OrangeBot.AI Digest — 2025-08-12

Open source alternative to AWS. Elastic compute, block storage (non replicated), firewall and load balancer, managed Postgres, K8s, AI inference, and IAM services.

Prompt Orchestration Markup Language

Generate audiobooks from EPUBs, PDFs and text with synchronized captions.

GPT4All: Run Local LLMs on Any Device. Open-source and available for commercial use.

Umami is a modern, privacy-focused alternative to Google Analytics.

100+ Fine-tuning LLM Notebooks on Google Colab, Kaggle, and more.

Full stack, modern web application template. Using FastAPI, React, SQLModel, PostgreSQL, Docker, GitHub Actions, automatic HTTPS and more.

OpenTelemetry Collector

Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.

Embedding Atlas is a tool that provides interactive visualizations for large embeddings. It allows you to visualize, cross-filter, and search embeddings and metadata.

FULL v0, Cursor, Manus, Same.dev, Lovable, Devin, Replit Agent, Windsurf Agent, VSCode Agent, Dia Browser, Xcode, Trae AI, Cluely & Orchids.app (And other Open Sourced) System Prompts, Tools & AI Models.

Simple Directmedia Layer

简简单单的看直播

Conductor is an event driven orchestration platform providing durable and highly resilient execution engine for your applications

Cursor for Analytics

Chat with everything you’ve read, heard, watched, or noted

AI workspace to unify, automate, and run your business

The AI builder for everyone

Autonomous AI LinkedIn Outreach

Turn your audience into an active economy

AI-Powered Events, Built on Community

Start your coding adventure ⋆˙⟡

Map how software gets used across teams

Concept to discover & analyze relevant open-source projects

The first to-do app that does it for you.

Your own encryption in any messenger

Go from raw footage to viral ads in seconds

An open-source alternative to Google Forms and Typeform

AI Analytics that fixes your Google Analytics headache

Large Language Model (LLM) based listwise ranking has shown superior performance in many passage ranking tasks. With the development of Large Reasoning Models, many studies have demonstrated that step-by-step reasoning during test-time helps improve listwise ranking performance. However, due to the scarcity of reasoning-intensive training data, existing rerankers perform poorly in many complex ranking scenarios and the ranking ability of reasoning-intensive rerankers remains largely underdeveloped. In this paper, we first propose an automated reasoning-intensive training data synthesis framework, which sources training queries and passages from diverse domains and applies DeepSeek-R1 to generate high-quality training labels. A self-consistency data filtering mechanism is designed to ensure the data quality. To empower the listwise reranker with strong reasoning ability, we further propose a two-stage post-training approach, which includes a cold-start supervised fine-tuning (SFT) stage for reasoning pattern learning and a reinforcement learning (RL) stage for further ranking ability enhancement. During the RL stage, based on the nature of listwise ranking, we design a multi-view ranking reward, which is more effective than a ranking metric-based reward. Extensive experiments demonstrate that our trained reasoning-intensive reranker ReasonRank outperforms existing baselines significantly and also achieves much lower latency than pointwise reranker Rank1. Through further experiments, our ReasonRank has achieved state-of-the-art (SOTA) performance 40.6 on the BRIGHT leaderboard\footnote{https://brightbenchmark.github.io/.} Our codes are available at https://github.com/8421BCD/ReasonRank.

From professional research to everyday planning, many tasks are bottlenecked by wide-scale information seeking, which is more repetitive than cognitively complex. With the rapid development of Large Language Models (LLMs), automated search agents powered by LLMs offer a promising solution to liberate humans from this tedious work. However, the capability of these agents to perform such "wide-context" collection reliably and completely remains largely unevaluated due to a lack of suitable benchmarks. To bridge this gap, we introduce WideSearch, a new benchmark engineered to evaluate agent reliability on these large-scale collection tasks. The benchmark features 200 manually curated questions (100 in English, 100 in Chinese) from over 15 diverse domains, grounded in real user queries. Each task requires agents to collect large-scale atomic information, which could be verified one by one objectively, and arrange it into a well-organized output. A rigorous five-stage quality control pipeline ensures the difficulty, completeness, and verifiability of the dataset. We benchmark over 10 state-of-the-art agentic search systems, including single-agent, multi-agent frameworks, and end-to-end commercial systems. Most systems achieve overall success rates near 0\%, with the best performer reaching just 5\%. However, given sufficient time, cross-validation by multiple human testers can achieve a near 100\% success rate. These results demonstrate that present search agents have critical deficiencies in large-scale information seeking, underscoring urgent areas for future research and development in agentic search. Our dataset, evaluation pipeline, and benchmark results have been publicly released at https://widesearch-seed.github.io/

