OrangeBot.AI Digest — 2025-09-28

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

Interactive roadmaps, guides and other educational content to help developers grow in their careers.

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

The best way to get AI coding agents to solve hard problems in complex codebases.

A new markup-based typesetting system that is powerful and easy to learn.

Collection of awesome LLM apps with AI Agents and RAG using OpenAI, Anthropic, Gemini and opensource models.

Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.

AI coding agent, built for the terminal.

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

人人都能用英语

Free and Open Source Enterprise Resource Planning (ERP)

Opinionated Arch/Hyprland Setup

The Free Software Media System - Server Backend & API

Metasploit Framework

Open Source AI Platform - AI Chat with advanced features that works with every LLM

Policy-based reinforcement learning currently plays an important role in improving LLMs on mathematical reasoning tasks. However, existing rollout-based reinforcement learning methods (GRPO, DAPO, GSPO, etc.) fail to explicitly consider LLMs' learning ability for samples of different difficulty levels, which is contrary to the human cognitive process of mathematical reasoning tasks from easy to difficult. Intuitively, we find that the variance of the rollout group's reward in RLVR partly reflects the difficulty of the current sample for LLMs. Samples that are too easy or too difficult have a lower variance, while samples with moderate difficulty have a higher variance. Based on this, we propose VCRL, a curriculum reinforcement learning framework that dynamically controls the difficulty of training samples based on the variance of group rewards. Experiments on five mathematical benchmarks and two models reveal the advantages of VCRL over the current LLM RL baselines.

Large multimodal reasoning models have achieved rapid progress, but their advancement is constrained by two major limitations: the absence of open, large-scale, high-quality long chain-of-thought (CoT) data, and the instability of reinforcement learning (RL) algorithms in post-training. Group Relative Policy Optimization (GRPO), the standard framework for RL fine-tuning, is prone to gradient vanishing when reward variance is low, which weakens optimization signals and impairs convergence. This work makes three contributions: (1) We propose Variance-Aware Sampling (VAS), a data selection strategy guided by Variance Promotion Score (VPS) that combines outcome variance and trajectory diversity to promote reward variance and stabilize policy optimization. (2) We release large-scale, carefully curated resources containing ~1.6M long CoT cold-start data and ~15k RL QA pairs, designed to ensure quality, difficulty, and diversity, along with a fully reproducible end-to-end training codebase. (3) We open-source a family of multimodal reasoning models in multiple scales, establishing standardized baselines for the community. Experiments across mathematical reasoning benchmarks demonstrate the effectiveness of both the curated data and the proposed VAS. Comprehensive ablation studies and analyses provide further insight into the contributions of each component. In addition, we theoretically establish that reward variance lower-bounds the expected policy gradient magnitude, with VAS serving as a practical mechanism to realize this guarantee. Our code, data, and checkpoints are available at https://github.com/LengSicong/MMR1.

We present a scientific reasoning foundation model that aligns natural language with heterogeneous scientific representations. The model is pretrained on a 206B-token corpus spanning scientific text, pure sequences, and sequence-text pairs, then aligned via SFT on 40M instructions, annealed cold-start bootstrapping to elicit long-form chain-of-thought, and reinforcement learning with task-specific reward shaping, which instills deliberate scientific reasoning. It supports four capability families, covering up to 103 tasks across workflows: (i) faithful translation between text and scientific formats, (ii) text/knowledge extraction, (iii) property prediction, (iv) property classification, (v) unconditional and conditional sequence generation and design. Compared with specialist systems, our approach broadens instruction coverage, improves cross-domain generalization, and enhances fidelity. We detail data curation and training and show that cross-discipline learning strengthens transfer and downstream reliability. The model, instruct tuning datasets and the evaluation code are open-sourced at https://huggingface.co/SciReason and https://github.com/open-sciencelab/SciReason.

