OrangeBot.AI Digest — 2025-12-25

CV/resume generator for academics and engineers, YAML to PDF

结合LightRAG 知识库的知识图谱智能体平台。 An agent platform that integrates a LightRAG knowledge base and knowledge graphs. Build with LangChain v1 + Vue + FastAPI, support DeepAgents、MinerU PDF、Neo4j 、MCP.

Source code for the X Recommendation Algorithm

The most customizable commerce platform built with TypeScript, NestJS and GraphQL.

RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device.

SoTA open-source TTS

🔥🔥🔥 Open-source Jira, Linear, Monday, and ClickUp alternative. Plane is a modern project management platform to manage tasks, sprints, docs, and triage.

A framework for efficient model inference with omni-modality models

Light Video Generation Inference Framework

We introduce TurboDiffusion, a video generation acceleration framework that can speed up end-to-end diffusion generation by 100-200x while maintaining video quality. TurboDiffusion mainly relies on several components for acceleration: (1) Attention acceleration: TurboDiffusion uses low-bit SageAttention and trainable Sparse-Linear Attention (SLA) to speed up attention computation. (2) Step distillation: TurboDiffusion adopts rCM for efficient step distillation. (3) W8A8 quantization: TurboDiffusion quantizes model parameters and activations to 8 bits to accelerate linear layers and compress the model. In addition, TurboDiffusion incorporates several other engineering optimizations. We conduct experiments on the Wan2.2-I2V-14B-720P, Wan2.1-T2V-1.3B-480P, Wan2.1-T2V-14B-720P, and Wan2.1-T2V-14B-480P models. Experimental results show that TurboDiffusion achieves 100-200x speedup for video generation even on a single RTX 5090 GPU, while maintaining comparable video quality. The GitHub repository, which includes model checkpoints and easy-to-use code, is available at https://github.com/thu-ml/TurboDiffusion.

Vision-language models (VLM) excel at general understanding yet remain weak at dynamic spatial reasoning (DSR), i.e., reasoning about the evolvement of object geometry and relationship in 3D space over time, largely due to the scarcity of scalable 4D-aware training resources. To bridge this gap across aspects of dataset, benchmark and model, we introduce DSR Suite. First, we propose an automated pipeline that generates multiple-choice question-answer pairs from in-the-wild videos for DSR. By leveraging modern vision foundation models, the pipeline extracts rich geometric and motion information, including camera poses, local point clouds, object masks, orientations, and 3D trajectories. These geometric cues enable the construction of DSR-Train for learning and further human-refined DSR-Bench for evaluation. Compared with previous works, our data emphasize (i) in-the-wild video sources, (ii) object- and scene-level 3D requirements, (iii) viewpoint transformations, (iv) multi-object interactions, and (v) fine-grained, procedural answers. Beyond data, we propose a lightweight Geometry Selection Module (GSM) to seamlessly integrate geometric priors into VLMs, which condenses question semantics and extracts question-relevant knowledge from pretrained 4D reconstruction priors into a compact set of geometry tokens. This targeted extraction avoids overwhelming the model with irrelevant knowledge. Experiments show that integrating DSR-Train and GSM into Qwen2.5-VL-7B significantly enhances its dynamic spatial reasoning capability, while maintaining accuracy on general video understanding benchmarks.

The "one-shot" technique represents a distinct and sophisticated aesthetic in filmmaking. However, its practical realization is often hindered by prohibitive costs and complex real-world constraints. Although emerging video generation models offer a virtual alternative, existing approaches typically rely on naive clip concatenation, which frequently fails to maintain visual smoothness and temporal coherence. In this paper, we introduce DreaMontage, a comprehensive framework designed for arbitrary frame-guided generation, capable of synthesizing seamless, expressive, and long-duration one-shot videos from diverse user-provided inputs. To achieve this, we address the challenge through three primary dimensions. (i) We integrate a lightweight intermediate-conditioning mechanism into the DiT architecture. By employing an Adaptive Tuning strategy that effectively leverages base training data, we unlock robust arbitrary-frame control capabilities. (ii) To enhance visual fidelity and cinematic expressiveness, we curate a high-quality dataset and implement a Visual Expression SFT stage. In addressing critical issues such as subject motion rationality and transition smoothness, we apply a Tailored DPO scheme, which significantly improves the success rate and usability of the generated content. (iii) To facilitate the production of extended sequences, we design a Segment-wise Auto-Regressive (SAR) inference strategy that operates in a memory-efficient manner. Extensive experiments demonstrate that our approach achieves visually striking and seamlessly coherent one-shot effects while maintaining computational efficiency, empowering users to transform fragmented visual materials into vivid, cohesive one-shot cinematic experiences.

