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    <name>Shenhuanjie</name>
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  <rights>All rights reserved 2026, Shenhuanjie</rights>
  <subtitle>一名的程序猿,梦想技术让世界变得更美好!</subtitle>
  <title>Hello World  分享知识,持续学习</title>
  <updated>2026-04-21T10:01:00.000Z</updated>
  <entry>
    <author>
      <name>Shenhuanjie</name>
    </author>
    <category term="产品分析" scheme="https://shenhuanjie.github.io/categories/%E4%BA%A7%E5%93%81%E5%88%86%E6%9E%90/"/>
    <category term="Todo清单" scheme="https://shenhuanjie.github.io/tags/Todo%E6%B8%85%E5%8D%95/"/>
    <category term="需求分析" scheme="https://shenhuanjie.github.io/tags/%E9%9C%80%E6%B1%82%E5%88%86%E6%9E%90/"/>
    <category term="GTD" scheme="https://shenhuanjie.github.io/tags/GTD/"/>
    <category term="番茄工作法" scheme="https://shenhuanjie.github.io/tags/%E7%95%AA%E8%8C%84%E5%B7%A5%E4%BD%9C%E6%B3%95/"/>
    <category term="产品设计" scheme="https://shenhuanjie.github.io/tags/%E4%BA%A7%E5%93%81%E8%AE%BE%E8%AE%A1/"/>
    <category term="效率工具" scheme="https://shenhuanjie.github.io/tags/%E6%95%88%E7%8E%87%E5%B7%A5%E5%85%B7/"/>
    <id>https://shenhuanjie.github.io/post/todo-qingdan-requirements-analysis-20260421.html</id>
    <link href="https://shenhuanjie.github.io/post/todo-qingdan-requirements-analysis-20260421.html"/>
    <published>2026-04-21T10:01:00.000Z</published>
    <summary>本文基于 Todo清单 Mac App、官网、帮助中心、App Store 与公开商店资料，对其产品定位、任务生命周期、今日计划、待办箱、日历、番茄专注、数据复盘、分类标签、同步商业化等能力进行需求拆解，并整理当前项目可参考的功能优先级。</summary>
    <title>Todo清单 App 需求功能分析：从任务闭环到数据复盘</title>
    <updated>2026-04-21T10:01:00.000Z</updated>
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  <entry>
    <author>
      <name>Shenhuanjie</name>
    </author>
    <category term="DevOps" scheme="https://shenhuanjie.github.io/categories/DevOps/"/>
    <category term="GitHub" scheme="https://shenhuanjie.github.io/categories/DevOps/GitHub/"/>
    <category term="GitHub Pages" scheme="https://shenhuanjie.github.io/tags/GitHub-Pages/"/>
    <category term="自定义域名" scheme="https://shenhuanjie.github.io/tags/%E8%87%AA%E5%AE%9A%E4%B9%89%E5%9F%9F%E5%90%8D/"/>
    <category term="DNS" scheme="https://shenhuanjie.github.io/tags/DNS/"/>
    <category term="HTTPS" scheme="https://shenhuanjie.github.io/tags/HTTPS/"/>
    <category term="SEO" scheme="https://shenhuanjie.github.io/tags/SEO/"/>
    <category term="域名解析" scheme="https://shenhuanjie.github.io/tags/%E5%9F%9F%E5%90%8D%E8%A7%A3%E6%9E%90/"/>
    <id>https://shenhuanjie.github.io/post/github-pages-custom-domain-guide-cost-20260421.html</id>
    <link href="https://shenhuanjie.github.io/post/github-pages-custom-domain-guide-cost-20260421.html"/>
    <published>2026-04-21T08:53:32.000Z</published>
    <summary>本文系统讲解 GitHub Pages 如何绑定自定义域名，覆盖域名购买成本、DNS 记录配置、apex 根域名与 www 子域名的选择、CNAME 文件、GitHub Pages 设置、HTTPS 生效、验证命令、常见故障和 SEO 迁移注意事项。</summary>
    <title>GitHub Pages 绑定自定义域名完整教程：DNS、HTTPS 与费用说明</title>
    <updated>2026-04-21T08:53:32.000Z</updated>
  </entry>
  <entry>
    <author>
      <name>Shenhuanjie</name>