Recent advances in reinforcement learning (RL) have significantly enhanced the agentic capabilities of large language models (LLMs). In long-term and multi-turn agent tasks, existing approaches driven solely by outcome rewards often suffer from the problem of sparse supervision. To address the challenge, we propose Tree-based Group Relative Policy Optimization (Tree-GRPO), a grouped agent RL method based on tree search, where each tree node represents the complete agent interaction step. By sharing common prefixes, the tree search sampling increases the number of rollouts achievable within a fixed budget of tokens or tool calls. Moreover, we find that the tree-structured trajectory naturally allows the construction of step-wise process supervised signals even using only the outcome reward. Based on this, Tree-GRPO estimates the grouped relative advantages both on intra-tree and inter-tree levels. Through theoretical analysis, we demonstrate that the objective of intra-tree level group relative policy optimization is equivalent to that of step-level direct preference learning. Experiments across 11 datasets and 3 types of QA tasks demonstrate the superiority of the proposed tree-based RL over the chain-based RL method.

We introduce Seedream 4.0, an efficient and high-performance multimodal image generation system that unifies text-to-image (T2I) synthesis, image editing, and multi-image composition within a single framework. We develop a highly efficient diffusion transformer with a powerful VAE which also can reduce the number of image tokens considerably. This allows for efficient training of our model, and enables it to fast generate native high-resolution images (e.g., 1K-4K). Seedream 4.0 is pretrained on billions of text-image pairs spanning diverse taxonomies and knowledge-centric concepts. Comprehensive data collection across hundreds of vertical scenarios, coupled with optimized strategies, ensures stable and large-scale training, with strong generalization. By incorporating a carefully fine-tuned VLM model, we perform multi-modal post-training for training both T2I and image editing tasks jointly. For inference acceleration, we integrate adversarial distillation, distribution matching, and quantization, as well as speculative decoding. It achieves an inference time of up to 1.8 seconds for generating a 2K image (without a LLM/VLM as PE model). Comprehensive evaluations reveal that Seedream 4.0 can achieve state-of-the-art results on both T2I and multimodal image editing. In particular, it demonstrates exceptional multimodal capabilities in complex tasks, including precise image editing and in-context reasoning, and also allows for multi-image reference, and can generate multiple output images. This extends traditional T2I systems into an more interactive and multidimensional creative tool, pushing the boundary of generative AI for both creativity and professional applications. Seedream 4.0 is now accessible on https://www.volcengine.com/experience/ark?launch=seedream.

Recent advances in 3D-native generative models have accelerated asset creation for games, film, and design. However, most methods still rely primarily on image or text conditioning and lack fine-grained, cross-modal controls, which limits controllability and practical adoption. To address this gap, we present Hunyuan3D-Omni, a unified framework for fine-grained, controllable 3D asset generation built on Hunyuan3D 2.1. In addition to images, Hunyuan3D-Omni accepts point clouds, voxels, bounding boxes, and skeletal pose priors as conditioning signals, enabling precise control over geometry, topology, and pose. Instead of separate heads for each modality, our model unifies all signals in a single cross-modal architecture. We train with a progressive, difficulty-aware sampling strategy that selects one control modality per example and biases sampling toward harder signals (e.g., skeletal pose) while downweighting easier ones (e.g., point clouds), encouraging robust multi-modal fusion and graceful handling of missing inputs. Experiments show that these additional controls improve generation accuracy, enable geometry-aware transformations, and increase robustness for production workflows.

AutoIntent is an automated machine learning tool for text classification tasks. Unlike existing solutions, AutoIntent offers end-to-end automation with embedding model selection, classifier optimization, and decision threshold tuning, all within a modular, sklearn-like interface. The framework is designed to support multi-label classification and out-of-scope detection. AutoIntent demonstrates superior performance compared to existing AutoML tools on standard intent classification datasets and enables users to balance effectiveness and resource consumption.

