Academic Website in a Day: The No-Code Playbook

If you are a new academic launching a lab, you already know the paradox: your research pace accelerates, but your website usually slows down. Between hiring, grants, teaching, and advising, the lab site often becomes the one thing everyone relies on and nobody has time to maintain.

That is risky now. As the internet fills with noise, a clear and frequently updated lab website is no longer optional. It is how your work is discovered, how students are recognized, how collaborators decide to reach out, and how non-specialists understand why your papers matter.

This is a meta post on how I built this SecretLab website in one day with Codex and Cursor, what actually worked, what took longer than expected, and how prompt strategy made the difference.

What “No-Code” Actually Meant

No-code did not mean “no engineering.” It meant I spent my energy on decisions and quality while the AI handled implementation details. I still had to define structure, enforce constraints, verify behavior, and keep the content model coherent.

Think of it this way: AI wrote most of the code, but I still owned product direction, system design, and correctness.

The One-Day Build Plan

1. Architecture first: set up modular templates and JSON-driven content before polishing visuals.
2. Shippable v1 second: get all core sections live (hero, news, projects, people, publications, blog, footer).
3. Iterative refinement third: run short feedback loops for layout, mobile behavior, and interaction details.
4. Hardening last: improve accessibility, metadata/SEO, consistency, and content integrity.

Why Modular Data Design Was the Real Multiplier

The biggest long-term win was separating content from rendering. I stored people, projects, publications, sponsors, and news as structured JSON, then rendered everything through reusable templates. Once that was in place, updates stopped being fragile copy-paste tasks and became predictable data edits.

That single-source model also powered interactivity: author chips, person popups, related-paper lists, section counters, and show more/show less controls all stayed synchronized. When data changes in one place, behavior updates everywhere.

Where AI Saved Time, and Where It Did Not

Importing publication metadata from Google Scholar saved a lot of time. It turned tedious backfill work (titles, venues, links, author ordering) into a much faster structured import-and-cleanup flow.

The real bottleneck was people data. Collecting student details I did not already have, name formatting, role/degree info, profile links, and usable photos, took the most time. AI can accelerate implementation, but it cannot replace missing source information.

Prompting Strategy That Worked

The highest-signal prompt structure was:

1. State one concrete goal.
2. Add hard constraints (what must not change).
3. Define acceptance criteria (what successful output must do).
4. Provide source-of-truth data or ordering rules.

A short prompt with sharp constraints usually outperformed a long prompt with vague intent.

Prompt Patterns That Produced Good Results

• “Redo the entire theme and verify the structure.”
• “Use consistent colors across sections. Darker hero left mask for readability.”
• “Show only 4 news, 6 projects, 15 papers by default; add Show more/Show less.”
• “Sort publications by year; avoid duplicate year in venue metadata.”
• “Make author names clickable and open profile popups from projects/publications/blog.”

What Went Right

• I committed early to modular templates + structured data.
• I changed from aesthetic feedback (“looks odd”) to measurable acceptance checks.
• I validated on desktop/tablet/mobile continuously instead of waiting for the end.
• I treated UI issues as reproducible bugs, not one-off visual anomalies.

What Slowed Me Down

• I sometimes bundled multiple concerns in one prompt, which created partial fixes.
• I occasionally optimized symptoms before root causes (especially in mobile overlay behavior).
• I pivoted themes frequently, which increased CSS churn and regression risk.
• I underestimated the manual coordination needed for profile data and photos.

Examples of Multi-Prompt Debug Loops

Some issues were inherently iterative and needed focused debugging:

• Mobile menu frost/blur: visual parity with header required layering, blur, width, and spacing fixes in sequence.
• People popup behavior: centering and scroll-lock side effects needed cross-browser checks and interaction tuning.
• Responsive card limits: section limits had to account for breakpoint-specific layouts and weighted cards.

Reusable Prompt Templates

Template 1: UI change with constraints

“Update only [component]. Keep [theme/layout] unchanged. On [breakpoint], implement [exact behavior]. Acceptance: [checklist].”

Template 2: Data update with ordering

“Add these records to [json file] in exact order: [list]. Keep IDs stable. Do not alter unrelated entries.”

Template 3: Bug fix

“Repro: [steps]. Current: [wrong behavior]. Expected: [target behavior]. Fix root cause and verify no regressions in [related feature].”

Starter Prompt You Can Copy

If you are building a new lab website from scratch, start with this:

I am a new faculty member launching a research lab website. Build a complete v1 website with a modular architecture and data-driven content.

Requirements:
- Use reusable templates/components for header, hero, news, projects, people, publications, blog, and footer.
- Keep content in JSON files (people, projects, publications, news, sponsors, site settings).
- Add responsive design for desktop, tablet, and mobile.
- Add light/dark theme toggle (dark default).
- Add SEO metadata (title, description, OpenGraph, Twitter tags, canonical URL, structured data where appropriate).
- Add interactive elements: person popups, show more/show less for long sections, and active section highlighting in nav.
- Keep accessibility in mind (semantic HTML, alt text, keyboard-close for modals, color contrast).

Content goals:
- Communicate research focus areas clearly in non-technical language.
- Showcase projects and publications with links and authors.
- Highlight students and collaborators with profile cards.
- Support easy updates by editing JSON only, not templates.

Output format:
1) Proposed file structure.
2) Implementation of templates, CSS, and JS.
3) Example JSON data for each section.
4) Final verification checklist for responsiveness, accessibility, and SEO.

Do not skip steps. Build a production-ready baseline that I can iterate on quickly.

This workflow is best run in an IDE-based AI coding agent. A plain chat interface usually cannot create the full file structure and documentation or support the same iterative edit-and-verify loop.

Final Takeaway

AI did not remove the need for technical judgment; it amplified the impact of clear judgment. The fastest path was modular structure, explicit constraints, tight iteration, and continuous verification. If you treat AI like a collaborator rather than an autocomplete tool, you can ship quickly without sacrificing quality and give your students a stronger public research presence from day one.