OrangeBot.AI Digest — 2025-09-30

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

☁️ Nextcloud server, a safe home for all your data

A new markup-based typesetting system that is powerful and easy to learn.

FastAPI framework, high performance, easy to learn, fast to code, ready for production

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

JavaScript animation engine

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

12 Lessons to Get Started Building AI Agents

Implement a ChatGPT-like LLM in PyTorch from scratch, step by step

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.

Linux, Jenkins, AWS, SRE, Prometheus, Docker, Python, Ansible, Git, Kubernetes, Terraform, OpenStack, SQL, NoSQL, Azure, GCP, DNS, Elastic, Network, Virtualization. DevOps Interview Questions

Collection of awesome LLM apps with AI Agents and RAG using OpenAI, Anthropic, Gemini and opensource models.

Prevalent semantic speech tokenizers, designed to capture linguistic content, are surprisingly fragile. We find they are not robust to meaning-irrelevant acoustic perturbations; even at high Signal-to-Noise Ratios (SNRs) where speech is perfectly intelligible, their output token sequences can change drastically, increasing the learning burden for downstream LLMs. This instability stems from two flaws: a brittle single-path quantization architecture and a distant training signal indifferent to intermediate token stability. To address this, we introduce StableToken, a tokenizer that achieves stability through a consensus-driven mechanism. Its multi-branch architecture processes audio in parallel, and these representations are merged via a powerful bit-wise voting mechanism to form a single, stable token sequence. StableToken sets a new state-of-the-art in token stability, drastically reducing Unit Edit Distance (UED) under diverse noise conditions. This foundational stability translates directly to downstream benefits, significantly improving the robustness of SpeechLLMs on a variety of tasks.

A prevailing view in Reinforcement Learning for Verifiable Rewards (RLVR) interprets recent progress through the lens of an exploration-exploitation trade-off, a perspective largely shaped by token-level metrics. We re-examine this perspective, proposing that this perceived trade-off may not be a fundamental constraint but rather an artifact of the measurement level. To investigate this, we shift the analysis to the semantically rich hidden-state space, adopting Effective Rank (ER) to quantify exploration and proposing its novel first- and second-order derivatives, named Effective Rank Velocity (ERV) and Effective Rank Acceleration (ERA), to capture exploitation dynamics. Our analysis reveals that at the hidden-state level, exploration and exploitation could be decoupled (Sec. 4). This finding reveals an opportunity to enhance both capacities simultaneously. This insight motivates our method, Velocity-Exploiting Rank-Learning (VERL), the first to operationalize the principle of synergistic exploration-exploitation enhancement by directly shaping the RL advantage function. The key innovation is leveraging the theoretically stable ERA as a predictive meta-controller to create a synergistic, dual-channel incentive structure. Instead of forcing a trade-off, VERL prospectively amplifies rewards for exploration to preempt overconfidence and reinforces exploitative gains to consolidate reasoning. Experiments across diverse LLMs and reasoning benchmarks show consistent gains, including up to 21.4% absolute accuracy improvement on the challenging Gaokao 2024 dataset.

Large Language Models (LLMs) with reasoning capabilities have achieved state-of-the-art performance on a wide range of tasks. Despite its empirical success, the tasks and model scales at which reasoning becomes effective, as well as its training and inference costs, remain underexplored. In this work, we rely on a synthetic data distillation framework to conduct a large-scale supervised study. We compare Instruction Fine-Tuning (IFT) and reasoning models of varying sizes, on a wide range of math-centric and general-purpose tasks, evaluating both multiple-choice and open-ended formats. Our analysis reveals that reasoning consistently improves model performance, often matching or surpassing significantly larger IFT systems. Notably, while IFT remains Pareto-optimal in training and inference costs, reasoning models become increasingly valuable as model size scales, overcoming IFT performance limits on reasoning-intensive and open-ended tasks.

