OrangeBot.AI Digest — 2025-10-21

The most intuitive desktop API client. Organize and execute REST, GraphQL, WebSockets, Server Sent Events, and gRPC 🦬

A fancy self-hosted monitoring tool

An Open Source implementation of Notebook LM with more flexibility and features

Generate audiobooks from e-books, voice cloning & 1107+ languages!

A collection of notebooks/recipes showcasing some fun and effective ways of using Claude.

A cat(1) clone with wings.

Automate browser-based workflows with LLMs and Computer Vision

MiniOB is a compact database that assists developers in understanding the fundamental workings of a database.

Speech-to-text, text-to-speech, speaker diarization, speech enhancement, source separation, and VAD using next-gen Kaldi with onnxruntime without Internet connection. Support embedded systems, Android, iOS, HarmonyOS, Raspberry Pi, RISC-V, x86_64 servers, websocket server/client, support 12 programming languages

Servo aims to empower developers with a lightweight, high-performance alternative for embedding web technologies in applications.

Free, local, open-source AI app builder ✨ v0 / lovable / Bolt alternative 🌟 Star if you like it!

The automation tower defense RTS

Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages.

Open source codebase powering the HuggingChat app

📚 Freely available programming books

Image editing has achieved remarkable progress recently. Modern editing models could already follow complex instructions to manipulate the original content. However, beyond completing the editing instructions, the accompanying physical effects are the key to the generation realism. For example, removing an object should also remove its shadow, reflections, and interactions with nearby objects. Unfortunately, existing models and benchmarks mainly focus on instruction completion but overlook these physical effects. So, at this moment, how far are we from physically realistic image editing? To answer this, we introduce PICABench, which systematically evaluates physical realism across eight sub-dimension (spanning optics, mechanics, and state transitions) for most of the common editing operations (add, remove, attribute change, etc). We further propose the PICAEval, a reliable evaluation protocol that uses VLM-as-a-judge with per-case, region-level human annotations and questions. Beyond benchmarking, we also explore effective solutions by learning physics from videos and construct a training dataset PICA-100K. After evaluating most of the mainstream models, we observe that physical realism remains a challenging problem with large rooms to explore. We hope that our benchmark and proposed solutions can serve as a foundation for future work moving from naive content editing toward physically consistent realism.

Autonomous data science, from raw data sources to analyst-grade deep research reports, has been a long-standing challenge, and is now becoming feasible with the emergence of powerful large language models (LLMs). Recent workflow-based data agents have shown promising results on specific data tasks but remain fundamentally limited in achieving fully autonomous data science due to their reliance on predefined workflows. In this paper, we introduce DeepAnalyze-8B, the first agentic LLM designed for autonomous data science, capable of automatically completing the end-toend pipeline from data sources to analyst-grade deep research reports. To tackle high-complexity data science tasks, we propose a curriculum-based agentic training paradigm that emulates the learning trajectory of human data scientists, enabling LLMs to progressively acquire and integrate multiple capabilities in real-world environments. We also introduce a data-grounded trajectory synthesis framework that constructs high-quality training data. Through agentic training, DeepAnalyze learns to perform a broad spectrum of data tasks, ranging from data question answering and specialized analytical tasks to open-ended data research. Experiments demonstrate that, with only 8B parameters, DeepAnalyze outperforms previous workflow-based agents built on most advanced proprietary LLMs. The model, code, and training data of DeepAnalyze are open-sourced, paving the way toward autonomous data science.

Large language models (LLMs) increasingly rely on long-context modeling for tasks such as document understanding, code analysis, and multi-step reasoning. However, scaling context windows to the million-token level brings prohibitive computational and memory costs, limiting the practicality of long-context LLMs. In this work, we take a different perspective-visual context scaling-to tackle this challenge. Instead of extending token-based sequences, we propose Glyph, a framework that renders long texts into images and processes them with vision-language models (VLMs). This approach substantially compresses textual input while preserving semantic information, and we further design an LLM-driven genetic search to identify optimal visual rendering configurations for balancing accuracy and compression. Through extensive experiments, we demonstrate that our method achieves 3-4x token compression while maintaining accuracy comparable to leading LLMs such as Qwen3-8B on various long-context benchmarks. This compression also leads to around 4x faster prefilling and decoding, and approximately 2x faster SFT training. Furthermore, under extreme compression, a 128K-context VLM could scale to handle 1M-token-level text tasks. In addition, the rendered text data benefits real-world multimodal tasks, such as document understanding. Our code and model are released at https://github.com/thu-coai/Glyph.