Text-to-Audio-Video (T2AV) generation aims to synthesize temporally coherent video and semantically synchronized audio from natural language, yet its evaluation remains fragmented, often relying on unimodal metrics or narrowly scoped benchmarks that fail to capture cross-modal alignment, instruction following, and perceptual realism under complex prompts. To address this limitation, we present T2AV-Compass, a unified benchmark for comprehensive evaluation of T2AV systems, consisting of 500 diverse and complex prompts constructed via a taxonomy-driven pipeline to ensure semantic richness and physical plausibility. Besides, T2AV-Compass introduces a dual-level evaluation framework that integrates objective signal-level metrics for video quality, audio quality, and cross-modal alignment with a subjective MLLM-as-a-Judge protocol for instruction following and realism assessment. Extensive evaluation of 11 representative T2AVsystems reveals that even the strongest models fall substantially short of human-level realism and cross-modal consistency, with persistent failures in audio realism, fine-grained synchronization, instruction following, etc. These results indicate significant improvement room for future models and highlight the value of T2AV-Compass as a challenging and diagnostic testbed for advancing text-to-audio-video generation.

We expose a significant popularity bias in state-of-the-art vision-language models (VLMs), which achieve up to 34% higher accuracy on famous buildings compared to ordinary ones, indicating a reliance on memorization over generalizable understanding. To systematically investigate this, we introduce the largest open benchmark for this task: the YearGuessr dataset, a collection of 55,546 building images with multi-modal attributes from 157 countries, annotated with continuous ordinal labels of their construction year (1001-2024), GPS data, and page-view counts as a proxy for popularity. Using this dataset, we frame the construction year prediction task as ordinal regression and introduce popularity-aware interval accuracy metrics to quantify this bias. Our resulting benchmark of 30+ models, including our YearCLIP model, confirms that VLMs excel on popular, memorized items but struggle significantly with unrecognized subjects, exposing a critical flaw in their reasoning capabilities. Project page: https://sytwu.github.io/BeyondMemo/

We present Nemotron 3 Nano 30B-A3B, a Mixture-of-Experts hybrid Mamba-Transformer language model. Nemotron 3 Nano was pretrained on 25 trillion text tokens, including more than 3 trillion new unique tokens over Nemotron 2, followed by supervised fine tuning and large-scale RL on diverse environments. Nemotron 3 Nano achieves better accuracy than our previous generation Nemotron 2 Nano while activating less than half of the parameters per forward pass. It achieves up to 3.3x higher inference throughput than similarly-sized open models like GPT-OSS-20B and Qwen3-30B-A3B-Thinking-2507, while also being more accurate on popular benchmarks. Nemotron 3 Nano demonstrates enhanced agentic, reasoning, and chat abilities and supports context lengths up to 1M tokens. We release both our pretrained Nemotron 3 Nano 30B-A3B Base and post-trained Nemotron 3 Nano 30B-A3B checkpoints on Hugging Face.

High-resolution video generation, while crucial for digital media and film, is computationally bottlenecked by the quadratic complexity of diffusion models, making practical inference infeasible. To address this, we introduce HiStream, an efficient autoregressive framework that systematically reduces redundancy across three axes: i) Spatial Compression: denoising at low resolution before refining at high resolution with cached features; ii) Temporal Compression: a chunk-by-chunk strategy with a fixed-size anchor cache, ensuring stable inference speed; and iii) Timestep Compression: applying fewer denoising steps to subsequent, cache-conditioned chunks. On 1080p benchmarks, our primary HiStream model (i+ii) achieves state-of-the-art visual quality while demonstrating up to 76.2x faster denoising compared to the Wan2.1 baseline and negligible quality loss. Our faster variant, HiStream+, applies all three optimizations (i+ii+iii), achieving a 107.5x acceleration over the baseline, offering a compelling trade-off between speed and quality, thereby making high-resolution video generation both practical and scalable.