Vision language models (VLMs) achieve unified modeling of images and text, enabling them to accomplish complex real-world tasks through perception, planning, and reasoning. Among these tasks, reasoning is particularly representative, with mathematical reasoning serving as a prominent example. It highlights the high-level capability of VLMs to comprehend mathematical information in images and to perform sophisticated reasoning. Recently, numerous visual mathematical reasoning benchmarks have been proposed, but they are often restricted to geometry, lack coverage of math word problems, and rarely assess reasoning across multiple images. To address these gaps, we introduce GSM8K-V, a purely visual multi-image mathematical reasoning benchmark. GSM8K-V is built by systematically mapping each sample from the widely used text-based GSM8K into visual form. Through a carefully designed automated image-generation pipeline combined with meticulous human annotation, we curate 1,319 high-quality samples. We evaluate a wide range of open-source and closed-source models on GSM8K-V. Results show that although existing VLMs have nearly saturated performance on text-based GSM8K, there remains substantial room for improvement on GSM8K-V. For example, the best-performing model, Gemini-2.5-Pro, achieves 95.22% accuracy on GSM8K but only 46.93% on GSM8K-V. We conduct a comprehensive analysis of GSM8K-V, examining the limitations of current models as well as potential directions for improvement. GSM8K-V offers a new perspective on visual mathematical reasoning and establishes a benchmark to guide the development of more robust and generalizable VLMs.

Deep Research Agents (DRAs) can autonomously conduct complex investigations and generate comprehensive reports, demonstrating strong real-world potential. However, existing evaluations mostly rely on close-ended benchmarks, while open-ended deep research benchmarks remain scarce and typically neglect personalized scenarios. To bridge this gap, we introduce Personalized Deep Research Bench, the first benchmark for evaluating personalization in DRAs. It pairs 50 diverse research tasks across 10 domains with 25 authentic user profiles that combine structured persona attributes with dynamic real-world contexts, yielding 250 realistic user-task queries. To assess system performance, we propose the PQR Evaluation Framework, which jointly measures (P) Personalization Alignment, (Q) Content Quality, and (R) Factual Reliability. Our experiments on a range of systems highlight current capabilities and limitations in handling personalized deep research. This work establishes a rigorous foundation for developing and evaluating the next generation of truly personalized AI research assistants.

RL with Verifiable Rewards (RLVR) has emerged as a promising paradigm for improving the reasoning abilities of large language models (LLMs). Current methods rely primarily on policy optimization frameworks like PPO and GRPO, which follow generalized policy iteration that alternates between evaluating the current policy's value and improving the policy based on evaluation. While effective, they often suffer from training instability and diversity collapse, requiring complex heuristic tricks and careful tuning. We observe that standard RLVR in math reasoning can be formalized as a specialized finite-horizon Markov Decision Process with deterministic state transitions, tree-structured dynamics, and binary terminal rewards. Though large in scale, the underlying structure is simpler than general-purpose control settings for which popular RL algorithms (e.g., PPO) were developed, suggesting that several sophisticated techniques in existing methods may be reduced or even omitted. Based on this insight, we prove a surprising result: the optimal action can be recovered from the Q-function of a fixed uniformly random policy, thereby bypassing the generalized policy iteration loop and its associated heuristics. We introduce Random Policy Valuation for Diverse Reasoning (ROVER) to translate this principle into a practical and scalable algorithm for LLM math reasoning, a minimalist yet highly effective RL method that samples actions from a softmax over these uniform-policy Q-values. ROVER preserves diversity throughout training, allowing sustained exploration of multiple valid pathways. Across multiple base models and standard math reasoning benchmarks, ROVER demonstrates superior performance in both quality (+8.2 on pass@1, +16.8 on pass@256) and diversity (+17.6\%), despite its radical simplification compared to strong, complicated existing methods.

