OrangeBot.AI Digest — 2026-01-06

Protocol Buffers - Google's data interchange format

🕵️‍♂️ All-in-one OSINT tool for analysing any website

Microsoft PowerToys is a collection of utilities that help you customize Windows and streamline everyday tasks

Official inference framework for 1-bit LLMs

A collaborative list of public APIs for developers

Kiro is an agentic IDE that works alongside you from prototype to production.

This repo powers my experiment where ChatGPT manages a real-money micro-cap stock portfolio.

📑 PageIndex: Document Index for Reasoning-based RAG

Free Introduction to Bash Scripting eBook

We present NextFlow, a unified decoder-only autoregressive transformer trained on 6 trillion interleaved text-image discrete tokens. By leveraging a unified vision representation within a unified autoregressive architecture, NextFlow natively activates multimodal understanding and generation capabilities, unlocking abilities of image editing, interleaved content and video generation. Motivated by the distinct nature of modalities - where text is strictly sequential and images are inherently hierarchical - we retain next-token prediction for text but adopt next-scale prediction for visual generation. This departs from traditional raster-scan methods, enabling the generation of 1024x1024 images in just 5 seconds - orders of magnitude faster than comparable AR models. We address the instabilities of multi-scale generation through a robust training recipe. Furthermore, we introduce a prefix-tuning strategy for reinforcement learning. Experiments demonstrate that NextFlow achieves state-of-the-art performance among unified models and rivals specialized diffusion baselines in visual quality.

Large language models (LLMs) generate fluent and complex outputs but often fail to recognize their own mistakes and hallucinations. Existing approaches typically rely on external judges, multi-sample consistency, or text-based self-critique, which incur additional compute or correlate weakly with true correctness. We ask: can LLMs predict their own failures by inspecting internal states during inference? We introduce Gnosis, a lightweight self-awareness mechanism that enables frozen LLMs to perform intrinsic self-verification by decoding signals from hidden states and attention patterns. Gnosis passively observes internal traces, compresses them into fixed-budget descriptors, and predicts correctness with negligible inference cost, adding only ~5M parameters and operating independently of sequence length. Across math reasoning, open-domain question answering, and academic knowledge benchmarks, and over frozen backbones ranging from 1.7B to 20B parameters, Gnosis consistently outperforms strong internal baselines and large external judges in both accuracy and calibration. Moreover, it generalizes zero-shot to partial generations, enabling early detection of failing trajectories and compute-aware control. These results show that reliable correctness cues are intrinsic to generation process and can be extracted efficiently without external supervision.

This technical report presents K-EXAONE, a large-scale multilingual language model developed by LG AI Research. K-EXAONE is built on a Mixture-of-Experts architecture with 236B total parameters, activating 23B parameters during inference. It supports a 256K-token context window and covers six languages: Korean, English, Spanish, German, Japanese, and Vietnamese. We evaluate K-EXAONE on a comprehensive benchmark suite spanning reasoning, agentic, general, Korean, and multilingual abilities. Across these evaluations, K-EXAONE demonstrates performance comparable to open-weight models of similar size. K-EXAONE, designed to advance AI for a better life, is positioned as a powerful proprietary AI foundation model for a wide range of industrial and research applications.

Video Face Swapping (VFS) requires seamlessly injecting a source identity into a target video while meticulously preserving the original pose, expression, lighting, background, and dynamic information. Existing methods struggle to maintain identity similarity and attribute preservation while preserving temporal consistency. To address the challenge, we propose a comprehensive framework to seamlessly transfer the superiority of Image Face Swapping (IFS) to the video domain. We first introduce a novel data pipeline SyncID-Pipe that pre-trains an Identity-Anchored Video Synthesizer and combines it with IFS models to construct bidirectional ID quadruplets for explicit supervision. Building upon paired data, we propose the first Diffusion Transformer-based framework DreamID-V, employing a core Modality-Aware Conditioning module to discriminatively inject multi-model conditions. Meanwhile, we propose a Synthetic-to-Real Curriculum mechanism and an Identity-Coherence Reinforcement Learning strategy to enhance visual realism and identity consistency under challenging scenarios. To address the issue of limited benchmarks, we introduce IDBench-V, a comprehensive benchmark encompassing diverse scenes. Extensive experiments demonstrate DreamID-V outperforms state-of-the-art methods and further exhibits exceptional versatility, which can be seamlessly adapted to various swap-related tasks.

