OrangeBot.AI Digest — 2026-09-15

AI-first video editor, just describe edits & refine in chat

Cloud agents, run on OpenAI's Codex harness

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Code robots in an offline Python learning simulator

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Turn AI noise into better business decisions

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A Mac dashboard for site traffic, deploys, uptime, and SEO

An alarm that calls you, and reads you your day

AI voice typing that sounds right in every app

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We present Vidu S2, which comprises Vidu S2-Avatar, a real-time interactive digital-character model, and Vidu S2-Editing, a real-time video editing model. Moreover, we explore the feasibility of real-time spatial video generation for both Vidu S2-Avatar and Vidu S2-Editing. Compared with Vidu S1, Vidu S2-Avatar supports real-time 720p video generation, generation with dynamic references that can be updated at any moment, and stronger instruction following, such as dancing. Vidu S2-Editing supports editing a video stream in real time, including style rendering, clothing replacement, character replacement, and background replacement. Experiments show that Vidu S2 outperforms all baselines. A playable online demo is available at https://vidu.com/vidu-stream.

As AI agents become participants in the development of their successors, they reshape both the production of intelligence and the role of human researchers. We introduce Atria Dawn Preview, a foundation agentic language model designed for scientific research and engineering workflows, with the goal of expanding the frontier of agent productivity in the real world. This model is trained via a Verifiable Experience Pipeline that connects tool-mediated interactions to executable environments and externally verified outcomes. Across 16 benchmarks spanning real-world research, engineering, and digital work, Atria Dawn Preview is competitive with frontier agents and achieves the highest reported score on five of them. Beyond standalone performance, we examine the real research-and-development process behind this model as a case study of human--AI collaboration, analyzing 769 task records from 56 participants together with agent logs. When asked to evaluate completed tasks under comparable conditions, participants rated about one-third of completed AI-assisted tasks as infeasible without AI. More strikingly, agents frequently propose methods and implement revisions, while humans retain most final decisions and guide exploration through judgment and feedback. These observations indicate a shift from task-level execution to project-level partnership, with human effort concentrating on what is worth pursuing and how evidence should guide research. Progress toward more autonomous AI research must therefore advance both the capacity for discovery and the capacity for meaningful human oversight, preserving accountable human authority over the risks and direction of continued development.

In this work, we present ZGCM-1, a fully open 7B dense foundation model trained from scratch with extreme data, system, and algorithmic efficiency. ZGCM-1 is founded on a core premise: compact models cannot passively memorize the open web, but can overcome parametric capacity limits by coupling deliberate internal thinking with active external tool use. To support this paradigm across a 256K context, we develop an end-to-end, high-efficiency open training recipe: Architecture & System Co-design: interleaved gated sliding-window and full attention, and a stable FP8 Muon optimizer; Progressive Curriculum & MDP Mid-Training: context scaling across 16K, 64K, and 256K, and the reformulation of interaction traces into Markov Decision Processes. Furthermore, we establish an AI-native R&D workflow where agent swarms autonomously manage cluster operations, data curation, and rapid diagnostic evaluation. Extensive evaluations show that ZGCM-1-7B is competitive across 7B model family on general benchmarks. On several challenging mathematical reasoning and agentic search suites, it remains competitive with frontier models orders of magnitude larger, such as Qwen3-235B-A22B and GLM-5.1. We also show that our pre-training design offers a ~4.2x efficiency improvement in 16K pre-training time-to-loss. Across the full development lifecycle, we distill eight actionable empirical findings-spanning architectural scaling, SFT quality pruning, long-context generalization, and agentic co-training dynamics. To facilitate community research, we open-source model weights from the pre-training, mid-training, and post-training stages, intermediate checkpoints, training code, per-stage data and data recipes, and W&B logs.

Recursive self-improvement is becoming increasingly vital for autonomous AI agents, where progress hinges on discovering high-value solutions across complex domains. The driver of this process is effective exploration, however, managing and improving exploration strategies remains a major bottleneck. Current systems face a fundamental dilemma: fixed strategies fail to adapt as search spaces scale, while online policy optimization requires navigating vast meta-search spaces under delayed and expensive feedback over long-horizon rollouts. We introduce Dream-RSI, a framework for scalable and recursively self-improving exploration. A lightweight orchestration layer makes exploration explicit and programmable while leaving the underlying coding agent unchanged. Our key insight is that accumulated discovery history can serve as a replay simulator over the realized search space. By performing dreaming in the replay simulator constructed from historical discovery trees, Dream-RSI secures immediate, low-cost off-policy feedback to evaluate and refine exploration policies without invoking repetitive, expensive online evaluations. The improved policy is subsequently redeployed online to drive further discovery, continuously expanding the simulator pool in a self-improving loop. Across algorithm engineering, mathematical optimization, and GPU kernel engineering, Dream-RSI achieves competitive or improved discovery quality while substantially reducing discovery cost in several settings.

