OrangeBot.AI Digest — 2025-12-11

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.

LLM-powered framework for deep document understanding, semantic retrieval, and context-aware answers using RAG paradigm.

an open source, extensible AI agent that goes beyond code suggestions - install, execute, edit, and test with any LLM

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

the blockchain for payments

Experimental menu for GTA 5: Enhanced

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

AGENTS.md — a simple, open format for guiding coding agents

Harden Windows Safely, Securely using Official Supported Microsoft methods and proper explanation | Always up-to-date and works with the latest build of Windows | Provides tools and Guides for Personal, Enterprise, Government and Military security levels | SLSA Level 3 Compliant for Secure Development and Build Process | Apps Available on MS Store✨

所有小初高、大学PDF教材。

Federated query engine for AI - The only MCP Server you'll ever need

Ship AI Agents to Google Cloud in minutes, not months. Production-ready templates with built-in CI/CD, evaluation, and observability.

An open-source remote desktop application designed for self-hosting, as an alternative to TeamViewer.

The growing adoption of XR devices has fueled strong demand for high-quality stereo video, yet its production remains costly and artifact-prone. To address this challenge, we present StereoWorld, an end-to-end framework that repurposes a pretrained video generator for high-fidelity monocular-to-stereo video generation. Our framework jointly conditions the model on the monocular video input while explicitly supervising the generation with a geometry-aware regularization to ensure 3D structural fidelity. A spatio-temporal tiling scheme is further integrated to enable efficient, high-resolution synthesis. To enable large-scale training and evaluation, we curate a high-definition stereo video dataset containing over 11M frames aligned to natural human interpupillary distance (IPD). Extensive experiments demonstrate that StereoWorld substantially outperforms prior methods, generating stereo videos with superior visual fidelity and geometric consistency. The project webpage is available at https://ke-xing.github.io/StereoWorld/.

Understanding how the human brain represents visual concepts, and in which brain regions these representations are encoded, remains a long-standing challenge. Decades of work have advanced our understanding of visual representations, yet brain signals remain large and complex, and the space of possible visual concepts is vast. As a result, most studies remain small-scale, rely on manual inspection, focus on specific regions and properties, and rarely include systematic validation. We present a large-scale, automated framework for discovering and explaining visual representations across the human cortex. Our method comprises two main stages. First, we discover candidate interpretable patterns in fMRI activity through unsupervised, data-driven decomposition methods. Next, we explain each pattern by identifying the set of natural images that most strongly elicit it and generating a natural-language description of their shared visual meaning. To scale this process, we introduce an automated pipeline that tests multiple candidate explanations, assigns quantitative reliability scores, and selects the most consistent description for each voxel pattern. Our framework reveals thousands of interpretable patterns spanning many distinct visual concepts, including fine-grained representations previously unreported.

Visual concept composition, which aims to integrate different elements from images and videos into a single, coherent visual output, still falls short in accurately extracting complex concepts from visual inputs and flexibly combining concepts from both images and videos. We introduce Bind & Compose, a one-shot method that enables flexible visual concept composition by binding visual concepts with corresponding prompt tokens and composing the target prompt with bound tokens from various sources. It adopts a hierarchical binder structure for cross-attention conditioning in Diffusion Transformers to encode visual concepts into corresponding prompt tokens for accurate decomposition of complex visual concepts. To improve concept-token binding accuracy, we design a Diversify-and-Absorb Mechanism that uses an extra absorbent token to eliminate the impact of concept-irrelevant details when training with diversified prompts. To enhance the compatibility between image and video concepts, we present a Temporal Disentanglement Strategy that decouples the training process of video concepts into two stages with a dual-branch binder structure for temporal modeling. Evaluations demonstrate that our method achieves superior concept consistency, prompt fidelity, and motion quality over existing approaches, opening up new possibilities for visual creativity.

