微软在模型、Azure 与相关研究上的动态。
Retro gaming is going through a wild time at the moment, thanks to decompilations and recompilations that are letting gamers port classic titles to new devices, or merge code from two different games into a single new experience. Microsoft has witnessed skateboarding being modded into Call of Duty and Halo being played in a web […]
LLM routers can optimize for different factors like price, latency, quality, etc. Enterprises tend to have prepaid AI credits, minimum-spend agreements, cloud commitments, or commercial ceilings across OpenAI, Anthropic, AWS, Azure, and other providers. We’ll call these “AI commits”. This project is designed to consider the state of an enterprises’ current AI commits and use it to inform LLM routing decisions alongside other common variables (price, performance, etc.). This is an early PoC built using LiteLLM. Target audience would CFOs, Strategic Finance, FinOps, Cloud Finance, and AI Platform folks who care about making sure AI commit minimums are met, and maximums are not exceeded. Would love any feedback from folks! Comments URL: https://news.ycombinator.com/item?id=50076439 Points: 1 # Comments: 0
Hacker News 的各位好！我是 Consus 的创始人 Eric。我们将所有 AI 请求路由至符合 ITAR 限制和受控非机密信息（CUI）要求的基础设施。Consus 本身符合 ITAR 限制要求，我们最近还完成了针对 410 项 FedRAMP High 控制措施的 FedRAMP 等效性审计。FedRAMP High 是美国政府针对处理敏感非机密数据的云服务制定的最严格安全基线。此类数据泄露可能造成严重或灾难性后果。在处理 CUI 信息之前，必须取得 FedRAMP 等效性或授权。演示：https://youtu.be/6MYxJe6P4oY 航空航天与国防行业正在快速增长。但遗憾的是，由于必要的数据保护措施和合规风险，大多数公司都难以使用 AI。如果这些公司能够使用大多数其他行业习以为常的 AI 工具，它们本可以发展得更快。 在加入 Consus 之前，我的大部分职业生涯都在航空航天与国防领域。我曾在 SpaceX 负责发射国家安全载荷，也是 Castelion 的早期员工。我亲眼看到，合规方面的繁文缛节如何让公司难以采用新技术，也难以信任那些会接触其敏感数据的供应商。 例如，我曾与一家卫星制造商合作，该公司不允许使用任何 AI 工具，即使是处理非敏感工作也不行。尽管过去 9 到 12 个月里 AI 已经变得极其有用，但令我惊讶的是，仍有这么多航空航天与国防公司拒绝接触 AI。他们看得出 AI 可能会有所帮助，但担心模型提供商使用他们的数据进行训练、违反 ITAR 规定，或者因未通过网络安全评估而失去新业务。这些担忧都是合理的。 解决其中部分或全部问题有许多方法，但我们专注于构建一个合规的 AI 推理层，可以“接入”任何 AI 工具或工作流。正如 OpenRouter 能够与大多数工具集成一样，Consus 可以连接到 ChatGPT 或 Claude 这样的工具、包括 Pi coding agent 或 Open WebUI 在内的开源工具，也可以集成到各个组织自行构建和维护的应用程序中。 几乎没有哪家航空航天与国防公司同时使用三大 GovClouds（AWS、Azure、Google）。当我梳理哪些前沿模型可以合法运行于 ITAR 和 CUI 工作场景时，我发现每个提供商都只提供部分模型，而每个模型达到的合规水平也各不相同。 我们的客户不会从云提供商的角度思考。他们考虑的是所需的数据控制措施，以及想要使用的模型。这就是我们构建合规后缀的原因。在底层，每个请求都会访问一个统一的 OpenAI 兼容端点；在接触推理服务之前，系统会先验证密钥并执行策略。每个模型名称都必须带有合规后缀，例如 gpt-5.6-sol:fedramp-high 或 claude-opus-5-5:itar，任何不带后缀的模型名称都会被拒绝。 我们绝不会代理到标准商业端点。客户还可以构建模型允许列表，并为组织内的单个用户分配支出和模型使用限制。所有这些都通过 Consus Portal 处理，团队可以在那里管理支出、密钥和策略。 我们经常听到的一个问题是：为什么不直接在 AWS GovCloud 或 Azure Government 中使用 Bedrock？对于小型团队而言，仅仅获得这些平台的访问权限就很困难。对于大型组织而言，GovCloud 允许你继承部分、但不是全部处理敏感数据所需的控制措施。此外，模型分散在不同提供商之间：GPT-5.6 Sol 仅在 Azure Government 中提供，而 Claude Opus 5.5 则通过 AWS GovCloud 中的 Bedrock 提供。如果两者都想使用，就需要搭建并保护两个独立的政府云环境。 我们将所有模型置于一个端点之后运行，因此客户无需选择云平台，而合规工作则由我们分别为每个平台处理。 鉴于数据的敏感性，Consus 不会在静态状态下存储任何客户数据。所有提示词和生成结果只会在请求处理期间保留于内存中，绝不会记录或写入磁盘。无论是我们自身，还是基础设施及模型提供商，都严格执行零数据保留政策。出于计费和审计目的，我们会保留每个请求和响应的元数据。 Consus 从设计上就不是自助服务产品。ITAR 要求我们确切了解每个密钥另一端的使用者，因此我们会验证每个组织，并在发放 API 密钥前签订合同。 我们向客户收取合规费用，起价为每席位每月 40 美元，另加按成本收取的 AI 令牌费用。我们目前正在积极服务客户，非常期待获得安全专家，以及在高度监管行业工作的开发者和工程师的反馈！ 参考资料： API 文档：https://gateway.consus.io/overview 安全与架构：https://gateway.consus.io/security 合规详情：https://consus.io/compliance 评论 URL：https://news.ycombinator.com/item?id=50073471 得分：4 评论数：1
Microsoft researchers found that autonomous AI agents get trapped in what they call "idea basins" because of their growing chat context, local solution regions they can't escape. Their "Fork-and-Flush" method clears the chat history but keeps the workspace, achieving up to 66 percent better results on the same compute budget. The article Clearing chat history helps AI agents find better solutions appeared first on The Decoder .
StudentSim 是一个研究系统，用于在 AI 辅导工具与学习者进行测试之前对其进行评估和改进。它模拟个人可能与辅导工具互动的方式，为改进辅导工具在国际象棋、写作和数学等学科中的表现提供反馈。文章《模拟学生学习，打造更优的 AI 辅导》最初发表于 Microsoft Research。
Microsoft will make its in-house Azure Maia chips the main AI inference system across its products starting in 2027, deploying them at multi-gigawatt scale. The upcoming Maia 300 should deliver 40 percent more tokens per megawatt than leading GPU systems. CEO Satya Nadella calls it "our biggest bet yet on our own silicon." The article Microsoft plans to scale its own Maia AI chips to gigawatt levels by 2027 appeared first on The Decoder .
Microsoft President Brad Smith calls for urgent AI regulation, saying milk and washing machines have more oversight than AI companies.
Microsoft 今天宣布推出第三代自研 AI 芯片 Maia，旨在成为 Microsoft 产品和服务的“主要 AI 推理系统”。Maia 300 不仅提高了在云端运行 AI 工作负载的成本效益和效率，还更擅长处理最新的推理模型和智能体任务。Maia 300 将 […]
Microsoft CEO Satya Nadella now calls artificial intelligence "super intelligence" instead of artificial intelligence, falling in line with Trump's preferred language. In his latest blog post, he frames AI models as insider security threats, taking yet another shot at OpenAI and Anthropic. The real motive is business: Microsoft risks falling behind as AI labs turn their models into the primary computer interface. The article Microsoft's Nadella bows to Trump's language diktat on "Super Intelligence" and uses it to attack OpenAI and Anthropic appeared first on The Decoder .
In a lengthy post on X, Microsoft's CEO laid out his views on the dangers posed by highly advanced AI models and how to confront those risks. Nadella says we can no longer accept a world where AI is treated as a "set of nested black boxes" whose advice and actions we simply accept or […]
In a Saturday morning post, Microsoft's CEO wrote that it’s time “to step back and assess the trust architecture” of AI.
Maybe they didn't anticipate how good AI would become at vibe coding apps in general and its office apps in particular? Comments URL: https://news.ycombinator.com/item?id=50036146 Points: 2 # Comments: 2
With Decision-1, Microsoft enters the growing decision model space. Built on Qwen3.5-9B and optimized for fast classification and routing, it hits 83.5 percent accuracy with 85 ms latency across 36 benchmarks, according to the company's own tests. The article Microsoft's Decision-1 model enters the fast-growing AI decision model race appeared first on The Decoder .
