OrangeBot.AI Digest — 2025-12-19

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A list of useful payloads and bypass for Web Application Security and Pentest/CTF

An open-source, code-first Typescript toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.

VIP cheatsheets for Stanford's CS 229 Machine Learning

The easy-to-use open source Business Intelligence and Embedded Analytics tool that lets everyone work with data 📊

We present Kling-Omni, a generalist generative framework designed to synthesize high-fidelity videos directly from multimodal visual language inputs. Adopting an end-to-end perspective, Kling-Omni bridges the functional separation among diverse video generation, editing, and intelligent reasoning tasks, integrating them into a holistic system. Unlike disjointed pipeline approaches, Kling-Omni supports a diverse range of user inputs, including text instructions, reference images, and video contexts, processing them into a unified multimodal representation to deliver cinematic-quality and highly-intelligent video content creation. To support these capabilities, we constructed a comprehensive data system that serves as the foundation for multimodal video creation. The framework is further empowered by efficient large-scale pre-training strategies and infrastructure optimizations for inference. Comprehensive evaluations reveal that Kling-Omni demonstrates exceptional capabilities in in-context generation, reasoning-based editing, and multimodal instruction following. Moving beyond a content creation tool, we believe Kling-Omni is a pivotal advancement toward multimodal world simulators capable of perceiving, reasoning, generating and interacting with the dynamic and complex worlds.

This paper presents LLaDA2.0 -- a tuple of discrete diffusion large language models (dLLM) scaling up to 100B total parameters through systematic conversion from auto-regressive (AR) models -- establishing a new paradigm for frontier-scale deployment. Instead of costly training from scratch, LLaDA2.0 upholds knowledge inheritance, progressive adaption and efficiency-aware design principle, and seamless converts a pre-trained AR model into dLLM with a novel 3-phase block-level WSD based training scheme: progressive increasing block-size in block diffusion (warm-up), large-scale full-sequence diffusion (stable) and reverting back to compact-size block diffusion (decay). Along with post-training alignment with SFT and DPO, we obtain LLaDA2.0-mini (16B) and LLaDA2.0-flash (100B), two instruction-tuned Mixture-of-Experts (MoE) variants optimized for practical deployment. By preserving the advantages of parallel decoding, these models deliver superior performance and efficiency at the frontier scale. Both models were open-sourced.

Inspired by the success of generative pretraining in natural language, we ask whether the same principles can yield strong self-supervised visual learners. Instead of training models to output features for downstream use, we train them to generate embeddings to perform predictive tasks directly. This work explores such a shift from learning representations to learning models. Specifically, models learn to predict future patch embeddings conditioned on past ones, using causal masking and stop gradient, which we refer to as Next-Embedding Predictive Autoregression (NEPA). We demonstrate that a simple Transformer pretrained on ImageNet-1k with next embedding prediction as its sole learning objective is effective - no pixel reconstruction, discrete tokens, contrastive loss, or task-specific heads. This formulation retains architectural simplicity and scalability, without requiring additional design complexity. NEPA achieves strong results across tasks, attaining 83.8% and 85.3% top-1 accuracy on ImageNet-1K with ViT-B and ViT-L backbones after fine-tuning, and transferring effectively to semantic segmentation on ADE20K. We believe generative pretraining from embeddings provides a simple, scalable, and potentially modality-agnostic alternative to visual self-supervised learning.

Cutting-edge agentic AI systems are built on foundation models that can be adapted to plan, reason, and interact with external tools to perform increasingly complex and specialized tasks. As these systems grow in capability and scope, adaptation becomes a central mechanism for improving performance, reliability, and generalization. In this paper, we unify the rapidly expanding research landscape into a systematic framework that spans both agent adaptations and tool adaptations. We further decompose these into tool-execution-signaled and agent-output-signaled forms of agent adaptation, as well as agent-agnostic and agent-supervised forms of tool adaptation. We demonstrate that this framework helps clarify the design space of adaptation strategies in agentic AI, makes their trade-offs explicit, and provides practical guidance for selecting or switching among strategies during system design. We then review the representative approaches in each category, analyze their strengths and limitations, and highlight key open challenges and future opportunities. Overall, this paper aims to offer a conceptual foundation and practical roadmap for researchers and practitioners seeking to build more capable, efficient, and reliable agentic AI systems.