The adoption of Large Language Models (LLMs) as automated evaluators (LLM-as-a-judge) has revealed critical inconsistencies in current evaluation frameworks. We identify two fundamental types of inconsistencies: (1) Score-Comparison Inconsistency, where lower-rated responses outperform higher-scored ones in pairwise comparisons, and (2) Pairwise Transitivity Inconsistency, manifested through circular preference chains (A>B>C>A) and equivalence contradictions (A=B=C\neq A). We argue that these issues come from information loss in discrete rating systems and ambiguous tie judgments during pairwise evaluation. We propose TrustJudge, a probabilistic framework that addresses these limitations through two key innovations: 1) distribution-sensitive scoring that computes continuous expectations from discrete rating probabilities, preserving information entropy for more precise scoring, and 2) likelihood-aware aggregation that resolves transitivity violations using bidirectional preference probabilities or perplexity. We also formalize the theoretical limitations of current LLM-as-a-judge frameworks and demonstrate how TrustJudge's components overcome them. When evaluated with Llama-3.1-70B-Instruct as judge using our dataset, TrustJudge reduces Score-Comparison inconsistency by 8.43% (from 23.32% to 14.89%) and Pairwise Transitivity inconsistency by 10.82% (from 15.22% to 4.40%), while maintaining higher evaluation accuracy. Our work provides the first systematic analysis of evaluation framework inconsistencies in LLM-as-a-judge paradigms, offering both theoretical insights and practical solutions for reliable automated assessment. The framework demonstrates consistent improvements across various model architectures and scales, enabling more trustworthy LLM evaluation without requiring additional training or human annotations. The codes can be found at https://github.com/TrustJudge/TrustJudge.

Recent advances in behavior cloning (BC) have enabled impressive visuomotor control policies. However, these approaches are limited by the quality of human demonstrations, the manual effort required for data collection, and the diminishing returns from increasing offline data. In comparison, reinforcement learning (RL) trains an agent through autonomous interaction with the environment and has shown remarkable success in various domains. Still, training RL policies directly on real-world robots remains challenging due to sample inefficiency, safety concerns, and the difficulty of learning from sparse rewards for long-horizon tasks, especially for high-degree-of-freedom (DoF) systems. We present a recipe that combines the benefits of BC and RL through a residual learning framework. Our approach leverages BC policies as black-box bases and learns lightweight per-step residual corrections via sample-efficient off-policy RL. We demonstrate that our method requires only sparse binary reward signals and can effectively improve manipulation policies on high-degree-of-freedom (DoF) systems in both simulation and the real world. In particular, we demonstrate, to the best of our knowledge, the first successful real-world RL training on a humanoid robot with dexterous hands. Our results demonstrate state-of-the-art performance in various vision-based tasks, pointing towards a practical pathway for deploying RL in the real world. Project website: https://residual-offpolicy-rl.github.io

This paper introduces a simple and scalable approach to improve the data efficiency of large language model (LLM) training by augmenting existing text data with thinking trajectories. The compute for pre-training LLMs has been growing at an unprecedented rate, while the availability of high-quality data remains limited. Consequently, maximizing the utility of available data constitutes a significant research challenge. A primary impediment is that certain high-quality tokens are difficult to learn given a fixed model capacity, as the underlying rationale for a single token can be exceptionally complex and deep. To address this issue, we propose Thinking augmented Pre-Training (TPT), a universal methodology that augments text with automatically generated thinking trajectories. Such augmentation effectively increases the volume of the training data and makes high-quality tokens more learnable through step-by-step reasoning and decomposition. We apply TPT across diverse training configurations up to 100B tokens, encompassing pre-training with both constrained and abundant data, as well as mid-training from strong open-source checkpoints. Experimental results indicate that our method substantially improves the performance of LLMs across various model sizes and families. Notably, TPT enhances the data efficiency of LLM pre-training by a factor of 3. For a 3B parameter model, it improves the post-training performance by over 10% on several challenging reasoning benchmarks.

Reinforcement learning (RL) has become a powerful paradigm for optimizing large language models (LLMs) to handle complex reasoning tasks. A core challenge in this process lies in managing policy entropy, which reflects the balance between exploration and exploitation during training. Existing methods, such as proximal policy optimization (PPO) and its variants, discard valuable gradient signals from low-probability tokens due to the clipping mechanism. We systematically analyze the entropy dynamics and reveal that these clipped tokens play a critical yet overlooked role in regulating entropy evolution. We propose Controlling Entropy via Gradient-Preserving Policy Optimization (CE-GPPO), a novel algorithm that reintroduces gradients from clipped tokens in native PPO in a gentle and bounded manner. By controlling the magnitude of gradients from tokens outside the clipping interval, CE-GPPO is able to achieve an exploration-exploitation trade-off. We provide theoretical justification and empirical evidence showing that CE-GPPO effectively mitigates entropy instability. Extensive experiments on mathematical reasoning benchmarks show that CE-GPPO consistently outperforms strong baselines across different model scales.

