OrangeBot.AI Digest — 2026-02-12

Generative UI SDK for React

Agentic AI Infrastructure for magnifying HUMAN capabilities.

A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization.

Chrome DevTools for coding agents

Microsoft PowerToys is a collection of utilities that supercharge productivity and customization on Windows

Free, local, open-source 24/7 Cowork and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!

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

Open-source AI coworker, with memory

GitHub Agentic Workflows

Fine-tuning & Reinforcement Learning for LLMs. 🦥 Train OpenAI gpt-oss, DeepSeek, Qwen, Llama, Gemma, TTS 2x faster with 70% less VRAM.

Yet another matrix client

66 Specialized Skills for Full-Stack Developers. Transform Claude Code into your expert pair programmer.

Official code repo for the O'Reilly Book - "Hands-On Large Language Models"

We introduce Step 3.5 Flash, a sparse Mixture-of-Experts (MoE) model that bridges frontier-level agentic intelligence and computational efficiency. We focus on what matters most when building agents: sharp reasoning and fast, reliable execution. Step 3.5 Flash pairs a 196B-parameter foundation with 11B active parameters for efficient inference. It is optimized with interleaved 3:1 sliding-window/full attention and Multi-Token Prediction (MTP-3) to reduce the latency and cost of multi-round agentic interactions. To reach frontier-level intelligence, we design a scalable reinforcement learning framework that combines verifiable signals with preference feedback, while remaining stable under large-scale off-policy training, enabling consistent self-improvement across mathematics, code, and tool use. Step 3.5 Flash demonstrates strong performance across agent, coding, and math tasks, achieving 85.4% on IMO-AnswerBench, 86.4% on LiveCodeBench-v6 (2024.08-2025.05), 88.2% on tau2-Bench, 69.0% on BrowseComp (with context management), and 51.0% on Terminal-Bench 2.0, comparable to frontier models such as GPT-5.2 xHigh and Gemini 3.0 Pro. By redefining the efficiency frontier, Step 3.5 Flash provides a high-density foundation for deploying sophisticated agents in real-world industrial environments.

With the rapid development of large multimodal models, reliable judge and critic models have become essential for open-ended evaluation and preference alignment, providing pairwise preferences, numerical scores, and explanatory justifications for assessing model-generated responses. However, existing critics are primarily trained in general visual domains such as captioning or image question answering, leaving physical AI tasks involving perception, causal reasoning, and planning largely underexplored. We introduce PhyCritic, a multimodal critic model optimized for physical AI through a two-stage RLVR pipeline: a physical skill warmup stage that enhances physically oriented perception and reasoning, followed by self-referential critic finetuning, where the critic generates its own prediction as an internal reference before judging candidate responses, improving judgment stability and physical correctness. Across both physical and general-purpose multimodal judge benchmarks, PhyCritic achieves strong performance gains over open-source baselines and, when applied as a policy model, further improves perception and reasoning in physically grounded tasks.

Unified Multimodal Models (UMMs) have shown remarkable progress in visual generation. Yet, existing benchmarks predominantly assess Crystallized Intelligence, which relies on recalling accumulated knowledge and learned schemas. This focus overlooks Generative Fluid Intelligence (GFI): the capacity to induce patterns, reason through constraints, and adapt to novel scenarios on the fly. To rigorously assess this capability, we introduce GENIUS (GEN Fluid Intelligence EvalUation Suite). We formalize GFI as a synthesis of three primitives. These include Inducing Implicit Patterns (e.g., inferring personalized visual preferences), Executing Ad-hoc Constraints (e.g., visualizing abstract metaphors), and Adapting to Contextual Knowledge (e.g., simulating counter-intuitive physics). Collectively, these primitives challenge models to solve problems grounded entirely in the immediate context. Our systematic evaluation of 12 representative models reveals significant performance deficits in these tasks. Crucially, our diagnostic analysis disentangles these failure modes. It demonstrates that deficits stem from limited context comprehension rather than insufficient intrinsic generative capability. To bridge this gap, we propose a training-free attention intervention strategy. Ultimately, GENIUS establishes a rigorous standard for GFI, guiding the field beyond knowledge utilization toward dynamic, general-purpose reasoning. Our dataset and code will be released at: https://github.com/arctanxarc/GENIUS{https://github.com/arctanxarc/GENIUS}.

