OrangeBot.AI Digest — 2025-11-25

🎯 告别信息过载，AI 助你看懂新闻资讯热点，简单的舆情监控分析 - 多平台热点聚合+基于 MCP 的AI分析工具。监控35个平台（抖音、知乎、B站、华尔街见闻、财联社等），智能筛选+自动推送+AI对话分析（用自然语言深度挖掘新闻：趋势追踪、情感分析、相似检索等13种工具）。支持企业微信/个人微信/飞书/钉钉/Telegram/邮件/ntfy/bark/slack 推送，30秒网页部署，1分钟手机通知，无需编程。支持Docker部署⭐ 让算法为你服务，用AI理解热点

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

Memory is critical for AI agents, yet the widely-adopted static memory, aiming to create readily available memory in advance, is inevitably subject to severe information loss. To address this limitation, we propose a novel framework called general agentic memory (GAM). GAM follows the principle of "just-in time (JIT) compilation" where it focuses on creating optimized contexts for its client at runtime while keeping only simple but useful memory during the offline stage. To this end, GAM employs a duo-design with the following components. 1) Memorizer, which highlights key historical information using a lightweight memory, while maintaining complete historical information within a universal page-store. 2) Researcher, which retrieves and integrates useful information from the page-store for its online request guided by the pre-constructed memory. This design allows GAM to effectively leverage the agentic capabilities and test-time scalability of frontier large language models (LLMs), while also facilitating end-to-end performance optimization through reinforcement learning. In our experimental study, we demonstrate that GAM achieves substantial improvement on various memory-grounded task completion scenarios against existing memory systems.

Humans naturally adapt to diverse environments by learning underlying rules across worlds with different dynamics, observations, and reward structures. In contrast, existing agents typically demonstrate improvements via self-evolving within a single domain, implicitly assuming a fixed environment distribution. Cross-environment learning has remained largely unmeasured: there is no standard collection of controllable, heterogeneous environments, nor a unified way to represent how agents learn. We address these gaps in two steps. First, we propose AutoEnv, an automated framework that treats environments as factorizable distributions over transitions, observations, and rewards, enabling low-cost (4.12 USD on average) generation of heterogeneous worlds. Using AutoEnv, we construct AutoEnv-36, a dataset of 36 environments with 358 validated levels, on which seven language models achieve 12-49% normalized reward, demonstrating the challenge of AutoEnv-36. Second, we formalize agent learning as a component-centric process driven by three stages of Selection, Optimization, and Evaluation applied to an improvable agent component. Using this formulation, we design eight learning methods and evaluate them on AutoEnv-36. Empirically, the gain of any single learning method quickly decrease as the number of environments increases, revealing that fixed learning methods do not scale across heterogeneous environments. Environment-adaptive selection of learning methods substantially improves performance but exhibits diminishing returns as the method space expands. These results highlight both the necessity and the current limitations of agent learning for scalable cross-environment generalization, and position AutoEnv and AutoEnv-36 as a testbed for studying cross-environment agent learning. The code is avaiable at https://github.com/FoundationAgents/AutoEnv.

Computer-Use Agents (CUA) are becoming increasingly capable of autonomously operating digital environments through Graphical User Interfaces (GUI). Yet, most GUI remain designed primarily for humans--prioritizing aesthetics and usability--forcing agents to adopt human-oriented behaviors that are unnecessary for efficient task execution. At the same time, rapid advances in coding-oriented language models (Coder) have transformed automatic GUI design. This raises a fundamental question: Can CUA as judges to assist Coder for automatic GUI design? To investigate, we introduce AUI-Gym, a benchmark for Automatic GUI development spanning 52 applications across diverse domains. Using language models, we synthesize 1560 tasks that simulate real-world scenarios. To ensure task reliability, we further develop a verifier that programmatically checks whether each task is executable within its environment. Building on this, we propose a Coder-CUA in Collaboration framework: the Coder acts as Designer, generating and revising websites, while the CUA serves as Judge, evaluating functionality and refining designs. Success is measured not by visual appearance, but by task solvability and CUA navigation success rate. To turn CUA feedback into usable guidance, we design a CUA Dashboard that compresses multi-step navigation histories into concise visual summaries, offering interpretable guidance for iterative redesign. By positioning agents as both designers and judges, our framework shifts interface design toward agent-native efficiency and reliability. Our work takes a step toward shifting agents from passive use toward active participation in digital environments. Our code and dataset are available at https://github.com/showlab/AUI.