We present PhysBrain 1.5, a unified model for understanding physical environments, generating actions, and predicting future states. Motivated by the physical loop of observation, interaction, and environmental change, we bring these capabilities into a common learning framework. Starting from a general vision--language model, we encode language responses, end-effector motion, and dense visual targets as discrete sequences and jointly optimize them with autoregressive next-token prediction. Pre-training draws its embodied supervision entirely from human interaction videos, using task-centered episodes to pair semantic and spatial context with recovered motion and subsequent observations. We then adapt the model through supervised fine-tuning on a mixture of human demonstrations, robot trajectories, and simulated experience. Across 28 embodied understanding benchmarks, our 8B model achieves an average score of 72.5, setting a new open-source state of the art and performing on par with leading proprietary models such as GPT-6-Astra and Gemini 3.6 Flash. It achieves the best open-source results on 14 benchmarks while retaining general multimodal capabilities. Beyond these understanding evaluations, qualitative examples show the model's ability to produce end-effector trajectories and predict future scenes through spatially aligned RGB, depth, and robot-mask outputs.

The KV cache is a primary bottleneck for Transformer decoding: its memory footprint and cache-read traffic grow with sequence length. Grouped-query attention (GQA) reduces this cost by sharing key-value heads, but still stores both a key and a value at every step. We introduce Grouped Value Attention (GVA), which stores grouped values and reconstructs content keys with a learned linear map. At inference, the map can be absorbed into the query, eliminating the need to materialize content keys in the intended decode path. A small shared decoupled RoPE channel retains positional information through a separately cached positional key. For the configurations studied, this representation reduces persistent cache scalars by approximately 45-47% relative to matched GQA. At the 350M-parameter scale with 30B FineWeb-Edu tokens, the 16-dimensional positional variant reaches 44.18 average accuracy across five tasks, compared with 44.36 for GQA and 43.88 for MLA. These results demonstrate near-GQA benchmark accuracy with a more compact cache representation. To translate this compact representation into faster autoregressive inference, we have developed custom decoding kernels and are currently evaluating their end-to-end inference performance with an open-source release planned soon.

Video diffusion models are stochastic and hard to control: precise content often requires repeated sampling without guaranteed success, and long-horizon scenes drift in appearance, interactions, and temporal coherence. Agentic visual creation provides explicit references, editable 3D scenes, or executable game states for stable control, but does not by itself guarantee high object or character fidelity. Combining the two can enable stable, high-quality generation. To realize this combination, we present LynnReal-Omni, a native multimodal video generation framework built on a 32B shared multimodal diffusion transformer that unifies text-to-video, image-conditioned generation, reference-guided generation, structural control, editing, degraded video restoration, and long-video generation. It accepts heterogeneous visual inputs, including appearance references, editable 3D renders, and game recordings, allowing agents to compose visual conditions within a unified model. We also train a dedicated 27B Flash shared multimodal diffusion transformer for real-time rendering. We build a systematic data pipeline for video cleaning, subject association, multimodal annotation, and aligned control construction, yielding a curated corpus of multi-shot audiovisual segments, and introduce MSAVP, a 100-prompt, 20-metric evaluation design that separates instruction following, generating plausibility, visual quality, temporal behavior, and audio coordination. LynnReal-Omni-Flash further reduces inference cost through model and decoding acceleration, including a lightweight VAE decoder; on one H100, warm generation and decoding of a 22-frame 540p video take 843 ms with LynnReal-Omni and 377 ms with Flash. These results provide a foundation for real-time streaming video generation, making LynnReal-Omni a unified, controllable, and efficient basis for agentic visual creation.