The advancement of vision-language models (VLMs) is hampered by a fragmented landscape of inconsistent and contaminated public datasets. We introduce FineVision, a meticulously collected, curated, and unified corpus of 24 million samples - the largest open resource of its kind. We unify more than 200 sources into 185 subsets via a semi-automated, human-in-the-loop pipeline: automation performs bulk ingestion and schema mapping, while reviewers audit mappings and spot-check outputs to verify faithful consumption of annotations, appropriate formatting and diversity, and safety; issues trigger targeted fixes and re-runs. The workflow further applies rigorous de-duplication within and across sources and decontamination against 66 public benchmarks. FineVision also encompasses agentic/GUI tasks with a unified action space; reviewers validate schemas and inspect a sample of trajectories to confirm executable fidelity. Models trained on FineVision consistently outperform those trained on existing open mixtures across a broad evaluation suite, underscoring the benefits of scale, data hygiene, and balanced automation with human oversight. We release the corpus and curation tools to accelerate data-centric VLM research.

Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for enhancing large language models (LLMs) by retrieving relevant documents from an external corpus. However, existing RAG systems primarily focus on unimodal text documents, and often fall short in real-world scenarios where both queries and documents may contain mixed modalities (such as text and images). In this paper, we address the challenge of Universal Retrieval-Augmented Generation (URAG), which involves retrieving and reasoning over mixed-modal information to improve vision-language generation. To this end, we propose Nyx, a unified mixed-modal to mixed-modal retriever tailored for URAG scenarios. To mitigate the scarcity of realistic mixed-modal data, we introduce a four-stage automated pipeline for generation and filtering, leveraging web documents to construct NyxQA, a dataset comprising diverse mixed-modal question-answer pairs that better reflect real-world information needs. Building on this high-quality dataset, we adopt a two-stage training framework for Nyx: we first perform pre-training on NyxQA along with a variety of open-source retrieval datasets, followed by supervised fine-tuning using feedback from downstream vision-language models (VLMs) to align retrieval outputs with generative preferences. Experimental results demonstrate that Nyx not only performs competitively on standard text-only RAG benchmarks, but also excels in the more general and realistic URAG setting, significantly improving generation quality in vision-language tasks.

Ensembling Large Language Models (LLMs) has gained attention as a promising approach to surpass the performance of individual models by leveraging their complementary strengths. In particular, aggregating models' next-token probability distributions to select the next token has been shown to be effective in various tasks. However, while successful for short-form answers, its application to long-form generation remains underexplored. In this paper, we show that using existing ensemble methods in long-form generation requires a careful choice of ensembling positions, since the standard practice of ensembling at every token often degrades performance. We identify two key factors for determining these positions: tokenization mismatch across models and consensus in their next-token probability distributions. Based on this, we propose SAFE, (Stable And Fast LLM Ensembling), a framework that selectively ensembles by jointly considering these factors. To further improve stability, we introduce a probability sharpening strategy that consolidates probabilities spread across multiple sub-word tokens representing the same word into a single representative token. Our experiments on diverse benchmarks, including MATH500 and BBH, demonstrate that SAFE outperforms existing methods in both accuracy and efficiency, with gains achieved even when ensembling fewer than 1% of tokens.