We present CHARM, a novel parametric representation and generative framework for anime hairstyle modeling. While traditional hair modeling methods focus on realistic hair using strand-based or volumetric representations, anime hairstyle exhibits highly stylized, piecewise-structured geometry that challenges existing techniques. Existing works often rely on dense mesh modeling or hand-crafted spline curves, making them inefficient for editing and unsuitable for scalable learning. CHARM introduces a compact, invertible control-point-based parameterization, where a sequence of control points represents each hair card, and each point is encoded with only five geometric parameters. This efficient and accurate representation supports both artist-friendly design and learning-based generation. Built upon this representation, CHARM introduces an autoregressive generative framework that effectively generates anime hairstyles from input images or point clouds. By interpreting anime hairstyles as a sequential "hair language", our autoregressive transformer captures both local geometry and global hairstyle topology, resulting in high-fidelity anime hairstyle creation. To facilitate both training and evaluation of anime hairstyle generation, we construct AnimeHair, a large-scale dataset of 37K high-quality anime hairstyles with separated hair cards and processed mesh data. Extensive experiments demonstrate state-of-the-art performance of CHARM in both reconstruction accuracy and generation quality, offering an expressive and scalable solution for anime hairstyle modeling. Project page: https://hyzcluster.github.io/charm/

Recent years, multimodal models have made remarkable strides and pave the way for intelligent browser use agents. However, when solving tasks on real world webpages in multi-turn, long-horizon trajectories, current agents still suffer from disordered action sequencing and excessive trial and error during execution. This paper introduces Recon-Act, a self-evolving multi-agent framework grounded in Reconnaissance-Action behavioral paradigm. The system comprises a Reconnaissance Team and an Action Team: the former conducts comparative analysis and tool generation, while the latter handles intent decomposition, tool orchestration, and execution. By contrasting the erroneous trajectories with successful ones, the Reconnaissance Team infers remedies, and abstracts them into a unified notion of generalized tools, either expressed as hints or as rule-based codes, and register to the tool archive in real time. The Action Team reinference the process empowered with these targeting tools, thus establishing a closed-loop training pipeline of data-tools-action-feedback. Following the 6 level implementation roadmap proposed in this work, we have currently reached Level 3 (with limited human-in-the-loop intervention). Leveraging generalized tools obtained through reconnaissance, Recon-Act substantially improves adaptability to unseen websites and solvability on long-horizon tasks, and achieves state-of-the-art performance on the challenging VisualWebArena dataset.

Image composition aims to seamlessly insert a user-specified object into a new scene, but existing models struggle with complex lighting (e.g., accurate shadows, water reflections) and diverse, high-resolution inputs. Modern text-to-image diffusion models (e.g., SD3.5, FLUX) already encode essential physical and resolution priors, yet lack a framework to unleash them without resorting to latent inversion, which often locks object poses into contextually inappropriate orientations, or brittle attention surgery. We propose SHINE, a training-free framework for Seamless, High-fidelity Insertion with Neutralized Errors. SHINE introduces manifold-steered anchor loss, leveraging pretrained customization adapters (e.g., IP-Adapter) to guide latents for faithful subject representation while preserving background integrity. Degradation-suppression guidance and adaptive background blending are proposed to further eliminate low-quality outputs and visible seams. To address the lack of rigorous benchmarks, we introduce ComplexCompo, featuring diverse resolutions and challenging conditions such as low lighting, strong illumination, intricate shadows, and reflective surfaces. Experiments on ComplexCompo and DreamEditBench show state-of-the-art performance on standard metrics (e.g., DINOv2) and human-aligned scores (e.g., DreamSim, ImageReward, VisionReward). Code and benchmark will be publicly available upon publication.

