OrangeBot.AI Digest — 2025-10-01

利用AI大模型，一键生成高清短视频 Generate short videos with one click using AI LLM.

Legacy-Mess Detector – assess the “legacy-mess level” of your code and output a beautiful report | 屎山代码检测器，评估代码的“屎山等级”并输出美观的报告

🤯 Lobe Chat - an open-source, modern design AI chat framework. Supports multiple AI providers (OpenAI / Claude 4 / Gemini / DeepSeek / Ollama / Qwen), Knowledge Base (file upload / RAG ), one click install MCP Marketplace and Artifacts / Thinking. One-click FREE deployment of your private AI Agent application.

☁️ Nextcloud server, a safe home for all your data

Community-contributed instructions, prompts, and configurations to help you make the most of GitHub Copilot.

Asynchronous image downloader with cache support as a UIImageView category

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

Find vulnerabilities, misconfigurations, secrets, SBOM in containers, Kubernetes, code repositories, clouds and more

pix2tex: Using a ViT to convert images of equations into LaTeX code.

The classic email sending library for PHP

openpilot is an operating system for robotics. Currently, it upgrades the driver assistance system on 300+ supported cars.

12 Lessons to Get Started Building AI Agents

使用ai生成多章节的长篇小说，自动衔接上下文、伏笔

FULL Augment Code, Claude Code, Cluely, CodeBuddy, Comet, Cursor, Devin AI, Junie, Kiro, Leap.new, Lovable, Manus Agent Tools, NotionAI, Orchids.app, Perplexity, Poke, Qoder, Replit, Same.dev, Trae, Traycer AI, VSCode Agent, Warp.dev, Windsurf, Xcode, Z.ai Code, dia & v0. (And other Open Sourced) System Prompts, Internal Tools & AI Models

MCP standardizes how LLMs interact with external systems, forming the foundation for general agents. However, existing MCP benchmarks remain narrow in scope: they focus on read-heavy tasks or tasks with limited interaction depth, and fail to capture the complexity and realism of real-world workflows. To address this gap, we propose MCPMark, a benchmark designed to evaluate MCP use in a more realistic and comprehensive manner. It consists of 127 high-quality tasks collaboratively created by domain experts and AI agents. Each task begins with a curated initial state and includes a programmatic script for automatic verification. These tasks demand richer and more diverse interactions with the environment, involving a broad range of create, read, update, and delete (CRUD) operations. We conduct a comprehensive evaluation of cutting-edge LLMs using a minimal agent framework that operates in a tool-calling loop. Empirical results show that the best-performing model, gpt-5-medium, reaches only 52.56\% pass@1 and 33.86\% pass^4, while other widely regarded strong models, including claude-sonnet-4 and o3, fall below 30\% pass@1 and 15\% pass^4. On average, LLMs require 16.2 execution turns and 17.4 tool calls per task, significantly surpassing those in previous MCP benchmarks and highlighting the stress-testing nature of MCPMark.

The relationship between computing systems and the brain has served as motivation for pioneering theoreticians since John von Neumann and Alan Turing. Uniform, scale-free biological networks, such as the brain, have powerful properties, including generalizing over time, which is the main barrier for Machine Learning on the path to Universal Reasoning Models. We introduce `Dragon Hatchling' (BDH), a new Large Language Model architecture based on a scale-free biologically inspired network of \n locally-interacting neuron particles. BDH couples strong theoretical foundations and inherent interpretability without sacrificing Transformer-like performance. BDH is a practical, performant state-of-the-art attention-based state space sequence learning architecture. In addition to being a graph model, BDH admits a GPU-friendly formulation. It exhibits Transformer-like scaling laws: empirically BDH rivals GPT2 performance on language and translation tasks, at the same number of parameters (10M to 1B), for the same training data. BDH can be represented as a brain model. The working memory of BDH during inference entirely relies on synaptic plasticity with Hebbian learning using spiking neurons. We confirm empirically that specific, individual synapses strengthen connection whenever BDH hears or reasons about a specific concept while processing language inputs. The neuron interaction network of BDH is a graph of high modularity with heavy-tailed degree distribution. The BDH model is biologically plausible, explaining one possible mechanism which human neurons could use to achieve speech. BDH is designed for interpretability. Activation vectors of BDH are sparse and positive. We demonstrate monosemanticity in BDH on language tasks. Interpretability of state, which goes beyond interpretability of neurons and model parameters, is an inherent feature of the BDH architecture.