Recent advances in text-to-video generation have produced increasingly realistic and diverse content, yet evaluating such videos remains a fundamental challenge due to their multi-faceted nature encompassing visual quality, semantic alignment, and physical consistency. Existing evaluators and reward models are limited to single opaque scores, lack interpretability, or provide only coarse analysis, making them insufficient for capturing the comprehensive nature of video quality assessment. We present VideoScore2, a multi-dimensional, interpretable, and human-aligned framework that explicitly evaluates visual quality, text-to-video alignment, and physical/common-sense consistency while producing detailed chain-of-thought rationales. Our model is trained on a large-scale dataset VideoFeedback2 containing 27,168 human-annotated videos with both scores and reasoning traces across three dimensions, using a two-stage pipeline of supervised fine-tuning followed by reinforcement learning with Group Relative Policy Optimization (GRPO) to enhance analytical robustness. Extensive experiments demonstrate that VideoScore2 achieves superior performance with 44.35 (+5.94) accuracy on our in-domain benchmark VideoScore-Bench-v2 and 50.37 (+4.32) average performance across four out-of-domain benchmarks (VideoGenReward-Bench, VideoPhy2, etc), while providing interpretable assessments that bridge the gap between evaluation and controllable generation through effective reward modeling for Best-of-N sampling. Project Page: https://tiger-ai-lab.github.io/VideoScore2/

Reinforcement Learning (RL) has emerged as a popular training paradigm, particularly when paired with reasoning models. While effective, it primarily focuses on generating responses and lacks mechanisms to explicitly foster critique or reflection. Several recent studies, like Critique-Fine-Tuning (CFT) and Critique-Guided-Distillation (CGD) have shown the benefits of explicitly teaching LLMs how to critique. Motivated by them, we propose Critique Reinforcement Learning (CRL), where the model is tasked with generating a critique for a given (question, solution) pair. The reward is determined solely by whether the final judgment label c in {True, False} of the generated critique aligns with the ground-truth judgment c^*. Building on this point, we introduce Critique-Coder, which is trained on a hybrid of RL and CRL by substituting 20\% of the standard RL data with CRL data. We fine-tune multiple models (Critique-Coder) and evaluate them on different benchmarks to show their advantages over RL-only models. We show that Critique-Coder consistently outperforms RL-only baselines on all the evaluated benchmarks. Notably, our Critique-Coder-8B can reach over 60\% on LiveCodeBench (v5), outperforming other reasoning models like DeepCoder-14B and GPT-o1. Beyond code generation, Critique-Coder also demonstrates enhanced general reasoning abilities, as evidenced by its better performance on logic reasoning tasks from the BBEH dataset. This indicates that the application of CRL on coding datasets enhances general reasoning and critique abilities, which are transferable across a broad range of tasks. Hence, we believe that CRL works as a great complement to standard RL for LLM reasoning.

Does RL teach LLMs genuinely new skills, or does it merely activate existing ones? This question lies at the core of ongoing debates about the role of RL in LLM post-training. On one side, strong empirical results can be achieved with RL even without preceding supervised finetuning; on the other, critics argue that RL contributes little beyond reweighting existing reasoning strategies. This work provides concrete evidence that LLMs can acquire genuinely new skills during RL by composing existing ones, mirroring one of the central mechanisms by which humans acquire new cognitive skills. To mitigate data contamination and other confounding factors, and to allow precise control over task complexity, we develop a synthetic framework for our investigation. Specifically, we define a skill as the ability to infer the output of a string transformation function f(x) given x. When an LLM has already learned f and g prior to RL, our experiments reveal that RL enables it to learn unseen compositions of them h(x)=g(f(x)). Further, this compositional ability generalizes to more difficult problems such as compositions of >2 functions unseen during RL training. Surprisingly, our experiments show that compositional skill acquired on a source task transfers to a different target task. This transfer happens even without compositional training on the target, requiring only prior knowledge of the target's atomic skills. Our qualitative analysis shows that RL fundamentally changes the reasoning behaviors of the models. In contrast, next-token training with the same data yields none of these findings. Our systematic experiments provide fresh insights into LLM learning, suggesting the value of first building base models with basic skills, then using RL to incentivize advanced, generalizable skills for complex problems.