Pixel diffusion aims to generate images directly in pixel space in an end-to-end fashion. This approach avoids the limitations of VAE in the two-stage latent diffusion, offering higher model capacity. Existing pixel diffusion models suffer from slow training and inference, as they usually model both high-frequency signals and low-frequency semantics within a single diffusion transformer (DiT). To pursue a more efficient pixel diffusion paradigm, we propose the frequency-DeCoupled pixel diffusion framework. With the intuition to decouple the generation of high and low frequency components, we leverage a lightweight pixel decoder to generate high-frequency details conditioned on semantic guidance from the DiT. This thus frees the DiT to specialize in modeling low-frequency semantics. In addition, we introduce a frequency-aware flow-matching loss that emphasizes visually salient frequencies while suppressing insignificant ones. Extensive experiments show that DeCo achieves superior performance among pixel diffusion models, attaining FID of 1.62 (256x256) and 2.22 (512x512) on ImageNet, closing the gap with latent diffusion methods. Furthermore, our pretrained text-to-image model achieves a leading overall score of 0.86 on GenEval in system-level comparison. Codes are publicly available at https://github.com/Zehong-Ma/DeCo.

Deep research models perform multi-step research to produce long-form, well-attributed answers. However, most open deep research models are trained on easily verifiable short-form QA tasks via reinforcement learning with verifiable rewards (RLVR), which does not extend to realistic long-form tasks. We address this with Reinforcement Learning with Evolving Rubrics (RLER), in which we construct and maintain rubrics that co-evolve with the policy model during training; this allows the rubrics to incorporate information that the model has newly explored and to provide discriminative, on-policy feedback. Using RLER, we develop Deep Research Tulu (DR Tulu-8B), the first open model that is directly trained for open-ended, long-form deep research. Across four long-form deep research benchmarks in science, healthcare and general domains, DR Tulu substantially outperforms existing open deep research models, and matches or exceeds proprietary deep research systems, while being significantly smaller and cheaper per query. To facilitate future research, we release all data, models, and code, including our new MCP-based agent infrastructure for deep research systems.

Diffusion transformers have recently delivered strong text-to-image generation around 1K resolution, but we show that extending them to native 4K across diverse aspect ratios exposes a tightly coupled failure mode spanning positional encoding, VAE compression, and optimization. Tackling any of these factors in isolation leaves substantial quality on the table. We therefore take a data-model co-design view and introduce UltraFlux, a Flux-based DiT trained natively at 4K on MultiAspect-4K-1M, a 1M-image 4K corpus with controlled multi-AR coverage, bilingual captions, and rich VLM/IQA metadata for resolution- and AR-aware sampling. On the model side, UltraFlux couples (i) Resonance 2D RoPE with YaRN for training-window-, frequency-, and AR-aware positional encoding at 4K; (ii) a simple, non-adversarial VAE post-training scheme that improves 4K reconstruction fidelity; (iii) an SNR-Aware Huber Wavelet objective that rebalances gradients across timesteps and frequency bands; and (iv) a Stage-wise Aesthetic Curriculum Learning strategy that concentrates high-aesthetic supervision on high-noise steps governed by the model prior. Together, these components yield a stable, detail-preserving 4K DiT that generalizes across wide, square, and tall ARs. On the Aesthetic-Eval at 4096 benchmark and multi-AR 4K settings, UltraFlux consistently outperforms strong open-source baselines across fidelity, aesthetic, and alignment metrics, and-with a LLM prompt refiner-matches or surpasses the proprietary Seedream 4.0.

