OrangeBot.AI Digest — 2025-12-04

Kanban as it should be. Not as it has been.

Incredibly fast JavaScript runtime, bundler, test runner, and package manager – all in one

A next.js web application that integrates AI capabilities with draw.io diagrams. This app allows you to create, modify, and enhance diagrams through natural language commands and AI-assisted visualization.

Lightweight coding agent that runs in your terminal

Truly independent web browser

The 500 AI Agents Projects is a curated collection of AI agent use cases across various industries. It showcases practical applications and provides links to open-source projects for implementation, illustrating how AI agents are transforming sectors such as healthcare, finance, education, retail, and more.

The AI coding agent built for the terminal.

The Social-Engineer Toolkit (SET) repository from TrustedSec - All new versions of SET will be deployed here.

12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

Production-Grade Container Scheduling and Management

Empower the Web community and invite more to build across platforms.

Master programming by recreating your favorite technologies from scratch.

Intelligent automation and multi-agent orchestration for Claude Code

We introduce Qwen3-VL, the most capable vision-language model in the Qwen series to date, achieving superior performance across a broad range of multimodal benchmarks. It natively supports interleaved contexts of up to 256K tokens, seamlessly integrating text, images, and video. The model family includes both dense (2B/4B/8B/32B) and mixture-of-experts (30B-A3B/235B-A22B) variants to accommodate diverse latency-quality trade-offs. Qwen3-VL delivers three core pillars: (i) markedly stronger pure-text understanding, surpassing comparable text-only backbones in several cases; (ii) robust long-context comprehension with a native 256K-token window for both text and interleaved multimodal inputs, enabling faithful retention, retrieval, and cross-referencing across long documents and videos; and (iii) advanced multimodal reasoning across single-image, multi-image, and video tasks, demonstrating leading performance on comprehensive evaluations such as MMMU and visual-math benchmarks (e.g., MathVista and MathVision). Architecturally, we introduce three key upgrades: (i) an enhanced interleaved-MRoPE for stronger spatial-temporal modeling across images and video; (ii) DeepStack integration, which effectively leverages multi-level ViT features to tighten vision-language alignment; and (iii) text-based time alignment for video, evolving from T-RoPE to explicit textual timestamp alignment for more precise temporal grounding. Under comparable token budgets and latency constraints, Qwen3-VL achieves superior performance in both dense and Mixture-of-Experts (MoE) architectures. We envision Qwen3-VL serving as a foundational engine for image-grounded reasoning, agentic decision-making, and multimodal code intelligence in real-world workflows.

Vision-Language-Action (VLA) models, trained via flow-matching or diffusion objectives, excel at learning complex behaviors from large-scale, multi-modal datasets (e.g., human teleoperation, scripted policies). However, since VLAs incorporate diverse data modes in the pre-training stage, and the finetuning dataset often contains demonstration data collected in a kinematically suboptimal or undesirable way, it exists redundant action modes that are irrelevant to the success action modes of the downstream task. Specifically, we observe a critical inference-time fragility among various sampled noises after supervised finetuning of pre-trained VLAs. In this paper, we attribute this instability to the distribution shift between the VLA policy and the policy induced by stable success modes of the downstream task dataset. Thus, we propose TACO, a test-time-scaling (TTS) framework that applies a lightweight pseudo-count estimator as a high-fidelity verifier of action chunks. The VLA models integrated with TACO can execute the actions with maximum pseudo-count from all sampled action chunks, thereby preventing distribution shifts while preserving the generalization ability of VLAs since the constraint is applied only during inference. Our method resembles the classical anti-exploration principle in offline reinforcement learning (RL), and being gradient-free, it incurs significant computational benefits compared to RL update, especially for flow or diffusion-based VLAs which are difficult to perform RL update due to denoising process. Extensive experiments across four simulation benchmarks (RoboTwin2.0, Robotwin, LIBERO, SimplerEnv) and a dual-arm platform demonstrate that our method significantly improves the inference stability and success rates in downstream-task adaptations.