Visual generation is dominated by three paradigms: AutoRegressive (AR), diffusion, and Visual AutoRegressive (VAR) models. Unlike AR and diffusion, VARs operate on heterogeneous input structures across their generation steps, which creates severe asynchronous policy conflicts. This issue becomes particularly acute in reinforcement learning (RL) scenarios, leading to unstable training and suboptimal alignment. To resolve this, we propose a novel framework to enhance Group Relative Policy Optimization (GRPO) by explicitly managing these conflicts. Our method integrates three synergistic components: 1) a stabilizing intermediate reward to guide early-stage generation; 2) a dynamic time-step reweighting scheme for precise credit assignment; and 3) a novel mask propagation algorithm, derived from principles of Reward Feedback Learning (ReFL), designed to isolate optimization effects both spatially and temporally. Our approach demonstrates significant improvements in sample quality and objective alignment over the vanilla GRPO baseline, enabling robust and effective optimization for VAR models.

Fine-tuning diffusion models via online reinforcement learning (RL) has shown great potential for enhancing text-to-image alignment. However, since precisely specifying a ground-truth objective for visual tasks remains challenging, the models are often optimized using a proxy reward that only partially captures the true goal. This mismatch often leads to reward hacking, where proxy scores increase while real image quality deteriorates and generation diversity collapses. While common solutions add regularization against the reference policy to prevent reward hacking, they compromise sample efficiency and impede the exploration of novel, high-reward regions, as the reference policy is usually sub-optimal. To address the competing demands of sample efficiency, effective exploration, and mitigation of reward hacking, we propose Gated and Adaptive Regularization with Diversity-aware Optimization (GARDO), a versatile framework compatible with various RL algorithms. Our key insight is that regularization need not be applied universally; instead, it is highly effective to selectively penalize a subset of samples that exhibit high uncertainty. To address the exploration challenge, GARDO introduces an adaptive regularization mechanism wherein the reference model is periodically updated to match the capabilities of the online policy, ensuring a relevant regularization target. To address the mode collapse issue in RL, GARDO amplifies the rewards for high-quality samples that also exhibit high diversity, encouraging mode coverage without destabilizing the optimization process. Extensive experiments across diverse proxy rewards and hold-out unseen metrics consistently show that GARDO mitigates reward hacking and enhances generation diversity without sacrificing sample efficiency or exploration, highlighting its effectiveness and robustness.

We present VINO, a unified visual generator that performs image and video generation and editing within a single framework. Instead of relying on task-specific models or independent modules for each modality, VINO uses a shared diffusion backbone that conditions on text, images and videos, enabling a broad range of visual creation and editing tasks under one model. Specifically, VINO couples a vision-language model (VLM) with a Multimodal Diffusion Transformer (MMDiT), where multimodal inputs are encoded as interleaved conditioning tokens, and then used to guide the diffusion process. This design supports multi-reference grounding, long-form instruction following, and coherent identity preservation across static and dynamic content, while avoiding modality-specific architectural components. To train such a unified system, we introduce a multi-stage training pipeline that progressively expands a video generation base model into a unified, multi-task generator capable of both image and video input and output. Across diverse generation and editing benchmarks, VINO demonstrates strong visual quality, faithful instruction following, improved reference and attribute preservation, and more controllable multi-identity edits. Our results highlight a practical path toward scalable unified visual generation, and the promise of interleaved, in-context computation as a foundation for general-purpose visual creation.

