Terms of Service & Disclaimer
Effective date: May 24, 2026
These Terms of Service and Disclaimer apply to haotianblog, a bilingual technical blog that publishes educational articles, code walkthroughs, datasets, diagrams, networking notes, and project materials. By using this website, you agree to read and use the content responsibly.
This page is written for a technical blog context and is not legal advice.
Educational and academic purpose
All Python scripts, C language code, datasets, diagrams, network architecture notes, configuration examples, security lab materials, and project files published on this website are provided for educational and academic research purposes only.
The materials are intended to help readers understand algorithms, machine learning workflows, implementation trade-offs, networking concepts, defensive security ideas, and engineering practice. They are not provided as production-ready software, professional consulting, managed infrastructure, or a guarantee of technical outcome.
As-Is code and project disclaimer
All content is provided on an As-Is and As-Available basis. haotianblog makes no warranty that any code sample, dataset, command, model, configuration, architecture, or explanation will be accurate, complete, secure, compatible with your environment, or stable in production.
You are responsible for reviewing, testing, securing, and adapting any material before using it. The site owner is not responsible for data loss, service interruption, security incidents, financial loss, academic misconduct, policy violations, infrastructure damage, or any other consequence caused by copying, running, modifying, deploying, or relying on materials from this website.
Production use and technical risk
Do not deploy code, models, network rules, firewall rules, reverse proxies, authentication flows, or server configuration examples from this site into production without independent review. Real systems require threat modeling, access control, logging, monitoring, backups, rollback plans, dependency review, license review, and environment-specific testing.
Examples may intentionally simplify error handling, authentication, scalability, input validation, or operational controls so that the educational idea is easier to see. Those simplifications should not be copied into production systems without correction.
| Material type | Appropriate use | Check before reuse | Not warranted by this site |
|---|---|---|---|
| Python/C code | Course practice, personal reproduction, and algorithm understanding | Dependency versions, input bounds, error handling, and licenses | Production stability, data loss prevention, performance, or compatibility |
| Datasets and downloads | Small experiments, format explanation, and visualization demos | Source, field meaning, privacy limits, and sample representativeness | Completeness, business suitability, or third-party authorization status |
| Networking and security examples | Local labs, defensive analysis, and protocol learning | Authorization boundary, isolated environment, logs, and misuse risk | Unauthorized testing, platform violations, or damage to real systems |
| AI/model workflows | Learning training, evaluation, explanation, and boundary documentation | Data split, metrics, bias, human review, and rollback plan | High-risk decisions, commercial suitability, or model accuracy guarantees |
Security, networking, and responsible use
Some articles may discuss network protocols, defensive traffic analysis, security controls, model privacy, adversarial machine learning, or similar technical topics. These materials are provided to support defensive research, privacy engineering, education, and system understanding.
You must not use this website’s materials for unauthorized access, disruption, evasion of lawful controls, exploitation of systems you do not own or have permission to test, abuse of public services, or any illegal activity. You are responsible for complying with applicable laws, institutional rules, platform policies, and network operator requirements.
Datasets and downloadable resources
Datasets, archives, diagrams, and downloadable materials may be modified, simplified, sampled, or created for teaching purposes. They may not represent complete, current, or production-grade data. You are responsible for verifying data quality, provenance, licensing, privacy constraints, and suitability before using any resource outside a learning context.
Examples, omissions, and environment differences
Technical examples on this site may omit details that are necessary in a real deployment, including secrets management, authentication flows, dependency pinning, input validation, monitoring, rate limits, backups, rollback plans, and compliance review. A command or code fragment may work in the environment used for the article but fail on another operating system, compiler, Python version, package version, CPU architecture, network path, browser, or hosting provider.
You should treat every example as a starting point for review rather than an instruction to copy unchanged. If an article reports an experiment result, that result is tied to the stated assumptions and sample data. Re-running the same method on another dataset, production service, or public network may produce different results and may introduce legal, operational, or ethical obligations that are not covered by the article.
Verification before reliance
Before relying on any article, identify what the page actually proves. A tutorial may prove that a small local example can run, that a metric can be calculated, that a protocol field has a specific meaning, or that a simplified model exposes a useful failure mode. It does not prove that the same method is safe, complete, licensed, performant, or compliant for a different environment.
If you adapt material for a class project, repository, server, research note, or internal tool, keep your own record of changes and tests. Note the source article, the input data, dependency versions, configuration differences, and any result that differs from the published example. That review is your responsibility even when the original article is written carefully.
Reader responsibility checklist
Before adapting any material from this site, review the purpose, input assumptions, dependency versions, licenses, security boundary, and expected output. For machine learning examples, confirm how the data was split and what metric is being optimized. For networking examples, confirm whether the environment is a local lab, a private system you control, or a third-party network with separate rules.
For security and AI safety content, keep the work defensive and bounded. A local toy example does not create permission to test a real service, bypass a platform policy, collect other people’s data, or deploy an automation against systems you do not own. You remain responsible for your own use of the material even when the article is written for education.
If a project is part of a class, workplace review, or public repository, disclose which material came from haotianblog and which changes, tests, or conclusions are your own.
Quality and availability of content
The site is maintained over time, but no guarantee is made that every article, download, command, package version, diagram, or external dependency will remain current. Technical material can become outdated when libraries change, operating systems remove behavior, protocols evolve, datasets move, or external services change their access rules.
If a page appears outdated, broken, duplicated, or too thin to support its claim, report the public URL and the specific issue through the contact page. The possible response may be correction, expansion, redirection, noindex treatment, or removal of the affected material.
There is also no obligation to preserve compatibility with every historical version of a script, notebook, package, browser, or operating system. When content is updated, the site may prioritize clarity, reproducibility, safety, and current educational usefulness over exact preservation of older commands or examples.
No professional advice
Articles on this site may discuss engineering, security, machine learning, infrastructure, or academic workflows. The content does not constitute legal, financial, medical, security consulting, academic integrity, or professional engineering advice. For high-risk decisions, consult qualified professionals and validate assumptions independently.
External links and third-party tools
Articles may link to external technical documentation, research papers, code repositories, package managers, cloud services, datasets, or third-party tools. These links are provided for convenience and context only.
haotianblog does not control and is not responsible for the availability, accuracy, license status, security, privacy practices, malware risk, policy compliance, or continued validity of external websites, tools, packages, scripts, or documentation. You should review third-party terms, licenses, and security properties before using them.
Intellectual property and acceptable reuse
Unless otherwise stated, articles, diagrams, and original explanations are published for reading, learning, and reference. Do not present the site’s content as your own work, remove attribution from copied material, or use content in a way that infringes the rights of the author or third parties.
When code, datasets, or third-party resources are referenced, their own license terms may apply and should be followed separately.
Changes and contact
These Terms of Service and Disclaimer may be updated as the website, content, infrastructure, or legal requirements change. The effective date at the top of this page indicates the latest published version.
Continued use of the site after updates means you should review the current version before relying on any code, dataset, configuration, or project material.
For questions, corrections, or takedown requests, contact haotianblog@gmail.com.