OrangeBot.AI Digest — 2025-07-04

小红书笔记 | 评论爬虫、抖音视频 | 评论爬虫、快手视频 | 评论爬虫、B 站视频 ｜ 评论爬虫、微博帖子 ｜ 评论爬虫、百度贴吧帖子 ｜ 百度贴吧评论回复爬虫 | 知乎问答文章｜评论爬虫

A generative world for general-purpose robotics & embodied AI learning.

Truly independent web browser

Swagger UI is a collection of HTML, JavaScript, and CSS assets that dynamically generate beautiful documentation from a Swagger-compliant API.

Label Studio is a multi-type data labeling and annotation tool with standardized output format

There can be more than Notion and Miro. AFFiNE(pronounced [ə‘fain]) is a next-gen knowledge base that brings planning, sorting and creating all together. Privacy first, open-source, customizable and ready to use.

An open source payments switch written in Rust to make payments fast, reliable and affordable

📚 从零开始的大语言模型原理与实践教程

Unified Backend Framework for APIs, Events, and AI Agents

Free, simple, and intuitive online database diagram editor and SQL generator.

Progressive Delivery for Kubernetes

macOS inside a Docker container.

The Cursor for Designers • An Open-Source Visual Vibecoding Editor • Visually build, style, and edit your React App with AI

Self-hosted collection of powerful web-based tools for everyday tasks. No ads, no tracking, just fast, accessible utilities right from your browser!

A cross-platform bilibili toolbox. 跨平台哔哩哔哩工具箱，支持下载视频、番剧等等各类资源

Your first personalized happy lifestyle index

Massive icon packs for vibe-coding

AI Agent for collaborative workspace

Get more traffic from ChatGPT & Google

Translate webpage and docs into SVG visual infographics

All AI Models in One Dashboard

Report Immigration and Customs Enforcement (ICE) activity

Speak, transcribe, and save, all in your browser

Cursor, Windsurf, Claude, Prompt, Code Generation

Save and restore your entire workspace — across all Spaces

One‑click SMTP server test with real‑time protocol logs

Create app icon mockups featuring iOS 26 home screen

Turn text, 2D, or 3D images into 3D models and 3D prints

Cursor-driven element inspection for DevTools workflow

Product hunt card generator

We present GLM-4.1V-Thinking, a vision-language model (VLM) designed to advance general-purpose multimodal reasoning. In this report, we share our key findings in the development of the reasoning-centric training framework. We first develop a capable vision foundation model with significant potential through large-scale pre-training, which arguably sets the upper bound for the final performance. Reinforcement Learning with Curriculum Sampling (RLCS) then unlocks the full potential of the model, leading to comprehensive capability enhancement across a diverse range of tasks, including STEM problem solving, video understanding, content recognition, coding, grounding, GUI-based agents, and long document understanding, among others. To facilitate research in this field, we open-source GLM-4.1V-9B-Thinking, which achieves state-of-the-art performance among models of comparable size. In a comprehensive evaluation across 28 public benchmarks, our model outperforms Qwen2.5-VL-7B on nearly all tasks and achieves comparable or even superior performance on 18 benchmarks relative to the significantly larger Qwen2.5-VL-72B. Notably, GLM-4.1V-9B-Thinking also demonstrates competitive or superior performance compared to closed-source models such as GPT-4o on challenging tasks including long document understanding and STEM reasoning, further underscoring its strong capabilities. Code, models and more information are released at https://github.com/THUDM/GLM-4.1V-Thinking.