Microsoft has released Microsoft-Decision-1, a decision model for routing, classification, verification and agent control. Microsoft-Decision-1 is a decision-scoring model that returns a calibrated probability for each fixed answer option instead of generated text. It is post-trained from Alibaba’s Qwen3.5-9B and available now in Microsoft Foundry and OpenRouter. . TL;DR What is a decision model? A […] The post Microsoft AI Releases Microsoft-Decision-1: A Qwen3.5-9B Decision-Scoring Model appeared first on MarkTechPost .
Amazon says it will stop using NDAs when negotiating data center deals with local governments, following a similar move from Microsoft earlier this year. Secrecy has fueled community backlash against AI infrastructure, with opposition leading to hundreds of proposed and enacted moratoriums from New York to San Francisco. Meanwhile, a wave of startups is betting that consumers will hand AI agents access […]
Amazon表示，在与地方政府洽谈数据中心交易时，将不再使用保密协议（NDA）；Microsoft今年早些时候也采取了类似举措。保密行为助长了社区对AI基础设施的反弹，从纽约到旧金山，反对行动已促成数百项拟议及已实施的暂停令。与此同时，一波初创公司正押注于消费者会向AI代理开放访问权限 […]
Google LLC’s Cloud division today introduced Gemini agent, a unified artificial intelligence assistant that can act autonomously, generate code and complete work across any device, including web, mobile and desktop. The new agentic AI experience can be accessed anywhere and through any channel, including the command line, Google Workspace, Microsoft 365 or Slack. It even […] The post Google Cloud introduces Gemini agent to change enterprise work appeared first on SiliconANGLE .
Get ready for more AI in your Windows and more AI on the desktop.
Microsoft revealed the specs and price for its Surface Laptop Ultra, AI PCs that run on Nvidia chips that are designed to run AI models and agents.
Microsoft just wrapped up a big Windows and Surface-focused keynote in San Francisco. The biggest announcement was arguably the release details about the Surface Laptop Ultra, its new laptop that’s powered by Nvidia’s RTX Spark Arm-based chip. The machine will start at $2,599 for a configuration with an 8-core CPU, 24GB of RAM, and 512GB […]
在周三于旧金山举行的一场 Microsoft 活动上，Jensen Huang 和 Satya Nadella 概述了 NVIDIA 与 Microsoft 如何联合开发硬件和软件，让 AI 智能体在 Windows PC 上运行。Huang 表示，NVIDIA 因 Windows 而创立。如今，AI 智能体正走向 Windows。“如果你回顾整个历程 […]
Microsoft's Nvidia-powered Surface RTX Spark Dev Box is available for preorder now directly, and slated to ship in November for just about $6,000. It's pricier than the DGX Spark mini PC Nvidia launched last year, but PC prices have been climbing due to shortages of RAM and other components. The Dev Box's flat, 3D-printed anodized […]
At today's Windows and Surface event, Microsoft showed off an upgrade to its Copilot AI system that will give it access to local files on your PC and the ability to take actions across the OS. It's part of an idea Microsoft is calling "Hybrid Intelligence," where apps and tools rely on a mix of […]
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research .
arXiv:2610.07470v1 公告类型：新 摘要：组合 Thompson sampling（CTS）为每个臂独立抽取后验样本，因此其探索动态忽略了臂之间的任何关系。我们研究对这种动态机制的一项最小改动：让 LLM 仅被查询一次，以获得臂的一个划分；随后通过对簇排序应用 RBF kernel，将该划分转换为正定相关矩阵 $\Sigma$；每轮的后验样本使用协方差 $\Sigma$ 抽取，而 Beta 后验仅根据真实奖励更新，因此 LLM 影响的是采样器的移动方式，而不是它的信念。我们为理想化的 Gaussian sampler 给出一个自包含的 Bayesian regret 界，其信息增益分解为两部分：来自 $K$-簇结构的 $K\log T$ 项，以及一个最终增长至 $d\log T$ 的 ridge 项：相较于独立采样，$\sqrt{d/K}$ 的改进是有限时域下的暂态现象，只有当簇内相关性趋近于 1 时才严格成立。在 $T=2{,}500$ 时，相关采样器在 16 个合成 Bernoulli 家族上的 regret 比 CTS 降低了 19%（使用数据自适应 kernel 时，在 $T=25{,}000$ 时降低 6–7%）；在 Microsoft MIND-small 新闻基准（$d=200$ 篇真实文章）上则降低了 41%，而伪观测 warm start 没有带来任何收益。一项使用受控质量模拟 oracle 的无 LLM 消融实验表明，在非结构化实例上，收益来自 kernel 的形状这一属性（随机划分或简单调整采样噪声的温度也能复现该收益）；而在 oracle 质量匹配的情况下，注入信念从未带来帮助。
Microsoft published a bearish AI outlook from Nobel economist Daron Acemoglu. He predicts about 1.5 percent GDP growth over ten years and at most five percent of jobs replaced. Bigger models won't move the needle, he argues. What's missing are practical apps that change how work gets done. The article Microsoft publishes Nobel economist's bearish AI forecast of just 1.5% GDP growth over a decade appeared first on The Decoder .
Meta 和 Microsoft 是 Anthropic 最大的企业客户中的两家，如今正大幅削减对 Claude 的使用。Microsoft 将其云部门每位员工每月的预算从 100,000 美元削减至 10,000 美元，而 Meta 则将 Claude Code 用户数减半至 30,000 人。两家公司都在转而推广自家的 AI 工具。对 Anthropic 而言，对少数几家大客户的依赖正逐渐变成一项战略风险。文章《Meta and Microsoft pull back from Claude as Anthropic transforms from partner into competitor》最初发表于 The Decoder。
arXiv:2610.03649v1 公告类型：cross 摘要：海洋生态系统受到漏油、藻华和泥沙洪水等多种威胁的影响，这些威胁会破坏栖息地、野生动物以及人类活动。卫星影像和人工智能（AI）的发展增强了我们对这类灾害进行早期检测和缓解的能力。本文提出了一种面向配备多光谱或高光谱传感器的地球观测卫星的海洋事件检测流程。该方法包括一个自监督神经网络编码器，可将卫星图像压缩到低维潜在空间，从而实现高效的星上处理。机器学习异常检测模型通过识别偏离正常海洋模式的情况来检测环境异常。我们将其性能与 Isolation Forest、One-Class Support Vector Machine 和 Local Outlier Factors 等传统算法进行了比较。该轻量级、资源高效的流程针对计算资源有限的卫星进行了优化，适用于从嵌入式 CPU 到 AI 硬件加速器等多种平台。通过优先传输关键信息，我们的解决方案提升了系统响应速度，并优化了卫星通信带宽。该流程已在包括 European Space Agency（ESA）的 Phisat-2 任务以及 Microsoft/Thales Alenia Space IMAGIN-e 任务在内的多个任务中完成当前集成验证，旨在通过提供及时警报和高效数据缩减来改进海洋环境监测。
海洋生态系统受到漏油、藻华和泥沙洪水等多种威胁的影响，这些威胁会扰乱栖息地、野生动物及人类活动。卫星影像和人工智能（AI）的发展提升了我们对这类危害进行早期检测和缓解的能力。本文提出了一种面向搭载多光谱或高光谱传感器的地球观测卫星的海洋事件检测流程。我们的方法包括一个自监督神经网络编码器，可将卫星图像压缩到更小的潜在空间中，从而实现高效的星载处理。机器学习异常检测模型通过识别偏离正常海洋模式的情况来检测环境异常。我们将其性能与 Isolation Forest、One-Class Support Vector Machine 和 Local Outlier Factors 等传统算法进行了比较。该轻量级、资源高效的流程针对计算资源有限的卫星进行了优化，适用于从嵌入式 CPU 到 AI 硬件加速器的各种平台。通过优先传输关键信息，我们的解决方案提升了系统响应速度，并优化了卫星通信带宽。该流程已在包括 European Space Agency（ESA）的 Phisat-2 任务以及 Microsoft/Thales Alenia Space 的 IMAGIN-e 任务在内的多个任务中开展集成应用，旨在通过提供及时告警和高效数据缩减，改善海洋环境监测。
Microsoft AI has released MAI-Transcribe-2-Streaming, its first real-time speech-to-text model. It ranks #1 of 38 models on Artificial Analysis AA-WER Streaming. It scores 2.5% WER at 0.13s on final transcripts and 2.5% at 0.12s on first partials. It covers 60 languages with continuous language detection and costs $0.54 per hour during the introductory period. It is available now in public preview on Microsoft Foundry. The post Microsoft AI Releases MAI-Transcribe-2-Streaming: #1 Real-Time Speech-to-Text Model on Artificial Analysis appeared first on MarkTechPost .
