OrangeBot.AI Digest — 2025-07-27

A generative world for general-purpose robotics & embodied AI learning.

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

Open Source HR and Payroll Software

An open source payments switch written in Rust to make payments fast, reliable and affordable

Examples and tutorials to help developers build AI systems

📚 Collaborative cheatsheets for console commands

Infisical is the open-source platform for secrets management, PKI, and SSH access.

AI E-com agent that converts 5x more from just a URL

Get to #1 on ChatGPT

Screen 1000 candidates before lunch

Professional resume review for just $1

Identify any insect in seconds, just by snapping a photo

Metabase AI Agent

An experimental AI-organized search results page

Crafted for calm, clear skies

From a word or image to an explorable 3D scene

The fastest way to tag images for object detection datasets

This paper introduces Group Sequence Policy Optimization (GSPO), our stable, efficient, and performant reinforcement learning algorithm for training large language models. Unlike previous algorithms that adopt token-level importance ratios, GSPO defines the importance ratio based on sequence likelihood and performs sequence-level clipping, rewarding, and optimization. We demonstrate that GSPO achieves superior training efficiency and performance compared to the GRPO algorithm, notably stabilizes Mixture-of-Experts (MoE) RL training, and has the potential for simplifying the design of RL infrastructure. These merits of GSPO have contributed to the remarkable improvements in the latest Qwen3 models.

Recent progress in transformer-based architectures has demonstrated remarkable success in video generation tasks. However, the quadratic complexity of full attention mechanisms remains a critical bottleneck, particularly for high-resolution and long-duration video sequences. In this paper, we propose NABLA, a novel Neighborhood Adaptive Block-Level Attention mechanism that dynamically adapts to sparsity patterns in video diffusion transformers (DiTs). By leveraging block-wise attention with adaptive sparsity-driven threshold, NABLA reduces computational overhead while preserving generative quality. Our method does not require custom low-level operator design and can be seamlessly integrated with PyTorch's Flex Attention operator. Experiments demonstrate that NABLA achieves up to 2.7x faster training and inference compared to baseline almost without compromising quantitative metrics (CLIP score, VBench score, human evaluation score) and visual quality drop. The code and model weights are available here: https://github.com/gen-ai-team/Wan2.1-NABLA

Large Language Models (LLMs) have achieved impressive performance on reasoning-intensive tasks, yet optimizing their reasoning efficiency remains an open challenge. While Test-Time Scaling (TTS) improves reasoning quality, it often leads to overthinking, wasting tokens on redundant computations. This work investigates how to efficiently and adaptively guide LLM test-time scaling without additional training. Inspired by the concept of momentum in physics, we propose Momentum Uncertainty-guided Reasoning (MUR), which dynamically allocates thinking budgets to critical reasoning steps by tracking and aggregating stepwise uncertainty over time. To support flexible inference-time control, we introduce gamma-control, a simple mechanism that tunes the reasoning budget via a single hyperparameter. We provide in-depth theoretical proof to support the superiority of MUR in terms of stability and biases. MUR is comprehensively evaluated against various TTS methods across four challenging benchmarks (MATH-500, AIME24, AIME25, and GPQA-diamond) using different sizes of recent Qwen3 models (1.7B, 4B, and 8B). Results demonstrate that MUR reduces computation by over 50% on average while improving accuracy by 0.62-3.37%.

Large reasoning models have achieved remarkable performance through extended chain-of-thought sequences, yet this computational freedom leads to excessive token generation even for simple problems. We present Length-Adaptive Policy Optimization (LAPO), a novel framework that transforms reasoning length control from an external constraint into an intrinsic model capability. Unlike existing approaches that impose rigid limits or rely on post-hoc interventions, LAPO enables models to internalize an understanding of appropriate reasoning depth through a two-stage reinforcement learning process. In the first stage, models learn natural reasoning patterns by discovering the statistical distribution of successful solution lengths. The second stage leverages these patterns as meta-cognitive guidance, embedding them directly within the model's reasoning context to ensure inference-time flexibility. Experiments on mathematical reasoning benchmarks demonstrate that LAPO reduces token usage by up to 40.9\% while improving accuracy by 2.3\%. Our analysis reveals that models trained with LAPO develop emergent abilities to allocate computational resources based on problem complexity, achieving efficient reasoning without sacrificing quality.