Although reinforcement learning (RL) can effectively enhance the reasoning capabilities of vision-language models (VLMs), current methods remain heavily dependent on labor-intensive datasets that require extensive manual construction and verification, leading to extremely high training costs and consequently constraining the practical deployment of VLMs. To address this challenge, we propose Vision-Zero, a domain-agnostic framework enabling VLM self-improvement through competitive visual games generated from arbitrary image pairs. Specifically, Vision-Zero encompasses three main attributes: (1) Strategic Self-Play Framework: Vision-Zero trains VLMs in "Who Is the Spy"-style games, where the models engage in strategic reasoning and actions across multiple roles. Through interactive gameplay, models autonomously generate their training data without human annotation. (2) Gameplay from Arbitrary Images: Unlike existing gamified frameworks, Vision-Zero can generate games from arbitrary images, thereby enhancing the model's reasoning ability across diverse domains and showing strong generalization to different tasks. We demonstrate this versatility using three distinct types of image datasets: CLEVR-based synthetic scenes, charts, and real-world images. (3) Sustainable Performance Gain: We introduce Iterative Self-Play Policy Optimization (Iterative-SPO), a novel training algorithm that alternates between Self-Play and reinforcement learning with verifiable rewards (RLVR), mitigating the performance plateau often seen in self-play-only training and achieving sustained long-term improvements. Despite using label-free data, Vision-Zero achieves state-of-the-art performance on reasoning, chart question answering, and vision-centric understanding tasks, surpassing other annotation-based methods. Models and code has been released at https://github.com/wangqinsi1/Vision-Zero.

As supervised fine-tuning (SFT) evolves from a lightweight post-training step into a compute-intensive phase rivaling mid-training in scale, data efficiency has become critical for aligning large language models (LLMs) under tight budgets. Existing data pruning methods suffer from a fragmented design: they operate either at the sample level or the token level in isolation, failing to jointly optimize both dimensions. This disconnect leads to significant inefficiencies--high-value samples may still contain redundant tokens, while token-level pruning often discards crucial instructional or corrective signals embedded in individual examples. To address this bottleneck, we introduce the Error-Uncertainty (EU) Plane, a diagnostic framework that jointly characterizes the heterogeneous utility of training data across samples and tokens. Guided by this insight, we propose Quadrant-based Tuning (Q-Tuning), a unified framework that strategically coordinates sample pruning and token pruning. Q-Tuning employs a two-stage strategy: first, it performs sample-level triage to retain examples rich in informative misconceptions or calibration signals; second, it applies an asymmetric token-pruning policy, using a context-aware scoring mechanism to trim less salient tokens exclusively from misconception samples while preserving calibration samples in their entirety. Our method sets a new state of the art across five diverse benchmarks. Remarkably, on SmolLM2-1.7B, Q-Tuning achieves a +38\% average improvement over the full-data SFT baseline using only 12.5\% of the original training data. As the first dynamic pruning approach to consistently outperform full-data training, Q-Tuning provides a practical and scalable blueprint for maximizing data utilization in budget-constrained LLM SFT.

