OrangeBot.AI Digest — 2025-06-25

Fullstack app framework for web, desktop, and mobile.

Next generation frontend tooling. It's fast!

Use Claude Code as the foundation for coding infrastructure, allowing you to decide how to interact with the model while enjoying updates from Anthropic.

✨ Innovative and open-source visualization application that transforms various data formats, such as JSON, YAML, XML, CSV and more, into interactive graphs.

A list of SaaS, PaaS and IaaS offerings that have free tiers of interest to devops and infradev

🔗 Some useful websites for programmers.

Collection of leaked system prompts

🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

A quick example of how one can "synchronize" a 3d scene across multiple windows using three.js and localStorage

Self-Hosting Guide. Learn all about locally hosting (on premises & private web servers) and managing software applications by yourself or your organization. Including Cloud, LLMs, WireGuard, Automation, Home Assistant, and Networking.

Master programming by recreating your favorite technologies from scratch.

😎 Awesome lists about all kinds of interesting topics

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

A curated list of engineering blogs

Find secrets with Gitleaks 🔑

The #1 open-source CRM

Custom Google Slides & PowerPoints via AI-powered API

Your fully private, open-source, on-device AI assistant

Get seen by target users automatically in YouTube comments

Observability copilot to resolve production issues instantly

World’s First Agentic Development Environment

Vibe code with the relaunched AI-native app platform.

your AI workforce in one agent

AI + humans vibe coding platform

The most powerful AI voice tools, now in your pocket.

Instantly clip meeting moments by chatting with AI

From raw data to charts in seconds with AI

Architect agent that builds professional software

Scan contracts & find gotchas before signing

Forward spam callers to an AI and waste their time

We propose Chain-of-Experts (CoE), a new Mixture-of-Experts (MoE) architecture that introduces sequential expert communication within each layer. Unlike traditional MoE models, where experts operate independently in parallel, CoE processes tokens iteratively across a chain of experts inside a layer. To support dynamic expert selection across iterations, CoE employs a dedicated router at each iteration step within a layer. This design allows tokens to re-evaluate and select different experts during each iteration, rather than being statically assigned. As a result, CoE introduces a flexible routing mechanism that increases the diversity of expert combinations and enriches the model's representational capacity. CoE demonstrates improved performance under fixed compute: on math reasoning tasks, it reduces validation loss from 1.20 to 1.12 compared to a standard MoE. Beyond performance, CoE offers a new scaling axis: depth through expert iteration, which complements conventional width/depth scaling. For example, using 2x iterations matches the performance of 3x expert selections (in width), while reducing memory usage by 17.6-42% relative to other scaling strategies. Our analysis reveals that CoE's benefits stem from its iterative residual structure and enhanced expert specialization empowered by iterative routing, which together unlock more expressive representations. Code is available at https://github.com/ZihanWang314/coe.

Generating high-dimensional visual modalities is a computationally intensive task. A common solution is progressive generation, where the outputs are synthesized in a coarse-to-fine spectral autoregressive manner. While diffusion models benefit from the coarse-to-fine nature of denoising, explicit multi-stage architectures are rarely adopted. These architectures have increased the complexity of the overall approach, introducing the need for a custom diffusion formulation, decomposition-dependent stage transitions, add-hoc samplers, or a model cascade. Our contribution, Decomposable Flow Matching (DFM), is a simple and effective framework for the progressive generation of visual media. DFM applies Flow Matching independently at each level of a user-defined multi-scale representation (such as Laplacian pyramid). As shown by our experiments, our approach improves visual quality for both images and videos, featuring superior results compared to prior multistage frameworks. On Imagenet-1k 512px, DFM achieves 35.2% improvements in FDD scores over the base architecture and 26.4% over the best-performing baseline, under the same training compute. When applied to finetuning of large models, such as FLUX, DFM shows faster convergence speed to the training distribution. Crucially, all these advantages are achieved with a single model, architectural simplicity, and minimal modifications to existing training pipelines.

