Microsoft / Copilot

微软的 AI 布局：Copilot 全家桶、Azure AI 基础设施与 OpenAI 合作关系的持续追踪。
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7月28日7月28日周二

星期二 · 3 条
16:58
MarkTechPost（RSS）精选
AI 评分 71/100
Microsoft 发布 MAI-Cyber-1-Flash：5B 活跃参数的网络安全模型，驱动 MDASH 在 CyberGym 上达到 95.95%

Microsoft 发布 MAI-Cyber-1-Flash，一款 137B 总参数（5B 活跃参数）、256k 上下文窗口的稀疏 MoE 网络安全模型，是 MAI-Code-1-Flash 的微调版本。

Microsoft安全/对齐开源/仓库模型发布
推荐理由：微软这个安全模型把漏洞挖掘系统推到95.95%，关键是90%任务由5B模型完成成本砍半，路由设计比模型本身更值得关注，做漏洞挖掘的可以马上跑起来试试。
03:50
GitHub Blog精选
AI 评分 74/100
GitHub Copilot 发布"Harness"工作流：用单一工具完成原型、规划、实现与代码审查

GitHub Copilot 推出“Harness”工作流，让开发者通过单一 AI 工具完成从原型设计、规划、实现到代码审查的完整软件开发流程，无需追逐多种新 AI 工具。该工作流强调实用性与集成性，旨在减少工具切换带来的效率损耗。

GitHub教程/实践编码
推荐理由：GitHub Copilot 官方出的实用工作流，不追新工具，把现有功能用透，用 Copilot 的开发者直接能套的「内功心得」。
00:50
GitHub Blog精选
AI 评分 68/100
GitHub Copilot app 入门指南：多 Agent 会话工作区与 Canvas 预览

GitHub Copilot app 将 AI 编码工具升级为多 Agent 会话工作区，支持同时管理多个任务线程而不丢失进度。用户可为每个会话绑定项目上下文，通过 /create-canvas 命令在浏览器 Canvas 中预览 UI 并直接点选修改，还能启用 Agent Merge 自动处理 PR 审查反馈和合并冲突。

智能体GitHub教程/实践编码
推荐理由：GitHub Copilot 应用把 AI 编程拆成多会话、画布和 Agent Merge，让 '一键修 bug' 变成真·项目管理流，Copilot 用户该上手试试。

7月25日7月25日周六

星期六 · 1 条
03:24
Hacker News 热门（buzzing.cc 中文翻译）精选
AI 评分 77/100
英伟达、微软和Meta联合警告：应避免对开放权重模型过度监管

英伟达、微软和Meta联合签署公开信，警告对开放权重AI模型的过度监管将削弱美国在AI领域的竞争力。信中指出，开放权重模型能促进创新、降低准入门槛，并支持学术研究。OpenAI和Anthropic未签署该信函。

MetaMicrosoft开源生态政策/监管
推荐理由：我觉得这份联名信信号明确，开源模型已成巨头护城河，监管收紧只会让中国模型更占优势，开发者要密切关注后续政策。

