欢迎来到浩天博客：这个双语技术站会发布什么

浩天博客是我用来长期整理技术学习、工程实验和代码说明的公开站点。它不是临时占位页，也不是只堆链接的导航页；每一条主要内容都尽量对应一篇可以复查的文章、一段可以运行的代码、一个可下载的配套资源，或者一次真实项目中的工程取舍。

站点目前以中文写作为主，同时维护英文入口和逐步扩展的英文文章。这样做的原因很直接：很多底层技术细节用中文写更完整，英文页面则方便把主题、关键词、项目结构和后续翻译路线组织起来。

这个站点的内容边界

这里的主题主要集中在三类技术问题。第一类是机器学习和深度学习：从数据集、特征工程、训练评估，到矩阵微积分、反向传播、卷积、Attention 和小型 CNN 项目。第二类是算法和 C/Python 实现：例如 8 皇后回溯、位运算优化、K-means 聚类、CSV 数据读取和可视化结果。第三类是网络协议与工程安全：包括 DNS、TCP、TLS、HTTP/3、代理边界、缓存再验证、AI 安全威胁建模和防御实验。

我会避免把文章写成只有结论的短摘要。技术内容应该说明为什么这样做、怎么验证、哪里容易出错，以及代码输出是否支持文章里的判断。如果一个页面只是工具、问卷、分享入口或资源索引，它会继续可访问，但不会被当作核心内容页来展示。

推荐阅读顺序

如果你从机器学习开始，可以先读 机器学习完整流程，再进入 特征工程、模型训练与评估 和 K-means Iris C 语言实现。这条路线会把数据、特征、模型和评估串起来，而不是直接跳到库函数调用。

如果你更关心底层实现，可以从 8 皇后回溯入门 和 位运算优化版 8 皇后 开始。这两篇文章使用同一个问题比较普通递归搜索和位掩码状态压缩，适合练习递归、剪枝和复杂度分析。

如果你想看网络协议，可以从 网络基础原理可视化 进入，再按 DNS、TCP、TLS、HTTP 缓存和代理边界阅读。网络内容默认使用本地模型或确定性脚本，不提供绕过访问控制的外部代理服务。

代码、数据和配套资源如何使用

站点中的 C/Python 代码、CSV 样例、流程图和实验包主要服务于文章解释。它们适合教育、学术研究、课程练习和个人复现实验，但不应该未经审查直接用于生产环境。每个资源都尽量关联到对应文章，避免只有下载文件而没有上下文。

下载资源会优先选择小而清楚的样例：能展示数据结构、算法过程、评估指标或可视化结果即可。对于大型数据集、真实攻击环境或可能造成滥用的系统配置，站点会保留边界说明，只提供安全的 toy example 或防御性分析。

站点内容证据地图

为了让读者和搜索审核系统更容易判断页面价值，核心文章会尽量留下可复查证据，而不是只给出概念性结论。

维护原则

这个博客会持续更新，但不会为了更新频率发布低信息量页面。更重要的是让文章之间形成清晰的知识路径：基础概念、可运行代码、实验结果、错误分析和后续阅读互相连接。新页面上线时，我会优先检查正文是否足够、是否有明确作者意图、是否能被读者独立复查。

如果你发现文章里的代码、下载链接、术语解释或中英文对应关系有问题，可以通过 联系页面 或 [email protected] 反馈。这个站点的目标是做成一个长期可维护、可复查、对读者有实际帮助的技术知识库。

Welcome to haotianblog: what this bilingual technical site covers

haotianblog is a public place for long-lived technical writing, engineering experiments, and implementation notes. It is not a placeholder page and it is not meant to be a collection of empty links. The main pages are organized around reviewable articles, runnable code, downloadable companion files, and engineering decisions that can be inspected later.

The site is currently strongest in Chinese while keeping English topic hubs and selected English articles. That structure lets the Chinese side carry detailed explanations and lets the English side expose the themes, keywords, project routes, and future translation paths more clearly.

Content scope

The writing is centered on three technical areas. The first is machine learning and deep learning: datasets, feature engineering, training and evaluation, matrix calculus, backpropagation, convolution, attention, and small CNN projects. The second is algorithms and C/Python implementation: eight queens backtracking, bitmask optimization, K-means clustering, CSV parsing, and visualized output. The third is network protocols and engineering security: DNS, TCP, TLS, HTTP/3, proxy boundaries, cache revalidation, AI security threat modeling, and defensive experiments.

The goal is to avoid short summaries that only state conclusions. A useful technical article should explain why a method is used, how to verify it, what can go wrong, and whether the code output supports the claim. Pages that are mainly tools, surveys, share actions, or resource indexes remain accessible, but they are not treated as the core editorial content of the site.

Where to start

If you are starting with machine learning, begin with Machine Learning Workflow, then continue into Model Training and Evaluation, Python AI Mini Practice, and the K-means Iris C implementation. That path connects data, features, models, and evaluation instead of jumping straight to library calls.

If you want lower-level implementation practice, start with eight queens backtracking and the bitmask version. These two articles use the same problem to compare recursive search with compact bitmask state, making recursion, pruning, and complexity easier to inspect.

If you are interested in networking, open Network Fundamentals Visualized and continue through DNS, TCP, TLS, HTTP caching, and proxy trust boundaries. The networking material uses local models or deterministic scripts by default and does not provide an external proxy service for bypassing access controls.

Code, data, and companion resources

The C/Python code, CSV samples, diagrams, and lab bundles on this site exist to support the articles. They are appropriate for education, academic study, course practice, and personal reproduction, but they should not be copied into production systems without review. Each resource is tied back to the article context so that a download is not separated from the assumptions behind it.

Companion files are intentionally small and inspectable. A file is useful when it reveals data layout, algorithm state, evaluation metrics, or visualization output. Large datasets, real attack environments, and configurations that could be misused are handled with additional boundaries and are represented with safe toy examples or defensive analysis instead.

Editorial Evidence Map

The core articles are designed to leave reviewable evidence. A reader should be able to see not only what the conclusion is, but how the conclusion can be checked.

Maintenance principles

This blog will continue to change, but it should not publish low-information pages just to look active. The stronger goal is to make the articles connect into clear knowledge routes: concepts, runnable code, experiment results, error analysis, and next reading steps. When a new page is published, it should have enough body content, a clear editorial purpose, and evidence that a reader can independently inspect.

If you find a problem in code, downloads, terminology, or bilingual page mapping, use the contact page or email [email protected]. The long-term aim is a maintainable technical knowledge base that is useful to readers, not a thin collection of search landing pages.