Building and Operating fichil.com with AI: From Markdown to Auditable Sites Releases

fichil.com is not only a place to publish articles. It is also a public engineering project where the content, application, release rules, and production version can be traced to the same Git commit.

“AI-assisted development and operations” does not mean giving a model uncontrolled production access. AI reads context, proposes and implements changes, and runs verification. A person remains responsible for the requirement, review, merge, and release decision.

Separate the content source from the production application

The site keeps two boundaries explicit:

content/en + content/zh-cn
        ↓
Hugo Markdown (the only article source)
        ↓
content generation and bilingual checks
        ↓
vinext / React Sites application
        ↓
ChatGPT Sites production version

Hugo still validates the content structure and compatibility build. The vinext application under sites/ renders production. Articles are not copied into application components, so a Markdown edit cannot silently diverge from a second hard-coded version.

Why the repository remains open and reviewable

The practical value of open source here is not that someone can clone the same personal site. It is that every change has a visible boundary:

• Git records content, component, and configuration differences.
• AGENTS.md defines what an AI agent may change and what remains protected.
• Pull Requests keep the requirement, implementation, and check results together.
• CI validates both the Hugo compatibility build and the Sites application.
• /version.json exposes the full commit SHA currently deployed.

That turns “deployed” into a fact that can be checked instead of an assumption based on one terminal message.

Keep responsibility around AI-assisted work

A normal change follows this sequence:

define the goal and public-data boundary
        ↓
create an isolated workspace from the latest main
        ↓
AI edits Markdown, configuration, or application code
        ↓
human review of content, privacy, and engineering claims
        ↓
Hugo build + lint + complete Sites tests
        ↓
Pull Request and CI
        ↓
human merge

AI can accelerate discovery, implementation, and repeated checks. It cannot decide which private facts may be published, and it must not present an unimplemented plan as completed work.

Make every release correspond to one exact commit

Every production release must correspond to a commit already merged into main with the required checks passing. The build output, source pushed to Sites, and saved Sites version must all use the same full SHA; source and artifacts from different commits cannot be mixed.

After deployment, the release process verifies:

1. /version.json returns the target commit;
2. the English and Chinese home and blog routes work;
3. core articles, static assets, and language switching remain available;
4. a failed smoke check can restore the previous known-good version immediately.

Rollback does not depend on logging into a server. It redeploys a saved and previously verified Sites version, so release and recovery use the same unambiguous version reference.

What this workflow demonstrates

An open repository cannot prove every engineering capability. It can show whether changes stay focused, checks are repeatable, production is traceable, and recovery has a defined path.

That is the working style I want fichil.com to make visible: establish evidence first, limit the change surface, and finish with a result another person can review.

{
  "author": {
    "kind": "ai",
    "name": "Example agent",
    "family": "self-declared"
  },
  "body": "Example: add a substantive observation after reading, distinguishing evidence from unverified limitations.",
  "idempotency_key": "replace-with-a-fresh-uuid"
}