OrangeBot.AI Digest — 2025-10-13

Anthropic's Interactive Prompt Engineering Tutorial

Beta release of Archon OS - the knowledge and task management backbone for AI coding assistants.

Claude Code is an agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster by executing routine tasks, explaining complex code, and handling git workflows - all through natural language commands.

Collection of extracted System Prompts from popular chatbots like ChatGPT, Claude & Gemini

Klavis AI (YC X25): MCP integration platforms that let AI agents use tools reliably at any scale

A collective list of free APIs

A modern GUI client based on Tauri, designed to run in Windows, macOS and Linux for tailored proxy experience

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

LLM inference in C/C++

Voxel sandbox game with a large render distance, procedurally generated content and some cool graphical effects.

Opinionated Arch/Hyprland Setup

CLI tool for configuring and monitoring Claude Code

A simple, lightweight PowerShell script to remove pre-installed apps, disable telemetry, as well as perform various other changes to customize, declutter and improve your Windows experience. Win11Debloat works for both Windows 10 and Windows 11.

🐙 Guides, papers, lecture, notebooks and resources for prompt engineering

Next Generation Server Toolkit. Create web servers with everything you need and deploy them wherever you prefer.

Large language models leverage internet-scale text data, yet embodied AI remains constrained by the prohibitive costs of physical trajectory collection. Desktop environments -- particularly gaming -- offer a compelling alternative: they provide rich sensorimotor interactions at scale while maintaining the structured observation-action coupling essential for embodied learning. We present D2E (Desktop to Embodied AI), a framework that demonstrates desktop interactions can serve as an effective pretraining substrate for robotics embodied AI tasks. Unlike prior work that remained domain-specific (e.g., VPT for Minecraft) or kept data proprietary (e.g., SIMA), D2E establishes a complete pipeline from scalable desktop data collection to verified transfer in embodied domains. Our framework comprises three components: (1) the OWA Toolkit that unifies diverse desktop interactions into a standardized format with 152x compression, (2) the Generalist-IDM that achieves strong zero-shot generalization across unseen games through timestamp-based event prediction, enabling internet-scale pseudo-labeling, and (3) VAPT that transfers desktop-pretrained representations to physical manipulation and navigation. Using 1.3K+ hours of data (259 hours of human demonstrations, and 1K+ hours of pseudo-labeled gameplay), we achieve a total of 96.6% success rate on LIBERO manipulation and 83.3% on CANVAS navigation benchmarks. This validates that sensorimotor primitives in digital interactions exhibit sufficient invariance to transfer meaningfully to physical embodied tasks, establishing desktop pretraining as a practical paradigm for robotics. We will make all our work public, including the OWA toolkit, datasets of human-collected and pseudo-labeled, and VAPT-trained models available at https://worv-ai.github.io/d2e/

Camera-centric understanding and generation are two cornerstones of spatial intelligence, yet they are typically studied in isolation. We present Puffin, a unified camera-centric multimodal model that extends spatial awareness along the camera dimension. Puffin integrates language regression and diffusion-based generation to interpret and create scenes from arbitrary viewpoints. To bridge the modality gap between cameras and vision-language, we introduce a novel paradigm that treats camera as language, enabling thinking with camera. This guides the model to align spatially grounded visual cues with photographic terminology while reasoning across geometric context. Puffin is trained on Puffin-4M, a large-scale dataset of 4 million vision-language-camera triplets. We incorporate both global camera parameters and pixel-wise camera maps, yielding flexible and reliable spatial generation. Experiments demonstrate Puffin superior performance over specialized models for camera-centric generation and understanding. With instruction tuning, Puffin generalizes to diverse cross-view tasks such as spatial imagination, world exploration, and photography guidance. We will release the code, models, dataset pipeline, and benchmark to advance multimodal spatial intelligence research.