The grand vision of enabling persistent, large-scale 3D visual geometry understanding is shackled by the irreconcilable demands of scalability and long-term stability. While offline models like VGGT achieve inspiring geometry capability, their batch-based nature renders them irrelevant for live systems. Streaming architectures, though the intended solution for live operation, have proven inadequate. Existing methods either fail to support truly infinite-horizon inputs or suffer from catastrophic drift over long sequences. We shatter this long-standing dilemma with InfiniteVGGT, a causal visual geometry transformer that operationalizes the concept of a rolling memory through a bounded yet adaptive and perpetually expressive KV cache. Capitalizing on this, we devise a training-free, attention-agnostic pruning strategy that intelligently discards obsolete information, effectively ``rolling'' the memory forward with each new frame. Fully compatible with FlashAttention, InfiniteVGGT finally alleviates the compromise, enabling infinite-horizon streaming while outperforming existing streaming methods in long-term stability. The ultimate test for such a system is its performance over a truly infinite horizon, a capability that has been impossible to rigorously validate due to the lack of extremely long-term, continuous benchmarks. To address this critical gap, we introduce the Long3D benchmark, which, for the first time, enables a rigorous evaluation of continuous 3D geometry estimation on sequences about 10,000 frames. This provides the definitive evaluation platform for future research in long-term 3D geometry understanding. Code is available at: https://github.com/AutoLab-SAI-SJTU/InfiniteVGGT

We study allowing large language models (LLMs) to process arbitrarily long prompts through the lens of inference-time scaling. We propose Recursive Language Models (RLMs), a general inference strategy that treats long prompts as part of an external environment and allows the LLM to programmatically examine, decompose, and recursively call itself over snippets of the prompt. We find that RLMs successfully handle inputs up to two orders of magnitude beyond model context windows and, even for shorter prompts, dramatically outperform the quality of base LLMs and common long-context scaffolds across four diverse long-context tasks, while having comparable (or cheaper) cost per query.

This work introduces Falcon-H1R, a 7B-parameter reasoning-optimized model that establishes the feasibility of achieving competitive reasoning performance with small language models (SLMs). Falcon-H1R stands out for its parameter efficiency, consistently matching or outperforming SOTA reasoning models that are 2times to 7times larger across a variety of reasoning-intensive benchmarks. These results underscore the importance of careful data curation and targeted training strategies (via both efficient SFT and RL scaling) in delivering significant performance gains without increasing model size. Furthermore, Falcon-H1R advances the 3D limits of reasoning efficiency by combining faster inference (through its hybrid-parallel architecture design), token efficiency, and higher accuracy. This unique blend makes Falcon-H1R-7B a practical backbone for scaling advanced reasoning systems, particularly in scenarios requiring extensive chain-of-thoughts generation and parallel test-time scaling. Leveraging the recently introduced DeepConf approach, Falcon-H1R achieves state-of-the-art test-time scaling efficiency, offering substantial improvements in both accuracy and computational cost. As a result, Falcon-H1R demonstrates that compact models, through targeted model training and architectural choices, can deliver robust and scalable reasoning performance.

We introduce Talk2Move, a reinforcement learning (RL) based diffusion framework for text-instructed spatial transformation of objects within scenes. Spatially manipulating objects in a scene through natural language poses a challenge for multimodal generation systems. While existing text-based manipulation methods can adjust appearance or style, they struggle to perform object-level geometric transformations-such as translating, rotating, or resizing objects-due to scarce paired supervision and pixel-level optimization limits. Talk2Move employs Group Relative Policy Optimization (GRPO) to explore geometric actions through diverse rollouts generated from input images and lightweight textual variations, removing the need for costly paired data. A spatial reward guided model aligns geometric transformations with linguistic description, while off-policy step evaluation and active step sampling improve learning efficiency by focusing on informative transformation stages. Furthermore, we design object-centric spatial rewards that evaluate displacement, rotation, and scaling behaviors directly, enabling interpretable and coherent transformations. Experiments on curated benchmarks demonstrate that Talk2Move achieves precise, consistent, and semantically faithful object transformations, outperforming existing text-guided editing approaches in both spatial accuracy and scene coherence.

