OrangeBot.AI Digest — 2025-09-29

利用AI大模型，一键生成高清短视频 Generate short videos with one click using AI LLM.

openpilot is an operating system for robotics. Currently, it upgrades the driver assistance system on 300+ supported cars.

Interactive roadmaps, guides and other educational content to help developers grow in their careers.

Legacy-Mess Detector – assess the “legacy-mess level” of your code and output a beautiful report | 屎山代码检测器，评估代码的“屎山等级”并输出美观的报告

Free and Open Source Enterprise Resource Planning (ERP)

A simple task management system for managing AI dev agents

The best way to get AI coding agents to solve hard problems in complex codebases.

A fast, clean, responsive Hugo theme.

12 Lessons to Get Started Building AI Agents

The Free Software Media System - Server Backend & API

Open Source AI Platform - AI Chat with advanced features that works with every LLM

A Collection of application ideas which can be used to improve your coding skills.

A reverse proxy that provides authentication with Google, Azure, OpenID Connect and many more identity providers.

Production-ready platform for agentic workflow development.

Plumb a PDF for detailed information about each char, rectangle, line, et cetera — and easily extract text and tables.

We present LongLive, a frame-level autoregressive (AR) framework for real-time and interactive long video generation. Long video generation presents challenges in both efficiency and quality. Diffusion and Diffusion-Forcing models can produce high-quality videos but suffer from low efficiency due to bidirectional attention. Causal attention AR models support KV caching for faster inference, but often degrade in quality on long videos due to memory challenges during long-video training. In addition, beyond static prompt-based generation, interactive capabilities, such as streaming prompt inputs, are critical for dynamic content creation, enabling users to guide narratives in real time. This interactive requirement significantly increases complexity, especially in ensuring visual consistency and semantic coherence during prompt transitions. To address these challenges, LongLive adopts a causal, frame-level AR design that integrates a KV-recache mechanism that refreshes cached states with new prompts for smooth, adherent switches; streaming long tuning to enable long video training and to align training and inference (train-long-test-long); and short window attention paired with a frame-level attention sink, shorten as frame sink, preserving long-range consistency while enabling faster generation. With these key designs, LongLive fine-tunes a 1.3B-parameter short-clip model to minute-long generation in just 32 GPU-days. At inference, LongLive sustains 20.7 FPS on a single NVIDIA H100, achieves strong performance on VBench in both short and long videos. LongLive supports up to 240-second videos on a single H100 GPU. LongLive further supports INT8-quantized inference with only marginal quality loss.

Reinforcement Learning with Verifiable Rewards (RLVR) strengthens LLM reasoning, but training often oscillates between {entropy collapse} and {entropy explosion}. We trace both hazards to the mean baseline used in value-free RL (e.g., GRPO and DAPO), which improperly penalizes negative-advantage samples under reward outliers. We propose {Quantile Advantage Estimation} (QAE), replacing the mean with a group-wise K-quantile baseline. QAE induces a response-level, two-regime gate: on hard queries (p <= 1 - K) it reinforces rare successes, while on easy queries (p > 1 - K) it targets remaining failures. Under first-order softmax updates, we prove {two-sided entropy safety}, giving lower and upper bounds on one-step entropy change that curb explosion and prevent collapse. Empirically, this minimal modification stabilizes entropy, sparsifies credit assignment (with tuned K, roughly 80% of responses receive zero advantage), and yields sustained pass@1 gains on Qwen3-8B/14B-Base across AIME 2024/2025 and AMC 2023. These results identify {baseline design} -- rather than token-level heuristics -- as the primary mechanism for scaling RLVR.

