OrangeBot.AI Digest — 2026-02-03

A Claude Code plugin that automatically captures everything Claude does during your coding sessions, compresses it with AI (using Claude's agent-sdk), and injects relevant context back into future sessions.

AI review prompts

Skills Catalog for Codex

Project management system for Claude Code using GitHub Issues and Git worktrees for parallel agent execution.

An agentic skills framework & software development methodology that works.

An autonomous agent for deep financial research

The best ChatGPT that $100 can buy.

The official source code repository for the calibre ebook manager

ChatDev 2.0: Dev All through LLM-powered Multi-Agent Collaboration

Agent Orchestration Command Center

the easiest way to run natural language-described workflows automatically

We introduce Green-VLA, a staged Vision-Language-Action (VLA) framework for real-world deployment on the Green humanoid robot while maintaining generalization across diverse embodiments. Green-VLA follows a five stage curriculum: (L0) foundational VLMs, (L1) multimodal grounding, (R0) multi-embodiment pretraining, (R1) embodiment-specific adaptation, and (R2) reinforcement-learning (RL) policy alignment. We couple a scalable data-processing pipeline (3,000 hours of demonstrations) with temporal alignment and quality filtering, and use a unified, embodiment-aware action interface enabling a single policy to control humanoids, mobile manipulators, and fixed-base arms. At inference, the VLA controller is enhanced with episode-progress prediction, out-of-distribution detection, and joint-prediction-based guidance to improve safety and precise target selection. Experiments on Simpler BRIDGE WidowX and CALVIN ABC-D, as well as real-robot evaluations, show strong generalization and performance gains from RL alignment in success rate, robustness, and long-horizon efficiency.

Unified multimodal models often struggle with complex synthesis tasks that demand deep reasoning, and typically treat text-to-image generation and image editing as isolated capabilities rather than interconnected reasoning steps. To address this, we propose UniReason, a unified framework that harmonizes these two tasks through a dual reasoning paradigm. We formulate generation as world knowledge-enhanced planning to inject implicit constraints, and leverage editing capabilities for fine-grained visual refinement to further correct visual errors via self-reflection. This approach unifies generation and editing within a shared representation, mirroring the human cognitive process of planning followed by refinement. We support this framework by systematically constructing a large-scale reasoning-centric dataset (~300k samples) covering five major knowledge domains (e.g., cultural commonsense, physics, etc.) for planning, alongside an agent-generated corpus for visual self-correction. Extensive experiments demonstrate that UniReason achieves advanced performance on reasoning-intensive benchmarks such as WISE, KrisBench and UniREditBench, while maintaining superior general synthesis capabilities.

We propose SWE-Universe, a scalable and efficient framework for automatically constructing real-world software engineering (SWE) verifiable environments from GitHub pull requests (PRs). To overcome the prevalent challenges of automatic building, such as low production yield, weak verifiers, and prohibitive cost, our framework utilizes a building agent powered by an efficient custom-trained model. This agent employs iterative self-verification and in-loop hacking detection to ensure the reliable generation of high-fidelity, verifiable tasks. Using this method, we scale the number of real-world multilingual SWE environments to a million scale (807,693). We demonstrate the profound value of our environments through large-scale agentic mid-training and reinforcement learning. Finally, we applied this technique to Qwen3-Max-Thinking and achieved a score of 75.3% on SWE-Bench Verified. Our work provides both a critical resource and a robust methodology to advance the next generation of coding agents.