Visual effects (VFX) are essential visual enhancements fundamental to modern cinematic production. Although video generation models offer cost-efficient solutions for VFX production, current methods are constrained by per-effect LoRA training, which limits generation to single effects. This fundamental limitation impedes applications that require spatially controllable composite effects, i.e., the concurrent generation of multiple effects at designated locations. However, integrating diverse effects into a unified framework faces major challenges: interference from effect variations and spatial uncontrollability during multi-VFX joint training. To tackle these challenges, we propose Omni-Effects, a first unified framework capable of generating prompt-guided effects and spatially controllable composite effects. The core of our framework comprises two key innovations: (1) LoRA-based Mixture of Experts (LoRA-MoE), which employs a group of expert LoRAs, integrating diverse effects within a unified model while effectively mitigating cross-task interference. (2) Spatial-Aware Prompt (SAP) incorporates spatial mask information into the text token, enabling precise spatial control. Furthermore, we introduce an Independent-Information Flow (IIF) module integrated within the SAP, isolating the control signals corresponding to individual effects to prevent any unwanted blending. To facilitate this research, we construct a comprehensive VFX dataset Omni-VFX via a novel data collection pipeline combining image editing and First-Last Frame-to-Video (FLF2V) synthesis, and introduce a dedicated VFX evaluation framework for validating model performance. Extensive experiments demonstrate that Omni-Effects achieves precise spatial control and diverse effect generation, enabling users to specify both the category and location of desired effects.

Recent advances in large language models have sparked growing interest in AI agents capable of solving complex, real-world tasks. However, most existing agent systems rely on manually crafted configurations that remain static after deployment, limiting their ability to adapt to dynamic and evolving environments. To this end, recent research has explored agent evolution techniques that aim to automatically enhance agent systems based on interaction data and environmental feedback. This emerging direction lays the foundation for self-evolving AI agents, which bridge the static capabilities of foundation models with the continuous adaptability required by lifelong agentic systems. In this survey, we provide a comprehensive review of existing techniques for self-evolving agentic systems. Specifically, we first introduce a unified conceptual framework that abstracts the feedback loop underlying the design of self-evolving agentic systems. The framework highlights four key components: System Inputs, Agent System, Environment, and Optimisers, serving as a foundation for understanding and comparing different strategies. Based on this framework, we systematically review a wide range of self-evolving techniques that target different components of the agent system. We also investigate domain-specific evolution strategies developed for specialised fields such as biomedicine, programming, and finance, where optimisation objectives are tightly coupled with domain constraints. In addition, we provide a dedicated discussion on the evaluation, safety, and ethical considerations for self-evolving agentic systems, which are critical to ensuring their effectiveness and reliability. This survey aims to provide researchers and practitioners with a systematic understanding of self-evolving AI agents, laying the foundation for the development of more adaptive, autonomous, and lifelong agentic systems.