Recent advances in diffusion models have greatly improved image generation and editing, yet generating or reconstructing layered PSD files with transparent alpha channels remains highly challenging. We propose OmniPSD, a unified diffusion framework built upon the Flux ecosystem that enables both text-to-PSD generation and image-to-PSD decomposition through in-context learning. For text-to-PSD generation, OmniPSD arranges multiple target layers spatially into a single canvas and learns their compositional relationships through spatial attention, producing semantically coherent and hierarchically structured layers. For image-to-PSD decomposition, it performs iterative in-context editing, progressively extracting and erasing textual and foreground components to reconstruct editable PSD layers from a single flattened image. An RGBA-VAE is employed as an auxiliary representation module to preserve transparency without affecting structure learning. Extensive experiments on our new RGBA-layered dataset demonstrate that OmniPSD achieves high-fidelity generation, structural consistency, and transparency awareness, offering a new paradigm for layered design generation and decomposition with diffusion transformers.

Window attention and linear attention represent two principal strategies for mitigating the quadratic complexity and ever-growing KV cache in Vision-Language Models (VLMs). However, we observe that window-based VLMs suffer performance degradation when sequence length exceeds the window size, while linear attention underperforms on information-intensive tasks such as OCR and document understanding. To overcome these limitations, we propose InfiniteVL, a linear-complexity VLM architecture that synergizes sliding window attention (SWA) with Gated DeltaNet. For achieving competitive multimodal performance under constrained resources, we design a three-stage training strategy comprising distillation pretraining, instruction tuning, and long-sequence SFT. Remarkably, using less than 2\% of the training data required by leading VLMs, InfiniteVL not only substantially outperforms previous linear-complexity VLMs but also matches the performance of leading Transformer-based VLMs, while demonstrating effective long-term memory retention. Compared to similar-sized Transformer-based VLMs accelerated by FlashAttention-2, InfiniteVL achieves over 3.6\times inference speedup while maintaining constant latency and memory footprint. In streaming video understanding scenarios, it sustains a stable 24 FPS real-time prefill speed while preserving long-term memory cache. Code and models are available at https://github.com/hustvl/InfiniteVL.

Vision-Language-Action (VLA) models have recently enabled robotic manipulation by grounding visual and linguistic cues into actions. However, most VLAs assume the Markov property, relying only on the current observation and thus suffering from temporal myopia that degrades long-horizon coherence. In this work, we view motion as a more compact and informative representation of temporal context and world dynamics, capturing inter-state changes while filtering static pixel-level noise. Building on this idea, we propose HiF-VLA (Hindsight, Insight, and Foresight for VLAs), a unified framework that leverages motion for bidirectional temporal reasoning. HiF-VLA encodes past dynamics through hindsight priors, anticipates future motion via foresight reasoning, and integrates both through a hindsight-modulated joint expert to enable a ''think-while-acting'' paradigm for long-horizon manipulation. As a result, HiF-VLA surpasses strong baselines on LIBERO-Long and CALVIN ABC-D benchmarks, while incurring negligible additional inference latency. Furthermore, HiF-VLA achieves substantial improvements in real-world long-horizon manipulation tasks, demonstrating its broad effectiveness in practical robotic settings.

Diffusion large language models (dLLMs) offer a promising alternative to autoregressive models, but their practical utility is severely hampered by slow, iterative sampling. We present SchED, a training-free, model-agnostic early-exit algorithm that aggregates full-span logit margins and halts decoding once a smooth, progress-dependent confidence threshold is met. We evaluated SchED on two dLLM families (Dream and LLaDA), in base and instruction-tuned variants across ten benchmarks spanning downstream tasks including multiple-choice question answering (MCQ), math, long-form QA/summarization, and translation. SchED delivers large, stable accelerations: on instruction-tuned models, it achieves 3.8-4.0times speedups while retaining 99.8-100% of the baseline score on average. On base models, SchED yields consistent speedup gains with 99.1-100% performance retention, with up to 2.34times under more aggressive settings. Using a conservative speed metric that heavily penalizes quality loss (QPS, γ{=}4), we show that SchED is robust and clearly outperforms prior confidence-based early-exit methods, which break down on long-form generation. An entropy analysis of the model's token predictions reveals that instruction tuning speeds up the decay of predictive entropy. By turning genuine confidence stabilization into computational savings, SchED makes dLLM decoding substantially more efficient.