We introduce the Nemotron 3 family of models - Nano, Super, and Ultra. These models deliver strong agentic, reasoning, and conversational capabilities. The Nemotron 3 family uses a Mixture-of-Experts hybrid Mamba-Transformer architecture to provide best-in-class throughput and context lengths of up to 1M tokens. Super and Ultra models are trained with NVFP4 and incorporate LatentMoE, a novel approach that improves model quality. The two larger models also include MTP layers for faster text generation. All Nemotron 3 models are post-trained using multi-environment reinforcement learning enabling reasoning, multi-step tool use, and support granular reasoning budget control. Nano, the smallest model, outperforms comparable models in accuracy while remaining extremely cost-efficient for inference. Super is optimized for collaborative agents and high-volume workloads such as IT ticket automation. Ultra, the largest model, provides state-of-the-art accuracy and reasoning performance. Nano is released together with its technical report and this white paper, while Super and Ultra will follow in the coming months. We will openly release the model weights, pre- and post-training software, recipes, and all data for which we hold redistribution rights.

Tokenizers provide the fundamental basis through which text is represented and processed by language models (LMs). Despite the importance of tokenization, its role in LM performance and behavior is poorly understood due to the challenge of measuring the impact of tokenization in isolation. To address this need, we present TokSuite, a collection of models and a benchmark that supports research into tokenization's influence on LMs. Specifically, we train fourteen models that use different tokenizers but are otherwise identical using the same architecture, dataset, training budget, and initialization. Additionally, we curate and release a new benchmark that specifically measures model performance subject to real-world perturbations that are likely to influence tokenization. Together, TokSuite allows robust decoupling of the influence of a model's tokenizer, supporting a series of novel findings that elucidate the respective benefits and shortcomings of a wide range of popular tokenizers.

Agentic reinforcement learning increasingly relies on experience-driven scaling, yet real-world environments remain non-adaptive, limited in coverage, and difficult to scale. World models offer a potential way to improve learning efficiency through simulated experience, but it remains unclear whether large language models can reliably serve this role and under what conditions they meaningfully benefit agents. We study these questions in text-based environments, which provide a controlled setting to reinterpret language modeling as next-state prediction under interaction. We introduce a three-level framework for evaluating LLM-based world models: (i) fidelity and consistency, (ii) scalability and robustness, and (iii) agent utility. Across five representative environments, we find that sufficiently trained world models maintain coherent latent state, scale predictably with data and model size, and improve agent performance via action verification, synthetic trajectory generation, and warm-starting reinforcement learning. Meanwhile, these gains depend critically on behavioral coverage and environment complexity, delineating clear boundry on when world modeling effectively supports agent learning.

Recent advances in pretraining general foundation models have significantly improved performance across diverse downstream tasks. While autoregressive (AR) generative models like GPT have revolutionized NLP, most visual generative pretraining methods still rely on BERT-style masked modeling, which often disregards the temporal information essential for video analysis. The few existing autoregressive visual pretraining methods suffer from issues such as inaccurate semantic localization and poor generation quality, leading to poor semantics. In this work, we propose NExT-Vid, a novel autoregressive visual generative pretraining framework that utilizes masked next-frame prediction to jointly model images and videos. NExT-Vid introduces a context-isolated autoregressive predictor to decouple semantic representation from target decoding, and a conditioned flow-matching decoder to enhance generation quality and diversity. Through context-isolated flow-matching pretraining, our approach achieves strong representations. Extensive experiments on large-scale pretrained models demonstrate that our proposed method consistently outperforms previous generative pretraining methods for visual representation learning via attentive probing in downstream classification.

Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for Large Language Models (LLMs) to address knowledge-intensive queries requiring domain-specific or up-to-date information. To handle complex multi-hop questions that are challenging for single-step retrieval, iterative RAG approaches incorporating reinforcement learning have been proposed. However, existing iterative RAG systems typically plan to decompose questions without leveraging information about the available retrieval corpus, leading to inefficient retrieval and reasoning chains that cascade into suboptimal performance. In this paper, we introduce Early Knowledge Alignment (EKA), a simple but effective module that aligns LLMs with retrieval set before planning in iterative RAG systems with contextually relevant retrieved knowledge. Extensive experiments on six standard RAG datasets demonstrate that by establishing a stronger reasoning foundation, EKA significantly improves retrieval precision, reduces cascading errors, and enhances both performance and efficiency. Our analysis from an entropy perspective demonstrate that incorporating early knowledge reduces unnecessary exploration during the reasoning process, enabling the model to focus more effectively on relevant information subsets. Moreover, EKA proves effective as a versatile, training-free inference strategy that scales seamlessly to large models. Generalization tests across diverse datasets and retrieval corpora confirm the robustness of our approach. Overall, EKA advances the state-of-the-art in iterative RAG systems while illuminating the critical interplay between structured reasoning and efficient exploration in reinforcement learning-augmented frameworks. The code is released at https://github.com/yxzwang/EarlyKnowledgeAlignment{Github}.

We present Streamo, a real-time streaming video LLM that serves as a general-purpose interactive assistant. Unlike existing online video models that focus narrowly on question answering or captioning, Streamo performs a broad spectrum of streaming video tasks, including real-time narration, action understanding, event captioning, temporal event grounding, and time-sensitive question answering. To develop such versatility, we construct Streamo-Instruct-465K, a large-scale instruction-following dataset tailored for streaming video understanding. The dataset covers diverse temporal contexts and multi-task supervision, enabling unified training across heterogeneous streaming tasks. After training end-to-end on the instruction-following dataset through a streamlined pipeline, Streamo exhibits strong temporal reasoning, responsive interaction, and broad generalization across a variety of streaming benchmarks. Extensive experiments show that Streamo bridges the gap between offline video perception models and real-time multimodal assistants, making a step toward unified, intelligent video understanding in continuous video streams.

Existing benchmarks for AI coding agents focus on isolated, single-issue tasks such as fixing a bug or implementing a small feature. However, real-world software engineering is fundamentally a long-horizon endeavor: developers must interpret high-level requirements, plan coordinated changes across many files, and evolve codebases over multiple iterations while preserving existing functionality. We introduce SWE-EVO, a benchmark that evaluates agents on this long-horizon software evolution challenge. Constructed from release notes and version histories of seven mature open-source Python projects, Tool comprises 48 evolution tasks that require agents to implement multi-step modifications spanning an average of 21 files, validated against comprehensive test suites averaging 874 tests per instance. Experiments with state-of-the-art models reveal a striking capability gap: even GPT-5 with OpenHands achieves only a 21 percent resolution rate on Tool, compared to 65 percent on the single-issue SWE-Bench Verified. This demonstrates that current agents struggle with sustained, multi-file reasoning. We also propose Fix Rate, a fine-grained metric that captures partial progress toward solving these complex, long-horizon tasks.

In this work, we introduce PhononBench, the first large-scale benchmark for dynamical stability in AI-generated crystals. Leveraging the recently developed MatterSim interatomic potential, which achieves DFT-level accuracy in phonon predictions across more than 10,000 materials, PhononBench enables efficient large-scale phonon calculations and dynamical-stability analysis for 108,843 crystal structures generated by six leading crystal generation models. PhononBench reveals a widespread limitation of current generative models in ensuring dynamical stability: the average dynamical-stability rate across all generated structures is only 25.83%, with the top-performing model, MatterGen, reaching just 41.0%. Further case studies show that in property-targeted generation-illustrated here by band-gap conditioning with MatterGen--the dynamical-stability rate remains as low as 23.5% even at the optimal band-gap condition of 0.5 eV. In space-group-controlled generation, higher-symmetry crystals exhibit better stability (e.g., cubic systems achieve rates up to 49.2%), yet the average stability across all controlled generations is still only 34.4%. An important additional outcome of this study is the identification of 28,119 crystal structures that are phonon-stable across the entire Brillouin zone, providing a substantial pool of reliable candidates for future materials exploration. By establishing the first large-scale dynamical-stability benchmark, this work systematically highlights the current limitations of crystal generation models and offers essential evaluation criteria and guidance for their future development toward the design and discovery of physically viable materials. All model-generated crystal structures, phonon calculation results, and the high-throughput evaluation workflows developed in PhononBench will be openly released at https://github.com/xqh19970407/PhononBench