While large language models (LLMs) have demonstrated strong performance on factoid question answering, they are still prone to hallucination and untruthful responses, particularly when tasks demand information outside their parametric knowledge. Indeed, truthfulness requires more than accuracy -- models must also recognize uncertainty and abstain when unsure to avoid hallucinations. This presents a fundamental challenge for existing methods: approaches that optimize for accuracy often amplify hallucinations, while those that encourage abstention can become overly conservative, sacrificing correct answers. Both extremes ultimately compromise truthfulness. In this work, we present TruthRL, a general reinforcement learning (RL) framework that directly optimizes the truthfulness of LLMs. Specifically, we implement TruthRL using GRPO with a simple yet effective ternary reward that distinguishes correct answers, hallucinations, and abstentions. It incentivizes models to reduce hallucinations not only by providing correct responses, but also by enabling abstention when uncertain, thereby improving truthfulness. Extensive experiments across four knowledge-intensive benchmarks show that, compared to vanilla RL, TruthRL significantly reduces hallucinations by 28.9% and improves truthfulness by 21.1%, with consistent gains across various backbone models (e.g., Qwen, Llama) under both retrieval and non-retrieval setups. In-depth ablation study demonstrates that vanilla accuracy-driven methods, such as supervised fine-tuning or RL with a binary reward, struggle to balance factual correctness and uncertainty. In contrast, our proposed truthfulness-driven TruthRL achieves strong performance in both accuracy and truthfulness, underscoring the importance of learning objective design for developing truthful LLMs.

We introduce OceanGym, the first comprehensive benchmark for ocean underwater embodied agents, designed to advance AI in one of the most demanding real-world environments. Unlike terrestrial or aerial domains, underwater settings present extreme perceptual and decision-making challenges, including low visibility, dynamic ocean currents, making effective agent deployment exceptionally difficult. OceanGym encompasses eight realistic task domains and a unified agent framework driven by Multi-modal Large Language Models (MLLMs), which integrates perception, memory, and sequential decision-making. Agents are required to comprehend optical and sonar data, autonomously explore complex environments, and accomplish long-horizon objectives under these harsh conditions. Extensive experiments reveal substantial gaps between state-of-the-art MLLM-driven agents and human experts, highlighting the persistent difficulty of perception, planning, and adaptability in ocean underwater environments. By providing a high-fidelity, rigorously designed platform, OceanGym establishes a testbed for developing robust embodied AI and transferring these capabilities to real-world autonomous ocean underwater vehicles, marking a decisive step toward intelligent agents capable of operating in one of Earth's last unexplored frontiers. The code and data are available at https://github.com/OceanGPT/OceanGym.

Reasoning has emerged as a pivotal capability in Large Language Models (LLMs). Through Reinforcement Learning (RL), typically Group Relative Policy Optimization (GRPO), these models are able to solve complex tasks such as mathematics and code generation. Building on these advances, recent research has sought to extend reasoning to Vision-Language Models (VLMs), yielding promising results across diverse visual tasks. Despite this progress, our study uncovers the dual nature of multimodal reasoning: while it substantially enhances logical inference and facilitates performance on challenging problems, it may gradually impair perceptual grounding, leading to recognition failures on otherwise basic visual questions. Through further analysis, we attribute this phenomenon to visual forgetting, wherein prolonged reasoning causes the model to increasingly disregard visual input. To address this, we propose Vision-Anchored Policy Optimization (VAPO), a simple yet effective method that explicitly steers the reasoning process toward visually grounded trajectories. Our result model, VAPO-Thinker-7B, significantly strengthens the model's reliance on visual information and achieves new state-of-the-art results on a wide range of established benchmarks. Project page: https://xytian1008.github.io/VAPO/

Reinforcement Learning with Verifiable Reward (RLVR) effectively solves complex tasks but demands extremely long context lengths during training, leading to substantial computational costs. While multi-stage training can partially mitigate this, starting with overly short contexts often causes irreversible performance degradation, ultimately failing to reduce overall training compute significantly. In this paper, we introduce **T**hinking-**F**ree **P**olicy **I**nitialization (**TFPI**), a simple yet effective adaptation to RLVR that bridges long Chain-of-Thought (CoT) distillation and standard RLVR. TFPI employs a simple *ThinkFree* operation, explicitly discarding the thinking content via a direct *</think>* append, to reduce token usage during inference. Training with *ThinkFree*-adapted inputs improves performance and lowers token consumption, even in the original slow-thinking mode. Extensive experiments across various benchmarks have shown that TFPI accelerates RL convergence, achieves a higher performance ceiling, and yields more token-efficient reasoning models without specialized rewards or complex training designs. With TFPI only, we train a 4B model to reach 89.0% accuracy on AIME24 and 65.5% on LiveCodeBench using less than 4K H20 hours.