Large Language Models have achieved strong performance on reasoning tasks, solving competition-level coding and math problems. However, their scalability is limited by human-labeled datasets and the lack of large-scale, challenging coding problem training data. Existing competitive coding datasets contain only thousands to tens of thousands of problems. Previous synthetic data generation methods rely on either augmenting existing instruction datasets or selecting challenging problems from human-labeled data. In this paper, we propose QueST, a novel framework which combines difficulty-aware graph sampling and difficulty-aware rejection fine-tuning that directly optimizes specialized generators to create challenging coding problems. Our trained generators demonstrate superior capability compared to even GPT-4o at creating challenging problems that benefit downstream performance. We leverage QueST to generate large-scale synthetic coding problems, which we then use to distill from strong teacher models with long chain-of-thought or to conduct reinforcement learning for smaller models, proving effective in both scenarios. Our distillation experiments demonstrate significant performance gains. Specifically, after fine-tuning Qwen3-8B-base on 100K difficult problems generated by QueST, we surpass the performance of the original Qwen3-8B on LiveCodeBench. With an additional 112K examples (i.e., 28K human-written problems paired with multiple synthetic solutions), our 8B model matches the performance of the much larger DeepSeek-R1-671B. These findings indicate that generating complex problems via QueST offers an effective and scalable approach to advancing the frontiers of competitive coding and reasoning for large language models.

While inference-time scaling through search has revolutionized Large Language Models, translating these gains to image generation has proven difficult. Recent attempts to apply search strategies to continuous diffusion models show limited benefits, with simple random sampling often performing best. We demonstrate that the discrete, sequential nature of visual autoregressive models enables effective search for image generation. We show that beam search substantially improves text-to-image generation, enabling a 2B parameter autoregressive model to outperform a 12B parameter diffusion model across benchmarks. Systematic ablations show that this advantage comes from the discrete token space, which allows early pruning and computational reuse, and our verifier analysis highlights trade-offs between speed and reasoning capability. These findings suggest that model architecture, not just scale, is critical for inference-time optimization in visual generation.

A dominant assumption in Multimodal Language Model (MLLM) research is that its performance is largely inherited from the LLM backbone, given its immense parameter scale and remarkable capabilities. This has created a void in the understanding of the vision encoder, which determines how MLLMs perceive images. The recent shift in MLLM training paradigms, from Supervised Finetuning (SFT) to Reinforcement Learning (RL), magnifies this oversight-namely, the significant lack of analysis on how such training reshapes the vision encoder as well as the MLLM. To address this, we first investigate the impact of training strategies on MLLMs, where RL shows a clear advantage over SFT in strongly vision-related VQA benchmarks. Motivated by this, we conduct a critical yet under-explored analysis of the vision encoder of MLLMs through diverse and in-depth experiments, ranging from ImageNet classification and segmentation to gradient visualization. Our results demonstrate that MLLM's post-training strategy (i.e., SFT or RL) not only leads to distinct outcomes on MLLM downstream tasks, but also fundamentally reshapes MLLM's underlying visual representations. Specifically, the key finding of our study is that RL produces stronger and precisely localized visual representations compared to SFT, boosting the ability of the vision encoder for MLLM. We then reframe our findings into a simple recipe for building strong vision encoders for MLLMs, Preference-Instructed Vision OpTimization (PIVOT). When integrated into MLLMs, a PIVOT-trained vision encoder outperforms even larger and more heavily-trained counterparts, despite requiring less than 1% of the computational cost of standard vision pretraining. This result opens an effective and efficient path for advancing the vision backbones of MLLMs. Project page available at https://june-page.github.io/pivot/

Honesty alignment-the ability of large language models (LLMs) to recognize their knowledge boundaries and express calibrated confidence-is essential for trustworthy deployment. Existing methods either rely on training-free confidence estimation (e.g., token probabilities, self-consistency) or training-based calibration with correctness annotations. While effective, achieving universal honesty alignment with training-based calibration requires costly, large-scale labeling. To support annotation-efficient training, we introduce Elicitation-Then-Calibration (EliCal), a two-stage framework that first elicits internal confidence using inexpensive self-consistency supervision, then calibrates this confidence with a small set of correctness annotations. To support a large-scale study, we release HonestyBench, a benchmark covering ten free-form QA datasets with 560k training and 70k evaluation instances annotated with correctness and self-consistency signals. Experiments show that EliCal achieves near-optimal alignment with only 1k correctness annotations (0.18% of full supervision) and better alignment performance on unseen MMLU tasks than the calibration-only baseline, offering a scalable solution toward universal honesty alignment in LLMs.

