OrangeBot.AI Digest — 2025-09-23

Gin is a high-performance HTTP web framework written in Go. It provides a Martini-like API but with significantly better performance—up to 40 times faster—thanks to httprouter. Gin is designed for building REST APIs, web applications, and microservices.

Truly independent web browser

⚡️ Express inspired web framework written in Go

Find and fix problems in your JavaScript code.

A modern formatting library

A collection of (mostly) technical things every software developer should know about

Node Version Manager - POSIX-compliant bash script to manage multiple active node.js versions

OpenZeppelin Contracts is a library for secure smart contract development.

🥢像老乡鸡🐔那样做饭。主要部分于2024年完工，非老乡鸡官方仓库。文字来自《老乡鸡菜品溯源报告》，并做归纳、编辑与整理。CookLikeHOC.

📚 Freely available programming books

大麦自动抢票，支持人员、城市、日期场次、价格选择

[NeurIPS2025] "AI-Researcher: Autonomous Scientific Innovation" -- A production-ready version: https://novix.science/chat

Foundry is a blazing fast, portable and modular toolkit for Ethereum application development written in Rust.

TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

The Kubernetes Package Manager

We define Agency as the emergent capacity of AI systems to function as autonomous agents actively discovering problems, formulating hypotheses, and executing solutions through self-directed engagement with environments and tools. This fundamental capability marks the dawn of the Age of AI Agency, driven by a critical industry shift: the urgent need for AI systems that don't just think, but work. While current AI excels at reasoning and generating responses, industries demand autonomous agents that can execute tasks, operate tools, and drive real-world outcomes. As agentic intelligence becomes the defining characteristic separating cognitive systems from productive workers, efficiently cultivating machine autonomy becomes paramount. Current approaches assume that more data yields better agency, following traditional scaling laws from language modeling. We fundamentally challenge this paradigm. LIMI (Less Is More for Intelligent Agency) demonstrates that agency follows radically different development principles. Through strategic focus on collaborative software development and scientific research workflows, we show that sophisticated agentic intelligence can emerge from minimal but strategically curated demonstrations of autonomous behavior. Using only 78 carefully designed training samples, LIMI achieves 73.5% on comprehensive agency benchmarks, dramatically outperforming state-of-the-art models: Kimi-K2-Instruct (24.1%), DeepSeek-V3.1 (11.9%), Qwen3-235B-A22B-Instruct (27.5%), and GLM-4.5 (45.1%). Most strikingly, LIMI demonstrates 53.7% improvement over models trained on 10,000 samples-achieving superior agentic intelligence with 128 times fewer samples. Our findings establish the Agency Efficiency Principle: machine autonomy emerges not from data abundance but from strategic curation of high-quality agentic demonstrations.

Recent advances in video insertion based on diffusion models are impressive. However, existing methods rely on complex control signals but struggle with subject consistency, limiting their practical applicability. In this paper, we focus on the task of Mask-free Video Insertion and aim to resolve three key challenges: data scarcity, subject-scene equilibrium, and insertion harmonization. To address the data scarcity, we propose a new data pipeline InsertPipe, constructing diverse cross-pair data automatically. Building upon our data pipeline, we develop OmniInsert, a novel unified framework for mask-free video insertion from both single and multiple subject references. Specifically, to maintain subject-scene equilibrium, we introduce a simple yet effective Condition-Specific Feature Injection mechanism to distinctly inject multi-source conditions and propose a novel Progressive Training strategy that enables the model to balance feature injection from subjects and source video. Meanwhile, we design the Subject-Focused Loss to improve the detailed appearance of the subjects. To further enhance insertion harmonization, we propose an Insertive Preference Optimization methodology to optimize the model by simulating human preferences, and incorporate a Context-Aware Rephraser module during reference to seamlessly integrate the subject into the original scenes. To address the lack of a benchmark for the field, we introduce InsertBench, a comprehensive benchmark comprising diverse scenes with meticulously selected subjects. Evaluation on InsertBench indicates OmniInsert outperforms state-of-the-art closed-source commercial solutions. The code will be released.

