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<!DOCTYPE html><html lang="zh-CN" data-default-color-scheme="auto"><head><meta charset="UTF-8"><link rel="apple-touch-icon" sizes="76x76" href="/img/fluid.png"><link rel="icon" href="/img/fluid.png"><meta name="viewport" content="width=device-width,initial-scale=1,maximum-scale=5,shrink-to-fit=no"><meta http-equiv="x-ua-compatible" content="ie=edge"><meta name="theme-color" content="#2f4154"><meta name="author" content="Evan Deng"><meta name="keywords" content=""><meta property="og:type" content="website"><meta property="og:title" content="Deng的博客"><meta property="og:url" content="http://coderedeng.github.io/index.html"><meta property="og:site_name" content="Deng的博客"><meta property="og:locale" content="zh_CN"><meta property="article:author" content="Evan Deng"><meta name="twitter:card" content="summary_large_image"><title>Deng的博客</title><link rel="stylesheet" href="https://lib.baomitu.com/twitter-bootstrap/4.6.1/css/bootstrap.min.css"><link rel="stylesheet" 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href="javascript:;" aria-label="Color Toggle"><i class="iconfont icon-dark" id="color-toggle-icon"></i></a></li></ul></div></div></nav><div id="banner" class="banner" parallax="true" style="background:url(/img/default.png) no-repeat center center;background-size:cover"><div class="full-bg-img"><div class="mask flex-center" style="background-color:rgba(0,0,0,.3)"><div class="banner-text text-center fade-in-up"><div class="h2"><span id="subtitle" data-typed-text="知识有两种，一种是你知道的，一种是你知道在哪里能找到的！"></span></div></div><div class="scroll-down-bar"><i class="iconfont icon-arrowdown"></i></div></div></div></div></div></header><main><div class="container nopadding-x-md"><div id="board" style="margin-top:0"><div class="container"><div class="row"><div class="col-12 col-md-10 m-auto"><div class="row mx-auto index-card"><div class="col-12 col-md-4 m-auto index-img"><a href="/2024/04/10/Go%E8%AF%AD%E8%A8%80%E5%8E%86%E5%8F%B2%E7%89%88%E6%9C%AC%E6%BC%94%E8%BF%9B%E5%92%8C%E6%96%B0%E7%89%B9%E6%80%A7%5B%E6%8C%81%E7%BB%AD%E6%9B%B4%E6%96%B0%5D/" target="_self"><img src="/img/cover35.png" srcset="/img/loading.gif" lazyload alt="Go语言历史版本演进和新特性[持续更新]"></a></div><article class="col-12 col-md-8 mx-auto index-info"><h1 class="index-header"><i class="index-pin iconfont icon-top" title="Pin on top"></i> <a href="/2024/04/10/Go%E8%AF%AD%E8%A8%80%E5%8E%86%E5%8F%B2%E7%89%88%E6%9C%AC%E6%BC%94%E8%BF%9B%E5%92%8C%E6%96%B0%E7%89%B9%E6%80%A7%5B%E6%8C%81%E7%BB%AD%E6%9B%B4%E6%96%B0%5D/" target="_self">Go语言历史版本演进和新特性[持续更新]</a></h1><a class="index-excerpt" href="/2024/04/10/Go%E8%AF%AD%E8%A8%80%E5%8E%86%E5%8F%B2%E7%89%88%E6%9C%AC%E6%BC%94%E8%BF%9B%E5%92%8C%E6%96%B0%E7%89%B9%E6%80%A7%5B%E6%8C%81%E7%BB%AD%E6%9B%B4%E6%96%B0%5D/" target="_self"><div>发布总览：Release History - The Go Programming Language GO 1.26 新特性发布时间：2026-02-10 官方说明：Go 1.26 Release Notes - The Go Programming Language Go 1.26 标志着垃圾回收器架构的重大升级。 