Mimicking human behavior to actively learning from general experience and achieve artificial general intelligence has always been a human dream. Recent reinforcement learning (RL) based large-thinking models demonstrate impressive expert-level abilities, i.e., software and math, but still rely heavily on verifiable rewards in specific domains, placing a significant bottleneck to extend the performance boundary of general reasoning capabilities. In this work, we propose PretrainZero, a reinforcement active learning framework built on the pretraining corpus to extend RL from domain-specific post-training to general pretraining. PretrainZero features the following characteristics: 1) Active pretraining: inspired by the active learning ability of humans, PretrainZero learns a unified reasoning policy to actively identify reasonable and informative contents from pretraining corpus, and reason to predict these contents by RL. 2) Self-supervised learning: without any verifiable labels, pretrained reward models, or supervised fine-tuning, we directly pretrain reasoners from 3 to 30B base models on the general Wikipedia corpus using RL, significantly breaking the verification data-wall for general reasoning. 3) Verification scaling: by tackling increasingly challenging masked spans, PretrainZero substantially enhances the general reasoning abilities of pretrained base models. In reinforcement pretraining, PretrainZero improves Qwen3-4B-Base for 8.43, 5.96 and 10.60 on MMLU-Pro, SuperGPQA and math average benchmarks. In post-training, the pretrained models can also serve as reasoning foundation models for downstream RLVR tasks.

Understanding visual differences between dynamic scenes requires the comparative perception of compositional, spatial, and temporal changes--a capability that remains underexplored in existing vision-language systems. While prior work on Image Difference Captioning (IDC) has enabled models to describe semantic changes between static images, these approaches fail to capture motion continuity, event evolution, or editing consistency over time. We introduce the ViDiC (Video Difference Captioning) task and its corresponding ViDiC-1K dataset, designed to evaluate the ability of Multimodal Large Language Models (MLLMs) to provide fine-grained descriptions of similarities and differences between video pairs. ViDiC-1K comprises 1,000 curated video pairs annotated with over 4,000 comparative checklist items, covering seven categories: subject, style, background, cinematography, motion, location, and playback techniques. To ensure reliable evaluation, we propose a dual-checklist framework that measures the accuracy of similarity and difference separately, based on the LLM-as-a-Judge protocol. Experiments on nineteen representative multimodal models reveal a significant performance gap in their comparative description and difference perception abilities. We hope ViDiC-1K can be a challenging benchmark that lays a solid foundation for advancing video understanding, edit awareness, and comparative reasoning in multimodal intelligence.

Reinforcement learning (RL) has recently achieved remarkable success in eliciting visual reasoning within Multimodal Large Language Models (MLLMs). However, existing approaches typically train separate models for different tasks and treat image and video reasoning as disjoint domains. This results in limited scalability toward a multimodal reasoning generalist, which restricts practical versatility and hinders potential knowledge sharing across tasks and modalities. To this end, we propose OneThinker, an all-in-one reasoning model that unifies image and video understanding across diverse fundamental visual tasks, including question answering, captioning, spatial and temporal grounding, tracking, and segmentation. To achieve this, we construct the OneThinker-600k training corpus covering all these tasks and employ commercial models for CoT annotation, resulting in OneThinker-SFT-340k for SFT cold start. Furthermore, we propose EMA-GRPO to handle reward heterogeneity in multi-task RL by tracking task-wise moving averages of reward standard deviations for balanced optimization. Extensive experiments on diverse visual benchmarks show that OneThinker delivers strong performance on 31 benchmarks, across 10 fundamental visual understanding tasks. Moreover, it exhibits effective knowledge transfer between certain tasks and preliminary zero-shot generalization ability, marking a step toward a unified multimodal reasoning generalist. All code, model, and data are released.