Large Language Model (LLM)-based judgments leverage powerful LLMs to efficiently evaluate candidate content and provide judgment scores. However, the inherent biases and vulnerabilities of LLM-generated judgments raise concerns, underscoring the urgent need for distinguishing them in sensitive scenarios like academic peer reviewing. In this work, we propose and formalize the task of judgment detection and systematically investigate the detectability of LLM-generated judgments. Unlike LLM-generated text detection, judgment detection relies solely on judgment scores and candidates, reflecting real-world scenarios where textual feedback is often unavailable in the detection process. Our preliminary analysis shows that existing LLM-generated text detection methods perform poorly given their incapability to capture the interaction between judgment scores and candidate content -- an aspect crucial for effective judgment detection. Inspired by this, we introduce J-Detector, a lightweight and transparent neural detector augmented with explicitly extracted linguistic and LLM-enhanced features to link LLM judges' biases with candidates' properties for accurate detection. Experiments across diverse datasets demonstrate the effectiveness of J-Detector and show how its interpretability enables quantifying biases in LLM judges. Finally, we analyze key factors affecting the detectability of LLM-generated judgments and validate the practical utility of judgment detection in real-world scenarios.

As LLM-based agents are increasingly deployed in real-life scenarios, existing benchmarks fail to capture their inherent complexity of handling extensive information, leveraging diverse resources, and managing dynamic user interactions. To address this gap, we introduce VitaBench, a challenging benchmark that evaluates agents on versatile interactive tasks grounded in real-world settings. Drawing from daily applications in food delivery, in-store consumption, and online travel services, VitaBench presents agents with the most complex life-serving simulation environment to date, comprising 66 tools. Through a framework that eliminates domain-specific policies, we enable flexible composition of these scenarios and tools, yielding 100 cross-scenario tasks (main results) and 300 single-scenario tasks. Each task is derived from multiple real user requests and requires agents to reason across temporal and spatial dimensions, utilize complex tool sets, proactively clarify ambiguous instructions, and track shifting user intent throughout multi-turn conversations. Moreover, we propose a rubric-based sliding window evaluator, enabling robust assessment of diverse solution pathways in complex environments and stochastic interactions. Our comprehensive evaluation reveals that even the most advanced models achieve only 30% success rate on cross-scenario tasks, and less than 50% success rate on others. Overall, we believe VitaBench will serve as a valuable resource for advancing the development of AI agents in practical real-world applications. The code, dataset, and leaderboard are available at https://vitabench.github.io/

Can humans identify AI-generated (fake) videos and provide grounded reasons? While video generation models have advanced rapidly, a critical dimension -- whether humans can detect deepfake traces within a generated video, i.e., spatiotemporal grounded visual artifacts that reveal a video as machine generated -- has been largely overlooked. We introduce DeeptraceReward, the first fine-grained, spatially- and temporally- aware benchmark that annotates human-perceived fake traces for video generation reward. The dataset comprises 4.3K detailed annotations across 3.3K high-quality generated videos. Each annotation provides a natural-language explanation, pinpoints a bounding-box region containing the perceived trace, and marks precise onset and offset timestamps. We consolidate these annotations into 9 major categories of deepfake traces that lead humans to identify a video as AI-generated, and train multimodal language models (LMs) as reward models to mimic human judgments and localizations. On DeeptraceReward, our 7B reward model outperforms GPT-5 by 34.7% on average across fake clue identification, grounding, and explanation. Interestingly, we observe a consistent difficulty gradient: binary fake v.s. real classification is substantially easier than fine-grained deepfake trace detection; within the latter, performance degrades from natural language explanations (easiest), to spatial grounding, to temporal labeling (hardest). By foregrounding human-perceived deepfake traces, DeeptraceReward provides a rigorous testbed and training signal for socially aware and trustworthy video generation.