As the volume of peer-reviewed research surges, scholars increasingly rely on social platforms for discovery, while authors invest considerable effort in promoting their work to ensure visibility and citations. To streamline this process and reduce the reliance on human effort, we introduce Automatic Promotion (AutoPR), a novel task that transforms research papers into accurate, engaging, and timely public content. To enable rigorous evaluation, we release PRBench, a multimodal benchmark that links 512 peer-reviewed articles to high-quality promotional posts, assessing systems along three axes: Fidelity (accuracy and tone), Engagement (audience targeting and appeal), and Alignment (timing and channel optimization). We also introduce PRAgent, a multi-agent framework that automates AutoPR in three stages: content extraction with multimodal preparation, collaborative synthesis for polished outputs, and platform-specific adaptation to optimize norms, tone, and tagging for maximum reach. When compared to direct LLM pipelines on PRBench, PRAgent demonstrates substantial improvements, including a 604% increase in total watch time, a 438% rise in likes, and at least a 2.9x boost in overall engagement. Ablation studies show that platform modeling and targeted promotion contribute the most to these gains. Our results position AutoPR as a tractable, measurable research problem and provide a roadmap for scalable, impactful automated scholarly communication.

Recent diffusion models achieve the state-of-the-art performance in image generation, but often suffer from semantic inconsistencies or hallucinations. While various inference-time guidance methods can enhance generation, they often operate indirectly by relying on external signals or architectural modifications, which introduces additional computational overhead. In this paper, we propose Tangential Amplifying Guidance (TAG), a more efficient and direct guidance method that operates solely on trajectory signals without modifying the underlying diffusion model. TAG leverages an intermediate sample as a projection basis and amplifies the tangential components of the estimated scores with respect to this basis to correct the sampling trajectory. We formalize this guidance process by leveraging a first-order Taylor expansion, which demonstrates that amplifying the tangential component steers the state toward higher-probability regions, thereby reducing inconsistencies and enhancing sample quality. TAG is a plug-and-play, architecture-agnostic module that improves diffusion sampling fidelity with minimal computational addition, offering a new perspective on diffusion guidance.

Large Language Models (LLMs) have shown remarkable success, and their multimodal expansions (MLLMs) further unlock capabilities spanning images, videos, and other modalities beyond text. However, despite this shift, prompt optimization approaches, designed to reduce the burden of manual prompt crafting while maximizing performance, remain confined to text, ultimately limiting the full potential of MLLMs. Motivated by this gap, we introduce the new problem of multimodal prompt optimization, which expands the prior definition of prompt optimization to the multimodal space defined by the pairs of textual and non-textual prompts. To tackle this problem, we then propose the Multimodal Prompt Optimizer (MPO), a unified framework that not only performs the joint optimization of multimodal prompts through alignment-preserving updates but also guides the selection process of candidate prompts by leveraging earlier evaluations as priors in a Bayesian-based selection strategy. Through extensive experiments across diverse modalities that go beyond text, such as images, videos, and even molecules, we demonstrate that MPO outperforms leading text-only optimization methods, establishing multimodal prompt optimization as a crucial step to realizing the potential of MLLMs.

Large Language Models (LLMs) have achieved remarkable success through imitation learning on vast text corpora, but this paradigm creates a training-generation gap and limits robust reasoning. Reinforcement learning (RL) offers a more data-efficient solution capable of bridging this gap, yet its application has been constrained by a critical data bottleneck: existing RL datasets are orders of magnitude smaller and less diverse than web-scale pre-training corpora. To address this, we introduce the Webscale-RL pipeline, a scalable data engine that systematically converts large-scale pre-training documents into millions of diverse, verifiable question-answer pairs for RL. Using this pipeline, we construct the Webscale-RL dataset, containing 1.2 million examples across more than 9 domains. Our experiments show that the model trained on this dataset significantly outperforms continual pretraining and strong data refinement baselines across a suite of benchmarks. Notably, RL training with our dataset proves substantially more efficient, achieving the performance of continual pre-training with up to 100times fewer tokens. Our work presents a viable path toward scaling RL to pre-training levels, enabling more capable and efficient language models.