While Large Reasoning Models (LRMs) generate extensive chain-of-thought reasoning, we lack a principled framework for understanding how these thoughts are structured. In this paper, we introduce a novel approach by applying Schoenfeld's Episode Theory, a classic cognitive framework for human mathematical problem-solving, to analyze the reasoning traces of LRMs. We annotated thousands of sentences and paragraphs from model-generated solutions to math problems using seven cognitive labels (e.g., Plan, Implement, Verify). The result is the first publicly available benchmark for the fine-grained analysis of machine reasoning, including a large annotated corpus and detailed annotation guidebooks. Our preliminary analysis reveals distinct patterns in LRM reasoning, such as the transition dynamics between cognitive states. This framework provides a theoretically grounded methodology for interpreting LRM cognition and enables future work on more controllable and transparent reasoning systems.

Firefox 将支持图像搜索，该功能由 Google Lens 技术提供。使用该功能非常简单，右键单击任意图像即可搜寻类似的产品、地点或对象；复制、翻译或搜索图像中的文字等。该功能只提供给桌面版本，将逐渐向全球推出，浏览器的默认搜索引擎需要是 Google。Mozilla 强调该功能是可选的，用户可自己控制是否激活，这与该公司长期以来对隐私和用户自主权的承诺一致。

比亚迪旗下高端品牌仰望宣布旗舰纯电超跑 U9 Xtreme 在德国 Papenburg 高速测试场上，成功创下 496.22 km/h 的惊人成绩，超越 Bugatti Chiron Super Sport 300+ 的单向极速纪录。然而由于这次仅以单一方向完成测试，世界最速量产车的官方头衔仍由 SSC Tuatara 所保持，两向平均纪录为 455.3 km/h。即便如此仰望 U9 的表现，已足以改写外界对中国高性能电动车的认知。U9 Xtreme 搭载四具电动马达，合计输出 2,978 马力，是全球首款采用 1,200 V 高压平台的量产车，比亚迪计划仅生产 30 辆 U9 Xtreme，价格尚未公布。这次纪录由德国耐久赛车手 Marc Basseng 驾驶完成。

Google 前 CEO Eric Schmidt 认为，为了与中国科技公司竞争，美国科技业从业者需要放弃工作生活平衡，拥抱中国的 996 工作制。他在 All-In 播客中表示，他不相信远程办公，部分是因为远程办公无助于美国科技公司与中国残酷的工作文化竞争。他说，如果你想要在科技领域获得成功，你必须权衡，我们的对手是中国人，中国工人的工作生活平衡是 996，即每周工作六天，早上 9 点到晚上 9 点。996 工作制于 2021 年被禁止，但 Schmidt 坚称中国科技公司仍然在推行 996 工作制。据《连线》报道，美国的初创公司，尤其是 AI 领域的初创公司，也热衷于推行中国的 996 工作制。

根据发表在《Brain, Behavior and Immunity - Health》期刊上的一项研究，从童年时期父母的温暖，到成年后的友谊、参与社区活动和宗教支持，贯穿一生的社会优势累积效应或能减缓衰老的生物过程，推迟表观遗传时钟，让一个人的生物年龄低于实际年龄。对逾 2100 名美国成年人的研究发现，更高水平的累积社会优势（cumulative social advantage）的人有着更慢的表观遗传衰老和更低的慢性炎症水平。研究人员称，累积的社会优势指的是一个人一生中社会联系的深度和广度。这种社会优势的积累会以可衡量的方式塑造人的健康轨迹。累积优势理论认为，无论是经济资源还是社会资源，都趋于累积，从而扩大生命历程中的差距。一个让人警醒的现实是：社会资源并非均匀分布，种族、阶级和教育程度塑造了在成长过程中有支持性父母、在社区机构中找到归属感或拥有提供稳定支持的朋友和伴侣的可能性。