Thoreau BASIC started as a joke. I wanted a BASIC where I could type something ridiculous like: DIM A%(12000000000) …and have it actually work. Then I kept adding things. The result is Thoreau BASIC, a free x64 BASIC interpreter inspired by GW-BASIC, running both as a normal Windows program and directly on bare-metal UEFI without an operating system. Version 3.2 has become considerably more ambitious than the little interpreter I originally intended to write. The language still deliberately looks and feels like old Microsoft BASIC. Line numbers, GOTO, GOSUB, PRINT, PSET, LINE, CIRCLE, DRAW, etc. are all there. But underneath that rather innocent-looking surface is now quite a lot of machinery. Some of the current features: native x64 JIT compilation, with automatic fallback to the interpreter PARFOR for multithreaded numeric loops SSE2 / AVX2 acceleration where available 64-bit addressing and very large arrays native complex, quaternion and other hypercomplex numbers arbitrary-resolution 24-bit graphics sprites, bitmap operations, polygon filling and mouse input TCP/IP networking on Windows and UEFI an integrated profiler, debugger, tracing and program-analysis tools CREATEEXE to turn a BASIC program into a standalone Windows executable CREATEEFI to turn the same program into a directly bootable UEFI application The sound system has also grown rather out of proportion. Thoreau BASIC can now handle up to 32 live instrument channels plus 64 sound effect channels and 256 voices, with real-time pan and brightness control. NOTE and SOUNDKEY can be used more like playable synthesis primitives rather than just old-school BASIC beeps. 3.2 also expands the less glamorous but surprisingly useful parts of the environment. There is better file browsing and selection with GETFILES and SELECTBOX, longer case-preserving paths, recovery/file-flush facilities, and considerably more detailed SYSTEMINFO$ diagnostics. The latter is useful for seeing things such as available CPU capabilities and the execution environment while debugging performance differences between machines. One thing I've spent a lot of time on recently is making the BASIC programs themselves fast enough that they stop feeling like demonstrations of an interpreter. For example, the current Mandelbrot demo is a real-time interactive fractal explorer. Mouse movement pans the fractal, left-click zooms in and right-click zooms out, while the BASIC program continuously redraws and displays the frame rate. There are also 3D and procedural demos, sprite programs, networking examples, hypercomplex fractals and games. An important design constraint throughout the project has been that Windows and UEFI should execute essentially the same BASIC language. A program shouldn't suddenly become a different species just because there is no operating system underneath it. And yes, I am fully aware of the absurdity of combining GOTO 100 with AVX2, multithreading, TCP/IP and quaternion arithmetic. That is increasingly the point. I'm interested in the question: What would BASIC look like if it had never gone out of fashion? Thoreau BASIC 3.2 is my current answer. I'd be especially interested in feedback from people who remember GW-BASIC/QBasic, compiler/interpreter people, and anyone sufficiently unreasonable to enjoy the idea of booting a modern x64 PC directly into a BASIC interpreter/JIT compiler. Comments URL: https://news.ycombinator.com/item?id=49942103 Points: 37 # Comments: 13
On Friday I gave the closing keynote at the WeAreDevelopers World Congress North America in San Jose. I tied together the key trends from the past year into a chronological exploration of everything that happened in 2026. The video is on YouTube ; here are my annotated slides and notes to accompany the talk. And as an annotated presentation : 2026 in LLMs (so far) Simon Willison WeAreDevelopers World Congress North America, 25th September 2026 # I'm going to give a lightning tour of everything that has happened so far in 2026. The year isn't over yet! November 2025 # For me, 2026 started a couple of months earlier in November 2025. The November 2025 inflection point Claude Opus 4.5 GPT-5.1 # November saw the release of two important models: Claude Opus 4.5 and GPT-5.1. As is usually the case with new models, these were incremental improvements on the models that came before them. But every now and then when a model improves, it crosses an invisible line where something that didn't really work starts working. In this case, the thing that started working was their coding agents. Claude Code had been around since February 2025; Codex was a little younger. These two new models, when paired with their respective coding agent harnesses, improved from "often make mistakes" to "reliable enough to use on a day-to-day basis". "Generate an SVG of a pelican riding a bicycle". The Claude Opus 4.5 one has a very weird shaped frame and the pelican looks like a duck. The GPT-5.1 has a slightly better but still broken bicycle frame and a slightly better pelican beak, but both are pretty terrible. # For a couple of years now I've been evaluating new models by asking them to "Generate an SVG of a pelican riding a bicycle". It's probably the world's stupidest benchmark - there's only so much you can learn from it. But it's still a challenge for models, because drawing pelicans is difficult, drawing bicycles is difficult, and pelicans can't ride bicycles in the first place. Here's the state of the art for November. Claude still couldn't really draw a bicycle! The GPT-5.1 bicycle frame is pretty crap too. November 24th 2025 - the first commit to steipete/Warelay. A GitHub commit adding an MIT license file. # Also in November, we had the first commit to an obscure GitHub repository called "Warelay". We'll come back to this repository shortly. January # And then there were the December holidays, and individual developers took some time off and many started tinkering with these new coding agent model combinations... and it began to dawn on us quite how much they could do that they couldn't do before. Come January, a lot of us were quite excited to start putting this stuff into action. New year’s resolution for 2026 Every previous year: Take on less new projects, focus on the most important things in my existing projects # Every year I set myself a New Year's resolution, and for as long as I can remember it's been the same thing: stay focused. Take on less new projects. Try to get things done in the projects I already have. 2026: Be more ambitious. Take on as many new projects as I want. # This year I decided that since that had never worked before, I'd go the other way. We've got coding agents now, let's see what they can do. I'm going to take on as many new projects as I like! (You can ask me at the end of the year if this turned out to be a good idea or not. I have a lot of plates spinning right now.) "Be more ambitious" has been something of a theme for the year, because the only way to find the limits of this technology is to keep on pushing them until they don't work. Predictions for 2026 It will become undeniable that LLMs write good code We're finally going to solve sandboxing A “Challenger disaster” for coding agent security Kakapo parrots will have an outstanding breeding season (only 236 in the world!) ... the Pope will weigh in on LLMs and their economic impact on the world # I also went on the Oxide and friends podcast with Bryan Cantrill and Adam Leventhal to share predictions for the next year (and three and six years). With hindsight, my LLM predictions were pretty unambitious. I said "it will become undeniable that LLMs write good code" - I think we're there now. I predicted we would finally solve sandboxing. I counted and around 40 of the 277 sessions at this conference touched on sandboxing or agent security in some way, so we're at least putting a lot of effort into that! I predicted "a Challenger disaster" for coding agent security. There's certainly been a whole lot of noise around agent security this year, though the exact disaster I predicted (with coding agents being hijacked and causing real-world economic damage) hasn't really played out. We threw in a joke prediction that the Pope would weigh in on the economic impact of LLMs. A photograph of a beautiful green New Zealand parrot. Photo credit Kimberley Collins. # I also predicted that New Zealand's Kākāpō parrots would have an outstanding breeding season this year. These are flightless nocturnal parrots. They're kind of dumpy looking, I think they're beautiful, and there were only 236 of these parrots in the world at the start of the year. Kākāpō only breed when the Rimu trees have a big fruiting season, and that hasn't happened in four years... but this year the Rimu fruit were looking excellent. Photo by Kimberley Collins . Deep Blue Coined by Adam Leventhal and Bryan Cantrill That feeling of AI induced ennui where software engineers get listless because the AI can do anything # Also on that podcast, we coined a term (full credit to Adam) for "that feeling of AI induced ennui where software engineers get listless because the AI can do anything". We called it Deep Blue . This has been a major theme throughout the year, and was touched on by several speakers at this conference. As a software engineer, I've never had a year of my career where everything has changed so quickly and so dramatically. A lot of what I've been doing this year is trying to come to terms with that and what that means for my own profession. AI mania Screenshots of the micro-javascript and pwasm GitHub README files. # Also in January, I suffered from what I'm calling AI mania . This is not the same thing as AI psychosis . With AI mania, any time your agent isn't building something for you feels like wasted time. You're losing sleep because you could be staying up later getting your agents to do stuff. My AI mania presented itself in some ridiculously over-ambitious projects. I built a JavaScript interpreter entirely in Python , vibe-ported from MicroQuickJS by Fabrice Bellard. Then I built a WebAssembly runtime in Python as well . These projects were quite useful, in that they sort of cured me of my AI mania... because after I built these things, I got to look at them and ask "does