We present Qwen3-Omni, a single multimodal model that, for the first time, maintains state-of-the-art performance across text, image, audio, and video without any degradation relative to single-modal counterparts. Qwen3-Omni matches the performance of same-sized single-modal models within the Qwen series and excels particularly on audio tasks. Across 36 audio and audio-visual benchmarks, Qwen3-Omni achieves open-source SOTA on 32 benchmarks and overall SOTA on 22, outperforming strong closed-source models such as Gemini-2.5-Pro, Seed-ASR, and GPT-4o-Transcribe. Qwen3-Omni adopts a Thinker-Talker MoE architecture that unifies perception and generation across text, images, audio, and video, yielding fluent text and natural real-time speech. It supports text interaction in 119 languages, speech understanding in 19 languages, and speech generation in 10 languages. To reduce first-packet latency in streaming synthesis, Talker autoregressively predicts discrete speech codecs using a multi-codebook scheme. Leveraging the representational capacity of these codebooks, we replace computationally intensive block-wise diffusion with a lightweight causal ConvNet, enabling streaming from the first codec frame. In cold-start settings, Qwen3-Omni achieves a theoretical end-to-end first-packet latency of 234 ms. To further strengthen multimodal reasoning, we introduce a Thinking model that explicitly reasons over inputs from any modality. Since the research community currently lacks a general-purpose audio captioning model, we fine-tuned Qwen3-Omni-30B-A3B to obtain Qwen3-Omni-30B-A3B-Captioner, which produces detailed, low-hallucination captions for arbitrary audio inputs. Qwen3-Omni-30B-A3B, Qwen3-Omni-30B-A3B-Thinking, and Qwen3-Omni-30B-A3B-Captioner are publicly released under the Apache 2.0 license.

Despite the growing interest in replicating the scaled success of large language models (LLMs) in industrial search and recommender systems, most existing industrial efforts remain limited to transplanting Transformer architectures, which bring only incremental improvements over strong Deep Learning Recommendation Models (DLRMs). From a first principle perspective, the breakthroughs of LLMs stem not only from their architectures but also from two complementary mechanisms: context engineering, which enriches raw input queries with contextual cues to better elicit model capabilities, and multi-step reasoning, which iteratively refines model outputs through intermediate reasoning paths. However, these two mechanisms and their potential to unlock substantial improvements remain largely underexplored in industrial ranking systems. In this paper, we propose OnePiece, a unified framework that seamlessly integrates LLM-style context engineering and reasoning into both retrieval and ranking models of industrial cascaded pipelines. OnePiece is built on a pure Transformer backbone and further introduces three key innovations: (1) structured context engineering, which augments interaction history with preference and scenario signals and unifies them into a structured tokenized input sequence for both retrieval and ranking; (2) block-wise latent reasoning, which equips the model with multi-step refinement of representations and scales reasoning bandwidth via block size; (3) progressive multi-task training, which leverages user feedback chains to effectively supervise reasoning steps during training. OnePiece has been deployed in the main personalized search scenario of Shopee and achieves consistent online gains across different key business metrics, including over +2% GMV/UU and a +2.90% increase in advertising revenue.

This paper introduces TempSamp-R1, a new reinforcement fine-tuning framework designed to improve the effectiveness of adapting multimodal large language models (MLLMs) to video temporal grounding tasks. We reveal that existing reinforcement learning methods, such as Group Relative Policy Optimization (GRPO), rely on on-policy sampling for policy updates. However, in tasks with large temporal search spaces, this strategy becomes both inefficient and limited in performance, as it often fails to identify temporally accurate solutions. To address this limitation, TempSamp-R1 leverages ground-truth annotations as off-policy supervision to provide temporally precise guidance, effectively compensating for the sparsity and misalignment in on-policy solutions. To further stabilize training and reduce variance in reward-based updates, TempSamp-R1 provides a non-linear soft advantage computation method that dynamically reshapes the reward feedback via an asymmetric transformation. By employing a hybrid Chain-of-Thought (CoT) training paradigm, TempSamp-R1 optimizes a single unified model to support both CoT and non-CoT inference modes, enabling efficient handling of queries with varying reasoning complexity. Experimental results demonstrate that TempSamp-R1 outperforms GRPO-based baselines, establishing new state-of-the-art performance on benchmark datasets: Charades-STA (R1@0.7: 52.9%, +2.7%), ActivityNet Captions (R1@0.5: 56.0%, +5.3%), and QVHighlights (mAP: 30.0%, +3.0%). Moreover, TempSamp-R1 shows robust few-shot generalization capabilities under limited data. Code: https://github.com/HVision-NKU/TempSamp-R1