Adapting LLM agents to domain-specific tool calling remains notably brittle under evolving interfaces. Prompt and schema engineering is easy to deploy but often fragile under distribution shift and strict parsers, while continual parameter-efficient fine-tuning improves reliability at the cost of training, maintenance, and potential forgetting. We identify a critical Lazy Agent failure mode where tool necessity is nearly perfectly decodable from mid-layer activations, yet the model remains conservative in entering tool mode, revealing a representation-behavior gap. We propose Activation Steering Adapter (ASA), a training-free, inference-time controller that performs a single-shot mid-layer intervention and targets tool domains via a router-conditioned mixture of steering vectors with a probe-guided signed gate to amplify true intent while suppressing spurious triggers. On MTU-Bench with Qwen2.5-1.5B, ASA improves strict tool-use F1 from 0.18 to 0.50 while reducing the false positive rate from 0.15 to 0.05, using only about 20KB of portable assets and no weight updates.

Recent advances in foundational models have yielded reasoning systems capable of achieving a gold-medal standard at the International Mathematical Olympiad. The transition from competition-level problem-solving to professional research, however, requires navigating vast literature and constructing long-horizon proofs. In this work, we introduce Aletheia, a math research agent that iteratively generates, verifies, and revises solutions end-to-end in natural language. Specifically, Aletheia is powered by an advanced version of Gemini Deep Think for challenging reasoning problems, a novel inference-time scaling law that extends beyond Olympiad-level problems, and intensive tool use to navigate the complexities of mathematical research. We demonstrate the capability of Aletheia from Olympiad problems to PhD-level exercises and most notably, through several distinct milestones in AI-assisted mathematics research: (a) a research paper (Feng26) generated by AI without any human intervention in calculating certain structure constants in arithmetic geometry called eigenweights; (b) a research paper (LeeSeo26) demonstrating human-AI collaboration in proving bounds on systems of interacting particles called independent sets; and (c) an extensive semi-autonomous evaluation (Feng et al., 2026a) of 700 open problems on Bloom's Erdos Conjectures database, including autonomous solutions to four open questions. In order to help the public better understand the developments pertaining to AI and mathematics, we suggest codifying standard levels quantifying autonomy and novelty of AI-assisted results. We conclude with reflections on human-AI collaboration in mathematics.

While reasoning over long context is crucial for various real-world applications, it remains challenging for large language models (LLMs) as they suffer from performance degradation as the context length grows. Recent work MemAgent has tried to tackle this by processing context chunk-by-chunk in an RNN-like loop and updating a textual memory for final answering. However, this naive recurrent memory update faces two crucial drawbacks: (i) memory can quickly explode because it can update indiscriminately, even on evidence-free chunks; and (ii) the loop lacks an exit mechanism, leading to unnecessary computation after even sufficient evidence is collected. To address these issues, we propose GRU-Mem, which incorporates two text-controlled gates for more stable and efficient long-context reasoning. Specifically, in GRU-Mem, the memory only updates when the update gate is open and the recurrent loop will exit immediately once the exit gate is open. To endow the model with such capabilities, we introduce two reward signals r^{update} and r^{exit} within end-to-end RL, rewarding the correct updating and exiting behaviors respectively. Experiments on various long-context reasoning tasks demonstrate the effectiveness and efficiency of GRU-Mem, which generally outperforms the vanilla MemAgent with up to 400\% times inference speed acceleration.