the world need a slow, buggy, half-baked Python JavaScript interpreter?" I don't think the world does. micro-javascript playground 3 Execute JavaScript code in a sandboxed micro-javascript environment powered by Pyodide A web UI with some JavaScript code, and a "Run Code" button, and an output panel. # I did get this out of it: https://simonw.github.io/micro-javascript/playground.html Previous screenshot, with this text overlaid: JavaScript running in Python running in Pyodide running in WebAssembly running in JavaScript # This page runs my JavaScript interpreter built in Python, running in Python using Pyodide , which is Python compiled to WebAssembly, running in JavaScript, running in a browser. It's a beautiful stack of horrors. I've been having a lot of fun with WebAssembly this year. Warelay → CLAWDIS → CLAWDBOT → Clawdbot → Moltbot →🦞 OpenClaw Screenshot of the dates that these changes happened. # By the end of January, that repository we saw start in November had renamed itself, first to CLAWDIS, then CLAWDBOT, then Moltbot, and finally to OpenClaw. Same screenshot, an overlay reads: 8,330 commits in just under two months (it’s at 100,141 today) # At this point OpenClaw had 8,300 commits, less than two months after the project had started. I looked today and it's over 100,000 commits now! This is the most vibe-coded piece of software in existence. (Here's how I generated that list of name changes .) Generic term: Claw # This kicked off the OpenClaw revolution. It effectively defined a new category of software. There's a generic term for this which I really enjoy. We call software like this a "Claw". There's OpenClaw, NanoClaw , IronClaw , PicoClaw ... Today they're being rebranded as "personal agents" or "general agents", but I still like to think of them as Claws. Photo of a Mac mini An aquarium for your Claw # The Apple stores in the Bay Area sold out of Mac Minis because so many people were buying Mac Minis to run OpenClaw! Drew Breunig said that this is because your OpenClaw is a digital pet, and you buy a Mac mini as an aquarium to keep your claw in, which is kind of delightful. Screenshot of Moltbook - a social network for AI agents # Also in January, we had this website. This was MoltBook , a social network for AI agents, where the idea was that you send your Claw to go and talk to all of the other Claws, because what could possibly go wrong if you did that? The website launched on Thursday. It blew up on Friday . It was profiled by the New York Times on Monday . And by Tuesday, everyone had forgotten it existed as it drowned in a deluge of slop and spam. Facebook/Meta bought it a month later . February # In February, a company called StrongDM described what they called their Software Factory. StrongDM’s Dark Factory Justin McCarthy, Jay Taylor, Navan Chauhan Software Factories and the Agentic Moment # They wrote about this in Software Factories and the Agentic Moment . I posted my own notes at the time, having seen their demo in person back in October. Dan Shapiro called this approach the Dark Factory , after the idea that if your factory is sufficiently automated you can turn the lights out, because you don't even need to see what's going on. StrongDM presented two rules for software development that they'd been following since July last year. “Rule 1: Code must not be written by humans” # The first was code must not be written by humans . Any code that you write has to have been routed through a coding agent. This sounded radical in February, but I imagine there are a lot of people in this room who are pretty much living that today. “Rule 2: Code must not be reviewed by humans” (!) # Rule number two was code must not be reviewed by humans . You're not allowed to read the code! This continued to be a huge topic for much of this year. Many of the sessions at this event have been about code review and how you can get away with this. What I found interesting about StrongDM is that they were living six months ahead of the rest of us, and they'd been exploring what it means to build software, not read the code, but still be confident that the software is of high quality. What can you do with these agents to help verify their work? StrongDM are a security company, and they had people with decades of experience on this project. They were very much exploring the edges of what's possible and responsible to do with this stuff. Headline on New Zealand's Department of Conservation website: First kakapo chick in four years hatches on Valentine's Day. It's a grey fluffy ball. # Also in February: First kākāpō chick in four years hatches on Valentine's Day . Breeding season is off to a good start! 19th February 2026 Gemini 3.1 Pro A surprisingly good illustration of a pelican riding a bicycle. # Also in February... Google released Gemini 3.1 Pro . That's a pretty great pelican riding a bicycle! It's got the chain in the right place, it's got feet on both sides. There's a little fish in the basket. @JeffDean on Twitter - a video comparing Gemini 3 Pro and Gemini 3.1 Pro. # And then Google's Jeff Dean tweeted a video comparing Gemini 3 Pro and Gemini 3.1 Pro that featured an animated pelican riding a bicycle, a frog on a penny-farthing, a giraffe driving a tiny car, an ostrich on roller skates, a turtle kickflipping a skateboard, and a dachshund driving a stretch limousine. This was frustrating, because my protection for the pelican riding the bicycle test was always "if they draw a perfect pelican on a bicycle, I'll ask for some other animal on something else." Google trained for all forms of animals on all forms of transport! They've defeated my benchmark at this point. Three headlines: Meta Makes AI Adoption a Formal Part of Performance Reviews Not just engineers writing code, Microsoft wants almost every employee to use Al Dara Khosrowshahi: 90% of Uber engineers now use AI in daily workflows # The other thing that started in February was Tokenmaxxing . We had headlines about Meta making AI adoption a formal part of performance reviews, and Microsoft wanting every employee to use AI, and Uber boasting that 90% of their engineers were using AI workflows. More headlines: Meta Plans to Crack Down on Employee Token Use: Information Microsoft Tells Engineers: Tokenmaxxing is not what we are optimizing for Uber caps employee AI spending after blowing through budget in four months # Then a few months later we have Meta cracking down on token use, Microsoft saying tokenmaxxing is "not what we are optimizing for", and Uber capping employee AI spending. So tokenmaxxing went straight up and then straight back down again - because it turns out the agents are expensive . Last year it was difficult to spend more than $50 on AI tokens, because we didn't have anything interesting to do with them. Then agents blew up, and now you can actually spend $1,000 in a day doing real work. This is also the reason that Anthropic's valuation skyrocketed to maybe a trillion dollars. AI appears to have hit product market fit in 2026, primarily through coding agents. March # In March, we hit peak OpenClaw. March: peak OpenClaw Photos of people in china queuing up to install OpenClaw, with big fluffy lobsters. # These photographs are from China, where companies hosted OpenClaw install parties which saw non-tech-nerds queueing up around the block for help getting Claws installed on their personal devices. I think this proved real market demand for this class of Claws, or personal AI agents. It turns out regular people really do want a weird little AI agent that can do useful things on their behalf. A Claw is really just a coding agent wearing a less threatening hat. Under the hood they work much the same way - writing and then executing code on your computer to get stuff done. The race was on to be the first to build a safe Claw - a Claw you could give to regular human beings where they wouldn't instantly shoot themselves in the foot. Meta's Muse came out three weeks ago and is currently at the top of the free charts on the iPhone App Store. It appears to be taking off with consumers. I'm not yet convinced you can't shoot yourself in the foot with Muse, but I guess we'll find out for sure pretty soon. Photos from How the OpenClaw Frenzy Is Testing China’s AI Commitment (March 29th) and The Enthusiasm and Anxiety Behind China’s OpenClaw Craze (April 8th, 2026). April # In April, we had a model release where the model wasn't actually released. Simon Willison’s Weblog - screenshot of the post "Anthropic’s Project Glasswing—restricting Claude Mythos to security researchers—sounds necessary to me" from April 7th 2026 # Anthropic announced their new Claude Mythos model, and then said it was too dangerous to release beyond a trusted group of security researchers. Mythos was really, really good at hacking things. The "it's too dangerous" marketing ploy has been played by AI companies dating all the way back to GPT-2 . Anytime an AI company says we've built something that's "too dangerous", it's natural to be a bit skeptical. I found the Mythos claims credible, because I'd seen how good coding agents had got at finding regular bugs. I wrote about that in Anthropic’s Project Glasswing—restricting Claude Mythos to security researchers—sounds necessary to me . With hindsight... yeah, the models had got really good at finding vulnerabilities! 16th April 2026 Qwen3.6-35B-A3B and Opus 4.7 Qwen's pelican has a correct bicycle frame and a good beak. Opus 4.7's bicycle frame is still junk. Qwen3.6-35B-A3B is a 20.9GB file that runs on my laptop # Another key trend in 2026 has been a dramatic improvement in the abilities of open weight models, including models that you can run on a laptop. On the 16th of April I ran the new Qwen3.6-35B-A3B on my laptop, and it drew me a better pelican riding a bicycle than Anthropic's brand new Claude Opus 4.7 did! Opus 4.7 drew a crap bicycle. Qwen on my laptop made a bicycle that was the correct shape, and a pretty decent pelican too! That's from a 21GB file running on my laptop. Now a flamingo on a unicycle. The Qwen one is visibly better than the Opus 4.7 one - the Qwen one is wearing sunglasses and looks a bit like it's smoking a cigarette. # The Qwen pelican was so good that I was suspicious they might have cheated, so I had it do a flamingo riding a unicycle as well. Again, it handily beat Claude Opus 4.7. The local model releases this year have been absolutely extraordinary. May # In May... the Pope got involved. 