Recent advancements in reinforcement learning (RL) have enhanced the reasoning abilities of large language models (LLMs), yet the impact on multimodal LLMs (MLLMs) is limited. Particularly in vision-intensive tasks like geometric reasoning, MLLMs hallucinate frequently, leading to inaccurate reasoning. We attribute this to the perceptual bottleneck in MLLMs, which caps the benefits of reasoning training. To quantify this, we design a Geo-Perception Question-Answering (GeoPQA) benchmark, targeting basic geometric concepts and spatial relationships. Experiments on GeoPQA reveal significant shortcomings of MLLMs in visual perception, which constrain RL reward signals for effective training. To address this bottleneck, we propose a two-stage RL training framework by first enhancing the visual perception of geometric structures, then fostering reasoning capabilities. Applied to Qwen2.5-VL-3B-Instruct, our two-stage training improves geometric reasoning by 9.7% and geometric problem solving by 9.1%, compared to the direct reasoning training approach. Our method also generalizes to other vision-intensive domains like figure understanding, highlighting the importance of perceptual grounding in effective MLLM reasoning.

Online reinforcement learning (RL) has been central to post-training language models, but its extension to diffusion models remains challenging due to intractable likelihoods. Recent works discretize the reverse sampling process to enable GRPO-style training, yet they inherit fundamental drawbacks, including solver restrictions, forward-reverse inconsistency, and complicated integration with classifier-free guidance (CFG). We introduce Diffusion Negative-aware FineTuning (DiffusionNFT), a new online RL paradigm that optimizes diffusion models directly on the forward process via flow matching. DiffusionNFT contrasts positive and negative generations to define an implicit policy improvement direction, naturally incorporating reinforcement signals into the supervised learning objective. This formulation enables training with arbitrary black-box solvers, eliminates the need for likelihood estimation, and requires only clean images rather than sampling trajectories for policy optimization. DiffusionNFT is up to 25times more efficient than FlowGRPO in head-to-head comparisons, while being CFG-free. For instance, DiffusionNFT improves the GenEval score from 0.24 to 0.98 within 1k steps, while FlowGRPO achieves 0.95 with over 5k steps and additional CFG employment. By leveraging multiple reward models, DiffusionNFT significantly boosts the performance of SD3.5-Medium in every benchmark tested.

We introduce SWE-Bench Pro, a substantially more challenging benchmark that builds upon the best practices of SWE-BENCH [25], but is explicitly designed to capture realistic, complex, enterprise-level problems beyond the scope of SWE-BENCH. SWE-BENCH PRO contains 1,865 problems sourced from a diverse set of 41 actively maintained repositories spanning business applications, B2B services, and developer tools. The benchmark is partitioned into a public set with open access to problems sourced from 11 repositories, a held-out set of 12 repositories and a commercial set of 18 proprietary repositories where we have formal partnership agreements with early-stage startups. Problems in the held-out and the commercial set are not publicly accessible, but we release results on the commercial set. Our benchmark features long-horizon tasks that may require hours to days for a professional software engineer to complete, often involving patches across multiple files and substantial code modifications. All tasks are human-verified and augmented with sufficient context to ensure resolvability. In our evaluation of widely used coding models, under a unified scaffold, we observe that their performance on SWE-Bench PRO remains below 25% (Pass@1), with GPT-5 achieving the highest score to date at 23.3%. To better understand these limitations, we cluster the failure modes observed in the collected agent trajectories for a clearer characterization of the error patterns exhibited by current models. Overall, SWE-BENCH PRO provides a contamination-resistant testbed that more faithfully captures the complexity and diversity of real-world software development, advancing the pursuit of truly autonomous software engineering agents at a professional level.