We present Klear-Reasoner, a model with long reasoning capabilities that demonstrates careful deliberation during problem solving, achieving outstanding performance across multiple benchmarks. Although there are already many excellent works related to inference models in the current community, there are still many problems with reproducing high-performance inference models due to incomplete disclosure of training details. This report provides an in-depth analysis of the reasoning model, covering the entire post-training workflow from data preparation and long Chain-of-Thought supervised fine-tuning (long CoT SFT) to reinforcement learning (RL), along with detailed ablation studies for each experimental component. For SFT data, our experiments show that a small number of high-quality data sources are more effective than a large number of diverse data sources, and that difficult samples can achieve better results without accuracy filtering. In addition, we investigate two key issues with current clipping mechanisms in RL: Clipping suppresses critical exploration signals and ignores suboptimal trajectories. To address these challenges, we propose Gradient-Preserving clipping Policy Optimization (GPPO) that gently backpropagates gradients from clipped tokens. GPPO not only enhances the model's exploration capacity but also improves its efficiency in learning from negative samples. Klear-Reasoner exhibits exceptional reasoning abilities in mathematics and programming, scoring 90.5\% on AIME 2024, 83.2\% on AIME 2025, 66.0\% on LiveCodeBench V5 and 58.1\% on LiveCodeBench V6.

Deep-Research agents, which integrate large language models (LLMs) with search tools, have shown success in improving the effectiveness of handling complex queries that require iterative search planning and reasoning over search results. Evaluations on current benchmarks like BrowseComp relies on black-box live web search APIs, have notable limitations in (1) fairness: dynamic and opaque web APIs hinder fair comparisons and reproducibility of deep research methods; (2) transparency: lack of control over the document corpus makes it difficult to isolate retriever contributions. In other words, the current evaluations may compare a complete deep research system at a given time, but they do not foster well-controlled experiments to provide insights into the capability of underlying deep research LLMs. To address these challenges, we introduce BrowseComp-Plus, a benchmark derived from BrowseComp, employing a fixed, carefully curated corpus. Each query in BrowseComp-Plus includes human-verified supporting documents and mined challenging negatives, enabling controlled experimentation. The benchmark is shown to be effective in distinguishing the performance of deep research systems. For instance, the open-source model Search-R1, when paired with the BM25 retriever, achieves 3.86% accuracy, whereas the GPT-5 achieves 55.9%. Integrating the GPT-5 with the Qwen3-Embedding-8B retriever further enhances its accuracy to 70.1% with fewer search calls. This benchmark allows comprehensive evaluation and disentangled analysis of deep research agents and retrieval methods, fostering insights into retrieval effectiveness, citation accuracy, and context engineering in Deep-Research system.

The recently proposed Large Concept Model (LCM) generates text by predicting a sequence of sentence-level embeddings and training with either mean-squared error or diffusion objectives. We present SONAR-LLM, a decoder-only transformer that "thinks" in the same continuous SONAR embedding space, yet is supervised through token-level cross-entropy propagated via the frozen SONAR decoder. This hybrid objective retains the semantic abstraction of LCM while eliminating its diffusion sampler and restoring a likelihood-based training signal. Across model sizes from 39M to 1.3B parameters, SONAR-LLM attains competitive generation quality. We report scaling trends, ablations, benchmark results, and release the complete training code and all pretrained checkpoints to foster reproducibility and future research.

Large Language Models (LLMs)-based agents have made impressive progress in reasoning and tool use, enabling them to solve complex tasks. However, their ability to proactively collaborate with users, especially when goals are vague, evolving, or indirectly expressed, remains underexplored. To address this gap, we introduce UserBench, a user-centric benchmark designed to evaluate agents in multi-turn, preference-driven interactions. UserBench features simulated users who start with underspecified goals and reveal preferences incrementally, requiring agents to proactively clarify intent and make grounded decisions with tools. Our evaluation of leading open- and closed-source LLMs reveals a significant disconnect between task completion and user alignment. For instance, models provide answers that fully align with all user intents only 20% of the time on average, and even the most advanced models uncover fewer than 30% of all user preferences through active interaction. These results highlight the challenges of building agents that are not just capable task executors, but true collaborative partners. UserBench offers an interactive environment to measure and advance this critical capability.