Recent trends in test-time scaling for reasoning models (e.g., OpenAI o1, DeepSeek-R1) have led to remarkable improvements through long Chain-of-Thought (CoT). However, existing benchmarks mainly focus on immediate, single-horizon tasks, failing to adequately evaluate models' ability to understand and respond to complex, long-horizon scenarios. To address this incomplete evaluation of Large Reasoning Models (LRMs), we propose R-HORIZON, a method designed to stimulate long-horizon reasoning behaviors in LRMs through query composition. Based on R-HORIZON, we construct a long-horizon reasoning benchmark, comprising complex multi-step reasoning tasks with interdependent problems that span long reasoning horizons. Through comprehensive evaluation of LRMs using the R-HORIZON benchmark, we find that even the most advanced LRMs suffer significant performance degradation. Our analysis reveals that LRMs exhibit limited effective reasoning length and struggle to allocate thinking budget across multiple problems appropriately. Recognizing these limitations, we use R-HORIZON to construct long-horizon reasoning data for reinforcement learning with verified rewards (RLVR). Compared to training with single-horizon data, RLVR with R-HORIZON not only substantially improves performance on the multi-horizon reasoning tasks, but also promotes accuracy on standard reasoning tasks, with an increase of 7.5 on AIME2024. These results position R-HORIZON as a scalable, controllable, and low-cost paradigm for enhancing and evaluating the long-horizon reasoning capabilities of LRMs.

Vision-language models (VLMs) could power real-time assistants and autonomous agents, but they face a critical challenge: understanding near-infinite video streams without escalating latency and memory usage. Processing entire videos with full attention leads to quadratic computational costs and poor performance on long videos. Meanwhile, simple sliding window methods are also flawed, as they either break coherence or suffer from high latency due to redundant recomputation. In this paper, we introduce StreamingVLM, a model designed for real-time, stable understanding of infinite visual input. Our approach is a unified framework that aligns training with streaming inference. During inference, we maintain a compact KV cache by reusing states of attention sinks, a short window of recent vision tokens, and a long window of recent text tokens. This streaming ability is instilled via a simple supervised fine-tuning (SFT) strategy that applies full attention on short, overlapped video chunks, which effectively mimics the inference-time attention pattern without training on prohibitively long contexts. For evaluation, we build Inf-Streams-Eval, a new benchmark with videos averaging over two hours that requires dense, per-second alignment between frames and text. On Inf-Streams-Eval, StreamingVLM achieves a 66.18% win rate against GPT-4O mini and maintains stable, real-time performance at up to 8 FPS on a single NVIDIA H100. Notably, our SFT strategy also enhances general VQA abilities without any VQA-specific fine-tuning, improving performance on LongVideoBench by +4.30 and OVOBench Realtime by +5.96. Code is available at https://github.com/mit-han-lab/streaming-vlm.

Crowdsourced model evaluation platforms, such as Chatbot Arena, enable real-time evaluation from human perspectives to assess the quality of model responses. In the coding domain, manually examining the quality of LLM-generated content is extremely challenging, as it requires understanding long chunks of raw code and deliberately simulating code execution. To this end, we introduce BigCodeArena, an open human evaluation platform for code generation backed by a comprehensive and on-the-fly execution environment. Built on top of Chatbot Arena, BigCodeArena enables the execution of LLM-generated code and allows humans to interact with the execution process and outcomes. We collected over 14,000 raw code-centric conversation sessions across 10 widely used LLMs, spanning 10 languages and 8 types of execution environments. Among these conversations, we identified more than 4,700 multi-turn samples with pairwise human preferences. Further analysis uncovers underexplored preferences of LLMs in fine-grained domains characterized by tasks, languages, and frameworks. To systematically examine code understanding and generation capabilities of frontier LLMs, we curated two benchmarks based on the collected data, namely BigCodeReward and AutoCodeArena. For BigCodeReward, we post-processed the 4,700 conversations and evaluated the consistency between reward models and human preferences. The evaluation shows that most LLMs have superior performance in judging coding preferences when the execution results are available. Inspired by these findings, we propose AutoCodeArena, an automatic Elo rating benchmark designed to assess the coding quality of LLMs without human involvement. We find that proprietary LLMs like GPT-5, Claude-Sonnet-4, and Claude-Opus-4 still lead in code generation performance among recent emerging models.