The rapid growth of stereoscopic displays, including VR headsets and 3D cinemas, has led to increasing demand for high-quality stereo video content. However, producing 3D videos remains costly and complex, while automatic Monocular-to-Stereo conversion is hindered by the limitations of the multi-stage ``Depth-Warp-Inpaint'' (DWI) pipeline. This paradigm suffers from error propagation, depth ambiguity, and format inconsistency between parallel and converged stereo configurations. To address these challenges, we introduce UniStereo, the first large-scale unified dataset for stereo video conversion, covering both stereo formats to enable fair benchmarking and robust model training. Building upon this dataset, we propose StereoPilot, an efficient feed-forward model that directly synthesizes the target view without relying on explicit depth maps or iterative diffusion sampling. Equipped with a learnable domain switcher and a cycle consistency loss, StereoPilot adapts seamlessly to different stereo formats and achieves improved consistency. Extensive experiments demonstrate that StereoPilot significantly outperforms state-of-the-art methods in both visual fidelity and computational efficiency. Project page: https://hit-perfect.github.io/StereoPilot/.

Recent strides in video generation have paved the way for unified audio-visual generation. In this work, we present Seedance 1.5 pro, a foundational model engineered specifically for native, joint audio-video generation. Leveraging a dual-branch Diffusion Transformer architecture, the model integrates a cross-modal joint module with a specialized multi-stage data pipeline, achieving exceptional audio-visual synchronization and superior generation quality. To ensure practical utility, we implement meticulous post-training optimizations, including Supervised Fine-Tuning (SFT) on high-quality datasets and Reinforcement Learning from Human Feedback (RLHF) with multi-dimensional reward models. Furthermore, we introduce an acceleration framework that boosts inference speed by over 10X. Seedance 1.5 pro distinguishes itself through precise multilingual and dialect lip-syncing, dynamic cinematic camera control, and enhanced narrative coherence, positioning it as a robust engine for professional-grade content creation. Seedance 1.5 pro is now accessible on Volcano Engine at https://console.volcengine.com/ark/region:ark+cn-beijing/experience/vision?type=GenVideo.

In this work, we present a panoramic metric depth foundation model that generalizes across diverse scene distances. We explore a data-in-the-loop paradigm from the view of both data construction and framework design. We collect a large-scale dataset by combining public datasets, high-quality synthetic data from our UE5 simulator and text-to-image models, and real panoramic images from the web. To reduce domain gaps between indoor/outdoor and synthetic/real data, we introduce a three-stage pseudo-label curation pipeline to generate reliable ground truth for unlabeled images. For the model, we adopt DINOv3-Large as the backbone for its strong pre-trained generalization, and introduce a plug-and-play range mask head, sharpness-centric optimization, and geometry-centric optimization to improve robustness to varying distances and enforce geometric consistency across views. Experiments on multiple benchmarks (e.g., Stanford2D3D, Matterport3D, and Deep360) demonstrate strong performance and zero-shot generalization, with particularly robust and stable metric predictions in diverse real-world scenes. The project page can be found at: https://insta360-research-team.github.io/DAP_website/ {https://insta360-research-team.github.io/DAP\_website/}

Depth-of-field control is essential in photography, but getting the perfect focus often takes several tries or special equipment. Single-image refocusing is still difficult. It involves recovering sharp content and creating realistic bokeh. Current methods have significant drawbacks. They need all-in-focus inputs, depend on synthetic data from simulators, and have limited control over aperture. We introduce Generative Refocusing, a two-step process that uses DeblurNet to recover all-in-focus images from various inputs and BokehNet for creating controllable bokeh. Our main innovation is semi-supervised training. This method combines synthetic paired data with unpaired real bokeh images, using EXIF metadata to capture real optical characteristics beyond what simulators can provide. Our experiments show we achieve top performance in defocus deblurring, bokeh synthesis, and refocusing benchmarks. Additionally, our Generative Refocusing allows text-guided adjustments and custom aperture shapes.