Orthogonal finetuning (OFT) offers highly parameter-efficient adaptation while preventing catastrophic forgetting, but its high runtime and memory demands limit practical deployment. We identify the core computational bottleneck in OFT as its weight-centric implementation, which relies on costly matrix-matrix multiplications with cubic complexity. To overcome this, we propose OFTv2, an input-centric reformulation that instead uses matrix-vector multiplications (i.e., matrix-free computation), reducing the computational cost to quadratic. We further introduce the Cayley-Neumann parameterization, an efficient orthogonal parameterization that approximates the matrix inversion in Cayley transform via a truncated Neumann series. These modifications allow OFTv2 to achieve up to 10x faster training and 3x lower GPU memory usage without compromising performance. In addition, we extend OFTv2 to support finetuning quantized foundation models and show that it outperforms the popular QLoRA in training stability, efficiency, and memory usage.

Reasoning models excel by generating long chain-of-thoughts, but decoding the resulting thousands of tokens is slow. Token-level speculative decoding (SD) helps, but its benefit is capped, because the chance that an entire gamma-token guess is correct falls exponentially as gamma grows. This means allocating more compute for longer token drafts faces an algorithmic ceiling -- making the speedup modest and hardware-agnostic. We raise this ceiling with Lookahead Reasoning, which exploits a second, step-level layer of parallelism. Our key insight is that reasoning models generate step-by-step, and each step needs only to be semantically correct, not exact token matching. In Lookahead Reasoning, a lightweight draft model proposes several future steps; the target model expands each proposal in one batched pass, and a verifier keeps semantically correct steps while letting the target regenerate any that fail. Token-level SD still operates within each reasoning step, so the two layers of parallelism multiply. We show Lookahead Reasoning lifts the peak speedup of SD both theoretically and empirically. Across GSM8K, AIME, and other benchmarks, Lookahead Reasoning improves the speedup of SD from 1.4x to 2.1x while preserving answer quality, and its speedup scales better with additional GPU throughput. Our code is available at https://github.com/hao-ai-lab/LookaheadReasoning

Human annotation variation (i.e., annotation disagreements) is common in NLP and often reflects important information such as task subjectivity and sample ambiguity. While Large Language Models (LLMs) are increasingly used for automatic annotation to reduce human effort, their evaluation often focuses on predicting the majority-voted "ground truth" labels. It is still unclear, however, whether these models also capture informative human annotation variation. Our work addresses this gap by extensively evaluating LLMs' ability to predict annotation disagreements without access to repeated human labels. Our results show that LLMs struggle with modeling disagreements, which can be overlooked by majority label-based evaluations. Notably, while RLVR-style (Reinforcement learning with verifiable rewards) reasoning generally boosts LLM performance, it degrades performance in disagreement prediction. Our findings highlight the critical need for evaluating and improving LLM annotators in disagreement modeling. Code and data at https://github.com/EdisonNi-hku/Disagreement_Prediction.

Vision-and-Language Navigation (VLN) in large-scale urban environments requires embodied agents to ground linguistic instructions in complex scenes and recall relevant experiences over extended time horizons. Prior modular pipelines offer interpretability but lack unified memory, while end-to-end (M)LLM agents excel at fusing vision and language yet remain constrained by fixed context windows and implicit spatial reasoning. We introduce Mem4Nav, a hierarchical spatial-cognition long-short memory system that can augment any VLN backbone. Mem4Nav fuses a sparse octree for fine-grained voxel indexing with a semantic topology graph for high-level landmark connectivity, storing both in trainable memory tokens embedded via a reversible Transformer. Long-term memory (LTM) compresses and retains historical observations at both octree and graph nodes, while short-term memory (STM) caches recent multimodal entries in relative coordinates for real-time obstacle avoidance and local planning. At each step, STM retrieval sharply prunes dynamic context, and, when deeper history is needed, LTM tokens are decoded losslessly to reconstruct past embeddings. Evaluated on Touchdown and Map2Seq across three backbones (modular, state-of-the-art VLN with prompt-based LLM, and state-of-the-art VLN with strided-attention MLLM), Mem4Nav yields 7-13 pp gains in Task Completion, sufficient SPD reduction, and >10 pp nDTW improvement. Ablations confirm the indispensability of both the hierarchical map and dual memory modules. Our codes are open-sourced via https://github.com/tsinghua-fib-lab/Mem4Nav.