Recent advances in large language models (LLMs) have extended context lengths, enabling assistants to sustain long histories for coherent, personalized responses. This ability, however, hinges on Key-Value (KV) caching, whose memory grows linearly with dialogue length and quickly dominates under strict resource constraints. An active line of research for reducing this overhead is KV cache compression, which seeks to limit cache size while preserving accuracy. Yet existing methods face two major limitations: (i) evicting entries after full-context prefill causes unbounded peak memory, and (ii) query-dependent eviction narrows the cache to a single query, leading to degraded accuracy in multi-turn conversations. We introduce EpiCache, a training-free KV cache management framework for long conversational question answering (LongConvQA) under fixed memory budgets. EpiCache bounds cache growth through block-wise prefill and preserves topic-relevant context via episodic KV compression, which clusters conversation history into coherent episodes and applies episode-specific KV cache eviction. We further design an adaptive layer-wise budget allocation strategy that measures each layer's sensitivity to eviction and distributes the memory budget across layers accordingly. Across three LongConvQA benchmarks, EpiCache improves accuracy by up to 40% over recent baselines, sustains near-full KV accuracy under 4-6x compression, and reduces latency and memory by up to 2.4x and 3.5x, thereby enabling efficient multi-turn interaction under strict resource constraints.

This paper introduces ByteWrist, a novel highly-flexible and anthropomorphic parallel wrist for robotic manipulation. ByteWrist addresses the critical limitations of existing serial and parallel wrists in narrow-space operations through a compact three-stage parallel drive mechanism integrated with arc-shaped end linkages. The design achieves precise RPY (Roll-Pitch-Yaw) motion while maintaining exceptional compactness, making it particularly suitable for complex unstructured environments such as home services, medical assistance, and precision assembly. The key innovations include: (1) a nested three-stage motor-driven linkages that minimize volume while enabling independent multi-DOF control, (2) arc-shaped end linkages that optimize force transmission and expand motion range, and (3) a central supporting ball functioning as a spherical joint that enhances structural stiffness without compromising flexibility. Meanwhile, we present comprehensive kinematic modeling including forward / inverse kinematics and a numerical Jacobian solution for precise control. Empirically, we observe ByteWrist demonstrates strong performance in narrow-space maneuverability and dual-arm cooperative manipulation tasks, outperforming Kinova-based systems. Results indicate significant improvements in compactness, efficiency, and stiffness compared to traditional designs, establishing ByteWrist as a promising solution for next-generation robotic manipulation in constrained environments.

We conduct a moderate-scale contamination-free (to some extent) evaluation of current large reasoning models (LRMs) with some preliminary findings. We also release ROME, our evaluation benchmark for vision language models intended to test reasoning from visual clues. We attach links to the benchmark, evaluation data, and other updates on this website: https://flageval-baai.github.io/LRM-Eval/

In this paper, we propose VideoFrom3D, a novel framework for synthesizing high-quality 3D scene videos from coarse geometry, a camera trajectory, and a reference image. Our approach streamlines the 3D graphic design workflow, enabling flexible design exploration and rapid production of deliverables. A straightforward approach to synthesizing a video from coarse geometry might condition a video diffusion model on geometric structure. However, existing video diffusion models struggle to generate high-fidelity results for complex scenes due to the difficulty of jointly modeling visual quality, motion, and temporal consistency. To address this, we propose a generative framework that leverages the complementary strengths of image and video diffusion models. Specifically, our framework consists of a Sparse Anchor-view Generation (SAG) and a Geometry-guided Generative Inbetweening (GGI) module. The SAG module generates high-quality, cross-view consistent anchor views using an image diffusion model, aided by Sparse Appearance-guided Sampling. Building on these anchor views, GGI module faithfully interpolates intermediate frames using a video diffusion model, enhanced by flow-based camera control and structural guidance. Notably, both modules operate without any paired dataset of 3D scene models and natural images, which is extremely difficult to obtain. Comprehensive experiments show that our method produces high-quality, style-consistent scene videos under diverse and challenging scenarios, outperforming simple and extended baselines.

We introduce Meta Agents Research Environments (ARE), a research platform for scalable creation of environments, integration of synthetic or real applications, and execution of agentic orchestrations. ARE provides simple abstractions to build complex and diverse environments, each with their own rules, tools, content, and verifiers, helping to bridge the gap between model development and real-world deployment. We also propose Gaia2, a benchmark built in ARE and designed to measure general agent capabilities. Beyond search and execution, Gaia2 requires agents to handle ambiguities and noise, adapt to dynamic environments, collaborate with other agents, and operate under temporal constraints. Unlike prior benchmarks, Gaia2 runs asynchronously, surfacing new failure modes that are invisible in static settings. Our experiments show that no system dominates across the intelligence spectrum: stronger reasoning often comes at the cost of efficiency, and budget scaling curves plateau, highlighting the need for new architectures and adaptive compute strategies. Perhaps more importantly, ARE abstractions enable continuous extension of Gaia2 to other environments, empowering the community to rapidly create new benchmarks tailored to their domains. In AI's second half, progress increasingly depends on defining meaningful tasks and robust evaluations to drive frontier capabilities forward.