Large-scale video generative models have recently demonstrated strong visual capabilities, enabling the prediction of future frames that adhere to the logical and physical cues in the current observation. In this work, we investigate whether such capabilities can be harnessed for controllable image-to-video generation by interpreting visual signals embedded within the frames as instructions, a paradigm we term In-Video Instruction. In contrast to prompt-based control, which provides textual descriptions that are inherently global and coarse, In-Video Instruction encodes user guidance directly into the visual domain through elements such as overlaid text, arrows, or trajectories. This enables explicit, spatial-aware, and unambiguous correspondences between visual subjects and their intended actions by assigning distinct instructions to different objects. Extensive experiments on three state-of-the-art generators, including Veo 3.1, Kling 2.5, and Wan 2.2, show that video models can reliably interpret and execute such visually embedded instructions, particularly in complex multi-object scenarios.

Scaling test-time computation improves performance across different tasks on large language models (LLMs), which has also been extended to tool-augmented agents. For these agents, scaling involves not only "thinking" in tokens but also "acting" via tool calls. The number of tool calls directly bounds the agent's interaction with the external environment. However, we find that simply granting agents a larger tool-call budget fails to improve performance, as they lack "budget awareness" and quickly hit a performance ceiling. To address this, we study how to scale such agents effectively under explicit tool-call budgets, focusing on web search agents. We first introduce the Budget Tracker, a lightweight plug-in that provides the agent with continuous budget awareness, enabling simple yet effective scaling. We further develop BATS (Budget Aware Test-time Scaling), an advanced framework that leverages this awareness to dynamically adapt its planning and verification strategy, deciding whether to "dig deeper" on a promising lead or "pivot" to new paths based on remaining resources. To analyze cost-performance scaling in a controlled manner, we formalize a unified cost metric that jointly accounts for token and tool consumption. We provide the first systematic study on budget-constrained agents, showing that budget-aware methods produce more favorable scaling curves and push the cost-performance Pareto frontier. Our work offers empirical insights toward a more transparent and principled understanding of scaling in tool-augmented agents.

Vision-Language Models (VLMs) excel at reasoning in linguistic space but struggle with perceptual understanding that requires dense visual perception, e.g., spatial reasoning and geometric awareness. This limitation stems from the fact that current VLMs have limited mechanisms to capture dense visual information across spatial dimensions. We introduce Chain-of-Visual-Thought (COVT), a framework that enables VLMs to reason not only in words but also through continuous visual tokens-compact latent representations that encode rich perceptual cues. Within a small budget of roughly 20 tokens, COVT distills knowledge from lightweight vision experts, capturing complementary properties such as 2D appearance, 3D geometry, spatial layout, and edge structure. During training, the VLM with COVT autoregressively predicts these visual tokens to reconstruct dense supervision signals (e.g., depth, segmentation, edges, and DINO features). At inference, the model reasons directly in the continuous visual token space, preserving efficiency while optionally decoding dense predictions for interpretability. Evaluated across more than ten diverse perception benchmarks, including CV-Bench, MMVP, RealWorldQA, MMStar, WorldMedQA, and HRBench, integrating COVT into strong VLMs such as Qwen2.5-VL and LLaVA consistently improves performance by 3% to 16% and demonstrates that compact continuous visual thinking enables more precise, grounded, and interpretable multimodal intelligence.

Radiology plays an integral role in modern medicine, yet rising imaging volumes have far outpaced workforce growth. Foundation models offer a path toward assisting with the full spectrum of radiology tasks, but existing medical models remain limited: they process volumetric CT and MRI as low-fidelity 2D slices, discard critical grayscale contrast information, and lack evaluation frameworks that reflect real clinical practice. We introduce Pillar-0, a radiology foundation model pretrained on 42,990 abdomen-pelvis CTs, 86,411 chest CTs, 14,348 head CTs, and 11,543 breast MRIs from a large academic center, together with RATE, a scalable framework that extracts structured labels for 366 radiologic findings with near-perfect accuracy using LLMs. Across internal test sets of 14,230 abdomen-pelvis CTs, 10,646 chest CTs, 4,906 head CTs, and 1,585 breast MRIs, Pillar-0 establishes a new performance frontier, achieving mean AUROCs of 86.4, 88.0, 90.1, and 82.9, outperforming MedGemma (Google), MedImageInsight (Microsoft), Lingshu (Alibaba), and Merlin (Stanford) by 7.8-15.8 AUROC points and ranking best in 87.2\% (319/366) tasks. Pillar-0 similarly outperforms all baselines in an external validation on the Stanford Abdominal CT dataset, including Merlin (82.2 vs 80.6 AUROC). Pillar-0 extends to tasks beyond its pretraining, such as long-horizon lung cancer risk prediction, where it improves upon the state-of-the-art Sybil by 3.0 C-index points on NLST, and generalizes with gains of 5.9 (MGH) and 1.9 (CGMH). In brain hemorrhage detection, Pillar-0 obtained a >95 AUROC when using only 1/20th of the data of the next most sample efficient baseline. Pillar-0 and RATE together provide an open, clinically rigorous foundation for building high-performance radiology systems, enabling applications that were previously infeasible due to computational, data, and evaluation constraints.