Reasoning is central to purposeful action, yet most robotic foundation models map perception and instructions directly to control, which limits adaptability, generalization, and semantic grounding. We introduce Action Reasoning Models (ARMs), a class of vision-language-action models that integrate perception, planning, and control through a structured three-stage pipeline. Our model, MolmoAct, encodes observations and instructions into depth-aware perception tokens, generates mid-level spatial plans as editable trajectory traces, and predicts precise low-level actions, enabling explainable and steerable behavior. MolmoAct-7B-D achieves strong performance across simulation and real-world settings: 70.5% zero-shot accuracy on SimplerEnv Visual Matching tasks, surpassing closed-source Pi-0 and GR00T N1; 86.6% average success on LIBERO, including an additional 6.3% gain over ThinkAct on long-horizon tasks; and in real-world fine-tuning, an additional 10% (single-arm) and an additional 22.7% (bimanual) task progression over Pi-0-FAST. It also outperforms baselines by an additional 23.3% on out-of-distribution generalization and achieves top human-preference scores for open-ended instruction following and trajectory steering. Furthermore, we release, for the first time, the MolmoAct Dataset -- a mid-training robot dataset comprising over 10,000 high quality robot trajectories across diverse scenarios and tasks. Training with this dataset yields an average 5.5% improvement in general performance over the base model. We release all model weights, training code, our collected dataset, and our action reasoning dataset, establishing MolmoAct as both a state-of-the-art robotics foundation model and an open blueprint for building ARMs that transform perception into purposeful action through structured reasoning. Blogpost: https://allenai.org/blog/molmoact

Large language models excel at abstract reasoning but their capacity for embodied agent reasoning remains largely unexplored. We present OmniEAR, a comprehensive framework for evaluating how language models reason about physical interactions, tool usage, and multi-agent coordination in embodied tasks. Unlike existing benchmarks that provide predefined tool sets or explicit collaboration directives, OmniEAR requires agents to dynamically acquire capabilities and autonomously determine coordination strategies based on task demands. Through text-based environment representation, we model continuous physical properties and complex spatial relationships across 1,500 scenarios spanning household and industrial domains. Our systematic evaluation reveals severe performance degradation when models must reason from constraints: while achieving 85-96% success with explicit instructions, performance drops to 56-85% for tool reasoning and 63-85% for implicit collaboration, with compound tasks showing over 50% failure rates. Surprisingly, complete environmental information degrades coordination performance, indicating models cannot filter task-relevant constraints. Fine-tuning improves single-agent tasks dramatically (0.6% to 76.3%) but yields minimal multi-agent gains (1.5% to 5.5%), exposing fundamental architectural limitations. These findings demonstrate that embodied reasoning poses fundamentally different challenges than current models can address, establishing OmniEAR as a rigorous benchmark for evaluating and advancing embodied AI systems. Our code and data are included in the supplementary materials and will be open-sourced upon acceptance.

The Mixture of Experts (MoE) architecture is a cornerstone of modern state-of-the-art (SOTA) large language models (LLMs). MoE models facilitate scalability by enabling sparse parameter activation. However, traditional MoE architecture uses homogeneous experts of a uniform size, activating a fixed number of parameters irrespective of input complexity and thus limiting computational efficiency. To overcome this limitation, we introduce Grove MoE, a novel architecture incorporating experts of varying sizes, inspired by the heterogeneous big.LITTLE CPU architecture. This architecture features novel adjugate experts with a dynamic activation mechanism, enabling model capacity expansion while maintaining manageable computational overhead. Building on this architecture, we present GroveMoE-Base and GroveMoE-Inst, 33B-parameter LLMs developed by applying an upcycling strategy to the Qwen3-30B-A3B-Base model during mid-training and post-training. GroveMoE models dynamically activate 3.14-3.28B parameters based on token complexity and achieve performance comparable to SOTA open-source models of similar or even larger size.

Recent advances at the intersection of reinforcement learning (RL) and visual intelligence have enabled agents that not only perceive complex visual scenes but also reason, generate, and act within them. This survey offers a critical and up-to-date synthesis of the field. We first formalize visual RL problems and trace the evolution of policy-optimization strategies from RLHF to verifiable reward paradigms, and from Proximal Policy Optimization to Group Relative Policy Optimization. We then organize more than 200 representative works into four thematic pillars: multi-modal large language models, visual generation, unified model frameworks, and vision-language-action models. For each pillar we examine algorithmic design, reward engineering, benchmark progress, and we distill trends such as curriculum-driven training, preference-aligned diffusion, and unified reward modeling. Finally, we review evaluation protocols spanning set-level fidelity, sample-level preference, and state-level stability, and we identify open challenges that include sample efficiency, generalization, and safe deployment. Our goal is to provide researchers and practitioners with a coherent map of the rapidly expanding landscape of visual RL and to highlight promising directions for future inquiry. Resources are available at: https://github.com/weijiawu/Awesome-Visual-Reinforcement-Learning.