Pixel diffusion generates images directly in pixel space in an end-to-end manner, avoiding the artifacts and bottlenecks introduced by VAEs in two-stage latent diffusion. However, it is challenging to optimize high-dimensional pixel manifolds that contain many perceptually irrelevant signals, leaving existing pixel diffusion methods lagging behind latent diffusion models. We propose PixelGen, a simple pixel diffusion framework with perceptual supervision. Instead of modeling the full image manifold, PixelGen introduces two complementary perceptual losses to guide diffusion model towards learning a more meaningful perceptual manifold. An LPIPS loss facilitates learning better local patterns, while a DINO-based perceptual loss strengthens global semantics. With perceptual supervision, PixelGen surpasses strong latent diffusion baselines. It achieves an FID of 5.11 on ImageNet-256 without classifier-free guidance using only 80 training epochs, and demonstrates favorable scaling performance on large-scale text-to-image generation with a GenEval score of 0.79. PixelGen requires no VAEs, no latent representations, and no auxiliary stages, providing a simpler yet more powerful generative paradigm. Codes are publicly available at https://github.com/Zehong-Ma/PixelGen.

Direct preference optimization methods have emerged as a computationally efficient alternative to Reinforcement Learning from Human Feedback (RLHF) for aligning Large Language Models (LLMs). Latest approaches have streamlined the alignment process by deriving implicit reward functions, yet they often suffer from a critical objective mismatch: optimizing the relative margin between chosen and rejected responses does not guarantee the preservation of the chosen response's absolute likelihood. This can lead to ``unlearning'', where the model degrades the probability of high-quality outputs to satisfy margin constraints, and ``formatting collapse'' caused by the over-penalization of rejected sequences. In this work, we introduce SLIME (Stabilized Likelihood Implicit Margin Enforcement), a reference-free alignment objective designed to decouple preference learning from generation quality. SLIME incorporates a three-pronged objective: (1) an anchoring term to maximize the likelihood of preferred responses; (2) a stabilizing penalty that prevents the probabilities of rejected tokens from collapsing to zero; and (3) a dual-margin mechanism that combines hard and soft constraints for precise boundary shaping. Our results demonstrate that SLIME achieves superior performance compared to state-of-the-art baselines while maintaining higher generation stability.

Post-training of reasoning LLMs is a holistic process that typically consists of an offline SFT stage followed by an online reinforcement learning (RL) stage. However, SFT is often optimized in isolation to maximize SFT performance alone. We show that, after identical RL training, models initialized from stronger SFT checkpoints can significantly underperform those initialized from weaker ones. We attribute this to a mismatch typical in current SFT-RL pipelines: the distribution that generates the offline SFT data can differ substantially from the policy optimized during online RL, which learns from its own rollouts. We propose PEAR (Policy Evaluation-inspired Algorithm for Offline Learning Loss Re-weighting), an SFT-stage method that corrects this mismatch and better prepares the model for RL. PEAR uses importance sampling to reweight the SFT loss, with three variants operating at the token, block, and sequence levels. It can be used to augment standard SFT objectives and incurs little additional training overhead once probabilities for the offline data are collected. We conduct controlled experiments on verifiable reasoning games and mathematical reasoning tasks on Qwen 2.5 and 3 and DeepSeek-distilled models. PEAR consistently improves post-RL performance over canonical SFT, with pass at 8 gains up to a 14.6 percent on AIME2025. Our results suggest that PEAR is an effective step toward more holistic LLM post-training by designing and evaluating SFT with downstream RL in mind rather than in isolation.

To achieve real-time interactive video generation, current methods distill pretrained bidirectional video diffusion models into few-step autoregressive (AR) models, facing an architectural gap when full attention is replaced by causal attention. However, existing approaches do not bridge this gap theoretically. They initialize the AR student via ODE distillation, which requires frame-level injectivity, where each noisy frame must map to a unique clean frame under the PF-ODE of an AR teacher. Distilling an AR student from a bidirectional teacher violates this condition, preventing recovery of the teacher's flow map and instead inducing a conditional-expectation solution, which degrades performance. To address this issue, we propose Causal Forcing that uses an AR teacher for ODE initialization, thereby bridging the architectural gap. Empirical results show that our method outperforms all baselines across all metrics, surpassing the SOTA Self Forcing by 19.3\% in Dynamic Degree, 8.7\% in VisionReward, and 16.7\% in Instruction Following. Project page and the code: https://thu-ml.github.io/CausalForcing.github.io/{https://thu-ml.github.io/CausalForcing.github.io/}