Instruction-based image editing has achieved remarkable progress; however, models solely trained via supervised fine-tuning often overfit to annotated patterns, hindering their ability to explore and generalize beyond training distributions. To this end, we introduce Edit-R1, a novel post-training framework for instruction-based image editing based on policy optimization. Specifically, we utilize Diffusion Negative-aware Finetuning (DiffusionNFT), a likelihood-free policy optimization method consistent with the flow matching forward process, thereby enabling the use of higher-order samplers and more efficient training. Another key challenge here is the absence of a universal reward model, resulting from the diverse nature of editing instructions and tasks. To bridge this gap, we employ a Multimodal Large Language Model (MLLM) as a unified, training-free reward model, leveraging its output logits to provide fine-grained feedback. Furthermore, we carefully design a low-variance group filtering mechanism to reduce MLLM scoring noise and stabilize optimization. UniWorld-V2, trained with this framework, achieves state-of-the-art results on the ImgEdit and GEdit-Bench benchmarks, scoring 4.49 and 7.83, respectively. Crucially, our framework is model-agnostic, delivering substantial performance gains when applied to diverse base models like Qwen-Image-Edit and FLUX-Kontext, demonstrating its wide applicability. Code and models are publicly available at https://github.com/PKU-YuanGroup/UniWorld-V2.

Recent advances in training-free attention control methods have enabled flexible and efficient text-guided editing capabilities for existing generation models. However, current approaches struggle to simultaneously deliver strong editing strength while preserving consistency with the source. This limitation becomes particularly critical in multi-round and video editing, where visual errors can accumulate over time. Moreover, most existing methods enforce global consistency, which limits their ability to modify individual attributes such as texture while preserving others, thereby hindering fine-grained editing. Recently, the architectural shift from U-Net to MM-DiT has brought significant improvements in generative performance and introduced a novel mechanism for integrating text and vision modalities. These advancements pave the way for overcoming challenges that previous methods failed to resolve. Through an in-depth analysis of MM-DiT, we identify three key insights into its attention mechanisms. Building on these, we propose ConsistEdit, a novel attention control method specifically tailored for MM-DiT. ConsistEdit incorporates vision-only attention control, mask-guided pre-attention fusion, and differentiated manipulation of the query, key, and value tokens to produce consistent, prompt-aligned edits. Extensive experiments demonstrate that ConsistEdit achieves state-of-the-art performance across a wide range of image and video editing tasks, including both structure-consistent and structure-inconsistent scenarios. Unlike prior methods, it is the first approach to perform editing across all inference steps and attention layers without handcraft, significantly enhancing reliability and consistency, which enables robust multi-round and multi-region editing. Furthermore, it supports progressive adjustment of structural consistency, enabling finer control.

Replicating AI research is a crucial yet challenging task for large language model (LLM) agents. Existing approaches often struggle to generate executable code, primarily due to insufficient background knowledge and the limitations of retrieval-augmented generation (RAG) methods, which fail to capture latent technical details hidden in referenced papers. Furthermore, previous approaches tend to overlook valuable implementation-level code signals and lack structured knowledge representations that support multi-granular retrieval and reuse. To overcome these challenges, we propose Executable Knowledge Graphs (xKG), a modular and pluggable knowledge base that automatically integrates technical insights, code snippets, and domain-specific knowledge extracted from scientific literature. When integrated into three agent frameworks with two different LLMs, xKG shows substantial performance gains (10.9% with o3-mini) on PaperBench, demonstrating its effectiveness as a general and extensible solution for automated AI research replication. Code will released at https://github.com/zjunlp/xKG.