Ensuring precise multimodal alignment between diffusion-generated images and input prompts has been a long-standing challenge. Earlier works finetune diffusion weight using high-quality preference data, which tends to be limited and difficult to scale up. Recent editing-based methods further refine local regions of generated images but may compromise overall image quality. In this work, we propose Implicit Multimodal Guidance (IMG), a novel re-generation-based multimodal alignment framework that requires no extra data or editing operations. Specifically, given a generated image and its prompt, IMG a) utilizes a multimodal large language model (MLLM) to identify misalignments; b) introduces an Implicit Aligner that manipulates diffusion conditioning features to reduce misalignments and enable re-generation; and c) formulates the re-alignment goal into a trainable objective, namely Iteratively Updated Preference Objective. Extensive qualitative and quantitative evaluations on SDXL, SDXL-DPO, and FLUX show that IMG outperforms existing alignment methods. Furthermore, IMG acts as a flexible plug-and-play adapter, seamlessly enhancing prior finetuning-based alignment methods. Our code will be available at https://github.com/SHI-Labs/IMG-Multimodal-Diffusion-Alignment.

Image-to-video generation has made remarkable progress with the advancements in diffusion models, yet generating videos with realistic motion remains highly challenging. This difficulty arises from the complexity of accurately modeling motion, which involves capturing physical constraints, object interactions, and domain-specific dynamics that are not easily generalized across diverse scenarios. To address this, we propose MotionRAG, a retrieval-augmented framework that enhances motion realism by adapting motion priors from relevant reference videos through Context-Aware Motion Adaptation (CAMA). The key technical innovations include: (i) a retrieval-based pipeline extracting high-level motion features using video encoder and specialized resamplers to distill semantic motion representations; (ii) an in-context learning approach for motion adaptation implemented through a causal transformer architecture; (iii) an attention-based motion injection adapter that seamlessly integrates transferred motion features into pretrained video diffusion models. Extensive experiments demonstrate that our method achieves significant improvements across multiple domains and various base models, all with negligible computational overhead during inference. Furthermore, our modular design enables zero-shot generalization to new domains by simply updating the retrieval database without retraining any components. This research enhances the core capability of video generation systems by enabling the effective retrieval and transfer of motion priors, facilitating the synthesis of realistic motion dynamics.

Audio-visual speech separation (AVSS) methods leverage visual cues to extract target speech and have demonstrated strong separation quality in noisy acoustic environments. However, these methods usually involve a large number of parameters and require high computational cost, which is unacceptable in many applications where speech separation serves as only a preprocessing step for further speech processing. To address this issue, we propose an efficient AVSS method, named Dolphin. For visual feature extraction, we develop DP-LipCoder, a dual-path lightweight video encoder that transforms lip-motion into discrete audio-aligned semantic tokens. For audio separation, we construct a lightweight encoder-decoder separator, in which each layer incorporates a global-local attention (GLA) block to efficiently capture multi-scale dependencies. Experiments on three benchmark datasets showed that Dolphin not only surpassed the current state-of-the-art (SOTA) model in separation quality but also achieved remarkable improvements in efficiency: over 50% fewer parameters, more than 2.4x reduction in MACs, and over 6x faster GPU inference speed. These results indicate that Dolphin offers a practical and deployable solution for high-performance AVSS in real-world scenarios. Our code and demo page are publicly available at http://cslikai.cn/Dolphin/.