Vision Language Models (VLMs) demonstrate strong qualitative visual understanding, but struggle with metrically precise spatial reasoning required for embodied applications. The agentic paradigm promises that VLMs can use a wide variety of tools that could augment these capabilities, such as depth estimators, segmentation models, and pose estimators. Yet it remains an open challenge how to realize this vision without solely relying on handcrafted prompting strategies or enforcing fixed, predefined tool pipelines that limit VLMs' ability to discover optimal tool-use patterns. Reinforcement Learning could overcome this gap, but has so far been limited to reasoning with a single visual tool due to the large search space in multi-tool reasoning. We introduce Double Interactive Reinforcement Learning (DIRL), a two-phase training framework where VLMs learn to coordinate multiple tools through interactive exploration and feedback. In the teaching phase, we combine demonstrations from a single tool specialist trained via interactive RL with traces from a frontier model using all tools. In the exploration phase, the model further refines multi-tool coordination through continued RL. Our model, SpaceTools, with tool-augmented spatial reasoning ability, achieves state-of-the-art performance on spatial understanding benchmarks (RoboSpatial-Home, BLINK, BOP-ASK) and demonstrates reliable real-world manipulation using a 7-DOF robot as a tool. DIRL provides substantial improvements over the vanilla SFT (+12% on RoboSpatial) and RL (+16% on RoboSpatial) baselines. Project page: https://spacetools.github.io/.

Achieving precise alignment between user intent and generated visuals remains a central challenge in text-to-visual generation, as a single attempt often fails to produce the desired output. To handle this, prior approaches mainly scale the visual generation process (e.g., increasing sampling steps or seeds), but this quickly leads to a quality plateau. This limitation arises because the prompt, crucial for guiding generation, is kept fixed. To address this, we propose Prompt Redesign for Inference-time Scaling, coined PRIS, a framework that adaptively revises the prompt during inference in response to the scaled visual generations. The core idea of PRIS is to review the generated visuals, identify recurring failure patterns across visuals, and redesign the prompt accordingly before regenerating the visuals with the revised prompt. To provide precise alignment feedback for prompt revision, we introduce a new verifier, element-level factual correction, which evaluates the alignment between prompt attributes and generated visuals at a fine-grained level, achieving more accurate and interpretable assessments than holistic measures. Extensive experiments on both text-to-image and text-to-video benchmarks demonstrate the effectiveness of our approach, including a 15% gain on VBench 2.0. These results highlight that jointly scaling prompts and visuals is key to fully leveraging scaling laws at inference-time. Visualizations are available at the website: https://subin-kim-cv.github.io/PRIS.

A truly interactive world model requires three key ingredients: real-time long-horizon streaming, consistent spatial memory, and precise user control. However, most existing approaches address only one of these aspects in isolation, as achieving all three simultaneously is highly challenging-for example, long-term memory mechanisms often degrade real-time performance. In this work, we present RELIC, a unified framework that tackles these three challenges altogether. Given a single image and a text description, RELIC enables memory-aware, long-duration exploration of arbitrary scenes in real time. Built upon recent autoregressive video-diffusion distillation techniques, our model represents long-horizon memory using highly compressed historical latent tokens encoded with both relative actions and absolute camera poses within the KV cache. This compact, camera-aware memory structure supports implicit 3D-consistent content retrieval and enforces long-term coherence with minimal computational overhead. In parallel, we fine-tune a bidirectional teacher video model to generate sequences beyond its original 5-second training horizon, and transform it into a causal student generator using a new memory-efficient self-forcing paradigm that enables full-context distillation over long-duration teacher as well as long student self-rollouts. Implemented as a 14B-parameter model and trained on a curated Unreal Engine-rendered dataset, RELIC achieves real-time generation at 16 FPS while demonstrating more accurate action following, more stable long-horizon streaming, and more robust spatial-memory retrieval compared with prior work. These capabilities establish RELIC as a strong foundation for the next generation of interactive world modeling.

Multimodal large language models (MLLMs) that think with images can interactively use tools to reason about visual inputs, but current approaches often rely on a narrow set of tools with limited real-world necessity and scalability. In this work, we first reveal a critical and previously overlooked weakness: even state-of-the-art MLLMs are surprisingly brittle, showing significant performance degradation on images with simple orientation changes or natural corruptions, underscoring the need for more robust tool-based reasoning. To address this, we propose CodeVision, a flexible and scalable code-as-tool framework where the model generates code as a universal interface to invoke any image operation, moving beyond fixed tool registries. We train our model using a two-stage methodology, beginning with Supervised Fine-Tuning (SFT) on a high-quality dataset curated for complex, multi-turn tool composition and error recovery, followed by Reinforcement Learning (RL) with a novel and dense process reward function to encourage strategic and efficient tool use. To facilitate this research, we construct new SFT and RL datasets and introduce a challenging new benchmark suite designed to rigorously evaluate robustness to orientation changes and multi-tool reasoning. Experiments on Qwen2.5-VL and Qwen3-VL series show that our approach significantly improves model performance and fosters emergent capabilities such as flexible tool composition, efficient chained execution, and robust error recovery from runtime feedback. Code is available at https://github.com/ByteDance-BandAI/CodeVision.