While confidence estimation is a promising direction for mitigating hallucinations in Large Language Models (LLMs), current research dominantly focuses on single-turn settings. The dynamics of model confidence in multi-turn conversations, where context accumulates and ambiguity is progressively resolved, remain largely unexplored. Reliable confidence estimation in multi-turn settings is critical for many downstream applications, such as autonomous agents and human-in-the-loop systems. This work presents the first systematic study of confidence estimation in multi-turn interactions, establishing a formal evaluation framework grounded in two key desiderata: per-turn calibration and monotonicity of confidence as more information becomes available. To facilitate this, we introduce novel metrics, including a length-normalized Expected Calibration Error (InfoECE), and a new "Hinter-Guesser" paradigm for generating controlled evaluation datasets. Our experiments reveal that widely-used confidence techniques struggle with calibration and monotonicity in multi-turn dialogues. We propose P(Sufficient), a logit-based probe that achieves comparatively better performance, although the task remains far from solved. Our work provides a foundational methodology for developing more reliable and trustworthy conversational agents.

We introduce CPPO, a Contrastive Perception Policy Optimization method for finetuning vision-language models (VLMs). While reinforcement learning (RL) has advanced reasoning in language models, extending it to multimodal reasoning requires improving both the perception and reasoning aspects. Prior works tackle this challenge mainly with explicit perception rewards, but disentangling perception tokens from reasoning tokens is difficult, requiring extra LLMs, ground-truth data, forced separation of perception from reasoning by policy model, or applying rewards indiscriminately to all output tokens. CPPO addresses this problem by detecting perception tokens via entropy shifts in the model outputs under perturbed input images. CPPO then extends the RL objective function with a Contrastive Perception Loss (CPL) that enforces consistency under information-preserving perturbations and sensitivity under information-removing ones. Experiments show that CPPO surpasses previous perception-rewarding methods, while avoiding extra models, making training more efficient and scalable.

While LLMs are powerful embedding backbones, their application in training-free settings faces two structural challenges: causal attention restricts early tokens from accessing subsequent context, and the next-token prediction objective biases representations toward generation rather than semantic compression. To address these limitations, we propose KV-Embedding, a framework that activates the latent representation power of frozen LLMs. Our method leverages the observation that the key-value (KV) states of the final token at each layer encode a compressed view of the sequence. By re-routing these states as a prepended prefix, we enable all tokens to access sequence-level context within a single forward pass. To ensure model-agnostic applicability, we introduce an automated layer selection strategy based on intrinsic dimensionality. Evaluations on MTEB across Qwen, Mistral, and Llama backbones show that KV-Embedding outperforms existing training-free baselines by up to 10%, while maintaining robust performance on sequences up to 4,096 tokens. These results demonstrate that internal state manipulation offers an efficient alternative to input modification, and we hope this work encourages further exploration of LLM internals for representation learning.

Human mesh recovery from multi-view images faces a fundamental challenge: real-world datasets contain imperfect ground-truth annotations that bias the models' training, while synthetic data with precise supervision suffers from domain gap. In this paper, we propose DiffProxy, a novel framework that generates multi-view consistent human proxies for mesh recovery. Central to DiffProxy is leveraging the diffusion-based generative priors to bridge the synthetic training and real-world generalization. Its key innovations include: (1) a multi-conditional mechanism for generating multi-view consistent, pixel-aligned human proxies; (2) a hand refinement module that incorporates flexible visual prompts to enhance local details; and (3) an uncertainty-aware test-time scaling method that increases robustness to challenging cases during optimization. These designs ensure that the mesh recovery process effectively benefits from the precise synthetic ground truth and generative advantages of the diffusion-based pipeline. Trained entirely on synthetic data, DiffProxy achieves state-of-the-art performance across five real-world benchmarks, demonstrating strong zero-shot generalization particularly on challenging scenarios with occlusions and partial views. Project page: https://wrk226.github.io/DiffProxy.html