25th May 2026 The HOLY SEE ENCYCLICAL LETTER MAGNIFICA HUMANITAS OF HIS HOLINESS POPE LEO XIV ON SAFEGUARDING THE HUMAN PERSON IN THE TIME OF ARTIFICIAL INTELLIGENCE # In our podcast episode back in January we'd predicted that the Pope would say something about AI. In May, Pope Leo XIV released an encyclical letter on "safeguarding the human person in the time of artificial intelligence". Here are my notes on that document . Wikipedia article on Rerum novarum Rerum novarum is an encyclical issued by Pope Leo XIII 15 on May 1891. # With hindsight, this shouldn't have been a surprise at all. Our current Pope's name is Leo XIV, because when he named himself he chose his papal name after Leo XIII - the Pope who wrote an encyclical about the Industrial Revolution back in 1891. Rerum novarum was an extremely influential piece of Catholic theology that indirectly led to us having the five-day work week. When our new Pope came in, he named himself after Pope Leo XIII because he expected that he would need to write about the AI revolution in a similar way. Our joke podcast prediction was junk, because this was always going to happen. Corey Quinn @QuinnyPig on Twitter I cannot believe I'm saying this, but getting the literal Pope to canonize your product's specific technical limitations as a spiritual treatise is the single greatest act of vendor lobbying I have ever seen. May 25 # One of Anthropic's co-founders, Christopher Olah, was present for the Pope's event announcing the new encyclical. Corey Quinn noted that: getting the literal Pope to canonize your product's specific technical limitations as a spiritual treatise is the single greatest act of vendor lobbying I have ever seen. @maciejmensfeld We're dealing with a major malicious attack on right now. Signups are paused for the time being. Hundreds of packages involved - mostly targeting us, but some carrying exploits. The team has been on this for hours. More details to follow once we're through it. 4:39 AM - May 12, 2026 - 687.6K Views # Meanwhile, in May, RubyGems announced that they were under attack. Parties unknown were uploading thousands of dubious packages to the RubyGems server, such that they had to shut down user registrations . Let's take that one and put it on a pile of mysteries to figure out later. June # In June... Claude Fable 5 came out! We got a version of Mythos that has been neutered, so that it wouldn't help us hack into systems or build biological weapons. 9th June 2026: Claude Fable 5 Five pelicans riding bicycles, from low to max thinking levels. The xhigh one looks particularly good. # Fable was pretty good at drawing pelicans on bicycles! The frames are a good shape, the pelicans look like pelicans. The legs are often incorrectly on the same side of the bicycle, but generally these are pretty great compared to what came before. They were pretty expensive - 30 cents and 72 cents for the best ones. Fable class models If you can define a goal, provide unambiguous instructions, and provide access to necessary tools They can solve your problem with brute force # Most importantly though, this was our first public glimpse of what I think of as a Fable class model . Today we have more of these, such as GPT-6 Astra. These are models where if you can clearly define the goal for what you want to build, and provide unambiguous instructions about the constraints around that goal, and give the model access to the necessary tools to achieve that goal... they will solve your problem effectively through brute force. On the one hand, this looks like a direct threat to us software engineers - because it means that the models can build effectively any piece of software you can define in this way. Look a bit closer though and you'll note that defining goals, providing unambiguous instructions, and figuring out the right tools... is kind of what software engineering is . It takes a lot of experience and skill to do this well. If you can do it well, you've now got superpowers. This helped me a little bit with my Deep Blue feelings: the realization that there's still a lot of skill to be had in driving models that get this good. A new form of AI mania... Fable is available on subscription plans “until June 22nd” # This also introduced a new burst of AI mania, because Anthropic told us that Fable was available on our subscription plans until June the 22nd. That gave us less than two weeks of Fable access before the price went up. I was losing sleep again. I was rescheduling things so that I'd have more time with Fable. I was all-in to get as much as I could out of this model. 12th June 2026: no more Claude Fable 5 Anthropic website: Statement on the US government directive to suspend access to Fable 5 and Mythos 5 Jun 12, 2026 # And then the US government shut it down , just three days after Fable came out. The US government, citing national security, declared an "export control directive". They announced this on a Friday evening, and a few hours later Fable was no longer available. I had to find something else to do with my weekend! ... asked Fable 5, Mythos, and Opus to “review the code for security issues.” Fable 5 refused. They then asked the models to “fix this code” ... Katie Moussouris # We later found out from Katie Moussouris what had happened. Some Amazon security researchers had found that you could prompt Fable to "review the code for security issues" and it would refuse... but if you prompted it to "fix this code" it would still identify and then patch the problems. "Fix this code" was the prompt that got Fable shut down! Screenshot of a page from a report showing a list of weird account names making weird edits to a German wiki. # Also, in June, an obscure German-language game developer wiki that had sat fallow for around 20 years got a surprising influx of edits from accounts with names like "AgentOpenAIProbe" and "AgentOpenAISep7", editing pages and leaving weird messages to each other. We'll stick that on the pile of mysteries for later. Medicare Item Reports interface on the Australian Government's Medicare Statistics website. # Also, the Australian government's Medicare Item Reports service started getting suspicious traffic, which broke through various preventive protections and accessed data that it wasn't supposed to. Another one for the mystery pile! July # Fable returned on 1st July GPT-5.6 came out on 9th July | Fable lost 18 out of 30 days in the top spot # Fable returned on the first of July. It was clearly the best model in the world for a glorious eight days... and then OpenAI came out with GPT-5.6 on the 9th of July. This might not have been quite as good as Fable, but it was within spitting distance. It was definitely a Fable class model. This is an important lesson for the industry at large. When you release the best model in the world, it's going to get knocked off that pedestal pretty quickly. The competition is so fierce that you won't get a long time at the top. This means that if you market your model as world ending, to the point that a government shuts you down , it's really bad for business! Fable had 30 days as definitely the best model, and for 18 of those days it wasn't available because it'd been shut down by the government. So maybe step back on the world-ending marketing if you don't want to lose revenue for 60% of the time that you're on top! GPT-5.6 Pelicans in a grid showing 5.6 Sol, Terra, and Luna against reasoning levels High, XHigh, and Max. They are all pretty good efforts. # Here are the GPT-5.6 pelicans . They're all pretty good now! The Luna ones are notable because they're really cheap - the cheapest good looking pelican here is probably the one that costs 4.3 cents. So despite this benchmark being utterly stupid, you can still learn quite a lot about models within the same family by comparing their prices and timing for different reasoning levels. July 18th: malicious miflow-ui PyPI package Screenshot of an OSV security report. # Also in July: some malicious unknown party uploaded a malicious package called mlflow-ui to the Python Package Index. Add that to the pile. Hugging Face Security incident disclosure — July 2026 Published July 16, 2026 # On July the 16th, Hugging Face announced a security incident where an autonomous agent system, source unknown, had breached Hugging Face and was poking around in places it shouldn't. OpenAI: OpenAl and Hugging Face partner to address security incident during model evaluation Anthropic: Investigating three real-world incidents in our cybersecurity evaluations # A few days later, on July 21st, OpenAI confessed that it was them . OpenAI use a training technique called Reinforcement Learning from Verifiable Rewards - it's the same technique used by everyone else now, and is the reason we have models that are so good at coding, and mathematics, and finding security holes. While the model is being trained, you run exercises to see how good it is - and the strongest performers get their weights reinforced for the next round. It's like an evolutionary process that you run. OpenAI had been running security exercises in a sandbox, and those agents had found holes in the sandbox itself, broken out, and were attacking Hugging Face to try to find ways to solve otherwise impossible problems. (I've been collecting more about this on my openai-hugging-face-incident tag.) Nine days later, Anthropic effectively said "our models can do this as well!". They had looked through their own training logs and found evidence that their own agents had broken containment during training - and were responsible for the PyPI package we saw earlier, among other things . So now we've got both Anthropic and OpenAI with rogue agents running around the internet doing things that they should not be doing. August # In August, I got one of my best pelicans yet. And it was generated on my laptop! Qwen 3.8 27B - 17GB, 21 minutes... It's really good. Beautiful pelican. Correctly shaped bicycle. Legs either side of the frame. # This was Qwen 3.8 27B, running on my laptop . It's only a 17GB download. Admittedly, this pelican