In-context diffusion models allow users to modify images with remarkable ease and realism. However, the same power raises serious privacy concerns: personal images can be easily manipulated for identity impersonation, misinformation, or other malicious uses, all without the owner's consent. While prior work has explored input perturbations to protect against misuse in personalized text-to-image generation, the robustness of modern, large-scale in-context DiT-based models remains largely unexamined. In this paper, we propose DeContext, a new method to safeguard input images from unauthorized in-context editing. Our key insight is that contextual information from the source image propagates to the output primarily through multimodal attention layers. By injecting small, targeted perturbations that weaken these cross-attention pathways, DeContext breaks this flow, effectively decouples the link between input and output. This simple defense is both efficient and robust. We further show that early denoising steps and specific transformer blocks dominate context propagation, which allows us to concentrate perturbations where they matter most. Experiments on Flux Kontext and Step1X-Edit show that DeContext consistently blocks unwanted image edits while preserving visual quality. These results highlight the effectiveness of attention-based perturbations as a powerful defense against image manipulation.

Latent diffusion models (LDMs) achieve state-of-the-art image synthesis, yet their reconstruction-style denoising objective provides only indirect semantic supervision: high-level semantics emerge slowly, requiring longer training and limiting sample quality. Recent works inject semantics from Vision Foundation Models (VFMs) either externally via representation alignment or internally by jointly modeling only a narrow slice of VFM features inside the diffusion process, under-utilizing the rich, nonlinear, multi-layer spatial semantics available. We introduce REGLUE (Representation Entanglement with Global-Local Unified Encoding), a unified latent diffusion framework that jointly models (i) VAE image latents, (ii) compact local (patch-level) VFM semantics, and (iii) a global (image-level) [CLS] token within a single SiT backbone. A lightweight convolutional semantic compressor nonlinearly aggregates multi-layer VFM features into a low-dimensional, spatially structured representation, which is entangled with the VAE latents in the diffusion process. An external alignment loss further regularizes internal representations toward frozen VFM targets. On ImageNet 256x256, REGLUE consistently improves FID and accelerates convergence over SiT-B/2 and SiT-XL/2 baselines, as well as over REPA, ReDi, and REG. Extensive experiments show that (a) spatial VFM semantics are crucial, (b) non-linear compression is key to unlocking their full benefit, and (c) global tokens and external alignment act as complementary, lightweight enhancements within our global-local-latent joint modeling framework. The code is available at https://github.com/giorgospets/reglue .

Recent advances in Text-to-Image (T2I) generative models, such as Imagen, Stable Diffusion, and FLUX, have led to remarkable improvements in visual quality. However, their performance is fundamentally limited by the quality of training data. Web-crawled and synthetic image datasets often contain low-quality or redundant samples, which lead to degraded visual fidelity, unstable training, and inefficient computation. Hence, effective data selection is crucial for improving data efficiency. Existing approaches rely on costly manual curation or heuristic scoring based on single-dimensional features in Text-to-Image data filtering. Although meta-learning based method has been explored in LLM, there is no adaptation for image modalities. To this end, we propose **Alchemist**, a meta-gradient-based framework to select a suitable subset from large-scale text-image data pairs. Our approach automatically learns to assess the influence of each sample by iteratively optimizing the model from a data-centric perspective. Alchemist consists of two key stages: data rating and data pruning. We train a lightweight rater to estimate each sample's influence based on gradient information, enhanced with multi-granularity perception. We then use the Shift-Gsampling strategy to select informative subsets for efficient model training. Alchemist is the first automatic, scalable, meta-gradient-based data selection framework for Text-to-Image model training. Experiments on both synthetic and web-crawled datasets demonstrate that Alchemist consistently improves visual quality and downstream performance. Training on an Alchemist-selected 50% of the data can outperform training on the full dataset.