While Multimodal Large Language Models (MLLMs) demonstrate remarkable capabilities on static images, they often fall short in comprehending dynamic, information-dense short-form videos, a dominant medium in today's digital landscape. To bridge this gap, we introduce Kwai Keye-VL, an 8-billion-parameter multimodal foundation model engineered for leading-edge performance in short-video understanding while maintaining robust general-purpose vision-language abilities. The development of Keye-VL rests on two core pillars: a massive, high-quality dataset exceeding 600 billion tokens with a strong emphasis on video, and an innovative training recipe. This recipe features a four-stage pre-training process for solid vision-language alignment, followed by a meticulous two-phase post-training process. The first post-training stage enhances foundational capabilities like instruction following, while the second phase focuses on stimulating advanced reasoning. In this second phase, a key innovation is our five-mode ``cold-start'' data mixture, which includes ``thinking'', ``non-thinking'', ``auto-think'', ``think with image'', and high-quality video data. This mixture teaches the model to decide when and how to reason. Subsequent reinforcement learning (RL) and alignment steps further enhance these reasoning capabilities and correct abnormal model behaviors, such as repetitive outputs. To validate our approach, we conduct extensive evaluations, showing that Keye-VL achieves state-of-the-art results on public video benchmarks and remains highly competitive on general image-based tasks (Figure 1). Furthermore, we develop and release the KC-MMBench, a new benchmark tailored for real-world short-video scenarios, where Keye-VL shows a significant advantage.

Animation colorization is a crucial part of real animation industry production. Long animation colorization has high labor costs. Therefore, automated long animation colorization based on the video generation model has significant research value. Existing studies are limited to short-term colorization. These studies adopt a local paradigm, fusing overlapping features to achieve smooth transitions between local segments. However, the local paradigm neglects global information, failing to maintain long-term color consistency. In this study, we argue that ideal long-term color consistency can be achieved through a dynamic global-local paradigm, i.e., dynamically extracting global color-consistent features relevant to the current generation. Specifically, we propose LongAnimation, a novel framework, which mainly includes a SketchDiT, a Dynamic Global-Local Memory (DGLM), and a Color Consistency Reward. The SketchDiT captures hybrid reference features to support the DGLM module. The DGLM module employs a long video understanding model to dynamically compress global historical features and adaptively fuse them with the current generation features. To refine the color consistency, we introduce a Color Consistency Reward. During inference, we propose a color consistency fusion to smooth the video segment transition. Extensive experiments on both short-term (14 frames) and long-term (average 500 frames) animations show the effectiveness of LongAnimation in maintaining short-term and long-term color consistency for open-domain animation colorization task. The code can be found at https://cn-makers.github.io/long_animation_web/.

Transcending human cognitive limitations represents a critical frontier in LLM training. Proprietary agentic systems like DeepResearch have demonstrated superhuman capabilities on extremely complex information-seeking benchmarks such as BrowseComp, a feat previously unattainable. We posit that their success hinges on a sophisticated reasoning pattern absent in open-source models: the ability to systematically reduce extreme uncertainty when navigating vast information landscapes. Based on this insight, we introduce WebSailor, a complete post-training methodology designed to instill this crucial capability. Our approach involves generating novel, high-uncertainty tasks through structured sampling and information obfuscation, RFT cold start, and an efficient agentic RL training algorithm, Duplicating Sampling Policy Optimization (DUPO). With this integrated pipeline, WebSailor significantly outperforms all opensource agents in complex information-seeking tasks, matching proprietary agents' performance and closing the capability gap.

Math reasoning has become the poster child of progress in large language models (LLMs), with new models rapidly surpassing human-level performance on benchmarks like MATH and AIME. But as math leaderboards improve week by week, it is worth asking: do these gains reflect broader problem-solving ability or just narrow overfitting? To answer this question, we evaluate over 20 open-weight reasoning-tuned models across a broad suite of tasks, including math, scientific QA, agent planning, coding, and standard instruction-following. We surprisingly find that most models that succeed in math fail to transfer their gains to other domains. To rigorously study this phenomenon, we conduct controlled experiments on Qwen3-14B models using math-only data but different tuning methods. We find that reinforcement learning (RL)-tuned models generalize well across domains, while supervised fine-tuning (SFT)-tuned models often forget general capabilities. Latent-space representation and token-space distribution shift analyses reveal that SFT induces substantial representation and output drift, while RL preserves general-domain structure. Our results suggest a need to rethink standard post-training recipes, particularly the reliance on SFT-distilled data for advancing reasoning models.

