OrangeBot.AI Digest — 2025-08-20

Sim is an open-source AI agent workflow builder. Sim Studio's interface is a lightweight, intuitive way to quickly build and deploy LLMs that connect with your favorite tools.

💖🧸 Self hosted, you owned Grok Companion, a container of souls of waifu, cyber livings to bring them into our worlds, wishing to achieve Neuro-sama's altitude. Capable of realtime voice chat, Minecraft, Factorio playing. Web / macOS / Windows supported.

JavaScript API for Chrome and Firefox

Bitwarden client apps (web, browser extension, desktop, and cli).

Leantime is a goals focused project management system for non-project managers. Building with ADHD, Autism, and dyslexia in mind.

The Self-hosted AI Starter Kit is an open-source template that quickly sets up a local AI environment. Curated by n8n, it provides essential tools for creating secure, self-hosted AI workflows.

Modern Backend Framework that unifies APIs, background jobs, workflows, and AI Agents into a single core primitive with built-in observability and state management.

This is a repo with links to everything you'd ever want to learn about data engineering

Implement a ChatGPT-like LLM in PyTorch from scratch, step by step

A benchmark for LLMs on complicated tasks in the terminal

New version of CDP software

Press shortcut → speak → get text. Free and open source. More local-first apps soon ❤️

PixiEditor is a Universal Editor for all your 2D needs

Official inference framework for 1-bit LLMs

Agentic guardrails for safe releases

From simple words to consistent, professional art.

Turn any set of notes into a powerful database

Database diagrams editor for teams

Reliable Web Agents and Workflows

All-in-One AI Video Production for Modern Teams

Introducing the first instrument-based AI music model

You can now build text-only conversational agents

Turn Screen Time into Skills Time.

Your AI Personal Assistant for ADHD

Photo habit tracker

Unified Memory for all of your apps

The billing system for AI products.

Whatsapp, Telegram, iMessage + AI + personal CRM

Proof of Human API

Recent advances in large language models (LLMs) and multi-agent systems have demonstrated remarkable capabilities in complex problem-solving tasks such as deep research, vibe coding, and mathematical reasoning. However, most existing multi-agent systems are built upon manual prompt/workflow engineering with sophisticated agent frameworks, making them computationally inefficient, less capable, and can not benefit from data-centric learning. In this work, we introduce Chain-of-Agents (CoA), a novel paradigm of LLM reasoning that enables native end-to-end complex problem-solving in the same way as a multi-agent system (i.e., multi-turn problem solving with multiple tools and multiple agents) within one model. In chain-of-agents problem-solving, the model dynamically activates different tool agents and role-playing agents to simulate multi-agent collaboration in an end-to-end fashion. To elicit end-to-end chain-of-agents problem-solving abilities in LLMs, we introduce a multi-agent distillation framework to distill state-of-the-art multi-agent systems into chain-of-agents trajectories for agentic supervised fine-tuning. We then use agentic reinforcement learning on verifiable agentic tasks to further improve the models' capabilities on chain-of-agents problem solving. We call the resulting models Agent Foundation Models (AFMs). Our empirical studies demonstrate that AFM establishes new state-of-the-art performance across diverse benchmarks in both web agent and code agent settings. We make the entire research, including the model weights, code for training and evaluation, and the training data, fully open-sourced, which offers a solid starting point for future research on agent models and agentic RL.

LongSplat addresses critical challenges in novel view synthesis (NVS) from casually captured long videos characterized by irregular camera motion, unknown camera poses, and expansive scenes. Current methods often suffer from pose drift, inaccurate geometry initialization, and severe memory limitations. To address these issues, we introduce LongSplat, a robust unposed 3D Gaussian Splatting framework featuring: (1) Incremental Joint Optimization that concurrently optimizes camera poses and 3D Gaussians to avoid local minima and ensure global consistency; (2) a robust Pose Estimation Module leveraging learned 3D priors; and (3) an efficient Octree Anchor Formation mechanism that converts dense point clouds into anchors based on spatial density. Extensive experiments on challenging benchmarks demonstrate that LongSplat achieves state-of-the-art results, substantially improving rendering quality, pose accuracy, and computational efficiency compared to prior approaches. Project page: https://linjohnss.github.io/longsplat/