We introduce MinerU2.5, a 1.2B-parameter document parsing vision-language model that achieves state-of-the-art recognition accuracy while maintaining exceptional computational efficiency. Our approach employs a coarse-to-fine, two-stage parsing strategy that decouples global layout analysis from local content recognition. In the first stage, the model performs efficient layout analysis on downsampled images to identify structural elements, circumventing the computational overhead of processing high-resolution inputs. In the second stage, guided by the global layout, it performs targeted content recognition on native-resolution crops extracted from the original image, preserving fine-grained details in dense text, complex formulas, and tables. To support this strategy, we developed a comprehensive data engine that generates diverse, large-scale training corpora for both pretraining and fine-tuning. Ultimately, MinerU2.5 demonstrates strong document parsing ability, achieving state-of-the-art performance on multiple benchmarks, surpassing both general-purpose and domain-specific models across various recognition tasks, while maintaining significantly lower computational overhead.

Training LLM agents in multi-turn environments with sparse rewards, where completing a single task requires 30+ turns of interaction within an episode, presents a fundamental challenge for reinforcement learning. We identify a critical failure mode unique to this setting: the exploration-exploitation cascade failure. This cascade begins with early-stage policy premature convergence, where sparse feedback causes agents to commit to flawed, low-entropy strategies. Subsequently, agents enter late-stage policy collapse, where conventional entropy regularization becomes counterproductive, promoting chaotic exploration that destabilizes training. We propose Entropy-regularized Policy Optimization (EPO), a general framework that breaks this failure cycle through three synergistic mechanisms: (1) adopting entropy regularization in multi-turn settings to enhance exploration, (2) an entropy smoothing regularizer that bounds policy entropy within historical averages to prevent abrupt fluctuations, and (3) adaptive phase-based weighting that balances exploration and exploitation across training. Our analysis justifies that EPO guarantees monotonically decreasing entropy variance while maintaining convergence. EPO achieves up to 152% performance improvement on ScienceWorld and up to 19.8% on ALFWorld. Our work demonstrates that multi-turn sparse-reward settings require fundamentally different entropy control than traditional RL, with broad implications for LLM agent training.

Peer review serves as a backbone of academic research, but in most AI conferences, the review quality is degrading as the number of submissions explodes. To reliably detect low-quality reviews, we define misinformed review points as either "weaknesses" in a review that contain incorrect premises, or "questions" in a review that can be already answered by the paper. We verify that 15.2% of weaknesses and 26.4% of questions are misinformed and introduce ReviewScore indicating if a review point is misinformed. To evaluate the factuality of each premise of weaknesses, we propose an automated engine that reconstructs every explicit and implicit premise from a weakness. We build a human expert-annotated ReviewScore dataset to check the ability of LLMs to automate ReviewScore evaluation. Then, we measure human-model agreements on ReviewScore using eight current state-of-the-art LLMs and verify moderate agreements. We also prove that evaluating premise-level factuality shows significantly higher agreements than evaluating weakness-level factuality. A thorough disagreement analysis further supports a potential of fully automated ReviewScore evaluation.

We introduce a variational reasoning framework for language models that treats thinking traces as latent variables and optimizes them through variational inference. Starting from the evidence lower bound (ELBO), we extend it to a multi-trace objective for tighter bounds and propose a forward-KL formulation that stabilizes the training of the variational posterior. We further show that rejection sampling finetuning and binary-reward RL, including GRPO, can be interpreted as local forward-KL objectives, where an implicit weighting by model accuracy naturally arises from the derivation and reveals a previously unnoticed bias toward easier questions. We empirically validate our method on the Qwen 2.5 and Qwen 3 model families across a wide range of reasoning tasks. Overall, our work provides a principled probabilistic perspective that unifies variational inference with RL-style methods and yields stable objectives for improving the reasoning ability of language models. Our code is available at https://github.com/sail-sg/variational-reasoning.

LLMs are often trained with RL from human or AI feedback, yet such methods typically compress nuanced feedback into scalar rewards, discarding much of their richness and inducing scale imbalance. We propose treating verbal feedback as a conditioning signal. Inspired by language priors in text-to-image generation, which enable novel outputs from unseen prompts, we introduce the feedback-conditional policy (FCP). FCP learns directly from response-feedback pairs, approximating the feedback-conditional posterior through maximum likelihood training on offline data. We further develop an online bootstrapping stage where the policy generates under positive conditions and receives fresh feedback to refine itself. This reframes feedback-driven learning as conditional generation rather than reward optimization, offering a more expressive way for LLMs to directly learn from verbal feedback. Our code is available at https://github.com/sail-sg/feedback-conditional-policy.