Recovering 3D structures with open-vocabulary scene understanding from 2D images is a fundamental but daunting task. Recent developments have achieved this by performing per-scene optimization with embedded language information. However, they heavily rely on the calibrated dense-view reconstruction paradigm, thereby suffering from severe rendering artifacts and implausible semantic synthesis when limited views are available. In this paper, we introduce a novel generative framework, coined LangScene-X, to unify and generate 3D consistent multi-modality information for reconstruction and understanding. Powered by the generative capability of creating more consistent novel observations, we can build generalizable 3D language-embedded scenes from only sparse views. Specifically, we first train a TriMap video diffusion model that can generate appearance (RGBs), geometry (normals), and semantics (segmentation maps) from sparse inputs through progressive knowledge integration. Furthermore, we propose a Language Quantized Compressor (LQC), trained on large-scale image datasets, to efficiently encode language embeddings, enabling cross-scene generalization without per-scene retraining. Finally, we reconstruct the language surface fields by aligning language information onto the surface of 3D scenes, enabling open-ended language queries. Extensive experiments on real-world data demonstrate the superiority of our LangScene-X over state-of-the-art methods in terms of quality and generalizability. Project Page: https://liuff19.github.io/LangScene-X.

We present Depth Anything at Any Condition (DepthAnything-AC), a foundation monocular depth estimation (MDE) model capable of handling diverse environmental conditions. Previous foundation MDE models achieve impressive performance across general scenes but not perform well in complex open-world environments that involve challenging conditions, such as illumination variations, adverse weather, and sensor-induced distortions. To overcome the challenges of data scarcity and the inability of generating high-quality pseudo-labels from corrupted images, we propose an unsupervised consistency regularization finetuning paradigm that requires only a relatively small amount of unlabeled data. Furthermore, we propose the Spatial Distance Constraint to explicitly enforce the model to learn patch-level relative relationships, resulting in clearer semantic boundaries and more accurate details. Experimental results demonstrate the zero-shot capabilities of DepthAnything-AC across diverse benchmarks, including real-world adverse weather benchmarks, synthetic corruption benchmarks, and general benchmarks. Project Page: https://ghost233lism.github.io/depthanything-AC-page Code: https://github.com/HVision-NKU/DepthAnythingAC

We present SciArena, an open and collaborative platform for evaluating foundation models on scientific literature tasks. Unlike traditional benchmarks for scientific literature understanding and synthesis, SciArena engages the research community directly, following the Chatbot Arena evaluation approach of community voting on model comparisons. By leveraging collective intelligence, SciArena offers a community-driven evaluation of model performance on open-ended scientific tasks that demand literature-grounded, long-form responses. The platform currently supports 23 open-source and proprietary foundation models and has collected over 13,000 votes from trusted researchers across diverse scientific domains. We analyze the data collected so far and confirm that the submitted questions are diverse, aligned with real-world literature needs, and that participating researchers demonstrate strong self-consistency and inter-annotator agreement in their evaluations. We discuss the results and insights based on the model ranking leaderboard. To further promote research in building model-based automated evaluation systems for literature tasks, we release SciArena-Eval, a meta-evaluation benchmark based on our collected preference data. The benchmark measures the accuracy of models in judging answer quality by comparing their pairwise assessments with human votes. Our experiments highlight the benchmark's challenges and emphasize the need for more reliable automated evaluation methods.

Despite significant progress in text-to-image diffusion models, achieving precise spatial control over generated outputs remains challenging. ControlNet addresses this by introducing an auxiliary conditioning module, while ControlNet++ further refines alignment through a cycle consistency loss applied only to the final denoising steps. However, this approach neglects intermediate generation stages, limiting its effectiveness. We propose InnerControl, a training strategy that enforces spatial consistency across all diffusion steps. Our method trains lightweight convolutional probes to reconstruct input control signals (e.g., edges, depth) from intermediate UNet features at every denoising step. These probes efficiently extract signals even from highly noisy latents, enabling pseudo ground truth controls for training. By minimizing the discrepancy between predicted and target conditions throughout the entire diffusion process, our alignment loss improves both control fidelity and generation quality. Combined with established techniques like ControlNet++, InnerControl achieves state-of-the-art performance across diverse conditioning methods (e.g., edges, depth).