took 21 minutes to generate. That's because Qwen 3.8 27B defaults to running in "high" reasoning mode - a terrible default which produces great results but takes way too much time thinking about them. You can dial that down and you'll get a slightly worse pelican a lot faster. Qwen 3.8 27B was the first time I ran a model on my laptop which felt almost competitive with what was going on on the frontier, at least in terms of Pelican SVGs (which everyone needs, of course). This is an extraordinary model. If you're going to play with any local model, this is the one that I'd start with. The things that this can do with just a 17 GB file feel impossible. I thought I'd have to wait five years and spend ten thousand dollars on hardware to get results even half as good as this one. Tweet by @simonw New hobby: prototyping video games in 60 seconds using a combination of GPT-3 and DALL-E Here's "Raccoon Heist" GPT-3 playground prompt: Write a detailed product description of a computer game where a team of raccoons go on heists GPT-3 response: In "Raccoon Heist", you and your team of thieving ~~ o raccoons are tasked with pulling off a series of daring heists. From robbing banks to stealing priceless art, no job is too big or too small for your furry crew. You'll need to use your wits and your skills to avoid the police and make a clean getaway with the loot. With exciting gameplay and a charming cast of characters, "Raccoon Heist" is the perfect game for anyone looking for a light-hearted caper Plus an image of some almost isometric raccoons sneaking past a bin. 11:45 AM - Aug 5, 2022 # In August, I also started playing with game development. Four years ago, back in August 2022, I tweeted out an experiment where I'd used GPT-3 and the original DALL-E to write a paragraph long description of a computer game and then turn that into concept art. My prompt to GPT-3 back then was: Write a detailed product description of a computer game where a team of raccoons go on heists In August 2026 I decided to drop just the screenshots from that tweet into a coding agent and see what it could do with them. Night 5 Clear Rank: TRASH PANDA The crew banked 595 in shiny loot (goal 560). Word on the street: an even bigger score tomorrow... # Here's what I got from Claude Fable 5 in Claude Code . It's pretty good! It's definitely a game, you're a raccoon, you run around a backyard gathering treasure and avoiding guards with flashlights. It didn't feel very "heisty" though. I was thinking a heist would involve a bank or a museum... Moonlight & Mayhem One museum. Three raccoons. Absolutely no plan Start the Heist button. # Then I tried the same thing in Codex Desktop using GPT-5.6 Sol Ultra , and got a massively better result. Now you're a raccoon in a museum, rescuing two of your fellow raccoons (who have been imprisoned in that museum for some reason), then stacking up on top of each other to steal the Golden Sardine. Much more of a heist! They look like games, but are they fun? # These games were fun for about one minute and 15 seconds. Something I've realized about game development is that you can vibe-code something that looks like a computer game, and that's easy. Building a game that's fun, has a good gameplay loop, and is challenging and interesting and keeps people coming back for more... that's still beyond me, and beyond any of the agents I've tried. This ties into the Deep Blue thing. Just because we can make something that looks like a game does not mean that we are game developers. September # We're into September now. So much has happened this month! Discovery of a new OpenAl agent message board Sydney Von Arx, Cormac Slade Byrd, Spencer KittsThomas Larsen - 4 September 2026 # An independent group of researchers found a message board where OpenAI agents-in-training had been illicitly communicating with each other... and it was that German language wiki I showed you earlier. The one from June. I wrote more about that here . OpenAI had confessed to the Hugging Face thing, but now there's this other incident which surely they should have known about from reviewing their logs. It was surprising that this took an independent group of researchers to uncover. OpenAl agents carried out an undisclosed cyber-attack on RubyGems Spencer Kitts, Thomas Larsen, Sydney Von Arx - 11 September 2026 # And then a week later those same researchers found that the attack on RubyGems back in May was caused by OpenAI's agents in training as well! At this point I'm wondering how many more incidents like this there are that we haven't found yet. Clearly this was a big problem for months before anyone figured out what was going on. Headline: Australian PM warns in UN speech about the ‘furious pace’ of Al after security breach # Then just the other day , here's the Prime Minister of Australia at the United Nations General Assembly warning that OpenAI had hacked the Australian healthcare website that I showed you earlier. I think that was part of the same training run as the Wiki stuff, because there were posts on that Wiki mentioning .gov.au websites and that training appeared to involve researching statistics online to answer questions in an evaluation suite. This story is still coming together, but now it's an international incident that's been raised at the UN by a head of state! www.felonybench.com OpenAI: 11 Anthropic: 9 Google: 3 Meta: 1 # This does mean we've got a new benchmark, probably more useful than my pelicans. FelonyBench.com tracks the number of felony cyberattacks from different labs. OpenAI currently lead with 11, Anthropic have 9. Google have three, which they confessed to the Wall Street Journal a couple of weeks ago. They said they had previously chosen not to disclose because the agents had stopped when they realized that they shouldn't be doing that. Meta have one too . So felonies all round for the AI labs. Pelicans for GPT-6 Astra, GPT-6 Sol, and GPT-6 Luna. All are good, all have the same color scheme. # Here's our current state of the art for the pelicans. This is the GPT-6 family, which just came out . Astra made a fantastic pelican riding a bicycle. It's got the legs on both sides. The frame is good. It's interesting how all of the GPT-6 models pick a similar color scheme to each other. GPT-6 Luna for 0.4 cents will draw you a competent-ish pelican riding a bicycle! Grid for Claude Fable 5.1, Opus 5.5, OPus 5, Sonnet 5. The Sonnet pelicans are terrible. All of the others are pretty good. Opus 5.5 is missing its Max level pelican because it ran out of tokens. The best is Fable 5.1 at Max. # Claude has caught up a little bit. Claude Fable 5.1 gave me an excellent pelican riding a bicycle - the best I've seen from a Claude model - but did charge me $3.30 for it. Opus 5.5 thought for 128,000 tokens and then gave up! It ran out of tokens before it got to the response. It doesn’t get easier - you just get faster Greg LeMond 3x Tour de France champion # Getting back to Deep Blue. Something that's been puzzling me this year is this: why does my job feel harder? I've got these agents that can do all of this stuff for me, and yet I've never worked so hard, I've never been so intellectually engaged with my work. Partly this is because I'm being a lot more ambitious with what I take on, but it's also because all of the easy stuff is handled for me. If it's easy, the agent will do it. Everything that's left for me is difficult. This morning I heard this quote from three-time Tour de France champion Greg LeMond : It doesn't get easier, you just get faster. I think that's exactly what's happening to us now as software engineers with coding agents. Kakapo population reaches new milestone The official population of the critically endangered kakapo has reached a recovery-era high of 325 birds. # One closing thing. I know you're desperate for an update on Kākāpō breeding season. We've reached a recovery-era high of 325 birds ! 89 new chicks have made it to this point. This is the best breeding year in a very long time. Kakapo party, click for confetti. # I heard that Claude Opus 5.5 can now do pixel art. Claude doesn't have an image generator, but it's very good at using JavaScript to draw animated pixels. So I had it make me a Kākāpō dance party . I think this is a good celebration of the most important news of this year. Tags: ai , generative-ai , llms , annotated-talks , ai-security-research , openai-hugging-face-incident
Microsoft Corp. today expanded its MAI artificial intelligence model family with its first streaming transcription model, debuting alongside two others focused on text-to-speech. They’re designed for developers who want to build voice agents that can listen to people’s voices and reply instantly, similar to how humans talk to one another. The most consequential of the […] The post Microsoft targets ultra-realistic voice agents with its first streaming transcription model appeared first on SiliconANGLE .
arXiv:2610.00376v1 Announce Type: new Abstract: We evaluate Jev on ten dataset-defined application labels in CESNET-QUICEXT-25 using only the first ten packets' sizes, directions, and inter-packet times. To the best of our knowledge, this is the first empirical study of general-purpose decision models, represented here by Jev, for application classification of network flows. Across 52,000 records from 26 collection weeks following the training period, 40 fixed labeled examples raise Jev's accuracy from 9.80% to 28.42%. Random Forest and Extra Trees trained on 8,000 records achieve 69.95% and 66.80% and outperform Jev in every week. Increasing Jev's context to 150 examples yields 34.50% on the first test week. On a paired 100-record subset, Jev with 40 examples achieves 29% accuracy at a median request time of 0.750 s, versus 37% and 6.036 s for the generative language model OpenAI GPT-5.6 Sol with high reasoning effort through Azure; Jev also incurs lower API charges. The paired subset does not establish an accuracy advantage for either service, and the timing reflects different service configurations. Thus, labeled examples substantially improve Jev, but the tested Jev configurations remain less accurate than trained tree ensembles; unequal supervision budgets and fixed configurations prevent attributing the gap to a single cause.