Chain-of-thought (CoT) reasoning has been highly successful in solving complex tasks in natural language processing, and recent multimodal large language models (MLLMs) have extended this paradigm to video reasoning. However, these models typically build on lengthy reasoning chains and large numbers of input visual tokens. Motivated by empirical observations from our benchmark study, we hypothesize that concise reasoning combined with a reduced set of visual tokens can be sufficient for effective video reasoning. To evaluate this hypothesis, we design and validate an efficient post-training and inference framework that enhances a video MLLM's reasoning capability. Our framework enables models to operate on compressed visual tokens and generate brief reasoning traces prior to answering. The resulting models achieve substantially improved inference efficiency, deliver competitive performance across diverse benchmarks, and avoid reliance on manual CoT annotations or supervised fine-tuning. Collectively, our results suggest that long, human-like CoT reasoning may not be necessary for general video reasoning, and that concise reasoning can be both effective and efficient. Our code will be released at https://github.com/LaVi-Lab/Rethink_CoT_Video.

Knowledge editing aims to update specific facts in large language models (LLMs) without full retraining. Prior efforts sought to tune the knowledge layers of LLMs, proving effective for making selective edits. However, a significant gap exists between their performance in controlled, teacher-forcing evaluations and their real-world effectiveness in lifelong learning scenarios, which greatly limits their practical applicability. This work's empirical analysis reveals two recurring issues associated with this gap: (1) Most traditional methods lead the edited model to overfit to the new fact, thereby degrading pre-trained capabilities; (2) There is a critical absence of a knowledge consolidation stage, leaving new facts insufficiently integrated into LLMs' inference-time behavior under autoregressive generation, thereby leading to a mismatch between parametric knowledge and actual generation behavior. To this end, we propose Edit-then-Consolidate, a novel knowledge editing paradigm that aims to bridge the gap between theoretical knowledge editing methods and their real-world applicability. Specifically, (1) our framework mitigates overfitting via Targeted Proximal Supervised Fine-Tuning (TPSFT) that localizes the edit via a trust-region objective to limit policy drift; (2) Then, a consolidation stage using Group Relative Policy Optimization (GRPO) aligns the edited knowledge with CoT-based inference policy by optimizing trajectory-level behavior under comprehensive reward signals. Extensive experiments demonstrate our framework consistently improves editing reliability and generalization under real-world evaluations, while better preserving locality and pre-trained capabilities.

We present WonderZoom, a novel approach to generating 3D scenes with contents across multiple spatial scales from a single image. Existing 3D world generation models remain limited to single-scale synthesis and cannot produce coherent scene contents at varying granularities. The fundamental challenge is the lack of a scale-aware 3D representation capable of generating and rendering content with largely different spatial sizes. WonderZoom addresses this through two key innovations: (1) scale-adaptive Gaussian surfels for generating and real-time rendering of multi-scale 3D scenes, and (2) a progressive detail synthesizer that iteratively generates finer-scale 3D contents. Our approach enables users to "zoom into" a 3D region and auto-regressively synthesize previously non-existent fine details from landscapes to microscopic features. Experiments demonstrate that WonderZoom significantly outperforms state-of-the-art video and 3D models in both quality and alignment, enabling multi-scale 3D world creation from a single image. We show video results and an interactive viewer of generated multi-scale 3D worlds in https://wonderzoom.github.io/

Autonomous driving (AD) systems struggle in long-tail scenarios due to limited world knowledge and weak visual dynamic modeling. Existing vision-language-action (VLA)-based methods cannot leverage unlabeled videos for visual causal learning, while world model-based methods lack reasoning capabilities from large language models. In this paper, we construct multiple specialized datasets providing reasoning and planning annotations for complex scenarios. Then, a unified Understanding-Generation-Planning framework, named UniUGP, is proposed to synergize scene reasoning, future video generation, and trajectory planning through a hybrid expert architecture. By integrating pre-trained VLMs and video generation models, UniUGP leverages visual dynamics and semantic reasoning to enhance planning performance. Taking multi-frame observations and language instructions as input, it produces interpretable chain-of-thought reasoning, physically consistent trajectories, and coherent future videos. We introduce a four-stage training strategy that progressively builds these capabilities across multiple existing AD datasets, along with the proposed specialized datasets. Experiments demonstrate state-of-the-art performance in perception, reasoning, and decision-making, with superior generalization to challenging long-tail situations.