Growing efforts to improve knowledge distillation (KD) in large language models (LLMs) replace dense teacher supervision with selective distillation, which uses a subset of token positions, vocabulary classes, or training samples for supervision. However, it remains unclear which importance signals, selection policies, and their interplay are most effective. In this work, we revisit where and how to distill in autoregressive LLMs. We disentangle selective KD along the position, class, and sample axes and systematically compare importance signals and selection policies. Then, guided by this analysis, we identify underexplored opportunities and introduce student-entropy-guided position selection (SE-KD). Across a suite of benchmarks, SE-KD often improves accuracy, downstream task adherence, and memory efficiency over dense distillation. Extending this approach across the class and sample axes (SE-KD 3X) yields complementary efficiency gains that make offline teacher caching feasible. In practice, this reduces wall time by 70% and peak memory by 18%, while cutting storage usage by 80% over prior methods without sacrificing performance.

Autoregressive video diffusion models enable streaming generation, opening the door to long-form synthesis, video world models, and interactive neural game engines. However, their core attention layers become a major bottleneck at inference time: as generation progresses, the KV cache grows, causing both increasing latency and escalating GPU memory, which in turn restricts usable temporal context and harms long-range consistency. In this work, we study redundancy in autoregressive video diffusion and identify three persistent sources: near-duplicate cached keys across frames, slowly evolving (largely semantic) queries/keys that make many attention computations redundant, and cross-attention over long prompts where only a small subset of tokens matters per frame. Building on these observations, we propose a unified, training-free attention framework for autoregressive diffusion: TempCache compresses the KV cache via temporal correspondence to bound cache growth; AnnCA accelerates cross-attention by selecting frame-relevant prompt tokens using fast approximate nearest neighbor (ANN) matching; and AnnSA sparsifies self-attention by restricting each query to semantically matched keys, also using a lightweight ANN. Together, these modules reduce attention, compute, and memory and are compatible with existing autoregressive diffusion backbones and world models. Experiments demonstrate up to x5--x10 end-to-end speedups while preserving near-identical visual quality and, crucially, maintaining stable throughput and nearly constant peak GPU memory usage over long rollouts, where prior methods progressively slow down and suffer from increasing memory usage.

We introduce FSVideo, a fast speed transformer-based image-to-video (I2V) diffusion framework. We build our framework on the following key components: 1.) a new video autoencoder with highly-compressed latent space (64times64times4 spatial-temporal downsampling ratio), achieving competitive reconstruction quality; 2.) a diffusion transformer (DIT) architecture with a new layer memory design to enhance inter-layer information flow and context reuse within DIT, and 3.) a multi-resolution generation strategy via a few-step DIT upsampler to increase video fidelity. Our final model, which contains a 14B DIT base model and a 14B DIT upsampler, achieves competitive performance against other popular open-source models, while being an order of magnitude faster. We discuss our model design as well as training strategies in this report.

Mobile Graphical User Interface (GUI) World Models (WMs) offer a promising path for improving mobile GUI agent performance at train- and inference-time. However, current approaches face a critical trade-off: text-based WMs sacrifice visual fidelity, while the inability of visual WMs in precise text rendering led to their reliance on slow, complex pipelines dependent on numerous external models. We propose a novel paradigm: visual world modeling via renderable code generation, where a single Vision-Language Model (VLM) predicts the next GUI state as executable web code that renders to pixels, rather than generating pixels directly. This combines the strengths of both approaches: VLMs retain their linguistic priors for precise text rendering while their pre-training on structured web code enables high-fidelity visual generation. We introduce gWorld (8B, 32B), the first open-weight visual mobile GUI WMs built on this paradigm, along with a data generation framework (gWorld) that automatically synthesizes code-based training data. In extensive evaluation across 4 in- and 2 out-of-distribution benchmarks, gWorld sets a new pareto frontier in accuracy versus model size, outperforming 8 frontier open-weight models over 50.25x larger. Further analyses show that (1) scaling training data via gWorld yields meaningful gains, (2) each component of our pipeline improves data quality, and (3) stronger world modeling improves downstream mobile GUI policy performance.