Long-form chain-of-thought reasoning has become a cornerstone of advanced reasoning in large language models. While recent verification-refinement frameworks have enabled proprietary models to solve Olympiad-level problems, their effectiveness hinges on strong, reliable verification and correction capabilities, which remain fragile in open-weight, smaller-scale models. This work demonstrates that even with weak verification and refinement capabilities on hard tasks, the reasoning limits of such models can be substantially extended through a probabilistic paradigm we call Deep Self-Evolving Reasoning (DSER). We conceptualize iterative reasoning as a Markov chain, where each step represents a stochastic transition in the solution space. The key insight is that convergence to a correct solution is guaranteed as long as the probability of improvement marginally exceeds that of degradation. By running multiple long-horizon, self-evolving processes in parallel, DSER amplifies these small positive tendencies, enabling the model to asymptotically approach correct answers. Empirically, we apply DSER to the DeepSeek-R1-0528-Qwen3-8B model. On the challenging AIME 2024-2025 benchmark, DSER solves 5 out of 9 previously unsolvable problems and boosts overall performance, enabling this compact model to surpass the single-turn accuracy of its 600B-parameter teacher through majority voting. Beyond its immediate utility for test-time scaling, the DSER framework serves to diagnose the fundamental limitations of current open-weight reasoners. By clearly delineating their shortcomings in self-verification, refinement, and stability, our findings establish a clear research agenda for developing next-generation models with powerful, intrinsic self-evolving capabilities.

Pretrained time series models have enabled inference-only forecasting systems that produce accurate predictions without task-specific training. However, existing approaches largely focus on univariate forecasting, limiting their applicability in real-world scenarios where multivariate data and covariates play a crucial role. We present Chronos-2, a pretrained model capable of handling univariate, multivariate, and covariate-informed forecasting tasks in a zero-shot manner. Chronos-2 employs a group attention mechanism that facilitates in-context learning (ICL) through efficient information sharing across multiple time series within a group, which may represent sets of related series, variates of a multivariate series, or targets and covariates in a forecasting task. These general capabilities are achieved through training on synthetic datasets that impose diverse multivariate structures on univariate series. Chronos-2 delivers state-of-the-art performance across three comprehensive benchmarks: fev-bench, GIFT-Eval, and Chronos Benchmark II. On fev-bench, which emphasizes multivariate and covariate-informed forecasting, Chronos-2's universal ICL capabilities lead to substantial improvements over existing models. On tasks involving covariates, it consistently outperforms baselines by a wide margin. Case studies in the energy and retail domains further highlight its practical advantages. The in-context learning capabilities of Chronos-2 establish it as a general-purpose forecasting model that can be used "as is" in real-world forecasting pipelines.

SpaceX 此前与 NASA 签署了一项价值 29 亿美元的合同，提供宇航员登陆月球表面的着陆器。NASA 代理局长 Sean Duffy 周一在 CNBC Squawk Box 上表示，SpaceX 推迟了时间表，而美国正致力于在中国之前载人登月，NASA 正考虑让其它公司与 SpaceX 竞争制造月球着陆器。如果 NASA 取消或修改与 SpaceX 的合同，可能预示着 NASA 的计划发生重大逆转。

美国联合航空公司一架从丹佛飞往洛杉矶的 UA1093 客机上周四 10 月 16 日发生了挡风玻璃撞击事件。这架 737 MAX 飞机前部两个玻璃窗之一破裂严重，但没有完全破碎，飞行员手臂显示疑似割伤痕迹。机长声称撞击玻璃的是太空碎片。碰撞高度在一万米左右。客机备降在盐湖城国际机场。美国国家运输安全委员会（NTSB）表示正对此事件展开调查。WindBorne System CEO 之后表示，与飞机发生相撞的物体可能是该公司的气象气球。气象气球当时正在该地区同一高度飞行。

KDE Plasma 桌面环境释出了 v6.5。主要变化包括：：窗口底部圆角、自动亮暗主题切换、改进系统设置、登录屏幕加入休眠选项、改进可访问性功能、通过调整色调映射曲线改进 HDR 显示支持、实验性的 Wayland 画中画协议支持、增强 Wayland 功能、新的桌面灰度选项，等等。