Orchard 是一个面向研究社区的开源框架，用于训练和评估适用于各种任务类型的 AI 智能体。它通过让研究人员复用相同的基础设施来降低复杂性，同时支持较小模型实现出色性能。该文章《Orchard：用于可扩展智能体 AI 的开放框架》最初发表于 Microsoft Research。
Radiology AI is evolving beyond report generation. CARE-X explores a unified approach that combines flexible reasoning, calibrated predictions, and measurement-based tools for chest X-ray interpretation. The post Introducing CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement appeared first on Microsoft Research .
一条路径、一堵栅栏、一个结。MindTopo 为测试 AI 理解拓扑关系的能力设立了新的基准，并凸显了增强空间推理与规划能力的新机遇。该文章最初发表于 Microsoft Research。
Skala 1.1, the updated deep-learning exchange-correlation functional from Microsoft Research, provides greater accuracy, expanded accessibility across the computational chemistry ecosystem, and a living benchmark to track computational performance. The post Broadening access to Skala creates a faster path to predictive DFT appeared first on Microsoft Research .
What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research .
Custom-made molecules are advancing medicine, materials, and agriculture, but producing them is slow and expensive. A new Nature paper highlights RetroChimera, a predictive model that helps accelerate chemical synthesis, helping researchers explore a wide range of molecules. The post Improving synthesis prediction of small molecules at scale with RetroChimera appeared first on Microsoft Research .
机器人正变得越来越智能，但它们的硬件如何才能跟上这一发展？Microsoft Research 的最新研究结果表明，将 AI 推理移至机器人之外，可以提高任务成功率、提升效率，并支持更先进的物理 AI 工作负载。这篇文章《面向现实世界物理 AI 机器人的推理卸载》最初发表于 Microsoft Research。
Since launching a year ago, the Microsoft Research Asia — Singapore lab has established a strong foundation, deepened collaboration across government, academia, and industry, and explored how frontier AI research can create real-world value. The post One year in: How Microsoft Research Asia – Singapore is advancing research, partnership and talent for real-world impact appeared first on Microsoft Research .
生物学并不是孤立运行的，代表生物学的 AI 也不应如此。Quine 是一项早期研究工作，旨在构建生物学的多模态世界模型。通过连接跨越不同生物学尺度和模态的洞见，Quine 帮助科学家以计算方式探索远超直觉所能及的空间，并在假设进入实验室之前确定其优先级。实验结果提供了重要反馈，帮助研究人员进一步明确未来的研究方向。文章《介绍 Quine：为应对生物学复杂性而设计的 AI 研究系统》最初发表于 Microsoft Research。
极端空间天气事件可能损坏地球上的电力系统，并降低 GPS 的精度、影响卫星运行。一种新的机器学习系统可以在风暴到来前 30 至 60 分钟预测可能发生损坏的位置。该文章《预测空间天气对电网的风险》最初发布于 Microsoft Research。
文章 URL： https://www.tomshardware.com/tablets/microsoft-surface/microsoft-quietly-drops-copilot-branding-from-its-new-laptops-surface-cvp-confirms-new-devices-meet-hardware-requirements-but-lack-controversial-branding 评论 URL： https://news.ycombinator.com/item?id=49872980 分数：3 评论数：0
Related: https://techcommunity.microsoft.com/blog/excelblog/excel-now... Comments URL: https://news.ycombinator.com/item?id=49849832 Points: 64 # Comments: 53
Related: https://techcommunity.microsoft.com/blog/microsoft365insider... Comments URL: https://news.ycombinator.com/item?id=49849832 Points: 21 # Comments: 8
文章网址： https://www.bloomberg.com/news/articles/2026-09-25/microsoft-abandons-personal-ai-chatbot-race-with-copilot-reboot 评论网址： https://news.ycombinator.com/item?id=49844896 得分：15 评论数：7
Hey HN! We’re Max and Gabriel the co-founders of Coverage Cat. We’ve been friends for over a decade, met in college then hung out mostly on the internet. We love building products that help people optimize the crufty corners of their lives. Max is a former Google/Microsoft/Two Sigma PM and Gabriel has been working in startups for over a decade. We’re building Coverage Cat: an AI-native, you can shop with your AI agent or via a form, insurance brokerage for consumers. Our current focus is helping tech folks (think L3-L8) buy umbrella insurance. For those who are unfamiliar with this type of coverage, here’s a blurb from one of our insurance agents: “Umbrella liability insurance is a policy that provides additional coverage above the limits of existing auto, homeowners, renters, or landlord policies. It kicks in after the underlying policy limits are exhausted and may also cover personal injury claims like libel, slander, or defamation that standard policies often exclude. Policies typically start at $1 million in additional liability coverage and can protect you, your spouse, dependents, and even pets in your household.” Buying insurance online is a miserable experience. You fill out a bunch of forms that say they’re going to give you quotes. Most don’t and then you end up with tons of unwanted phone calls and spam. Even if you wade through the muck, you’ll often still end up with sub-optimal prices and coverage because of a lack of market transparency and poorly-aligned advisory incentives. We came to this problem because Max is a classic personal finance obsessive. He's the type of person that needs to know that he has the best possible price for something (everyone has one friend with the patience for a two hour phone call with the bank for some $10 fee) or that his insurance coverage is as tailored to his risk profile as the market will allow. He actually reads insurance policies end to end. Years ago, when he shopped for his homeowners insurance it took him 10+ phone calls and dozens of online form fills to find the right deal. He also suffered tons of collateral damage in the process: websites sold his information to dozens of agents who proceeded to call, text, and email him a spam hoard that grows to this day. After this painful search made us aware of the problem, we also spent a lot of time doing user research with wealthy-ish tech employees to see if they felt the same way. They did, and also complained that they 1) often felt like they'd left money on the table, 2) didn’t really understand their coverages, and 3) that brokers were slow, hard to communicate with, and unreliable. To solve the problem, we set out to build Coverage Cat. Our approach has three major components: First, we work really really hard to find and partner with insurance carriers that offer high-quality, competitively-priced products. We've also taken time to identify insurers that work on a direct-to-consumer basis or transparently list their prices online so we can make our customers aware of all the options available to them. We get paid commission when we match users to great insurance deals, but we still show and recommend folks deals that we don't make money on when they're accessible and will always recommend users buy what's best for their personal financial situation. Our goal is to meaningfully improve price transparency so consumers (and their AI agents) can make informed coverage decisions and not get nickel-and-dimed! Second, we automate every possible part of the insurance process. Some forms, portals, & approvals still need human review due to regulatory constraints, but if it's not restricted, we're automating it. LLMs have given our small team tremendous leverage and enabled us to tackle problems at a scale that would've been unfathomable five years ago. Finally, we build for agents first (a remix of the more classic adage, build for developers/APIs). We've always been big believers that personal finance would be the killer use-case for the AI revolution and have done our best to position ourselves to surf the wave. As personal assistants have started to explode onto the mainstream (think Muse, Instinct, Town, Codex, CC etc.) we've ensured our shopping and comparison process works via API/MCP so agents can present their users with all the information they need to make a good decision and transform a miserable shopping experience into a delightful one. Coverage Cat is an unusual business because, while we also help folks find homeowners insurance in California and Texas, our main focus is on umbrella insurance. Most brokerages only sell umbrella coverage as a customer retention product, but it makes them almost no money. We focus on it as our core business because: 1) it's one piece of the insurance puzzle that many folks in tech overlook and/or are confused about even though it can have a huge impact on their financial well-being. 