7月24日7月24日周五

星期五 · 2 条
21:55
Satya Nadella@satyanadella精选
AI 评分 74/100
微软阐述开源模型助力美国竞争力路径Open-weight models are essential to a healthy AI ecosystem. Together with others across our industry, we are outlining a path for open-weight models to strengthen American competitiveness and expand economic opportunity, while protecting national security. https://www.microsoft.com/en-us/corporate-responsibility/topics/open-weight/译开放权重模型对健康的 AI 生态系统至关重要。我们与行业同仁一道，正在规划一条路径，让开放权重模型在保护国家安全的同时，增强美国竞争力并扩大经济机会。
Microsoft开源生态政策/监管
推荐理由：微软等公司联合提出开放权重模型路径，把开源与国家竞争力、国家安全挂钩，这份文件对开源模型生态的边界有定调作用，开发者和政策研究者都应该细读。
00:55
Satya Nadella@satyanadella精选
AI 评分 65/100
微软MAI模型：以更低成本实现前沿能力规模化http://x.com/i/article/2080328073724260352Frontier Diffusion & ControlIn a world where software has real marginal cost for the first time, how do we ensure frontier benefits are diffused across the entire ecosystem?The key is to optimize the cost-to-outcome frontier in real world context. In practical terms, that means using the right model for each task, and optimizing the context, skills, tools, and agent harness around it.This is the motivation behind our MAI model family. These models have been built ground up with clean data lineage and optimized for learning transfer from generalist to specialized skills in enterprise RLEs. We continue to make rapid progress in this pursuit.We can now take saturated frontier capabilities and deliver them at scale and at lower cost through models optimized for high-usage products, while continuing to use frontier models for frontier needs. We are proving this out across our first party products, and thereby creating a template for every other AI native, SaaS, or Enterprise company out there.In our products, frontier models from OpenAI and Anthropic are part of the orchestration system alongside MAI. But the model is only one part of the hill-climbing system. Harness, memory, context, tools, skills, user interactions, etc. all shape the evals and performance of these agentic systems.The other key criteria to ensure that you are in control, is your evals should continue to hill climb even when any given model has been removed. Therefore we build RLEs where models learn inside the product system and are rewarded for completing the tasks customers actually care about. We train models against the actual product harness, interactions, and outcomes they will encounter. And strategically ensure that the harness, memory, context, skills are externalized outside of the model.Product-specific evals and model independence give us the control and a direct hill to climb, and to keep refining until we reach the right quality-cost target. We are now seeing MAI models outperform general-purpose frontier models in many use cases while using a fraction of the tokens.We believe the biggest opportunity is to optimize all of these layers together in the products where the world works every day. And we are beginning to route traffic across our first-party surfaces to MAI whenever our models match or outperform frontier alternatives.We are seeing promising early results across GitHub Copilot, Excel, and Outlook and are beginning to take the same approach across Copilot Chat, PowerPoint, and more. And all these results will only get better as the entire system keeps hill-climbing!What we are doing across our first party products is also what every enterprise customer can be doing in their real world agentic systems with their proprietary evals, their proprietary RLEs, workflows, and context. We are making all this available as part of Foundry and our toolchain.Read more here: https://microsoft.ai/news/hill-climbing-mai-models-for-github-copilot-and-excel/译微软CEO Satya Nadella详解MAI模型家族战略：通过优化成本-效果前沿，MAI模型在GitHub Copilot、Excel等产品中已用更少token超越通用前沿模型。核心是构建独立于模型的评估系统，让模型在产品真实环境中学习并完成用户关心的任务。微软正将这一模板通过Foundry平台开放给企业客户。
智能体Microsoft大佬观点部署/工程
推荐理由：微软CEO详细阐述MAI模型战略，从通用模型转向产品内优化，透露GitHub Copilot和Excel已开始路由流量到MAI，对微软生态开发者和企业是个风向标。

7月23日7月23日周四

星期四 · 1 条
02:22
Microsoft Research@MSFTResearch精选
AI 评分 73/100
微软 MagenticLite 模型全面开源MagenticLite's models are now fully open source.MagenticBrain and Fara 1.5, previously available on Microsoft Foundry, are live on Hugging Face with open weights.The app, the harness, and every model in the stack are now open.译MagenticLite 的模型现已完全开源。 此前在 Microsoft Foundry 上提供的 MagenticBrain 和 Fara 1.5，现已在 Hugging Face 上开放权重。 该应用、测试工具以及堆栈中的每个模型现已全部开放。
Hugging FaceMicrosoft开源/仓库开源生态
推荐理由：微软把这些模型全开源了，从 Foundry 搬到 Hugging Face，对不想被平台锁定的开发者是个好信号，但模型能力本身不算顶尖，更偏生态布局。

7月22日7月22日周三

星期三 · 1 条
00:49
GitHub Blog精选
AI 评分 70/100
GitHub Copilot 推出 canvases 扩展，实现开发者与 AI 智能体实时协作

GitHub Copilot 在应用中推出 canvases 扩展，这是一种共享交互式界面，开发者和 AI 智能体可在其中实时协作。用户通过 /create-canvas 指令创建画布，Copilot 可动态更新内容，用户则通过点击、编辑等操作与同一工作区交互。示例包括快速分类 Issue、生成交互式代码库关系图、管理会话工作树、优化提示词质量以及跨平台搜索知识联系人。

GitHub教程/实践编码
推荐理由：Copilot 的 canvas 把对话变成可拖拽可点击的交互界面，处理 issue、探索代码都变得直观，日常依赖 Copilot 的开发者可以直接用起来。

