execution graph, allowing the model to learn adaptive decomposition via trial and error; and 3) a robust NPR Engine that refactors memory management and flow control of SGLang to enable stable, large-scale parallel RL training. Across eight reasoning benchmarks, NPR trained on Qwen3-4B achieves performance gains of up to 24.5% and inference speedups up to 4.6x. Unlike prior baselines that often fall back to autoregressive decoding, NPR demonstrates 100% genuine parallel execution, establishing a new standard for self-evolving, efficient, and scalable agentic reasoning.

Rotary Position Embeddings (RoPE) have become a standard for encoding sequence order in Large Language Models (LLMs) by applying rotations to query and key vectors in the complex plane. Standard implementations, however, utilize only the real component of the complex-valued dot product for attention score calculation. This simplification discards the imaginary component, which contains valuable phase information, leading to a potential loss of relational details crucial for modeling long-context dependencies. In this paper, we propose an extension that re-incorporates this discarded imaginary component. Our method leverages the full complex-valued representation to create a dual-component attention score. We theoretically and empirically demonstrate that this approach enhances the modeling of long-context dependencies by preserving more positional information. Furthermore, evaluations on a suite of long-context language modeling benchmarks show that our method consistently improves performance over the standard RoPE, with the benefits becoming more significant as context length increases. The code is available at https://github.com/OpenMOSS/rope_pp.

Existing video editing methods face a critical trade-off: expert models offer precision but rely on task-specific priors like masks, hindering unification; conversely, unified temporal in-context learning models are mask-free but lack explicit spatial cues, leading to weak instruction-to-region mapping and imprecise localization. To resolve this conflict, we propose VideoCoF, a novel Chain-of-Frames approach inspired by Chain-of-Thought reasoning. VideoCoF enforces a ``see, reason, then edit" procedure by compelling the video diffusion model to first predict reasoning tokens (edit-region latents) before generating the target video tokens. This explicit reasoning step removes the need for user-provided masks while achieving precise instruction-to-region alignment and fine-grained video editing. Furthermore, we introduce a RoPE alignment strategy that leverages these reasoning tokens to ensure motion alignment and enable length extrapolation beyond the training duration. We demonstrate that with a minimal data cost of only 50k video pairs, VideoCoF achieves state-of-the-art performance on VideoCoF-Bench, validating the efficiency and effectiveness of our approach. Our code, weight, data are available at https://github.com/knightyxp/VideoCoF.

Voxel art is a distinctive stylization widely used in games and digital media, yet automated generation from 3D meshes remains challenging due to conflicting requirements of geometric abstraction, semantic preservation, and discrete color coherence. Existing methods either over-simplify geometry or fail to achieve the pixel-precise, palette-constrained aesthetics of voxel art. We introduce Voxify3D, a differentiable two-stage framework bridging 3D mesh optimization with 2D pixel art supervision. Our core innovation lies in the synergistic integration of three components: (1) orthographic pixel art supervision that eliminates perspective distortion for precise voxel-pixel alignment; (2) patch-based CLIP alignment that preserves semantics across discretization levels; (3) palette-constrained Gumbel-Softmax quantization enabling differentiable optimization over discrete color spaces with controllable palette strategies. This integration addresses fundamental challenges: semantic preservation under extreme discretization, pixel-art aesthetics through volumetric rendering, and end-to-end discrete optimization. Experiments show superior performance (37.12 CLIP-IQA, 77.90\% user preference) across diverse characters and controllable abstraction (2-8 colors, 20x-50x resolutions). Project page: https://yichuanh.github.io/Voxify-3D/

Reference-to-video (R2V) generation aims to synthesize videos that align with a text prompt while preserving the subject identity from reference images. However, current R2V methods are hindered by the reliance on explicit reference image-video-text triplets, whose construction is highly expensive and difficult to scale. We bypass this bottleneck by introducing Saber, a scalable zero-shot framework that requires no explicit R2V data. Trained exclusively on video-text pairs, Saber employs a masked training strategy and a tailored attention-based model design to learn identity-consistent and reference-aware representations. Mask augmentation techniques are further integrated to mitigate copy-paste artifacts common in reference-to-video generation. Moreover, Saber demonstrates remarkable generalization capabilities across a varying number of references and achieves superior performance on the OpenS2V-Eval benchmark compared to methods trained with R2V data.