Image captioning is a fundamental task that bridges the visual and linguistic domains, playing a critical role in pre-training Large Vision-Language Models (LVLMs). Current state-of-the-art captioning models are typically trained with Supervised Fine-Tuning (SFT), a paradigm that relies on expensive, non-scalable data annotated by humans or proprietary models. This approach often leads to models that memorize specific ground-truth answers, limiting their generality and ability to generate diverse, creative descriptions. To overcome the limitation of SFT, we propose applying the Reinforcement Learning with Verifiable Rewards (RLVR) paradigm to the open-ended task of image captioning. A primary challenge, however, is designing an objective reward function for the inherently subjective nature of what constitutes a "good" caption. We introduce Captioning Reinforcement Learning (CapRL), a novel training framework that redefines caption quality through its utility: a high-quality caption should enable a non-visual language model to accurately answer questions about the corresponding image. CapRL employs a decoupled two-stage pipeline where an LVLM generates a caption, and the objective reward is derived from the accuracy of a separate, vision-free LLM answering Multiple-Choice Questions based solely on that caption. As the first study to apply RLVR to the subjective image captioning task, we demonstrate that CapRL significantly enhances multiple settings. Pretraining on the CapRL-5M caption dataset annotated by CapRL-3B results in substantial gains across 12 benchmarks. Moreover, within the Prism Framework for caption quality evaluation, CapRL achieves performance comparable to Qwen2.5-VL-72B, while exceeding the baseline by an average margin of 8.4%. Code is available here: https://github.com/InternLM/CapRL.

Reinforcement Learning with Verifiable Rewards (RLVR) is a powerful framework for improving the reasoning abilities of Large Language Models (LLMs). However, current methods such as GRPO rely only on problems where the model responses to the same input differ in correctness, while ignoring those where all responses receive the same reward - so-called zero-variance prompts. In this work, we argue that such prompts are not useless but can, in fact, provide meaningful feedback for policy optimization. To this end, we introduce RL with Zero-Variance Prompts (RL-ZVP), a novel algorithm that extract learning signals from zero-variance prompts. RL-ZVP directly rewards correctness and penalizes errors even without contrasting responses, modulating feedback with token-level characteristics to preserve informative, nuanced signals. Across six math reasoning benchmarks, RL-ZVP achieves significant improvements of up to 8.61 points in accuracy and 7.77 points in pass rate over GRPO, while consistently outperforming other baselines that filter out zero-variance prompts. These results highlight the untapped potential of learning from zero-variance prompts in RLVR.

The ability of robots to interpret human instructions and execute manipulation tasks necessitates the availability of task-relevant tabletop scenes for training. However, traditional methods for creating these scenes rely on time-consuming manual layout design or purely randomized layouts, which are limited in terms of plausibility or alignment with the tasks. In this paper, we formulate a novel task, namely task-oriented tabletop scene generation, which poses significant challenges due to the substantial gap between high-level task instructions and the tabletop scenes. To support research on such a challenging task, we introduce MesaTask-10K, a large-scale dataset comprising approximately 10,700 synthetic tabletop scenes with manually crafted layouts that ensure realistic layouts and intricate inter-object relations. To bridge the gap between tasks and scenes, we propose a Spatial Reasoning Chain that decomposes the generation process into object inference, spatial interrelation reasoning, and scene graph construction for the final 3D layout. We present MesaTask, an LLM-based framework that utilizes this reasoning chain and is further enhanced with DPO algorithms to generate physically plausible tabletop scenes that align well with given task descriptions. Exhaustive experiments demonstrate the superior performance of MesaTask compared to baselines in generating task-conforming tabletop scenes with realistic layouts. Project page is at https://mesatask.github.io/