While Large Language Models (LLMs) have recently shown promise in Automated Heuristic Design (AHD), existing approaches typically formulate AHD around constructive priority rules or parameterized local search guidance, thereby restricting the search space to fixed heuristic forms. Such designs offer limited capacity for structural exploration, making it difficult to escape deep local optima in complex Combinatorial Optimization Problems (COPs). In this work, we propose G-LNS, a generative evolutionary framework that extends LLM-based AHD to the automated design of Large Neighborhood Search (LNS) operators. Unlike prior methods that evolve heuristics in isolation, G-LNS leverages LLMs to co-evolve tightly coupled pairs of destroy and repair operators. A cooperative evaluation mechanism explicitly captures their interaction, enabling the discovery of complementary operator logic that jointly performs effective structural disruption and reconstruction. Extensive experiments on challenging COP benchmarks, such as Traveling Salesman Problems (TSP) and Capacitated Vehicle Routing Problems (CVRP), demonstrate that G-LNS significantly outperforms LLM-based AHD methods as well as strong classical solvers. The discovered heuristics not only achieve near-optimal solutions with reduced computational budgets but also exhibit robust generalization across diverse and unseen instance distributions.

Decoder-only large language models are increasingly used as behavioral encoders for user representation learning, yet the impact of attention masking on the quality of user embeddings remains underexplored. In this work, we conduct a systematic study of causal, hybrid, and bidirectional attention masks within a unified contrastive learning framework trained on large-scale real-world Alipay data that integrates long-horizon heterogeneous user behaviors. To improve training dynamics when transitioning from causal to bidirectional attention, we propose Gradient-Guided Soft Masking, a gradient-based pre-warmup applied before a linear scheduler that gradually opens future attention during optimization. Evaluated on 9 industrial user cognition benchmarks covering prediction, preference, and marketing sensitivity tasks, our approach consistently yields more stable training and higher-quality bidirectional representations compared with causal, hybrid, and scheduler-only baselines, while remaining compatible with decoder pretraining. Overall, our findings highlight the importance of masking design and training transition in adapting decoder-only LLMs for effective user representation learning. Our code is available at https://github.com/JhCircle/Deepfind-GGSM.

This paper proposes Omni Dense Captioning, a novel task designed to generate continuous, fine-grained, and structured audio-visual narratives with explicit timestamps. To ensure dense semantic coverage, we introduce a six-dimensional structural schema to create "script-like" captions, enabling readers to vividly imagine the video content scene by scene, akin to a cinematographic screenplay. To facilitate research, we construct OmniDCBench, a high-quality, human-annotated benchmark, and propose SodaM, a unified metric that evaluates time-aware detailed descriptions while mitigating scene boundary ambiguity. Furthermore, we construct a training dataset, TimeChatCap-42K, and present TimeChat-Captioner-7B, a strong baseline trained via SFT and GRPO with task-specific rewards. Extensive experiments demonstrate that TimeChat-Captioner-7B achieves state-of-the-art performance, surpassing Gemini-2.5-Pro, while its generated dense descriptions significantly boost downstream capabilities in audio-visual reasoning (DailyOmni and WorldSense) and temporal grounding (Charades-STA). All datasets, models, and code will be made publicly available at https://github.com/yaolinli/TimeChat-Captioner.

Agents powered by large language models (LLMs) are increasingly adopted in the software industry, contributing code as collaborators or even autonomous developers. As their presence grows, it becomes important to assess the current boundaries of their coding abilities. Existing agentic coding benchmarks, however, cover a limited task scope, e.g., bug fixing within a single pull request (PR), and often rely on non-executable evaluations or lack an automated approach for continually updating the evaluation coverage. To address such issues, we propose FeatureBench, a benchmark designed to evaluate agentic coding performance in end-to-end, feature-oriented software development. FeatureBench incorporates an execution-based evaluation protocol and a scalable test-driven method that automatically derives tasks from code repositories with minimal human effort. By tracing from unit tests along a dependency graph, our approach can identify feature-level coding tasks spanning multiple commits and PRs scattered across the development timeline, while ensuring the proper functioning of other features after the separation. Using this framework, we curated 200 challenging evaluation tasks and 3825 executable environments from 24 open-source repositories in the first version of our benchmark. Empirical evaluation reveals that the state-of-the-art agentic model, such as Claude 4.5 Opus, which achieves a 74.4% resolved rate on SWE-bench, succeeds on only 11.0% of tasks, opening new opportunities for advancing agentic coding. Moreover, benefiting from our automated task collection toolkit, FeatureBench can be easily scaled and updated over time to mitigate data leakage. The inherent verifiability of constructed environments also makes our method potentially valuable for agent training.