While previous AI Scientist systems can generate novel findings, they often lack the focus to produce scientifically valuable contributions that address pressing human-defined challenges. We introduce DeepScientist, a system designed to overcome this by conducting goal-oriented, fully autonomous scientific discovery over month-long timelines. It formalizes discovery as a Bayesian Optimization problem, operationalized through a hierarchical evaluation process consisting of "hypothesize, verify, and analyze". Leveraging a cumulative Findings Memory, this loop intelligently balances the exploration of novel hypotheses with exploitation, selectively promoting the most promising findings to higher-fidelity levels of validation. Consuming over 20,000 GPU hours, the system generated about 5,000 unique scientific ideas and experimentally validated approximately 1100 of them, ultimately surpassing human-designed state-of-the-art (SOTA) methods on three frontier AI tasks by 183.7\%, 1.9\%, and 7.9\%. This work provides the first large-scale evidence of an AI achieving discoveries that progressively surpass human SOTA on scientific tasks, producing valuable findings that genuinely push the frontier of scientific discovery. To facilitate further research into this process, we will open-source all experimental logs and system code at https://github.com/ResearAI/DeepScientist/.

根据 Netblocks 的监测数据，阿富汗断网已超过 48 小时。互联网和移动电话服务全部中断，全国居民生活在通讯几乎完全中断的状况下。阿富汗的全面断网始于周一晚上，进入周二后互联网和电话服务继续中断。首都喀布尔一名 42 岁的店主 Najibullah 说，"没有电话和互联网我们都是盲人，所有业务都依赖于手机。送货是用手机。这一情况就像是假期：每个人都在家里。市场完全冻结。"这是塔利班政府首次切断全国的通信，官方没有对此做出解释。法新社在断网前曾收到一名政府官员的警告，称有八到九千个通信支柱（telecommunications pillars）将被关闭，通信中断将持续到另行通知为止。目前阿富汗有限的通信只能依靠无线电和少数卫星链路。

在 Linux 6.17 将 Bcachefs 文件系统列为由外部维护并且没有合并任何 Bcachefs 维护者 Kent Overstreet 递交的拉取请求之后，Linus Torvalds 在 Linux 6.18 中完全移除了 Bcachefs，总共删除了 11.7 万行代码。Torvalds 评论说，Bcachefs 现在是一个 DKMS 模块，内核代码过时了，删除内核中的代码以避免版本混淆。

贵州花江峡谷大桥正式通车。大桥桥面距水面625米，高度超过北盘江第一桥近 60 米，成为新的世界第一高桥；大桥主桥跨径 1420 米，居山区桥梁跨径世界第一。大桥全长 2890 米，可将两岸通行时间从两个多小时缩短到两分钟左右。从 2022 年开工到正式通车，这座“超级工程”的建造只花了三年多。花江峡谷大桥钢桁梁吊装有 93 个节段，总重达 2.1 万吨，需在 600 多米高空实现毫米级精准对接。建设团队借助研发的“智慧缆索吊装系统”，全部吊装仅用了 73 天就全面完成；3.8 万平方米的桥面，建设团队在 1 个多月里完成了 5 层铺装。

以研究数字取证和深度伪造而知名的加州伯克利计算机科学教授 Hany Farid 表示，计算机科学在极短时间内从经得住时间考验的职业变成了剧变中的行业。他说，计算机科学专业的学生通常会在前四年获得五份实习机会，毕业时会收到多份高薪的工作机会。但如今这种情况不会发生了，如果能收到一份工作邀约他们就很高兴了。Farid 教授认为 AI 只是因素之一。计算机科学行业正在发生某种变化。他现在给学生的建议是掌握多种技能，因为不知道未来会发生什么。他说，AI 不会让律师失业，但会用 AI 的律师会让不会用 AI 的律师失业。他认为每个职业都如此。

美国调查公司 Omdia 的数据显示，10～12 月服务器用 DRAM 的预测价格为 4.3 美元/GB，比 2023 年 10～12 月高出 2.4 美元。PC 用产品的预测价格为 2.8 美元，比 2023 年 10～12 月上涨 1.2 美元。这一趋势背后的原因是 AI 服务器的需求猛增。AI 服务器主要使用 HBM 内存。主要 DRAM 内存芯片制造商三星电子、SK 海力士及美光缩小产量或停产了上一代的 DDR4，转为生产和销售 HBM。AI 服务器的内存需求正在推动整个半导体市场。美国半导体行业协会（SIA）公布的全球半导体销售额7月达到了 620.7 亿美元，同比增长 20.6%，首次突破 600 亿美元。已连续 21 个月超过去年同期。