Large language models (LLMs) are evolving from conversational systems into strong reasoners for tasks such as Olympiad mathematics and competitive programming. While scaling parameters and test-time computation has driven progress, a key bottleneck is the lack of high-quality training problems: human-curated datasets are costly and limited, while existing synthetic corpora are often too easy or narrow. PromptCoT 1.0 showed that injecting rationales into prompt synthesis increases problem difficulty. Building on this, we present PromptCoT 2.0, a scalable framework that replaces hand-crafted heuristics with an expectation-maximization (EM) loop, where rationales are iteratively refined to guide prompt construction. This produces problems that are both harder and more diverse than prior corpora. The synthetic prompts support two post-training regimes: (1) Self-Play, where strong models improve autonomously via verifiable feedback without stronger teachers; and (2) Supervised Fine-Tuning (SFT), where weaker models learn from teacher-distilled traces. Extensive experiments demonstrate the effectiveness of this approach. In self-play, applying PromptCoT 2.0 to Qwen3-30B-A3B-Thinking-2507 sets new state-of-the-art results at the 30B scale, with +4.4, +4.8, and +5.3 on AIME 24/25 and HMMT 25, +6.1 and +5.0 on LiveCodeBench v5/v6, and +35 Elo on Codeforces. In SFT, training Qwen2.5-7B-Instruct solely on synthetic prompts boosts accuracy to 73.1 (AIME 24), 65.6 (AIME 25), and 53.4 (LiveCodeBench v5), surpassing models trained on human or hybrid data. Analyses further confirm that PromptCoT 2.0 yields fundamentally harder and distributionally distinct problems. These results establish prompt synthesis as a new axis for scaling reasoning and position PromptCoT 2.0 as a scalable foundation for future open-source models. The implementation is available at https://github.com/inclusionAI/PromptCoT.

Autonomous agents have recently achieved remarkable progress across diverse domains, yet most evaluations focus on short-horizon, fully observable tasks. In contrast, many critical real-world tasks, such as large-scale software development, commercial investment, and scientific discovery, unfold in long-horizon and partially observable scenarios where success hinges on sustained reasoning, planning, memory management, and tool use. Existing benchmarks rarely capture these long-horizon challenges, leaving a gap in systematic evaluation. To bridge this gap, we introduce UltraHorizon a novel benchmark that measures the foundational capabilities essential for complex real-world challenges. We use exploration as a unifying task across three distinct environments to validate these core competencies. Agents are designed in long-horizon discovery tasks where they must iteratively uncover hidden rules through sustained reasoning, planning, memory and tools management, and interaction with environments. Under the heaviest scale setting, trajectories average 200k+ tokens and 400+ tool calls, whereas in standard configurations they still exceed 35k tokens and involve more than 60 tool calls on average. Our extensive experiments reveal that LLM-agents consistently underperform in these settings, whereas human participants achieve higher scores, underscoring a persistent gap in agents' long-horizon abilities. We also observe that simple scaling fails in our task. To better illustrate the failure of agents, we conduct an in-depth analysis of collected trajectories. We identify eight types of errors and attribute them to two primary causes: in-context locking and functional fundamental capability gaps. https://github.com/StarDewXXX/UltraHorizon{Our code will be available here.}