We present WorldCanvas, a framework for promptable world events that enables rich, user-directed simulation by combining text, trajectories, and reference images. Unlike text-only approaches and existing trajectory-controlled image-to-video methods, our multimodal approach combines trajectories -- encoding motion, timing, and visibility -- with natural language for semantic intent and reference images for visual grounding of object identity, enabling the generation of coherent, controllable events that include multi-agent interactions, object entry/exit, reference-guided appearance and counterintuitive events. The resulting videos demonstrate not only temporal coherence but also emergent consistency, preserving object identity and scene despite temporary disappearance. By supporting expressive world events generation, WorldCanvas advances world models from passive predictors to interactive, user-shaped simulators. Our project page is available at: https://worldcanvas.github.io/.

While current multimodal models can answer questions based on 2D images, they lack intrinsic 3D object perception, limiting their ability to comprehend spatial relationships and depth cues in 3D scenes. In this work, we propose N3D-VLM, a novel unified framework that seamlessly integrates native 3D object perception with 3D-aware visual reasoning, enabling both precise 3D grounding and interpretable spatial understanding. Unlike conventional end-to-end models that directly predict answers from RGB/RGB-D inputs, our approach equips the model with native 3D object perception capabilities, enabling it to directly localize objects in 3D space based on textual descriptions. Building upon accurate 3D object localization, the model further performs explicit reasoning in 3D, achieving more interpretable and structured spatial understanding. To support robust training for these capabilities, we develop a scalable data construction pipeline that leverages depth estimation to lift large-scale 2D annotations into 3D space, significantly increasing the diversity and coverage for 3D object grounding data, yielding over six times larger than the largest existing single-image 3D detection dataset. Moreover, the pipeline generates spatial question-answering datasets that target chain-of-thought (CoT) reasoning in 3D, facilitating joint training for both 3D object localization and 3D spatial reasoning. Experimental results demonstrate that our unified framework not only achieves state-of-the-art performance on 3D grounding tasks, but also consistently surpasses existing methods in 3D spatial reasoning in vision-language model.

Recent advances in reinforcement learning for large language models have converged on increasing complexity: multi-stage training pipelines, dynamic hyperparameter schedules, and curriculum learning strategies. This raises a fundamental question: Is this complexity necessary? We present JustRL, a minimal approach using single-stage training with fixed hyperparameters that achieves state-of-the-art performance on two 1.5B reasoning models (54.9\% and 64.3\% average accuracy across nine mathematical benchmarks) while using 2times less compute than sophisticated approaches. The same hyperparameters transfer across both models without tuning, and training exhibits smooth, monotonic improvement over 4,000+ steps without the collapses or plateaus that typically motivate interventions. Critically, ablations reveal that adding ``standard tricks'' like explicit length penalties and robust verifiers may degrade performance by collapsing exploration. These results suggest that the field may be adding complexity to solve problems that disappear with a stable, scaled-up baseline. We release our models and code to establish a simple, validated baseline for the community.

Recent advances have shown that multimodal large language models (MLLMs) benefit from multimodal interleaved chain-of-thought (CoT) with vision tool interactions. However, existing open-source models often exhibit blind tool-use reasoning patterns, invoking vision tools even when they are unnecessary, which significantly increases inference overhead and degrades model performance. To this end, we propose AdaTooler-V, an MLLM that performs adaptive tool-use by determining whether a visual problem truly requires tools. First, we introduce AT-GRPO, a reinforcement learning algorithm that adaptively adjusts reward scales based on the Tool Benefit Score of each sample, encouraging the model to invoke tools only when they provide genuine improvements. Moreover, we construct two datasets to support training: AdaTooler-V-CoT-100k for SFT cold start and AdaTooler-V-300k for RL with verifiable rewards across single-image, multi-image, and video data. Experiments across twelve benchmarks demonstrate the strong reasoning capability of AdaTooler-V, outperforming existing methods in diverse visual reasoning tasks. Notably, AdaTooler-V-7B achieves an accuracy of 89.8\% on the high-resolution benchmark V*, surpassing the commercial proprietary model GPT-4o and Gemini 1.5 Pro. All code, models, and data are released.