Large Language Models (LLMs) require sophisticated prompting, yet current practices face challenges in structure, data integration, format sensitivity, and tooling. Existing methods lack comprehensive solutions for organizing complex prompts involving diverse data types (documents, tables, images) or managing presentation variations systematically. To address these gaps, we introduce POML (Prompt Orchestration Markup Language). POML employs component-based markup for logical structure (roles, tasks, examples), specialized tags for seamless data integration, and a CSS-like styling system to decouple content from presentation, reducing formatting sensitivity. It includes templating for dynamic prompts and a comprehensive developer toolkit (IDE support, SDKs) to improve version control and collaboration. We validate POML through two case studies demonstrating its impact on complex application integration (PomLink) and accuracy performance (TableQA), as well as a user study assessing its effectiveness in real-world development scenarios.

Visual designers naturally draw inspiration from multiple visual references, combining diverse elements and aesthetic principles to create artwork. However, current image generative frameworks predominantly rely on single-source inputs -- either text prompts or individual reference images. In this paper, we focus on the task of controllable image generation using multiple visual references. We introduce MultiRef-bench, a rigorous evaluation framework comprising 990 synthetic and 1,000 real-world samples that require incorporating visual content from multiple reference images. The synthetic samples are synthetically generated through our data engine RefBlend, with 10 reference types and 33 reference combinations. Based on RefBlend, we further construct a dataset MultiRef containing 38k high-quality images to facilitate further research. Our experiments across three interleaved image-text models (i.e., OmniGen, ACE, and Show-o) and six agentic frameworks (e.g., ChatDiT and LLM + SD) reveal that even state-of-the-art systems struggle with multi-reference conditioning, with the best model OmniGen achieving only 66.6% in synthetic samples and 79.0% in real-world cases on average compared to the golden answer. These findings provide valuable directions for developing more flexible and human-like creative tools that can effectively integrate multiple sources of visual inspiration. The dataset is publicly available at: https://multiref.github.io/.

Evaluating personalized recommendations remains a central challenge, especially in long-form audio domains like podcasts, where traditional offline metrics suffer from exposure bias and online methods such as A/B testing are costly and operationally constrained. In this paper, we propose a novel framework that leverages Large Language Models (LLMs) as offline judges to assess the quality of podcast recommendations in a scalable and interpretable manner. Our two-stage profile-aware approach first constructs natural-language user profiles distilled from 90 days of listening history. These profiles summarize both topical interests and behavioral patterns, serving as compact, interpretable representations of user preferences. Rather than prompting the LLM with raw data, we use these profiles to provide high-level, semantically rich context-enabling the LLM to reason more effectively about alignment between a user's interests and recommended episodes. This reduces input complexity and improves interpretability. The LLM is then prompted to deliver fine-grained pointwise and pairwise judgments based on the profile-episode match. In a controlled study with 47 participants, our profile-aware judge matched human judgments with high fidelity and outperformed or matched a variant using raw listening histories. The framework enables efficient, profile-aware evaluation for iterative testing and model selection in recommender systems.

While large language models (LLMs) have demonstrated remarkable performance across diverse tasks, they fundamentally lack self-awareness and frequently exhibit overconfidence, assigning high confidence scores to incorrect predictions. Accurate confidence estimation is therefore critical for enhancing the trustworthiness and reliability of LLM-generated outputs. However, existing approaches suffer from coarse-grained scoring mechanisms that fail to provide fine-grained, continuous confidence estimates throughout the generation process. To address these limitations, we introduce FineCE, a novel confidence estimation method that delivers accurate, fine-grained confidence scores during text generation. Specifically, we first develop a comprehensive pipeline for constructing training data that effectively captures the underlying probabilistic distribution of LLM responses, and then train a model to predict confidence scores for arbitrary text sequences in a supervised manner. Furthermore, we propose a Backward Confidence Integration (BCI) strategy that leverages information from the subsequent text to enhance confidence estimation for the current sequence during inference. We also introduce three strategies for identifying optimal positions to perform confidence estimation within the generation process. Extensive experiments on multiple benchmark datasets demonstrate that FineCE consistently outperforms existing classical confidence estimation methods. Our code and all baselines used in the paper are available on GitHub.