Multimodal embedding models, built upon causal Vision Language Models (VLMs), have shown promise in various tasks. However, current approaches face three key limitations: the use of causal attention in VLM backbones is suboptimal for embedding tasks; scalability issues due to reliance on high-quality labeled paired data for contrastive learning; and limited diversity in training objectives and data. To address these issues, we propose MoCa, a two-stage framework for transforming pre-trained VLMs into effective bidirectional multimodal embedding models. The first stage, Modality-aware Continual Pre-training, introduces a joint reconstruction objective that simultaneously denoises interleaved text and image inputs, enhancing bidirectional context-aware reasoning. The second stage, Heterogeneous Contrastive Fine-tuning, leverages diverse, semantically rich multimodal data beyond simple image-caption pairs to enhance generalization and alignment. Our method addresses the stated limitations by introducing bidirectional attention through continual pre-training, scaling effectively with massive unlabeled datasets via joint reconstruction objectives, and utilizing diverse multimodal data for enhanced representation robustness. Experiments demonstrate that MoCa consistently improves performance across MMEB and ViDoRe-v2 benchmarks, achieving new state-of-the-art results, and exhibits strong scalability with both model size and training data on MMEB.

Despite the critical role of reward models (RMs) in reinforcement learning from human feedback (RLHF), current state-of-the-art open RMs perform poorly on most existing evaluation benchmarks, failing to capture the spectrum of nuanced and sophisticated human preferences. Even approaches that incorporate advanced training techniques have not yielded meaningful performance improvements. We hypothesize that this brittleness stems primarily from limitations in preference datasets, which are often narrowly scoped, synthetically labeled, or lack rigorous quality control. To address these challenges, we present a large-scale preference dataset comprising 40 million preference pairs, named SynPref-40M. To enable data curation at scale, we design a human-AI synergistic two-stage pipeline that leverages the complementary strengths of human annotation quality and AI scalability. In this pipeline, humans provide verified annotations, while large language models perform automatic curation based on human guidance. Training on this preference mixture, we introduce Skywork-Reward-V2, a suite of eight reward models ranging from 0.6B to 8B parameters, trained on a carefully curated subset of 26 million preference pairs from SynPref-40M. We demonstrate that Skywork-Reward-V2 is versatile across a wide range of capabilities, including alignment with human preferences, objective correctness, safety, resistance to stylistic biases, and best-of-N scaling, achieving state-of-the-art performance across seven major reward model benchmarks. Ablation studies confirm that the effectiveness of our approach stems not only from data scale but also from high-quality curation. The Skywork-Reward-V2 series represents substantial progress in open reward models, highlighting the untapped potential of existing preference datasets and demonstrating how human-AI curation synergy can unlock significantly higher data quality.

Recent advances in diffusion models have enabled high-quality video generation, but the additional temporal dimension significantly increases computational costs, making training and inference on long videos prohibitively expensive. In this paper, we identify a phenomenon we term Spatiotemporal Energy Decay in video diffusion models: post-softmax attention scores diminish as spatial and temporal distance between tokens increase, akin to the physical decay of signal or waves over space and time in nature. Motivated by this, we propose Radial Attention, a scalable sparse attention mechanism with O(n log n) complexity that translates energy decay into exponentially decaying compute density, which is significantly more efficient than standard O(n^2) dense attention and more expressive than linear attention. Specifically, Radial Attention employs a simple, static attention mask where each token attends to spatially nearby tokens, with the attention window size shrinking with temporal distance. Moreover, it allows pre-trained video diffusion models to extend their generation length with efficient LoRA-based fine-tuning. Extensive experiments show that Radial Attention maintains video quality across Wan2.1-14B, HunyuanVideo, and Mochi 1, achieving up to a 1.9times speedup over the original dense attention. With minimal tuning, it enables video generation up to 4times longer while reducing training costs by up to 4.4times compared to direct fine-tuning and accelerating inference by up to 3.7times compared to dense attention inference.

