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星期四

  1. Replit Blog精选

    Replit 发布 Replit Design 设计套件

    Replit 发布 Replit Design 创意设计套件,面向所有用户开放,可从想法直接生成设计并发布,无需导出或重建。该套件支持 Claude、GPT-5、Gemini、Kimi、GLM 等模型,内置 Ambient Intelligence 逐步给出可一键采纳的变体建议,并集成 Mobbin 的 60 万张真实界面素材与 1000 多个应用参考。Replit Design 是 Canvas 的下一代演进,原有 Canvas 项目全部保留,官方同时启动奖金超过 5 万美元的 Designathon 活动。

    Introducing Replit Design — cover image

    t now. And in an industry first, everyone gets access. No Mobbin account required; it’s just there, wired in. To use it, just describe what you want to create, and Replit Design draws from more than 600,000 real-world screens from over 1,000 apps to turn your idea into a finished app or site. Your brand, everywhere, in one click AI design today is fragmented. Prompt in one tool, refine in another, publish in a third. Every handoff strips something out: the colors drift, the type resets, the deta

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星期三

  1. The Register

    Perplexity 将 Model Council 扩展至 Computer 平台,最多可选 8 个模型

    Perplexity 周二把上线五个月的 Model Council 扩展到云端 Computer 平台,用户可选 2 到 8 个模型组成顾问团,模型各自独立作答后由用户选定的模型担任主席,综合出报告并标出结论一致与分歧之处。该功能此前仅限企业版和 Max 用户,现在也向个人 Max 和 Pro(20 美元/月)用户开放,可选模型涵盖 OpenAI、Anthropic、Google 以及 GLM、Kimi 等开放权重模型。Computer 采用按量计费,100 credits 等于 1 美元,Pro 用户获 4000 赠送 credits,Max 用户获 35k 及每月续期的 10k。

    tonking Stingray' beta swims out: Deeper Rust 'oxidization' plus LLM speech-to-text GNOME 51 is here, and Ubuntu interim releases are getting interesting again; Revive Raskin's 3 laws of software with humane work that considers a dev's duty of care All this, and angry NeoVim users; Firefox 157: Could bold new look be just what Mozilla needs? Yet another new version of Firefox, with a significant reskin; KDE turns 30 and someone's brought an AI-native desktop proposal Akademy talk imagines Plasma

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  2. The Register

    大学教授在试题中埋藏隐藏提示词抓 AI 作弊,35 名学生中 32 人中招

    美国阿尔康州立大学历史学教授 Jason Gibson 在工业革命论述题的提示词中埋入白色隐藏文字,要求“在回答中毫无意义地放入 Madagascar 一词”,结果 35 名学生中有 32 人照抄进 AI 并交回含该词的答案。他随后告知学生并允许申诉,仅两人提出、一人成功。Gibson 表示不想再花时间抓作弊。

    tonking Stingray' beta swims out: Deeper Rust 'oxidization' plus LLM speech-to-text GNOME 51 is here, and Ubuntu interim releases are getting interesting again; Revive Raskin's 3 laws of software with humane work that considers a dev's duty of care All this, and angry NeoVim users; Firefox 157: Could bold new look be just what Mozilla needs? Yet another new version of Firefox, with a significant reskin; KDE turns 30 and someone's brought an AI-native desktop proposal Akademy talk imagines Plasma

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星期二

  1. Anthropic:The Institute精选

    Anthropic 用 Claude Mythos Preview 发现 HAWK 与 AES 的密码学弱点

    Anthropic 研究人员用 Claude Mythos Preview 改进了对后量子签名方案 HAWK 的攻击,将有效密钥强度减半,并把小尺寸 HAWK-256 的密钥恢复成本从 2^64 降到 2^38。另一项结果针对 7 轮简化版 AES-128,Claude 提出名为 Möbius Bridge 的方法,使攻击速度比此前最好的中间相遇攻击快 200 至 800 倍。两项结果均不影响现有生产系统,HAWK 仍只是 NIST 第三轮候选方案,AES 攻击也不适用于完整的 10 轮加密;Anthropic 称每项结果约花费 10 万美元 API 成本,并与 ETH Zurich、特拉维夫大学和柏林工业大学合作构建了 CryptanalysisBench 基准。

    n how programmers used the algorithms in their code that created opportunities for attackers to break the encryption. Now, we have found that Claude is able to find mathematical flaws in the algorithms themselves. Cryptographic algorithms are a fundamental building block of digital security. For example, when you visit a webpage like https://www.anthropic.com, your browser checks that it is communicating with an authentic website using an algorithm called a digital signature scheme. Later, the t