This study explores the effectiveness of predictive maintenance models and the optimization of intelligent Operation and Maintenance (O&M) systems in improving wind power generation efficiency. Through qualitative research, structured interviews were conducted with five wind farm engineers and maintenance managers, each with extensive experience in turbine operations. Using thematic analysis, the study revealed that while predictive maintenance models effectively reduce downtime by identifying major faults, they often struggle with detecting smaller, gradual failures. Key challenges identified include false positives, sensor malfunctions, and difficulties in integrating new models with older turbine systems. Advanced technologies such as digital twins, SCADA systems, and condition monitoring have significantly enhanced turbine maintenance practices. However, these technologies still require improvements, particularly in AI refinement and real-time data integration. The findings emphasize the need for continuous development to fully optimize wind turbine performance and support the broader adoption of renewable energy.

We introduce Matrix-Game, an interactive world foundation model for controllable game world generation. Matrix-Game is trained using a two-stage pipeline that first performs large-scale unlabeled pretraining for environment understanding, followed by action-labeled training for interactive video generation. To support this, we curate Matrix-Game-MC, a comprehensive Minecraft dataset comprising over 2,700 hours of unlabeled gameplay video clips and over 1,000 hours of high-quality labeled clips with fine-grained keyboard and mouse action annotations. Our model adopts a controllable image-to-world generation paradigm, conditioned on a reference image, motion context, and user actions. With over 17 billion parameters, Matrix-Game enables precise control over character actions and camera movements, while maintaining high visual quality and temporal coherence. To evaluate performance, we develop GameWorld Score, a unified benchmark measuring visual quality, temporal quality, action controllability, and physical rule understanding for Minecraft world generation. Extensive experiments show that Matrix-Game consistently outperforms prior open-source Minecraft world models (including Oasis and MineWorld) across all metrics, with particularly strong gains in controllability and physical consistency. Double-blind human evaluations further confirm the superiority of Matrix-Game, highlighting its ability to generate perceptually realistic and precisely controllable videos across diverse game scenarios. To facilitate future research on interactive image-to-world generation, we will open-source the Matrix-Game model weights and the GameWorld Score benchmark at https://github.com/SkyworkAI/Matrix-Game.

Resolution of complex SQL issues persists as a significant bottleneck in real-world database applications. Current Large Language Models (LLMs), while adept at text-to-SQL translation, have not been rigorously evaluated on the more challenging task of debugging SQL issues. To address this gap, we introduce BIRD-CRITIC, a new SQL issue debugging benchmark comprising 530 PostgreSQL tasks (BIRD-CRITIC-PG) and 570 multi-dialect tasks (BIRD-CRITIC-Multi), distilled from authentic user issues and replayed within new environments to facilitate rigorous evaluation. Baseline evaluations underscore the task's complexity, with the leading reasoning model O3-Mini achieving only 38.87% success rate on BIRD-CRITIC-PG and 33.33% on BIRD-CRITIC-Multi. Meanwhile, advancing open-source models for database tasks is crucial for empowering local development while safeguarding data privacy. Therefore, we present Six-Gym (Sql-fIX-Gym), a training environment for elevating open-source model capabilities for SQL issue debugging. This environment leverages SQL-Rewind strategy, which automatically generates executable issue-solution datasets by reverse-engineering issues from verified SQLs. However, popular trajectory-based fine-tuning methods do not explore substantial supervisory signals. We further propose f-Plan Boosting, which extracts high-level debugging plans from SQL solutions, enabling teacher LLMs to produce 73.7% more successful trajectories for training. We integrate these components into an open-source agent, Bird-Fixer. Based on Qwen-2.5-Coder-14B, Bird-Fixer achieves 38.11% success rate on BIRD-CRITIC-PG and 29.65% on BIRD-CRITIC-Multi, surpassing leading proprietary models such as Claude-3.7-Sonnet and GPT-4.1, marking a significant step toward democratizing sophisticated SQL-debugging capabilities. The leaderboard and source code are available: https://bird-critic.github.io/

Vision-language-action models (VLAs) have garnered significant attention for their potential in advancing robotic manipulation. However, previous approaches predominantly rely on the general comprehension capabilities of vision-language models (VLMs) to generate action signals, often overlooking the rich temporal and causal structure embedded in visual observations. In this paper, we present UniVLA, a unified and native multimodal VLA model that autoregressively models vision, language, and action signals as discrete token sequences. This formulation enables flexible multimodal tasks learning, particularly from large-scale video data. By incorporating world modeling during post-training, UniVLA captures causal dynamics from videos, facilitating effective transfer to downstream policy learning--especially for long-horizon tasks. Our approach sets new state-of-the-art results across several widely used simulation benchmarks, including CALVIN, LIBERO, and Simplenv-Bridge, significantly surpassing previous methods. For example, UniVLA achieves 95.5% average success rate on LIBERO benchmark, surpassing pi0-FAST's 85.5%. We further demonstrate its broad applicability on real-world ALOHA manipulation and autonomous driving.