Self-Rewarding Language Models propose an architecture in which the Large Language Models(LLMs) both generates responses and evaluates its own outputs via LLM-as-a-Judge prompting, dynamically improving its generative capabilities through iterative Direct Preference Optimization (DPO). However, our analysis reveals a critical limitation in existing Self-Rewarding paradigms: the synchronized improvement of chosen and rejected responses progressively narrows the representational difference between contrasting samples, undermining effective preference learning. We propose Temporal Self-Rewarding Language Models that strategically coordinate past, present, and future model generations to sustain learning signals. Our dual-phase framework introduces: (1) Anchored Rejection - fixing rejected responses using the past initial model's outputs and (2) Future-Guided Chosen - dynamically curating chosen samples using next-generation model predictions. Extensive experiments across three model families (Llama, Qwen, Mistral) and different model sizes (Llama3B/8B/70B) demonstrate significant improvements when trained with our method compared to Self-Rewarding using same computation resources. For example, Llama3.1-8B reaches a 29.44 win rate on AlpacaEval 2.0 with our method, outperforming the Self-Rewarding baseline (19.69) by 9.75. Notably, our method also demonstrates superior out-of-distribution generalization across mathematical reasoning (GSM8K), knowledge-based QA (ARC, TruthfulQA), and code generation (HumanEval) tasks, even though we do not specifically collect such training data.

Reinforcement learning for LLM reasoning has rapidly emerged as a prominent research area, marked by a significant surge in related studies on both algorithmic innovations and practical applications. Despite this progress, several critical challenges remain, including the absence of standardized guidelines for employing RL techniques and a fragmented understanding of their underlying mechanisms. Additionally, inconsistent experimental settings, variations in training data, and differences in model initialization have led to conflicting conclusions, obscuring the key characteristics of these techniques and creating confusion among practitioners when selecting appropriate techniques. This paper systematically reviews widely adopted RL techniques through rigorous reproductions and isolated evaluations within a unified open-source framework. We analyze the internal mechanisms, applicable scenarios, and core principles of each technique through fine-grained experiments, including datasets of varying difficulty, model sizes, and architectures. Based on these insights, we present clear guidelines for selecting RL techniques tailored to specific setups, and provide a reliable roadmap for practitioners navigating the RL for the LLM domain. Finally, we reveal that a minimalist combination of two techniques can unlock the learning capability of critic-free policies using vanilla PPO loss. The results demonstrate that our simple combination consistently improves performance, surpassing strategies like GRPO and DAPO.

Large reasoning models achieve strong performance through test-time scaling but incur substantial computational overhead, particularly from excessive token generation when processing short input prompts. While sparse attention mechanisms can reduce latency and memory usage, existing approaches suffer from significant accuracy degradation due to accumulated errors during long-generation reasoning. These methods generally require either high token retention rates or expensive retraining. We introduce LessIsMore, a training-free sparse attention mechanism for reasoning tasks, which leverages global attention patterns rather than relying on traditional head-specific local optimizations. LessIsMore aggregates token selections from local attention heads with recent contextual information, enabling unified cross-head token ranking for future decoding layers. This unified selection improves generalization and efficiency by avoiding the need to maintain separate token subsets per head. Evaluation across diverse reasoning tasks and benchmarks shows that LessIsMore preserves -- and in some cases improves -- accuracy while achieving a 1.1times average decoding speed-up compared to full attention. Moreover, LessIsMore attends to 2times fewer tokens without accuracy loss, achieving a 1.13times end-to-end speed-up compared to existing sparse attention methods.