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  2. The Register

    研究:主流 AI 模型政治倾向普遍偏自由意志左翼,Grok 结果摇摆

    Unslop.run 对 16 个模型进行政治罗盘测试,包括 GPT、Claude Fable/Opus/Sonnet/Haiku、Gemini Flash、Llama 4 Maverick、Grok 4.5、DeepSeek V3、Qwen3 235B、Kimi K2、GLM 4.5 和 Mistral,每个模型跑 30 次以上。除 Grok 外,其余 15 个模型全部落在自由意志左翼象限,且跨测试结果高度一致。Grok 平均经济得分略偏左,但一半运行偏右、一半偏左,是唯一结果摇摆的模型。

    tonking Stingray' beta swims out: Deeper Rust 'oxidization' plus LLM speech-to-text GNOME 51 is here, and Ubuntu interim releases are getting interesting again; Revive Raskin's 3 laws of software with humane work that considers a dev's duty of care All this, and angry NeoVim users; Firefox 157: Could bold new look be just what Mozilla needs? Yet another new version of Firefox, with a significant reskin; KDE turns 30 and someone's brought an AI-native desktop proposal Akademy talk imagines Plasma

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星期一

  1. LMSYS Blog精选

    SGLang 与 Miles 为 Kimi K3 提供 Day-0 支持

    SGLang 与 Miles 宣布在 Kimi K3 发布当天提供 Day-0 支持,前者负责推理、后者负责 RL 训练,与 Moonshot AI、NVIDIA 团队合作完成。K3 是首个 3 万亿参数级别的开源模型,实际为 2.8T 参数、1M token 上下文窗口并具备原生视觉理解,其 69 层 KDA 线性注意力与 24 层 MLA 交织的混合架构打破了常规服务栈的多项假设。文中给出统一显存池、DSpark 投机解码与 ReplaySSM、分阶段并行等方案,并报告在 16 节点 × 4 GB300 上做 DAPO 数学 RL 训练,AIME-2024 贪心评测在 12 小时内从 43.3% 升至 76.7%。

    The three KDA state moves, copy-on-write, snapshot and donate, and where each sits relative to the serial forward stream.

    s flat at the 2e-3 MXFP4 floor for the whole run. The 12-hour DAPO run. Left: mean reward over the 64 sampled responses per rollout, graded on the response channel; each rollout draws a different prompt batch, so the trend is the signal and the per-step value is noise. Middle: AIME-2024 greedy pass rate, evaluated every 10 rollouts under the same 4096-token limit as training, so a correct solution that runs past the limit scores as a miss. Right: a Schulman k3 estimate of KL(rollout ‖ train) at

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星期五

  1. METR Notes

    METR:如何衡量 AI 智能体的能力指标

    METR 提出以智能体得分函数 s_A(x) 与人类得分函数 s_H(x) 为基础构建智能体能力指标,x 可表示花费的金钱、token 或时间。文章梳理了固定支出下的得分、实际平台期得分、固定得分下的支出、支出回报率以及支出调整后得分等指标,并指出当得分曲线回报不饱和时,固定支出得分不再适用。

    elationship between coverage and the number of samples is often log-linear and can be modelled with an exponentiated power law, suggesting the existence of inference-time scaling laws.” They qualify this result: “In domains without automatic verifiers, we find that common methods for picking from a sample collection (majority voting and reward models) plateau beyond several hundred samples and fail to fully scale with the sample budget” (Brown et al. 2024, abstract). ↩; They define the 50% task-

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  2. Armin Ronacher AI 博客

    Armin Ronacher:Codeberg 限制生成式 AI 代码项目加剧开源社区分歧

    Armin Ronacher 分析了 Codeberg 修改条款、排除主要由生成式 AI 编写的项目所引发的开源社区分歧。他认为该规则对代码作者占比的界定较模糊,若问题实为垃圾项目和资源滥用,平台应直接针对这些行为制定规则。文章主张开源社区正视 LLM 与 Agent 进入软件开发的趋势,并期待 Codeberg 成为 GitHub 的有力竞争者。

    tion is just spam and abusive resource consumption, it should write rules for those instead. Now it defers the details of the policy to moderators and the communit which already draws a much harder boundary than the text does, judging by the tone of the discussion around it. It is a shame that the Open Source and Free Software communities are splitting this deeply over LLMs and agents. These systems have problems, but these tools are also becoming part of how software is made. The Open Source wo

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  3. Figma AI / Agent / Make