Large language models (LLMs) acquire substantial world knowledge during pre-training, which is further shaped by post-training techniques such as supervised fine-tuning (SFT). However, the impact of SFT on a model's knowledge remains underexplored, limiting our ability to control knowledge change behavior in fine-tuned models. To address this gap, we evaluate closed-book question answering (CBQA) performance across five LLMs from the LLaMA-2 and LLaMA-3 families. Surprisingly, models fine-tuned on 1,920 samples perform up to 14% worse than those fine-tuned on only 240 samples. Furthermore, varying the level of knowledge mastery in the fine-tuning data leads to performance fluctuations of over 12%. To investigate these effects, we analyze model behavior at both the token and parameter levels. Our analysis reveals that up to 90% of parameter updates during SFT do not contribute to knowledge enhancement. Restoring these updates can improve performance on the CBQA task, depending on the characteristics of the fine-tuning data. These insights offer practical guidance for developing fine-tuning strategies that more effectively strengthen model knowledge.

The demand for efficient deployment of large language models (LLMs) has driven interest in quantization, which reduces inference cost, and parameter-efficient fine-tuning (PEFT), which lowers training overhead. This motivated the development of quantization-aware PEFT to produce accurate yet efficient quantized models. In this setting, reducing quantization error prior to fine-tuning is crucial for achieving high model accuracy. However, existing methods that rely on low-rank adaptation suffer from limited representational capacity. Recent Fourier-related transform (FT)-based adapters offer greater representational power than low-rank adapters, but their direct integration into quantized models often results in ineffective error reduction and increased computational overhead. To overcome these limitations, we propose QWHA, a method that integrates FT-based adapters into quantized models by employing the Walsh-Hadamard Transform (WHT) as the transform kernel, together with a novel adapter initialization scheme incorporating adaptive parameter selection and value refinement. We demonstrate that QWHA effectively mitigates quantization errors while facilitating fine-tuning, and that its design substantially reduces computational cost. Experimental results show that QWHA consistently outperforms baselines in low-bit quantization accuracy and achieves significant training speedups over existing FT-based adapters. The code is available at https://github.com/vantaa89/qwha.

内蒙古自治区锡林浩特市公安局发布关于锡林浩特市集中采集男性居民血样并录入本地 DN A数据库的通告，引发网友关注。在锡林浩特市之前，多地都曾开展集中采集男性居民血样的工作，此举是为了推进“Y库”建设。“Y库”的全称为“Y库家系工匠系统”，此前白银连环杀人案、南医大女生被害案等案件告破，“Y库”都立了功。据锡林浩特市公安局通告，为进一步夯实公安基础工作，完善该市居民基础信息库数据，健全居民个人信息管理，提升重大风险防范与应对能力，精准落实相关工作举措，根据上级部门统一部署，锡林浩特市公安局各派出所将开展男性居民血样集中采集工作。采集时间为2025年9月5日起，采集对象为锡林浩特市辖区内男性居民，采集地点为居民户籍所在地派出所。通告还称，本次血样采集的作用是完善公民身份信息，直接关联到个人身份证、护照等证件的办理。并且对于防范老人儿童走失、人员身份信息确认等方面，具有重大作用。请广大男性居民积极支持配合此项工作，携带本人有效身份证件（身份证、户口本等）前往指定采集点完成信息登记与血样采集。采集过程严格遵循相关规范，居民个人信息及生物样本将依法严格保密，确保信息安全。此项工作功在当代、利在千秋，望全体市民理解支持，共同推动工作顺利开展。

一项研究表明，“肥胖但健康”是有科学依据的。基于数万丹麦人数据的研究发现，在 5 年随访期间，体重指数（BMI）为超重，甚至部分肥胖的人群，其死亡风险并不比 BMI 处于正常范围上限（22.5~25）的人群高。研究人员对 85761 人（女性占 81.4%，基线中位年龄 66.4 岁）的 BMI 和死亡率之间的关系进行了研究。随访期间，共有 7555 人（占比 8%）死亡。分析发现，低体重人群的死亡风险几乎是接近健康上限的参照组（22.5~25）的 3 倍。BMI ≥40 的严重肥胖人群的死亡风险则是参照组的 2.1 倍。BMI 在 35 以下并未显示出与较高的死亡风险相关，即使在 35~40 区间也仅与轻微增加的风险有关。