    </author>
    <category term="量化交易" scheme="https://shenhuanjie.github.io/categories/%E9%87%8F%E5%8C%96%E4%BA%A4%E6%98%93/"/>
    <category term="投资研究" scheme="https://shenhuanjie.github.io/categories/%E9%87%8F%E5%8C%96%E4%BA%A4%E6%98%93/%E6%8A%95%E8%B5%84%E7%A0%94%E7%A9%B6/"/>
    <category term="机器学习" scheme="https://shenhuanjie.github.io/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/"/>
    <category term="量化交易" scheme="https://shenhuanjie.github.io/tags/%E9%87%8F%E5%8C%96%E4%BA%A4%E6%98%93/"/>
    <category term="Regime" scheme="https://shenhuanjie.github.io/tags/Regime/"/>
    <category term="因子轮动" scheme="https://shenhuanjie.github.io/tags/%E5%9B%A0%E5%AD%90%E8%BD%AE%E5%8A%A8/"/>
    <category term="Factor Rotation" scheme="https://shenhuanjie.github.io/tags/Factor-Rotation/"/>
    <category term="风险模型" scheme="https://shenhuanjie.github.io/tags/%E9%A3%8E%E9%99%A9%E6%A8%A1%E5%9E%8B/"/>
    <category term="HMM" scheme="https://shenhuanjie.github.io/tags/HMM/"/>
    <category term="Alpha" scheme="https://shenhuanjie.github.io/tags/Alpha/"/>
    <id>https://shenhuanjie.github.io/post/quant-regime-factor-rotation.html</id>
    <link href="https://shenhuanjie.github.io/post/quant-regime-factor-rotation.html"/>
    <published>2026-04-21T03:38:40.000Z</published>
    <summary>
      <![CDATA[<p>这篇文章整理自一段关于量化交易的 ChatGPT 分享对话，主题是 <strong>市场状态（Regime）识别</strong> 与 <strong>因子轮动（Factor]]>
    </summary>
    <title>市场状态 Regime 与因子轮动：量化交易框架进阶</title>
    <updated>2026-04-21T03:38:40.000Z</updated>
  </entry>
  <entry>
    <author>
      <name>Shenhuanjie</name>
    </author>
    <category term="机器学习" scheme="https://shenhuanjie.github.io/categories/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/"/>
    <category term="模型评估" scheme="https://shenhuanjie.github.io/tags/%E6%A8%A1%E5%9E%8B%E8%AF%84%E4%BC%B0/"/>
    <category term="可解释性" scheme="https://shenhuanjie.github.io/tags/%E5%8F%AF%E8%A7%A3%E9%87%8A%E6%80%A7/"/>
    <category term="AI治理" scheme="https://shenhuanjie.github.io/tags/AI%E6%B2%BB%E7%90%86/"/>
    <category term="可信AI" scheme="https://shenhuanjie.github.io/tags/%E5%8F%AF%E4%BF%A1AI/"/>
    <category term="风险管理" scheme="https://shenhuanjie.github.io/tags/%E9%A3%8E%E9%99%A9%E7%AE%A1%E7%90%86/"/>
    <id>https://shenhuanjie.github.io/post/machine-learning-model-evaluation-interpretability-risk-governance-20260421.html</id>
    <link href="https://shenhuanjie.github.io/post/machine-learning-model-evaluation-interpretability-risk-governance-20260421.html"/>
    <published>2026-04-21T02:20:00.000Z</published>
    <summary>本文围绕机器学习可信落地展开，系统介绍离线评估、交叉验证、阈值选择、概率校准、分组评估、可解释性、公平性、隐私安全、鲁棒性、上线监控和 AI 风险治理框架。</summary>
    <title>模型评估、可解释性与风险治理：把机器学习做成可信系统</title>
    <updated>2026-04-21T02:20:00.000Z</updated>
  </entry>
  <entry>
    <author>
      <name>Shenhuanjie</name>
    </author>
    <category term="机器学习" scheme="https://shenhuanjie.github.io/categories/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/"/>