Generalization in embodied AI is hindered by the "seeing-to-doing gap," which stems from data scarcity and embodiment heterogeneity. To address this, we pioneer "pointing" as a unified, embodiment-agnostic intermediate representation, defining four core embodied pointing abilities that bridge high-level vision-language comprehension with low-level action primitives. We introduce Embodied-R1, a 3B Vision-Language Model (VLM) specifically designed for embodied reasoning and pointing. We use a wide range of embodied and general visual reasoning datasets as sources to construct a large-scale dataset, Embodied-Points-200K, which supports key embodied pointing capabilities. We then train Embodied-R1 using a two-stage Reinforced Fine-tuning (RFT) curriculum with a specialized multi-task reward design. Embodied-R1 achieves state-of-the-art performance on 11 embodied spatial and pointing benchmarks. Critically, it demonstrates robust zero-shot generalization by achieving a 56.2% success rate in the SIMPLEREnv and 87.5% across 8 real-world XArm tasks without any task-specific fine-tuning, representing a 62% improvement over strong baselines. Furthermore, the model exhibits high robustness against diverse visual disturbances. Our work shows that a pointing-centric representation, combined with an RFT training paradigm, offers an effective and generalizable pathway to closing the perception-action gap in robotics.

Text-guided color editing in images and videos is a fundamental yet unsolved problem, requiring fine-grained manipulation of color attributes, including albedo, light source color, and ambient lighting, while preserving physical consistency in geometry, material properties, and light-matter interactions. Existing training-free methods offer broad applicability across editing tasks but struggle with precise color control and often introduce visual inconsistency in both edited and non-edited regions. In this work, we present ColorCtrl, a training-free color editing method that leverages the attention mechanisms of modern Multi-Modal Diffusion Transformers (MM-DiT). By disentangling structure and color through targeted manipulation of attention maps and value tokens, our method enables accurate and consistent color editing, along with word-level control of attribute intensity. Our method modifies only the intended regions specified by the prompt, leaving unrelated areas untouched. Extensive experiments on both SD3 and FLUX.1-dev demonstrate that ColorCtrl outperforms existing training-free approaches and achieves state-of-the-art performances in both edit quality and consistency. Furthermore, our method surpasses strong commercial models such as FLUX.1 Kontext Max and GPT-4o Image Generation in terms of consistency. When extended to video models like CogVideoX, our approach exhibits greater advantages, particularly in maintaining temporal coherence and editing stability. Finally, our method also generalizes to instruction-based editing diffusion models such as Step1X-Edit and FLUX.1 Kontext dev, further demonstrating its versatility.

Recent advances in self-refinement have demonstrated significant potential for improving the outputs of large language models (LLMs) through iterative refinement. However, most existing self-refinement methods rely on a reactive process with a fixed number of iterations, making it difficult to determine the optimal timing and content of refinement based on the evolving generation context. Inspired by the way humans dynamically refine their thoughts during execution, we propose ProActive Self-Refinement (PASR), a novel method that enables LLMs to refine their outputs during the generation process. Unlike methods that regenerate entire responses, PASR proactively decides whether, when, and how to refine based on the model's internal state and evolving context. We conduct extensive experiments on a diverse set of 10 tasks to evaluate the effectiveness of PASR. Experimental results show that PASR significantly enhances problem-solving performance. In particular, on Qwen3-8B, PASR reduces average token consumption by 41.6 percent compared to standard generation, while also achieving an 8.2 percent improvement in accuracy. Our code and all baselines used in the paper are available in the GitHub.