根据发表于《科学进展》的研究，基于 700 万年前的化石，利用强有力的解剖学证据表明，外表像猿、大脑很小的撒海尔人乍得种能直立行走。这意味着，人类祖先直立行走的时间比预期的早得多。法国普瓦提埃大学的古生物学家在中非乍得德乍腊沙漠发现了撒海尔人乍得种化石。这些化石可追溯至 700 万年前。这些化石到底属于人类直系祖先，还是一种已灭绝的旁支类人猿，学术界对此长期存在争议，其中一个关键争论点就是撒海尔人乍得种是否能直立行走。研究人员利用先进的三维成像技术等，对撒海尔人乍得种的肢体骨骼化石进行分析，发现了支撑其双足行走的三个关键特征：一是股骨近端前侧有个结节。这个结构虽小却很重要，它是人体最强韧带——髂股韧带的附着点。这种韧带是直立行走的关键。这一特征迄今仅在人科动物中观察到。二是股骨自然旋转扭曲，即股骨前倾，其角度处于人科动物范围内，有助于腿部向前伸展，从而实现高效行走。三是臀肌与早期人科动物相似，能够稳定髋关节，并有助于站立、行走和奔跑。后两个特征此前已有研究提及，而这项新研究证实了它们的存在。

去年 CES 上 AI 戴尔宣布了一项受争议的决定：重塑了其产品品牌，废弃了有几十年历史的 XPS、Inspiron、Latitude 品牌，改用 Dell、Dell Pro 和 Dell Pro Max，每个品牌下有三个级别：Base、Plus 和 Premium。戴尔称，此举是为了让客户更方便的找到满足其需求的 AI PC。Dell 品牌 PC 针对娱乐、教育和工作，Dell Pro 针对生产力，Dell Pro Max 瞄准最高性能。一年之后戴尔在 2026 年 CES 展会上承认放弃 XPS 品牌是错误的决定，它决定重启 XPS，将其定位为高端笔记本电脑产品，不过它无意重启 Inspiron 和 Latitude 品牌。

世嘉联合创始人 David Rosen 去世，享年 95 岁。David Rosen 在朝鲜战争期间是驻扎在日本的美国空军飞行员。战后他因为喜欢日本而留下，1954 年创办了 Rosen Enterprises，1965 年与另一家公司 Nihon Goraku Bussan 合并，该公司的投币游戏业务 Service Games 在新公司缩写为世嘉（Sega）。世嘉在之后的 15 年里从进口游戏转向自主设计游戏，从点唱机和弹珠台转向街机游戏，它还建立起了街机厅。Rosen 担任世嘉董事直到 1996 年，之后退休。在其任职期间，世嘉的街机业务是行业的领导者，但游戏机业务输给了任天堂。

美国的民意调查显示美国人对高等教育的态度在恶化。皮尤研究中心发现，认为大学“非常重要”的成年人比例从 2013 年的 70% 降至如今的 35%；NBC 民调显示认为大学学位“不值钱”的比例从同期的 40% 增至如今的 63%。但大学入学数据却与多数人的感知截然不同。2023 年四年制大学授予 200 万个学士学位，而 2010 年是 160 万；过去 15 年 25 岁群体拥有学士学位的比例稳步增长。从经济角度看，高等教育仍然具有强大的吸引力。拥有学士学位的人平均收入比拥有类似工作经验的高中毕业生高约 70%。如果将奖学金考虑在内，自 2015 年以来美国公立四年制大学的学费下降逾 20%。即使考虑学生贷款，大学毕业生每年的净收入比无学位者高约 8000 美元。造成认知偏差的部分原因可能是对大学收费存在误解。近半数美国成年人认为所有人学费都一样，但实际上只有不到 20% 的家庭支付了官方公布的学费。