Normalizing Flows (NFs) are a class of generative models distinguished by a mathematically invertible architecture, where the forward pass transforms data into a latent space for density estimation, and the reverse pass generates new samples from this space. This characteristic creates an intrinsic synergy between representation learning and data generation. However, the generative quality of standard NFs is limited by poor semantic representations from log-likelihood optimization. To remedy this, we propose a novel alignment strategy that creatively leverages the invertibility of NFs: instead of regularizing the forward pass, we align the intermediate features of the generative (reverse) pass with representations from a powerful vision foundation model, demonstrating superior effectiveness over naive alignment. We also introduce a novel training-free, test-time optimization algorithm for classification, which provides a more intrinsic evaluation of the NF's embedded semantic knowledge. Comprehensive experiments demonstrate that our approach accelerates the training of NFs by over 3.3times, while simultaneously delivering significant improvements in both generative quality and classification accuracy. New state-of-the-art results for NFs are established on ImageNet 64times64 and 256times256. Our code is available at https://github.com/MCG-NJU/FlowBack.

Aligning Large Language Models (LLMs) with human preferences typically relies on external supervision, which faces critical limitations: human annotations are scarce and subjective, reward models are vulnerable to reward hacking, and self-evaluation methods suffer from prompt sensitivity and biases. In this work, we propose stable rank, an intrinsic, annotation-free quality signal derived from model representations. Stable rank measures the effective dimensionality of hidden states by computing the ratio of total variance to dominant-direction variance, capturing quality through how information distributes across representation dimensions. Empirically, stable rank achieves 84.04% accuracy on RewardBench and improves task accuracy by an average of 11.3 percentage points over greedy decoding via Best-of-N sampling. Leveraging this insight, we introduce Stable Rank Group Relative Policy Optimization (SR-GRPO), which uses stable rank as a reward signal for reinforcement learning. Without external supervision, SR-GRPO improves Qwen2.5-1.5B-Instruct by 10% on STEM and 19% on mathematical reasoning, outperforming both learned reward models and self-evaluation baselines. Our findings demonstrate that quality signals can be extracted from internal model geometry, offering a path toward scalable alignment without external supervision.

While Neural Processing Units (NPUs) offer high theoretical efficiency for edge AI, state-of-the-art Vision--Language Models (VLMs) tailored for GPUs often falter on these substrates. We attribute this hardware-model mismatch to two primary factors: the quantization brittleness of Vision Transformers (ViTs) and the I/O-bound nature of autoregressive attention mechanisms, which fail to utilize the high arithmetic throughput of NPUs. To bridge this gap, we propose AutoNeural, an NPU-native VLM architecture co-designed for integer-only inference. We replace the standard ViT encoder with a MobileNetV5-style backbone utilizing depthwise separable convolutions, which ensures bounded activation distributions for stable INT4/8/16 quantization. Complementing this, our language backbone integrates State-Space Model (SSM) principles with Transformer layers, employing efficient gated convolutions to achieve linear-time complexity. This hybrid design eliminates the heavy memory I/O overhead of Key-Value caching during generation. Our approach delivers substantial efficiency gains, reducing quantization error of vision encoder by up to 7x and end-to-end latency by 14x compared to conventional baselines. The AutoNeural also delivers 3x decoding speed and 4x longer context window than the baseline. We validate these improvements via a real-world automotive case study on the Qualcomm SA8295P SoC, demonstrating real-time performance for cockpit applications. Our results highlight that rethinking model topology specifically for NPU constraints is a prerequisite for robust multi-modal edge intelligence.