Large language model (LLM)-based multi-agent systems are challenging to debug because failures often arise from long, branching interaction traces. The prevailing practice is to leverage LLMs for log-based failure localization, attributing errors to a specific agent and step. However, this paradigm has two key limitations: (i) log-only debugging lacks validation, producing untested hypotheses, and (ii) single-step or single-agent attribution is often ill-posed, as we find that multiple distinct interventions can independently repair the failed task. To address the first limitation, we introduce DoVer, an intervention-driven debugging framework, which augments hypothesis generation with active verification through targeted interventions (e.g., editing messages, altering plans). For the second limitation, rather than evaluating on attribution accuracy, we focus on measuring whether the system resolves the failure or makes quantifiable progress toward task success, reflecting a more outcome-oriented view of debugging. Within the Magnetic-One agent framework, on the datasets derived from GAIA and AssistantBench, DoVer flips 18-28% of failed trials into successes, achieves up to 16% milestone progress, and validates or refutes 30-60% of failure hypotheses. DoVer also performs effectively on a different dataset (GSMPlus) and agent framework (AG2), where it recovers 49% of failed trials. These results highlight intervention as a practical mechanism for improving reliability in agentic systems and open opportunities for more robust, scalable debugging methods for LLM-based multi-agent systems. Project website and code will be available at https://aka.ms/DoVer.

We study instruction-guided editing of egocentric videos for interactive AR applications. While recent AI video editors perform well on third-person footage, egocentric views present unique challenges - including rapid egomotion and frequent hand-object interactions - that create a significant domain gap. Moreover, existing offline editing pipelines suffer from high latency, limiting real-time interaction. To address these issues, we present a complete ecosystem for egocentric video editing. First, we construct EgoEditData, a carefully designed and manually curated dataset specifically designed for egocentric editing scenarios, featuring rich hand-object interactions, while explicitly preserving hands. Second, we develop EgoEdit, an instruction-following egocentric video editor that supports real-time streaming inference on a single GPU. Finally, we introduce EgoEditBench, an evaluation suite targeting instruction faithfulness, hand and interaction preservation, and temporal stability under egomotion. Across both egocentric and general editing tasks, EgoEdit produces temporally stable, instruction-faithful results with interactive latency. It achieves clear gains on egocentric editing benchmarks-where existing methods struggle-while maintaining performance comparable to the strongest baselines on general editing tasks. EgoEditData and EgoEditBench will be made public for the research community. See our website at https://snap-research.github.io/EgoEdit

Most visual generative models compress images into a latent space before applying diffusion or autoregressive modelling. Yet, existing approaches such as VAEs and foundation model aligned encoders implicitly constrain the latent space without explicitly shaping its distribution, making it unclear which types of distributions are optimal for modeling. We introduce Distribution-Matching VAE (DMVAE), which explicitly aligns the encoder's latent distribution with an arbitrary reference distribution via a distribution matching constraint. This generalizes beyond the Gaussian prior of conventional VAEs, enabling alignment with distributions derived from self-supervised features, diffusion noise, or other prior distributions. With DMVAE, we can systematically investigate which latent distributions are more conducive to modeling, and we find that SSL-derived distributions provide an excellent balance between reconstruction fidelity and modeling efficiency, reaching gFID equals 3.2 on ImageNet with only 64 training epochs. Our results suggest that choosing a suitable latent distribution structure (achieved via distribution-level alignment), rather than relying on fixed priors, is key to bridging the gap between easy-to-model latents and high-fidelity image synthesis. Code is avaliable at https://github.com/sen-ye/dmvae.