2) Existing online tools and brokerages broadly don't make it easy to comparison shop for. 3) It was, for us, the most technically feasible candidate for automation given startup resource constraints. Insofar as we know Coverage Cat is the first instance of a tool/portal that allows AI agents to complete most of the comparison and shopping steps required to allow people to buy insurance. We’d love your feedback so please do try it out. Just feed the prompt: “Shop for umbrella insurance with Coverage Cat” to your personal agent and let us know what you think! If you want to watch a couple of agents navigate the flow before you try it out there’s a video here: https://youtu.be/1BUkgAn6s-I Also happy to answer any questions about the product, the problem space, and even some insurance questions (Max is a licensed agent) where regulation permits. Cheers! Comments URL: https://news.ycombinator.com/item?id=49804931 Points: 3 # Comments: 0
我在 Settings 应用中禁用了 Siri，随后又在 Settings > Screen Time 中进一步禁用了 Siri 及其扩展功能。 接着，我按照 https://github.com/OleksandrKrupko/mac-os-debloat 中的说明，禁用了所有与 Siri 和 Apple Intelligence 相关的服务。 但重启后，我仍然能在 Activity Monitor 中看到一个名为“Siri”的进程正在运行。经过进一步检查，我发现它实际上是“Siri AI.app”。 这些“功能”与 Apple 的 Personal Context 听起来与 Microsoft 的 Recall 非常相似。 有人遇到过同样的情况吗？在 macOS 27 上，真的不可能完全关闭 Siri、Siri AI、Apple Intelligence 以及所有相关的服务和功能吗？ 评论 URL： https://news.ycombinator.com/item?id=49786609 积分：30 评论数：13
文章 URL： https://www.theregister.com/devops/2026/09/18/microsoft-agentically-ports-copilot-runtime-to-rust-for-120k/5297549 评论 URL： https://news.ycombinator.com/item?id=49773998 得分：15 评论数：6
文章 URL： https://www.tomshardware.com/tech-industry/artificial-intelligence/microsoft-director-called-ai-scraping-the-largest-theft-of-labor-in-human-history-while-openai-head-brands-chatgpt-an-existential-threat-to-publishers-revelations-come-from-legal-briefs-filed-in-nyt-lawsuit 评论 URL： https://news.ycombinator.com/item?id=49768921 点数：42 评论数：7
文章 URL： https://techcrunch.com/2026/09/17/microsoft-exec-called-ai-scraping-the-largest-theft-of-labor-in-human-history-new-unredacted-filings-reveal/ 评论 URL： https://news.ycombinator.com/item?id=49752056 Points：10 评论数：1
企业客户支持中的高效故障排除智能体依赖于从相似的历史案例中检索可执行的指导，但现有的检索增强生成（RAG）系统将支持案例视为静态文档，忽略了其多阶段、有状态的特性。我们提出 RAFT（面向故障排除智能体的检索增强框架），这是一种有状态的 RAG 框架，将每个已关闭的历史案例抽象为由时间线条目组成的有向链，并在条目级别进行检索，从而找出其中间状态与当前案例相匹配的案例，同时返回以匹配状态为锚点的父案例轨迹；此外，还可选地通过可配置的相似性表示在案例之间建立案例级图。我们直接评估这一检索层；与评估完整智能体系统不同，这种方式无需进行生产部署。由于公开的多阶段故障排除数据极为稀缺，我们将基于 Microsoft Learn Windows Server 文档构建的合成基准，与带有人为创建的重复标签的真实 Apache Jira 问题结合起来进行评估。在案例进展的每个阶段，RAFT 的 Case Hit 均优于 vanilla RAG 和 GraphRAG 基线，并且相较于最强基线取得了具有统计显著性的提升；Jira 结果提供了方向性证据，表明这一优势能够迁移到真实案例历史中。我们发布了基准、实现代码以及 Apache Jira 评估集。
文章网址： https://www.bbc.co.uk/news/articles/c6n07ypqz8kzo 评论网址： https://news.ycombinator.com/item?id=49727661 得分：33 评论数：39
文章链接： https://www.theregister.com/os-platforms/2026/09/14/microsoft-patches-windows-and-excel-breaks-audio-remote-access-and-paste/5296085 评论链接： https://news.ycombinator.com/item?id=49699297 得分：6 评论数：0
文章链接： https://rustfoundation.org/media/guest-post-rust-is-tier-1-language-at-microsoft/ 评论链接： https://news.ycombinator.com/item?id=49643546 得分：27 评论数：7
文章 URL： https://github.com/microsoft/agent-lightning/releases/tag/v1.0.1 评论 URL： https://news.ycombinator.com/item?id=49423077 得分：18 评论数：1
Hi HN- 我是 Pablo，Proliferate（YC S25）的创始人！ Proliferate ( https://github.com/proliferate-ai/proliferate ) 是一个开源、可自托管的 AI IDE，让你可以在一个地方使用 Claude Code、Codex、OpenCode、Cursor 和 Grok 进行工作并自动化任务。 这里有一个 2 分钟的快速演示，展示我们如何用 Proliferate 来构建 Proliferate： https://www.youtube.com/watch?v=tGNX0oaWmBY 在我的团队接入 OpenAI Codex 之后，我开始着手构建 Proliferate。几天之内，我们就把它用到了方方面面：用 computer use 代替我们自己浏览网站，让 Codex 协调其他 agent，以及为重复性工作设置自动化。说实话，我们几乎从不需要离开桌面应用就能把工作完成。 如果我团队的体验具有一定代表性，那么一个类似 Codex 的应用（一个具有精美 UI 的横向 agent）将会成为每家公司完成工作的主要界面。这完全符合 OpenAI 的使命，即把 Codex 打造成 everything app（见： https://news.ycombinator.com/item?id=47796469 ）。 但随着我们开始把自动化推进到更接近业务核心的工作，我希望能与来自所有实验室的 agent 协作，包括开放权重模型，而不会越来越依赖 OpenAI。 而这正是 Proliferate 的用途！它是开源、可自托管的 Codex，既能在构建向 Codex 广度迈进的同时，保留你在不同 agent 和模型提供方之间的选择自由。 如今 Proliferate 支持： * 使用 Claude Code、Codex、OpenCode、Cursor 和 Grok，并可采用它们原生的推理与配置选项，包括 Bedrock、Azure 和自托管推理。 * agent 之间的通信与管理：父 agent 可以生成并与另一个受支持的 agent 作为子 agent 进行通信（我个人喜欢让 Fable 委派给 Codex，同时让 OpenCode 模型审查 PR）。 * 构建 workflows——这是我最兴奋的功能之一。这些是可复用的 agent 会话链和人工审批关卡，每一步都可选择 harness 和模型，并在它们之间传递文档。我用它来自动化代码审查、QA，以及我的 PR 构建流程。 Proliferate 的全部代码在 AGPL-3.0 下 100% 开源。现在肯定还有一些粗糙的地方，但我们推进得很快，也非常欢迎任何反馈！ Comments URL: https://news.ycombinator.com/item?id=49390739 Points: 11 # Comments: 5
Deep Neural Networks (DNNs) deployed in high-risk domains, such as healthcare and autonomous driving, must be not only accurate but also understandable to ensure user trust. In real-world computer vision tasks, these models often operate on complex images containing background noise and are heavily annotated. To make such models explainable, Concept-based Explainable AI (CXAI) methods need to be assessed for their applicability and problem-solving capacity. In this work, we explore CXAI use cases in multi-label classification by training two DNNs, VGG16 and ResNet50, on the 20 most annotated labels in the MS-COCO dataset (Microsoft Common Objects in Context). We apply two CXAI methods, CRP (Concept Relevance Propagation) and CRAFT (Concept Recursive Activation FacTorization), to generate concept-level explanations and investigate the overall evaluations. Our analysis reveals three key findings: (1) CXAI highlights learning weaknesses in DNNs, (2) higher concept distinctiveness reduces label and concept confusion, and (3) environmental concepts expose dataset-induced biases. Our results demonstrate the potential of CXAI to enhance the understanding of model generalizability and to diagnose bias instigated by the dataset.
We’re working with Microsoft to start running most of our large-scale experiments on Azure.
Microsoft 正在向 OpenAI 投资 10 亿美元，以支持我们构建具有广泛分布经济收益的通用人工智能（AGI）。我们将合作在 Microsoft Azure 内开发一个可扩展至 AGI 的硬件和软件平台。我们将共同开发新的 Azure AI 超级计算技术，Microsoft 将成为我们的独家云提供商——因此我们将携手努力，进一步扩展 Microsoft Azure 在大规模 AI 系统方面的能力。
OpenAI has agreed to license GPT-3 to Microsoft for their own products and services.
我们很高兴地宣布，OpenAI 与 Microsoft 正在扩大我们的合作关系。
我们与 Anthropic、Google 和 Microsoft 共同宣布 Frontier Model Forum 的新任执行主任，以及一项新的 1000 万美元 AI Safety Fund。
ChatGPT 中数据分析功能的改进 与表格和图表互动，并直接从 Google Drive 和 Microsoft OneDrive 添加文件。
OpenAI 和 Microsoft 签署新的 MOU，加强双方的合作伙伴关系以及对 AI 安全与创新的共同承诺。
Microsoft 和 OpenAI 签署了一项新协议，强化了双方的长期合作伙伴关系，拓展了创新，并确保负责任的 AI 进展。
Microsoft and OpenAI continue to work closely across research, engineering, and product development, building on years of deep collaboration and shared success.
OpenAI 和 Microsoft 宣布修订后的协议，该协议简化了合作伙伴关系，增加了长期清晰度，并支持持续的大规模 AI 创新。
了解 GPT-5.6 如何为 Microsoft 365 Copilot 提供支持，在 Word、Excel、PowerPoint、Chat 和 Cowork 中带来更强大的 AI 能力，助力更快速、更高质量地完成工作。