Since 2019, the Hugging Face Model Hub has been the primary global platform for sharing open weight AI models. By releasing a dataset of the complete history of weekly model downloads (June 2020-August 2025) alongside model metadata, we provide the most rigorous examination to-date of concentration dynamics and evolving characteristics in the open model economy. Our analysis spans 851,000 models, over 200 aggregated attributes per model, and 2.2B downloads. We document a fundamental rebalancing of economic power: US open-weight industry dominance by Google, Meta, and OpenAI has declined sharply in favor of unaffiliated developers, community organizations, and, as of 2025, Chinese industry, with DeepSeek and Qwen models potentially heralding a new consolidation of market power. We identify statistically significant shifts in model properties, a 17X increase in average model size, rapid growth in multimodal generation (3.4X), quantization (5X), and mixture-of-experts architectures (7X), alongside concerning declines in data transparency, with open weights models surpassing truly open source models for the first time in 2025. We expose a new layer of developer intermediaries that has emerged, focused on quantizing and adapting base models for both efficiency and artistic expression. To enable continued research and oversight, we release the complete dataset with an interactive dashboard for real-time monitoring of concentration dynamics and evolving properties in the open model economy.

We present Jina-VLM, a 2.4B parameter vision-language model that achieves state-of-the-art multilingual visual question answering among open 2B-scale VLMs. The model couples a SigLIP2 vision encoder with a Qwen3 language backbone through an attention-pooling connector that enables token-efficient processing of arbitrary-resolution images. Across standard VQA benchmarks and multilingual evaluations, Jina-VLM outperforms comparable models while preserving competitive text-only performance.

Cooking is a sequential and visually grounded activity, where each step such as chopping, mixing, or frying carries both procedural logic and visual semantics. While recent diffusion models have shown strong capabilities in text-to-image generation, they struggle to handle structured multi-step scenarios like recipe illustration. Additionally, current recipe illustration methods are unable to adjust to the natural variability in recipe length, generating a fixed number of images regardless of the actual instructions structure. To address these limitations, we present CookAnything, a flexible and consistent diffusion-based framework that generates coherent, semantically distinct image sequences from textual cooking instructions of arbitrary length. The framework introduces three key components: (1) Step-wise Regional Control (SRC), which aligns textual steps with corresponding image regions within a single denoising process; (2) Flexible RoPE, a step-aware positional encoding mechanism that enhances both temporal coherence and spatial diversity; and (3) Cross-Step Consistency Control (CSCC), which maintains fine-grained ingredient consistency across steps. Experimental results on recipe illustration benchmarks show that CookAnything performs better than existing methods in training-based and training-free settings. The proposed framework supports scalable, high-quality visual synthesis of complex multi-step instructions and holds significant potential for broad applications in instructional media, and procedural content creation.

虽然 Windows 10 已经停止了主流支持（即免费更新），转向了付费的扩展支持阶段，但 Windows 10 用户并没有迅速拥抱 Windows 11。Statcounter 的数据显示，2025 年 11 月 Windows 11 份额为 53.7%，而 Windows 10 仍然有 42.7%。分析师认为，一个原因是 Windows 11 提高了硬件需求，很多现有的 Windows 10 PC 无法升级。而客户通常抱着一个信念是“如果东西没坏就没必要动它”。Windows 11 也没有提供什么必不可少的新功能能促使企业客户更新换代。

今天的学科日益细化，我们很难再看到精通多个领域的“大师”。以数学为例，数学被划分为 63 个大类，这些大类又进一步细分为 529 个子类，每个子类都发展出自己专门的术语，用于阐述和证明技术定理，而掌握这些术语需要多年的学习。这些专门的术语阻碍了数学家或科学家与非专业人士进行沟通。研究人员已经发现，科学文献的可读性随时间在下降。去年夏天几何朗兰兹猜想获得证明，但真正能读懂证明的人寥寥无几。

安全公司 Wiz 周三披露了危险等级 10/10 的 React Server 高危漏洞。React Server 被网站和云环境广泛使用，安全研究人员督促管理员尽快打上补丁，因为漏洞极其容易被利用（成功率差不多 100%）。漏洞利用代码已经公开，攻击者可利用漏洞远程执行代码。约 6% 的网站和 39% 的云环境使用 React。受影响的 React 版本包括 v19.0.1、v19.1.2 或 v19.2.1，受影响的第三方组件包括 Vite RSC、Parcel RSC、React Router RSC、RedwoodSDK、Waku 和 Next.js 等。漏洞编号为 CVE-2025-55182，存在于 React Server Components 的 Flight 协议中，源自于不安全的反序列化。