Classifier-free guidance (CFG) has become an essential component of modern conditional diffusion models. Although highly effective in practice, the underlying mechanisms by which CFG enhances quality, detail, and prompt alignment are not fully understood. We present a novel perspective on CFG by analyzing its effects in the frequency domain, showing that low and high frequencies have distinct impacts on generation quality. Specifically, low-frequency guidance governs global structure and condition alignment, while high-frequency guidance mainly enhances visual fidelity. However, applying a uniform scale across all frequencies -- as is done in standard CFG -- leads to oversaturation and reduced diversity at high scales and degraded visual quality at low scales. Based on these insights, we propose frequency-decoupled guidance (FDG), an effective approach that decomposes CFG into low- and high-frequency components and applies separate guidance strengths to each component. FDG improves image quality at low guidance scales and avoids the drawbacks of high CFG scales by design. Through extensive experiments across multiple datasets and models, we demonstrate that FDG consistently enhances sample fidelity while preserving diversity, leading to improved FID and recall compared to CFG, establishing our method as a plug-and-play alternative to standard classifier-free guidance.

Code-switching (CSW) is the act of alternating between two or more languages within a single discourse. This phenomenon is widespread in multilingual communities, and increasingly prevalent in online content, where users naturally mix languages in everyday communication. As a result, Large Language Models (LLMs), now central to content processing and generation, are frequently exposed to code-switched inputs. Given their widespread use, it is crucial to understand how LLMs process and reason about such mixed-language text. This paper presents a systematic evaluation of LLM comprehension under code-switching by generating CSW variants of established reasoning and comprehension benchmarks. While degradation is evident when foreign tokens disrupt English textx2013even under linguistic constraintsx2013embedding English into other languages often improves comprehension. Though prompting yields mixed results, fine-tuning offers a more stable path to degradation mitigation.

This paper presents ScaleCap, an inference-time scalable image captioning strategy that generates comprehensive and detailed image captions. The key challenges of high-quality image captioning lie in the inherent biases of LVLMs: multimodal bias resulting in imbalanced descriptive granularity, offering detailed accounts of some elements while merely skimming over others; linguistic bias leading to hallucinated descriptions of non-existent objects. To address these issues, we propose a scalable debiased captioning strategy, which continuously enriches and calibrates the caption with increased inference budget. Specifically, we propose two novel components: heuristic question answering and contrastive sentence rating. The former generates content-specific questions based on the image and answers them to progressively inject relevant information into the caption. The latter employs sentence-level offline contrastive decoding to effectively identify and eliminate hallucinations caused by linguistic biases. With increased inference cost, more heuristic questions are raised by ScaleCap to progressively capture additional visual details, generating captions that are more accurate, balanced, and informative. Extensive modality alignment experiments demonstrate the effectiveness of ScaleCap. Annotating 450K images with ScaleCap and using them for LVLM pretraining leads to consistent performance gains across 11 widely used benchmarks. Furthermore, ScaleCap showcases superb richness and fidelity of generated captions with two additional tasks: replacing images with captions in VQA task, and reconstructing images from captions to assess semantic coverage. Code is available at https://github.com/Cooperx521/ScaleCap.