TikTok CEO 周受资通知员工，TikTok 母公司字节跳动与甲骨文、银湖资本以及阿布达比 AI 投资机构 MGX 签署协议成立合资企业，接管 TikTok 美国业务。交易预计将在明年 1 月 22 日完成。根据协议：甲骨文、银湖与 MGX 各持有 15% 股权；30.1% 由字节跳动现有投资人关联实体持有；字节跳动保留 19.9%。合资公司的董事会有 7 人，多数为美国人。美国用户数据将储存在甲骨文的服务器上，而算法将根据美国用户资料重新训练。TikTok 在美国有逾 1.7 亿活跃用户。

路透社报道，在前 ASML 工程师的帮助下中国科学家研制出极紫外光刻机（Extreme Ultra-violet 或 EUV）原型。原型机于 2025 年初完成制造，尚未开始制造工作芯片，政府的目标是在 2028 年制造工作芯片，但更现实的目标可能是 2030 年。荷兰 ASML 公司垄断了 EUV 光刻机市场，在此之前它是唯一一家能制造 EUV 光刻机的公司。

美国计算机协会（ACM）宣布，从 2026 年 1 月起 ACM 数字图书馆所有出版物和相关文献都将开放获取。此举反映了全世界计算机领域长期以来日益增长的呼声，即研究应当更易获取、更易发现、更易复用。ACM 表示对于所有开放获取的出版物：作者保留知识产权：作品将受益于更广泛的影响力和可见度：研究成果将免费向全球所有人开放，提升阅读量、引用率和实际应用价值；学生、教育工作者和研究人员都将受益；促进合作、透明度和累积进步，增强计算机学科的整体发展。和其它开放获取出版物类似，ACM 将对发表的论文收取处理费用，每篇收取 1450 美元，低收入国家的作者有折扣。

伊朗的水危机严重到总统宣布要迁都，但远水解不了近渴，迁都预计将需要花费数十年时间花费上千亿美元，无法缓解当前的危机。水文学家表示这场危机的直接原因是降雨量不足，但根源可以追溯到过去半个世纪鲁莽的水利工程。伊朗在 20世纪后半期是世界三大水坝建设国之一，数十座水坝建在原本水量不足以维持其正常运转的河流上，水库没有解决缺水，反而因为水面蒸发加剧流失，同时降低下游河流的水流量，导致湿地和地下水资源枯竭。今天水坝后的许多水库都干涸了。上游的阿富汗也在修建水坝，导致进入伊朗境内的河流水量减少。地表水资源匮乏，地下水情况则更糟糕。过去 40 年伊朗打了逾百万口井，配备了大功率水泵，目的是灌溉干旱的农田，实现粮食自给自足。但结果是曾经储量丰富的含水层遭到过度抽取。过去二十年伊朗地下水储量损失逾 210 立方公里。一项国际研究发现，全球过度抽取地下水的 50 个含水层有 32 个位于伊朗。水文专家表示伊朗正处于水资源破产的边缘。

奥斯卡颁奖典礼自 1976 年起一直由迪士尼的 ABC 转播，但从 2029 年起它将由全球最大的视频平台、Google 的 YouTube 免费转播。ABC 转播权到 2028 年第 100 届颁奖典礼截至。YouTube 的转播除了颁奖典礼外，还将提供大量花絮，包括红地毯前秀、幕后花絮、访谈、电影教育节目，播客等等。Google 还将为美国电影艺术与科学学院博物馆藏品数字化提供协助。YouTube 的目标是打造成为全世界最强大的平台，获得全球最有知名度的颁奖典礼无疑是其一大成就。