Post-training compression of large language models (LLMs) largely relies on low-rank weight approximation, which represents each column of a weight matrix in a shared low-dimensional subspace. While this is a computationally efficient strategy, the imposed structural constraint is rigid and can lead to a noticeable model accuracy drop. In this work, we propose CoSpaDi (Compression via Sparse Dictionary Learning), a novel training-free compression framework that replaces low-rank decomposition with a more flexible structured sparse factorization in which each weight matrix is represented with a dense dictionary and a column-sparse coefficient matrix. This formulation enables a union-of-subspaces representation: different columns of the original weight matrix are approximated in distinct subspaces spanned by adaptively selected dictionary atoms, offering greater expressiveness than a single invariant basis. Crucially, CoSpaDi leverages a small calibration dataset to optimize the factorization such that the output activations of compressed projection layers closely match those of the original ones, thereby minimizing functional reconstruction error rather than mere weight approximation. This data-aware strategy preserves better model fidelity without any fine-tuning under reasonable compression ratios. Moreover, the resulting structured sparsity allows efficient sparse-dense matrix multiplication and is compatible with post-training quantization for further memory and latency gains. We evaluate CoSpaDi across multiple Llama and Qwen models under per-layer and per-group settings at 20-50\% compression ratios, demonstrating consistent superiority over state-of-the-art data-aware low-rank methods both in accuracy and perplexity. Our results establish structured sparse dictionary learning as a powerful alternative to conventional low-rank approaches for efficient LLM deployment.

The growing capabilities of large language models and multimodal systems have spurred interest in voice-first AI assistants, yet existing benchmarks are inadequate for evaluating the full range of these systems' capabilities. We introduce VoiceAssistant-Eval, a comprehensive benchmark designed to assess AI assistants across listening, speaking, and viewing. VoiceAssistant-Eval comprises 10,497 curated examples spanning 13 task categories. These tasks include natural sounds, music, and spoken dialogue for listening; multi-turn dialogue, role-play imitation, and various scenarios for speaking; and highly heterogeneous images for viewing. To demonstrate its utility, we evaluate 21 open-source models and GPT-4o-Audio, measuring the quality of the response content and speech, as well as their consistency. The results reveal three key findings: (1) proprietary models do not universally outperform open-source models; (2) most models excel at speaking tasks but lag in audio understanding; and (3) well-designed smaller models can rival much larger ones. Notably, the mid-sized Step-Audio-2-mini (7B) achieves more than double the listening accuracy of LLaMA-Omni2-32B-Bilingual. However, challenges remain: multimodal (audio plus visual) input and role-play voice imitation tasks are difficult for current models, and significant gaps persist in robustness and safety alignment. VoiceAssistant-Eval identifies these gaps and establishes a rigorous framework for evaluating and guiding the development of next-generation AI assistants. Code and data will be released at https://mathllm.github.io/VoiceAssistantEval/ .

Universal image restoration (UIR) aims to recover images degraded by unknown mixtures while preserving semantics -- conditions under which discriminative restorers and UNet-based diffusion priors often oversmooth, hallucinate, or drift. We present LucidFlux, a caption-free UIR framework that adapts a large diffusion transformer (Flux.1) without image captions. LucidFlux introduces a lightweight dual-branch conditioner that injects signals from the degraded input and a lightly restored proxy to respectively anchor geometry and suppress artifacts. Then, a timestep- and layer-adaptive modulation schedule is designed to route these cues across the backbone's hierarchy, in order to yield coarse-to-fine and context-aware updates that protect the global structure while recovering texture. After that, to avoid the latency and instability of text prompts or MLLM captions, we enforce caption-free semantic alignment via SigLIP features extracted from the proxy. A scalable curation pipeline further filters large-scale data for structure-rich supervision. Across synthetic and in-the-wild benchmarks, LucidFlux consistently outperforms strong open-source and commercial baselines, and ablation studies verify the necessity of each component. LucidFlux shows that, for large DiTs, when, where, and what to condition on -- rather than adding parameters or relying on text prompts -- is the governing lever for robust and caption-free universal image restoration in the wild.