Visual personalization is essential in user-facing AI systems such as smart homes and healthcare, where aligning model behavior with user-centric concepts is critical. However, recent large Vision-Language Models (VLMs), despite their broad applicability, remain underexplored in their ability to adapt to individual users. In this paper, we introduce MMPB, the first extensive benchmark for evaluating VLMs on personalization. MMPB comprises 10k image-query pairs and includes 111 personalizable concepts across four categories: humans, animals, objects, and characters, with the human category enriched with preference-grounded queries. We structure personalization into three main task types, each highlighting a different key property of VLMs. Using 23 widely used VLMs including both open- and closed-source models, we evaluate personalization performance via a three-stage protocol: concept injection, multi-turn dialogue, and personalized querying. Our findings indicate that most VLMs (including some closed-source models) struggle with personalization, particularly in maintaining consistency over dialogue, handling user preferences, and adapting to visual cues. Our analysis reveals that the challenges in VLM personalization (such as refusal behaviors and long-context forgetting) highlight substantial room for improvement. By identifying these limitations and offering a scalable benchmark, MMPB offers valuable insights and a solid foundation for future research toward truly personalized multi-modal AI. Project Page: aidaslab.github.io/MMPB

We study the problem of applying 3D Foundation Models (3DFMs) to dense Novel View Synthesis (NVS). Despite significant progress in Novel View Synthesis powered by NeRF and 3DGS, current approaches remain reliant on accurate 3D attributes (e.g., camera poses and point clouds) acquired from Structure-from-Motion (SfM), which is often slow and fragile in low-texture or low-overlap captures. Recent 3DFMs showcase orders of magnitude speedup over the traditional pipeline and great potential for online NVS. But most of the validation and conclusions are confined to sparse-view settings. Our study reveals that naively scaling 3DFMs to dense views encounters two fundamental barriers: dramatically increasing VRAM burden and imperfect outputs that degrade initialization-sensitive 3D training. To address these barriers, we introduce VGGT-X, incorporating a memory-efficient VGGT implementation that scales to 1,000+ images, an adaptive global alignment for VGGT output enhancement, and robust 3DGS training practices. Extensive experiments show that these measures substantially close the fidelity gap with COLMAP-initialized pipelines, achieving state-of-the-art results in dense COLMAP-free NVS and pose estimation. Additionally, we analyze the causes of remaining gaps with COLMAP-initialized rendering, providing insights for the future development of 3D foundation models and dense NVS. Our project page is available at https://dekuliutesla.github.io/vggt-x.github.io/

Monocular Depth Estimation (MDE) is a foundational task for computer vision. Traditional methods are limited by data scarcity and quality, hindering their robustness. To overcome this, we propose BRIDGE, an RL-optimized depth-to-image (D2I) generation framework that synthesizes over 20M realistic and geometrically accurate RGB images, each intrinsically paired with its ground truth depth, from diverse source depth maps. Then we train our depth estimation model on this dataset, employing a hybrid supervision strategy that integrates teacher pseudo-labels with ground truth depth for comprehensive and robust training. This innovative data generation and training paradigm enables BRIDGE to achieve breakthroughs in scale and domain diversity, consistently outperforming existing state-of-the-art approaches quantitatively and in complex scene detail capture, thereby fostering general and robust depth features. Code and models are available at https://dingning-liu.github.io/bridge.github.io/.