Agents, language model (LM)-based systems that are capable of reasoning, planning, and acting are becoming the dominant paradigm for real-world AI applications. Despite this widespread adoption, the principles that determine their performance remain underexplored, leaving practitioners to rely on heuristics rather than principled design choices. We address this gap by deriving quantitative scaling principles for agent systems. We evaluate this across four diverse benchmarks: Finance-Agent, BrowseComp-Plus, PlanCraft, and Workbench. Using five canonical architectures (Single, Independent, Centralized, Decentralized, Hybrid) instantiated across three LLM families, we perform a controlled evaluation spanning 180 configurations with standardized tools and token budgets. We derive a predictive model using empirical coordination metrics, including efficiency, overhead, error amplification, and redundancy, that achieves cross-validated R^2=0.513. We identify three dominant effects: (1) a tool-coordination trade-off: under fixed computational budgets, tool-heavy tasks suffer disproportionately from multi-agent overhead. (2) a capability saturation: coordination yields diminishing or negative returns (beta=-0.408, p<0.001) once single-agent baselines exceed ~45%. (3) topology-dependent error amplification: independent agents amplify errors 17.2x through unchecked propagation, while centralized coordination contains this to 4.4x. Centralized coordination improves performance by 80.9% on parallelizable tasks like financial reasoning, while decentralized coordination excels on dynamic web navigation (+9.2% vs. +0.2%). Yet for sequential reasoning tasks, all multi-agent variants degraded performance by 39-70%. The framework predicts the optimal coordination strategy for 87% of held-out configurations, providing a predictive principle of agentic scaling based on measurable task properties.

Diffusion (Large) Language Models (dLLMs) now match the downstream performance of their autoregressive counterparts on many tasks, while holding the promise of being more efficient during inference. One particularly successful variant is masked discrete diffusion, in which a buffer filled with special mask tokens is progressively replaced with tokens sampled from the model's vocabulary. Efficiency can be gained by unmasking several tokens in parallel, but doing too many at once risks degrading the generation quality. Thus, one critical design aspect of dLLMs is the sampling procedure that selects, at each step of the diffusion process, which tokens to replace. Indeed, recent work has found that heuristic strategies such as confidence thresholding lead to both higher quality and token throughput compared to random unmasking. However, such heuristics have downsides: they require manual tuning, and we observe that their performance degrades with larger buffer sizes. In this work, we instead propose to train sampling procedures using reinforcement learning. Specifically, we formalize masked diffusion sampling as a Markov decision process in which the dLLM serves as the environment, and propose a lightweight policy architecture based on a single-layer transformer that maps dLLM token confidences to unmasking decisions. Our experiments show that these trained policies match the performance of state-of-the-art heuristics when combined with semi-autoregressive generation, while outperforming them in the full diffusion setting. We also examine the transferability of these policies, finding that they can generalize to new underlying dLLMs and longer sequence lengths. However, we also observe that their performance degrades when applied to out-of-domain data, and that fine-grained tuning of the accuracy-efficiency trade-off can be challenging with our approach.

Recent advances in multimodal large language models (MLLMs) have led to impressive progress across various benchmarks. However, their capability in understanding infrared images remains unexplored. To address this gap, we introduce IF-Bench, the first high-quality benchmark designed for evaluating multimodal understanding of infrared images. IF-Bench consists of 499 images sourced from 23 infrared datasets and 680 carefully curated visual question-answer pairs, covering 10 essential dimensions of image understanding. Based on this benchmark, we systematically evaluate over 40 open-source and closed-source MLLMs, employing cyclic evaluation, bilingual assessment, and hybrid judgment strategies to enhance the reliability of the results. Our analysis reveals how model scale, architecture, and inference paradigms affect infrared image comprehension, providing valuable insights for this area. Furthermore, we propose a training-free generative visual prompting (GenViP) method, which leverages advanced image editing models to translate infrared images into semantically and spatially aligned RGB counterparts, thereby mitigating domain distribution shifts. Extensive experiments demonstrate that our method consistently yields significant performance improvements across a wide range of MLLMs. The benchmark and code are available at https://github.com/casiatao/IF-Bench.