原计划参加 SpaceX Crew-12 任务前往国际空间站的俄罗斯宇航员 Oleg Artemyev 被从乘组名单中移除，他的位置由另一名俄罗斯宇航员 Andrey Fedyaev 接替。原因是他被逮到在加州 Hawthorne 基地使用手机偷拍了 SpaceX 火箭引擎和内部机密材料，违反了美国出口管制规定。Artemyev 上周被从训练基地驱逐。报道称，NASA 不希望围绕 Artemyev 的争议公开化。

Django Python Web 框架项目宣布释出 Django 6.0。主要新特性包括：支持内容安全政策（Content Security Policy 或CSP）；模板语言支持模板局部（Template Partials）；使用 Python 的 email API 处理邮件；等等。

稳定版内核维护者 Greg Kroah-Hartman 宣布释出 Linux 5.4.302。这是 5.4 系列最后一个稳定版本，该系列已终止支持，他不再建议任何人使用，因为有大量 bug 没有修复，他提供了未修复 CVE 漏洞的列表，目前数量高达 1539 个，未来这个数字只会继续增加。Linux 5.4 于 2019 年 11 月 24 日释出，是第 20 个长期支持（LTS）版本。

Valve 早些时候披露了 VR 头显产品 Steam Frame，使用了 Arm SoC，运行 Linux 操作系统 SteamOS。Valve 此前开发了在 x86 架构 Linux 操作系统上运行 Windows 游戏的兼容层项目 Proton，但在非 x86 架构上如何运行 Windows 游戏？根据 The Verge 报道，Valve 对此早就做好了充分准备。SteamOS 和 Steam Deck 的架构师 Pierre-Loup Griffais 透露 Valve 早在几年前就开始资助在 Arm 架构上运行 Windows 游戏的开源项目的开发工作，未来 Windows 应用开发商无需再在移植上花费时间就让包括游戏在内的应用在基于 x86 以及 Arm 架构处理器的 Linux 操作系统上运行。最新披露的开源项目是 Fex。Griffais 称 Valve 从 2016 年和 2017 年起就开始招募并资助开源开发者，Fex 首席开发者 Ryan Houdek 称他在 2018 年完成了首个原型，而 Valve 提供的薪水让他能全职投入 Fex 项目。

过去一个月内存和 SSD 等存储产品价格飙升，短期内这一趋势无法缓解，而主要内存和存储制造商之一的美光宣布了令消费者更沮丧的消息：它将押注 AI 退出消费者内存和存储业务，砍掉了面向消费者的品牌英睿达（Crucial）。美光并没有停止制造内存和 SSD 等存储产品，而是这些产品将只面向企业销售，服务于利润更高的 AI 数据中心市场。

科学家发现了一种微小的单细胞变形虫，它可在 63°C 下生长，创下真核生物的耐热纪录。“复杂生命”通常指细胞内含有细胞核和内部结构的生物。该变形虫能在足以杀死所有其他已知复杂生命体的高温下茁壮成长，这挑战了以往的观点，即包括所有动植物在内的“真核生物”无法适应细菌等无核生物所能耐受的极端环境。研究人员在美国加州北部喀斯喀特山脉拉森火山国家公园发现该生物，将其命名为 Incendiamoeba cascadensis，意为“来自喀斯喀特的火变形虫”。I.cascadensis 在 63°C 时仍能分裂繁殖，在 64°C 下依然能够活动。即使在高达 70°C 的环境中，这些细胞也能形成休眠的“包囊”，并在温度降低后重新被激活。相比之下，一些最耐热的细菌和古菌能承受高得多的温度：古菌 Methanopyrus kandleri 保持着目前已知生命形式的最高耐热纪录——122°C。而此前真核生物的耐热纪录由几种真菌和红藻保持；人类及其他哺乳动物细胞的耐热上限则大约只有 43°C。