    <category term="机器学习" scheme="https://shenhuanjie.github.io/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/"/>
    <category term="特征工程" scheme="https://shenhuanjie.github.io/tags/%E7%89%B9%E5%BE%81%E5%B7%A5%E7%A8%8B/"/>
    <category term="数据治理" scheme="https://shenhuanjie.github.io/tags/%E6%95%B0%E6%8D%AE%E6%B2%BB%E7%90%86/"/>
    <category term="数据泄漏" scheme="https://shenhuanjie.github.io/tags/%E6%95%B0%E6%8D%AE%E6%B3%84%E6%BC%8F/"/>
    <category term="特征平台" scheme="https://shenhuanjie.github.io/tags/%E7%89%B9%E5%BE%81%E5%B9%B3%E5%8F%B0/"/>
    <id>https://shenhuanjie.github.io/post/machine-learning-feature-engineering-data-governance-20260421.html</id>
    <link href="https://shenhuanjie.github.io/post/machine-learning-feature-engineering-data-governance-20260421.html"/>
    <published>2026-04-21T02:00:00.000Z</published>
    <summary>本文详细介绍机器学习中的数据治理、样本构建、缺失值处理、异常值处理、类别编码、数值缩放、时间窗口、数据泄漏、特征选择和特征平台建设，强调高质量特征体系对模型效果和生产稳定性的决定性作用。</summary>
    <title>特征工程与数据治理：决定机器学习上限的关键能力</title>
    <updated>2026-04-21T02:00:00.000Z</updated>
  </entry>
  <entry>
    <author>
      <name>Shenhuanjie</name>
    </author>
    <category term="机器学习" scheme="https://shenhuanjie.github.io/categories/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/"/>
    <category term="深度学习" scheme="https://shenhuanjie.github.io/tags/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/"/>
    <category term="神经网络" scheme="https://shenhuanjie.github.io/tags/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/"/>
    <category term="反向传播" scheme="https://shenhuanjie.github.io/tags/%E5%8F%8D%E5%90%91%E4%BC%A0%E6%92%AD/"/>
    <category term="PyTorch" scheme="https://shenhuanjie.github.io/tags/PyTorch/"/>
    <category term="模型优化" scheme="https://shenhuanjie.github.io/tags/%E6%A8%A1%E5%9E%8B%E4%BC%98%E5%8C%96/"/>
    <id>https://shenhuanjie.github.io/post/deep-learning-backpropagation-optimization-practice-20260421.html</id>
    <link href="https://shenhuanjie.github.io/post/deep-learning-backpropagation-optimization-practice-20260421.html"/>
    <published>2026-04-21T01:40:00.000Z</published>
    <summary>本文从神经元、层、激活函数和损失函数讲起，深入解释前向传播、反向传播、自动微分、梯度下降、优化器、正则化、归一化和训练稳定性，帮助读者建立深度学习的工程化理解。</summary>
    <title>深度学习基础详解：神经网络、反向传播与优化实践</title>
    <updated>2026-04-21T01:40:00.000Z</updated>
  </entry>
  <entry>
    <author>
      <name>Shenhuanjie</name>
    </author>
    <category term="机器学习" scheme="https://shenhuanjie.github.io/categories/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/"/>
    <category term="监督学习" scheme="https://shenhuanjie.github.io/tags/%E7%9B%91%E7%9D%A3%E5%AD%A6%E4%B9%A0/"/>
    <category term="分类算法" scheme="https://shenhuanjie.github.io/tags/%E5%88%86%E7%B1%BB%E7%AE%97%E6%B3%95/"/>
    <category term="回归算法" scheme="https://shenhuanjie.github.io/tags/%E5%9B%9E%E5%BD%92%E7%AE%97%E6%B3%95/"/>
    <category term="集成学习" scheme="https://shenhuanjie.github.io/tags/%E9%9B%86%E6%88%90%E5%AD%A6%E4%B9%A0/"/>
    <category term="scikit-learn" scheme="https://shenhuanjie.github.io/tags/scikit-learn/"/>