We present Captain Cinema, a generation framework for short movie generation. Given a detailed textual description of a movie storyline, our approach firstly generates a sequence of keyframes that outline the entire narrative, which ensures long-range coherence in both the storyline and visual appearance (e.g., scenes and characters). We refer to this step as top-down keyframe planning. These keyframes then serve as conditioning signals for a video synthesis model, which supports long context learning, to produce the spatio-temporal dynamics between them. This step is referred to as bottom-up video synthesis. To support stable and efficient generation of multi-scene long narrative cinematic works, we introduce an interleaved training strategy for Multimodal Diffusion Transformers (MM-DiT), specifically adapted for long-context video data. Our model is trained on a specially curated cinematic dataset consisting of interleaved data pairs. Our experiments demonstrate that Captain Cinema performs favorably in the automated creation of visually coherent and narrative consistent short movies in high quality and efficiency. Project page: https://thecinema.ai

Large reasoning models achieve remarkable performance through extensive chain-of-thought generation, yet exhibit significant computational inefficiency by applying uniform reasoning strategies regardless of problem complexity. We present Hierarchical Budget Policy Optimization (HBPO), a reinforcement learning framework that enables models to learn problem-specific reasoning depths without sacrificing capability. HBPO addresses the fundamental challenge of exploration space collapse in efficiency-oriented training, where penalties on long output length systematically bias models away from necessary long reasoning paths. Through hierarchical budget exploration, our approach partitions rollout samples into multiple subgroups with distinct token budgets, aiming to enable efficient resource allocation while preventing degradation of capability. We introduce differentiated reward mechanisms that create budget-aware incentives aligned with the complexity of the problem, allowing models to discover natural correspondences between task requirements and computational effort. Extensive experiments demonstrate that HBPO reduces average token usage by up to 60.6% while improving accuracy by 3.14% across four reasoning benchmarks. Unlike existing methods that impose external constraints or rely on discrete mode selection, HBPO exhibits emergent adaptive behavior where models automatically adjust reasoning depth based on problem complexity. Our results suggest that reasoning efficiency and capability are not inherently conflicting, and can be simultaneously optimized through appropriately structured hierarchical training that preserves exploration diversity.

Scaling visual generation models is essential for real-world content creation, yet requires substantial training and computational expenses. Alternatively, test-time scaling has garnered growing attention due to resource efficiency and promising performance. In this work, we present TTS-VAR, the first general test-time scaling framework for visual auto-regressive (VAR) models, modeling the generation process as a path searching problem. To dynamically balance computational efficiency with exploration capacity, we first introduce an adaptive descending batch size schedule throughout the causal generation process. Besides, inspired by VAR's hierarchical coarse-to-fine multi-scale generation, our framework integrates two key components: (i) At coarse scales, we observe that generated tokens are hard for evaluation, possibly leading to erroneous acceptance of inferior samples or rejection of superior samples. Noticing that the coarse scales contain sufficient structural information, we propose clustering-based diversity search. It preserves structural variety through semantic feature clustering, enabling later selection on samples with higher potential. (ii) In fine scales, resampling-based potential selection prioritizes promising candidates using potential scores, which are defined as reward functions incorporating multi-scale generation history. Experiments on the powerful VAR model Infinity show a notable 8.7% GenEval score improvement (from 0.69 to 0.75). Key insights reveal that early-stage structural features effectively influence final quality, and resampling efficacy varies across generation scales. Code is available at https://github.com/ali-vilab/TTS-VAR.