Digital agents must often adapt to new environments whose interfaces, tools, and failure modes are not fully captured by pretrained models. We introduce RSIAgent, a training-free multi-agent framework for recursive self-improvement through autonomous memory construction. RSIAgent coordinates curriculum, actor, and verifier agents to continually explore the environment, validate outcomes, and retain environment-specific knowledge, including reusable causal relationships between actions, conditions, and consequences. It further adopts a broad-then-deep exploration strategy, combining parallel broad recursive self-exploration for discovering diverse environment structures with focused deep self-exploration for uncovering hard cases, hidden constraints, boundary conditions, and previously unknown causal dependencies. The resulting memory is frozen and can be directly reused for downstream tasks without updating model parameters. Experiments on OSWorld-v2 and Agent's Last Exam show that RSIAgent substantially improves strong open-source models, enabling Kimi-K3 and GLM-5.3 to outperform frontier closed-source models including GPT-6.

Orthrus is a hybrid autoregressive-diffusion architecture that accelerates autoregressive language-model inference by generating multiple tokens in parallel while using a frozen autoregressive backbone. Its central claim is that an intra-model consensus mechanism enables lossless speculative decoding, producing the same output sequence as the autoregressive model. We independently reproduce Orthrus and examine this claim under different numerical precisions. Under BF16 inference, exact trajectory matching occurs in only 45% of cases for the authors' checkpoint and 43% for our independently trained model across 1,190 prompts from 12 domains. The probability of exact matching is also strongly associated with the response-conditional perplexity of the reference model. Despite this trajectory divergence, Orthrus does not show systematic degradation on downstream lm-eval-harness benchmarks. In contrast, repeating the trajectory evaluation with FP32 yields exact trajectory matching on all evaluated prompts. These results show that the practical losslessness of Orthrus depends on numerical precision and that exact trajectory equivalence should be evaluated separately from downstream task performance.

Foundation models have progressed from learning and reasoning over existing knowledge, to increasingly learning through action, tool use, and outcome feedback. We argue that the next frontier is a further transition: from solving and acting within problems specified by humans to participating in the process by which new problems, representations, explanations, and knowledge are created. We refer to this capability as Discovery Intelligence. We formulate Discovery Foundation Models (DFMs) as general-purpose model systems for open-ended discovery. A DFM operates over a revisable research state and supports seven coupled capabilities spanning problem discovery, formulation, representation construction, hypothesis formation, intervention, evidence-grounded revision, and continual discovery improvement. We instantiate this framework with Zetema, which couples explicit research-state dynamics, verification and experimental gating, external grounding, and cross-task Discovery Skill evolution. We further ground the framework with GALILEO, a real therapeutic-discovery system in which Dry-Lab reasoning, robotic and hands-on Wet-Lab experimentation, external biological evidence, and iterative hypothesis and design revision form a closed physical discovery loop. We then formulate a unified approach to capability formation and process-centered evaluation, enabling discovery behavior to be trained, improved, and measured beyond final-answer performance. Together, these components establish discovery as a learnable, executable, and evaluable capability of foundation-model systems. We view this shift as a broader progression in intelligence scaling: from learning over existing knowledge, to learning from action outcomes, and ultimately to participating in the construction, testing, and revision of the structures through which new knowledge is discovered. Code: https://github.com/Gen-Verse/DFM-Plans

Multimodal agents can create complex videos in software such as Blender by coding without relying on diffusion models. Yet video understanding benchmarks still evaluate models mainly through question answering. If an agent truly understands a video, it can reconstruct it programmatically. We introduce BVB, Blender-VideoBench, a benchmark that tests this ability by asking agents to reconstruct real-world videos as animated Blender scenes. To ensure fair comparison, each agent programs the reconstruction through a lightweight harness, Mini-BVB, in an identical sandbox under a shared cost limit. The benchmark renders each reconstruction from its animated camera and evaluates it on two axes: (1) Dual VQA measures how many spatiotemporal facts the reconstruction preserves. (2) Latent Similarity measures how closely the reconstruction matches the source video perceptually. Our overall score, a square-root mean, favors balanced performance. We evaluate 51 configurations from 10 model families and analyze semantic retention, perceptual similarity, reasoning effort, and cost. The best model reaches 88.6 Latent Similarity but retains only 53.7% of the source-correct spatiotemporal answers. Additional reasoning improves visual similarity but does not close this gap in factual accuracy. In a blind study with 15 raters and five configurations, Latent Similarity correlates strongly with human preference. These results show that programmatic reconstruction is a viable test of agentic video understanding, and that semantic retention remains the main challenge.