7月14日7月14日周二

星期二 · 1 条
00:00

7月12日7月12日周日

星期日 · 1 条
23:34
Satya Nadella@satyanadella精选
AI 评分 75/100
纳德拉提出"反向信息悖论"：企业使用AI时需保护自身知识http://x.com/i/article/2076319195718090753The Reverse Information ParadoxIn the age of intelligence, how should firms protect their core IP?Nobel Prize winning economist Kenneth Arrow famously described a paradox in the market for information. “Its value for the purchaser is not known until he has the information, but then he has in effect acquired it without cost.” In Arrow’s “Information Paradox,” the seller risks giving away knowledge in order to sell it.AI creates the reverse problem. In the AI age, the buyer risks giving away knowledge, just in order to use what they bought.You essentially pay for intelligence twice, once with money, and again with something even more valuable: the proprietary knowledge you must reveal to make that intelligence useful. The better you want the model to perform, the more of that knowledge you have to feed it!Over time, the information asymmetry becomes increasingly skewed. The seller learns more and more about you as you use what you purchased, while you learn very little about what the seller is learning in return.That is what I think of as the Reverse Information Paradox.Patents solve one aspect of Arrow’s paradox. They let an inventor disclose an idea without simply giving it away. The Reverse Information Paradox needs its own equivalent.This requires more than data protection. Models learn from "exhaust," the prompts people write, the tools agents use, and especially the corrections people make when the model is wrong. Every correction is distilled into institutional know-how. It's the kind of knowledge a competitor could never buy, and the kind that leaks almost imperceptibly: trace by trace, correction by correction, eval by eval.In consuming intelligence, you are creating intelligence. And what you create should belong to you. This is your particular intelligence, in Hayek's sense: the knowledge of time, place, and circumstance that no one else can hold. It knows what you think, what you value, and how you measure success.While the great innovation that comes from model providers having fair use rights to train models on public data is needed, I find it ironic that the status quo is to then turn around and impose restrictive terms on distillation, and to reserve the right to learn from customer usage and interaction data. If learning flows in only one direction, economic value converges toward the owners of the learning infrastructure rather than the creators of the knowledge itself. Therefore, it's imperative that we distribute the learning infrastructure to every firm so that they can control their own learning loop.As Alex Karp put it: "What the technical customers want is control over their compute, their models, their data stack, and their alpha. They want to know they own the means of production, and it's not being transferred to someone else." The current regime does precisely the transfer Karp and companies fear.That is why enterprises need a real trust boundary for their human capital and token capital to compound. It is where an organization’s data, traces, evals, adapted weights, and memory accumulate and improve together. And it is a hard boundary across which nothing crosses, not even the intelligence exhaust, without consent. Enterprises will demand the rights to use model outputs to fine tune and/or train their own models. I think of this as every firm’s right to align models to their enterprise accountability obligations.In the cloud era, enterprises accumulated data. In the AI era, they accumulate learning. The trust boundary must evolve accordingly, from protecting information to protecting the mechanisms through which organizations learn, adapt, and compound intelligence. There are a few things every enterprise must do to ensure this:• Control: Create your private evals, because evals define what “good” looks like inside the organization. Also, retain ownership of your organization’s memory, traces, feedbacks, decisions, and institutional context, and ability to use outputs of models from your own tasks and queries.• Capability: Build your own proprietary learning environments within the tenant boundary to train or tune models, where models learn against real workflows without exposing the company’s knowledge.• Choice: Ensure the orchestration layer is decoupled from any single model. Ask yourself: If any one model you are using is taken away, do you still have the ability to operate and optimize for your evals using other models? Does your company “veteran” capability remain with you even if a given “generalist” model is taken away?• Cost: By decoupling the orchestration layer, you are also able to bring together context, models, and tasks in the most efficient and cost-effective way without sacrificing quality.• Compound: Bring these four together and you create your own continuous learning loop (i.e. hill climbing machine) that will allow your AI investments to compound the value of your firm.In other words, a company should be able to use a model without giving up the knowledge that makes it unique. That is the reverse information paradox we need to confront.译微软CEO萨提亚·纳德拉提出"反向信息悖论"：AI时代，买家为使用AI支付金钱，同时必须暴露专有知识（提示词、工具使用、纠正反馈等），这些"智力废气"被模型学习，导致信息不对称向卖家倾斜。企业需要真正的信任边界，确保自身数据、痕迹、评估、适配权重和记忆在边界内积累，未经同意不得外泄。纳德拉呼吁企业拥有私有评估、保留组织记忆所有权，并主张企业应有权使用模型输出微调或训练自有模型，以控制自身学习循环。
Microsoft大佬观点数据/训练
推荐理由：Satya 提出的「反向信息悖论」直指企业 AI 部署的核心顾虑，建议具体可操作，尤其是评估集控制和编排层解耦，做企业 AI 的产品人现在就该读。