Streaming video generation, as one fundamental component in interactive world models and neural game engines, aims to generate high-quality, low-latency, and temporally coherent long video streams. However, most existing work suffers from severe error accumulation that often significantly degrades the generated stream videos over long horizons. We design Rolling Forcing, a novel video generation technique that enables streaming long videos with minimal error accumulation. Rolling Forcing comes with three novel designs. First, instead of iteratively sampling individual frames, which accelerates error propagation, we design a joint denoising scheme that simultaneously denoises multiple frames with progressively increasing noise levels. This design relaxes the strict causality across adjacent frames, effectively suppressing error growth. Second, we introduce the attention sink mechanism into the long-horizon stream video generation task, which allows the model to keep key value states of initial frames as a global context anchor and thereby enhances long-term global consistency. Third, we design an efficient training algorithm that enables few-step distillation over largely extended denoising windows. This algorithm operates on non-overlapping windows and mitigates exposure bias conditioned on self-generated histories. Extensive experiments show that Rolling Forcing enables real-time streaming generation of multi-minute videos on a single GPU, with substantially reduced error accumulation.

Long-sequence processing is a critical capability for modern large language models. However, the self-attention mechanism in the standard Transformer architecture faces severe computational and memory bottlenecks when processing long sequences. While trainable sparse attention methods offer a promising solution, existing approaches such as NSA introduce excessive extra parameters and disrupt the conventional pretrain-on-short, finetune-on-long workflow, resulting in slow convergence and difficulty in acceleration. To overcome these limitations, we introduce dense-sparse switchable attention framework, termed as InfLLM-V2. InfLLM-V2 is a trainable sparse attention that seamlessly adapts models from short to long sequences. Specifically, InfLLM-V2 reuses dense attention parameters through parameter-free architecture modification, maintaining consistency between short and long sequence processing. Additionally, InfLLM-V2 ensures computational efficiency across all sequence lengths, by using dense attention for short inputs and smoothly transitioning to sparse attention for long sequences. To achieve practical acceleration, we further introduce an efficient implementation of InfLLM-V2 that significantly reduces the computational overhead. Our experiments on long-context understanding and chain-of-thought reasoning demonstrate that InfLLM-V2 is 4times faster than dense attention while retaining 98.1% and 99.7% of the performance, respectively. Based on the InfLLM-V2 framework, we have trained and open-sourced MiniCPM4.1 (https://huggingface.co/openbmb/MiniCPM4.1-8B), a hybrid reasoning model, providing a reproducible implementation for the research community.

We posit that to achieve continual model improvement and multifaceted alignment, future models must learn from natural human interaction. Current conversational models are aligned using pre-annotated, expert-generated human feedback. In this work, we introduce Reinforcement Learning from Human Interaction (RLHI), a paradigm that learns directly from in-the-wild user conversations. We develop two complementary methods: (1) RLHI with User-Guided Rewrites, which revises unsatisfactory model outputs based on users' natural-language follow-up responses, (2) RLHI with User-Based Rewards, which learns via a reward model conditioned on knowledge of the user's long-term interaction history (termed persona). Together, these methods link long-term user personas to turn-level preferences via persona-conditioned preference optimization. Trained on conversations derived from WildChat, both RLHI variants outperform strong baselines in personalization and instruction-following, and similar feedback enhances performance on reasoning benchmarks. These results suggest organic human interaction offers scalable, effective supervision for personalized alignment.

根据 Netblocks 的监测数据，阿富汗断网已超过 24 小时。互联网和移动电话服务全部中断，全国居民生活在通讯几乎完全中断的状况下。阿富汗的全面断网始于周一晚上，进入周二后互联网和电话服务继续中断。首都喀布尔一名 42 岁的店主 Najibullah 说，"没有电话和互联网我们都是盲人，所有业务都依赖于手机。送货是用手机。这一情况就像是假期：每个人都在家里。市场完全冻结。"这是塔利班政府首次切断全国的通信，官方没有对此做出解释。法新社在断网前曾收到一名政府官员的警告，称有八到九千个通信支柱（telecommunications pillars）将被关闭，通信中断将持续到另行通知为止。目前阿富汗有限的通信只能依靠无线电和少数卫星链路。