天文学家利用韦伯太空望远镜观测一颗在宇宙中自由漂流的行星 SIMP-0136，意外发现在高层大气不时出现极光，而且行星大气循环由这些极光加热所驱动。漂流行星 SIMP-0136 距离地球约 20 光年，质量约为木星的 12.7 倍、半径约为木星的 1.2 倍。由于此行星自转一周只需约 2.4 小时，让天文学家得以快速观察大气层的完整变化。结果发现大气的垂直温度分布出现「温度反转」现象，也就是高度愈低，气温愈低，越往高空则气温越高，与地球等行星的大气温度垂直分布完全不同。这种异常主要源于极光不断将能量注入并加热高层大气所致。 这颗行星的云层并非由水或冰构成，而是矽酸盐颗粒组成，类似地球沙滩上的沙子。整颗行星几乎被云层平均覆盖，与地球云系经常出现云缝或空隙的情况大不相同。它的平均气温超过摄氏一千五百度，远比木星或土星平均气温约在零下百度炙热得多。研究显示，极光不只出现于地球或木星，也能在孤单的漂流行星上扮演塑造大气结构与提供动力来源的关键角色。

瑞士周日公投以微弱多数批准电子身份证。这是瑞士电子身份证计划的第二次全民公决。第一次是在 2021 年，当时由于选民担心数据的隐私保护问题，以及该系统主要由私营企业运营而投了反对票。政府之后修改了计划，新的电子身份证将由政府运营，而且是可选的，且限制了数据的访问——举例来说，需要访问年龄的机构将只能访问到年龄信息。用户可选择将电子身份证数据与手机捆绑，如果更换手机将需要重新申请一张电子身份证。在周日的公投中，50.4% 的选民支持电子身份证，49.6% 的选民反对。投票率 49.55%。

上个月 Google 以安全的名义宣布将验证所有 Android 应用开发者的身份，从明年开始，Google 将屏蔽未经身份验证的开发者的 Android 应用的侧载（sideload）。开源自由软件 Android 应用商店 F-Droid 发表声明反对 Google 的决定。F-Droid 认为，如果这一政策强制推行，包括它在内的第三方应用商店将面临终结。Google 声称是为了安全，但过去几年它的官方应用商店 Google Play 被发现托管了大量恶意程序。它要求验证应用开发者的身份不是为了安全而是为了巩固权力，加强对曾经开放的生态系统的控制。Google 正在构建一个限制竞争和用户自由的阻塞点（choke point）。F-Droid 呼吁对此问题关心的用户向自己所在地区的议员递交反对意见，向欧盟 DMA 请愿，捍卫应用的自由分发。

Linus Torvalds 在内核邮件列表上宣布释出 Linux 6.17，Linux 6.18 合并窗口开启。Linux 6.17 的主要新特性包括：更好的控制针对 x86 CPU Spectre 漏洞的缓解措施；64 位 Arm 平台的实时补丁（live patching）支持；改进 pidfd；移除对单处理器系统的特殊支持；初步支持代理执行；file_getattr() 和 file_setattr()系统调用；Btrfs 文件系统的大页（large folio）实验性支持；支持 DualPI2 拥塞控制协议，等等。更多可浏览 kernelnewbies 网页。

中科院等单位的研究人员，在对一件距今约 100 万年前的古人类头骨化石经重新分析后，不仅揭示出一个与神秘古人类“丹尼索瓦人”密切相关的新演化支系——“龙人”（Homo longi），更将现代人、尼安德特人与这一亚洲古人类支系的分化时间大幅推前，远超此前学界共识。研究发表于《科学》。研究人员对 1990 年发现于湖北省郧县的“郧县人2号”头骨化石进行了高精度 CT 扫描与结构光表面扫描，进行数字重建。研究结果显示，重建后的郧县人2号头骨脑容量超过了 1100 毫升，并且呈现出原始与进步特征交融的形态：低平的额骨和突出的吻部类似更古老的直立人或海德堡人；而扁平低矮的颧骨、更宽的后脑颅以及较大的脑容量，则与龙人以及大荔、金牛山、华龙洞、许家窑等地出土的中更新世人类化石相似。研究发现，智人、龙人和尼安德特人这三支的分化发生得非常早，虽然早于目前化石记录所示，但与基因组数据推测的结果高度吻合。进一步研究结果显示，郧县人并非直立人，而是与丹尼索瓦人密切相关的龙人支系的早期代表，表明早在 100 万年前，人类祖先已经分化成多个独立演化的群体。