Humans do not just see attribute similarity -- we also see relational similarity. An apple is like a peach because both are reddish fruit, but the Earth is also like a peach: its crust, mantle, and core correspond to the peach's skin, flesh, and pit. This ability to perceive and recognize relational similarity, is arguable by cognitive scientist to be what distinguishes humans from other species. Yet, all widely used visual similarity metrics today (e.g., LPIPS, CLIP, DINO) focus solely on perceptual attribute similarity and fail to capture the rich, often surprising relational similarities that humans perceive. How can we go beyond the visible content of an image to capture its relational properties? How can we bring images with the same relational logic closer together in representation space? To answer these questions, we first formulate relational image similarity as a measurable problem: two images are relationally similar when their internal relations or functions among visual elements correspond, even if their visual attributes differ. We then curate 114k image-caption dataset in which the captions are anonymized -- describing the underlying relational logic of the scene rather than its surface content. Using this dataset, we finetune a Vision-Language model to measure the relational similarity between images. This model serves as the first step toward connecting images by their underlying relational structure rather than their visible appearance. Our study shows that while relational similarity has a lot of real-world applications, existing image similarity models fail to capture it -- revealing a critical gap in visual computing.

We propose Multi-view Pyramid Transformer (MVP), a scalable multi-view transformer architecture that directly reconstructs large 3D scenes from tens to hundreds of images in a single forward pass. Drawing on the idea of ``looking broader to see the whole, looking finer to see the details," MVP is built on two core design principles: 1) a local-to-global inter-view hierarchy that gradually broadens the model's perspective from local views to groups and ultimately the full scene, and 2) a fine-to-coarse intra-view hierarchy that starts from detailed spatial representations and progressively aggregates them into compact, information-dense tokens. This dual hierarchy achieves both computational efficiency and representational richness, enabling fast reconstruction of large and complex scenes. We validate MVP on diverse datasets and show that, when coupled with 3D Gaussian Splatting as the underlying 3D representation, it achieves state-of-the-art generalizable reconstruction quality while maintaining high efficiency and scalability across a wide range of view configurations.

Recent video generation models demonstrate impressive synthesis capabilities but remain limited by single-modality conditioning, constraining their holistic world understanding. This stems from insufficient cross-modal interaction and limited modal diversity for comprehensive world knowledge representation. To address these limitations, we introduce UnityVideo, a unified framework for world-aware video generation that jointly learns across multiple modalities (segmentation masks, human skeletons, DensePose, optical flow, and depth maps) and training paradigms. Our approach features two core components: (1) dynamic noising to unify heterogeneous training paradigms, and (2) a modality switcher with an in-context learner that enables unified processing via modular parameters and contextual learning. We contribute a large-scale unified dataset with 1.3M samples. Through joint optimization, UnityVideo accelerates convergence and significantly enhances zero-shot generalization to unseen data. We demonstrate that UnityVideo achieves superior video quality, consistency, and improved alignment with physical world constraints. Code and data can be found at: https://github.com/dvlab-research/UnityVideo

We introduce LongCat-Image, a pioneering open-source and bilingual (Chinese-English) foundation model for image generation, designed to address core challenges in multilingual text rendering, photorealism, deployment efficiency, and developer accessibility prevalent in current leading models. 1) We achieve this through rigorous data curation strategies across the pre-training, mid-training, and SFT stages, complemented by the coordinated use of curated reward models during the RL phase. This strategy establishes the model as a new state-of-the-art (SOTA), delivering superior text-rendering capabilities and remarkable photorealism, and significantly enhancing aesthetic quality. 2) Notably, it sets a new industry standard for Chinese character rendering. By supporting even complex and rare characters, it outperforms both major open-source and commercial solutions in coverage, while also achieving superior accuracy. 3) The model achieves remarkable efficiency through its compact design. With a core diffusion model of only 6B parameters, it is significantly smaller than the nearly 20B or larger Mixture-of-Experts (MoE) architectures common in the field. This ensures minimal VRAM usage and rapid inference, significantly reducing deployment costs. Beyond generation, LongCat-Image also excels in image editing, achieving SOTA results on standard benchmarks with superior editing consistency compared to other open-source works. 4) To fully empower the community, we have established the most comprehensive open-source ecosystem to date. We are releasing not only multiple model versions for text-to-image and image editing, including checkpoints after mid-training and post-training stages, but also the entire toolchain of training procedure. We believe that the openness of LongCat-Image will provide robust support for developers and researchers, pushing the frontiers of visual content creation.