2020 年，骗子利用苹果联合创始人沃茨（Steve Wozniak）的片段在 YouTube 上发布视频以骗取比特币，沃茨的妻子 Janet Wozniak 多次举报了该视频，但 YouTube 对此没有采取任何行动，两人都认为 YouTube 是在助纣为虐，他们为此提起了诉讼。然而五年之后，沃茨接受采访透露案件停滞不前，原因是名为 Section 230 的联邦法规。Section 230 是非常宽泛的法规，它限制了对社媒平台提起任何诉讼的能力。沃茨说，Section 230 规定平台不需要对上面发布的任何内容承担任何责任。对于沃茨的诉讼，Google/YouTube 公关部门的 José Castañeda 发表了一份冠冕堂皇但没有任何正面回应的措辞，声称公司严肃对待平台上滥用行为，会在发现违规行为时迅速采取行动。

GitHub 首席执行官 Thomas Dohmke 宣布将于年底离职，而微软不再任命新 CEO，GitHub 领导团队将直接向 CoreAI 部门汇报。微软于 2018 年以 75 亿美元收购 GitHub 后，这家代码托管平台一直在公司内部独立运营，但最新的人事变动意味着 GitHub 的运营方式发生了重大改变。微软的 CoreAI 部门由 Meta 前高管 Jay Parikh 领导，专注于为微软及其客户构建 AI 平台和工具。

两个俄罗斯网络犯罪组织过去两周正通过含有恶意附件的钓鱼邮件利用一个高危 WinRAR 0day。WinRAR 是广泛使用的文件压缩工具，用户数多达 5 亿，安全公司 ESET 于 7 月 18 日首次检测到针对 WinRAR 的攻击，7 月 24 日确定利用了一个 WinRAR 0day，同一天通知 WinRAR 开发商，6 天后漏洞修复。该漏洞滥用了名为交换数据流（Alternate Data Streams，ADS）的 Windows 功能，该功能允许同一文件路径可以有不同的表示方式。漏洞利用滥用该功能触发了一个此前未知的路径遍历漏洞，导致 WinRAR 将恶意可执行文件植入攻击者选择的文件路径 %TEMP% 和 %LOCALAPPDATA%，因为能执行代码 Windows 通常禁止访问这些路径。利用该漏洞的俄罗斯黑客组织包括 RomCom 和 Paper Werewolf。

从吸血鬼传说到实验室培养的组织，年轻血逆转衰老不再是纯粹的神话。一项新研究发现，在实验室环境中，年轻血液激活骨髓细胞分泌蛋白质能逆转皮肤衰老。皮肤品公司德国拜尔斯道夫（Beiersdorf AG）的研究人员发现，仅仅凭借年轻血液并不能逆转衰老，必须存在骨髓细胞。如果不存在骨髓细胞，皮肤衰老标志没有改善的迹象。只有将年轻血清和骨髓细胞共同培养，年轻血清引发骨髓细胞分泌出恢复皮肤活力的蛋白质因子。研究人员识别出 55 种与年龄相关的蛋白质，其中 7 种在测试中表现出了明显的抗衰老效果。

根据发表在《The American Journal of Clinical Nutrition》期刊上的一项研究，素食者罹患癌症的风险比肉食者低 12%，纯素食者罹患癌症的风险比肉食者低 24%。研究使用了始于 2002-2007 年的 Adventist Health Study 数据，涉及到 95,863 名北美基督复临安息日会信徒，有 79,468 名信徒最初未患有癌症。基督复临安息日会是基督新教教派之一，推崇素食饮食。结果显示，相比肉食者，素食者整体癌症风险低约 12%，中等发病率癌症的风险降低约 18%。素食者患乳腺癌、结直肠癌、前列腺癌、胃癌和淋巴增生性癌症的风险较低。