    <id>https://shenhuanjie.github.io/post/supervised-learning-algorithms-practice-20260421.html</id>
    <link href="https://shenhuanjie.github.io/post/supervised-learning-algorithms-practice-20260421.html"/>
    <published>2026-04-21T01:20:00.000Z</published>
    <summary>本文系统讲解监督学习的基本假设、训练目标、泛化能力、常见算法与指标选择，覆盖线性回归、逻辑回归、KNN、朴素贝叶斯、决策树、随机森林、梯度提升树和支持向量机，帮助读者建立可落地的算法选择框架。</summary>
    <title>监督学习核心算法详解：从线性模型到集成学习的实践指南</title>
    <updated>2026-04-21T01:20:00.000Z</updated>
  </entry>
  <entry>
    <author>
      <name>Shenhuanjie</name>
    </author>
    <category term="机器学习" scheme="https://shenhuanjie.github.io/categories/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/"/>
    <category term="机器学习" scheme="https://shenhuanjie.github.io/tags/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/"/>
    <category term="MLOps" scheme="https://shenhuanjie.github.io/tags/MLOps/"/>
    <category term="模型部署" scheme="https://shenhuanjie.github.io/tags/%E6%A8%A1%E5%9E%8B%E9%83%A8%E7%BD%B2/"/>
    <category term="数据工程" scheme="https://shenhuanjie.github.io/tags/%E6%95%B0%E6%8D%AE%E5%B7%A5%E7%A8%8B/"/>
    <category term="AI工程化" scheme="https://shenhuanjie.github.io/tags/AI%E5%B7%A5%E7%A8%8B%E5%8C%96/"/>
    <id>https://shenhuanjie.github.io/post/machine-learning-engineering-lifecycle-20260421.html</id>
    <link href="https://shenhuanjie.github.io/post/machine-learning-engineering-lifecycle-20260421.html"/>
    <published>2026-04-21T01:00:00.000Z</published>
    <summary>本文系统梳理机器学习项目从问题定义、数据建设、特征工程、模型训练、离线评估、部署上线到持续监控的完整生命周期，强调把机器学习从一次性建模任务升级为可复现、可治理、可迭代的工程系统。</summary>
    <title>机器学习工程全流程：从业务问题到可持续上线的系统方法</title>
    <updated>2026-04-21T01:00:00.000Z</updated>
  </entry>
  <entry>
    <author>
      <name>Shenhuanjie</name>
    </author>
    <category term="DevOps" scheme="https://shenhuanjie.github.io/categories/DevOps/"/>
    <category term="Docker" scheme="https://shenhuanjie.github.io/categories/DevOps/Docker/"/>
    <category term="Docker" scheme="https://shenhuanjie.github.io/tags/Docker/"/>
    <category term="GitHub Actions" scheme="https://shenhuanjie.github.io/tags/GitHub-Actions/"/>
    <category term="Docker Hub" scheme="https://shenhuanjie.github.io/tags/Docker-Hub/"/>
    <category term="Node.js" scheme="https://shenhuanjie.github.io/tags/Node-js/"/>
    <category term="CI/CD" scheme="https://shenhuanjie.github.io/tags/CI-CD/"/>
    <category term="GHCR" scheme="https://shenhuanjie.github.io/tags/GHCR/"/>
    <category term="容器化" scheme="https://shenhuanjie.github.io/tags/%E5%AE%B9%E5%99%A8%E5%8C%96/"/>
    <category term="镜像发布" scheme="https://shenhuanjie.github.io/tags/%E9%95%9C%E5%83%8F%E5%8F%91%E5%B8%83/"/>
    <id>https://shenhuanjie.github.io/post/github-actions-build-and-push-node-docker-image.html</id>
    <link href="https://shenhuanjie.github.io/post/github-actions-build-and-push-node-docker-image.html"/>
    <published>2026-04-20T11:33:36.000Z</published>
    <summary>
      <![CDATA[<p>最近整理一段 ChatGPT 分享对话时，里面有个很常见的问题：</p>
<blockquote>
<p>GitHub Actions 是否支持将 Node 项目打包成 Docker 镜像？</p>
</blockquote>
<p>答案是支持，而且这正是 GitHub]]>