We introduce IntFold, a controllable foundation model for both general and specialized biomolecular structure prediction. IntFold demonstrates predictive accuracy comparable to the state-of-the-art AlphaFold3, while utilizing a superior customized attention kernel. Beyond standard structure prediction, IntFold can be adapted to predict allosteric states, constrained structures, and binding affinity through the use of individual adapters. Furthermore, we introduce a novel confidence head to estimate docking quality, offering a more nuanced assessment for challenging targets such as antibody-antigen complexes. Finally, we share insights gained during the training process of this computationally intensive model.

The remarkable advancements of vision and language foundation models in multimodal understanding, reasoning, and generation has sparked growing efforts to extend such intelligence to the physical world, fueling the flourishing of vision-language-action (VLA) models. Despite seemingly diverse approaches, we observe that current VLA models can be unified under a single framework: vision and language inputs are processed by a series of VLA modules, producing a chain of action tokens that progressively encode more grounded and actionable information, ultimately generating executable actions. We further determine that the primary design choice distinguishing VLA models lies in how action tokens are formulated, which can be categorized into language description, code, affordance, trajectory, goal state, latent representation, raw action, and reasoning. However, there remains a lack of comprehensive understanding regarding action tokens, significantly impeding effective VLA development and obscuring future directions. Therefore, this survey aims to categorize and interpret existing VLA research through the lens of action tokenization, distill the strengths and limitations of each token type, and identify areas for improvement. Through this systematic review and analysis, we offer a synthesized outlook on the broader evolution of VLA models, highlight underexplored yet promising directions, and contribute guidance for future research, hoping to bring the field closer to general-purpose intelligence.

Recent progress in multimodal reasoning has been significantly advanced by textual Chain-of-Thought (CoT), a paradigm where models conduct reasoning within language. This text-centric approach, however, treats vision as a static, initial context, creating a fundamental "semantic gap" between rich perceptual data and discrete symbolic thought. Human cognition often transcends language, utilizing vision as a dynamic mental sketchpad. A similar evolution is now unfolding in AI, marking a fundamental paradigm shift from models that merely think about images to those that can truly think with images. This emerging paradigm is characterized by models leveraging visual information as intermediate steps in their thought process, transforming vision from a passive input into a dynamic, manipulable cognitive workspace. In this survey, we chart this evolution of intelligence along a trajectory of increasing cognitive autonomy, which unfolds across three key stages: from external tool exploration, through programmatic manipulation, to intrinsic imagination. To structure this rapidly evolving field, our survey makes four key contributions. (1) We establish the foundational principles of the think with image paradigm and its three-stage framework. (2) We provide a comprehensive review of the core methods that characterize each stage of this roadmap. (3) We analyze the critical landscape of evaluation benchmarks and transformative applications. (4) We identify significant challenges and outline promising future directions. By providing this structured overview, we aim to offer a clear roadmap for future research towards more powerful and human-aligned multimodal AI.

由 YouTube 主播 Accursed Farms 发起的 Stop Killing Games 运动赢得了广泛关注，该运动旨在让游戏和书籍等类似，玩家购买之后拥有所有权，可以在任何时候使用，而不是在游戏发行商关闭服务器之后就无法访问。Stop Killing Games 在英国的请愿获得了 15 万人签名——达到递交英国议会辩论所需的要求，在欧盟的请愿赢得了 107 万人签名。可能需要政府监管部门涉足，游戏行业可能才会改变现有的做法。

一项针对学术文献的大规模分析显示，去年发表的生物医学论文摘要中，约 1/7 可能借助 AI 完成撰写。2024 年医学数据库 PubMed 收录的 150 万篇摘要中，超过 20 万篇包含大模型（LLM）常推荐使用的词汇。许多团队试图评估 LLM 对学术产出的影响，但这一过程颇具挑战性，因为大多数使用者并未披露这种行为。研究人员利用了 LLM 流行后的风格化词汇去估计摘要是否是 AI 帮助撰写。研究发现，2024年有 454 个词汇的出现频率远高于 2010 年以来的任何年份。它们多为与研究内容无关的“风格词”，且以动词和形容词为主。科学词汇的演变是长期过程。2021年有 190 个“冗余词汇”，多为与研究内容相关的名词。但自 LLM 普及以来的词汇变化更为显著，且主要体现在风格层面。研究人员发现，在计算科学和生物信息学等领域，超过 1/5 的摘要由 LLM 辅助撰写。