Diffusion Transformers have demonstrated remarkable capabilities in visual synthesis, yet they often struggle with high-level semantic reasoning and long-horizon planning. This limitation frequently leads to visual hallucinations and mis-alignments with user instructions, especially in scenarios involving complex scene understanding, human-object interactions, multi-stage actions, and in-context motion reasoning. To address these challenges, we propose Plan-X, a framework that explicitly enforces high-level semantic planning to instruct video generation process. At its core lies a Semantic Planner, a learnable multimodal language model that reasons over the user's intent from both text prompts and visual context, and autoregressively generates a sequence of text-grounded spatio-temporal semantic tokens. These semantic tokens, complementary to high-level text prompt guidance, serve as structured "semantic sketches" over time for the video diffusion model, which has its strength at synthesizing high-fidelity visual details. Plan-X effectively integrates the strength of language models in multimodal in-context reasoning and planning, together with the strength of diffusion models in photorealistic video synthesis. Extensive experiments demonstrate that our framework substantially reduces visual hallucinations and enables fine-grained, instruction-aligned video generation consistent with multimodal context.

We present HunyuanVideo 1.5, a lightweight yet powerful open-source video generation model that achieves state-of-the-art visual quality and motion coherence with only 8.3 billion parameters, enabling efficient inference on consumer-grade GPUs. This achievement is built upon several key components, including meticulous data curation, an advanced DiT architecture featuring selective and sliding tile attention (SSTA), enhanced bilingual understanding through glyph-aware text encoding, progressive pre-training and post-training, and an efficient video super-resolution network. Leveraging these designs, we developed a unified framework capable of high-quality text-to-video and image-to-video generation across multiple durations and resolutions.Extensive experiments demonstrate that this compact and proficient model establishes a new state-of-the-art among open-source video generation models. By releasing the code and model weights, we provide the community with a high-performance foundation that lowers the barrier to video creation and research, making advanced video generation accessible to a broader audience. All open-source assets are publicly available at https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5.

We present M^3-Bench, the first benchmark for evaluating multimodal tool use under the Model Context Protocol. The benchmark targets realistic, multi-hop and multi-threaded workflows that require visual grounding and textual reasoning, cross-tool dependencies, and persistence of intermediate resources across steps. We introduce a similarity-driven alignment that serializes each tool call, embeds signatures with a sentence encoder, and performs similarity-bucketed Hungarian matching to obtain auditable one-to-one correspondences. On top of this alignment, we report interpretable metrics that decouple semantic fidelity from workflow consistency. The benchmark spans 28 servers with 231 tools, and provides standardized trajectories curated through an Executor & Judge pipeline with human verification; an auxiliary four large language models (LLMs) judge ensemble reports end-task Task Completion and information grounding. Evaluations of representative state-of-the-art Multimodal LLMs (MLLMs) reveal persistent gaps in multimodal MCP tool use, particularly in argument fidelity and structure consistency, underscoring the need for methods that jointly reason over images, text, and tool graphs. Our Benchmark's anonymous repository is at https://github.com/EtaYang10th/Open-M3-Bench