根据 Cloudflare Radar 的监测，美国对委内瑞拉发动突然袭击前一天该国国有电信公司 CANTV 的自治系统 AS8048 发生了路由泄漏事件。当 BGP 流量从 A 点发送到 B 点，它可以被重路由经过 C 点。如果控制了 C 点，即使只有几个小时，理论上也可以收集到大量情报，这对政府机构非常有用。1 月 2 日 AS8048 的流量被路由经过了一条它原本不会经过的路线，该路线上的中转服务提供商 Sparkle 被公认不安全，也就是没有部署 BGP 安全功能如 RPKI 过滤。暂时还不清楚究竟发生了什么。

影子图书馆安娜的档案（Anna's Archive）主域名 annas-archive.org 被封禁。运营者在其 subreddit 上表示这与该网站最近发布 300TB 的 Spotify 存档有关。管理.org 域名的美国非营利组织 Public Interest Registry(PIR)通常很少停用域名，它对 annas-archive.org 采取行动应该是收到了法庭命令。运营者建议用户访问它的其它域名如 annas-archive.in 或 annas-archive.pm。

去年 12 月 27 日，《彩虹六号：围攻X》服务器遭到入侵，黑客向所有玩家赠送了逾 20 亿虚拟点数和稀有皮肤。12 月 29 日育碧对此次攻击采取了回滚操作。本周玩家再次报道黑客入侵了游戏服务器，这一次黑客没有送礼物，而是送出了封禁，封禁日期是 67 天——67 是著名的网络模因。根据育碧官方服务器状态页，《彩虹六号：围攻X》的 PC、PS4/5、Xbox Series X/S 和 Xbox One 服务器都报告遭遇计划外问题（unplanned issues）。有数以千计的玩家报告账号被封禁，其中包括游戏主播 VarsityGaming——其 YouTube 账号有 143 万粉丝。

日本在留资格约有 30 种，其中永住者资格持有者最多，截至 2025 年 6 月底，有约 93 万人持有该资格，占在留外国人总数的 2 成。日本政府本月将召开会议，收紧永住资格的认可条件。讨论中的新规定包括将日语能力纳入申请要求；可能只有持 5 年期限资格的人能申请转换永住；第二大在留资格“技术·人文知识·国际业务”将限制资格外就业行为；“留学生”在留资格取消打工许可；居住 10 年以上才能申请加入日本国籍，对特殊人才有例外。

Palo Alto Networks 首席安全情报官 Wendi Whitmore 认为，AI 智能体将成为企业在 2026 年最大的内部威胁。企业安全团队面临需要尽快部署新技术的巨大压力，他们承受着巨大的压力和工作量，需要快速完成采购流程、安全检查，了解新 AI 应用是否足够安全，能满足企业实际应用场景。根据 Gartner 的估计，到 2026 年底，40% 的企业应用将集成特定任务的 AI 智能体，而 2025 年该比例不到 5%。Whitmore 指出这种激增是一把双刃剑。根据其配置和权限，AI 智能体可能拥有敏感数据和系统的访问权限，这使得 AI 智能体成为极具吸引力的攻击目标。

捷克研究人员在《Communications Medicine》期刊上发表研究，分析了 95 个捷克家庭——其中 47 个纯素、23 个素食和 25 个杂食，共涉及 187 名成年人和 142 名儿童，而纯素和素食组的儿童会服用维生素 B12 和维生素 D 补充剂。研究人员比较了不同饮食组和不同年龄段儿童的生长发育、心血管健康、骨转换、碘摄入量和总体微量营养素水平，结果显示素食和非素食儿童生长发育无显著差异。他们的生长发育和骨骼健康都相似，而纯素组的胆固醇和心血管健康指标最出色，但碘含量较低——研究人员认为这值得关注。