Interactive video world models must maintain broad scene context under camera motion while producing high-fidelity observations with low latency. Existing approaches face a representation trade-off: perspective models operate on local views and must preserve off-screen content over long rollouts, whereas broader spatial coverage is typically obtained by synthesizing full-sphere videos or constructing explicit 3D representations. Motivated by the complementary roles of global context and selective local acuity in visual perception, we present AlayaVista, a camera-controllable streaming video world model that decouples panoramic world evolution from perspective observation synthesis. Given a single perspective image, AlayaVista constructs a 360-degree scene prior using a pretrained panorama expansion model and then evolves the scene as a camera-conditioned panoramic latent state. A latent viewport renderer maps this state to the requested perspective video latents, while a perspective refiner restores details, suppresses artifacts, and performs super-resolution. To support efficient streaming, we adapt the panoramic generator to chunk-autoregressive generation and distill both panoramic generation and perspective refinement into few-step processes. To provide the supervision required by this design, we construct MUGEN, a large-scale real-world panoramic video dataset containing 1,318 hours of videos at resolutions of at least 4K, together with rich semantic and geometric annotations.

Streaming omni-modal models must decide what and when to answer from the video chunks and synchronized audio observed so far. Visual cues often support an interpretation before an utterance or sound event is complete. If that interpretation enters memory as a fact, later reasoning can keep relaying it even after audio contradicts it. We call this failure premature cross-modal commitment. We propose Omni-Streaming Thinking (OST), which generates structured outputs that include evidence observed so far, forecasts of future evidence, and claims based on this evidence. Each claim is initially marked as pending and linked to a future verification interval. Audio and visual evidence are stored separately, and OST checks a claim against the evidence from the specified modality at the end of the verification interval. When contradictory evidence is detected, a refutation process reduces the influence of the claim and its dependent states, and then guides a state update using the new evidence. An answer gate decides whether the answer-critical claims meet the conditions for giving a response. Using a frozen Qwen3-Omni-30B-A3B-Instruct backbone with lightweight adaptation, OST outperforms the strongest open baselines on five streaming and audio-visual benchmarks by more than 10% relative on average. We also introduce OST-DiagBench, which holds video fixed and edits audio to test agreement, absence, contradiction, coexistence, and subtitle-speech conflict. OST reaches d-prime = 2.95, compared with at most 1.38 for open baselines, while reducing vision-induced auditory hallucinations.

Native 3D generators turn one image into a single mesh. TRELLIS.2 and its peers deliver high-fidelity non-watertight geometry with materials, but the output is one fused object, while downstream work such as editing, rigging and simulation operates on part-level assets. A naive idea is to run a 3D segmentation network on the fused mesh that TRELLIS.2 generates, but such pipelines are slow and bounded by the accuracy of the segmentation. We want a simple way to extend an existing native 3D generator to the part level. But we face a critical problem: the O-Voxel grid stores one sheet of surface per voxel, so a single volume cannot represent the interface where two parts touch, at any resolution. We introduce a dual-volume representation to solve this problem and put forward KaiNinja, a part-level extension of TRELLIS.2 built on a dual-volume form of its O-Voxel representation. KaiNinja keeps the generation speed and quality of TRELLIS.2 while extending it to the part level, with no mask or segmenter in the pipeline. Its training data come from sources of many kinds, including CAD models and assets authored by an LLM-driven agent; to our knowledge it is the first 3D generative model trained on agent-authored part data. Surprisingly, we also find that whole-object fidelity improves over the same backbone fine-tuned on the same dataset. Against part generation pipelines of different paradigms, it lowers whole-object Chamfer distance by 40% and raises strict part F-score by 16%.

Computer-use agents increasingly interact with browsers, terminals, file systems, and external services, introducing safety risks that emerge through runtime behavior rather than generated content alone. Existing guard models target static prompts and responses and are poorly suited to agent execution; existing executable safety platforms produce evaluation verdicts rather than the normalized supervision a guard model needs to learn across heterogeneous agent frameworks. We introduce HazardAuditor, an execution-grounded framework that closes both gaps. Its infrastructure runs heterogeneous agents (Claude Code, Codex, Hermes, and OpenClaw) in controlled environments and normalizes their interactions into a canonical event representation for cross-framework supervision. We further observe that token-level post-training objectives create a structural mismatch for generative guards, causing longer rationales to dominate gradient updates. Guard Policy Optimization (GuardPO) addresses this by converting deterministic safety outcomes into sequence-level advantages and normalizing rationale and verdict regions, making the safety decision the effective unit of optimization. Across multiple benchmarks and heterogeneous computer-use systems, HazardAuditor improves accuracy by up to 16.5 percentage points over the strongest prior guard. Code, models, and evaluation artifacts will be available at https://yunhao-feng.github.io/HazardAuditor/.