Information extraction (IE) is fundamental to numerous NLP applications, yet existing solutions often require specialized models for different tasks or rely on computationally expensive large language models. We present GLiNER2, a unified framework that enhances the original GLiNER architecture to support named entity recognition, text classification, and hierarchical structured data extraction within a single efficient model. Built pretrained transformer encoder architecture, GLiNER2 maintains CPU efficiency and compact size while introducing multi-task composition through an intuitive schema-based interface. Our experiments demonstrate competitive performance across extraction and classification tasks with substantial improvements in deployment accessibility compared to LLM-based alternatives. We release GLiNER2 as an open-source pip-installable library with pre-trained models and documentation at https://github.com/fastino-ai/GLiNER2.

Despite the remarkable developments achieved by recent 3D generation works, scaling these methods to geographic extents, such as modeling thousands of square kilometers of Earth's surface, remains an open challenge. We address this through a dual innovation in data infrastructure and model architecture. First, we introduce Aerial-Earth3D, the largest 3D aerial dataset to date, consisting of 50k curated scenes (each measuring 600m x 600m) captured across the U.S. mainland, comprising 45M multi-view Google Earth frames. Each scene provides pose-annotated multi-view images, depth maps, normals, semantic segmentation, and camera poses, with explicit quality control to ensure terrain diversity. Building on this foundation, we propose EarthCrafter, a tailored framework for large-scale 3D Earth generation via sparse-decoupled latent diffusion. Our architecture separates structural and textural generation: 1) Dual sparse 3D-VAEs compress high-resolution geometric voxels and textural 2D Gaussian Splats (2DGS) into compact latent spaces, largely alleviating the costly computation suffering from vast geographic scales while preserving critical information. 2) We propose condition-aware flow matching models trained on mixed inputs (semantics, images, or neither) to flexibly model latent geometry and texture features independently. Extensive experiments demonstrate that EarthCrafter performs substantially better in extremely large-scale generation. The framework further supports versatile applications, from semantic-guided urban layout generation to unconditional terrain synthesis, while maintaining geographic plausibility through our rich data priors from Aerial-Earth3D. Our project page is available at https://whiteinblue.github.io/earthcrafter/

We introduce the latest series of TeleChat models: TeleChat2, TeleChat2.5, and T1, offering a significant upgrade over their predecessor, TeleChat. Despite minimal changes to the model architecture, the new series achieves substantial performance gains through enhanced training strategies in both pre-training and post-training stages. The series begins with TeleChat2, which undergoes pretraining on 10 trillion high-quality and diverse tokens. This is followed by Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) to further enhance its capabilities. TeleChat2.5 and T1 expand the pipeline by incorporating a continual pretraining phase with domain-specific datasets, combined with reinforcement learning (RL) to improve performance in code generation and mathematical reasoning tasks. The T1 variant is designed for complex reasoning, supporting long Chain-of-Thought (CoT) reasoning and demonstrating substantial improvements in mathematics and coding. In contrast, TeleChat2.5 prioritizes speed, delivering rapid inference. Both flagship models of T1 and TeleChat2.5 are dense Transformer-based architectures with 115B parameters, showcasing significant advancements in reasoning and general task performance compared to the original TeleChat. Notably, T1-115B outperform proprietary models such as OpenAI's o1-mini and GPT-4o. We publicly release TeleChat2, TeleChat2.5 and T1, including post-trained versions with 35B and 115B parameters, to empower developers and researchers with state-of-the-art language models tailored for diverse applications.

Learning from non-stationary data streams subject to concept drift requires models that can adapt on-the-fly while remaining resource-efficient. Existing adaptive ensemble methods often rely on coarse-grained adaptation mechanisms or simple voting schemes that fail to optimally leverage specialized knowledge. This paper introduces DriftMoE, an online Mixture-of-Experts (MoE) architecture that addresses these limitations through a novel co-training framework. DriftMoE features a compact neural router that is co-trained alongside a pool of incremental Hoeffding tree experts. The key innovation lies in a symbiotic learning loop that enables expert specialization: the router selects the most suitable expert for prediction, the relevant experts update incrementally with the true label, and the router refines its parameters using a multi-hot correctness mask that reinforces every accurate expert. This feedback loop provides the router with a clear training signal while accelerating expert specialization. We evaluate DriftMoE's performance across nine state-of-the-art data stream learning benchmarks spanning abrupt, gradual, and real-world drifts testing two distinct configurations: one where experts specialize on data regimes (multi-class variant), and another where they focus on single-class specialization (task-based variant). Our results demonstrate that DriftMoE achieves competitive results with state-of-the-art stream learning adaptive ensembles, offering a principled and efficient approach to concept drift adaptation. All code, data pipelines, and reproducibility scripts are available in our public GitHub repository: https://github.com/miguel-ceadar/drift-moe.