索尼 PS2 游戏机的模拟器项目 PCSX2 释出了 v2.6.0 版本。开发者表示这是至今规模最大的版本发布。主要变化包括：Big Picture 模式和 Qt 界面功能维持一致性；完成韩语游戏名翻译；Windows 和 Linux 支持创建游戏快捷方式；Big Picture 模式支持表情符号；改进混合和硬件渲染器，显著提升 Direct3D12 渲染器速度，部分游戏的性能提升逾 5 倍（主要是使用 AMD GPU 的系统）；等等。

中科院等离子体物理研究所的全超导托卡马克核聚变实验装置（EAST）通过实验证实了托卡马克密度自由区的存在。托卡马克装置是一种利用磁约束来实现受控核聚变的环形装置，犹如一个螺旋形“磁跑道”，锁住高温等离子体，达到核聚变目的。等离子体密度是托卡马克性能的关键参数之一，直接影响聚变反应速率。过去，科研人员发现，等离子体密度存在一个极限，一旦达到极限，等离子体就会破裂并逃脱磁场约束，巨大能量释放到装置内壁，影响安全运行。国际聚变界通过长期研究发现，触发密度极限的物理过程发生于等离子体和装置内壁的边界区域，但对其中的物理机制并不十分清楚。最新研究中研究团队首次证实了托卡马克密度自由区的存在。

行星通常与一颗或多颗恒星相互绑定，越来越多的证据表明，一部分行星正孤独地漫游于银河系中。被称为自由漂浮行星或流浪行星的天体不与任何已知恒星构成伴星系统。由于此类天体本身发光极其微弱，它们仅能通过其微弱的引力效应才能被人类探测到。微引力透镜探测法的局限之一是无法确定距离，因而难以独立测算其质量。研究人员通过一次转瞬即逝的微引力透镜事件发现了一颗新的流浪行星。与之前的探测不同，他们利用多个地面巡天观测和 Gaia 太空望远镜从地球和太空同步观测了这次微引力透镜事件。由于光线到达这些相距遥远观测点的时间存在细微差异，因此可以测量微引力透镜视差；结合有限源点透镜模型分析，作者得以确定该行星的质量和位置。其质量约为木星的 22%，距离银河系中心约 3000 秒差距。由于该行星的质量与土星相当，研究人员认为它诞生于恒星周围，通过引力激变而被逐出其原有轨道。

Reddit 在英国超过 TikTok 成为访问量第四大的社媒平台。英国用户是仅次于美国用户的第二大访问人群，过去两年英国用户人数增长了 88%，Ofcom 的数据显示三分之二的英国网民会访问 Reddit，而 2023 年是三分之一。Reddit 在英国年轻人群中非常受欢迎，18-24 岁英国用户中 Reddit 是访问量第六大的网站，而一年前这一数字是第十。Reddit 的崛起背后的因素包括了 Google 调整了算法突出了论坛类内容，而 Reddit 就是论坛形式的社媒。在 AI 时代，用户也越来越多的转向人工撰写的内容，Reddit 也受益于这一趋势。在 Reddit 的英国用户中女性占了半数以上。

Windows 11 是目前微软唯一支持的 Windows 版本，但它因为更高的硬件需求、更臃肿的系统以及 AI 而在用户中间口碑不佳。在六部旧笔电 ThinkPad X220——配置英特尔 Core i5-2520M CPU、8GB 内存和 256GB 硬盘——上测试最新版本的 Windows XP、Windows Vista、Windows 7、Windows 8.1、Windows 10 和 Windows 11，结果显示：Windows 11 启动速度最慢；安装容量为 37.3GB，略低于 Windows Vista 的 37.8GB 和 Windows 7 的 44.6GB；内存占用 3.3GB 最多 3.7GB；在旧硬件上更容易出现卡顿。