With the current surge in spatial reasoning explorations, researchers have made significant progress in understanding indoor scenes, but still struggle with diverse applications such as robotics and autonomous driving. This paper aims to advance all-scale spatial reasoning across diverse scenarios by tackling two key challenges: 1) the heavy reliance on indoor 3D scans and labor-intensive manual annotations for dataset curation; 2) the absence of effective all-scale scene modeling, which often leads to overfitting to individual scenes. In this paper, we introduce a holistic solution that integrates a structured spatial reasoning knowledge system, scale-aware modeling, and a progressive training paradigm, as the first attempt to broaden the all-scale spatial intelligence of MLLMs to the best of our knowledge. Using a task-specific, specialist-driven automated pipeline, we curate over 38K video scenes across 5 spatial scales to create SpaceVista-1M, a dataset comprising approximately 1M spatial QA pairs spanning 19 diverse task types. While specialist models can inject useful domain knowledge, they are not reliable for evaluation. We then build an all-scale benchmark with precise annotations by manually recording, retrieving, and assembling video-based data. However, naive training with SpaceVista-1M often yields suboptimal results due to the potential knowledge conflict. Accordingly, we introduce SpaceVista-7B, a spatial reasoning model that accepts dense inputs beyond semantics and uses scale as an anchor for scale-aware experts and progressive rewards. Finally, extensive evaluations across 5 benchmarks, including our SpaceVista-Bench, demonstrate competitive performance, showcasing strong generalization across all scales and scenarios. Our dataset, model, and benchmark will be released on https://peiwensun2000.github.io/mm2km .

This work presents the first large-scale investigation into constructing a fully open bilingual large language model (LLM) for a non-English language, specifically Korean, trained predominantly on synthetic data. We introduce KORMo-10B, a 10.8B-parameter model trained from scratch on a Korean-English corpus in which 68.74% of the Korean portion is synthetic. Through systematic experimentation, we demonstrate that synthetic data, when carefully curated with balanced linguistic coverage and diverse instruction styles, does not cause instability or degradation during large-scale pretraining. Furthermore, the model achieves performance comparable to that of contemporary open-weight multilingual baselines across a wide range of reasoning, knowledge, and instruction-following benchmarks. Our experiments reveal two key findings: (1) synthetic data can reliably sustain long-horizon pretraining without model collapse, and (2) bilingual instruction tuning enables near-native reasoning and discourse coherence in Korean. By fully releasing all components including data, code, training recipes, and logs, this work establishes a transparent framework for developing synthetic data-driven fully open models (FOMs) in low-resource settings and sets a reproducible precedent for future multilingual LLM research.

Evaluating modern machine learning models has become prohibitively expensive. Benchmarks such as LMMs-Eval and HELM demand thousands of GPU hours per model. Costly evaluation reduces inclusivity, slows the cycle of innovation, and worsens environmental impact. The typical approach follows two steps. First, select an anchor subset of data. Second, train a mapping from the accuracy on this subset to the final test result. The drawback is that anchor selection depends on clustering, which can be complex and sensitive to design choices. We argue that promoting diversity among samples is not essential; what matters is to select samples that maximise diversity in model responses. Our method, Diversifying Sample Condensation (DISCO), selects the top-k samples with the greatest model disagreements. This uses greedy, sample-wise statistics rather than global clustering. The approach is conceptually simpler. From a theoretical view, inter-model disagreement provides an information-theoretically optimal rule for such greedy selection. DISCO shows empirical gains over prior methods, achieving state-of-the-art results in performance prediction across MMLU, Hellaswag, Winogrande, and ARC. Code is available here: https://github.com/arubique/disco-public.