We present ROCKET, a training-free model compression method that achieves state-of-the-art performance in comparison with factorization, structured-sparsification and dynamic compression baselines. Operating under a global compression budget, ROCKET comprises two key innovations: First, it formulates layer-wise compression allocation as a multi-choice knapsack problem, selecting the optimal compression level for each layer to minimize total reconstruction error while adhering to a target model size. Second, it introduces a single-step sparse matrix factorization inspired by dictionary learning: using only a small calibration set, it sparsifies weight coefficients based on activation-weights sensitivity and then updates the dictionary in closed form via least squares bypassing iterative optimization, sparse coding, or backpropagation entirely. ROCKET consistently outperforms existing compression approaches across different model architectures at 20-50\% compression rates. Notably, it retains over 90\% of the original model's performance at 30\% compression without any fine-tuning. Moreover, when applying a light fine-tuning phase, recovery is substantially enhanced: for instance, compressing Qwen3-14B to an 8B-parameter model and healing it with just 30 million tokens yields performance nearly on par with the original Qwen3-8B. The code for ROCKET is at github.com/mts-ai/ROCKET/tree/main.

Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as an effective approach for enhancing the reasoning capabilities of Large Language Models (LLMs). Despite its efficacy, RLVR faces a meta-learning bottleneck: it lacks mechanisms for error attribution and experience internalization intrinsic to the human learning cycle beyond practice and verification, thereby limiting fine-grained credit assignment and reusable knowledge formation. We term such reusable knowledge representations derived from past errors as meta-experience. Based on this insight, we propose Meta-Experience Learning (MEL), a novel framework that incorporates self-distilled meta-experience into the model's parametric memory. Building upon standard RLVR, we introduce an additional design that leverages the LLM's self-verification capability to conduct contrastive analysis on paired correct and incorrect trajectories, identify the precise bifurcation points where reasoning errors arise, and summarize them into generalizable meta-experience. The meta-experience is further internalized into the LLM's parametric memory by minimizing the negative log-likelihood, which induces a language-modeled reward signal that bridges correct and incorrect reasoning trajectories and facilitates effective knowledge reuse. Experimental results demonstrate that MEL achieves consistent improvements on benchmarks, yielding 3.92%--4.73% Pass@1 gains across varying model sizes.

In the current landscape of Large Language Models (LLMs), the curation of large-scale, high-quality training data is a primary driver of model performance. A key lever is the data recipe, which comprises a data processing pipeline to transform raw sources into training corpora. Despite the growing use of LLMs to automate individual data processing steps, such as data synthesis and filtering, the overall design of data recipes remains largely manual and labor-intensive, requiring substantial human expertise and iteration. To bridge this gap, we formulate end-to-end data recipe generation for LLM adaptation. Given a target benchmark and a pool of available data sources, a model is required to output a complete data recipe that adapts a base LLM to the target task. We present DataChef-32B, which performs online reinforcement learning using a proxy reward that predicts downstream performance for candidate recipes. Across six held-out tasks, DataChef-32B produces practical recipes that reach comparable downstream performance to those curated by human experts. Notably, the recipe from DataChef-32B adapts Qwen3-1.7B-Base to the math domain, achieving 66.7 on AIME'25 and surpassing Qwen3-1.7B. This work sheds new light on automating LLM training and developing self-evolving AI systems.

Supervised fine-tuning (SFT) on chain-of-thought data is an essential post-training step for reasoning language models. Standard machine learning intuition suggests that training with more unique training samples yields better generalization. Counterintuitively, we show that SFT benefits from repetition: under a fixed update budget, training for more epochs on smaller datasets outperforms single-epoch training on larger datasets. On AIME'24/25 and GPQA benchmarks, Olmo3-7B trained for 128 epochs on 400 samples outperforms the equivalent 1 epoch on 51200 samples by 12-26 percentage points, with no additional catastrophic forgetting. We find that training token accuracy reliably signals when repetition has saturated; improvements from additional epochs plateau at full memorization, a pattern consistent across all settings. These findings provide a practical approach for reasoning SFT, where scaling epochs with token accuracy as a stopping criterion can replace expensive undirected data scaling. We pose the repetition advantage, where full memorization coincides with improved generalization, as a new open problem for the community in understanding the training dynamics of large language models.