在 Linux 6.17 将 Bcachefs 文件系统列为由外部维护并且没有合并任何 Bcachefs 维护者 Kent Overstreet 递交的拉取请求之后，Linus Torvalds 在 Linux 6.18 中完全移除了 Bcachefs，总共删除了 11.7 万行代码。Torvalds 评论说，Bcachefs 现在是一个 DKMS 模块，内核代码过时了，删除内核中的代码以避免版本混淆。

贵州花江峡谷大桥正式通车。大桥桥面距水面625米，高度超过北盘江第一桥近 60 米，成为新的世界第一高桥；大桥主桥跨径 1420 米，居山区桥梁跨径世界第一。大桥全长 2890 米，可将两岸通行时间从两个多小时缩短到两分钟左右。从 2022 年开工到正式通车，这座“超级工程”的建造只花了三年多。花江峡谷大桥钢桁梁吊装有 93 个节段，总重达 2.1 万吨，需在 600 多米高空实现毫米级精准对接。建设团队借助研发的“智慧缆索吊装系统”，全部吊装仅用了 73 天就全面完成；3.8 万平方米的桥面，建设团队在 1 个多月里完成了 5 层铺装。

以研究数字取证和深度伪造而知名的加州伯克利计算机科学教授 Hany Farid 表示，计算机科学在极短时间内从经得住时间考验的职业变成了剧变中的行业。他说，计算机科学专业的学生通常会在前四年获得五份实习机会，毕业时会收到多份高薪的工作机会。但如今这种情况不会发生了，如果能收到一份工作邀约他们就很高兴了。Farid 教授认为 AI 只是因素之一。计算机科学行业正在发生某种变化。他现在给学生的建议是掌握多种技能，因为不知道未来会发生什么。他说，AI 不会让律师失业，但会用 AI 的律师会让不会用 AI 的律师失业。他认为每个职业都如此。

美国调查公司 Omdia 的数据显示，10～12 月服务器用 DRAM 的预测价格为 4.3 美元/GB，比 2023 年 10～12 月高出 2.4 美元。PC 用产品的预测价格为 2.8 美元，比 2023 年 10～12 月上涨 1.2 美元。这一趋势背后的原因是 AI 服务器的需求猛增。AI 服务器主要使用 HBM 内存。主要 DRAM 内存芯片制造商三星电子、SK 海力士及美光缩小产量或停产了上一代的 DDR4，转为生产和销售 HBM。AI 服务器的内存需求正在推动整个半导体市场。美国半导体行业协会（SIA）公布的全球半导体销售额7月达到了 620.7 亿美元，同比增长 20.6%，首次突破 600 亿美元。已连续 21 个月超过去年同期。

根据发表在《Osteoporosis International》上的一篇综述，研究人员分析了 62 项研究，发现微塑料会破坏骨髓干细胞，刺激破骨细胞——一种削弱骨组织的细胞，从而削弱骨骼。实验室实验发现，微塑料颗粒会降低细胞活性，诱导细胞过早衰老，改变基因表达，引发炎症反应。动物研究发现，微塑料的积累会降低白细胞数量，破坏骨骼微结构，导致细胞结构不规则，增加骨折风险。巴西 Campinas 州立大学的 Rodrigo Bueno de Oliveira 表示，这些影响会阻碍实验动物的骨骼生长。

几周前，阿富汗切断了多省的光纤连接，本周一则发生了全国性断网。网络监测组织 Netblocks 报告阿富汗的网络连接率仅为正常水平的 14%。法新社报告在 6:15 pm (1315 GMT)与该新闻社位于喀布尔的分社失去联系。阿富汗此前表示它切断光纤接入是为了防止不良行为。