Building on the success of 3D Gaussian Splatting (3DGS) in static 3D scene representation, its extension to dynamic scenes, commonly referred to as 4DGS or dynamic 3DGS, has attracted increasing attention. However, designing more compact and efficient deformation schemes together with rate-distortion-optimized compression strategies for dynamic 3DGS representations remains an underexplored area. Prior methods either rely on space-time 4DGS with overspecified, short-lived Gaussian primitives or on canonical 3DGS with deformation that lacks explicit temporal control. To address this, we present TED-4DGS, a temporally activated and embedding-based deformation scheme for rate-distortion-optimized 4DGS compression that unifies the strengths of both families. TED-4DGS is built on a sparse anchor-based 3DGS representation. Each canonical anchor is assigned learnable temporal-activation parameters to specify its appearance and disappearance transitions over time, while a lightweight per-anchor temporal embedding queries a shared deformation bank to produce anchor-specific deformation. For rate-distortion compression, we incorporate an implicit neural representation (INR)-based hyperprior to model anchor attribute distributions, along with a channel-wise autoregressive model to capture intra-anchor correlations. With these novel elements, our scheme achieves state-of-the-art rate-distortion performance on several real-world datasets. To the best of our knowledge, this work represents one of the first attempts to pursue a rate-distortion-optimized compression framework for dynamic 3DGS representations.

校园供餐计划旨在减少饥饿并提升儿童的学习能力、专注力及整体健康。中、低收入国家约占全球营养不良现象的 90%。最新研究涉及 9.1 万名中、小学生。大多数研究来自中、低收入国家。整体而言，研究作者发现，中、低收入国家的校园供餐计划可使学生的数学测验成绩与入学率略有提升，并可能促进儿童在年龄别身高及年龄别体重等相对成长指标上的些微改善。供餐计划对阅读测验成绩及学校出席率可能影响不大或几乎没有影响。研究的第一作者、渥太华大学荣誉教授 Elizabeth Kristjansson 表示：校园供餐计划在改善弱势儿童的健康与教育成果方面扮演关键角色。我们看到的成效虽然不大，但是真实存在。就我来看，喂饱饥饿的孩子是一项道德责任。

马斯克的 X 平台已经弃用了他收购时使用的 Twitter 名称、商标以及相关 logo。一家叫 Operation Bluebird 的新创公司向 USPTO 申请撤销 X 的 Twitter 和 tweet 商标，希望以 Twitter 的名字推出新社交网络，吸引现有用户重现 Twitter 旧日的辉煌。Operation Bluebird 已经发布了 Twitter.new 的原型，正邀请用户预留用户名。创始人 Michael Peroff 表示，类 Twitter 社交网络如 Threads、Mastodon 和 Bluesky 都没有达到 Twitter 当年的规模和知名度。

印度工业和内部贸易促进部发表了一项提议框架，允许 AI 公司使用所有受版权保护的作品训练模型，但需向一个由版权所有者组织组成的新收款机构支付版税，版税随后将分配给创作者。该提案认为这种“强制性一揽子许可”将降低 AI 公司的合规成本，同时确保作家、音乐家、艺术家等版权所有者在其作品被用于训练商业模型时获得补偿。

英伟达开发出了一种地理位置验证技术去判断 AI 芯片运行时所处的区域位置。它已经在内部演示了该功能，但尚未正式发布。该功能将作为可选的软件工具提供给客户安装。该技术利用了英伟达 GPU 的机密计算能力，通过与英伟达服务器通信的时间延迟估算芯片的地理位置。该技术将首先应用于最新一代的基于 Blackwell 架构的 AI 芯片，英伟达还在考虑将其应用于上一代的 Hopper 架构和 Ampere 架构芯片。该技术有助于解决美国政府对先进 AI 芯片走私到中国的担忧。

苹果周三发布了最受欢迎应用和游戏年度榜单。在美国市场，OpenAI 的 ChatGPT 应用位居 2025 年免费应用（不包括游戏）下载量榜首。紧随其后的是 Threads、Google、TikTok、WhatsApp、Instagram、YouTube、Google Maps、Gmail 和 Google 的 Gemini。ChatGPT 去年排名第四，榜首是拼多多的跨境购物应用 Temu。ChatGPT 应用于 2023 年 5 月在 iPhone 上发布，虽然表现不凡，但在 2023 年它未能进入前十。