Zig 软件基金会总裁兼首席开发者 Andrew Kelly 宣布项目将从 GitHub 迁移到 Codeberg，理由是微软对 AI 的迷恋正在毁掉 GitHub。他给出的一个例子是 GitHub Actions 的一个严重 bug 在 2022 年 2 月引入直到 2025 年 8 月才解决，该 bug 会导致死循环，导致进程一直运行其它任务无法执行。Andrew Kelly 认为 GitHub 不再致力于提供卓越的服务，用前 CEO 在离职前的“名言”——要么拥抱 AI 要么走——他们选择沉迷于 AI。Zig 不是唯一一个选择离开的项目，Dillo 浏览器项目也决定离开。与此同时，Codeberg 的支持会员数从 1 月的 600 人增加到了 1200 多人。

在引发公众强烈抗议后，印度政府撤回了要求智能手机制造商在新手机上预装网络安全应用 Sanchar Saathi 的命令。这一命令于上个月底传达给智能手机制造商，本周一公开。该命令遭到了厂商的抵制和安全专家的强烈反对。印度电信部长 Jyotiraditya Scindia 否认该应用是为了加强监控。印度电信部称，迄今已有 1400 万用户下载了该应用，每天报告 2000 起诈骗案件。仅周二一天就有 60 万新用户注册——十倍增长。

在英特尔之后，特朗普政府再次以注资方式获得企业股份，这次是英特尔前 CEO Pat Gelsinger 创办的 xLight。xLight 致力于改进极紫外光刻（EUV）工艺使用的激光器，希望在荷兰 ASML 的光刻机中集成它的改良激光器。去年因为英特尔业绩不佳而被董事会解雇的 Pat Gelsinger 担任 xLight 的执行董事长。特朗普政府将向 xLight 注资 1.5 亿美元，预计将成为 xLight 的最大股东。这笔资金来自于拜登政府在 2022 年通过的芯片法案《Chips and Science Act》。英特尔获得的政府资金原本也是来自于芯片法案。

《绝地潜兵 2（HELLDIVERS 2）》开发商 Arrowhead Game Studios 释出最新更新，将 PC 版本的游戏容量从 154GB 减少到 23GB，瘦身高达 85%。Arrowhead 此前曾在官方博客上解释了为什么 PC 版本的容量如此之大，原因是 PC 版本包含了大量重复数据，旨在加快机械硬盘上游戏的加载速度，而游戏机使用的是固态硬盘，因此主机版本的容量没有这么大。今天绝大部分 PC 使用的硬盘已从机械硬盘过渡到固态硬盘，Arrowhead 估计只有 12% 的《绝地潜兵 2》玩家仍然使用机械硬盘。

安全公司 Koi Security 披露了被称为 ShadyPanda 的攻击者利用浏览器扩展感染了 430 万 Chrome 和 Edge 用户。攻击者采取了长线方案，首先通过合法应用吸引积累用户群，然后通过后续更新植入恶意代码。攻击者的活动分为多个阶段，第一阶段是在扩展中嵌入联盟营销追踪代码，拦截电商平台购物链接嵌入自己的联盟营销代码获取佣金；第二阶段是劫持搜索和窃取 cookie；第三阶段植入远程访问后门变成间谍软件窃取敏感浏览器数据。 受影响的扩展包括了 Clean Master、以及 Infinity 和 WeTab 等。WeTab 开发商随后发表声明，称 Clean Master 扩展已被该公司出售，与 WeTab 和 Infinity 已经没有关联，而 WeTab 和 Infinity 并没有恶意代码，

Google DeepMind 在 2020 年 11 月宣布了它的 AI 工具 AlphaFold2，2021 年发布了 AlphaFold2 代码和数据库。问世五年来，AlphaFold2 不仅改变了结构生物学的研究方式，也推动了计算生物学的进步。不过将其生物学洞见转化为药物开发等实际应用仍需时间。AlphaFold 数据库目前已收录超过 2.4 亿个结构预测，覆盖绝大多数已知蛋白质，为全球 100 多个国家的 330 万名研究者提供支持。如今科学家已利用 AlphaFold2 设计应对抗生素耐药性的方案、寻找疟疾等疾病的新疗法，并深入理解疾病机制、加速靶向药物开发。