LLM-based deep research agents are largely built on the ReAct framework. This linear design makes it difficult to revisit earlier states, branch into alternative search directions, or maintain global awareness under long contexts, often leading to local optima, redundant exploration, and inefficient search. We propose Re-TRAC, an agentic framework that performs cross-trajectory exploration by generating a structured state representation after each trajectory to summarize evidence, uncertainties, failures, and future plans, and conditioning subsequent trajectories on this state representation. This enables iterative reflection and globally informed planning, reframing research as a progressive process. Empirical results show that Re-TRAC consistently outperforms ReAct by 15-20% on BrowseComp with frontier LLMs. For smaller models, we introduce Re-TRAC-aware supervised fine-tuning, achieving state-of-the-art performance at comparable scales. Notably, Re-TRAC shows a monotonic reduction in tool calls and token usage across rounds, indicating progressively targeted exploration driven by cross-trajectory reflection rather than redundant search.

Methods for controlling large language models (LLMs), including local weight fine-tuning, LoRA-based adaptation, and activation-based interventions, are often studied in isolation, obscuring their connections and making comparison difficult. In this work, we present a unified view that frames these interventions as dynamic weight updates induced by a control signal, placing them within a single conceptual framework. Building on this view, we propose a unified preference-utility analysis that separates control effects into preference, defined as the tendency toward a target concept, and utility, defined as coherent and task-valid generation, and measures both on a shared log-odds scale using polarity-paired contrastive examples. Across methods, we observe a consistent trade-off between preference and utility: stronger control increases preference while predictably reducing utility. We further explain this behavior through an activation manifold perspective, in which control shifts representations along target-concept directions to enhance preference, while utility declines primarily when interventions push representations off the model's valid-generation manifold. Finally, we introduce a new steering approach SPLIT guided by this analysis that improves preference while better preserving utility. Code is available at https://github.com/zjunlp/EasyEdit/blob/main/examples/SPLIT.md.

Progressive Learning (PL) reduces pre-training computational overhead by gradually increasing model scale. While prior work has extensively explored depth expansion, width expansion remains significantly understudied, with the few existing methods limited to the early stages of training. However, expanding width during the mid-stage is essential for maximizing computational savings, yet it remains a formidable challenge due to severe training instabilities. Empirically, we show that naive initialization at this stage disrupts activation statistics, triggering loss spikes, while copy-based initialization introduces gradient symmetry that hinders feature diversity. To address these issues, we propose SPARKLING (balancing {S}ignal {P}reservation {A}nd symmet{R}y brea{K}ing for width-progressive {L}earn{ING}), a novel framework for mid-stage width expansion. Our method achieves signal preservation via RMS-scale consistency, stabilizing activation statistics during expansion. Symmetry breaking is ensured through asymmetric optimizer state resetting and learning rate re-warmup. Extensive experiments on Mixture-of-Experts (MoE) models demonstrate that, across multiple width axes and optimizer families, SPARKLING consistently outperforms training from scratch and reduces training cost by up to 35% under 2times width expansion.