日本大阪公立大学与韩国科学技术院研究团队首次在量子流体中观测到“量子开尔文—亥姆霍兹不稳定性”（KHI），并发现了一种形态酷似梵高名画《星空(The Starry Night)》中弯月的新型涡旋结构，即偏心分数斯格明子（EFS）。这一现象早在数十年前便被理论预测，却从未在实验中直接观测到。KHI 是经典流体力学中的重要现象，当两种速度不同的流体在边界处相遇时，会形成波浪与涡旋。这种现象可在风吹起的海浪、翻卷的云层，甚至《星空》旋动的天空中找到。研究团队提出疑问：量子流体中也会发生类似的不稳定性吗？为验证这一设想，团队将锂原子气体冷却至接近绝对零度，制备出一种多组分玻色—爱因斯坦凝聚态（量子超流体），并在其中形成两股速度不同的流体。在它们的交界面上，首先出现了波状指形结构，类似经典湍流；随后，在量子力学与拓扑学规则的作用下，生成了特殊涡旋。

Steam 创意工坊知名模组遭遇大规模恶意 DMCA 举报，问题始于《钢铁雄心4》，然后迅速扩散到其它流行游戏，包括 Left 4 Dead 2、Terraria、Garry's Mod、Stellaris、Wallpaper Engine 和 Rimworld 等等。恶意人士滥用了 Steam 平台创意工坊 DMCA 举报机制。当有模组遭遇 DMCA 投诉时，Valve 采取了一种非常节省它自己时间的做法：模组开发者首先收到警告邮件，要求解决 DMCA 问题，模组开发者然后需要递交证据证明没有违反 DMCA，Valve 会进行审核，但你知道谁去审核证据？是举报人。当举报人发现他们不需要任何证据就可以举报流行模组后，他们开始恶意将各个流行游戏的流行模组全部举报。问题起源于《钢铁雄心4》，《钢铁雄心4》有一个模组叫 The Fire Rises (TFR)，TFR 有一个来自中国的子模组 Loong Rising of Darkness(LROD)，双方的开发者不知何故发生了冲突，TFR 向 LROD 发出了 DMCA，双方试图和解，但 TFR 发出的 DMCA 没有撤销，最终导致 LROD 下架，随后 LROD 发起了反击，举报了 TFR，并发现可以滥用 DMCA，因此扩大了举报范围，导致各大游戏的流行模组几乎无一幸免。玩家呼吁 Valve 立即采取行动，改变它处理 DMCA 的方法。

刚刚发布的 Debian 13 正式加入了对  RISC-V CPU 架构的支持，而下一个版本考虑正式支持龙芯的 LoongArch CPU 架构。Debian 14 代号为 Forky，预计将在 2027 年发布。Debian 发布团队在邮件列表上表示，他们准备在不久之后接受为 Loong64 构建的软件包。

Linus Torvalds 在内核邮件列表上宣布释出 Linux 6.17-rc1，最引入瞩目的一个变化是 Bcachefs 维护者 Kent Overstreet 递交的任何 pull request 都没有被接受。此前 Linux 作者和 Bcachefs 维护者之间曾爆发冲突，Linus Torvalds 表示考虑移除 Bcachefs 文件系统。新版的主要变化包括：标准化部分笔记本电脑上的性能提升（Performance Boost）键的键码值；gconfig 内核配置编辑器使用 GTK3，改进 Rust 语言支持，新的 GPU、ARM 和 RISC-V SoC 支持，等等。

数据显示，全球最常见四种塑料有三分之二由七个国家生产，其中中国的产量是另外六个国家的总和。在聚乙烯（PE）、聚丙烯（PP）、聚对苯二甲酸乙二醇酯（PET）和聚苯乙烯（PS）四种最常用塑料中，中国占比 34%，美国 13%，沙特 5%，韩国 5%，印度 4%，日本 3% 以及 德国 2%。数据还显示，塑料生产高度集中于少数几家大型企业，18 家公司占全球塑料总产量的一半以上，其中最大的塑料生产商是中石化，占全球产量的 5.4%，其次是 埃克森美孚的 5%，巴赛尔公司（LyondellBasell）的 4.5%，沙特阿美的 4.3%，中石油的 4.2%。