美国白宫公布了字节跳动剥离 TikTok 美国业务的细节，美国总统特朗普预计将在本周晚些时候批准这笔交易。TikTok 美国业务将移交给以甲骨文和银湖资本（Silver Lake）为中心的企业联合体，并由该联合体负责运营。但作为核心技术的算法仍由中国以授权方式提供，并未完全切割。合资公司中美国董事会成员占半数以上，美国政府没有计划派人员进入董事会，也不会获取在重大事项上行使否决权的“黄金股”或实施资本注入。字节跳动对合资公司的持股将控制在 20% 以下。因为相关法规定义，中国资本若在美企持股比例超过 20%，该企业将被视为“在中国的管理之下”。合资公司预计还有多家企业和投资者参与，但“资本构成尚未最终确定”。甲骨文将在美国境内利用用户数据来运行算法的复制版本，并负责安全措施。甲骨文接收的算法副本包括“源代码”，将被纳入甲骨文管理的系统中，以便该公司实施验证。

Google 开始将其 Gemini AI 助手推送给 Google TV 设备。用户将能向 Gemini 寻求电视推荐、节目回顾、评论，甚至执行家庭作业辅导、假期计划或学习新技能等任务的帮助。Gemini AI 将首先推送给 TCL 的 QM9K 系列智能电视，晚些时候推送给 Google TV Streamer、Walmart onn 4K Pro、2025 Hisense U7、U8 和 UX 型号，以及 2025 款 TCL QM7K、QM8K 和 X11K 系列型号。

微软正在为 Windows 11 加入将视频设为桌面墙纸的功能。最新的 Windows 11 预览版包含了该功能，允许用户将 MP4、MOV、AVI、WMV、M4V 或 MKV 文件设置为墙纸，用户查看桌面时视频就会播放。视频墙纸并非是新特性，Windows 操作系统早就支持该功能。Windows Vista 的终极版通过 DreamScene 功能支持视频墙纸，很多 Linux 发行版都支持，macOS 也支持将移动背景设为锁屏墙纸。

AI 芯片最大的供应商宣布与 AI 行业估值最高的公司展开合作，投资建造用于训练 AI 的数据中心。英伟达宣布将向 OpenAI 投资千亿美元，OpenAI 的估值达到了 5000 亿美元，但英伟达手中并没有这么多现金，它的投资承诺更像是助长 AI 泡沫的意向书。

埃及总统赦免了已被关押了六年的活动人士 Alaa Abdel Fattah。Alaa Abd El Fattah 是一位活跃的民主人士，同时也是埃及开源运动的积极倡导者。2014 年，他因未经授权组织政治抗议活动被捕，之后获准保释，但重审后被判处五年监禁。他于 2019 年 3 月获释，但 9 月再次被国家安全局以传播虚假新闻的罪名逮捕，2021 年被判入狱五年。他本应该于 2024 年 9 月 29 日重获自由，但埃及政府拒绝将审前拘留的两年时间计算在服刑时间内。他的家人通过社交媒体证实了赦免的消息。

Multikernel Technologies 公司的 Cong Wang 公布了代码递交了 RFC。代码为 Linux 内核加入多内核架构支持，让多个独立内核实例能在一台物理机器上共存并通信，每个内核实例能在专用 CPU 核心上运行，共享底层硬件资源。Multikernel Technologies 公司承诺将采用社区优先的开发方法。

能通过 U 盘运行的便捷式匿名发行版 Tails 释出了 7.0 版本。Tails 7.0 是基于 Linux 6.12.43、Debian 13 ("trixie") 和 GNOME 48 的首个版本，使用 z std 而不是 xz 压缩 USB 和 ISO 镜像，实现了更快的启动速度。开发者将 Tails 7.0 献给 Tails、Tor 和 Debian 项目的资深开发者 Jérémy Bobbio aka Lunar——他于 2024 年 11 月 8 日去世。