    </summary>
    <title>GitHub Actions 如何将 Node 项目打包成 Docker 镜像并自动推送</title>
    <updated>2026-04-20T11:42:54.000Z</updated>
  </entry>
  <entry>
    <author>
      <name>Shenhuanjie</name>
    </author>
    <category term="Java" scheme="https://shenhuanjie.github.io/tags/Java/"/>
    <category term="RestTemplate" scheme="https://shenhuanjie.github.io/tags/RestTemplate/"/>
    <category term="POST" scheme="https://shenhuanjie.github.io/tags/POST/"/>
    <category term="请求" scheme="https://shenhuanjie.github.io/tags/%E8%AF%B7%E6%B1%82/"/>
    <category term="设置" scheme="https://shenhuanjie.github.io/tags/%E8%AE%BE%E7%BD%AE/"/>
    <id>https://shenhuanjie.github.io/post/java-resttemplate-send-post-request-set-the-request-body-example-zvmkr2.html</id>
    <link href="https://shenhuanjie.github.io/post/java-resttemplate-send-post-request-set-the-request-body-example-zvmkr2.html"/>
    <published>2025-01-15T12:12:06.000Z</published>
    <summary>本文介绍了如何在Java中使用RestTemplate发送POST请求并设置请求体参数。通过创建RestTemplate实例、设置请求URL、请求头和请求体，并使用HttpEntity封装，可以发送POST请求并获取响应。文章还提供了发送JSON和表单数据的示例代码，并说明了处理响应的方法。</summary>
    <title>Java RestTemplate 发送 POST 请求设置请求体示例</title>
    <updated>2025-01-15T12:13:43.000Z</updated>
  </entry>
  <entry>
    <author>
      <name>Shenhuanjie</name>
    </author>
    <category term="rss" scheme="https://shenhuanjie.github.io/tags/rss/"/>
    <category term="订阅" scheme="https://shenhuanjie.github.io/tags/%E8%AE%A2%E9%98%85/"/>
    <category term="源" scheme="https://shenhuanjie.github.io/tags/%E6%BA%90/"/>
    <category term="中文" scheme="https://shenhuanjie.github.io/tags/%E4%B8%AD%E6%96%87/"/>
    <category term="优质" scheme="https://shenhuanjie.github.io/tags/%E4%BC%98%E8%B4%A8/"/>
    <id>https://shenhuanjie.github.io/post/the-most-subscriber-rss-source-chinese-high-quality-rss-source-1bvhxe.html</id>
    <link href="https://shenhuanjie.github.io/post/the-most-subscriber-rss-source-chinese-high-quality-rss-source-1bvhxe.html"/>
    <published>2025-01-15T08:12:25.000Z</published>
    <summary>本文列出了订阅人数最多的中文RSS源，包括知乎、阮一峰博客、少数派、美团技术团队、V2EX、酷壳、爱范儿、知乎热榜、南方周末、机核、编程随想、Solidot、煎蛋、ONE·一个、云风博客、知乎日报、小众软件等，并提供链接方便查看。</summary>
    <title>订阅人数最多的中文RSS源推荐</title>
    <updated>2025-01-15T08:16:53.000Z</updated>
  </entry>
  <entry>
    <author>
      <name>Shenhuanjie</name>
    </author>
    <category term="跨平台" scheme="https://shenhuanjie.github.io/tags/%E8%B7%A8%E5%B9%B3%E5%8F%B0/"/>
    <category term="React" scheme="https://shenhuanjie.github.io/tags/React/"/>
    <category term="Flutter" scheme="https://shenhuanjie.github.io/tags/Flutter/"/>
    <category term="Xamarin" scheme="https://shenhuanjie.github.io/tags/Xamarin/"/>
    <category term="Electron" scheme="https://shenhuanjie.github.io/tags/Electron/"/>
    <id>https://shenhuanjie.github.io/post/multi-end-development-plan-advantages-and-disadvantages-comparison-and-selection-guide-1hq2wo.html</id>
    <link href="https://shenhuanjie.github.io/post/multi-end-development-plan-advantages-and-disadvantages-comparison-and-selection-guide-1hq2wo.html"/>