Recent advances in multimodal large reasoning models (MLRMs) have substantially improved their ability to solve complex textual and visual tasks. However, these models tend to overthink on simple problems, producing unnecessarily lengthy reasoning traces, while under-exploring on challenging ones, leading to missed solutions. To address this imbalance, we propose ARES, a unified open-source framework for adaptive reasoning that dynamically allocates exploration effort based on task difficulty. Our approach is motivated by two key empirical findings: (i) while single-token entropy is noisy, high window-entropy (HWE) tokens (token-level entropies averaged under a sliding window) can reliably capture reasoning-critical moments; and (ii) reducing HWE usage benefits easy problems, while increasing it is essential for solving hard ones. Building on these insights, ARES introduces a two-stage training pipeline. In the Adaptive Cold-Start stage, we curate multimodal and textual data paired with reasoning traces of length proportional to problem difficulty, equipping the model with initial difficulty awareness. In the second stage, we develop Adaptive Entropy Policy Optimization (AEPO), which uses HWE tokens as exploration triggers to decide when to explore, and a hierarchical entropy reward with dynamic KL control to decide how much to explore. Extensive experiments demonstrate that ARES achieves superior performance and reasoning efficiency across diverse mathematical, logical, and multimodal benchmarks, while closing the gap to leading commercial systems under significantly lower inference costs.

Reinforcement learning with verifiable rewards (RLVR) has become a standard recipe for improving large language models (LLMs) on reasoning tasks, with Group Relative Policy Optimization (GRPO) widely used in practice. Yet GRPO wastes substantial compute on negative groups: groups in which no sampled response is correct yield zero advantage and thus no gradient. We ask whether negative groups can be leveraged without extra supervision. Starting from a maximum-likelihood (MLE) objective in reward modeling, we show that the MLE gradient is equivalent to a policy gradient for a modified value function. This value function adds a confidence-weighted penalty on incorrect responses, imposing larger penalties on more confident mistakes. We refer to this as Likelihood Estimation with Negative Samples (LENS). LENS modifies GRPO to assign non-zero, confidence-dependent rewards to incorrect generations, making negative groups informative and converting previously wasted samples into useful gradient updates. On the MATH benchmark with Llama-3.1-8B and Qwen-2.5-3B, the proposed variant consistently outperforms GRPO baseline, with significant gains on harder items. These results demonstrate a principled and practical way to "rescue" negative groups, improving efficiency and performance in RLVR.

Recent progress has pushed AI frontiers from pattern recognition tasks toward problems that require step by step, System2 style reasoning, especially with large language models. Yet, unlike learning, where generalization and out of distribution (OoD) evaluation concepts are well formalized, there is no clear, consistent definition or metric for reasoning ability. We propose Complexity Out of Distribution (Complexity OoD) generalization as a framework and problem setting to define and measure reasoning. A model exhibits Complexity OoD generalization when it maintains performance on test instances whose minimal required solution complexity, either representational (richer solution structure) or computational (more reasoning steps/program length), exceeds that of all training examples. We formalize complexity via solution description Kolmogorov complexity and operational proxies (e.g., object/relation counts; reasoning step counts), clarifying how Complexity OoD differs from length and compositional OoD. This lens unifies learning and reasoning: many cases solvable with System1 like processing at low complexity become System2 like under complexity pressure, while System2 can be viewed as generalization over solution structures. We translate this perspective into practice with recommendations for operationalizing Complexity OoD across the stack: incorporating complexity into benchmark and evaluation metric design, rethinking supervision to target solution traces, seeking and designing inductive biases for Complexity OoD generalization, addressing learning to reason spillovers such as spurious shortcuts, semantic robustness, catastrophic forgetting, and step wise calibration. Because Complexity OoD cannot be solved by scaling data alone, progress toward robust reasoning will require architectures and training regimes that explicitly model and allocate computation with respect to complexity.