Valve 从 Steam 商店下架了 2D 平台游戏《BlockBlasters》，原因是该游戏最近释出的一个补丁被发现含有恶意程序。《BlockBlasters》于 7 月 31 日发布，8 月 30 日释出了补丁 Build 19799326，其中的文件 game2.bat 表现出了恶意行为，它会收集用户的 IP 和位置信息，检测安装的杀毒软件；收集用户的登录信息，上传收集的信息，执行 VBS 启动器脚本。它最终会安装一个后门和一个窃取程序，从 Google Chrome、Brave Browser 和 Microsoft Edge 窃取信息，它主要窃取加密货币。可能有数百名玩家受到这次攻击的影响。

新华社报道，中国海军宣布，歼-15T、歼-35和空警-600三型舰载机，已于此前成功完成在福建舰上的首次弹射起飞和着舰训练。这是我国首次在弹射型航母上，实现多型号先进舰载机的电磁弹射和阻拦着舰。美国在 2010 年完成了最早的陆基电磁弹射，福特号航母在 2013 年安装了第一套电磁弹射器，但因为种种问题至今没有进行舰载机电磁弹射测试。

英国银行仍然运行 1960 年代写的代码，而了解这些代码的人寥寥无几。根据一项对 200 家英国银行的调查，16% 的银行依赖 1960 年代的软件，近 40% 的银行仍在维护 1970 年代的代码。50% 的银行承认，他们依赖的软件只有一两位已到或接近退休年龄的员工了解。31.5% 的银行表示，他们依赖一两位未到退休年龄的员工掌握旧系统。38 家银行称，他们仍在使用设计用于在穿孔卡等物理系统上运行的代码，15% 的银行运行的是为房间大小的大型机编写的代码。银行机构庞大，不太可能在每一次科技创新时都重构基础设施。一位受访者表示，其银行核心系统建于 1970 年代，至今仍在使用 Cobol 语言。

微软雷德蒙德总部附近的 Five Stones 咖啡店几个月前招聘咖啡师，收到的应聘者简历列出了在微软等科技公司任职的经历，应聘者通常有硕士学位，有平面设计或市场营销经验，甚至拥有高级职位，而他们应聘的职位薪水是当地的最低薪水：时薪 16.66 美元。Five Stones 咖啡店没有录取这些高学历应聘者，而是优先考虑传统的入门级咖啡师，如学历为高中的人。根据跟踪裁员的 Layoffs.fyi 网站的数据，西雅图最大的两家科技公司微软和亚马逊自 2023 年以来裁员逾 4.6 万人，占到了当地科技公司裁员总数的 85%。科技行业大规模裁员冲击了西雅图的其它领域。亚马逊和微软园区周边商业和购物区的餐饮和零售支出减少，热门地区交易额下降 7%。西雅图在 2025 年上半年有 450 家餐厅关门，相当于当地餐厅总数的 16%。Uber 司机 Juan Prado 在 2021 年的收入达到六位数，经常接送乘客去面试，但今年此类的需求要少得多。当地的商业地产空置率也创历史新高。

天文学家在地球附近发现一颗准卫星（quasi-moon）。被称为 2025 PN7 的天体是一颗近地小行星，围绕太阳飞行一周大约一年时间，可能在地球附近徘徊了约 60 年，直到今年夏天近距离掠过地球时才被望远镜观测到。此类准卫星因体积小且暗淡无光而难以被发现，夏威夷的 Pan-STARRS 天文台是在 8 月 29 日观测到 2025 PN7，历史档案数据显示它在类地球轨道上运行了数十年。天文学家仍致力于确定 2025 PN7 的大小，有估计认为其直径为 19 米或 30 米，它可能是已知绕地球运行的最小准卫星。

世界各地的企业过去十年将其数字基础设施从自托管服务器迁移至云端，它们受益于云服务提供商如微软提供的安全功能。但如果云服务商本身出现问题，后果可能将是灾难性的。安全研究员 Dirk-jan Mollema 在微软云服务 Azure 的身份和访问管理平台 Entra ID 发现了两个漏洞，可用于获得管理员权限，允许他访问 Entra ID 中储存的所有用户账号，从而造成灾难性影响。Mollema 于 7 月 14 日向微软披露了漏洞，微软于 7 月 17 日发布了补丁。微软之后向 Mollema 确认，问题已于 7 月 23 日修复，8 月实施了额外补救措施。微软于 9 月 4 日公开了漏洞的 CVE。