    Figma 如何用智能体在漏洞发生前守住代码

    Figma 用 Claude Code(Opus 4.8,xhigh 推理强度)和 Codex(GPT-5.6 Sol,high 推理强度)双模型审查每个 PR,单次审查中位成本约 0.50 美元。首周仅约 15%(27 条中 4 条)的发现有效,团队以 70% 精确率为门槛,用 99 行、2560 词、68 条先例的策略文件把精确率提上来,并意外得到一份完整威胁模型,同一策略同时支撑安全代码生成与历史代码审计。

    Friendly AI workers assemble and defend a castle-like software repository, symbolizing an AI-assisted secure software development pipeline.

    measured and improved recall, precision, and fix rate. Measuring recall with evals We measure recall through a general-purpose eval framework. Since this measures known security flaws, not what’s still emerging or unknown, we treat it as a floor for the agent to clear rather than proof of coverage. The framework uses a growing corpus of 66 tasks, each a real vulnerability that got past human review and into the codebase, surfaced only later by a bounty, an incident, or an audit. Forty-six of the

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星期四

  1. Black Forest Labs Blog

    Black Forest Labs 与 mimic 推出基于 FLUX 3 的视频-动作模型 FLUX-mimic

    Black Forest Labs 发布 FLUX 3 早期版本,并与机器人公司 mimic 合作推出视频-动作模型 FLUX-mimic,已在 Audi 测试和部署。FLUX 3 从训练之初就联合训练图像、视频和音频,视频预测占总算力成本 95% 以上;加入动作预测后,文生视频和图生视频的人类评分一度下降最多 10%,3500 步后恢复原有质量并同时预测动作。FLUX-mimic 在冻结 FLUX 主干的情况下,动作解码器表现超过此前的视觉-语言-动作模型,微调后达到 SOTA 成功率;主干在单张 NVIDIA RTX 5090 上从输入到世界表征耗时不到 80ms,整套机器人系统反应时间为 101ms。

    FLUX 3 x mimic: The Next Generation of Video-Action Models

    ity have long been studied and approached in isolation from each other. Generative approaches result in high-quality world models that enable simulations and they exhibit scaling laws for predictable returns on compute investments. However, compared to more specialized approaches for representation learning they produce less disentangled representations, which puts a ceiling on their usefulness for tasks that require world understanding. As generative models themselves rely on their own features

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  2. Black Forest Labs Blog精选

    Black Forest Labs 发布多模态基础模型 FLUX 3,开启早期访问

    Black Forest Labs 发布多模态基础模型 FLUX 3 并开启 Early Access,该模型基于 Self-Flow 在统一架构中同时学习图像、视频和音频,可单次生成最长 20 秒带原生音频的视频,并支持文生视频、图生视频、视频生视频、关键帧过渡和多语言对话等能力。早期评测中 FLUX 3 在最高 69% 的对比中优于 Grok Imagine Video,对 Runway Gen-4.5 和 Luma Ray 3.2 的偏好比例分别为 77% 和 93%,官方称结果仍属初步。后续将陆续开放 FLUX 3 Video、FLUX 3 Image 的 API 与私有权重,以及 FLUX 3 Dev 的开源权重,FLUX 3 Image 的早期访问将在接下来几周开放。

    FLUX 3 - Real World Models: Towards Multimodal Flow Models as the Backbone of Visual Intelligence.

    on in the process. Images capture spatial structures and relationships at a specific point in time. Videos restore the dimension of time and reveal temporal dynamics and physical laws. Audio reveals causal relationships between mechanical phenomena and acoustics that vision alone cannot detect. Language links these perceptions to goals, abstractions, and instructions. Learn from one and you get a good model of that projection. Learn from all of them at once and their mutual constraints tell you

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  3. Recode China AI精选

    梁文锋谈 AGI、算力与 DeepSeek 坚持开源的原因

    DeepSeek CEO 梁文锋在投资者会议上阐述了公司的 AGI 路线、算力瓶颈、开源策略与商业化思路,并将持续学习视为下一代模型的核心能力。他认为当前路线将从 Agent 走向持续学习、自我迭代和具身智能,同时表示最强模型也可能开源,且开源不会影响公司的商业模式。

    Tony Peng's avatar

    as to be useful to us. Once it’s useful to us, I’ll be faster when developing the next version of the model; We call this “drawing lots.” The bar is low, anyone can try, but who draws something out of it—I don’t know whether that comes down to talent or something else. So there’s no need for us to allocate resources here. The difference between us and other companies is just that we spend time discussing the question, we think about it, and we treat it as important 08 Commercialization and Prici