The field of speech separation, addressing the "cocktail party problem", has seen revolutionary advances with DNNs. Speech separation enhances clarity in complex acoustic environments and serves as crucial pre-processing for speech recognition and speaker recognition. However, current literature focuses narrowly on specific architectures or isolated approaches, creating fragmented understanding. This survey addresses this gap by providing systematic examination of DNN-based speech separation techniques. Our work differentiates itself through: (I) Comprehensive perspective: We systematically investigate learning paradigms, separation scenarios with known/unknown speakers, comparative analysis of supervised/self-supervised/unsupervised frameworks, and architectural components from encoders to estimation strategies. (II) Timeliness: Coverage of cutting-edge developments ensures access to current innovations and benchmarks. (III) Unique insights: Beyond summarization, we evaluate technological trajectories, identify emerging patterns, and highlight promising directions including domain-robust frameworks, efficient architectures, multimodal integration, and novel self-supervised paradigms. (IV) Fair evaluation: We provide quantitative evaluations on standard datasets, revealing true capabilities and limitations of different methods. This comprehensive survey serves as an accessible reference for experienced researchers and newcomers navigating speech separation's complex landscape.

This study investigates the use of Large Language Models (LLMs) for predicting human-perceived misery scores from natural language descriptions of real-world scenarios. The task is framed as a regression problem, where the model assigns a scalar value from 0 to 100 to each input statement. We evaluate multiple prompting strategies, including zero-shot, fixed-context few-shot, and retrieval-based prompting using BERT sentence embeddings. Few-shot approaches consistently outperform zero-shot baselines, underscoring the value of contextual examples in affective prediction. To move beyond static evaluation, we introduce the "Misery Game Show", a novel gamified framework inspired by a television format. It tests LLMs through structured rounds involving ordinal comparison, binary classification, scalar estimation, and feedback-driven reasoning. This setup enables us to assess not only predictive accuracy but also the model's ability to adapt based on corrective feedback. The gamified evaluation highlights the broader potential of LLMs in dynamic emotional reasoning tasks beyond standard regression. Code and data link: https://github.com/abhi1nandy2/Misery_Data_Exps_GitHub

Virtual Try-ON (VTON) is a practical and widely-applied task, for which most of existing works focus on clothes. This paper presents OmniTry, a unified framework that extends VTON beyond garment to encompass any wearable objects, e.g., jewelries and accessories, with mask-free setting for more practical application. When extending to various types of objects, data curation is challenging for obtaining paired images, i.e., the object image and the corresponding try-on result. To tackle this problem, we propose a two-staged pipeline: For the first stage, we leverage large-scale unpaired images, i.e., portraits with any wearable items, to train the model for mask-free localization. Specifically, we repurpose the inpainting model to automatically draw objects in suitable positions given an empty mask. For the second stage, the model is further fine-tuned with paired images to transfer the consistency of object appearance. We observed that the model after the first stage shows quick convergence even with few paired samples. OmniTry is evaluated on a comprehensive benchmark consisting of 12 common classes of wearable objects, with both in-shop and in-the-wild images. Experimental results suggest that OmniTry shows better performance on both object localization and ID-preservation compared with existing methods. The code, model weights, and evaluation benchmark of OmniTry will be made publicly available at https://omnitry.github.io/.

Recent flow matching models for text-to-image generation have achieved remarkable quality, yet their integration with reinforcement learning for human preference alignment remains suboptimal, hindering fine-grained reward-based optimization. We observe that the key impediment to effective GRPO training of flow models is the temporal uniformity assumption in existing approaches: sparse terminal rewards with uniform credit assignment fail to capture the varying criticality of decisions across generation timesteps, resulting in inefficient exploration and suboptimal convergence. To remedy this shortcoming, we introduce TempFlow-GRPO (Temporal Flow GRPO), a principled GRPO framework that captures and exploits the temporal structure inherent in flow-based generation. TempFlow-GRPO introduces two key innovations: (i) a trajectory branching mechanism that provides process rewards by concentrating stochasticity at designated branching points, enabling precise credit assignment without requiring specialized intermediate reward models; and (ii) a noise-aware weighting scheme that modulates policy optimization according to the intrinsic exploration potential of each timestep, prioritizing learning during high-impact early stages while ensuring stable refinement in later phases. These innovations endow the model with temporally-aware optimization that respects the underlying generative dynamics, leading to state-of-the-art performance in human preference alignment and standard text-to-image benchmarks.