在推出键盘外形的一体机 Raspberry Pi 500 九个月之后，树莓派宣布了升级版 Raspberry Pi 500+，价格也提高了一倍达到 200 美元。Raspberry Pi 500+ 基本配置与 Raspberry Pi 500 相同，CPU 仍然是四核 2.4GHz Arm Cortex-A76，但内存从 8GB 升级到 16GB，提供了一个 NVMe 插槽，配备了 256GB M.2 2280 SSD，用户可升级为更高容量的 SSD。除此之外，Raspberry Pi 500+ 还改进了一体机的键盘，配备机械开关、可更换键帽和可单独编程的 RGB LED 灯。用户可选配 220 美元的版本，包括鼠标、27W USB-C 电源、2 米长的 micro HDMI 转 HDMI 线和初学者指南。

亚马逊 kindle 电子书阅读器的围墙花园如今比巴比伦塔还要高。亚马逊一周前向 11 代和 12 代 Kindle，Kindle Scribe 1 和 Scribe 2 以及 Kindle Colorsoft 推送了新版本固件（v5.18.5），更新了 DRM 系统。新的 DRM 使用了一个储存在 Kindle 无法访问区域的文件(account secret)作为解密加密电子书的密钥的一部分，这意味着除非设备在更新新固件前已经越狱，否则下载到设备上的新电子书将无法解密。亚马逊是否会对第 9 代和 10 代 Kindle 推送新 DRM 还有待观察。阻止下载新固件的一个权宜之计是让 Kindle 设备的可用空间降至 300MB 以内，因为下载新固件需要 300MB 的空间。

广泛使用的 YouTube 视频下载工具 yt-dlp 项目宣布，为了工具正常工作未来将需要安装 Deno 或其它支持的 JavaScript 运行时，原因是 YouTube 设置了越来越多的障碍，项目目前使用的 JavaScript 解释器越来越力不从心，必须使用真正的 JavaScript 运行时。推荐使用 Deno 是因为它是完全独立的单一可执行文件，默认沙盒化，不允许文件系统或网络访问。开发者同时指出，YouTube 未来将对所有客户端强制执行 proof-of-origin (PO) token，超出了 yt-dlp 现有的 JavaScript 功能能力范围。

根据发表在《科学》期刊上的一项研究，白蚁会主动清理危害其培育的鸡枞菌的有害菌。肥大齿白蚁（Odontotermes obesus）等会培植真菌的白蚁与一种名为“鸡枞菌”（Termitomyces）的真菌保持着至关重要的共生关系；这些白蚁会在一种名为菌圃（comb）的特殊营养基质中培育这种真菌；菌圃既能给予白蚁可靠的食源，也可为鸡枞菌提供理想的生长环境。这些富含营养的菌圃也会吸引真菌型杂草的入侵，尤其是生长迅速的伪炭角菌（Pseudoxylaria）：如果任其发展，伪炭角菌很快会反客为主。研究人员发现，在面对小规模感染时，白蚁会主动从受污染的菌圃中移除伪炭角菌，并将其埋在土壤团块（boluses）之下；这可有效地将该有害真菌隔离在缺氧的土壤环境中，从而抑制其进一步生长。在严重感染的情况下，白蚁会将感染菌圃与健康菌圃完全隔离，并在必要时将整个感染部分埋入土块之中以遏制该威胁。

Fedora 发行版项目公布了一项提案，讨论使用 AI 辅助工具的社区政策。在两周的反馈期结束后，Fedora 理事会将投票批准该提案。提案建议：开发者需要对自己的贡献承担责任，AI 生成的内容必须被视为建议，不能作为最终代码或最终文本；开发者有责任审核、测试和理解所有提交的内容，提交未经验证或质量低劣的机器生成内容（常被称为“AI 垃圾”）会给社区带来不公平的审核负担，不属于可接受的贡献。开发者需要透明的使用 AI，如果贡献的内容得到了 AI 的帮助，在递交时应尽可能明示。限制将 AI 工具用于审核代码或文档。提供给 Fedora 用户的 AI 功能必须是可选的，不得默认启用将数据发送到云端的选项。

PostgreSQL 数据库项目释出了 v18 版本。主要新特性包括：异步 I/O (AIO) 子系统改进顺序扫描、位图堆扫描、vacuums 清理等操作的性能，基准测试显示在部分情况下性能提升最高 3 倍；pg_upgrade 保留优化器统计信息；支持对多列 B-tree 索引的“跳过扫描”查找；虚拟生成列，改进文本处理，OAuth 身份验证，等等。更多浏览发布公告。