Matt Bracken / FedScoop : At a US House hearing, Treasury Secretary Scott Bessent said AI labs should get no liability exemptions and called for more open-source models built in the US —  The secretary told House Financial Services Committee lawmakers that the “best way to guarantee safety” is for AI creators to be held …

@openrouter : OpenRouter users spent more on OpenAI's models than on Anthropic's in the week of September 7, the first time that happened since the week of February 26, 2024 —  OpenRouter users spent more on OpenAI models than on Anthropic models last week. This hasn't happened for more than 2.5 years

Melia Robinson / Business Insider : Pulley, which offers cap table management software, says it will cease operations after December 8; it had raised $50M+ from investors, including Founders Fund —  Pulley, a software that startups rely on to track their funding, is shutting down after seven years.

New York Times : At the Future of Life Institute's Pro-Human Assembly, Bernie Sanders, Steve Bannon, and others called for tighter restrictions on AI and denounced tech CEOs —  At an event in Washington, partisanship took a back seat as elected officials, religious leaders, parents and artists called for reining in artificial intelligence.

Brody Ford / Bloomberg : During a Salesforce event, Jensen Huang says the AI industry doesn't need any new laws or regulations and market forces will help companies safely innovate —  Nvidia Corp. Chief Executive Officer Jensen Huang dismissed the need for new artificial intelligence security regulations on Tuesday …

Dylan Tokar / Wall Street Journal : Crypto exchange CoinEx says it is closing, citing a lengthy downturn and rising compliance costs; a report said it moved $3B+ for Iran-linked wallets since 2019 —  CoinEx says it is ceasing operations less than three months after a Wall Street Journal article spotlighted its use in Iran

CNBC : The Senate fails to advance the Clarity Act, dealing a major blow to the crypto industry's push for a comprehensive market structure framework —  The Senate on Tuesday voted to block the Clarity Act from advancing, dealing a major blow to the crypto industry's push for a comprehensive market structure framework.

Jeremy Kahn / Fortune : CADDi, which sells AI tools to help manufacturers organize engineering and production data, raised a $114M Series D at a $1.2B valuation, up from $470M in 2025 —  CADDi, a startup that sells AI software to help manufacturers organize and use their engineering and production data …

Google : Google launches Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking, its “most advanced live dialogue models yet”, to more effectively enable voice agents —  Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking are our most advanced live dialogue models yet.

Alix Coutures / The Information : Some developers are using the Claude Code harness to access cheaper non-Anthropic models, such as OpenAI's GPT-5.6 Sol, via proxies and services like OpenRouter —  Boris Cherney, the head of Anthropic's coding tool Claude Code, last month found himself in the middle of an online spat …

Marina Temkin / TechCrunch : Thatch, a healthcare plan marketplace where employers set a fixed budget that employees can use for individual plans, raised a $108M Series C at a $1B valuation —  Thatch, a platform that lowers healthcare costs for employers while expanding plan choices for workers, has raised $108 million …

Tim Ingham / Music Business Worldwide : UMG sues DistroKid, alleging it distributes AI-generated music masquerading as artist-backed releases and knowingly “proliferates” copyright-infringing tracks —  Universal Music Group is suing the world's biggest music distributor by volume, DistroKid.

Angel Au-Yeung / Wall Street Journal : Factory, whose AI coding agents, called Droids, switch between models depending on a task's complexity, raised $200M at a $5B valuation, up from $1.5B in April —  Khosla Ventures, Blackstone and Marc Benioff are among the investors putting $200 million into the startup

The Information : Sources: Palmer Luckey-backed Erebor Bank ended its free stablecoin-to-cash redemption offer after crypto trading firms like Wintermute used it for arbitrage —  When Palmer Luckey's Erebor Bank launched earlier this year, it made an attractive pitch to lure new crypto customers: It would convert their stablecoins to cash for free.