主要特性： 全新垃圾回收器（Green Tea）：全新的 GC 实现，显著提升</div></a><div class="index-btm post-metas"><div class="post-meta mr-3"><i class="iconfont icon-date"></i> <time datetime="2024-04-10 21:34" pubdate>2024-04-10</time></div><div class="post-meta mr-3 d-flex align-items-center"><i class="iconfont icon-category"></i> <span class="category-chains"><span class="category-chain"><a href="/categories/Go%E5%9F%BA%E7%A1%80/" class="category-chain-item">Go基础</a></span></span></div><div class="post-meta"><i class="iconfont icon-tags"></i> <a href="/tags/Go%E5%9F%BA%E7%A1%80/">#Go基础</a></div></div></article></div><div class="row mx-auto index-card"><div class="col-12 col-md-4 m-auto index-img"><a href="/2026/06/18/%E5%BE%AE%E8%BD%AFMAI-Seven%E6%B7%B1%E5%BA%A6%E8%A7%A3%E6%9E%90-Build-2026%E5%8F%91%E5%B8%83%E4%B8%83%E6%AC%BE%E8%87%AA%E7%A0%94AI%E6%A8%A1%E5%9E%8B/" target="_self"><img src="/img/cover43.png" srcset="/img/loading.gif" lazyload alt="微软MAI Seven深度解析：Build 2026发布七款自研AI模型，AI独立战略迈出关键一步"></a></div><article class="col-12 col-md-8 mx-auto index-info"><h1 class="index-header"><a href="/2026/06/18/%E5%BE%AE%E8%BD%AFMAI-Seven%E6%B7%B1%E5%BA%A6%E8%A7%A3%E6%9E%90-Build-2026%E5%8F%91%E5%B8%83%E4%B8%83%E6%AC%BE%E8%87%AA%E7%A0%94AI%E6%A8%A1%E5%9E%8B/" target="_self">微软MAI Seven深度解析：Build 2026发布七款自研AI模型，AI独立战略迈出关键一步</a></h1><a class="index-excerpt" href="/2026/06/18/%E5%BE%AE%E8%BD%AFMAI-Seven%E6%B7%B1%E5%BA%A6%E8%A7%A3%E6%9E%90-Build-2026%E5%8F%91%E5%B8%83%E4%B8%83%E6%AC%BE%E8%87%AA%E7%A0%94AI%E6%A8%A1%E5%9E%8B/" target="_self"><div>引言：微软告别”代工厂”时代在刚刚落幕的 Microsoft Build 2026 大会上，一个曾被外界视为”不可能”的时刻正式到来——微软首次完整展示了其自研 AI 模型家族 MAI Seven（Microsoft AI Superintelligence），共包含七款覆盖推理、代码生成、图像理解、语音转录和声音合成的全栈模型。 这标志着自 2019 年与 OpenAI 达成战略合作以来，微软在</div></a><div class="index-btm post-metas"><div class="post-meta mr-3"><i class="iconfont icon-date"></i> <time datetime="2026-06-18 22:00" pubdate>2026-06-18</time></div><div class="post-meta mr-3 d-flex align-items-center"><i class="iconfont icon-category"></i> <span class="category-chains"><span class="category-chain"><a href="/categories/Tech%E5%89%8D%E6%B2%BF/" class="category-chain-item">Tech前沿</a></span></span></div><div class="post-meta"><i class="iconfont icon-tags"></i> <a href="/tags/AI%E5%89%8D%E6%B2%BF/">#AI前沿</a> <a href="/tags/%E5%BE%AE%E8%BD%AF/">#微软</a> <a href="/tags/MAI/">#MAI</a></div></div></article></div><div class="row mx-auto index-card"><div class="col-12 col-md-4 m-auto index-img"><a