根据发表在《Osteoporosis International》上的一篇综述，研究人员分析了 62 项研究，发现微塑料会破坏骨髓干细胞，刺激破骨细胞——一种削弱骨组织的细胞，从而削弱骨骼。实验室实验发现，微塑料颗粒会降低细胞活性，诱导细胞过早衰老，改变基因表达，引发炎症反应。动物研究发现，微塑料的积累会降低白细胞数量，破坏骨骼微结构，导致细胞结构不规则，增加骨折风险。巴西 Campinas 州立大学的 Rodrigo Bueno de Oliveira 表示，这些影响会阻碍实验动物的骨骼生长。

几周前，阿富汗切断了多省的光纤连接，本周一则发生了全国性断网。网络监测组织 Netblocks 报告阿富汗的网络连接率仅为正常水平的 14%。法新社报告在 6:15 pm (1315 GMT)与该新闻社位于喀布尔的分社失去联系。阿富汗此前表示它切断光纤接入是为了防止不良行为。

沙特基金 Public Investment Fund (PIF)、Silver Lake 和 Affinity Partners 组成的财团宣布与 EA（Electronic Arts） 达成了 550 亿美元的收购协议。财团将收购 100% 的 EA 股份，其中 PIF 将其持有的 EA 股份（截至 2023 年持有 55% 的股份）转入此次收购。作为收购的一部分，EA 股东将以每股 210 美元的价格获得现金。此次收购以现金方式出资。这次交易被认为是史上最大规模的全现金私有化收购。交易预计将于 2027 财年第一季度完成。

根据发表在《Foods》期刊上的一项研究，每天食用芒果有助于降低糖尿病风险。芒果含糖量属于较高水平，对糖尿病前期患者而言可能不是好的水果选择。但对照测试发现，结果并非如此。研究人员将参与者分成两组，一组每天吃一个新鲜芒果，另一组则每天食用一根低糖燕麦棒。研究人员在 6 个月中测量了参与者的血糖水平、身体对胰岛素的反应以及体脂。结果显示，含 32 克糖的高糖芒果比含 11 克糖的低糖燕麦棒更有益。每天食用芒果的参与者血糖控制得到改善、胰岛素敏感性增强，并且体脂减少。芒果等水果中天然存在糖分，辅以纤维、维生素及营养素，能提供额外的健康益处。而像早餐麦片，甚至是一些低糖零食这些添加糖分的食物，则可能不具备同等的营养价值，甚至还会增加患糖尿病的风险。研究人员称，“重要的不仅是含糖量，食物的整体构成也很重要。”

日本DM三井制糖开发出了使用魔芋薯等制成的植物基金枪鱼。 该产品将面向孕妇和老年人等因健康原因无法食用生鱼片的人群，从 2026 年起向医院和护理设施推广。在全球变暖及渔业从业者数量减少等原因导致产量下降的情况下，日本涉足植物基生鱼片业务的企业正在增加。植物基金枪鱼生鱼片价格设定为每公斤 2000 日元（约合人民币 95.5 元）区间，比真金枪鱼便宜，旨在推动产品普及。该公司预计到 2028 年年产量将达到 10 吨左右。除金枪鱼外，该公司还力争推动三文鱼、乌贼等品种的实用化。