根据 Clothoff 告密者披露的信息，该深度伪造色情应用正计划向全球扩张，试图支配深度伪造色情领域。Clothoff 已经收购了至少 10 款类似服务，这些服务每月吸引了数十万到数百万流量。告密者称，Clothoff 年度预算约 350 万美元，它目前的营销方式主要是依靠 Telegram 机器人和 X 频道向可能使用该应用的年轻男性投放广告。Clothoff 大部分营销预算都花在 Telegram 频道、Reddit Sex Sub 和 4chan 上。

在一项研究中，科学家对埃及一座墓葬中的一名古埃及人进行了全基因组测序。测序对象为男性，其放射性碳测年为公元前 2855 年-公元前 2570 年左右。他被发现埋葬于古埃及 Nuwayrat 地区的一个密封陶罐中，说明他的社会地位较高，活到了他那个时代的高龄——44-64 岁之间。 在提取的 7 个DNA样本中，有两个保存足够完好，能用于测序，并与 3233 个现代个体和 805 个古代个体的数据库进行了对比分析。通过遗传模拟，该 Nuwayrat 遗体基因组的绝大部分可以追溯到北非新石器时代的祖先。该基因组约 20% 与东新月沃土人群有关，补充了这两个地区有贸易往来和相互影响的考古学证据。

地球上的大部分水资源都是海水，由于盐分过高而无法饮用。海水淡化厂可将海水淡化处理成饮用水，然而该过程需要消耗大量能源。香港研究团队在《ACS Energy Letters》发表研究成果，其研发出一种具有长链微气囊结构的海绵结构材料，结合阳光照射与简易塑料罩，成功实现盐水资源向淡水的转化。一项户外原理验证实验成功在自然光照条件下产出可直接饮用的淡水，标志着实现低能耗可持续海水淡化技术的重大进展。在户外测试中，研究人员将这种材料置于盛有海水的蒸发容器中，上方覆盖弧形透明塑料罩。阳光加热海绵结构材料顶部时，仅会将水分蒸发为水蒸气（盐分会被阻隔）。蒸气在塑料罩内壁凝结为液态水，沿罩壁汇集至边缘，最终滴入蒸发容器下方的漏斗中，以另一容器盛放。经过 6 小时自然光照，该系统最终产出约 3 汤匙的饮用水。

天文学家最近发现一颗名为 HIP 67522b 的系外行星，跟它的母恒星 HIP 67522 的互动关系非常不寻常。这颗行星靠母星非常近，导致恒星表面频繁发生激烈的耀斑，也让行星的大气层持续受热膨胀。HIP 67522 是一颗年轻的 G 型恒星，位于半人马座，距离地球约 417 光年，年龄大约只有 1,700 万年。这颗恒星拥有两颗行星，其中 HIP 67522b 是一颗「热木星」——体积接近木星，由于公转轨道非常靠近母星，绕转一圈只需 7 天的时间。研究团队发现，这颗行星似乎能与母恒星的磁场产生某种奇特的连结，进而引发恒星表面出现剧烈的耀斑活动。这些耀斑朝向行星爆发时，又把大量能量「反馈」到行星身上，使它的大气层像吹气球一样不断膨胀。长期下来，行星的大气可能会被严重剥离，甚至从一颗巨大的热木星，缩小成像「热海王星」或「亚海王星」那样的体积。这类母星与行星之间的强烈互动，早就在理论上被预测过，但直到现在才首次被实际观测到。

丹麦奥胡斯大学的一项研究发现，女性并非天生比男性更容易被婴儿晚上的哭泣声惊醒。不过女性花在夜间照顾的可能性三倍于男性。研究人员开展了两项独立研究。第一项实验针对 142 名无孩成年人，结果发现女性对非常安静的声音的反应略强于男性。对于耳语级别的声音，无论是婴儿哭声还是常见的闹钟声，女性吵醒的可能性比男性高 14%。但如果声音的响度加强，男女之间不存在显著差异。第二项研究中丹麦 117 位初为人父母的夫妇记录了他们一周内的夜间照护情况。结果显示，母亲夜间婴儿照护的可能性是父亲的三倍。研究人员认为，社会因素而非生理差异才能解释其中的差异。丹麦最近将陪产假从两周延长至十一周，可能有助于平衡父母之间的育儿责任。