href="/2026/06/16/Kimi-K2-7-Code%E6%B7%B1%E5%BA%A6%E8%A7%A3%E6%9E%90-Moonshot-AI%E4%B8%87%E4%BA%BF%E5%8F%82%E6%95%B0%E5%BC%80%E6%BA%90%E7%BC%96%E7%A8%8B%E6%A8%A1%E5%9E%8B/" target="_self"><img src="/img/cover42.png" srcset="/img/loading.gif" lazyload alt="Kimi K2.7-Code深度解析：Moonshot AI万亿参数MoE编程模型，推理Token减少30%"></a></div><article class="col-12 col-md-8 mx-auto index-info"><h1 class="index-header"><a href="/2026/06/16/Kimi-K2-7-Code%E6%B7%B1%E5%BA%A6%E8%A7%A3%E6%9E%90-Moonshot-AI%E4%B8%87%E4%BA%BF%E5%8F%82%E6%95%B0%E5%BC%80%E6%BA%90%E7%BC%96%E7%A8%8B%E6%A8%A1%E5%9E%8B/" target="_self">Kimi K2.7-Code深度解析：Moonshot AI万亿参数MoE编程模型，推理Token减少30%</a></h1><a class="index-excerpt" href="/2026/06/16/Kimi-K2-7-Code%E6%B7%B1%E5%BA%A6%E8%A7%A3%E6%9E%90-Moonshot-AI%E4%B8%87%E4%BA%BF%E5%8F%82%E6%95%B0%E5%BC%80%E6%BA%90%E7%BC%96%E7%A8%8B%E6%A8%A1%E5%9E%8B/" target="_self"><div>引言：编程大模型的又一次跃迁2026年6月12日，北京AI初创公司月之暗面（Moonshot AI）正式发布了 Kimi K2.7-Code——一款专为代码生成、调试和重构场景优化的万亿参数MoE模型。该模型以Modified MIT许可证开源，权重已发布至Hugging Face（moonshotai/Kimi-K2.7-Code），上线首周下载量即突破5万次。 在Claude Fab</div></a><div class="index-btm post-metas"><div class="post-meta mr-3"><i class="iconfont icon-date"></i> <time datetime="2026-06-16 08:00" pubdate>2026-06-16</time></div><div class="post-meta mr-3 d-flex align-items-center"><i class="iconfont icon-category"></i> <span class="category-chains"><span class="category-chain"><a href="/categories/Tech%E5%89%8D%E6%B2%BF/" class="category-chain-item">Tech前沿</a></span></span></div><div class="post-meta"><i class="iconfont icon-tags"></i> <a href="/tags/AI%E5%89%8D%E6%B2%BF/">#AI前沿</a> <a href="/tags/Kimi-K2-7-Code/">#Kimi K2.7-Code</a></div></div></article></div><div class="row mx-auto index-card"><div class="col-12 col-md-4 m-auto index-img"><a href="/2026/06/15/NVIDIA-SkillSpector%E6%B7%B1%E5%BA%A6%E8%A7%A3%E6%9E%90-AI%E4%BB%A3%E7%90%86%E6%8A%80%E8%83%BD%E5%AE%89%E5%85%A8%E6%89%AB%E6%8F%8F%E5%99%A8/" target="_self"><img src="/img/cover42.png" srcset="/img/loading.gif" lazyload alt="NVIDIA SkillSpector 深度解析：为 AI 代理技能装上"安检仪""></a></div><article class="col-12 col-md-8 mx-auto index-info"><h1 class="index-header"><a href="/2026/06/15/NVIDIA-SkillSpector%E6%B7%B1%E5%BA%A6%E8%A7%A3%E6%9E%90-AI%E4%BB%A3%E7%90%86%E6%8A%80%E8%83%BD%E5%AE%89%E5%85%A8%E6%89%AB%E6%8F%8F%E5%99%A8/" target="_self">NVIDIA SkillSpector 深度解析：为 AI 代理技能装上"安检仪"</a></h1><a class="index-excerpt" href="/2026/06/15/NVIDIA-SkillSpector%E6%B7%B1%E5%BA%A6%E8%A7%A3%E6%9E%90-AI%E4%BB%A3%E7%90%86%E6%8A%80%E8%83%BD%E5%AE%89%E5%85%A8%E6%89%AB%E6%8F%8F%E5%99%A8/" target="_self"><div>背景：AI 代理技能的”信任危机”随着 Claude Code、Codex CLI、Gemini CLI 等 AI 编程代理的爆发式增长，开发者开始大量使用社区发布的 Skill（技能）来扩展代理能力——从自动化代码审查到数据库管理，无所不包。然而，这些 Skill 在安装后以隐式信任的方式执行，几乎未经任何安全审查。 