Multi-agent systems perform well on general reasoning tasks. However, the lack of training in specialized areas hinders their accuracy. Current training methods train a unified large language model (LLM) for all agents in the system. This may limit the performances due to different distributions underlying for different agents. Therefore, training multi-agent systems with distinct LLMs should be the next step to solve. However, this approach introduces optimization challenges. For example, agents operate at different frequencies, rollouts involve varying sub-agent invocations, and agents are often deployed across separate servers, disrupting end-to-end gradient flow. To address these issues, we propose M-GRPO, a hierarchical extension of Group Relative Policy Optimization designed for vertical Multi-agent systems with a main agent (planner) and multiple sub-agents (multi-turn tool executors). M-GRPO computes group-relative advantages for both main and sub-agents, maintaining hierarchical credit assignment. It also introduces a trajectory-alignment scheme that generates fixed-size batches despite variable sub-agent invocations. We deploy a decoupled training pipeline in which agents run on separate servers and exchange minimal statistics via a shared store. This enables scalable training without cross-server backpropagation. In experiments on real-world benchmarks (e.g., GAIA, XBench-DeepSearch, and WebWalkerQA), M-GRPO consistently outperforms both single-agent GRPO and multi-agent GRPO with frozen sub-agents, demonstrating improved stability and sample efficiency. These results show that aligning heterogeneous trajectories and decoupling optimization across specialized agents enhances tool-augmented reasoning tasks.

We propose a fully data-driven approach to designing mutual information (MI) estimators. Since any MI estimator is a function of the observed sample from two random variables, we parameterize this function with a neural network (MIST) and train it end-to-end to predict MI values. Training is performed on a large meta-dataset of 625,000 synthetic joint distributions with known ground-truth MI. To handle variable sample sizes and dimensions, we employ a two-dimensional attention scheme ensuring permutation invariance across input samples. To quantify uncertainty, we optimize a quantile regression loss, enabling the estimator to approximate the sampling distribution of MI rather than return a single point estimate. This research program departs from prior work by taking a fully empirical route, trading universal theoretical guarantees for flexibility and efficiency. Empirically, the learned estimators largely outperform classical baselines across sample sizes and dimensions, including on joint distributions unseen during training. The resulting quantile-based intervals are well-calibrated and more reliable than bootstrap-based confidence intervals, while inference is orders of magnitude faster than existing neural baselines. Beyond immediate empirical gains, this framework yields trainable, fully differentiable estimators that can be embedded into larger learning pipelines. Moreover, exploiting MI's invariance to invertible transformations, meta-datasets can be adapted to arbitrary data modalities via normalizing flows, enabling flexible training for diverse target meta-distributions.

固态硬盘（SSD）基本上取代了机械硬盘成为最流行的存储设备，SSD 速度更快，功耗更低。但如果你计划将 SSD 作为冷存储设备使用，将数据储存在 SSD 里面然后离线保存几年，那么你可能需要三思而行，因为 SSD 在长时间断电后会缓慢损失或丢失数据。最便宜的 QLC NAND SSD 能在完全断电的情况下安全保存数据约一年时间，而价格更贵的 TLC NAND SSD 能保存数据三年，MLC 和 SLC NAND SSD 在断电的情况下分别能保存数据 5 年和 10 年。绝大多数消费级 SSD 使用的是 TLC 或 QLC NAND，因此断电超过一年就可能面临数据丢失的风险。相比下，机械硬盘比 SSD 更适合长时间断电保存数据。

微软正在 Windows 11 中集成越来越多的 AI 功能，最新的测试版本 v26220.7262 加入了 Copilot Actions，但默认没有启用，需要管理员权限才能激活。对于这些基于大模型的 AI 功能，微软试图推卸掉自己的责任，它通过其支持文档警告，称 Copilot Actions 之类的 AI 功能会引入新的安全风险，如跨提示注入(cross-prompt injection 或 XPIA)——文档或 UI 元素中的恶意内容能覆盖 AI 指令，导致数据泄露或安装恶意程序等意外操作。它建议用户在了解安全风险的情况下启用 AI 功能。Copilot Actions 具有高访问权限，能对用户的 Documents、Downloads、Desktop、Pictures、Videos 和 Music 等文件夹进行读写操作。微软还表示 AI 也可能会产生幻觉，“产生意外之外的输出”。