天文学家长期以来一直依赖超级计算机去模拟宇宙的宏大结构，但一款名为 Effort.jl 的新开源工具正改变这一现状。意大利和加拿大研究团队开发了模拟器 Effort.jl，模拟复杂宇宙模型如 EFTofLSS 如何响应。他们的测试显示，Effort.jl 只需几分钟即可在标准笔记本电脑上提供具有相同精度的结果。这项突破性技术将神经网络与物理知识的巧妙运用相结合，在保持可靠性的同时，大幅缩短了计算时间。Effort.jl 源代码发布在 GitHub 上，采用 MIT 许可证。

微软和华硕宣布开放预购将于 10 月 16 日上市的 ROG Xbox Ally 系列掌机，正式公布了掌机价格。ROG Xbox Ally 系列掌机运行 Windows 11，操作系统为掌机进行了优化，支持包括 Valve Steam 和 Epic Games Store 在内的游戏商店，虽然使用 Xbox 品牌，但并不支持 Xbox 游戏，而是 Windows PC 游戏。ROG Xbox Ally 系列共两个型号，均采用 7 英寸 1080p IPS 显示屏，刷新率 120 Hz，支持 Wi-Fi 6E 和蓝牙 5.4，但内部配置有显著差异。低端的 Xbox Ally 搭载 AMD Ryzen Z2 A 芯片，其配置与 Valve 三年前的 Steam Deck 掌机几乎完全相同，售价 599.99 美元。高端的 Xbox Ally X 搭载了 Ryzen AI Z2 Extreme 处理器，配备 8 核 Zen 5 CPU、16 核 RDNA3.5 GPU、1TB 存储空间、24GB LPDDR5X-8000 内存以及 NPU，售价 999.99 美元。

OpenAI 准备建造的数据中心耗电量相当于纽约和圣迭戈两大城市之和。芝加哥大学计算机科学教授 Andrew Chien 表示，作为从业 40 年的计算机科学家，大部分时间里，计算的耗电量通常是经济用电量中非常小的一部分，而如今计算的耗电量正占到经济用电量的很大一部分。他对此既感到兴奋又感到担忧，认为现有的电力基础设施以及在建设施发电量都满足不了 AI 公司日益膨胀的耗电需求。他说，到 2030 年计算可能占到全球总电力的 10% 或 12%。我们即将迎来思考 AI 及其社会影响的重要时刻。OpenAI 准备建造的数据中心耗电量达到惊人的 10-17GW，而美国在 2030 年之前可并网的核电容量不到 1 GW，如此巨大的鸿沟如何填补？

在发现证据表明以色列国防部使用微软云服务 Azure 监视加沙居民后，微软禁止以国防部访问某些服务和订阅。《卫报》今年八月报告以色列使用 Azure 存储加沙居民数据并对其进行监视，微软随后启动了一项内部调查，调查仍然在进行之中。微软总裁 Brad Smith 通过官方博客宣称，微软长期以来将隐私作为一项基本权利加以保护，作为员工他们都对隐私保护有着共同的兴趣，因为隐私保护通过确保客户能完全信任微软的服务而创造商业价值。

Maria Branyas 能活到 117 岁的原因之一是她拥有异常年轻的基因组。Branyas 于 2024 年去世，当时她是世界最长寿的人，她去世前提供了自己的血液、唾液、尿液和粪便样本供科学家研究。科学家分析后发现，她体内细胞的生物年龄看起来比她的实际年龄更年轻。她晚年总体健康状况良好，心血管健康状况极佳，炎症水平极低。她的免疫系统和肠道菌群指标与更年轻的人​​群相当，她的“坏”胆固醇和甘油三酯水平极低，“好”胆固醇水平极高。所有这些因素都有助于解释她健康状况如此出色的和寿命如此高。科学家还发现，她的染色体末端端粒异常短。非常短的端粒可能为她带来了优势。科学家认为她体内细胞的短寿可能阻止了癌症的增殖。