一项名为《Agent Skills in the Wild》的研究揭示了令人不</div></a><div class="index-btm post-metas"><div class="post-meta mr-3"><i class="iconfont icon-date"></i> <time datetime="2026-06-15 08:30" pubdate>2026-06-15</time></div><div class="post-meta mr-3 d-flex align-items-center"><i class="iconfont icon-category"></i> <span class="category-chains"><span class="category-chain"><a href="/categories/Tech%E5%89%8D%E6%B2%BF/" class="category-chain-item">Tech前沿</a></span></span></div><div class="post-meta"><i class="iconfont icon-tags"></i> <a href="/tags/AI%E5%AE%89%E5%85%A8/">#AI安全</a> <a href="/tags/NVIDIA/">#NVIDIA</a></div></div></article></div><div class="row mx-auto index-card"><div class="col-12 col-md-4 m-auto index-img"><a href="/2026/06/14/LMCache%E6%B7%B1%E5%BA%A6%E8%A7%A3%E6%9E%90-LLM%E6%8E%A8%E7%90%86%E7%9A%84KV%E7%BC%93%E5%AD%98%E9%9D%A9%E5%91%BD/" target="_self"><img src="/img/cover43.png" srcset="/img/loading.gif" lazyload alt="LMCache深度解析：LLM推理的KV缓存革命"></a></div><article class="col-12 col-md-8 mx-auto index-info"><h1 class="index-header"><a href="/2026/06/14/LMCache%E6%B7%B1%E5%BA%A6%E8%A7%A3%E6%9E%90-LLM%E6%8E%A8%E7%90%86%E7%9A%84KV%E7%BC%93%E5%AD%98%E9%9D%A9%E5%91%BD/" target="_self">LMCache深度解析：LLM推理的KV缓存革命</a></h1><a class="index-excerpt" href="/2026/06/14/LMCache%E6%B7%B1%E5%BA%A6%E8%A7%A3%E6%9E%90-LLM%E6%8E%A8%E7%90%86%E7%9A%84KV%E7%BC%93%E5%AD%98%E9%9D%A9%E5%91%BD/" target="_self"><div>引言：KV缓存——LLM推理的隐形瓶颈在大语言模型（LLM）部署中，GPU显存是最昂贵的资源。当你的Llama 3.1 70B模型在H100上运行时，你可能会惊讶地发现：在一个并发用户、128K上下文长度的场景下，KV Cache alone就要占用42GB显存——这已经接近单张80GB H100的极限。而当你把并发数提升到8时，KV Cache的需求飙升至343GB，相当于近5张H100的显存量</div></a><div class="index-btm post-metas"><div class="post-meta mr-3"><i class="iconfont icon-date"></i> <time datetime="2026-06-14 08:00" pubdate>2026-06-14</time></div><div class="post-meta mr-3 d-flex align-items-center"><i class="iconfont icon-category"></i> <span class="category-chains"><span class="category-chain"><a href="/categories/Tech%E5%89%8D%E6%B2%BF/" class="category-chain-item">Tech前沿</a></span></span></div><div class="post-meta"><i class="iconfont icon-tags"></i> <a href="/tags/AI%E5%89%8D%E6%B2%BF/">#AI前沿</a> <a href="/tags/KV-Cache/">#KV Cache</a> <a href="/tags/LLM%E6%8E%A8%E7%90%86%E4%BC%98%E5%8C%96/">#LLM推理优化</a></div></div></article></div><div class="row mx-auto index-card"><div class="col-12 col-md-4 m-auto index-img"><a href="/2026/06/13/%E6%B7%B1%E5%BA%A6%E8%A7%A3%E6%9E%90-Agent-Skills-Addy-Osmani%E5%A6%82%E4%BD%95%E7%94%A8%E5%B7%A5%E7%A8%8B%E8%A7%84%E8%8C%83%E9%A9%AF%E6%9C%8D-AI-%E7%BC%96%E7%A8%8B%E4%BB%A3%E7%90%86/" target="_self"><img src="/img/cover42.png" srcset="/img/loading.gif" lazyload alt="深度解析 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