Diffusion-based text-to-speech (TTS) systems have made remarkable progress in zero-shot speech synthesis, yet optimizing all components for perceptual metrics remains challenging. Prior work with DMOSpeech demonstrated direct metric optimization for speech generation components, but duration prediction remained unoptimized. This paper presents DMOSpeech 2, which extends metric optimization to the duration predictor through a reinforcement learning approach. The proposed system implements a novel duration policy framework using group relative preference optimization (GRPO) with speaker similarity and word error rate as reward signals. By optimizing this previously unoptimized component, DMOSpeech 2 creates a more complete metric-optimized synthesis pipeline. Additionally, this paper introduces teacher-guided sampling, a hybrid approach leveraging a teacher model for initial denoising steps before transitioning to the student model, significantly improving output diversity while maintaining efficiency. Comprehensive evaluations demonstrate superior performance across all metrics compared to previous systems, while reducing sampling steps by half without quality degradation. These advances represent a significant step toward speech synthesis systems with metric optimization across multiple components. The audio samples, code and pre-trained models are available at https://dmospeech2.github.io/.

Segments in computer vision are often defined by semantic considerations and are highly dependent on category-specific conventions. In contrast, developmental psychology suggests that humans perceive the world in terms of Spelke objects--groupings of physical things that reliably move together when acted on by physical forces. Spelke objects thus operate on category-agnostic causal motion relationships which potentially better support tasks like manipulation and planning. In this paper, we first benchmark the Spelke object concept, introducing the SpelkeBench dataset that contains a wide variety of well-defined Spelke segments in natural images. Next, to extract Spelke segments from images algorithmically, we build SpelkeNet, a class of visual world models trained to predict distributions over future motions. SpelkeNet supports estimation of two key concepts for Spelke object discovery: (1) the motion affordance map, identifying regions likely to move under a poke, and (2) the expected-displacement map, capturing how the rest of the scene will move. These concepts are used for "statistical counterfactual probing", where diverse "virtual pokes" are applied on regions of high motion-affordance, and the resultant expected displacement maps are used define Spelke segments as statistical aggregates of correlated motion statistics. We find that SpelkeNet outperforms supervised baselines like SegmentAnything (SAM) on SpelkeBench. Finally, we show that the Spelke concept is practically useful for downstream applications, yielding superior performance on the 3DEditBench benchmark for physical object manipulation when used in a variety of off-the-shelf object manipulation models.

This report documents, describes, and evaluates new 2024 English GloVe (Global Vectors for Word Representation) models. While the original GloVe models built in 2014 have been widely used and found useful, languages and the world continue to evolve and we thought that current usage could benefit from updated models. Moreover, the 2014 models were not carefully documented as to the exact data versions and preprocessing that were used, and we rectify this by documenting these new models. We trained two sets of word embeddings using Wikipedia, Gigaword, and a subset of Dolma. Evaluation through vocabulary comparison, direct testing, and NER tasks shows that the 2024 vectors incorporate new culturally and linguistically relevant words, perform comparably on structural tasks like analogy and similarity, and demonstrate improved performance on recent, temporally dependent NER datasets such as non-Western newswire data.