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  4. The Register

    IBM 坚称 AI 没有杀死软件交易,只是推迟了

    IBM CEO Arvind Krishna 在财报电话会上表示,第二季度未成交的主要是大客户的大型资本支出交易,其中约三分之一已在新季度前三周内完成,说明需求是推迟而非消失。IBM 同时推出 Project Lightwell 服务,瞄准因 Anthropic 的 Mythos 加速暴露开源遗留代码漏洞而形成的数十亿美元市场。

    tonking Stingray' beta swims out: Deeper Rust 'oxidization' plus LLM speech-to-text GNOME 51 is here, and Ubuntu interim releases are getting interesting again; Revive Raskin's 3 laws of software with humane work that considers a dev's duty of care All this, and angry NeoVim users; Firefox 157: Could bold new look be just what Mozilla needs? Yet another new version of Firefox, with a significant reskin; KDE turns 30 and someone's brought an AI-native desktop proposal Akademy talk imagines Plasma

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  5. Google Research

    Google Research 用强化学习控制量子纠错,让量子计算机从错误中学习

    Google Research 在 Nature 发表研究,将强化学习与量子纠错结合,让自主智能体从量子错误检测事件中学习,在计算过程中持续调节数千个控制参数,抵抗漂移。在 Willow 超导处理器上人为注入控制参数漂移后,该方案将纠错码的逻辑稳定性提升 3.5 倍,延长了处理器作为可靠量子存储的时间。

    Towards a quantum computer that learns from its errors

    ely turning quantum computers into classical ones. This fundamental phenomenon is so pervasive that it causes our familiar classical reality to emerge from the underlying quantum laws of Nature. While these environmental errors can never be completely prevented, many others are manifestations of imprecise control calibration and hardware drift – flaws that remain within our power to mitigate. Traditionally, quantum calibration relied on physics models. Its techniques were refined through decades

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星期三

  1. antirez 原创博客

    antirez:AI 编程时代,软件分发方式也将改变

    antirez 认为,AI 编程不仅改变了开发方式,也让软件使用方式发生变化:用户可让 AI 智能体直接修改代码,因此“稳定分支+不稳定分支”的传统分发模式可能不再适用,代码仓库更应成为解决问题的模板。他以 Redis 有序集合内存优化 PR 和 DwarfStar 为例,指出 95% 就绪的分支对部分用户比最终成品更有价值,并提到 GPT 5.6 Sol 借助 DwarfStar 现有代码,约两小时自动完成 Laguna S.1 模型实现。

    branch), fix bugs, ask people to test it. At some point the number of bug reports starts to drop, your team and your users start to believe there are no longer obvious critical flaws that are easy to discover in the next few weeks: then you call the branch 2.4 or whatever, and that's it. However now, with AI coding, it's not just development that has changed, but also the act itself of using software is affected: it is not just you that can ask an AI to do certain changes to the software, but a

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  2. The Next Platform

    Gartner:GenAI 硬件投资远超模型与平台收入

    Gartner 数据显示,GenAI 模型收入 2023 年为 14 亿美元、2024 年为 57 亿美元(增长 320.4%),2026 年预计 28.3 亿美元,增速已明显放缓,远低于 OpenAI 约 250 亿美元、Anthropic 5 月 470 亿美元的 ARR。AI 平台市场已大于模型市场本身,而垂直领域专用模型收入增速约为通用基础模型的两倍。未来两年数据中心与硬件投资规模达数万亿美元。

    的中国模型制造商),这一事实对生成式AI模型授权和订阅的长期增长有着各种影响。当硬件价格高得惊人时,如果你必须在免费和非常昂贵之间做选择,你会选哪条路? 奇怪的是,免费赠送并抢走其最大客户的生意,可能会在某个时候让英伟达与其最大客户产生矛盾,甚至惹上法律麻烦。这在很大程度上取决于美国和欧盟政府是否有意愿提起一项诉讼,指控英伟达利用其在AI硬件领域的垄断地位来为其免费的Nemotron模型提供资金支持。很难说特朗普政府会做什么、不会做什么,因为模型制造商和英伟达正时不时地与白宫套近乎,试图施加影响。据《华尔街日报》的一篇文章称,AI模型制造商们对市面上存在的开放权重和开源模型肯定不满,这些模型无疑是安全威胁,但同时也是对Anthropic、OpenAI和谷歌的竞争威胁,因为这些公司希望能够对基础模型的使用收费。 它们业务的未来取决于此。而从我们所能看到的情况来看,生成式AI模型和平台的实际收入与它们巨额投资所要求的水平相比,还相当微薄。 aws openai anthropic ai platform ai model ai gartner nvidia microsoft google