Notepad++ 发布安全警告，它遭遇了流量劫持，部分地区的更新程序被植入恶意程序。调查发现，Notepad++ 更新程序 WinGUp 的流量被劫持到恶意服务器，下载恶意可执行文件。更新程序使用版本检查功能查询 URL“https://notepad-plus-plus.org/update/getDownloadUrl.php”并评估返回的 XML 文件。更新程序使用 XML 文件中列出的 Download-URL，将文件保存到 %TEMP% 文件夹并执行。任何能拦截和篡改此流量的攻击者都可以更改 Download-URL。Notepad++ v8.8.7 之前的版本使用了自签名证书，允许攻击者创建篡改后的更新并将其推送给受害者。从 v8.8.7 开始 Notepad++ 使用了来自 GlobalSign 签发的合法证书进行签名。

众筹平台 GoFundMe 发表年度报告《Year in Help》，称有更多的人在 2025 年众筹房租和食品等生活必需品。报告称房租、水电费和食品杂货等基本开支的筹款活动数量增长了 20%，“每月账单（Monthly bills）”是增长速度第二快的众筹类别，仅次于对非营利组织的捐助。生活必需品众筹增长的英语国家包括了美国、加拿大、英国和澳大利亚。GoFundMe CEO Tim Cadogan 称，在美国政府停摆期间，随着每月福利 SNAP 的中断，食品相关的众筹活动增加了近六倍。

希腊前财长、经济学家 Yanis Varoufakis 认为今天的科技巨头是新时代的苏维埃，推行基于算法的计划经济。Palantir 联合创始人 Peter Thiel 宣称失败者才要竞争。他的意思是赢家不仅仅是通过消灭竞争对手去垄断市场，它们还会不断扩张直至摧毁市场本身，复活苏联式的国家计划经济。苏联的国家计划经济之所以失败，是因为它缺乏科技巨头最强大的武器：云资本——以算法、数据中心和光纤构成的集成网络。一位年长的托洛茨基主义者认为苏联以社会主义的名义创造了一种工业封建主义，而今天的科技巨头以资本主义和自由市场的名义创造了一种科技封建主义。随着科技巨头的云资本不断积累并集中到越来越少的人手中，各国政府愈来愈依赖科技巨头。通过在租用的云基础设施上构建核心功能，各国政府实际上是从亚马逊 AWS、微软和 Google 等公司回租其运作能力。这种依赖催生了一种科技封建权力维度。云资本用源自苏联时代的计划工具取代了市场，在此过程中它杀死了资本主义。

2023 年，拜登的国务卿布林肯（Antony Blinken）通知所有大使馆淘汰 Times New Roman 字体改用 Calibri 字体。Times New Roman 是诞生于 1930 年代的衬线字体，而 Calibri 是诞生于 2004 年的无衬线字体。改变字体不是出于美学而是出于可访问性的考虑，因为无衬线字体更容易阅读，尤其是在屏幕上。它对于使用光学字符识别和文本语音工具的人也更方便。在现任总统特朗普治下，美国很多政策在后退，最新的倒退就是恢复 Times New Roman 字体。特朗普的国务卿卢比奥（Marco Rubio）通知大使馆弃用 Calibri 字体，声称此举是浪费资源的 DEIA（代表多元化、公平和包容性），而恢复使用 Times New Roman 字体有助于恢复国务院书面工作的得体性和专业性。