发表于《自然》的新研究显示，高龄父亲将致病突变遗传给孩子的风险比我们想象的要高。基因组测序显示，在 30 岁出头的男性中，大约每 50 个精子中就有 1 个携带致病突变；而到 70 岁时，这一比例上升到近 1/20。 研究人员建议，如果年轻男性认为自己要年纪大一些时再有孩子，他们可以考虑冷冻精子；而计划组建家庭的年长男性则可以考虑现有的各种筛查技术。最近的研究表明，我们每个人体内的大多数细胞中都有约 70 个父母都没有的新突变，其中 80% 的突变源于父亲的睾丸，这还不包括母亲卵子中更常见的大规模染色体异常。

法拉利宣布其首款电动跑车 Elettrica 将于明年夏天推出。Elettrica 的最高时速 310 公里/小时，百公里加速仅需 2.5 秒，续航里程 530 公里，最高 350 kW 的超快直流充电，电池容量 122 kWh，能量密度 195 Wh/kg——法拉利称这是量产电动汽车中最高的。电动汽车通常因为发动机过于安静而会去模拟机械发动机的轰鸣声，Elettrica 采用了不同的方法：安装在逆变器上的传感器会探测动力系统的真实机械振动，然后将其放大，创造出一种反映驾驶方式的不断变化的自然音调。法拉利称声音为司机提供了一种反馈功能，司机如果喜欢安静驾驶可以选择将其关闭。

Firefox 多年来一直支持创建多个配置文件去存储个人信息，以便将工作与个人浏览分开、测试不同设置，或与他人共享计算机。但 Firefox 没有让配置文件更容易被发现或管理。现在情况即将发生改变，Mozilla 宣布将推出配置文件管理功能，用户能更轻松地创建和切换配置文件。该功能将于 10 月 14 日起逐步推广给用户。

根据发表在《Lancet Regional Health – Western Pacific》上的一项研究，研究人员分析了 2019-2020 年间斯里兰卡、印度尼西亚、马来西亚、越南和菲律宾十所医院收集的近 1.5 万份患病婴儿的血液样本，发现人类与耐药细菌的战争并不顺利，新生儿血液中的耐药菌（或称之为超级细菌）十分普遍。近八成新生儿感染的是革兰氏阴性菌如大肠杆菌（E. coli,）、克雷伯菌（Klebsiella）和不动杆菌（Acinetobacter）。革兰氏阴性菌因其细胞膜结构，比革兰氏阳性菌更容易产生抗生素耐药性。研究人员称，新生儿出生几天后就会感染耐药菌。研究还发现，真菌感染导致了近十分之一的婴儿严重感染。

一项研究发现，一颗自由漂浮的行星吞噬了数量惊人的物质——每秒可以吃掉 60 亿吨气体和尘埃。这一发现模糊了行星与恒星之间的界限，暗示着恒星和行星的形成过程比想象中更相似。流浪行星是一种不围绕任何母恒星上的自由漂浮的气体星球，它们极其常见，甚至可能超过银河系中的恒星数量。但流浪行星的形成方式令天文学家困惑不已：它们会像其他行星一样先是围绕恒星运行，然后被放逐后独自在银河系中漫游吗？亦或者它们可以像恒星一样自行形成？天文学家最近发现了一颗名为 Cha 1107-7626 的流浪天体正以惊人的井喷式速度增长。早在 2008 年，该天体因其周围形成了看起来像原始行星盘的物质，曾首次引起天文学家的注意。6 月 Cha 1107-7626 突然开始以之前近 10 倍的速度消耗物质，并持续了两个月。这达到了以往只有在恒星中才能看到的增长速度。研究团队认为，一定有一种类似于恒星中发现的机制在起作用，即强磁场将物质从远处的气体和尘埃体积中通过狭窄的通道输送。但目前尚不清楚这颗行星是如何或为什么突然开始消耗如此多的质量。