根据 Circana 的数据，18-24 岁的美国年轻人四月份的游戏支出比去年同期减少了 25%，总支出比去年同期减少 13%。减少开支的可能原因是经济的不确定性和就业前景黯淡。相比之下，年龄较大的群体的支出保持了稳定。美国的经济环境可能促使年轻一代改变消费习惯，对已经面临裁员的游戏行业而言，这可能不是好消息。

MediaMatters 报告，短视频平台 TikTok 上涌现了大量由 Google Veo 3 生成的种族主义视频。攻击对象主要是黑人，称他们是“嫌疑惯犯”、父母缺席和喜欢吃西瓜的猴子。TikTok 的服务条款禁止此类内容。但相关内容的传播并未受到多少限制。TikTok 发言人表示，MediaMatters 报告中提及的账户逾半数在报告发布前就因违反政策而被封禁，其余账户现已删除。

微软宣布了最新一轮裁员，将裁掉约九千名员工，占员工总数的不到 4%，其中 XBox 游戏业务深受影响。微软今年至今已经经历了多轮裁员，年初根据绩效裁掉了不到 1% 的员工，5 月裁掉了逾 6000 人，6 月小规模裁员 300 人左右。这一波裁员的背景是微软仍然是标普 500 中最赚钱的公司之一。在 XBox 部门，游戏工作室 The Initiative 被关，微软同时取消了多个游戏项目，包括 Perfect Dark、Everwild 等等。

ESA 宣布，天文学家可能发现了已知第三个星际天体（第一个 Oumuamua，第二个是星际彗星 2I/Borisov）。该天体暂时命名为 A11pl3Z。A11pl3Z 是近期发现的，目前位于木星轨道内，将于今年 10 月抵达近日点穿越火星轨道。天文学家测量发现该天体的偏心率约为 6，为双曲线轨道，意味着 A11pl3Z 可能起源于太阳系之外。

Linux 新闻网站 Phoronix 在一台配备了 AMD Ryzen 9 9950X 的 Ubuntu Linux 系统上测试了 Firefox 120 到 Firefox 141 Beta 的性能。Firefox 差不多一个月发布一个新版本，Firefox 120 是在 2023 年 11 月发布的，而 Firefox 最新正式版本是 Firefox 140，141 还是 Beta 状态。结果显示，Firefox 141 Beta 平均比 Firefox 120 快约 12%，内存占用也更低。Mozilla 还是在努力改进浏览器的。

外设制造商称，任天堂使用新的加密方案有意锁定了 Switch 2 的 USB-C 端口，阻止第三方扩展坞和外设与之兼容。因抢先推出 Steam Deck 扩展坞而名声大振的 Jsaux 表示，因任天堂的行动该公司暂停了制造 Switch 2 扩展坞的计划。

在 GPLv3 发布 18 年、GPLv2 发布 34 年之际，已经停滞很久的 Copyleft-next 项目宣布重新启动。该项目旨在开发下一代 Copyleft 许可证。项目发起人表示 FOSS 需要新方法强化 copyleft。Richard Fontana 和 Bradley Kuhn 担任项目的联合主编，他们都参与过 Drafting Committees of GPLv3，从中汲取了很多教训，Software Freedom Conservancy(SFC)将为该项目提供资源并托管该项目。

西北工业大学空天组合动力团队牵头研制的飞天二号于 2025 年 6 月 23 日成功完成试飞试验，报道称飞行速度达到了 12 马赫，创造了世界纪录。此次试验在国际上首次获取了煤油/过氧化氢推进剂火箭冲压组合动力在变结构进气、变推力加速、变攻角自主飞行等关键工况下的科学数据。该飞行器将火箭和冲压式喷气发动机合二为一，在飞行中能自主切换两种推进系统，能应对高速飞行带来的巨大压力。