最近发生多起发射事故的俄罗斯宇航局 Roscosmos 宣布计划到 2036 年在月球上建造一座核电站，已经与宇航公司 Lavochkin Association 签署了一份合同。Roscosmos 表示参与者还包括了国家原子能公司（Rosatom）和核能研究机构 Kurchatov Institute。Roscosmos 表示该电站将为俄罗斯的月球计划提供动力，包括月球车、天文台以及俄中联合国际月球研究站（Russian-Chinese International Lunar Research Station）的基础设施。

欧盟 2024 年使用的能量有 25.2% 来自可再生能源，比 2023 年增加 0.7 个百分点，距离 20230 年可再生能源占比 42.5% 还差 17.3 个百分点，意味着要实现目标从 2025 年到 2030 年可再生能源占比每年要增长 2.9%。欧盟国家中，瑞典的可再生能源占比最高达到 62.8%。瑞典主要依赖固体生物质能、水力发电和风能。芬兰紧随其后占比 52.1%，同样依赖固体生物质能、风能和水力发电。丹麦第三占比 46.8%，大部分可再生能源来自固体生物质能、风能和沼气。比利时（14.3%）、卢森堡（14.7%）和爱尔兰（16.1%）的可再生能源占比最低。

微软杰出工程师 Galen Hunt 在 LinkedIn 上畅谈要在 AI 和算法的帮助下，到 2030 年用 Rust 语言重写所有 C 和 C++ 代码，目标是一名工程师一个月一百万行代码。这番话引发了一片哗然，以至于微软进行澄清，而 Galen Hunt 则修改了他的帖子。他在帖子里大量使用“我们（We）”这个词，因此在外界看来他是代表公司发言。微软高管 Frank X. Shaw 澄清，公司没有计划使用 AI 重写 Windows 11。而 Galen Hunt 也在帖子里澄清：微软没有采用 AI 使用 Rust 重写 Windows 11，表示读者过度解读了。

Ruby 语言在圣诞节释出了 v4.0.0。Ruby 语言一直习惯在圣诞节发布大更新。Ruby 4.0.0 的新特性包括：新实验性功能 Ruby Box——提供定义隔离；新的 JIT 编译器 ZJIT，它是作为 YJIT 的下一代开发的，但目前速度还不如 YJIT，不建议用于生产环境；改进并行执行机制 Ractor；语法方面的改变，等等。

在获得出口许可之后，英伟达通知中国客户计划春节前发货 H200。初始订单将使用现有库存完成，现有库存有 5,000-10,000 块 HGX 主板，总计提供 40,000 到 80,000 块 GPU。这意味着英伟达将优先供应性能更强的 SXM 版的 H200 显卡，比基于 PCIe 的 NVL 显卡更适合训练应用。根据英伟达与美国政府的协议，它将上缴 25% 的销售收入。英伟达同时通知客户，在中国政府批准之后才能确定发货时间。

美国国务院禁止五名欧洲人入境，其中四人是欧洲 NGO 组织负责人，一人是前欧盟委员，原因是他们推动对美国科技巨头的监管。五人包括：2019-2024 年之间负责内部市场的欧盟委员 Thierry Breton，“打击网络仇恨中心”的 Imran Ahmed、总部位于英国的“全球虚假信息指数”的 Clare Melford，以及德国“仇恨援助”（HateAid）组织的 Anna-Lena von Hodenberg 和 Josephine Ballon，“仇恨援助”负责举报网络上的极右翼仇恨言论。美国国务卿鲁比奥表示：“长期以来，欧洲的意识形态分子一直在有组织地胁迫美国平台惩罚他们反对的美国观点。特朗普政府不会再容忍这种恶劣的域外审查行为。”