7月10日7月10日周五

星期五 · 3 条
06:21
Hacker News 热门（buzzing.cc 中文翻译）精选
AI 评分 75/100
微软发布Flint：面向AI智能体的可视化语言

微软研究院推出Flint，一种可视化中间语言，让AI智能体通过简洁的人类可编辑spec自动生成美观图表。用户只需提供数据、语义类型和图表类型，Flint编译器即可推导坐标轴、配色、布局等底层参数。支持46种图表类型，可渲染到Vega-Lite、ECharts和Chart.js三个后端。项目通过npm安装（TypeScript/JavaScript），并提供MCP服务器用于智能体工作流集成。采用弹性布局模型自动优化图表尺寸与间距，已开源。

智能体Microsoft开源/仓库
推荐理由：微软这个 Flint 用语义类型代替了低层图表参数，是给 AI 代理装上了一双会画图的手，开发者现在就能装 MCP server 跑起来，做数据可视化代理的不用再跟 Vega-Lite 的细节搏斗了。
03:59
OpenAI：官网动态（RSS · 排除企业/客户案例）精选
AI 评分 69/100
GPT-5.6 成为 Microsoft 365 Copilot 首选模型

OpenAI 宣布 GPT-5.6 将作为 Microsoft 365 Copilot 的首选模型，覆盖 Word、Excel、PowerPoint、Chat 和 Cowork。该集成使用户能在日常工具中以更少提示轮次创建更高质量文档、进行更深入数据分析、生成更精美演示，并完成跨职能协作。GPT-5.6 在每个 token 上提供更实用的工作产出，性价比更强，并支持按需处理最复杂任务。微软通过 OpenAI API 直接调用该模型服务 Microsoft 365 客户。

MicrosoftOpenAI模型发布
推荐理由：GPT-5.6进入Microsoft 365 Copilot，标志着OpenAI的最强模型直接嵌入全球最广泛使用的办公套件，普通用户终于能无感体验到前沿AI，但别期望太多颠覆，只是模型升级带来的自然提升。
00:46
Microsoft Research 博客（RSS）精选
AI 评分 64/100
微软发布开源地球系统基础模型 Aurora 1.5，新增 22 个天气变量与集合预报

微软发布 Aurora 1.5，对开源 Aurora 地球系统基础模型进行重大扩展，新增 22 个天气变量（原 4 个）、支持小时级时间分辨率和概率集合预报，已在 GitHub 开源并放出的 Hugging Face 模型检查点。

Microsoft多模态开源生态模型发布
推荐理由：原文来自模型发布方，给出新增变量数、小时级分辨率和与 ECMWF 集合的对比数字，读者可据此评估其在能源等场景的适用性。

7月8日7月8日周三

星期三 · 1 条
03:07
The Decoder：AI News（RSS）精选
AI 评分 71/100
微软为降成本在Copilot中用自研MAI模型替换OpenAI和Anthropic模型

微软正用自研MAI模型替换Copilot产品中的OpenAI和Anthropic模型以降低支出。MAI模型已在Excel和Outlook中每周处理数万次请求，但占比仍小。Build大会上发布推理模型MAI-Thinking 1，声称编码媲美Sonnet 4.6和Opus 4.6，但基准测试大幅落后，仅与DeepSeek V3.2相当。AI负责人承认目标是削减并消除对Anthropic的支出。CEO暗示未来可能按用量计费，MAI为默认，第三方模型付费附加。微软称MAI使用干净商业许可数据，实际基于Common Crawl。

Microsoft现象/趋势行业动态
推荐理由：微软正悄悄把Copilot里的OpenAI和Anthropic模型换成自家MAI，用户可能要为更弱的AI付同样的钱。这对所有依赖Copilot的团队是个警告，现在就该重新评估工作流。

7月7日7月7日周二

星期二 · 3 条
07:10
Hacker News 热门（buzzing.cc 中文翻译）精选
AI 评分 75/100
OfficeCLI：为AI智能体设计的开源Office套件