Amanda Silberling / TechCrunch : DoorDash agrees to acquire Wonder's Grubhub Campus Dining business, formerly Tapingo, for $300M, and will add $125M to Wonder's $650M Series D announced in July —  The delivery platform DoorDash is investing in and partnering with Marc Lore-owned food tech company Wonder in deals worth …

研究人员分析了 英国生物样本库（UK Biobank）11,071 名参与者的数据，参与者在一周时间内手腕佩戴了光线和运动传感器。研究开始时参与者均未有心血管疾病。在几年之后他们接受了心脏 MRI 检查。研究人员主要针对两类人群，其一是夜间睡眠时几乎没有任何光；其二是接触至少 3 lux（照度单位）的光，这些光线可能来自透过窗帘射入的街灯，家用电器上的 LED 灯。在考虑个人背景、生活方式、健康状况和环境因素后，研究人员发现，夜间睡眠时的光照水平如果超过3 lux，每增加一点光照都与可测量的、细微的心脏损伤有关。相比在最黑暗房间内睡觉的参与者，光照暴露量最高的参与者左心室体积增大 2.4%、心壁增厚 1.5%，以及心脏收缩能力下降 1.9%。虽然心脏变化微小，但与心血管疾病及中风存在关联。

WHO 的数据显示，全球逾 10 亿人有心理健康障碍，其中焦虑症和抑郁症等病症造成了巨大的个人痛苦和经济损失。全世界约有七分之一 10-19 岁青少年有心理障碍，占该年龄段疾病负担的 15%。抑郁症、焦虑症和行为障碍是导致疾病和残疾的主因，而自杀则是 15-29 岁人群的第三大死因，凸显了为青少年提供心理健康支持的迫切性。人们已经认识到，环境因素会影响大脑健康、认知能力、心理健康及身体健康，而这些因素可通过改变行为加以改善。因此通过改善生活方式，人们不仅能提升大脑健康和认知能力，还能降低患心理健康障碍和躯体疾病的风险。剑桥大学的研究人员指出，出于兴趣阅读以及参加读书会，是一种有助于促进终身身心健康的低成本干预措施。阅读投入与大脑及心理健康的改善、认知表现的提升以及认知衰退风险的降低密切相关。对成人的调查数据显示，阅读与压力减轻、共情能力增强、幸福感提升以及孤独感降低有关。对青少年研究显示，出于兴趣阅读与注意力、记忆力、执行功能及学业成绩相关，同时也与较少的心理健康问题相关。

在英国舰队于 1788 年登陆澳大利亚前，这块大陆生活了多少原居民？在英国殖民澳大利亚 140 多年后的 1930 年代，人口学家 Alfred Radcliffe-Brown 首次对原居民的人口总数进行了估计。他估计澳洲原居民的人口在 25 万到 30 万之间，他强调这是一个最低估计值。现在研究人员使用了五种不同的方法重新进行了估计，得出的中位数是——殖民前澳大利亚的原住民约有 222 万。研究人员称，原住民人口至少 100 万以上，有可能在 200 万至 300 万之间，甚至可能超过 500 万。殖民后原居民的人口锐减则是疾病以及暴力导致的。到 1861 年，原住民人口仅剩约 17.7-19.3 万人。时至今日原居民人口仍然未达到殖民前的水平。

今天有无数开发者在 LLM 帮助下编写程序，其中包括了开源开发者。那么 Android FOSS 应用商店 F-Droid 中 AI 辅助开发应用的比例有多高？一位 FOSS 维护者对 9 月 12 日 F-Droid 推送更新的 102 款应用及其代码库进行了分析，发现其中 74 款应用（72.5%）主要是 AI 编写的，10 款应用难以明确归类（9.8%）， 18 款应用几乎没有 AI 参与的迹象（17.6%）。有 4 个托管在 Codeberg 上的应用主要是 AI 编写的，而 Codeberg 最近宣布了 AI 政策，禁止了此类 AI 应用，但要清除此类应用显然需要更多时间。

巴基斯坦水泥公司 Bestway Cement 正在扩建其太阳能发电设施，计划年底前在现有 26MW 装机容量的基础上增加 6.34MW 装机容量。太阳能满足了该公司逾四分之一的电力需求。受益于中国制造的廉价太阳能组件，Bestway 及其竞争对手加入了全球数百万企业和家庭的行列，在屋顶、庭院、花园等空地上安装太阳能电池板。截至 2025 年底，全球太阳能装机容量已接近 1.2TW。由廉价中国光伏板推动的太阳能革命——以及个人发电模式的兴起——正在改变发展中国家乃至工业化国家的能源格局。在较贫穷国家，数以百万计的人们如今获得了更可靠的电力供应，而这是通过他们自身努力实现的，而非依赖于大规模的基础设施建设。标普全球太阳能与储能研究经理 Josefin Berg 表示，太阳能的增长正在彻底改变电力系统，使其从集中式结构转变为一种任何人都能发电的模式。本世纪初，太阳能电池板的成本约为每瓦发电容量 5-6 美元。如今已降至每瓦约 12 美分。Ember 预计非洲今年将新增约 17 GW 的太阳能装机容量。菲律宾电力分销商 Meralco 表示，今年上半年屋顶太阳能发电量达到了 372 GWh，该国的家用太阳能电池板只需三年多时间即可收回成本。南非国有电力公司 Eskom 估计，截至今年 3 月的一年内，其售电量减少了 11.7 TWh，约 7% 的降幅归因于屋顶太阳能电池板和电池系统的普及。太阳能在阴雨天气发电量会大幅下降，未来的电网系统将需要考虑这一情况。