    <published>2025-01-15T07:35:28.000Z</published>
    <summary>本文对比了多种多端开发方案的优缺点，包括React Native、Flutter、Xamarin、Electron、Ionic、NativeScript、Unity和Qt等。分析了它们在跨平台、性能、开发效率、社区支持、学习曲线等方面的表现，并提供了选择多端开发方案的指南，帮助开发者根据项目需求和团队技能做出合适的选择。</summary>
    <title>多端开发方案：优缺点对比及选择指南</title>
    <updated>2025-01-15T07:35:46.000Z</updated>
  </entry>
  <entry>
    <author>
      <name>Shenhuanjie</name>
    </author>
    <category term="神经网络" scheme="https://shenhuanjie.github.io/tags/%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/"/>
    <category term="Java" scheme="https://shenhuanjie.github.io/tags/Java/"/>
    <category term="MLP" scheme="https://shenhuanjie.github.io/tags/MLP/"/>
    <category term="感知器" scheme="https://shenhuanjie.github.io/tags/%E6%84%9F%E7%9F%A5%E5%99%A8/"/>
    <category term="分类" scheme="https://shenhuanjie.github.io/tags/%E5%88%86%E7%B1%BB/"/>
    <id>https://shenhuanjie.github.io/post/java-realizes-simple-multi-layer-perception-neural-network-z1kdxr7.html</id>
    <link href="https://shenhuanjie.github.io/post/java-realizes-simple-multi-layer-perception-neural-network-z1kdxr7.html"/>
    <published>2025-01-15T07:31:36.000Z</published>
    <summary>本文介绍了如何使用Java实现一个简单的多层感知器（MLP）神经网络。通过定义神经网络结构、初始化权重和偏置、前向传播和反向传播等步骤，实现了对简单分类问题的求解。文章详细阐述了神经网络的设计与实现过程，为读者提供了基于Java的多层感知器神经网络的基本实现示例。</summary>
    <title>Java实现简单多层感知器神经网络</title>
    <updated>2025-01-15T07:32:21.000Z</updated>
  </entry>
  <entry>
    <author>
      <name>Shenhuanjie</name>
    </author>
    <category term="注册步骤" scheme="https://shenhuanjie.github.io/tags/%E6%B3%A8%E5%86%8C%E6%AD%A5%E9%AA%A4/"/>
    <category term="Google" scheme="https://shenhuanjie.github.io/tags/Google/"/>
    <category term="开发者" scheme="https://shenhuanjie.github.io/tags/%E5%BC%80%E5%8F%91%E8%80%85/"/>
    <category term="账号" scheme="https://shenhuanjie.github.io/tags/%E8%B4%A6%E5%8F%B7/"/>
    <category term="详解" scheme="https://shenhuanjie.github.io/tags/%E8%AF%A6%E8%A7%A3/"/>
    <id>https://shenhuanjie.github.io/post/detailed-explanation-of-google-developer-account-registration-steps-g5yuq.html</id>
    <link href="https://shenhuanjie.github.io/post/detailed-explanation-of-google-developer-account-registration-steps-g5yuq.html"/>
    <published>2025-01-15T07:25:58.000Z</published>
    <summary>本文详细介绍了注册 Google 开发者账号的步骤，包括访问 Google Developers 网站、登录或创建 Google 账号、访问 Google Play Console 或 Google Cloud Console、支付注册费用、填写开发者信息、同意开发者协议、完成注册等，并简要说明了如何启用 API 和服务以及获取 API 密钥。</summary>
    <title>Google开发者账号注册步骤详解</title>
    <updated>2025-01-15T07:26:32.000Z</updated>
  </entry>
  <entry>
    <author>
      <name>Shenhuanjie</name>
    </author>
    <category term="开发者" scheme="https://shenhuanjie.github.io/tags/%E5%BC%80%E5%8F%91%E8%80%85/"/>
    <category term="ip查询" scheme="https://shenhuanjie.github.io/tags/ip%E6%9F%A5%E8%AF%A2/"/>
    <category term="免费API" scheme="https://shenhuanjie.github.io/tags/%E5%85%8D%E8%B4%B9API/"/>
    <category term="IP地址" scheme="https://shenhuanjie.github.io/tags/IP%E5%9C%B0%E5%9D%80/"/>