Omni-modal large language models (OLLMs) aim to unify multimodal understanding and generation, yet incorporating speech with 3D facial animation remains largely unexplored despite its importance for natural interaction. A key challenge arises from the representation mismatch between discrete, token-level semantic reasoning in LLMs and the dense, fine-grained temporal dynamics required for 3D facial motion, which makes direct modeling difficult to optimize under limited data. We propose Expressive Omni (Ex-Omni), an open-source omni-modal framework that augments OLLMs with speech-accompanied 3D facial animation. Ex-Omni reduces learning difficulty by decoupling semantic reasoning from temporal generation, leveraging speech units as temporal scaffolding and a unified token-as-query gated fusion (TQGF) mechanism for controlled semantic injection. We further introduce InstructEx, a dataset aims to facilitate augment OLLMs with speech-accompanied 3D facial animation. Extensive experiments demonstrate that Ex-Omni performs competitively against existing open-source OLLMs while enabling stable aligned speech and facial animation generation.

《Highguard》开发商 Wildlight Entertainment 证实了裁员的消息，但没有透露裁掉了多少员工。该公司的开发者称大部分员工都被辞退了。《Highguard》是一款以突袭为主题的英雄射击游戏，于 1 月 26 日上线，一度吸引了 9.7 万玩家同时在线，但这一热度并没有持续太长时间，在短短 17 天内同时在线玩家人数已经锐减到 2200 人左右，对一款需要长期运营的免费 PvP 游戏而言，结局可能已经注定了。

瑞士研究团队采用部分重编程技术，短暂启动三个关键基因 Oct4、Sox2 和 Klf4（简称 OSK）。此前研究表明，这组因子可在一定程度上逆转细胞老化迹象。他们利用腺相关病毒作为载体，通过精确脑部注射，将标记学习激活神经元的荧光系统和可控开启 OSK 表达的时间开关两个元件送入两大关键脑区——海马体齿状回与内侧前额叶皮层。前者主导近期记忆的形成与提取，后者则负责远期记忆和回忆。结果显示，在老年小鼠中，仅需短暂激活海马体内的印痕神经元 OSK 表达，其记忆表现便恢复至年轻小鼠水平；而靶向前额叶皮层时，几周前形成的遥远记忆也得以重现。

根据发表在《Current Biology》期刊上的一项研究，人类和其它动物的总能量消耗受到限制。这种能量支出模型被称为约束模型，约束模型认为人体的总能量预算是有限的，并会努力维持在一个相对稳定的区间内。当我们通过体育锻炼显著增加能量输出时，身体会悄悄减少其他方面的能量开支进行补偿，例如降低基础代谢率、睡眠时代谢率或减少用于细胞修复、免疫等内部生理活动的能量。研究人员认为，约束模型可能源于远古祖先的生存策略：在食物不稳定的时代，过度消耗能量会危及生命，因此身体进化出总能量控制系统，确保总支出稳定在安全区间。这解释了为什么现代人即使每天多跑几公里，体重减轻也往往缓慢。

惠普推出了游戏笔记本订阅服务 OMEN Gaming Subscription，允许用户以按月付费的方式租用笔记本电脑，用户不拥有所有权，长期租用的费用会超过笔记本的零售价。用户可租用的笔记本电脑包括：低端的 HP Victus 15——RTX 4050 移动显卡，Ryzen 7 8845HS CPU、16 GB 内存 和 1TB SSD，月费 50 美元，其零售价为 950 美元，也就是租用两年的价格就超过了零售价；高端的 HP Omen Max 16—— Intel Core Ultra 9 处理器，RTX 5080 移动显卡、32 GB 内存 和 1 TB SSD，月费 130 美元，目前零售价 2,110 美元。放弃所有权的好处包括了：年度硬件升级、次日更换服务、全天候支持以及持续保修。用户无法在订阅之后中途取消，否则需要支付昂贵的提前终止费用——Victus 15 为 550 美元，Omen Max 16 为 1430 美元。用户需要至少订阅 12 个月之后才可以在第 13 个月取消订阅而无需支付终止费用。