Text-to-image (T2I) generation has achieved remarkable progress, yet existing methods often lack the ability to dynamically reason and refine during generation--a hallmark of human creativity. Current reasoning-augmented paradigms most rely on explicit thought processes, where intermediate reasoning is decoded into discrete text at fixed steps with frequent image decoding and re-encoding, leading to inefficiencies, information loss, and cognitive mismatches. To bridge this gap, we introduce LatentMorph, a novel framework that seamlessly integrates implicit latent reasoning into the T2I generation process. At its core, LatentMorph introduces four lightweight components: (i) a condenser for summarizing intermediate generation states into compact visual memory, (ii) a translator for converting latent thoughts into actionable guidance, (iii) a shaper for dynamically steering next image token predictions, and (iv) an RL-trained invoker for adaptively determining when to invoke reasoning. By performing reasoning entirely in continuous latent spaces, LatentMorph avoids the bottlenecks of explicit reasoning and enables more adaptive self-refinement. Extensive experiments demonstrate that LatentMorph (I) enhances the base model Janus-Pro by 16% on GenEval and 25% on T2I-CompBench; (II) outperforms explicit paradigms (e.g., TwiG) by 15% and 11% on abstract reasoning tasks like WISE and IPV-Txt, (III) while reducing inference time by 44% and token consumption by 51%; and (IV) exhibits 71% cognitive alignment with human intuition on reasoning invocation.

根据发表在《Milbank Quarterly》期刊上的一项研究，哈佛、杜克和密歇根大学的研究人员认为，超加工食品（Ultra-processed foods）与香烟的相似之处远多于与水果或蔬菜的相似之处，需要更严格的监管。超加工食品是经过工业化生产、通常使用乳化剂或人工色素和香精的食品，如软饮料、薯片和饼干。研究人员称，超加工食品和香烟的生产过程存在相似之处，制造商都在努力优化产品“剂量”以及对人体奖赏通路的作用速度。宣传食品“低脂”或“无糖”都是在误导消费者，类似 1950 年代宣传香烟的过滤嘴是一种保护性创新，实际上几乎没有任何实质性益处。研究人员认为应该借鉴烟草管理去监管超加工食品。

西班牙首相 Pedro Sanchez 周二表示，计划禁止 16 岁以下未成年人使用社交媒体，社交平台需要引入年龄验证系统。他表示要保护儿童远离数字狂野西部。澳大利亚于去年 12 月成为首个禁止 16 岁以下儿童使用社交媒体的国家，英法等国正在考虑采取类似年龄限制措施。Sanchez 称西班牙将于下周提出一项法案，追究社交媒体高管对非法和仇恨言论内容的责任，将算法操纵和放大非法内容定为犯罪行为。

巴黎检方突击搜查 X 在法办公室。执行搜查的是网络犯罪部门，欧洲刑警组织协助。搜查与 2025 年 1 月启动的调查相关，这次调查涉及对 X 算法及其推荐内容的投诉。巴黎检方还传唤了马斯克（Elon Musk）以及 X 前 CEO Linda Yaccarino，要求 4 月出席听证会。检方在声明中称，X 平台流传深度伪造的色情视频以及否认纳粹大屠杀的内容。检方还宣布将退出 X 平台，将通过 LinkedIn 和 Instagram 与外界沟通。

工信部发布了新的强制性安全标准《汽车车门把手安全技术要求》，禁止电动汽车使用隐藏式门把手，成为世界上首个禁止这种设计的国家。这种特斯拉推广的设计因一系列致命事件而面临全球监管机构的审查。新规定要求在中国销售的汽车必须配备机械释放车门外把手。新规将于 2027 年 1 月 1 日起开始实施。已获得型式批准的车型，应于 2029 年 1 月前修改其设计以符合要求。在此之前，中国国内发生多起引发高度关注的事故，其中包括两起小米电动汽车起火事故。事故中车门疑似因断电而无法打开，造成车内人员既无法逃生，也无法获救，最终身亡。

乌克兰和 SpaceX 最近合作阻止俄罗斯无人机使用 Starlink 发动攻击。乌克兰国防部表示，乌克兰的 Starlink 用户在不久之后将被要求登记其终端，未来经过验证和登记的 Starlink 终端将被加入到白名单，能继续在乌克兰境内访问卫星互联网，未登记的终端将被断开连接。俄罗斯通过黑市交易获得了 Starlink 终端，它的 Molniya-2 无人机的攻击型号和侦察型号通过配备 Starlink 实现超视距的控制和数据传输，在更远的距离上进行精确打击。Molniya-2 被发现使用了 F8 迷你 PC ，运行正版授权的 Windows 11 操作系统。