根据发表在《自然》期刊上的一项研究，罗格斯大学和中山大学等机构的研究人员报告中国东南沿海地区同时遭遇海平面加速上升和地面加速下沉。研究人员称这种现象在全新世地质记录中以前从未观察到过。中国东南沿海是世界人口最稠密的地区之一。研究人员分析了过去 11,700 年海平面上升速度。海平面变化经历了三个阶段：早期因冰川融化推动海平面快速上升，之后从 4200 年前到 19 世纪中叶海平面变化趋稳，自 1900 年以来海平面上升速度超过了过去 4000 年任何一个世纪。在海平面加速上升的同时，中国东南沿海地区还面临人为的地面加速沉降，大规模城市化导致的地面沉降速度远高于海平面上升速度，如潮州、福州、绍兴、汕头、杭州等城市沉降速度数倍于海平面上升速度。双重效应增加了该地区的洪涝风险。

根据发表在 PLoS Biology 期刊上的一项研究，被切断大脑区域的脑电图与深度睡眠脑电图相似。这一研究发现加深了对意识和无意识脑状态的理解。对于药物无效的严重癫痫患儿，医生会通过名为脑半球切除术（hemispherotomy）的手术切断引发癫痫的脑半球与大脑其余部分的连接，以阻止癫痫扩散。被切断的脑组织会被留在颅骨内，保留了完整的血液供应。被切断的脑区域是否具有某种形式的意识，或者是否能表现出意识？研究人员分析了十名儿童在手术前以及手术后六个月至三年期间的脑电图。他们发现，被切断脑区域的电活动在术后减缓，而其余大脑部分的电活动没有变化，其脑电图也与对照组儿童脑电图相似。被切断脑区域的脑电波主要是 δ波，脑电图与对照组儿童深度睡眠时的脑电图相似。

SpaceX 于 10 月 19 日发射了两枚 Falcon 9 火箭，分别将 28 颗 Starlink 宽带卫星送入轨道。其中第一枚在佛罗里达发射升空，其第一级创下了第 31 次发射的重复使用记录；第二枚不到两个小时后从加州范登堡太空军基地发射升空，这枚火箭携带了第 10,000 颗进入轨道的 Starlink 卫星，这是 Falcon 9 火箭今年的第 132 次发射，追平了去年创下的纪录，而距离 2026 年还有近两个半月的时间。

游戏行业研究机构 GameDiscoverCo 发表报告，分析了 Steam 平台上一款游戏的愿望单数字如何转化为实际销量。当玩家将一款游戏加入愿望单后，Steam 会在游戏发售时以及特价时通知玩家，增加了游戏的曝光度，因此也能增加游戏的销量。但两者之间是如何转化的？GameDiscoverCo 编辑统计了 2024 年 9 月至 2025 年 9 月首周销售数据和转化率最高的 20 款游戏，发现高转化率游戏通常在发售后口碑更好，而在线合作类型一般有更高的转化率。针对休闲类玩家的畅销游戏如 NBA 2K26 和 EA Sports FC 25，玩家通常不会把它们加入到愿望单，因此销量/愿望单的转化率会显得异常高，有时候时候可能达到 1 到 3 倍。游戏愿望单数字很高，但销量很低，原因通常是发售后口碑太差。有一个特例是 NSFW 类型游戏，其转化率相当高，格外突出，原因是购买成人游戏的 Steam 玩家很可能属于转化率更高的“硬核”群体。

未来的汽车会是完全自主驾驶，但今天的技术距离自主驾驶还有很长的距离。打车软件巨头 Uber 开始向打零工的司机们提供新的收入来源：帮助训练和改进该公司的 AI 系统。此举旨在加速自主化，但完全自主化之后人类司机也不再需要了。司机们是在在训练会取代他们的技术。

冰岛首次在野外发现蚊子。Björn Hjaltason 在 Facebook 群组 Insects in Iceland 报告了他的发现，利用昆虫陷阱他在 10 月 16 日捕获了一只雌性环跗脉毛蚊（Culiseta annulata），之后他又捕获了两只，这些蚊子被送到了冰岛的自然历史研究所，昆虫学家 Matthías Alfreðsson 表示这一发现意义重大，因为这是首次在野外发现蚊子，此前偶尔发现的蚊子是搭乘飞机抵达冰岛的。