Multi-agent reinforcement learning (MARL) is a powerful paradigm for solving cooperative and competitive decision-making problems. While many MARL benchmarks have been proposed, few combine continuous state and action spaces with challenging coordination and planning tasks. We introduce CAMAR, a new MARL benchmark designed explicitly for multi-agent pathfinding in environments with continuous actions. CAMAR supports cooperative and competitive interactions between agents and runs efficiently at up to 100,000 environment steps per second. We also propose a three-tier evaluation protocol to better track algorithmic progress and enable deeper analysis of performance. In addition, CAMAR allows the integration of classical planning methods such as RRT and RRT* into MARL pipelines. We use them as standalone baselines and combine RRT* with popular MARL algorithms to create hybrid approaches. We provide a suite of test scenarios and benchmarking tools to ensure reproducibility and fair comparison. Experiments show that CAMAR presents a challenging and realistic testbed for the MARL community.

Copyright protection for large language models is of critical importance, given their substantial development costs, proprietary value, and potential for misuse. Existing surveys have predominantly focused on techniques for tracing LLM-generated content-namely, text watermarking-while a systematic exploration of methods for protecting the models themselves (i.e., model watermarking and model fingerprinting) remains absent. Moreover, the relationships and distinctions among text watermarking, model watermarking, and model fingerprinting have not been comprehensively clarified. This work presents a comprehensive survey of the current state of LLM copyright protection technologies, with a focus on model fingerprinting, covering the following aspects: (1) clarifying the conceptual connection from text watermarking to model watermarking and fingerprinting, and adopting a unified terminology that incorporates model watermarking into the broader fingerprinting framework; (2) providing an overview and comparison of diverse text watermarking techniques, highlighting cases where such methods can function as model fingerprinting; (3) systematically categorizing and comparing existing model fingerprinting approaches for LLM copyright protection; (4) presenting, for the first time, techniques for fingerprint transfer and fingerprint removal; (5) summarizing evaluation metrics for model fingerprints, including effectiveness, harmlessness, robustness, stealthiness, and reliability; and (6) discussing open challenges and future research directions. This survey aims to offer researchers a thorough understanding of both text watermarking and model fingerprinting technologies in the era of LLMs, thereby fostering further advances in protecting their intellectual property.

亚马逊 AWS CEO Matt Garman 表示用 AI 工具取代初级员工是他听过的最蠢的主意之一，因为企业需要精通业务的资深员工，但资深员工都是从初级员工一步步成长起来的。他说，如果 10 年后企业没有此类员工，你的公司如何运作？他认为，企业应该继续招聘应届生，教他们如何构建软件、分解问题和采用最佳实践。他说 AI 时代最有价值的技能与大学学位不相关。要保住自己的工作员工必须不停的继续学习更新技能。

Mozilla 释出了 Firefox 142.0。主要新特性包括：美国用户新标签页的文章推荐将按照体育、美食等主题分类，用户可选择关注自己感兴趣的主题，以及移除不感兴趣的主题；链接预览功能，用户单击右键还可选择 AI 生成摘要，AI 在本地运行，不会泄漏用户隐私，链接预览功能将逐步推广，目前提供给有 3GB 可用内存的 en-US、en-CA、en-GB 和 en-AU 地区用户；放宽对部分网站的严格跟踪保护功能，否则网站无法正常展示；等等。

苹果正在印度而不是中国组装更多新款 iPhone 17 手机，且首次所有 iPhone 新机型在印度组装出货。苹果正在开发 iPhone 16E 后续机型，计划由印度组装。为了减少对中国制造的依赖，苹果正将 iPhone 大部分的组装转移到印度。苹果预计本季度将缴纳 11 亿美元的关税，而目前从印度向美国出口的 iPhone 可享受关税豁免。分析师称，iPhone 的组件仍然主要在中国生产，然后运往印度进行最终组装。

世界人口第一大国还有足够的时间先富后老。印度要到 2050 年代末才会跨过人口老龄化的门槛——中位数年龄 41 岁，而中国已经跨过这一门槛。印度需要在 35 年内保持 10.4% 的 GDP 增长率才能在老龄化前实现富裕，而中国则需要保持 32% 的 GDP 年增长率。印度劳动年龄人口占总人口的比例将从 2021 年的 67.5% 增长到 2031 年的 69.2%，到 2036 年中位年龄为 34.5 岁。中国面临的困境和发达国家差不多，欧洲 65 岁以上人口比例从 1950 年的 8% 增加到了 2050 年的 30%，提高退休年龄将面临老年选民群体的抵制，老年人口占到了欧洲选民人口的四成。美国的老龄化问题将在 2033 年出现。