澳大利亚海洋科学研究所(AIMS)报告，大堡礁珊瑚白化事件创下纪录。大堡礁（Great Barrier Reef）是世界最大最长的珊瑚礁群，绵延 2,300 公里。珊瑚白化现象是珊瑚礁所表现出来的病理特征，造成珊瑚白化的原因有很多，最主要是全球暖化导致海水温度过高。如果珊瑚经历超过其热极限 1 摄氏度的水温，它们可能会在两个月内死亡。如果水温高出 2 摄氏度，它们会在一个月内死亡。AIMS 在 2024 年 8 月至 2025 年 5 月期间对 124 个珊瑚礁的健康状况进行了调查。报告显示，大堡礁三个地区中有两个发生珊瑚数量最大幅度的下降。北部和南部地区受影响最严重，珊瑚覆盖下降四分之一至三分之一。

作为获得向中国出口芯片的许可证的一部分，英伟达和 AMD 同意将 15% 的中国营收上缴给美国。英伟达称，它一直遵守美国政府制定的参与全球市场的规则。它已经几个月没有向中国交付 H2O 芯片，希望出口管控规则能让美国公司参与中国的竞争。AMD 尚未置评。根据出口许可证协议，英伟达将把在华销售的 H20 芯片营收的 15% 上缴给美国政府，而 AMD 将把 MI308 芯片营收的 15% 上缴给美国政府。

日本读卖新闻集团向东京地方法院起诉了使用生成 AI 提供搜索服务的美国新兴公司 Perplexity。诉讼称Perplexity 通过 AI 搜索未经授权使用文章侵犯了著作权，要求赔偿约 21.68 亿日元。这是日本媒体首次围绕AI搜 索提起诉讼。诉状显示，Perplexity 于 2025 年 2～6 月获取并复制了 11 万 9467 篇读卖新闻在线文章的信息，制作并向用户发送了包含相似文本和图像的内容。诉状指出，Perplexity 侵犯了著作权法规定的复制权和公众传播权，并因用户不能访问原始网站的“零点击搜索”妨碍了经营。诉讼还要求停止复制文章等行为。

《地球》（Earth）期刊发表的一项研究显示，自 1800 年以来，人类与自然的联结度下降了 60% 以上。通过城市化进程、社区野生动植物减少的数据，以及父母不再向子女传递亲近自然的习惯等因素，研究人员追踪了 220 年来人类生活中自然元素的缺失。结果显示从 1800 年到 2020 年，自然词汇在书籍中逐渐消失，其中 1990 年的降幅达到 60.6% 的峰值。计算机模型预测，如果没有深远的政策和社会变革，人类与自然的联结度将继续下降。随着社区日益城市化、父母不再传递“面向自然”的价值观，下一代将继续失去对自然的认知。而最有效的干预措施是让儿童从小接触自然，以及对城市环境进行大规模绿化。

手机已经成为日常生活的一部分，储存了大量敏感信息，但我们如何确保手机安全可靠？Google 的 Android 系统提供了开源版本 AOSP，而 Android 本身并未以安全为重心设计的，但基于开源系统，社区发行版如 GrapheneOS 对 Android 进行了加固。GrapheneOS 始于 CopperheadOS 项目，它的两位创始人因分歧而分道扬镳，其中之一的 Daniel Micay 创建了独立的项目 GrapheneOS。它旨在加强安全，并没有优先考虑支持更多设备，它支持的设备类型非常有限，仅限于 Google Pixel 6 到 Pixel 9，新的 Pixel 设备使用了新的 ARMv9 CPU 核心，支持如硬件内存标记（memory tagging）等安全功能。GrapheneOS 默认使用硬件内存标记保护操作系统和用户安装的兼容应用免遭攻击。它没有预装原版 Android 提供的大量开箱即用的应用，没有 Google Play store，而是提供了自己的浏览器、相机应用、PDF 阅读器以及总共只有 13 款应用的应用商店。浏览器是 Chromium 分支 Vanadium，启用了严格的网站隔离，它并不推荐 Firefox，认为它容易受到攻击，用户可选择安装的一个浏览器是 IronFox——Firefox 加固版。