天文学家利用韦伯望远镜追踪今年 3 月由多项望远镜侦测到的伽玛射线暴 GRB 250314A，确认它就是一颗在宇宙诞生后约 7.3 亿年爆炸的超新星所发出的伽玛射线暴，这是目前天文学家成功观测到、爆炸时间最早的超新星事件，将超新星观测年代推前至宇宙 7.3 亿年时，超越先前约 18 亿年的纪录。韦伯的近红外影像让研究团队不但辨认出这颗大质量恒星崩塌后爆炸的余辉，还首次观测到那个极为遥远、在影像中仅呈现微弱红色斑点的宿主星系。这项成果显示，我们已能藉由伽玛射线暴与超新星的余辉，一颗一颗找出宇宙仅有现今约百分之五年龄时就已形成的恒星与星系，为探索早期宇宙开启崭新视野。相比现代超新星，这颗遥远超新星的光学与红外特性都十分相似，令天文学家颇为意外。一般预期宇宙前十亿年的恒星金属量更低、质量更大、寿命更短，且处在宇宙仍相当不透明的再游离时期，因此爆炸型态或光谱应可能有所不同；然而至少在这个案例中，早期宇宙的超新星与今日恒星系统中观测到的十分接近。

十年前发现的 CRISPR 基因编辑技术开始逐渐应用于治疗疾病。2023 年科学家将 CRISPR 用于治疗镰状细胞贫血症。全球大约有 800 万镰状细胞贫血症患者，他们大多携带相同的基因突变。但罕见病的治疗与镰状细胞贫血症不同，它们需要面对不同的基因突变，没有企业会为只有 50 个人携带相同的突变而去研究疗法。但 CRISPR 正逐渐改变这一状况。如果一类或几类罕见病可以使用一个 CRISPR 平台，然后微调模块为每位患者定制疗法，那么罕见病将能更快更经济的治疗。一位名叫 KJ Muldoon 的婴儿患有罕见的遗传病尿素循环障碍，患者通常只有五成几率活过婴儿期。今年二月，六个月大的 Muldoon 接受了 CRISPR 定制疗法治疗去修复特定的基因突变，如今他已是健康的一岁男孩。他的治疗证明定制基因编辑疗法是有效的，可以相对快速安全地投入使用。

Let's Encrypt 项目回顾了过去的十年。该项目旨在让每一个网站都启用 HTTPS 加密，它颁发的免费证书用于加密设备与互联网之间的连接，确保无人能拦截及窃取传输数据。目前全球有数以百万计的网站依赖 Let’s Encrypt 作为安全保障。Let’s Encrypt 如今已是全球最大的证书签发 CA 机构。它于 2014 年 11 月宣布成立，2015 年 9 月 14 日签发了首张证书；2016 年 3 月签发了第一百万张证书；2017 年 6 月签发了第一亿张证书；2020 年 2 月签发了第十亿张证书。2018 年 9 月实现单日签发一百万张证书，2025 年 9 月实现单日签发一千万张证书。

Linux 内核年度维护者峰会讨论了 Rust 语言实验性相关的主题，与会者一致认为，Rust 不再是实验性的，它现在是内核的核心组成部分，将会长期存在。因此 Rust 的实验性标签将会被移除。

与微软、Google、亚马逊和 Meta 等科技巨头不同，苹果在 AI 热下非常保守，没有将 AI 视为天要塌下来需要立即采取行动的事情。今年初，由于 AI 战略匮乏，苹果饱受批评，反映在市值上就是股价大跌。2025 年上半年，苹果在七大科技巨头中表现倒数第二，股价下跌了 18%。但随着对 AI 巨额投资的质疑，情况发生了逆转，苹果股价飙升 35%，此前的宠儿 Meta 和微软则股价暴跌，英伟达的表现也不如苹果。标普 500 指数同期上涨 10%，科技股为主的纳斯达克 100 指数上涨了 13%。目前苹果的市值达到 4.1 万亿美元，超过了微软，正逼近英伟达。财富管理公司认为苹果的股票从某种程度上是反 AI 的。

澳大利亚禁止 16 岁以下青少年使用社媒的禁令正式生效。12 岁悉尼女孩 Paloma 在接受采访时对禁令表达了悲伤之情，称她通过 Snapchat 和 TikTok 等应用结识了来自不同国家的朋友，表示自己认识的每个人都对禁令感到生气，称通过禁止使用社交媒体政府剥夺了他们的一部分权利。15 岁少年 Noah Jones 和 Macy Neyland 在一家权利团体的支持下向澳大利亚最高法院提起诉讼，认为禁令剥夺了他们自由交流的权利。澳大利亚电信部长 Anika Wells 表示不会屈服，不会被法律战吓到，“我们将代表澳大利亚的父母坚定立场。”