AI 热导致内存和固态硬盘价格不断上涨，也迫使 PC 组装厂商不断调整价格应对主要零部件价格的上涨。树莓派宣布了两个月内的第二次价格上调。所有配备 2GB 以上内存的 Raspberry Pi 4 和 Raspberry Pi 5 都将涨价。2GB 内存版本上涨 10 美元，4GB 内存上涨 15 美元，8GB 内存上涨 30 美元，16GB 内存版本将大幅上涨 60 美元。16GB 版本的 Pi 5 如今售价高达 205 美元，而树莓派之类的单板电脑曾以低价著称。

马斯克（Elon Musk）旗下的火箭公司正式宣布收购他旗下的 AI 公司 xAI，这也意味着作为 xAI 一部分的社交网络 X/Twitter 也将成为 SpaceX 家族的一员。SpaceX 是马斯克目前最成功的公司，预计今年 IPO，其估值超过 1 万亿美元。马斯克声称他计划使用 SpaceX 的火箭为 xAI 发射部署 100 万个轨道数据中心。他的很多言论不能当真。

Phoronix 评测了龙芯科技 LoongArch 架构 12 核 24 线处理器 3B6000 在 Linux 下的性能，并与 AMD 和英特尔推出的主流 CPU 以及树莓派的 Raspberry Pi 500+ 进行了对比。3B6000 于 2025 年推出，支持 DDR4 ECC 内存，其开发板售价为 3000 元。测试结果并不出人意料，3B6000 性能要远逊色于 AMD Zen 5 架构的处理器如 9600x，但显著强于 ARM 架构的 Pi 500+。

根据发表在 PNAS 期刊上的一项研究，犹他大学的研究人员分析了近一个世纪的人类头发样本，发现自 1970 年代禁止含铅汽油等含铅产品以来，头发中的铅含量减少到峰值期间的百分之一。铅是强效神经毒素。研究人员使用了犹他州居民身上收集的头发样本，部分样本保存在家族数代人的剪贴簿中。分析发现，头发铅含量在 1916-1969 年间达到峰值的 100 ppm，1990 年降至 10ppm，2024 年降至 1ppm 以下。这一下降趋势与美国淘汰含铅汽油的进程基本一致。

芬兰交通管理局（Fintraffic）准备今年夏天推出新交通信号灯系统，该系统将赋予紧急车辆优先通行权，允许救护车、消防车和其它紧急救援车辆通过“绿波”式的交通信号灯通过路口。名为“national traffic light priority system”的交通灯优先系统能识别紧急车辆的位置，自动将交通信号灯切换为绿灯方便其快速通行。 新系统将于 7 月正式投入使用，但由于需要对交通系统进行测试和更新，因此在全国范围内推广还需要时间。

Mozilla 宣布为 Firefox 用户提供禁用所有 AI 功能的选项。从 2 月 24 日推出的 Firefox 148 起，用户可以在设置中启用“Block AI enhancements”选项，一旦启用，用户将不会看到任何现在或未来 AI 功能的弹出窗口或提醒。新的 AI 控制选项还允许用户单独管理各项 AI 功能。Mozilla 新 CEO Anthony Enzor-DeMeo 表示，AI 应该始终是一种选择，可以关闭，让用户知道如何运作，能带来什么价值。