根据慕尼黑地方法院的判决，亚马逊不得在德国单方面更改其流媒体服务 Prime Video 的合同条款。该判决尚未生效，亚马逊保留上诉权。亚马逊发言人表示将评估判决以决定下一步行动。德国消费者权益组织 Bundesverband der Verbraucherzentralen 针对亚马逊的诉讼获胜。法院表示。亚马逊需向客户发送一份“更正信函”。根据判决，亚马逊在 2024 年初通过邮件通知 Prime Video 用户，自 2 月起将在有限范围内插播广告；不愿观看广告的用户需每月额外支付 2.99 欧元。法院第 33 民事庭认为，这一做法违反了公平竞争原则。法官认为，亚马逊的邮件内容具有误导性。判决指出，亚马逊向客户暗示其有权单方面更改合同条款，但法院认为，亚马逊的用户条款以及相关法律均不允许单方面变更条款。客户在签订合同时是基于无广告的服务承诺，亚马逊已将无广告内容作为“合同标的”，因此必须遵守这一承诺。

丹麦 IT 部长向交通部移交了首台没有安装任何微软软件的 Linux 笔记本电脑。交通部是名为 SIA Open 的试点项目的首个客户，最终目标是让 1.5 万用户使用开源替代，避免过度依赖微软等私有软件供应商，掌控系统和数据的主权。交通部门的负责人 Stefan Søsted 表示要确保自己知道重要的信息储存在什么地方。相比私有软件，开源替代并不逊色且价格更低。微软丹麦分公司回应称，微软的解决方案定价合理且具有竞争力，兼具高安全性、创新性和高效协作性，微软欢迎竞争，开源与微软产品之间并不存在矛盾。

扫地机器人 Roomba 的制造商 iRobot 最近申请破产重组。根据重组协议，iRobot 的控制权将转交给其代工厂及最大债权人深圳杉川机器人公司。公司 CEO Gary Cohen 接受日经采访时表示，破产原因是产品技术创新方面落后中国竞争企业四年。关于进入杉川集团旗下后的业务运营，他表示：“将保持 Roomba 的品牌和各地区的销售体制，并引入中国的产品开发速度”。他主张称，计划将总部功能和营销部门留在美国，以此“与（其他）中国企业划清界限”。关于 Roomba 收集的数据的管理，他明确表示：“现在和今后都不会（保存在中国的服务器上）”。他同时强调，云服务的利用和应用程序开发将继续保持以美国为中心的体制。

索尼互动于今年 7 月向加州法庭起诉腾讯抄袭其《地平线》系列游戏，指控腾讯版权和商标侵犯。腾讯旗下工作室 Polaris Quest 最近发布了《Light of Motiram》的预告片，该游戏背景与索尼旗下工作室 Guerrilla Games 开发的《地平线（Horizon）》 系列游戏《地平线零之曙光》和《地平线 西之绝境》几乎如出一辙，都发生在机械占领的末世后文明世界，女主角身穿类似猎人的服装与机械生物进行战斗。索尼称，腾讯曾试图获得《地平线》系列的 IP 授权但遭到了它的拒绝。本周双方达成了不公开的和解，《Light of Motiram》已经在 Steam 和 Epic Games Store 下架。索尼和腾讯都拒绝对此发表进一步的评论。

塑料开启了全新的便利时代，从此廉价的一次性用品充斥着千家万户。但普通人也因此暴露在成千上万种化学物质中，这些物质会从塑料制品渗入尘埃、食物和水，最终进入人体。瑞士和挪威科学家建立了一个数据库，记录了与塑料相关的 1.6 万种化学物质。这 1.6 万种化学物质中，有逾 5400 种被认为对人类健康有害，仅 161 种被认定为无害，其余逾万种物质尚缺乏足够数据评估其安全性。这些化学物质通过多种途径进入食物：黑色塑料餐具和托盘通常含有溴化阻燃剂，溴化阻燃剂已被证实与儿童智商下降和神经发育问题有关，研究发现孕妇体内溴化阻燃剂水平每增加 10 倍，其子女的智商平均会下降 3.7 点；不粘锅、可降解餐盘和餐具通常含有全氟烷基和多氟烷基物质（PFAS）。这些化学物质在环境中或人体内需要数年才能分解，它们与肾癌、睾丸癌以及儿童自闭症和多动症等发育问题相关。