美国正考虑制定一项规定，要求芯片公司在国内制造和从国外进口的芯片各占一半，否则进口的芯片将需要缴纳关税。此举旨在促进半导体制造回流美国，在美国国内制造芯片的企业将获得关税豁免。但如果企业不能长期维持国内制造和国外进口 1:1 比例，它们将需要缴关税。美国商务部长 Howard Lutnick 向半导体行业高管提出了这一想法，告诉他们这是出于经济安全的需要。根据提案，承诺在美国国内制造芯片的企业将获得承诺产量的信用，允许在工厂竣工前免关税进口芯片。

睡眠对身心健康至关重要，而年轻成年人的睡眠问题成为了一项重大的公共卫生挑战。根据发表在《Science Advances》期刊上的一项研究，丹麦研究人员调查了该国出生于 1980-2015 年的 220 万人口的数据，发现 15-45 岁人群自我报告有睡眠问题的比例从 2010 年的 34% 增至 2021 年的 49%，帮助睡眠的褪黑素使用量增加了 10 倍——开褪黑素处方的比例从每千人 2.43 增加到 20.9。

Cristian Fleming 斥资 7 万美元购买了一辆 Fisker Ocean 中型跨界电动 SUV。7 个月后的 2024 年 6 月，制造商 Fisker 公司申请破产，它总共交付了 1.1 万辆电动汽车。斥巨资购买汽车的车主面临汽车失去维修的难题，零部件替换、电池、软件、电子钥匙等问题横在他们面前，如果无法解决他们的汽车将会沦为昂贵的垃圾。车主们不愿意认命，他们组织成立了一个非盈利组织 Fisker Owners Association(FOA)，发布了第三方软件，构建了一个全球零部件供应链，保障自己的汽车在可预计的未来能正常运行。Fleming 目前担任 FOA 的主席。FOA 自称是全世界第一个完全由车主控制的电动汽车车队。至今有 4,055 名车主注册，每年缴纳 550 美元的会费，只有经过验证的 Ocean 车主才能成为正式会员，但任何人都可以捐款。FOA 成立了三家企业：Tsunami Automotive 负责北美地区的零部件业务；Tidal Wave 则负责欧洲地区的零部件业务，从保险拍卖回收零部件，与摸具制造商合作复制零部件；UnderCurrent Automotive 负责开发软件。其中 UnderCurrent 的第一款产品是 OceanLink Pro 用于恢复电动汽车的基本功能，OceanLink Pulse 支持无线 CarPlay 和 Android Auto，计划支持无钥匙进入汽车。

Firefox 将支持图像搜索，该功能由 Google Lens 技术提供。使用该功能非常简单，右键单击任意图像即可搜寻类似的产品、地点或对象；复制、翻译或搜索图像中的文字等。该功能只提供给桌面版本，将逐渐向全球推出，浏览器的默认搜索引擎需要是 Google。Mozilla 强调该功能是可选的，用户可自己控制是否激活，这与该公司长期以来对隐私和用户自主权的承诺一致。

比亚迪旗下高端品牌仰望宣布旗舰纯电超跑 U9 Xtreme 在德国 Papenburg 高速测试场上，成功创下 496.22 km/h 的惊人成绩，超越 Bugatti Chiron Super Sport 300+ 的单向极速纪录。然而由于这次仅以单一方向完成测试，世界最速量产车的官方头衔仍由 SSC Tuatara 所保持，两向平均纪录为 455.3 km/h。即便如此仰望 U9 的表现，已足以改写外界对中国高性能电动车的认知。U9 Xtreme 搭载四具电动马达，合计输出 2,978 马力，是全球首款采用 1,200 V 高压平台的量产车，比亚迪计划仅生产 30 辆 U9 Xtreme，价格尚未公布。这次纪录由德国耐久赛车手 Marc Basseng 驾驶完成。