发表在 PNAS 期刊上的一项研究显示，2024 年夏季，连续 6 周的创纪录高温导致北极斯瓦尔巴群岛的冰融化量创历史新高。到今年夏末，该群岛上 1% 的陆地冰层已经消失，足以使全球海平面平均上升 0.16 毫米。一半以上的斯瓦尔巴群岛都被冰覆盖。1991 年以来，平均每年夏季融化的冰不到 100 亿吨。但在过去 5 年中，有 4 年创下了夏季冰流失的新纪录。研究团队估计，去年夏季总共损失了大约 620 亿吨冰，且几乎全部源于表层融化而非冰川入海。气候模型显示，随着地球持续变暖，这种情况将变得更加普遍。

不少人选择通过 LASIK 等激光矫正技术矫正视力。但这种手术需切削角膜组织，存在一定风险。加州大学尔湾分校研究人员正在探索一种通过电流重塑角膜，而不是切割角膜的“去激光化”视力矫正新技术。人类角膜是位于眼睛前部的一种透明拱形结构，其作用是折射环境光线并将其聚焦到视网膜上，再传送到大脑形成图像。如果角膜形状异常，就无法正确聚焦光线，从而导致视力模糊。LASIK 手术通过激光去除部分角膜组织以矫正角膜形态。人们普遍认为，这一手术相对安全，但仍存在局限和风险，而且激光切割角膜会削弱眼睛的结构稳定性。研究人员此次探索的是一种名为“电—机械重塑”的方法，其原理是许多富含胶原蛋白的组织（包括角膜）依靠带相反电荷分子的吸引力维持形态，当在组织中施加电流时，会改变其pH值，使这些分子间的吸引力暂时减弱，从而让组织变得柔软可塑。当pH值恢复后，组织则固定在新形态上。

Pistachio CTO Zack Korman 披露微软 Windows AI 助手 M365 Copilot 的一个 bug，它对文件的访问会不记录在审计日志内，这意味着如果有人能操纵 Copilot 他们能匿名其访问痕迹，对企业而言这是一个严重的安全隐患。微软和其它科技公司一样，正全力押注 AI，将 AI 整合到旗下的各种产品中，Windows 11 就整合了 Copilot 助手。Zack Korman 发现如果要求 Copilot 访问一个文件，摘录其内容，在回复中不要链接文档地址，那么在日志里就不会留下访问记录。他在 7 月初向微软报告了该 bug，微软在 8 月中旬修复了 bug，但没有赋予 CVE 编号，也没有披露该 bug。

根据 NetBlocks 报告，巴基斯坦周二互联网连接速度降至平常的五分之一，该国 1.16 亿网户受到影响。网络问题主要影响该国骨干运营商 PTCL。目前尚不清楚故障原因。此前有报道称巴基斯坦的 Web 管理系统进口了中国的网络设备，新系统从 2024 年下半年开始投入使用，相比旧系统提供了更先进的监控能力，支持从加密连接收集元数据。Web 管理系统是工作在网络通道内（in-path）而不是旁路（on-path），被认为会对网络吞吐产生负面影响，巴基斯坦网民从去年下半年就开始抱怨他们的网速显著下降。

8 月 20 日北京时间约 00:34 至 01:48 期间，中国 HTTPS 访问短暂受限。苹果报告 APP Store 在此期间遇到问题，但没有给出更多解释。分析显示，所有指向 TCP 443 端口的连接无条件注入伪造的 TCP RST+ACK 包，导致连接中断；无条件的 RST+ACK 注入仅发生在 TCP 443 端口（常用于 HTTPS），未见于其他常见端口（如 22、80、8443）；无条件注入同时扰乱了出入境双方向的连接，但触发机制不对称。