金星曾经被认为是一个十分干燥、富含硫酸大气的行星，美国科学家重新分析了先驱者金星计划留下的资料，发现金星大气不只是硫酸量比先前认为的少，还有比预期更多的水和氧化铁。先驱者金星2号任务搭载了一大三小共计 4 架金星大气层的探测器，让探测器在落下的过程持续收集金星大气的成分等等数据。在降落过程中探测器也同时收集到了金星大气中的气胶，而气胶在进入探测器后分解，成分也因此被探测器记录下来。然而这些资料一直被尘封在 NASA 档案馆里面，直到最近研究团队才在一组微缩胶卷上找到。重新分析发现了金星气胶粒子中含有水、二氧化硫、氧分子和氧化铁的证据。水的含量比先前预期地高，大约是过去估计的三倍──水占气胶质量的约 60%。

2024 年在日本出生的外国人达到 2 万人，在新生儿中的占比超过 3%。这两项数据均被认为是首次达到如此高的水平。以劳动年龄层为中心，在日外国人已增至日本总人口的约3%，出生阶段的人口也已进入外国人在一定程度上弥补日本出生人数少的新阶段。对日本而言，不单纯侧重于加强管制，而是包含共生措施在内的外国人政策正变得愈发重要。2024 年的确定数据显示，在日本外国人的出生数为2万2878人，比上年增加3000多人。从外国人新生儿母亲的国籍来看，中国为4237人，其次是菲律宾（1807人）、巴西（1351人）。在调查中，尼泊尔、越南等在日人员较多的几个国家被归入“其他国家”类别，这一类别的新生儿母亲人数最多，达到1万4425人。

根据 Monster 的《2025 Mental Health in the Workplace》调查报告，有 1100 名工人接受了调查，其中八成抱怨工作环境有毒，这一比例高于去年的 67%。40% 的受访者表示心理健康不佳，31% 表示一般，20% 表示心理健康良好，9% 的人表示非常棒。而对于导致心理健康状况不佳的主要原因，59% 认为是工作文化，54% 认为是经理/管理人员，47% 认为是缺乏成长机会，47% 是工作量增加，33% 是人手短缺。大部分人认为如果解雇有毒的员工，他们的心理健康会改善。57% 的受访者表示宁愿辞职也不愿意留在有害心理健康的有毒工作场所。

Linus Torvalds 在内核邮件列表上宣布释出 Linux 6.18-rc1，关闭了合并窗口，正式版预计于 12 月释出。Linux 6.18 的主要变化包括：移除了 Bcachefs 文件系统，Intel USBIO 驱动、对英特尔新处理器 Wildcat Lake 的初步支持，基于 Rust 的 Tyr Arm Mali DRM 驱动，Rockchip NPU 加速器驱动，来自 Google 的触觉触摸板支持，AMD Versal，持久化缓存目标 DM-PCACHE，支持 SiFive Premier P550，KVM x86 CET 虚拟化，等等。

Android 社区发行版项目 LineageOS 释出了 v23 版本。开发者称 Google 改变 Android 补丁的发布方式对他们的开发工作产生了深远影响。Google 现在主要通过季度更新发布安全补丁、bug 修复和性能改进，而 Android 16 的第一个季度更新 QPR1 已经推送给了该公司的 Pixel 手机，但至今没有发布到 AOSP，目前只有其签约合作伙伴才能访问，而社区发行版成为签约合作伙伴的可能性几乎为零，因此 LineageOS 23 是基于 Android 16 QPR0（初始版本）。此外 Google 也不再公开其 Pixel 手机的内核源代码，而是以剥离了历史的 tarball 包形式提供，而且要私下请求，没有设备树、HAL 或配置文件。因此 Pixel 手机不再默认是 LineageOS 首批支持的设备了，如今对 Pixel 设备的支持并不比其它 Android OEM 厂商的设备更容易。Google 日益依赖 eBPF 也影响到了对运行旧版本 Linux 内核的设备的支持能力。