Medical image segmentation is crucial for many healthcare tasks, including disease diagnosis and treatment planning. One key area is the segmentation of skin lesions, which is vital for diagnosing skin cancer and monitoring patients. In this context, this paper introduces SegDT, a new segmentation model based on diffusion transformer (DiT). SegDT is designed to work on low-cost hardware and incorporates Rectified Flow, which improves the generation quality at reduced inference steps and maintains the flexibility of standard diffusion models. Our method is evaluated on three benchmarking datasets and compared against several existing works, achieving state-of-the-art results while maintaining fast inference speeds. This makes the proposed model appealing for real-world medical applications. This work advances the performance and capabilities of deep learning models in medical image analysis, enabling faster, more accurate diagnostic tools for healthcare professionals. The code is made publicly available at https://github.com/Bekhouche/SegDT{GitHub}.

分析显示，今天的环法自行车选手已经超越了兴奋剂时代的阿姆斯特朗（Lance Armstrong）。去年环法自行车赛的一个山地赛段中 Tadej Pogacar 在近 40 分钟内的功率输出约 7瓦/千克。Jonas Vingegaard 曾在近 15 分钟内功率输出超过 7瓦/千克。相比下，阿姆斯特朗在 20 年前靠兴奋剂实现了 6瓦/千克的功率输出，他完成路段的时间比今天的顶尖选手慢。阿姆斯特朗靠服用兴奋剂从 1999 年到 2005 年连续七次获得环法自行车赛冠军。于 2012年 被取消自 1998 年 8 月之后的所有成绩，被终身禁赛。今天的选手表现更出色源于技术进步：每位选手都使用提供实时性能数据的功率计；营养摄入使用精确测量的食物摄入量持续补充热量；自行车使用风洞测试以降低阻力系数，等等。

《科学》期刊撤下了受争议的砷基生命论文。2010 年《科学》期刊发表了 F. Wolfe-Simon 等人的论文《A bacterium that can grow by using arsenic instead of phosphorus》，声称在加州湖泊中发现了一种砷基细菌 GFAJ-1，它利用砷而不是磷生长。论文发表之后引发了很多争议，2012 年《科学》发表了两篇未能复制这一发现的论文。《科学》期刊主编 Holden Thorp 在声明中称，他们没有在 2012 年撤回论文是因为当时的政策主要针对存在科学不端行为，而这篇论文的作者没有故意欺骗或犯有不端行为。《科学》后来扩大了撤稿的政策：如果一篇论文报告的实验结果不支持其核心结论，撤下是合适的。

Pebble 创始人 Eric Migicovsky 宣布他拿回了原始商标，因此他的公司准备推出的智能手表产品将使用 Pebble 商标：Core 2 Duo 改为 Pebble 2 Duo，Core Time 2 改名 Pebble Time 2。Pebble 诞生于 2012 年，Eric Migicovsky 通过 Kickstarter 筹集到了创纪录的 1030 万美元，而其第二代智能手表通过 Kickstarter 再次筹集到破纪录的 2030 万美元。但在 2016 年 12 月 Pebble 出售给 Fitbit 后关闭，创始人也离开了公司。Google 通过收购 Fitbit 获得了 Pebble 的所有权。今年初 Google 宣布在 Apache License 2.0 下开源 Pebble 智能手表操作系统，源代码托管在 GitHub 上。而 Migicovsky 同一时间宣布推出能运行 Pebble OS 的新智能手表产品。

一项初步研究显示，全球的民用机场与军事设施所操作的雷达系统，可能正无意间将地球的存在广播给科技先进的外星文明，这些讯号可被视为智慧生命的间接证据。研究调查了雷达系统泄漏出的电波讯号若由距离地球 200 光年的观测者侦测到，会呈现出怎样的样貌，前提是他们拥有与地球上同等级的电波望远镜。研究结果同时也意味着，理论上我们也能在相同范围内侦测到类似等级的外星文明。研究人员的目标是评估六个邻近的恒星系统，尤其是巴纳德星（Barnard’s Star，5.98光年） 与显微镜座AU（AU Microscopii，32.3光年）来看，这些讯号的可侦测程度。分析显示，机场用来监控飞机的雷达系统，合计产生约 2×10¹⁵瓦特的功率，这样的能量输出足以让如绿堤望远镜（Green Bank Telescope）等级的电波望远镜在 200 光年外仍能侦测到讯号。军事雷达系统具有更高的指向性，形成如同灯塔光束扫过天空般的独特模式。