根据皮尤研究中心（Pew Research Center）周三公布的报告，大部分美国人不付费阅读新闻。皮尤去年 12 月调查了 3560 名美国成年人，结果显示他们对于关注新闻的重要性没有共识，但对于是否为新闻付费他们的共识是不付费。83% 的受访者过去一年没有通过订阅、捐赠或成为会员的方式为任何新闻源付费，他们可以通过免费渠道获得新闻，为新闻付费对他们而言是一种奢侈。最有可能为新闻付费的群体是高收入人群（30%）、有研究生学历的成年人（35%）和自由派民主党人（29%）。只有 8% 的受访者认为美国民众有责任为新闻付费。最不认为为新闻付费是个人责任的群体是低收入者、共和党人或倾向共和党的人士、30 岁以下的成年人、高中及以下学历者。

Meta 旗下的 WhatsApp 透露，俄罗斯正尝试全面屏蔽该消息应用。WhatsApp 以及 Telegram 都是俄罗斯最受欢迎的消息应用，用户数超过一亿。俄罗斯官方塔斯社（Tass Media）早些时候报道，俄罗斯预计在 2026 年永久封禁 WhatsApp。俄罗斯已将 WhatsApp 母公司 Meta 认定为极端组织，官员表示屏蔽 WhatsApp 是绝对正当的。俄罗斯正在努力推广本土消息应用 Max，该应用被誉为俄罗斯的微信，它组合了即时通讯和政府服务，但没有加密功能。

安娜的档案（Anna’s Archive）去年宣布抓取了音乐流媒体巨头 Spotify 的音乐文件，震惊了音乐行业。它随后发布了 Spotify 的元数据，但并没有公开音乐文件，尽管如此 Spotify 和唱片公司对安娜的档案提起了诉讼，导致了它失去了包括 .org 在内的多个域名。2 月 8 日有人发布了数十个新的 Spotify 种子文件，每个包含大约 6 万个文件，总共约 280 万个文件，约 6 TB 的音乐。安娜的档案此前表示它存档了 300 TB 的 Spotify 音乐文件，总共 8600 万首音乐，预计它未来可能会释出更多 Spotify 音乐文件。

博主 Jani Patokallio 在 2023 年 8 月发表了一篇文章，利用公开信息试图找出 Archive.today aka Archive.is 幕后运营者的身份，这篇文章并没有引起多少关注，点击量在 1 万左右。但 2025 年 10 月 FBI 向域名注册商 Tucows 发出传票，要求提供 Archive.today 注册者的信息。媒体在报道此事时引用了 Patokallio 博客，认为 Archive.today 的创始人可能来自俄罗斯。媒体报道之后，Patokallio 收到邮件要求撤下他在 2023 年发表的文章，邮件被 Gmail 归类为垃圾邮件，因此 Patokallio 是在邮件发出五天之后才看到。在此期间他遭遇了 DDoS 攻击，攻击代码嵌入在 Archive.today 的 CAPTCHA 页面，用户访问 Archive.today 时必须通过 CAPTCHA 的测试，只要该页面保持在打开状态，每隔 300 毫秒它就会向 Patokallio 的博客发去请求，此举显然是通过 DDoS 攻击增加网站的托管费用。Patokallio 在多次联络之后表示不会撤下文章，攻击者则对他进行辱骂和发出威胁，声称要开发与他名字相关的 AI 色情。这起事件促使维基百科志愿者讨论是否应将 Archive.today 加入黑名单。Patokallio 表示他的托管费用是固定的，DDoS 攻击并没有增加他的负担。

Moderna 周二披露 FDA 拒绝审核其实验性 mRNA 流感疫苗。FDA 由美国卫生部直接管辖，而现任卫生部长 Robert F. Kennedy Jr.是一位反疫苗者。在耗资数亿美元、招募近 41000 名受试者的试验中，Moderna 将mRNA-1010 疫苗的安全性和有效性与已批准流感疫苗进行对比。试验结果表明，mRNA-1010 疫苗优于对照疫苗。但今年 2 月 3 日 FDA 以试验不够充分控制不够好为由拒绝对疫苗的上市申请进行审核。Moderna 表示已请求与 FDA 会面以了解拒绝的理由。mRNA-1010 疫苗已获得欧盟、加拿大和澳大利亚的审核批准。