知情人士透露，微软正在重新评估 Windows 11 的 AI 战略，收缩或移除 Windows 内置应用与 AI 应用 Copilot 的集成。过去几个月 Windows 深度整合 AI 遭遇了用户的强烈反对。微软正在评估记事本和画图等应用中的 Copilot 功能，可能会完全移除相关功能或移除 Copilot 标识以提供更简洁的用户体验。微软已经暂停了在其它内置应用中引入 Copilot 按钮的工作。早先引发争议的 Windows Recall 功能也在接受评估，微软内部认为目前的实现方案是失败的，探索重新设计或重命名该功能，但没有完全放弃。

公安部发布《网络犯罪防治法（征求意见稿）》，意见反馈截止时间 2026 年 3 月 2 日。根据《征求意见稿》：第十五条 任何个人和组织制作、销售、提供具有下列功能的设备、软件、工具、服务的，应当到公安机关、电信等主管部门备案，并登记购买者、使用者的真实身份信息： （一）具有批量控制网络账号、上网线路、智能终端等功能的； （二）具有网络虚拟定位功能的； （三）具有侵入、控制计算机信息系统功能的； （四）其他由省级以上公安机关会同电信等主管部门认定的，可能被大量用于网络违法犯罪的设备、软件、工具、服务。 第二十四条 任何个人和组织不得违反国家有关规定，实施网络产品安全漏洞发现、收集、发布等违法犯罪活动，或者散布、传播重要信息系统的设计方案、网络拓扑、核心源代码等可能危害网络安全的信息。 第二十五条 未经省级以上网信部门、公安机关批准或者行业主管部门、运营者授权，任何个人、组织不得对网络安全等级保护第三级（含）以上的网络开展网络安全漏洞探测、渗透性测试等可能影响网络安全的活动。 未经设区的市级以上网信部门、公安机关批准或者行业主管部门、运营者授权，任何个人、组织不得对网络安全等级保护第二级（含）以下的网络开展网络安全漏洞探测、渗透性测试等可能影响网络安全的活动。 依法或者经批准、授权开展的，应当在活动实施五个工作日前向县级以上公安机关报告。法律、行政法规另有规定的，从其规定。

去年 12 月 Notepad++ 发布安全警告，它遭遇了流量劫持，部分地区的更新程序被植入恶意程序。调查发现，Notepad++ 更新程序 WinGUp 的流量被劫持到恶意服务器，下载恶意可执行文件。现在 Notepad++ 公布了最新调查结果：其流量被国家支持黑客劫持。托管 https://notepad-plus-plus.org/update/getDownloadUrl.php 的服务器在 2025 年 6 月遭到入侵，这次入侵在 2025 年 9 月 2 日因服务器更新而被阻止，但攻击者仍然持有 Notepad++ 内部服务凭据直至 12 月 2 日，允许攻击者将部分 https://notepad-plus-plus.org/getDownloadUrl.php 的流量重定向到他们控制的服务器，返回篡改后的 URL。攻击者是专门针对 Notepad++。

去年 4 月一位叫 Amani 的办公室经理在公司内部的 WhatsApp 群里鼓励同事和下属好好工作，称“每天都带来新机会”。Amani 的工作地点是老挝的一个电诈园区，他鼓励的也不是普通的销售团队，而是从事杀猪盘的诈骗成员。和诈骗目标一样，他们很多人也都是受害者。告密者 Mohammad Muzahir 向《连线》泄漏了其所在园区的内部文件，包括三个月的 WhatsApp 内部群聊记录，揭示了电诈园区的工作文化。在 Amani 发表甜言蜜语几小时后，一名高层恐吓说不要违规否则会死，群聊里的员工纷纷用点赞和敬礼的表情符号回复。群聊显示从事电诈的工作人员并没有被直接监禁，园区是依靠契约奴隶制和债务制控制他们。Muzahir 的基本月薪是 3500 人民币，他只要赚到 5400 美元就能买断合同离开，然而各种罚款让这一目标遥不可及。在办公室睡觉、看无关视频、与朋友聊天以及任何工作无关活动都将被罚款 200 元，等等。Muzahir 称罚款让他几乎身无分文。