Recent reinforcement learning (RL) techniques have yielded impressive reasoning improvements in language models, yet it remains unclear whether post-training truly extends a model's reasoning ability beyond what it acquires during pre-training. A central challenge is the lack of control in modern training pipelines: large-scale pre-training corpora are opaque, mid-training is often underexamined, and RL objectives interact with unknown prior knowledge in complex ways. To resolve this ambiguity, we develop a fully controlled experimental framework that isolates the causal contributions of pre-training, mid-training, and RL-based post-training. Our approach employs synthetic reasoning tasks with explicit atomic operations, parseable step-by-step reasoning traces, and systematic manipulation of training distributions. We evaluate models along two axes: extrapolative generalization to more complex compositions and contextual generalization across surface contexts. Using this framework, we reconcile competing views on RL's effectiveness. We show that: 1) RL produces true capability gains (pass@128) only when pre-training leaves sufficient headroom and when RL data target the model's edge of competence, tasks at the boundary that are difficult but not yet out of reach. 2) Contextual generalization requires minimal yet sufficient pre-training exposure, after which RL can reliably transfer. 3) Mid-training significantly enhances performance under fixed compute compared with RL only, demonstrating its central but underexplored role in training pipelines. 4) Process-level rewards reduce reward hacking and improve reasoning fidelity. Together, these results clarify the interplay between pre-training, mid-training, and RL, offering a foundation for understanding and improving reasoning LM training strategies.

We propose ReCamDriving, a purely vision-based, camera-controlled novel-trajectory video generation framework. While repair-based methods fail to restore complex artifacts and LiDAR-based approaches rely on sparse and incomplete cues, ReCamDriving leverages dense and scene-complete 3DGS renderings for explicit geometric guidance, achieving precise camera-controllable generation. To mitigate overfitting to restoration behaviors when conditioned on 3DGS renderings, ReCamDriving adopts a two-stage training paradigm: the first stage uses camera poses for coarse control, while the second stage incorporates 3DGS renderings for fine-grained viewpoint and geometric guidance. Furthermore, we present a 3DGS-based cross-trajectory data curation strategy to eliminate the train-test gap in camera transformation patterns, enabling scalable multi-trajectory supervision from monocular videos. Based on this strategy, we construct the ParaDrive dataset, containing over 110K parallel-trajectory video pairs. Extensive experiments demonstrate that ReCamDriving achieves state-of-the-art camera controllability and structural consistency.

Process reward models (PRMs) that provide dense, step-level feedback have shown promise for reinforcement learning, yet their adoption remains limited by the need for expensive step-level annotations or ground truth references. We propose SPARK: a three-stage framework where in the first stage a generator model produces diverse solutions and a verifier model evaluates them using parallel scaling (self-consistency) and sequential scaling (meta-critique). In the second stage, we use these verification outputs as synthetic training data to fine-tune generative process reward models, which subsequently serve as reward signals during training. We show that aggregating multiple independent verifications at the step level produces training data for process reward models that surpass ground-truth outcome supervision, achieving 67.5 F1 on ProcessBench (a benchmark for identifying erroneous steps in mathematical reasoning) compared to 66.4 for reference-guided training and 61.9 for GPT-4o. In the final stage, we apply our generative PRM with chain-of-thought verification (PRM-CoT) as the reward model in RL experiments on mathematical reasoning, and introduce format constraints to prevent reward hacking. Using Qwen2.5-Math-7B, we achieve 47.4% average accuracy across six mathematical reasoning benchmarks, outperforming ground-truth-based RLVR (43.9%). Our work enables reference-free RL training that exceeds ground-truth methods, opening new possibilities for domains lacking verifiable answers or accessible ground truth.