Google 释出了 Chrome 145，主要变化包括：重新加入对 JPEG-XL 图像的支持，text-justify CSS 属性、支持多列换行、设备绑定会话凭据、IndexedDB 的 SQLite 后端、默认情况下减少用户代理字符串、Upsert 等。Google Chrome 是在 2023 年 移除了对实验性的 JPEG-XL 图像格式的支持，此举引发了很多争议，因为 Chrome/Chromium 占据了九成市场份额，它是 Web 标准事实上的仲裁者。但到了 2025 年事情有了戏剧性转变。Google 改变了主意，开始恢复对 JPEG-XL 图像的支持，去年 12 月 Chrome/Chromium 代码库合并了 Rust 语言开发的 JPEG-XL 图像解码器 jxl-rs。

NetBSD 项目释出了 NetBSD 11.0 的首个 RC 版本。主要新特性包括：支持 64 位 RISC-V 平台；增强对 POSIX.1-2024 和 C23 编程接口标准的兼容性；增强对 compat_linux(8) 中 Linux 系统调用的支持；初步支持高通骁龙 X Elite 平台；改进 npf(7) 防火墙；新 MICROVM kernel for x86，专为实现极速虚拟机启动设计，在 2020 年时代 x86 CPU 上启动仅需 10 毫秒；新 virt68k，等等。

微软最近几年为其以精简著称的记事本应用引入了新功能，其中包括 AI 和 Markdown，新增功能也扩大了其攻击面，它刚刚爆出了一个远程代码执行漏洞 CVE-2026-20841，该漏洞与处理外链有关：当用户用记事本打开一个 Markdown 文件，攻击者可以引诱用户点击一个恶意链接，导致应用启动未经验证的协议去加载并执行远程文件。

字节跳动最近发布了 AI 视频生成工具 Seedance 2.0，它能同时处理多达四种类型的输入：图像、视频、音频和文本。用户能组合九张图像、三个视频和三个音频文件最多十二个文件。生成的视频时长为 4-15 秒（或 60 秒），能自动添加音效或音乐。但由于潜在的安全风险，字节跳动禁用了 Seedance 2.0 的人脸转语音功能。模型展现了能仅仅根据面部图像生成高度精确的个人语音的能力。根据脸部照片生成个人声音不是新研究，早在 2024 年的 USENIX 安全会议上，新加坡国立大学的研究人员就发表论文《Can I Hear Your Face? Pervasive Attack on Voice Authentication Systems with a Single Face Image》，介绍根据人脸生成语音攻击语音身份验证系统，因为人脸特征与语音特征之间存在高度关联。

自 1790 年普查人口以来，美国首次面临人口总数下降。美国人口普查局原本预测要到 2081 年才可能出现人口减少，但在特朗普政府的加速努力下，人口下降有望提前 50 年发生，最快今年就可能到来。根据上月底公布的最新人口普查数据，截至 2025 年 7 月的一年内美国人口增长率放缓至 0.5%，为疫情爆发以来的最低水平，净移民人数从前一年的 270 万降至 130 万。人口普查专家预计截至 2026 年 7 月的一年内净移民人数将降至 31.6 万，表示美国正朝着净移民负值的方向发展。美国企业研究所和布鲁金斯学会的研究人员估计，2026 年美国的净移民总数在 + 18.5 万和 -92.5万之间。而美国最新的出生人口减去死亡人口的差额是 51.9 万，这一差额预计到 2030 年将消失。移民外流加上净人口增长减少，将导致美国人口比预期的更快出现收缩。

Nala Ginrut 写道： 代表着日本技术荣耀以及号成“能养活一亿人”、“有丰田在就有日本在”的丰田最近发布了一款开源游戏引擎，结果是在北美发布的，IP也归属北美了。所以大部份人看到的日本，是否真的是日本呢？本文从另一个侧面来探讨一下，知己知彼，搞不好还能从日本顺走一些亚洲科技企业出海的经验。