Windows 10 于 10 月 14 日终止了主流支持，IT 管理公司 Lansweeper 发现其 3000 万个企业系统逾四成不支持 Windows 11，因为微软提高了操作系统的硬件需求，要求 PC 配备 Treacherous Platform Module 2.0。自由软件基金会（FSF）认为 Windows 10 终止支持是切换到 GNU/Linux 摆脱私有软件制造的计划报废循环的极佳机会。GNU/Linux 发行版尊重用户的自由，用户可以按自己想法运行自己的计算机，学习和修改源代码，重新发布副本，GNU/Linux 发行版无需更新合同，允许用户掌控一切。

韦伯望远镜（JWST）在回顾过去观测宇宙早期时的图像时，向天文学家展示了一个极为奇特的景象：数百个小红点莫名其妙地点缀在远古宇宙中。它们究竟是什么？过去几个月里，学界逐渐形成共识——这些被称作“红宝石”的斑点是宇宙中一种全新类型的天体。这些斑点因其在 JWST 图像中紧凑的尺寸以及发射长波“红”光而得名，最初令天文学家困惑不解。因为它们看起来过于致密而不像星系，但发射的光谱类型又不符合黑洞特征。在首次观测到小红点 3 年后，已有约 200 篇研究手稿在 arXiv 上发布，其中一些尚未经过同行评审。《自然》汇总了近 3 个月内发表的几项亮点研究，这些研究正在揭示这一诱人的新天体类别。部分研究认为它是名为“悬崖”的天体，得名于其发射光谱的急剧断裂：在一个显示该天体在某一时刻发出光的波长的图表中，在可见光谱之外，其发出的紫外线辐射几乎为零，但在能量稍低的波长的光中，辐射却突然激增。对光辐射峰值的分析表明，该天体一定能量极高，如同黑洞，但也一定被包裹温暖、致密的气体中，类似于恒星的大气层。

用 Rust 语言开发的浏览器引擎项目 Servo 释出了 v0.0.1 版本。Servo 源自 Mozilla，2020 年 8 月 Mozilla 在裁员时砍掉了 Servo 引擎团队的大部分成员。Servo 项目之后脱离 Mozilla 成为一个独立项目，由 Linux 基金会托管，旨在为其它项目提供一个嵌入的高性能的、安全的渲染引擎。Servo v0.0.1 支持 Linux、Windows 和 macOS 以及 Android。

亚马逊 AWS 发生严重宕机事故，影响了数以百万计的网站和服务，包括亚马逊自己、PrimeVideo、Perplexity AI、Canva 等网站以及《堡垒之夜》等游戏。亚马逊在其 AWS 状态页面发表声明，称确认 US-EAST-1 区域DynamoDB 端点的请求存在严重的错误率，工程师正致力于缓解问题和全面理解问题根因。

使用 Xfce 桌面环境的 Ubuntu Linux 衍生发行版 Xubuntu 官网遭到黑客入侵，黑客在下载页面提供了一个 zip 文件，其中包含了一个可疑的 exe 文件和一个 tos.txt 文件. 该文件的版权声明是 Copyright (c) 2026 Xubuntu.org。黑客嵌入的恶意程序旨在窃取加密货币，方法是扫描剪切板上的加密货币地址，然后替换黑客控制的钱包地址。扫描的加密货币包括比特币、莱特币、以太坊和狗币等。

Windows Recovery Environment(WinRE)是 Windows 的恢复环境，用于在启动失败之后对计算机进行故障排除，包括启动到 BIOS 或以安全模式启动计算机。但 Windows 11 十月更新 KB5066835 存在 bug，会导致在 WinRE 下 USB 键盘和鼠标失去响应，这将导致 WinRE 对大部分用户而言失去了作用。PS/2 接口的键盘和鼠标不受影响，但今天的计算机几乎不再使用此类接口的外设。微软表示正在开发修复程序解决该问题。