    <category term="网络" scheme="https://shenhuanjie.github.io/tags/%E7%BD%91%E7%BB%9C/"/>
    <id>https://shenhuanjie.github.io/post/ipifyorg-detailed-explanation-of-free-ip-query-service-z3xiy9.html</id>
    <link href="https://shenhuanjie.github.io/post/ipifyorg-detailed-explanation-of-free-ip-query-service-z3xiy9.html"/>
    <published>2025-01-15T07:22:34.000Z</published>
    <summary>ipify.org是一个免费IP查询服务，提供简单易用的API，开发者可通过HTTP请求获取客户端IP地址。支持纯文本、JSON和JSONP格式返回，适用于客户端和服务器端获取IP，方便调试和测试。服务稳定，响应速度快，但需注意隐私问题和请求频率限制。</summary>
    <title>ipify.org：免费IP查询服务详解</title>
    <updated>2025-01-15T07:23:07.000Z</updated>
  </entry>
  <entry>
    <author>
      <name>Shenhuanjie</name>
    </author>
    <category term="开发者" scheme="https://shenhuanjie.github.io/tags/%E5%BC%80%E5%8F%91%E8%80%85/"/>
    <category term="gcp" scheme="https://shenhuanjie.github.io/tags/gcp/"/>
    <category term="firebase" scheme="https://shenhuanjie.github.io/tags/firebase/"/>
    <category term="云服务" scheme="https://shenhuanjie.github.io/tags/%E4%BA%91%E6%9C%8D%E5%8A%A1/"/>
    <category term="工具" scheme="https://shenhuanjie.github.io/tags/%E5%B7%A5%E5%85%B7/"/>
    <id>https://shenhuanjie.github.io/post/google-developer-weapon-cloud-platform-and-firebase-service-analysis-1vmcn0.html</id>
    <link href="https://shenhuanjie.github.io/post/google-developer-weapon-cloud-platform-and-firebase-service-analysis-1vmcn0.html"/>
    <published>2025-01-15T06:57:43.000Z</published>
    <summary>本文介绍了Google为开发者提供的多种云服务和工具，包括Google Cloud Platform、Firebase、Google Cloud Functions、Google Cloud AI等，这些服务涵盖了计算、存储、数据库、机器学习、大数据、人工智能、API管理、网站分析、广告管理、地图服务、消息传递、数据库、内容分发网络和统一控制台等方面，帮助开发者构建现代、可扩展和高效的软件解决方案。</summary>
    <title>Google开发者利器：云平台与Firebase服务解析</title>
    <updated>2025-01-15T06:58:19.000Z</updated>
  </entry>
  <entry>
    <author>
      <name>Shenhuanjie</name>
    </author>
    <category term="CSS" scheme="https://shenhuanjie.github.io/tags/CSS/"/>
    <category term="Google" scheme="https://shenhuanjie.github.io/tags/Google/"/>
    <category term="字体库" scheme="https://shenhuanjie.github.io/tags/%E5%AD%97%E4%BD%93%E5%BA%93/"/>
    <category term="网页" scheme="https://shenhuanjie.github.io/tags/%E7%BD%91%E9%A1%B5/"/>
    <category term="使用" scheme="https://shenhuanjie.github.io/tags/%E4%BD%BF%E7%94%A8/"/>
    <id>https://shenhuanjie.github.io/post/google-fonts-font-library-use-guide-z9nwfx.html</id>
    <link href="https://shenhuanjie.github.io/post/google-fonts-font-library-use-guide-z9nwfx.html"/>
    <published>2025-01-15T06:54:28.000Z</published>
    <summary>Google Fonts 是一个提供丰富字体样式的免费在线库，用户可轻松将其添加到网页中。本文介绍了 Google Fonts 的特点，包括多样性、易用性、性能优化等，并详细说明了如何选择字体、获取链接、添加到 HTML 和 CSS 中，以及优化字体加载。同时，文章也提醒了使用字体时需要注意的版权和知识产权问题。</summary>
    <title>Google Fonts字体库使用指南</title>
    <updated>2025-01-15T06:55:11.000Z</updated>
  </entry>
  <entry>
    <author>
      <name>Shenhuanjie</name>
    </author>
    <category term="小程序" scheme="https://shenhuanjie.github.io/tags/%E5%B0%8F%E7%A8%8B%E5%BA%8F/"/>
    <category term="手机号" scheme="https://shenhuanjie.github.io/tags/%E6%89%8B%E6%9C%BA%E5%8F%B7/"/>