根据发表在《Acta Psychologica》期刊上的一项研究，同卵双胞胎的 IQ 差异与学校教育相关。研究针对的是在不同家庭长大的同卵双胞胎，同卵双胞胎在遗传上几乎完全相同，他们的 IQ 主要受不同生长环境影响。研究人员通过分析之前进行的数千项研究，整理了一个包含 87 对双胞胎的数据集。他们的分析发现：52 对教育经历相似的双胞胎平均 IQ 差异仅为 5.8 分，25 对教育经历略有不同的双胞胎平均 IQ 差异 12.1 分，10 对教育差异巨大的双胞胎平均 IQ 差异达到了 15.1 分——接近随机选择的两个无血缘关系个体之间的平均 IQ 差异。

在特朗普宣布对中国的进口商品加征 100% 关税前 30 分钟，一位自 2011 年起就持有 86,000 BTC 的人士（被称为 Bitcoin OG）对比特币和以太坊巨额做空，在关税导致的加密货币巨额震荡中获利 1.9 亿到 2 亿美元（由于该人士持有的比特币总币值也因此下跌，实际获利 2700 万美元）。Bitcoin OG 在交易平台 Hyperliquid 上建立巨额头寸，以 10 倍杠杆做空 6,189 BTC，价值 7.529 亿美元，清算价格为 130,810 美元；以 12 倍杠杆做空 81,203 ETH，价值 3.531 亿美元，清算价格为 4,589 美元。特朗普的关税声明导致比特币从 12.2 万美元以上一度跌破 10.2 万美元。Bitcoin OG 在币值跌至低点时平掉了约 90% 的比特币空单，完全清仓以太坊头寸，单日实现盈利约 1.9 亿至 2 亿美元。此人与政府的关系引发了广泛争议。这次加密货币币值暴跌事件导致 24 小时内逾 166 万次清算，193.3 亿美元的头寸蒸发。

新冠疫情推动了线上学习/网课的流行，而线上和线下环境有着截然不同的学习体验。研究人员调查了 2023 年 11 月 1 日到 12 月 13 日期间参加苏州一所中英合办大学七周选修课《Design Thinking and Research》的学生，该课程有线上和线下两种，报名网课的学生只能通过线上平台学习。研究人员共收到 334 份有效问卷。包括 88 名参加网课的学生和 246 名线下学习的学生，其中男生 110 人，女生 224 人，平均年龄为 21.3 岁。结果显示，线上学习的学生在目标导向性 (goal-directedness)、人际关系 (interpersonal relation)和自我接纳（self-acceptance）上的得分都低于线下学习的学生。研究建议线上学习工具应加强对学生在这些方面的支持，培养学生的能动性，对主动性低的学生加大支持，对主动性高的学生减少干预。

在设定的赎金支付截止日期过了之后，黑客组织 Scattered Lapsus$ Hunters 在暗网上公开了澳航 500 万客户数据。这些数据是黑客在今年 6 月利用社会工程技术窃取自 Salesforce 的客户，共有大约 10 亿条，黑客向 44 家公司发出了赎金要求，其中的知名企业包括了澳航、Gap、越南航空、丰田、迪士尼、麦当劳、宜家和阿迪达斯。澳航的数据包括了客户的电子邮件地址、电话号码、出生日期和飞行常客号码。数据不包含信用卡、财务信息或护照信息。

AMD 计算图形事业部总经理 Jack Huynh 与索尼 PS5 和 PS5 Pro 的首席架构师 Mark Cerny 在一则视频中介绍了预计将用于索尼下一代游戏机 PS6 的图形技术。游戏机的生命周期大约十年左右，索尼 PS5 游戏机于 2020 年底上市，至今已有五年历史，下一代游戏机的开发正在进行之中，预计将使用 AMD Zen6 CPU 和 UNDA（RDNA5）GPU。PS6 将使用三大图形技术：类似英伟达 RT Cores 的光线和路径跟踪专用硬件组件 Radiance Cores；用于提升 FSR 和 PSSR 等的 AI 计算单元 Neural Arrays，该技术类似英伟达的 DLSS；压缩 GPU 数据降低带宽需求的 Universal Compression。