YouTube 是最受欢迎的视频平台，但它的优势主要是在白天而非晚上的黄金时间段。尼尔森的数据显示，10 月的上午 11 点，YouTube 在美国的平均电视观看人数为 630 万，而 Netflix 为 280 万。亚马逊在同一时间段为 100 万，HBO Max、Paramount+ 和 Peacock 则不足 60 万。到了晚上，其它流媒体服务与 YouTube 之间的观看人数差距显著缩小。Netflix 在晚上 9 点的观看人数增至 1100 万以上，略低于 YouTube 的 1200 万。为了挑战 YouTube 对白天电视时间的支配，主要流媒体公司都在增加适合用户在白天观看的内容，Netflix 计划明年推出至少 34 个视频播客节目，亚马逊则在 9 月上线了播客 New Heights。数据显示，播客的观看时间主要集中在早上 6 点至下午 6 点之间。YouTube 称在 10 月份用户在电视上观看视频播客的时长达到了 7 亿小时，比上年同期增长了 75%。

安全研究员 Anurag Sen 发现，乌兹别克斯坦的汽车牌追踪监控系统在没有密码保护的情况下暴露在互联网上。数据显示，该系统的数据库于 2024 年 9 月设置，交通监控则始于 2025 年中期。该系统由乌兹别克斯坦内政部公共安全局运营，由深圳公司 Maxvision 开发，该公司的外国客户包括布基纳法索、科威特、阿曼、墨西哥、沙特阿拉伯和乌兹别克斯坦。

华硕演示了它的 5K@180Hz 27 英寸 ROG Strix 27 Pro 游戏显示器。5K 分辨率为 5120 x 2880，比 4K 分辨率 3840 x 2160 的像素数多 78%，因此 4K 下能流畅运行游戏的显卡在 5K 下也力不从心了。华硕测试了英伟达的旗舰显卡 RTX 5090D（中国特供版，已被另一个特供版 5090Dv2 取代），测试游戏是 Cyberpunk 2077，开启超高光线追踪设置，其帧数仅为 51 帧/秒。测试系统配备了 AMD Ryzen 9950X3D CPU，DLSS 设置为平衡，关闭了帧生成。同样的配置在 4K 下运行 Cyberpunk 2077，帧数达到了 77 帧/秒，比 5K 分辨率高出约 50%。ROG Strix 27 Pro 使用了 IPS 面板，支持双模，可在两种分辨率下切换：5K@180Hz 或 2K@330Hz。该显示器的售价约 800 美元。

一项调查显示，国际象棋大师、奥运会金牌得主和诺贝尔奖得主很少是神童。同样童年的卓越表现和高强度的训练也很少能引领人们在成人的世界里取得最高成就。这项基于 19 项研究、涉及近 3.5 万名优秀人才的分析表明，绝大多数在各自专业领域处于全球顶尖水平的成年人都是在参与各种活动的过程中成长起来的，并逐渐发展出最精湛的技能。在不同的专业领域中，早期的高成就者与后来的世界级水平者在很大程度上是不同的人。事实上，在那些成年后表现出色的人中，只有约 10% 在未成年时表现也出色，而在未成年时表现出色的人中，大约只有 10% 在成年后取得了卓越成就。在童年和青少年时期减少高强度的训练安排，可能有助于防止倦怠和伤病，后者会影响长期职业生涯。

主流显示器的分辨率正缓慢从 4K 升级到 6K。三星计划在 2026 年推出的一款游戏显示器是 Odyssey 3D G90XH，32 英寸 IPS 显示屏，6K 分辨率并支持裸眼3D，刷新率 165Hz，显示器支持实时眼动追踪，根据用户位置“自动调整深度和透视效果”。显示器支持在两种分辨率下切换：6K@165Hz 或 3K@330Hz。三星还将推出一款刷新率 1040Hz 的游戏显示器 Odyssey G6 G60H，显示屏大小 27 英寸，同样支持两种分辨率，1040Hz 仅限于 720p 分辨率：720p@1040Hz 或者 1440p@600Hz。显示器兼容 AMD FreeSync Premium 和 NVIDIA G-Sync。