自 1992 年以来，美国 50 岁以下人群八种癌症的诊断率翻了一番。美国癌症研究协会(American Association for Cancer Research)表示本周将召开特别会议，讨论年轻人群癌症率上升的问题。部分专家认为亟需找出这一现象背后的原因。还有一部分专家则认为没必要担忧，很多癌症是过早被发现了，本就不会致命。数十年来人们已经知道不是所有癌症都危险。部分癌症会自己消失。部分癌症会停止生长或不构成任何风险——不会引起症状也不会扩散。但问题在于不可能知道一个人的癌症是否致命。哈佛医学院的 H. Gilbert Welch 博士认为，判断癌症诊断人数上升是虚惊一场还是真正危险信号的一种方法是观察死亡人数是否同时上升。如果癌症发病率飙升，但死亡率保持稳定，那么很多患者其实不需要接受诊断。美国八种癌症诊断率上升并没有伴随着死亡人数增加。八种癌症中只有结直肠癌和子宫内膜癌死亡率略有增加，其中子宫内膜癌被认为与肥胖流行相关。耶鲁大学的 Cary Gross 博士认为，癌症诊断率上升可能反应了检测工具如 CT、超声和 MRI 的灵敏度改进和使用频率的增加。

走私者、洗钱者以及面临制裁的人过去通常使用奢侈品如钻石、黄金和艺术品藏匿非法财富。这些奢侈品的转移和消费都不很方便。今天的稳定币让犯罪分子能轻松洗钱和逃避制裁。稳定币是一种与美元挂钩的加密货币。区块链分析公司 Chainalysis 在 2 月发布的一份报告估计，去年涉及稳定币的非法交易额高达 250 亿美元。稳定币的兴起危及到了制裁这一美国最强大的外交政策工具。区块链数据公司 TRM Labs 政策主管 Ari Redbord 表示，当犯罪分子只需点击几下鼠标就能转移数百万美元时，制裁等经济处罚的效力就大打折扣了。美国财政部几十年来一直依赖银行和信用卡公司通过执行合规措施去打击非法金融活动，而稳定币完全绕过了这一系统。

曾在 MIT AI 实验室长期工作的 Richard Stallman（RMS）认为 ChatGPT 没有智能，不应该称之为 AI。他对智能的定义是至少在某个领域知道、理解或掌握相关知识。ChatGPT 既不知道也不理解任何事物，因此它不具有智能。它不知道自己输出的意思，也不知道文字能包容万象。他将 ChatGPT 称之为胡扯生成器，以根本不在乎事实是否属实的方式生成输出。其它生成式 AI 系统都有类似的问题。他说人们不应该相信那些机械地玩弄文字、却不真正理解文字含义的系统。RMS 同时表示 ChatGPT 是私有软件，运行在云端服务器上，因此会危害用户的计算自由。

欧盟周二宣布对 Google 展开调查。调查将评估 Google 是否在未给予适当补偿的情况下，使用媒体和其他出版机构在网上发布的内容来训练和提供 AI 服务，从而违反反垄断法规。欧盟委员会表示，调查将关注 Google 是否通过向出版商和内容创作者施加不公平条款，或通过为自己提供对这些内容的优先访问权，从而扭曲竞争。欧盟竞争事务主管 Teresa Ribera 表示，一个自由且民主的社会，依赖多元媒体，也依赖开放的信息获取渠道和充满活力的创意环境。她表示，AI 正带来显著的创新，也为整个欧洲的人们和商业带来许多益处。但进步不能以牺牲社会核心原则为代价。