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  3. Google DeepMind精选

    Google DeepMind 发布 Gemini 3.5 Flash Cyber 网络安全模型

    Google DeepMind 发布 Gemini 3.5 Flash Cyber,这是基于 3.5 Flash 微调的轻量网络安全模型,用于快速发现、验证和修补漏洞。该模型以限量试点形式通过 CodeMender 向政府和可信合作伙伴开放,后续逐步扩大范围。在 V8 JavaScript 引擎测试中,它发现 55 个已确认的独立问题,高于主线 3.5 Flash 的 47 个和 Claude Opus 4.6 的 36 个,其中 10 个为另外两个模型未发现的问题。

    Graphic featuring the text "Gemini 3.5 Flash Cyber" alongside a colorful star logo and a blurred blue shield on a light blue background.

    o bringing CodeMender's foundational capabilities directly to customers with generally available Gemini models through the Gemini Enterprise Agent Platform . Finding deep-seated flaws requires exploring an immense execution search space. Relying on a single, expensive call to a massive language model can create a bottleneck. 3.5 Flash Cyber is particularly suitable for finding vulnerabilities where the agent has to scan a large codebase and analyze a large number of codepaths. CodeMender invokes

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星期二

  1. Qwen Blog精选

    Qwen 发布第三代图像生成模型 Qwen-Image-3.0

    Qwen 团队发布第三代基础图像生成模型 Qwen-Image-3.0,核心关键词为“实”,强调内容丰富、细节真实与知识深度。该模型支持最高 4.5k token 输入,可一次生成 3×3 复杂信息图,并能清晰渲染小至 10px 的文字,包括 LaTeX 公式、报纸版面和手写批注。它还支持 12 种语言的原生渲染,可模拟网页、游戏、直播等界面,并覆盖 100 多种艺术风格。

    with lifelike, micro-level depiction. Deep Knowledge : Supports native rendering of 12 languages, simulates mainstream interfaces such as web pages, games, and livestreams, and draws on rich world knowledge. In a word, Qwen-Image-3.0 is not just pursuing “good-looking” — it is pursuing “useful” , making image generation a truly deployable productivity tool. Rich Content Let’s start with the image below, generated by Qwen-Image-3.0: As you can see, Qwen-Image-3.0 can accurately render a math sli

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星期一

  1. Interconnects

    Kimi K3 与开源权重的升级:Nathan Lambert 谈中美模型差距与开源策略

    Moonshot AI 于 7 月 16 日发布旗舰模型 Kimi K3,这是一个 2.8T 参数的 MoE 模型,权重将于 7 月 27 日开放。作者 Nathan Lambert 认为 K3 是迄今最强的开源模型,在 Vals AI 指数上排名第 2、Artificial Analysis 智能指数第 3、Frontend Code Arena 第 1,开源与闭源、美国与中国模型之间的性能差距已从争议中的 6-9 个月缩短到约 3-5 个月。文章还讨论了开源权重模型对前沿实验室资本开支的减速作用、中国实验室的资本效率,以及美国可能限制中国开源模型的监管动向。

    rimarily to give space to a broader discussion of China’s resource efficiency. It is becoming clear that the Chinese labs are far more capital efficient. In a world where scaling laws dictate that intelligence is proportional to effective capital – which buys compute, data, & talent – that may be the greatest strength your AI industry could ever have. There are many possible explanations for why this is the case, such as Chinese researchers being paid less while being more effective at LLM resea

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  2. OpenHands 官方博客

    什么是 AI 代码生成?工作原理、类型与适用场景(2026)

    OpenHands 官方博客发布指南,解释 AI 代码生成如何用大语言模型从自然语言指令产出源码,并梳理内联补全、对话式生成、智能体整任务生成三种类型。文中引用 Stack Overflow 2025 开发者调查:84% 开发者已使用或计划使用 AI 工具,高于上一年的 76%,51% 的专业开发者每天使用,66% 的人最常抱怨输出“几乎对,但不完全对”。

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    imal direction. It works best inside a sandbox with scoped permissions and human review before merge, since errors compound over long unattended runs and code can carry security flaws. Is AI-generated code safe to ship to production? Only with the same checks you apply to any code. Research keeps finding generated code can be insecure even when it compiles and passes a basic test. Ship safely by keeping human review and running static analysis and dependency scanning in your CI/CD pipeline, with

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