AI Lab: building a static-first bilingual career website

This is a personal technical practice, not an enterprise client delivery case. It uses a static-first architecture to present verified career information, bilingual content and ongoing Explore writing. AI assistance supports content structuring, implementation and verification.

The site includes bilingual career pages, structured content collections, Explore listing and article routes, and configuration for static hosting. Static generation keeps each publication step deliberate and reviewable.

Content before systems

Career history, project cases and long-form reflections are stored in central data files and Markdown collections. Changes remain version-controlled, and public pages are generated at build time without depending on a runtime database.

Architecture is useful when it makes truthful content easier to maintain—not when it makes a simple problem look complex.

Bilingual does not mean duplicated

English and Chinese serve different hiring contexts. They share a content model and visual components, while the writing remains independent enough to sound natural. A translationKey establishes the relationship without forcing line-by-line translation.

AI assistance and human review

AI supports content structure, code implementation and quality checks. Career facts, disclosure permissions and final wording remain subject to human review. Unverified clients, project scale, outcomes and contact details are not published simply because the site can display them.

Room to grow

New AI Lab notes, reading notes or project cases can be added as Markdown files that satisfy the schema. Page components read the collection and generate routes, so the site grows through content instead of repeated template work.