根据 Letterboxd 和消费者调查，在观众想要看到更多的电影类型中喜剧片排名第二，但好莱坞喜剧片产量自 1990 年以来却下降了 27%。喜剧片制作预算平均为 2650 万美元，投资回报率达到 102%，然而只有 9.3% 的喜剧片有续集，而动作片则高达 27.6%。为什么好莱坞停止制作喜剧片？相比动作片、恐怖片或传记片，喜剧片公式化程度不那么高，而且更难翻译到其它语言和文化，喜剧片的票房主要来自本土市场而非国际市场，如 《妙探出更(Beverly Hills Cop)》和《捉鬼敢死队（Ghostbusters）》大部分票房都来自北美市场。今天的好莱坞更倾向于制作具有国际市场潜力的电影。

去年初震惊整个开源和网络安全社区的 XZ 后门事件并没有离我们而去。在 XZ 事件中，攻击者 Jia Tan（化名，未必是华人）潜伏 XZ Utils 项目长达两年多时间，最终获得信任成为项目的共同维护者，之后他或他们利用其权限悄悄在 xz-utils 包中植入了一个复杂的后门。在恶意版本大规模传播前，后门就被发现了，因此没有造成大问题。但 Binarly REsearch 的调查发现，在攻击期间构建的部分 Docker 镜像仍然包含有 XZ Utils 后门。安全研究人员从 DockerHub 上发现了超过 35 个含有后门的镜像。虽然数字不多，但研究人员只扫描了一小部分镜像，而且只针对 Debian 发行版，其它发行版如 Fedora 和 OpenSUSE 情况未知。

英国芯片设计公司 Arm 的大股东日本软银集团向陷入困境的英特尔公司投资 20 亿美元，入股芯片巨人。与此同时，美国政府也在与英特尔公司谈判入股 10%。软银将以每股 23 美元的价格收购英特尔的普通股，将持有约 2% 的英特尔股份。软银今年二季度是四年来首次盈利，英特尔现任 CEO 陈立武曾是软银董事。英特尔曾是最强大的半导体公司，如今已经落后英伟达和 AMD 等公司。它目前正在重组裁员。

MIT 发表报告《The GenAI Divide: State of AI in Business 2025》称，95% 的企业生成式 AI 试验失败了。虽然企业纷纷整合大模型，但只有 5% 的 AI 试点项目实现了收入的快速增长，大多数项目停滞，对损益表几乎没有产生可衡量的影响。研究基于对 150 名高管的访谈，350 名员工的调查以及对 300 个公开的 AI 部署项目的分析。报告主要作者 Aditya Challapally 解释说，95% 的企业部署生成式 AI 表现不佳不是因为大模型的质量，而是因为 ChatGPT 之类的通用工具因其灵活性对个人用户非常有用，但它们无法从工作流程中学习或适应工作流程，因此企业部署停滞不前。逾半数的生成式 AI 预算是投入在销售和营销工具上面，但研究显示后台业务自动化投资回报率最高——在后台部署 AI 有助于消除业务流程外包、削减外部营力成本和简化运营。

在美国登月计划受挫之际，中国的登月计划过去几个月则取得了显著进展，中国有可能在美国之前登陆月球。中国载人航天工程办公室于 8 月 6 日成功测试了 26 吨的揽月着陆器高保真模型。揽月月面着陆器是登月舱和推进舱组成，主要用于环月轨道和月球表面间的航天员运输，可搭载 2 名航天员往返，中国国家航天局在声明中再次确认计划在 2030 年前登月。中国还在上周完成了新一代载人运载火箭长征十号的首次系留点火试验，6 月份完成了梦舟载人飞船零高度逃逸试验。

英格兰儿童事务专员 Rachel de Souza 表示，政府需要阻止儿童使用 VPN 绕过色情网站的年龄验证。她表示该漏洞需要堵上，呼吁 VPN 服务也要验证年龄。在 PornHub、Reddit 和 X 等网站开始对访问成人内容的英国用户验证年龄后，VPN 成为英国苹果 App Store 下载量最多的应用。对于限制 VPN 的评论，英国科学、创新和科技部发言人回应称，VPN 是成人使用的合法工具，目前没有禁止的计划。但如果平台故意向儿童推销 VPN 作为绕过年龄验证方法，将面临严厉执法和巨额罚款。