根据 Steam Replay 2025 年度总结，2025 年 Steam 用户 14% 的游戏时间是花在新游戏上，44% 的游戏时间花在 1 至 7 年历史的游戏上，40% 的游戏时长花在超过八年的游戏上——长久不衰的游戏如 DOTA 2 和《绝地求生（PUBG: Battlegrounds）》都属于这一类别。Steam 用户平均玩的游戏数量只有 4 款，PC 玩家和主机玩家类似，都越来越倾向于少数几款游戏。

亿万富翁 Jared Isaacman 的 NASA 局长任命堪称一波三折：特朗普最初提名 Isaacman 为 NASA 局长，但五月撤回了提名，当时特朗普与马克斯（Elon Musk）关系破裂，而 Isaacman 是马斯克青睐的人选。Isaacman 此后加强了对特朗普阵营的个人攻势，包括向特朗普的政治行动委员会 MAGA Inc.两次捐赠了 100 万美元，以及向共和党全国委员会捐赠 17.5 万美元，从而改善了与特朗普的关系，重新获得了特朗普的任命。本周美国参议院以 67 票赞成 30 票反对确认了对 Isaacman 的 NASA 局长任命。

美国在线学习平台 Coursera 宣布达成全股票交易收购竞争对手 Udemy，合并后的公司估值 25 亿美元。新冠疫情期间在线教育的需求一度激增，但疫情后需求下降，在线教育行业陷入困境。根据收购协议，Udemy 股东每持有 1 股 Udemy 股票将获得 0.8 股 Coursera 股票，对 Udemy 的估值约为 9.3 亿美元。交易预计将于明年下半年完成，仍需获得监管机构和股东的批准。Coursera 业务模式是与大学和机构合作提供学位课程和专业证书；而 Udemy 则运营着一个让独立讲师直接向消费​​者和企业销售课程的市场。两家公司的股价今年都出现了下跌，合并后的公司将致力于吸引企业客户，为公司员工提供 AI、数据科学和软件开发方面的再培训。

日本科学家报告研发出一种堪称完美的塑料，由植物纤维素制成，植物纤维素是世界最丰富的有机化合物。新塑料强度高、柔韧性好，能在自然环境中快速分解，没有任何微塑料污染。微塑料是一种全球性污染物，几乎存在于所有生态系统中，甚至包括人体组织和血液。大多数标榜“可生物降解”的塑料要么无法在海洋环境中降解，要么需要长时间才能降解，而且会在降解过程中留下微塑料。基于植物的新塑料由木浆衍生物羧甲基纤维和正电荷聚乙烯亚胺胍离子，以及一种增塑剂有机盐氯化胆碱构成。根据氯化胆碱的用量，柔韧性可从坚硬如玻璃到拉伸至其原始长度的 130%。

骨骼肌组织和功能随年龄增长会逐渐衰退，到 60 岁左右可能会影响独立生活和健康。瑞典的一项长达 47 年的研究发现，即使持续进行有规律的训练，体能、力量和耐力在 35 岁左右开始逐渐下降。研究发现，最大有氧能力和肌肉耐力在 26-36 岁达到峰值，一开始缓慢下降，然后加速下降，男女之间的下降速度没有什么差异。肌肉力量男性在 27 岁达到峰值，女性在 19 岁达到峰值。从巅峰期到 63 岁，总体体能下降幅度为 30% 至 48%。即使成年人开始锻炼的时间较晚，他们的身体机能仍然可以提高 5%-10%，证明锻炼永远不晚。