根据发表在《SLEEP Advances》期刊上的一项研究，睡眠不足与预期寿命减少相关。研究针对的是美国，发现睡眠对预期寿命的影响仅次于吸烟，超过了饮食、运动、孤独感等因素。论文第一作者、OHSU School of Nursing 的副教授 Andrew McHill 博士表示，研究强调了每天有七到九小时充足睡眠时间的重要性。研究没有深入探讨睡眠不足为何会缩短预期寿命，McHill 博士指出，睡眠会影响心血管健康、免疫系统和大脑功能。他说，研究表明，我们应像重视饮食和运动一样重视睡眠。良好的睡眠不仅能改善精神状态，还能延长寿命。

2010 年代的 Google 眼镜再次复活。Google 官方博客透露它正在研发两款不同类型的 AI 智能眼镜，计划明年推出，与 Meta 的现有产品进行竞争。其中一款配备了屏幕，另一款则专注于音频。它的硬件合作伙伴包括了韩国三星、美国 Warby Parker 以及韩国的 Gentle Monster 等。Google 展示了与中国公司 Xreal 合作开发的代号为 Project Aura 的 AI 眼镜样品。Project Aura 运行 Android XR，需要连接外置电池组才能工作，它提供了 70 度的视场。

联想预计将在下个月举行的 CES 展会上展示水平扩展屏幕的卷曲 OLED 游戏本。联想此前推出过一款可扩展屏幕的产品——ThinkBook Plus Gen 6，但它只是在垂直方向扩展屏幕。而被称为 Lenovo Legion Pro Rollable 的游戏本则在水平方向将屏幕扩展为 21:9 的超宽显示屏。目前不清楚屏幕分辨率、刷新率，两种状态下的屏幕尺寸，以及价格或发布时间。该笔记本将使用英特尔酷睿 Ultra 处理器。

美国政府将允许英伟达恢复向中国出售 H200 芯片，前提是政府要从营收中抽取 25%。美国总统特朗普在其 Truth Social 平台上表示，他将允许英伟达向中国出售 H200 芯片——H200 基于 Hopper 架构的芯片，是英伟达上一代产品，最新一代是基于 Blackwell 架构。特朗普称他将对 AMD、英特尔等公司的类似芯片采取相同的抽成法。英伟达最新一代的 Blackwell 系列芯片仍然被禁止出口，该公司 CEO 黄仁勋表示他不知道中国是否会需要这些旧芯片。

Mozilla 释出了 Firefox 146。主要新特性包括：对所有用户开放 Firefox Labs；macOS 版本将有一个专门的 GPU 进程，图形代码中的致命错误将不会导致浏览器崩溃，而是重启 GPU 进程；Linux 版本原生支持 Wayland 分数缩放显示，提升渲染效率；Windows 版本停止支持 Direct2D，需要 Direct2D 的用户建议用 140ESR 版本；WebCrypto 支持压缩椭圆曲线点；更新 Skia 图形库；支持 CSS text-decoration-inset 属性、支持 @scope 规则，等等。

在与 Netflix 之间展开的竞购失败之后，Paramount Skydance 周一宣布将对 Warner Bros. Discovery 发起敌意收购，将直接向该公司股东提出每股 30 美元的全现金收购要约。Netflix 以 830 亿美元收购 Warner Bros. Discovery 的流媒体和影视游戏业务，而 Paramount Skydance 的要约是以 1080 亿美元收购整家公司，囊括了有线电视业务如 CNN 和 TNT。1080 亿美元来自埃里森家族和私募股权公司 RedBird Capital 的股权融资，以及来自美国银行、花旗集团和 Apollo Global Management 的 540 亿美元债务承诺。