1980 年代后期，《韦氏大学词典(Merriam-Webster's Collegiate Dictionary)》曾连续 155 周进入《纽约时报》畅销书榜，最终销量高达 5700 万册，在美国仅次于《圣经》。但词典辉煌时代早已一去不复返了。在互联网时代，词典都在苦苦挣扎。25 年前美国约有 200 名全职词典编纂者，今天可能不到 30 人。韦氏词典如今隶属于大英百科全书，后者也在 2012 年停止出版了纸质版本。大英百科全书的网站每年吸引约 10 亿次页面浏览量，但主要内容不是字典树，而是文字游戏、流行俚语和广告。一项对数字图书馆的分析发现，英语词汇量从 1950 年的约 60 万单词增长到 2000 年的逾百万单词，印刷书籍中 52% 的英语单词是“词汇暗物质”，即没有出现在任何标准词典中。

2018 年的法律 US CLOUD Act 允许美国当局要求该国科技公司交出储存在任何地方的数据。这一法律对欧洲企业和公共机构的数字主权构成了威胁，成为欧盟数据保护条例 GDPR 面临的重大且不可接受的风险。欧洲公共机构正缓慢摆脱对美国科技公司的软件和云服务的依赖。奥地利联邦经济、能源和旅游部最近完成了 1200 名员工向欧洲开源协作平台 Nextcloud 的迁移，这一示范作用也促使奥地利其它部门采用 Nextcloud。欧洲的数字基础设施几乎完全依赖于非欧洲供应商。如果一家美国主要云服务商限制欧洲用户的访问或停止运营，后果将十分严重。欧洲正在采取行动加强数字主权。海牙国际刑事法院(ICC) 宣布从微软切换到德国的开源办公软件 OpenDesk。德国 Schleswig-Holstein 州的 3 万名公务员正迁移到开源软件 LibreOffice、Nextcloud、Open Xchange 和 Thunderbird。

MIT Technology Review 采访逾 30 名开发者、科技公司高管、分析师和研究人员后发现，基于大模型的 AI 工具是否加快程序员编程速度不是一个一锤定音的问题。随着一线程序员认识到大模型的局限性，他们对 AI 工具的狂热开始消退。众多研究表明，AI 工具所宣称的生产力提升可能只是一种假象。GitClear 的数据显示 2022 年以来工程师所写代码的持久性——数周内代码不会被删除或重写——提高约 10%，这一改进可能需要归功于 AI。但与此同时，代码的多项质量指标在快速下降。编程问答平台 Stack Overflow 的调查首次显示对 AI 工具的信任度和好感度显著下降。程序员普遍认同 AI 工具的优势在于生成“样板代码”，编写测试、修 bug 以及向新手解释不熟悉的代码。但对于经验丰富的程序员而言，此类任务只占工作量的一小部分，AI 工具对于解决复杂难题帮助不大。基于大模型的 AI 工具也不可避免存在幻觉，它们生成的代码看起来完美，因此很难发现错误。所以使用 AI 工具就像是玩老虎机，有的时候大有帮助，但其它情况可能完全不可靠。

国家发展改革委、市场监管总局和国家网信办联合发布《互联网平台价格行为规则》，其中规定：平台经营者不得以排挤竞争对手或者独占市场为目的，以低于成本的价格销售商品或者提供服务，扰乱正常的生产经营秩序...；平台经营者不得在消费者不知情的情况下，基于支付意愿、支付能力、消费偏好、消费习惯等信息，运用数据和算法、平台规则等手段，对同一商品或者服务在同等交易条件下设置不同的价格或者收费标准；平台经营者不得利用平台规则、数据和算法等手段，相互串通，操纵市场价格，损害其他经营者、消费者合法权益；在突发事件发生期间，平台经营者、平台内经营者销售应急物资和重要民生商品，应当合理制定价格，不得在成本未明显增加时大幅度提高商品价格，或者成本虽有增加但商品价格上涨幅度明显高于成本增长幅度；未提高商品价格，但不合理大幅度提高运输费用或者收取其他不合理费用。