域名系统（DNS）的原始设计不包含任何安全细节，域名系统安全扩展（DNSSEC）尝试在其中添加安全性，同时仍保持向后兼容性。DNSSEC 能阻止 DNS 缓存污染等攻击，它的 RFC 是在 28 年前发布的，根据 Internet Society 的数据，DNSSEC 普及率仅为 34%，相比下 HTTPS 的开发时间线与 DNSSEC 基本相同——在 Top 1000 网站中，HTTPS 的普及率为 96%，HTTP/3 仅发布四年时间普及率就达到了 25%。大约三成的国家域名尚未实现 DNSSEC。

2017 年 Google 的一辆街景车在阿根廷拍摄到了一名警察在自家院子里裸体的画面，将这名警察的光屁股以及门牌号和街道名都公布在地图上，此事经过阿根廷媒体报道之后被广泛传播。这位警察对搜索巨人提起了诉讼，指控其侵犯了他的尊严，他表示自家院子的围墙高 6.5 英尺，称自己在邻居和同事中间沦为笑柄。Google 对此的回应是围墙不够高。阿根廷法庭去年驳回了诉讼，认为责任在于他本人的行为不恰当。本周上诉法庭推翻了原判，判决这名男子的尊严受到了公然的侵犯，Google 需要为此赔偿 1.25 万美元。

Wayback 项目释出了 v0.1 版本。Wayland 是目前主流的显示服务器，它正在取代 X.org X11 server，后者目前处于维护模式，不再继续开发。虽然 Wayland 取得了长足进步，但仍然有很多基于 X11 的窗口管理器和桌面环境不支持 Wayland。Wayback 项目就是旨在解决这一问题，允许用户在 Wayland 上运行旧的 X11 桌面环境，它有望成为 X.org 的一个更简单更直接的替代，减轻 Linux 发行版的维护负担。v0.1 的版本号意味着它处于 alpha 状态，功能远不完整，有很多功能尚未实现或正在开发中，如多显示器支持。

AMD CEO 苏姿丰表示，台积电美国工厂制造的芯片会比台湾工厂贵 5%-20%，预计今年年底台积电亚利桑那州工厂会为 AMD 生产芯片。她表示额外的支出是值得的，有助于实现芯片供应的多元化。她称新冠疫情期间得到的教训促使他们关注供应链的弹性。苏姿丰认为 AI 芯片的供应将会持续保持高位。英伟达是最主要的 AI 芯片供应商，而 AMD 是英伟达最接近的竞争对手。

微软 CEO 纳德拉（Satya Nadella）周四在给员工的备忘录中简单回应了大规模裁员引发员工担忧一事。微软在股价创新高利润创纪录向 AI 大规模投资的同时进行了大规模裁员，今年至今裁员逾 1.5 万人，其中 7 月初裁掉了 9 千人。为什么公司状况如此之好却仍然进行裁员？纳德拉使用了标准的模糊语言，没有正面回应，只是说裁员给员工带来了压力，但是进步不是沿着一条直线，是动态的，有时候不协调，但总是苛刻的，我们还是来谈谈使命吧。这是高管或政客在不愿意正面回应时使用的话术，没什么意义，他不愿意告诉员工他的薪酬是与利润和股价挂钩，而不是与员工的忠诚挂钩，裁员能让投资者或股东满意，对投资者有利，对他也有利。

为提高透明度，法国 AI 公司 Mistral AI 与 Carbone 4 和生态转型机构 ADEME 合作发布了其大模型 Mistral Large 2 的环境报告，证实 AI 是一个饥渴的怪物。Mistral Large 2 大模型的推理过程占到了温室气体排放的 85.5% 和水消耗的 91%；Mistral Large 2 有 1230 亿个参数，模型训练产生了约 2 万吨二氧化碳当量，消耗了 28.1 万立方米水，相当于约 112 个奥运会标准游泳池的蓄水量；为了产生 400 个 token 的响应，模型消耗了约 45 毫升水，产生了约 1.14 克二氧化碳当量。Mistral 称测试显示，大模型的环境影响与参数规模成正比，生成相同数量的 token，一个参数规模大十倍的模型的环境影响比较小的模型大一个量级。