    <category term="登录" scheme="https://shenhuanjie.github.io/tags/%E7%99%BB%E5%BD%95/"/>
    <category term="UNI-APP" scheme="https://shenhuanjie.github.io/tags/UNI-APP/"/>
    <category term="Spring Boot" scheme="https://shenhuanjie.github.io/tags/Spring-Boot/"/>
    <id>https://shenhuanjie.github.io/post/uniapp-spring-boot-implements-a-small-program-mobile-phone-number-login-j33si.html</id>
    <link href="https://shenhuanjie.github.io/post/uniapp-spring-boot-implements-a-small-program-mobile-phone-number-login-j33si.html"/>
    <published>2025-01-15T06:09:55.000Z</published>
    <summary>本文介绍了使用UNI-APP和Spring Boot实现小程序手机号登录的完整方案。前端通过获取用户手机号授权并发送code到后端，后端通过微信小程序API获取手机号，处理用户登录逻辑，包括查询用户、创建新用户并返回登录凭证。方案详细描述了前端和后端的实现步骤和关键代码。</summary>
    <title>UNI-APP + Spring Boot 实现小程序手机号登录</title>
    <updated>2025-01-15T06:10:41.000Z</updated>
  </entry>
  <entry>
    <author>
      <name>Shenhuanjie</name>
    </author>
    <category term="MySQL" scheme="https://shenhuanjie.github.io/categories/MySQL/"/>
    <category term="MySQL" scheme="https://shenhuanjie.github.io/tags/MySQL/"/>
    <category term="禁用" scheme="https://shenhuanjie.github.io/tags/%E7%A6%81%E7%94%A8/"/>
    <category term="GROUP" scheme="https://shenhuanjie.github.io/tags/GROUP/"/>
    <category term="模式" scheme="https://shenhuanjie.github.io/tags/%E6%A8%A1%E5%BC%8F/"/>
    <category term="配置" scheme="https://shenhuanjie.github.io/tags/%E9%85%8D%E7%BD%AE/"/>
    <id>https://shenhuanjie.github.io/post/how-to-disable-onlyfullgroupby-z28azoh.html</id>
    <link href="https://shenhuanjie.github.io/post/how-to-disable-onlyfullgroupby-z28azoh.html"/>
    <published>2025-01-15T03:01:39.000Z</published>
    <summary>MySQL 8.0 中，可以通过三种方法禁用 ONLY_FULL_GROUP_BY 模式：临时禁用（仅当前会话）、全局禁用（所有会话）和通过配置文件永久禁用。临时禁用和全局禁用分别使用 SET SESSION 和 SET GLOBAL 命令，而永久禁用则需要修改 MySQL 配置文件并重启服务。禁用该模式后，需注意数据准确性问题，并谨慎使用。</summary>
    <title>MySQL 8.0 如何禁用 ONLY_FULL_GROUP_BY</title>
    <updated>2025-01-15T03:03:51.000Z</updated>
  </entry>
  <entry>
    <author>
      <name>Shenhuanjie</name>
    </author>
    <category term="Linux" scheme="https://shenhuanjie.github.io/tags/Linux/"/>
    <category term="Ollama" scheme="https://shenhuanjie.github.io/tags/Ollama/"/>
    <category term="安装" scheme="https://shenhuanjie.github.io/tags/%E5%AE%89%E8%A3%85/"/>
    <category term="服务" scheme="https://shenhuanjie.github.io/tags/%E6%9C%8D%E5%8A%A1/"/>
    <category term="systemd" scheme="https://shenhuanjie.github.io/tags/systemd/"/>
    <id>https://shenhuanjie.github.io/post/how-to-install-ollama-and-enable-the-service-1nixot.html</id>
    <link href="https://shenhuanjie.github.io/post/how-to-install-ollama-and-enable-the-service-1nixot.html"/>
    <published>2025-01-09T03:10:16.000Z</published>
    <summary>本文介绍了在Linux系统上安装和配置Ollama服务的步骤。首先通过一键命令安装Ollama，然后使用systemd创建服务文件并启动服务，并设置开机自启。最后介绍了直接使用ollama命令启动服务的方法，并简要说明了可能遇到的问题及解决方案。</summary>
    <title>Linux安装Ollama并启用服务教程</title>
    <updated>2025-01-09T03:30:30.000Z</updated>
  </entry>
</feed>