OfficeCLI是全球首个专为AI智能体设计的开源Office套件，以单二进制文件运行，无需安装Office或任何依赖。它内置HTML渲染引擎，可将.docx/.xlsx/.pptx转换为HTML或PNG，形成“渲染→查看→修复”的视觉闭环，使AI代理能自主创建、读取和修改Word、Excel、PowerPoint文档。支持公式、图表、条件格式、RTL布局、修订追踪、表格、数据透视表等复杂功能。提供CLI命令和基于自然语言的桌面应用AionUi，并可一键安装到Claude Code、Cursor、Windsurf、GitHub Copilot等AI编码工具中。

智能体MCP/工具开源/仓库
推荐理由：我觉得这个工具把 AI 代理操作 Office 的最后一公里走通了，特别是内置渲染引擎让代理能‘看见’文档布局，而不是盲猜 DOM，对自动化报表和批处理文档的团队很有价值。
02:49
TechCrunch：AI（RSS）精选
AI 评分 78/100
2026年科技公司AI裁员名单：Microsoft、Oracle、GitLab等十家公司裁减数千岗位

2026年以来，多家科技公司以AI为由大规模裁员。Microsoft裁减约4800岗位（2.1%），Oracle裁减21000人（13%），GitLab裁减350人（14%）以投资AI基础设施，Google Cloud持续裁减员工（外界估计1500-3000+工程师），Intuit裁减3000人（17%），Meta裁减8000人（10%）并转岗7000人至AI，Cisco裁减近4000人（5%），Cloudflare裁减1100人（20%），GM裁减500-600 IT岗位，Coinbase裁减700人（14%）。据Layoffs.fyi统计，2026年累计已裁约12万个技术岗位。

GoogleMicrosoft行业动态
推荐理由：这不是简单的裁员名单，而是AI正在改写工作方式的证据链，每一条都让“AI不会导致失业”的安慰显得苍白。职场人该面对现实了。
00:00

7月3日7月3日周五

星期五 · 1 条
03:45
The Decoder：AI News（RSS）精选
AI 评分 78/100
Microsoft 成立"Frontier Company"，斥资 25 亿美元派驻 6000 名 AI 工程师到企业客户现场

Microsoft 新设业务部门“Frontier Company”，拨款 25 亿美元，将 6000 名行业与工程专家派驻企业客户现场，“共同设计、共同创新、部署并持续改进 AI 系统”。该部门由 Rodrigo Kede Lima 领导，旨在超越“前部署工程”模式，成为“最大、以结果为导向的工程组织”。Microsoft 将自己定位为 OpenAI 和 Anthropic 的“平台中立”替代方案，后两者也已设立专门部署公司。Microsoft 将借助埃森哲、凯捷、安永等系统集成商扩大覆盖范围。

AnthropicMicrosoftOpenAI行业动态
推荐理由：微软砸 25 亿美元成立 Frontier Company，把 6000 名工程师直接塞进企业客户现场，正面应战 OpenAI 和 Anthropic 的部署子公司。这一手既是补齐落地能力也是巩固生态，对 CIO 来说是选择多了，但对 AI 行业意味着部署军备竞赛正式开打。

7月2日7月2日周四

星期四 · 1 条
08:00
PromptArmor：Threat Intelligence精选
AI 评分 65/100
PromptArmor 披露 Microsoft Copilot Cowork Skill 可绕过管理员设置调用 DeepSeek

PromptArmor 披露，即使组织未加入 DeepSeek Preview，Microsoft Copilot Cowork 的 Skill 也能通过 agent 自身的访问路径调用 DeepSeek（Mistral 亦验证可行），且无需 API key，认证由用户的 Cowork 会话自动授予。

智能体Microsoft安全/对齐部署/工程
推荐理由：原文实测展示了 Copilot Cowork Skill 如何绕过组织级模型封锁调用 DeepSeek，并给出管理员目前唯一可用的关闭路径。

6月30日6月30日周二

星期二 · 1 条
08:00
PromptArmor：Threat Intelligence精选
AI 评分 68/100
PromptArmor 测试 104 万个 Skills，微软 Copilot Cowork 安全扫描混淆后漏检 99.6%

PromptArmor 从 SkillsMP、Claude Marketplaces、skills.sh、ClawHub、Composio、LobeHub 六个市场收集约 195 万条 Skills。

智能体论文/研究
推荐理由：作者用约百万条真实市场 Skills 和微软自家 BIPIA 攻击样本实测其扫描器，给出了具体检出率与绕过手法，可帮助读者评估 Skill 安全审查的局限。
OpenAI / ChatGPTAI 编码产品更新