皮尤研究中心于 2 月 5 日至 6 月 18 日之间调查了 5022 名美国人的社媒使用情况。结果显示：YouTube 84%，Facebook 71%，Instagram 50%，TikTok 37%，WhatsApp 32%，Reddit 26%，Snapchat 25%，X.com（Twitter）21%，Threads 8%，Bluesky 4% 和 Truth Social 3%。YouTube 和 Facebook 仍然是美国占据主导地位的社交媒体，但其使用比例长期保持稳定，而年轻人更可能使用 YouTube，30-49 岁人群则更可能使用 Facebook（80%）。逾半数女性使用 Instagram (55%)，男性则只有 44%；男性更可能使用 X 和 Reddit，民主党人和倾向民主党的独立人士更可能使用 WhatsApp、Reddit、TikTok、Bluesky 和 ​​Threads。

人、猴甚至北极熊都会接吻。研究人员将嘴对嘴的接吻行为追溯到 2100 万年前。接吻行为可能传播疾病，且似乎并不能直接提高生存或繁殖能力。虽然接吻对许多人类群体具有强烈的情感和文化意义，但其演化背景却很少被深入研究。在这项研究中，研究人员首次尝试跨物种追踪接吻的起源——利用灵长类动物间的演化关系进行分析。结果表明，接吻在大型类人猿中有很深的渊源，在 2150 万至 1690 万年前的祖先中就已出现。这种行为似乎在演化过程中持续存在，并且在该类群的大多数物种中仍可观察到。研究小组还得出结论，已经灭绝的人类近亲——尼安德特人可能也会接吻。这一结论得到了早期研究的支持，人类与尼安德特人曾交换口腔微生物（通过唾液转移）并进行杂交，这意味着接吻是他们互动的一部分。

英伟达上周释出了 GeForce Hotfix Display Driver v581.94，称微软在十月份释出的 Windows 11 24H2 和 Windows 11 25H 更新导致游戏性能出现问题。存在问题的补丁是 KB5066835，安装之后部分游戏的性能可能会下降。除此之外，微软的十月份例行更新还被发现会破坏 localhost HTTP 连接，智能卡身份验证问题，以及 Windows Recovery Environment(WinRE)无法使用 USB 鼠标和键盘。

研究公司 Alinea Analytics 估计，Valve 在 2025 年的年收入在 160-170 亿美元之间。而 Valve 大约有 350 名员工，意味着每名员工产生约 5000 万美元的收入。Valve 是一家私营公司，它无需公开披露收入等数据。它的员工数据还是因为诉讼而泄露的。Valve 为员工提供了丰厚的薪酬，根据泄露的数据，它在员工工资上花了近 4.5 亿美元，平均每位员工逾 130 万美元。

根据发表在《Mental Health Research》期刊上的一项研究，打扮成蝙蝠侠可能会在公共场合促进亲社会行为。意大利研究人员在米兰地铁展开了研究，观察了 138 次乘车。对照组由一名装扮成孕妇的女性与一位观察员组成，她们一起登上列车。实验组成员打扮成蝙蝠侠登上列车。结果显示，当蝙蝠侠出现时，乘客让座的概率显著高于对照组。值得注意的是，实验组中 44% 的让座者表示并没有看到蝙蝠侠。这表明意外事件能促进亲社会行为，这项发现对于在公共场合鼓励善意行为有重要意义。

从 Git 3.0 起，默认分支名将是 main 而不是 master。关于 main 和 master 名字的争论可以追溯到 2020 年，而 GitHub 早在 2020 年 10 月 1 日将新建代码库的默认分支名改为 main。Git 3.0 预计会在 2026 年底左右发布，主要变化包括：默认哈希函数从 SHA-1 改为 SHA-256 以提高安全性；改变默认存储格式以更好支持 macOS 和 Windows 并提升性能；更正式的将 Rust 集成到 Git 自身构建流程中。

看不见的微塑料通过空气扩散到全球。早稻田大学环境化学教授 Hiroshi Okochi 称，最近的研究表明空气传播的塑料污染正以惊人速度扩散。空气传播的微塑料直径小于 2.5 微米。Okochi 团队在 2023 年发表的一项研究发现，富士山顶云层中的水每升含有 6.7 个微塑料颗粒。德国和瑞士的团队报告，他们在北极每升雪中发现了逾万个微塑料颗粒。这些微塑料可能是通过空气传播随雪沉积。尽管在人体各部位都发现微塑料，但目前尚不清楚空气中的塑料颗粒对健康的影响。1 微米或更小的通过空气传播的塑料颗粒被认为能到达肺泡。英国一项研究表明，在 13 名接受肺部手术的患者的肺组织样本中，有 11 份检测到了微塑料。其中肺下部的微塑料含量最高。人每天呼吸超过 2 万次，一生累计呼吸 6-7 亿次。Okochi 表示人类不可避免的会吸入空气中的微塑料，但因为看不见所以也丝毫不知。