Yusuf Gundogdu 写道：LibreDB Studio 是一个 MIT 协议的 SQL 客户端，不装在本地而是跑在服务器上，浏览器打开就能用，一条 docker run 就起来。16 个驱动覆盖 42 种数据库，PostgreSQL、MySQL、MongoDB、Redis、ClickHouse 这些都在内。9 月 8 日发布了 0.15.0 版本。我觉得值得一提的是他们把 AI 那部分做了实测：28 个模型跑同一套六项数据库任务，27 个通过 Ollama 完全在本地运行，最快的 qwen2.5:7b 只有 4.7 GB，一次完整运行中位数 6 秒，最小的 2.5 GB。数据逐个模型公开，包括没通过的和卡在哪一步。另外只读不是靠解析 SQL 挡的，是数据库自己挡的：PostgreSQL 上开只读事务，SQLite 上每条语句前重设 query_only。

美国空军部长 Troy Meink 周一在 Air and Space Forces Association 的《Air, Space & Cyber Conference》会议上首次公开宣传，美国已在轨道上部署了太空控制武器。他没有披露太空武器的任何细节，但披露其存在这一事实，凸显了五角大楼在轨道战思维上的重大转变——直到几年前，还没有任何美国军方高级官员愿意公开讨论这一话题。如今军方官员已开始公开讨论轨道作战、卫星防御性反制措施，以及一种旨在瘫痪敌方在轨卫星的新型陆基武器。Meink 也未说明这些太空武器是何时部署的。

西南联合研究生院与云南大学研究员研究员张文华与合作者研究表明，太阳能电池可在南海海面下 10 米处正常运行，可在水下工作两小时，可产生 324 兆瓦时的电能，足以给锂离子电池充电；若持续置于 10 米水深的环境中，其运行寿命可达约 5.5 年。这些发现表明，水下太阳能电池有望为远离陆地的水下传感器、摄像头和通信系统提供动力。尽管太阳能电池早已能够为陆地上的各类技术提供绿色能源，但由于阳光在水下短距离内就会迅速衰减，研究人员一直难以在海底收集太阳能，用于水产养殖监测及其他用途。为克服这一难题，张文华团队首先开发了一套定制化的实验室系统，配备定制的光学滤光片，以模拟不同水深下的光照条件。研究人员开发的高效宽能带隙浸没式太阳能电池，能够吸收从蓝光到橙光的光谱。将电池在充满氮气的手套箱中储存 300 天后，其效率仍保留了初始值的约 96%。此外，在模拟 10米 水深条件下运行 1160 小时后，电池几乎未出现性能衰减，表明其在水下具有卓越的耐久性。研究团队将钙钛矿太阳能电池与水下机器人集成，并将其部署在南海涠洲岛附近 10 米水深处，以测试其实际应用性能。

Valve 正式发售了新 VR 设备 Steam Frame，即日起开放排队预购，起售价 1059 美元（捆绑《Half-Life: Alyx》）。Steam Frame 重 440g，其一大创新是后置的电池模组与前置的镜片模组分离，用户体验到的前部重量感要轻得多，整个设备的重量主要集中在脑后面的电池上，这一设计的缺点是如果用户想要头向后靠在头枕上，那么电池可能会成为障碍。Steam Frame 运行基于 Arch 的 SteamOS 3，桌面环境是 KDE Plasma，它是一款 Linux VR 设备。