沙特基金 Public Investment Fund (PIF)、Silver Lake 和 Affinity Partners 组成的财团宣布与 EA（Electronic Arts） 达成了 550 亿美元的收购协议。财团将收购 100% 的 EA 股份，其中 PIF 将其持有的 EA 股份（截至 2023 年持有 55% 的股份）转入此次收购。作为收购的一部分，EA 股东将以每股 210 美元的价格获得现金。此次收购以现金方式出资。这次交易被认为是史上最大规模的全现金私有化收购。交易预计将于 2027 财年第一季度完成。

根据发表在《Foods》期刊上的一项研究，每天食用芒果有助于降低糖尿病风险。芒果含糖量属于较高水平，对糖尿病前期患者而言可能不是好的水果选择。但对照测试发现，结果并非如此。研究人员将参与者分成两组，一组每天吃一个新鲜芒果，另一组则每天食用一根低糖燕麦棒。研究人员在 6 个月中测量了参与者的血糖水平、身体对胰岛素的反应以及体脂。结果显示，含 32 克糖的高糖芒果比含 11 克糖的低糖燕麦棒更有益。每天食用芒果的参与者血糖控制得到改善、胰岛素敏感性增强，并且体脂减少。芒果等水果中天然存在糖分，辅以纤维、维生素及营养素，能提供额外的健康益处。而像早餐麦片，甚至是一些低糖零食这些添加糖分的食物，则可能不具备同等的营养价值，甚至还会增加患糖尿病的风险。研究人员称，“重要的不仅是含糖量，食物的整体构成也很重要。”

日本DM三井制糖开发出了使用魔芋薯等制成的植物基金枪鱼。 该产品将面向孕妇和老年人等因健康原因无法食用生鱼片的人群，从 2026 年起向医院和护理设施推广。在全球变暖及渔业从业者数量减少等原因导致产量下降的情况下，日本涉足植物基生鱼片业务的企业正在增加。植物基金枪鱼生鱼片价格设定为每公斤 2000 日元（约合人民币 95.5 元）区间，比真金枪鱼便宜，旨在推动产品普及。该公司预计到 2028 年年产量将达到 10 吨左右。除金枪鱼外，该公司还力争推动三文鱼、乌贼等品种的实用化。

Ruby Central 据报道在最大支持者 Shopify 的施压下，未经长期维护者同意接管了多个 Ruby 旗舰开源项目的控制权，其中包括 bundler 和 rubygems-update，此举可能加剧社区的分裂。Ruby 项目的主要赞助商 Sidekiq 撤回了每年 25 万美元的赞助承诺，导致 Ruby Central 严重依赖于 Shopify 的赞助。Shopify 前雇员、Ruby 开发者和维护者 Joel Drapper 称，Ruby Central 此前已经陷入财务困境，Shopify 在此背景下施压 Ruby Central 获得对 RubyGems GitHub 组织以及对部分核心 Gem 如 bundler 和 rubygems-update 的完整控制权，威胁如果不这样做将停止资助。此后 Ruby Central 采取了一系列行动，包括将 RubyGems GitHub 企业重命名为 Ruby Central，移除了众多维护者的权限，停用了邮件账号，撤销了对 RubyGems 的所有权。在社区引发争论之后，Ruby Central 回应称此举是为了确保供应链的安全。

加州按 GDP 计算相当于世界第四大经济体。自 2000 年以来，加州的 GDP 增长了 78%，但同期排放下降了 20%。加州过去几年在加速建造支持电动汽车的充电基础设施，2019 年它有 4.2 万个公共和共享充电桩，六个月前这一数字达到了 17.8 万，相比下加油站数量为 12 万。上周加州宣布它在六个月内又增加了 2.3 万公共共享充电桩，总数超过了 20 万，比加油站多 68%。与此同时，加州的家用充电桩数量达到了 80 万。加州能源政策机构称，94% 的加州居民居住在距离充电桩 10 分钟内的地方。加州还宣布去年所有新卡车中有 23% 是零排放，该州为电动卡车提供补贴。

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。