由于谈判不顺利，在三次给予 TikTok 宽限期之后美国政府官员威胁关闭 TikTok。TikTok 美国业务出售或被禁止服务的禁令原本于 2025 年 1 月 19 日生效，特朗普于 1 月 20 日上任之后就给了它 75 天宽限期。该宽限期于 4 月 5 日截至，但将 TikTok 美国业务出售给美国公司的谈判仍然在进行之中，特朗普之后第二次给了 75 天的宽限期。在 6 月 19 日第二次宽限期即将结束之际，特朗普再次延长 90 天。这位最早在 2020 年威胁要关闭 TikTok 的美国总统表示只有他能达成交易让它能在美国继续运营。然而与中国方面的谈判并不顺利，商务部长 Howard Lutnick 表示如果中国方面不批准交易，美国政府愿意关闭 TikTok。谈判的焦灼点是美国要求字节跳动出售 TikTok 使用的推荐算法。

代号为 Trixie 的 Debian 13.0 将于 8 月 9 日释出，新版本将有哪些变化？apt 更新到 v3.0，支持以不同颜色区分更新或下载；systemd 升级到 257.7-1；Kernel 为 6.12 LTS 版；Prometheus server 更新到 v2.53，OpenSSH 更新到 v10.0p1-5；此外还有大量软件包更新。

深圳中软赢科准备在 Chinajoy 2025 上展示其最高端的掌机：使用 AMD Ryzen AI Max+ 395 APU 的 GPD WIN 5。AMD Ryzen AI Max 395 此前主要用于工作站，由基于 Zen5 架构的 16 核 32 线 CPU 和 Radeon 8060S GPU 组成，华硕和惠普推出的 Ryzen AI Max 395 笔记本电脑售价在 1.5-2 万元之间，很难想象如此强大的 APU 会用于掌机，也很难想象掌机的电池续航时间会有多久。

日本内阁府的生命伦理专门调查会达成基本共识，允许使用人类 iPS 细胞制作受精卵（胚胎）。培养期限定为 14 天以内。在遵守一定规则的前提下允许进行相关研究，这将有助于查明不孕症及遗传性疾病的病因。使用受精卵的传统研究，大多使用在不孕治疗过程中获得的受精卵。如果能够使用 iPS 细胞等轻松获取受精卵，繁殖相关研究有可能取得进展。在使用 iPS 细胞来调查“繁殖”机制的研究领域，日本一直走在世界前列。已经在小鼠实验中成功使用 iPS 细胞制作卵子和精子，并使其受精后培育出后代。在人类身上实现这一点也只是时间问题，因此需要加紧建立相关规则。京都大学教授斋藤通纪的研究团队于 2011～2012 年首次成功利用小鼠的 iPS 细胞制作出卵子和精子，并使其受精后培育出后代。

作为 CEO 陈立武（Lip-Bu Tan）全面重组计划的一部分，英特尔宣布在 2025 年内裁员约 2.4 万人，取消或者缩减位于德国、波兰、哥斯达黎加和俄亥俄州的项目规模。截至 2024 年底，英特尔员工总数为 10.98 万名，其中 9.95 万是“核心员工”。芯片巨人表示，它计划到 2025 年底核心员工总数为 7.5 万。这意味着今年内裁员 2.4 万人，占到了员工总数的四分之一。陈立武在财报电话会议上表示，新工厂过度投资，他不认同只要建了工厂客户就会来的观点。英特尔取消了在德国和波兰投资数百亿美元建造晶圆厂、组装和测试设施的计划。哥斯达黎加的组装和测试业务将整合到越南的工厂，而逾 3400 名员工中的 2000 多人将会继续留在工程和企业部门。