Google 前 CEO Eric Schmidt 认为，为了与中国科技公司竞争，美国科技业从业者需要放弃工作生活平衡，拥抱中国的 996 工作制。他在 All-In 播客中表示，他不相信远程办公，部分是因为远程办公无助于美国科技公司与中国残酷的工作文化竞争。他说，如果你想要在科技领域获得成功，你必须权衡，我们的对手是中国人，中国工人的工作生活平衡是 996，即每周工作六天，早上 9 点到晚上 9 点。996 工作制于 2021 年被禁止，但 Schmidt 坚称中国科技公司仍然在推行 996 工作制。据《连线》报道，美国的初创公司，尤其是 AI 领域的初创公司，也热衷于推行中国的 996 工作制。

根据发表在《Brain, Behavior and Immunity - Health》期刊上的一项研究，从童年时期父母的温暖，到成年后的友谊、参与社区活动和宗教支持，贯穿一生的社会优势累积效应或能减缓衰老的生物过程，推迟表观遗传时钟，让一个人的生物年龄低于实际年龄。对逾 2100 名美国成年人的研究发现，更高水平的累积社会优势（cumulative social advantage）的人有着更慢的表观遗传衰老和更低的慢性炎症水平。研究人员称，累积的社会优势指的是一个人一生中社会联系的深度和广度。这种社会优势的积累会以可衡量的方式塑造人的健康轨迹。累积优势理论认为，无论是经济资源还是社会资源，都趋于累积，从而扩大生命历程中的差距。一个让人警醒的现实是：社会资源并非均匀分布，种族、阶级和教育程度塑造了在成长过程中有支持性父母、在社区机构中找到归属感或拥有提供稳定支持的朋友和伴侣的可能性。

在推出键盘外形的一体机 Raspberry Pi 500 九个月之后，树莓派宣布了升级版 Raspberry Pi 500+，价格也提高了一倍达到 200 美元。Raspberry Pi 500+ 基本配置与 Raspberry Pi 500 相同，CPU 仍然是四核 2.4GHz Arm Cortex-A76，但内存从 8GB 升级到 16GB，提供了一个 NVMe 插槽，配备了 256GB M.2 2280 SSD，用户可升级为更高容量的 SSD。除此之外，Raspberry Pi 500+ 还改进了一体机的键盘，配备机械开关、可更换键帽和可单独编程的 RGB LED 灯。用户可选配 220 美元的版本，包括鼠标、27W USB-C 电源、2 米长的 micro HDMI 转 HDMI 线和初学者指南。

亚马逊 kindle 电子书阅读器的围墙花园如今比巴比伦塔还要高。亚马逊一周前向 11 代和 12 代 Kindle，Kindle Scribe 1 和 Scribe 2 以及 Kindle Colorsoft 推送了新版本固件（v5.18.5），更新了 DRM 系统。新的 DRM 使用了一个储存在 Kindle 无法访问区域的文件(account secret)作为解密加密电子书的密钥的一部分，这意味着除非设备在更新新固件前已经越狱，否则下载到设备上的新电子书将无法解密。亚马逊是否会对第 9 代和 10 代 Kindle 推送新 DRM 还有待观察。阻止下载新固件的一个权宜之计是让 Kindle 设备的可用空间降至 300MB 以内，因为下载新固件需要 300MB 的空间。

广泛使用的 YouTube 视频下载工具 yt-dlp 项目宣布，为了工具正常工作未来将需要安装 Deno 或其它支持的 JavaScript 运行时，原因是 YouTube 设置了越来越多的障碍，项目目前使用的 JavaScript 解释器越来越力不从心，必须使用真正的 JavaScript 运行时。推荐使用 Deno 是因为它是完全独立的单一可执行文件，默认沙盒化，不允许文件系统或网络访问。开发者同时指出，YouTube 未来将对所有客户端强制执行 proof-of-origin (PO) token，超出了 yt-dlp 现有的 JavaScript 功能能力范围。