NVIDIA 宣布将于 2026 年 10 月 15—16 日在苏州举办中国开发者日。日程分为两日：首日为动手实践日，包含 2026 黑客松总决赛路演、全天实战培训及实训营，同时开放四门 Associate 级别认证现场考试；次日为主论坛，设 LLM、AI 模型与运行时、物理AI/机器人、AI基础设施四个分论坛，并举行黑客松颁奖典礼。 值得注意的细节：认证考试可在现场完成，对希望获得 NVIDIA 官方技术背书但不愿单独约考的开发者有一定吸引力。活动偏向实战而非产品宣讲，适合关注推理优化、训练基础设施和机器人方向的工程师。注册已开放，名额限制未公布，建议提早。 https://www.nvidia.cn/developer-day/?ncid=partn-270555

微软上周推送了 9 月份的例行安全更新，修复了近千个 bug。一周后，微软证实这次更新又给用户带来了一系列新 bug：Windows 11 26H1 和 Windows Server 2012 等多个 Windows 版本的远程桌面服务（RDS）出现问题； Windows 11 26H1、25H2 和 24H2 版本对 USB Audio Class 1.0 设备的支持出现问题，受影响的用户可能会遇到无音频输出、声音设置和音量控制失效，多声道音频问题；对电子表格 Excel 的 bug 修正导致粘贴功能失效。 微软表示它正在着手修复新 bug，但没有给出修复时间。

在重新上线一周时间后，基于 Nitter 开源项目的实例 XCancel 再次下线，运营者表示“由于正在进行的法律诉讼有了新进展，我们不得不再次暂停此项服务，直至另行通知。我们无法透露更多详情”。8 月 24 日， X/Twitter 向 Nitter 及其实例发出停止侵权通知 ，指控 Nitter 抓取数据并要求永久关闭 Nitter 实例和项目代码库。8 月 25 日，Nitter 的 GitHub 项目归档。9 月 6 日，Nitter 项目宣布在听取法律建议之后决定恢复服务，一天后 XCancel 成为第一个重新上线的 Nitter 实例。9 月 14 日，XCancel 再次下线。

根据发表在《Ecology》期刊上的一项研究，一群非洲野犬完成了横跨大陆、创纪录的 4000 公里之旅。科学家表示这是有记录以来非洲陆生哺乳动物为寻找配偶而行进的最远距离。三只雄犬行进的直线距离大约为 418 公里，但为了绕过人类活动区域它们迂回走了 4000 公里路。非洲野犬是非洲最稀有的捕食者之一，目前野外仅存约 6000 只。它们生活在高度社会化的家族群中，集体狩猎，四处游荡、寻找新领地以及与其它群体进行繁殖机会而闻名。它们无法在自己出生的家族群内繁衍，因此要么等待可能最终继承该家族群，要么在两三岁时出发寻找配偶。在这次寻找配偶而进行的迁徙中，三只雌性犬因落入人类陷阱而有两只死亡。

Kinryū Labs 发现了一个因错误配置而能被访问的数据库，该数据库 Advance Passenger Information 记录了过去九年进出越南的几乎所有旅客和机组人员的信息。在接到通知之后该数据库的访问于 2026 年 6 月关闭。Kinryu Labs 是在 6 月 3 日发现了名为 pax-info 的 Elasticsearch 集群，该数据库可使用默认凭证登陆，运营者没有改变默认的用户名和密码，它包含了 29 个索引和约 107 GB 的数据。其中两个主要索引分别存储了 210,318,069 条乘客记录和 10,465,631 条机组人员记录，总计 220,783,700 条记录，时间是从 2017 年 1 月 7 日至 2026 年 4 月 30 日。泄露的信息包括乘客和机组人员的姓名、出生日期、性别、国籍、护照或旅行证件号码、证件有效期及签发国。相关的旅行数据则包括航班号与日期、航空公司、出发地、目的地及中转机场、座位信息、行李编号，以及计划、预计和实际飞行时间。涉及的旅客国籍包括韩国、中国、加拿大、新西兰等。

中国地震局监测司上周五表示，中国地震台网中心正在与苹果公司沟通，力争加快推进地震预警信息接入 iOS 系统。苹果手机用户目前可通过微信小程序获取该局统一发布的地震预警信息。今年 8  月 24 日，四川宜宾长宁发生 4.7 级地震，但成都高新减灾研究所用自己的系统生成了一个“7.7级”的地震预警，并以“中国地震预警网”的名义，通过荣耀、vivo、魅族手机以及小天才手表等终端向用户推送。中国地震局后来把这种行为定性为“擅自生成”“违规推送”。 成都高新减灾研究所对此提出异议，称“中国地震预警网”是它与中国地震局此前合作建设的，否认是“冒用”。