根据发表在《PLOS Medicine》期刊上的一项研究，减少吸烟并不能消除心血管疾病风险，必须完全戒烟才能改善健康。研究人员使用了包含 323,826 名成年人的数据集，相比从不吸烟者，每日吸烟 2-5 支的人会增加心血管疾病和全因死亡风险；每日吸烟 11-15 支会显著增加心血管疾病和全因死亡风险。只有戒烟才能逐渐降低风险，风险的最大降幅出现在戒烟后前 10 年内。戒烟 20 年后，既往吸烟者的相对风险比当前吸烟者低 80% 以上。

欧盟委员会上周五根据《数字服务法》(Digital Services Act)对马斯克(Elon Musk)旗下的 X/Twitter 平台处以 1.2 亿欧元的罚款，理由包括 X 违反了欧盟透明度规定、提供的数据访问权限不足，以及其认证账户的蓝勾设计具有欺骗性——该公司并没有真正验证用户身份而是只要付钱就行。马斯克随后抨击欧盟应该废除，而 X 的高级官员 Nikita Bier 则宣布封禁欧盟的广告账户，声称欧盟试图利用其广告系统中的“漏洞”宣传上周五发布的罚款推文。欧盟委员会发言人对此回应称他们只是在使用 X 提供给企业账户的工具。

30 年前的 12 月 4 日，Netscape Communications 和 Sun Microsystems 发表新闻稿，正式宣布推出设计用于创建交互式 Web 应用的对象脚本语言 JavaScript。Netscape 工程师 Brendan Eich 在 1995 年 5 月的 10 天内冲刺开发出了一个内部原型，1996 年 3 月发布了 JavaScript 的 1.0 版本。30 年后的今天 JavaScript 运行在 98.9% 的支持客户端代码的网站上，是 Web 领域最具支配性的编程语言。除浏览器之外，JavaScript 还驱动着服务器后端、移动应用、桌面软件，甚至部分嵌入式系统。JavaScript 一直是全球使用最广泛的语言之一。包括 Netscape 和 Sun 在内的众多最早支持 JavaScript 的科技公司基本都已经消失，而 JavaScript 比它们都活得更久。JavaScript 使用过多个名字，最早叫 Mocha，然后改为 LiveScript，12 月 Netscape 和 Sun 签署授权协议正式将其命名为 JavaScript。JavaScript 与 Sun 的 Java 语言一度引起混淆和困惑，其实除了名字和部分语法规范，两者基本上毫无关系。甲骨文在收购 Sun 之后继承了 JavaScript 商标，但从未使用 JavaScript 名字构建产品，Brendan Eich 等人在一封公开信中认为甲骨文因从未使用而放弃了该商标，因此 JavaScript 成为一个通用术语。

家暴是一个全球性问题。澳大利亚研究人员调查了常用抗抑郁药舍曲林(Sertraline)减少家暴的效果。研究人员从新南威尔士州 1738 名男性中随机挑选出 630 人，分别让他们服用舍曲林或安慰剂。这些人大多数都是有家暴前科的，是从社区矫正机构和法院招募来的。舍曲林是通过增强大脑中血清素功能去发挥作用，而血清素在调节冲动控制和情绪反应上发挥重要作用，因此这有助于缓解暴力行为中的一个关键驱动因素——无法冷静下来控制情绪。结果显示，服用 12 个月后舍曲林组再犯率（19.1%）低于安慰剂组（24.8%）；服用 24 个月后舍曲林组再犯率（28.2%）低于安慰剂组（35.7%）。服药更规律的男性 24 个月后再犯率降低 30%。

俄罗斯保时捷车主遭遇了汽车启动无反应，发动机不转，仪表盘指示灯不亮等众多问题，就好像汽车变砖了。这一问题最早是在 11 月底报道的。俄罗斯最大的保时捷经销商 Rolf 证实，问题源于汽车配备的跟踪系统（Vehicle Tracking System 或 VTS）与卫星完全切断连接。VTS 是基于卫星的防盗系统，当卫星连接中断后，系统会认为汽车可能被盗，因此激活了防盗功能，切断燃油供应并且完全锁定发动机。问题影响所有安装了 VTS 的保时捷车型。