Photo retouching has become integral to contemporary visual storytelling, enabling users to capture aesthetics and express creativity. While professional tools such as Adobe Lightroom offer powerful capabilities, they demand substantial expertise and manual effort. In contrast, existing AI-based solutions provide automation but often suffer from limited adjustability and poor generalization, failing to meet diverse and personalized editing needs. To bridge this gap, we introduce JarvisArt, a multi-modal large language model (MLLM)-driven agent that understands user intent, mimics the reasoning process of professional artists, and intelligently coordinates over 200 retouching tools within Lightroom. JarvisArt undergoes a two-stage training process: an initial Chain-of-Thought supervised fine-tuning to establish basic reasoning and tool-use skills, followed by Group Relative Policy Optimization for Retouching (GRPO-R) to further enhance its decision-making and tool proficiency. We also propose the Agent-to-Lightroom Protocol to facilitate seamless integration with Lightroom. To evaluate performance, we develop MMArt-Bench, a novel benchmark constructed from real-world user edits. JarvisArt demonstrates user-friendly interaction, superior generalization, and fine-grained control over both global and local adjustments, paving a new avenue for intelligent photo retouching. Notably, it outperforms GPT-4o with a 60% improvement in average pixel-level metrics on MMArt-Bench for content fidelity, while maintaining comparable instruction-following capabilities. Project Page: https://jarvisart.vercel.app/.

Large Language Models (LLMs), particularly slow-thinking models, often exhibit severe hallucination, outputting incorrect content due to an inability to accurately recognize knowledge boundaries during reasoning. While Reinforcement Learning (RL) can enhance complex reasoning abilities, its outcome-oriented reward mechanism often lacks factual supervision over the thinking process, further exacerbating the hallucination problem. To address the high hallucination in slow-thinking models, we propose Knowledge-enhanced RL, KnowRL. KnowRL guides models to perform fact-based slow thinking by integrating a factuality reward, based on knowledge verification, into the RL training process, helping them recognize their knowledge boundaries. KnowRL guides models to perform fact-based slow thinking by integrating a factuality reward, based on knowledge verification, into the RL training process, helping them recognize their knowledge boundaries. This targeted factual input during RL training enables the model to learn and internalize fact-based reasoning strategies. By directly rewarding adherence to facts within the reasoning steps, KnowRL fosters a more reliable thinking process. Experimental results on three hallucination evaluation datasets and two reasoning evaluation datasets demonstrate that KnowRL effectively mitigates hallucinations in slow-thinking models while maintaining their original strong reasoning capabilities. Our code is available at https://github.com/zjunlp/KnowRL.

在安克（Anker）等多个充电宝品牌宣布召回存在自燃风险的移动电源后，问题根源被认为与电芯有关，而电芯供应商是安普瑞斯（Amprius）无锡公司。安普瑞斯是移动电源最大的电芯供应商，其客户包括了安克、罗马仕、小米、绿联、倍思、麦多多、傲基、电友等，目前宣布召回产品的主要是安克和罗马仕。报道称，安普瑞斯在未经客户允许下，私自使用未送检的隔膜材料，导致更换后的隔膜无法和之前送测的样品有相同的强度。安普瑞斯的 11 个 3C 证书自 6 月 10 日起变更为“已暂停”状态，该公司目前处于停产状态。

Windows 10 将于今年 10 月 14 日终止支持，微软最新的官方博客继续向 Windows 10 用户推销 Windows 11（需要购买新 PC），但面对数量可能多达数亿的 Windows 10 用户，微软也表现出了妥协姿态，为 Windows 10 用户提供为期一年的扩展安全更新（Extended Security Updates）。获得扩展安全更新有三种选择：支付 30 美元，支付 1000 Microsoft points，注册登陆 Windows Backup 同步系统设置到微软云端（该选项某种意义上是免费的，代价是用户需要使用 OneDrive 云储存，微软会掌握用户的部分数据）。扩展安全更新通常只提供给企业级客户，这是首次提供给普通客户，

太阳可以很快把水晒干，但为什么阳光比加热炉子更高效？北卡罗来纳州立大学的一项新研究给出了答案：关键是阳光中的振荡电场！研究团队利用计算模拟的方法，系统地分析了阳光的不同特性。他们发现：阳光中本就包含振荡电场，它能极大地加快水分子的逃逸速度。更令人惊讶的是，电场不仅“赶走”了单个水分子，它还能把一整簇水分子——即“水分子团簇”——从液体中分离出来！这效率可比一个个赶单分子高多了。研究者们还比较了纯水和水凝胶中的蒸发差异。结果发现，在水凝胶中，由于交联聚合物链的存在，更多的水团簇出现在水表面附近。也就是说，在这种环境中，电场能更容易“切断”水团簇，让蒸发更快发生。