Ruby Central 据报道在最大支持者 Shopify 的施压下，未经长期维护者同意接管了多个 Ruby 旗舰开源项目的控制权，其中包括 bundler 和 rubygems-update，此举可能加剧社区的分裂。Ruby 项目的主要赞助商 Sidekiq 撤回了每年 25 万美元的赞助承诺，导致 Ruby Central 严重依赖于 Shopify 的赞助。Shopify 前雇员、Ruby 开发者和维护者 Joel Drapper 称，Ruby Central 此前已经陷入财务困境，Shopify 在此背景下施压 Ruby Central 获得对 RubyGems GitHub 组织以及对部分核心 Gem 如 bundler 和 rubygems-update 的完整控制权，威胁如果不这样做将停止资助。此后 Ruby Central 采取了一系列行动，包括将 RubyGems GitHub 企业重命名为 Ruby Central，移除了众多维护者的权限，停用了邮件账号，撤销了对 RubyGems 的所有权。在社区引发争论之后，Ruby Central 回应称此举是为了确保供应链的安全。

加州按 GDP 计算相当于世界第四大经济体。自 2000 年以来，加州的 GDP 增长了 78%，但同期排放下降了 20%。加州过去几年在加速建造支持电动汽车的充电基础设施，2019 年它有 4.2 万个公共和共享充电桩，六个月前这一数字达到了 17.8 万，相比下加油站数量为 12 万。上周加州宣布它在六个月内又增加了 2.3 万公共共享充电桩，总数超过了 20 万，比加油站多 68%。与此同时，加州的家用充电桩数量达到了 80 万。加州能源政策机构称，94% 的加州居民居住在距离充电桩 10 分钟内的地方。加州还宣布去年所有新卡车中有 23% 是零排放，该州为电动卡车提供补贴。

天文学家利用韦伯太空望远镜观测一颗在宇宙中自由漂流的行星 SIMP-0136，意外发现在高层大气不时出现极光，而且行星大气循环由这些极光加热所驱动。漂流行星 SIMP-0136 距离地球约 20 光年，质量约为木星的 12.7 倍、半径约为木星的 1.2 倍。由于此行星自转一周只需约 2.4 小时，让天文学家得以快速观察大气层的完整变化。结果发现大气的垂直温度分布出现「温度反转」现象，也就是高度愈低，气温愈低，越往高空则气温越高，与地球等行星的大气温度垂直分布完全不同。这种异常主要源于极光不断将能量注入并加热高层大气所致。 这颗行星的云层并非由水或冰构成，而是矽酸盐颗粒组成，类似地球沙滩上的沙子。整颗行星几乎被云层平均覆盖，与地球云系经常出现云缝或空隙的情况大不相同。它的平均气温超过摄氏一千五百度，远比木星或土星平均气温约在零下百度炙热得多。研究显示，极光不只出现于地球或木星，也能在孤单的漂流行星上扮演塑造大气结构与提供动力来源的关键角色。

瑞士周日公投以微弱多数批准电子身份证。这是瑞士电子身份证计划的第二次全民公决。第一次是在 2021 年，当时由于选民担心数据的隐私保护问题，以及该系统主要由私营企业运营而投了反对票。政府之后修改了计划，新的电子身份证将由政府运营，而且是可选的，且限制了数据的访问——举例来说，需要访问年龄的机构将只能访问到年龄信息。用户可选择将电子身份证数据与手机捆绑，如果更换手机将需要重新申请一张电子身份证。在周日的公投中，50.4% 的选民支持电子身份证，49.6% 的选民反对。投票率 49.55%。

上个月 Google 以安全的名义宣布将验证所有 Android 应用开发者的身份，从明年开始，Google 将屏蔽未经身份验证的开发者的 Android 应用的侧载（sideload）。开源自由软件 Android 应用商店 F-Droid 发表声明反对 Google 的决定。F-Droid 认为，如果这一政策强制推行，包括它在内的第三方应用商店将面临终结。Google 声称是为了安全，但过去几年它的官方应用商店 Google Play 被发现托管了大量恶意程序。它要求验证应用开发者的身份不是为了安全而是为了巩固权力，加强对曾经开放的生态系统的控制。Google 正在构建一个限制竞争和用户自由的阻塞点（choke point）。F-Droid 呼吁对此问题关心的用户向自己所在地区的议员递交反对意见，向欧盟 DMA 请愿，捍卫应用的自由分发。