马斯克（Elon Musk）旗下的社交媒体 X/Twitter 开始展示账号注册的地理位置，如果该账号使用了 VPN 隐藏 IP 它还会提示可能使用了 VPN。该功能在上线之后一度下线，之后又恢复上线。地理位置信息显示很多政治网红的账号其实都是在美国之外运营的。MAGA NATION 有逾 39.2 万粉丝，其运营地点位于东欧；Dark Maga 有逾 1.5 万粉丝，其运营地点位于泰国；MAGA Scope 有逾 5.1 万粉丝，其运营地点位于尼日利亚；America First 有逾 6.7 万粉丝，其运营地点位于孟加拉国。反 MAGA 账号 Ron Smith 有逾 5.2 万名粉丝，其运营地点位于肯尼亚；Republicans Against Trump 有逾 97 万粉丝，其运营地点位于奥地利，目前使用美国 IP 的 VPN 隐藏原 IP。

2023 年 Google Chrome 移除了对实验性的 JPEG-XL 图像格式的支持。JPEG-XL 是免专利新的图像格式。Google 此举引发了很多争议，因为 Chrome/Chromium 占据了九成市场份额，它是 Web 标准事实上的仲裁者。到了 2025 年事情有了戏剧性转变。Google 开发者 Rick Byers 表示考虑恢复支持 JPEG-XL，预计将使用 JPEG-XL 的 Rust 语言实现。Google 开发者称，Safari 加入了对 JPEG-XL 支持，Firefox 也表明了立场，PDF 也准备添加 JPEG-XL 支持。Chromium 要默认启用 JPEG XL 解码器，需要有长期维护的承诺，满足这些条件的话将会恢复支持。

美国疾病控制与预防中心（CDC）的科学家近日已接到逐步停止所有猴子研究工作的指令。这将导致约 200 只恒河猴和豚尾猴参与的研究工作停止。这些猴子曾被用于艾滋病、肝炎和其他传染病研究。目前它们前路未卜，其中一部分可能被转移到灵长类动物保护区，另一部分可能会被安乐死。此举将是美国政府机构首次终止其内部的非人灵长类动物研究项目。多年来一直致力于推动政府终止对动物研究支持的美国非营利组织“白大褂废物项目(White Coat Waste Project)”对这上述决定表示欢迎。而生物医学科学家则警告称，此举将是一个重大错误。他们表示，CDC 的猴子研究项目对于艾滋病病毒暴露前预防药物研发至关重要，这种预防策略已经大幅降低全球艾滋病感染率。

在 Linux 操作系统上 Firefox 浏览器将所有文件都储存在 ~/.mozilla 目录下。2004 年 9 月递交的一份 bug 报告呼吁 Firefox 遵守 Freedesktop.org 的 XDG Base Directory Specification 目录标准：将配置文件、缓存数据、用户数据等储存在不同目录，如 ~/.config 和 ~/.local/share。21 年后，Firefox 终于解决了该 bug，从 Firefox 147 起它将支持 XDG Base Directory Specification 目录标准。

总部位于美国华盛顿 Bellevue 的密码学会 International Association of Cryptologic Research（IACR）在全世界有数千名会员，该组织于 10 月 17 日至 11 月 16 日之间举行了包括主席在内的多个领导职位的选举，使用名为 Helios 的电子投票系统。该系统对每张选票进行加密，允许投票者追踪自己投的票。投票结果使用三个密钥进行解密，这三个密钥掌握在三位选举委员会成员手中。然而问题是其中一人——Google 的 Moti Yung 的密钥丢了，导致结果无法解密。密码学会表示这一轮投票作废，新一轮投票将于 11 月 21 日至 12 月 20 日举行。该组织同时表示 Moti Yung 已经辞去了选举委员会成员职位。IACR 表示为避免再次出现类似的问题，他们将放宽密钥使用要求，采用三分之二密钥使用门槛。