美国众议院首席行政官以数据漏洞问题为由禁止国会工作人员在政府发放的设备上使用 WhatsApp。众议院的网络安全办公室判断 Meta 旗下的这款即时通讯应用属于“高风险”应用，原因是数据保护缺乏透明度、存储数据缺乏加密以及存在潜在的安全风险。工作人员不得在任何众议院设备上下载或保留 WhatsApp，无论是移动版、桌面版还是网页版。

Fedora 发行版的开发者正在讨论是否在 Fedora 44 之后放弃支持 32 位软件包。Fedora 最新的稳定版本是 v42，Fedora 44 预计在 2026 年上半年发布。放弃支持 i686 软件包的提议需要获得 Fedora Engineering and Steering Committee(FESCo)的批准之后才会正式推行，目前还处于讨论阶段。提议的开发者称，停止支持 32 位 x86 肯定会在某个时间点发生，提前做好充分准备总比手忙脚乱被迫应对要好得多。

官方记录显示，中国五月份太阳能装机容量创下记录。单月新增装机容量超过其他国家 2024 年全年的装机容量。根据国家能源局的数据，5 月新增太阳能装机容量 93 GW，打破了 2024 年 12 月创下的 71 GW 的纪录。太阳能装机容量大幅增长的一个原因是政府从 6 月 1 日起取消了对太阳能项目的电价保护：在保护政策下太阳能项目只要投入运营就能确保盈利。另一项于 5 月 1 日生效的政策加大了屋顶太阳能板接入电网的难度。分析人士预测，新政策将会放缓太阳能装机容量的增长。中国累计太阳能装机容量至今已超过 1 TW。

Vera C. Rubin 天文台周一公开第一批宇宙全景照，宣告展开为期 10 年的时空遗珍巡天项目（Legacy Survey of Space and Time，LSST），这将会是人类史上最全面的南天巡天计划。天文台位于智利帕乔恩山顶，海拔1,600 米，配有口径 8.4 米的望远镜以及史上最大与最高解析度的数字相机 LSSTCam，其大小与一台汽车相当。这台超级相机每三个晚上就能扫描整个南半球夜空。在首批释出的影像中，LSSTCam 捕捉到距离地球约五千万光年的室女座星系团，画面中包含多达一千万个星系，然而这一千万个星系，只占 LSST 任务期间预计将观测到 200 亿个星系的 0.05%。

Google 正将 AI 功能集成到其越来越多的产品中，最新集成的产品是它面向教育领域的笔记本电脑 Chromebook。虽然 AI 的运算主要是在云端进行，但 Chromebook 要使用 AI 仍然需要较高的硬件配置。Google 和联想合作推出的 Chromebook Plus 14 配备了联发科 Kompanio Ultra 处理器，Google 称是 Chromebook 史上最强大的 ARM 芯片。Kompanio Ultra NPU 的 AI 运算能力达到了 50 TOPS，足以本地运行部分 AI 模型，接近微软的 Copilot+ PC。这款 Chromebook 售价 749 美元。

ULA 周一从佛罗里达州卡纳维拉尔角使用 Atlas V火箭为亚马逊发射了 27 颗 Project Kuiper 互联网宽带卫星。Project Kuiper 至今共完成了三次发射，其中第一次是测试，目前在轨宽带卫星 54 颗，亚马逊计划发射 3232 颗宽带卫星，覆盖大部分人口密集地区。亚马逊已与四家发射公司购买了 80 多次发射合同，其中 ULA 使用 Atlas V 火箭发射九次，之后火箭退役，改用 Vulcan 火箭发射 38 次——每次发射的卫星数量将增加到 45 颗。欧洲的 Ariane 6 火箭将执行 18 次，贝佐斯旗下 Blue Origin 的 New Glenn 火箭将至少发射 12 次。竞争对手 SpaceX 将在下个月执行 Project Kuiper 的第四次发射。SpaceX 的 Starlink 宽带卫星星座总数已经超过 7000 颗。

从 Google X 分拆出来的创业公司 IYO 就 IO 商标起诉了 OpenAI 和 Jony Ive 的 IO Products, Inc. 公司。 OpenAI 于 2025 年 5 月 21 日宣布以 65 亿美元收购 IO，但前几天悄悄撤销了相关的宣传材料。IYO 生产名为 IYO ONE 的耳戴式设备，允许用户通过语音命令与计算机和 AI 进行交互，无需屏幕或键盘。起诉书指控被告故意为竞争产品使用一个易混淆的名称。起诉书称，OpenAI CEO Sam Altman 和 Ive 的设计工作室 LoveFrom 在 2022-2025 年间多次与 IYO 代表会面，了解 IYO 的技术和商业计划细节。2025 年 3 月，Altman 告诉 IYO 正在开发名为 io 的竞争产品。IO Products 成立于 2023 年 9 月，致力于开发与 IYO 产品类似的无屏电脑交互硬件。诉讼寻求禁制令(injunctive relief)，要求对商标侵权和不正当竞争赔偿。

Mozilla 释出了 Firefox 140，这是一个长期支持版本（LTS）。主要新特性包括：右键标签页会显示“Unload Tab”选项，此举可减少未使用标签页占用的内存节省 CPU 资源；支持 CSS Custom Highlighting API，Chrome 从 v121 开始支持该 API；改进垂直标签，支持添加自定义搜索引擎；支持 SVG fetchpriority 属性、Cookie Store API 等。

法国食品、环境和职业健康与安全局（ANSES）发表研究报告，所有饮料都含有微塑料，但玻璃瓶装饮料的微塑料颗粒含量显著高于塑料瓶、纸盒或罐装饮料。对各种包装的饮料的检测发现，玻璃瓶装软饮料、柠檬水、冰茶和啤酒中平均每升含有 100 个微塑料颗粒，是塑料瓶或金属罐装饮料的 5-50 倍。科学家推测，玻璃瓶装饮料的塑料颗粒可能来自瓶盖上的塑料涂层。瓶盖可能是在运输过程中因为摩擦等导致了塑料颗粒脱落。制造商可采取措施减少塑料颗粒的脱落。

过去几十年全世界博士毕业生数量持续增长。传统上博士学位是终身学术生涯的垫脚石，但今天的博士毕业生数量远远超过了大学和研究机构的职位空缺数量。在 38 个经合组织（OECD）成员国中，1998-2017 年之间新增博士数量翻了几乎一番。中国博士生人数从 2013 年的 30 万增加到 2023 年的 60 多万，香港大学的 Hugo Horta 解释说，推动中国博士生人数增长的因素包括了学士和硕士学位人数快速增长，期望投资高等教育能带来更好的经济和社会前景。博士数量超过学界需求迫使博士毕业生以前所未有的速度转向非学术领域。2023 年针对英国 4500 多名博士毕业生的调查发现，逾三分之二博士在学术界以外就业。南非调查的 6000 多名博士毕业生中有 18% 表示难以找到与其专业相关的工作。部分国家开始调整博士课程。日本、德国和英国提供了博士学习期间的培训和带薪实习，其中包括“产业博士”项目，允许与企业合作开展研究。

马斯克参加Y Combinator AI创业学校活动，与数百名年轻工程师分享了50分钟的创业公开课。他宣布从华盛顿DOGE工作回归技术领域，用"支线任务与主线任务"比喻解释这一选择——政府效率改革虽重要但只是支线任务，技术建设才是主线任务。他预测数字超级智能可能在今年或明年实现，强调人类正处于"智能大爆炸的早期阶段"。马斯克分享了三个核心观点：选择"不可能成功"的项目因为"小概率成功比零概率成功好"；AI安全最重要的是"对真理的严格坚持"而非技术本身；面对人类智能将占总智能不到1%的未来，应从长期视角思考个人选择。他详述了从Zip2到PayPal、SpaceX、Tesla的创业历程，强调第一性原理思维在技术突破中的关键作用，并对脑机接口、机器人技术和多行星文明等未来技术发展做出了预测。

微软六月例行安全更新的一个补丁 KB5060842 修改了 Windows 11 管理系统还原点的方式：将有效时间设为 60 天，超过 60 天的还原点将不可用。Windows 11 v24H2 没有改变还原点的创建或使用方式；它只是为还原点的存储时间设定了明确的期限。如果分配的磁盘空间已满，系统仍然会删除时间较旧的还原点。现在无论可用磁盘空间多大，还原点的存储时间上限都固定为 60 天。