This website—the one you're reading right now—was built almost entirely with AI assistance. Not as an experiment, but as a real production site that needed to work.

Here's how that actually happened.

The Starting Point

I had a WordPress site that was showing its age. Slow, expensive to host, plugins breaking with every update. I wanted to migrate to Next.js, but rebuilding an entire website solo seemed like a months-long project.

Then I started using Claude Code.

What Claude Code Is

Claude Code is a command-line interface that lets you interact with Claude directly from your terminal. It can:

  • Read and understand your codebase
  • Write and edit files
  • Run commands
  • Maintain context across a conversation

The key difference from chat-based AI is that it operates in your development environment. It can see your files, understand your project structure, and make changes directly.

The Build Process

Phase 1: Initial Setup (Day 1)

What I asked for:

Set up a new Next.js 14 project with:
- App Router
- TypeScript
- A basic layout with header and footer
- Dark/light theme toggle
- CSS custom properties for theming

What I got: A working project skeleton with theme switching in about 20 minutes.

What I learned: Being specific about the tech stack upfront saved significant back-and-forth. Claude knew exactly what patterns to use for Next.js 14 App Router vs Pages Router.

Phase 2: Blog System (Days 2-3)

The blog was the core of the site. I needed:

  • MDX support for rich content
  • Categories and subcategories
  • Dynamic routing
  • SEO metadata

The conversation went like this:

Me: "I need a blog system using MDX. Posts should have frontmatter for title, date, category, subcategory, and author. Subcategory is optional."

Claude: [Created the blog utilities, interfaces, and initial routing]

Me: "The subcategory routing isn't working. Posts with subcategories should go to /blog/[category]/[subcategory]/[slug], posts without should go to /blog/[category]/[slug]"

Claude: [Fixed the routing logic, explained the Next.js catch-all route approach]

Lesson: AI doesn't always get it right the first time, especially with complex routing. But iterating was fast. Instead of debugging for hours, I described the problem and got a solution in minutes.

Phase 3: Cookie Consent (Day 4)

GDPR compliance meant I needed proper cookie consent. This is where things got interesting.

Initial request: "I need GDPR-compliant cookie consent with different defaults for EU vs non-EU users."

The implementation included:

  • Geolocation detection via Vercel headers
  • Consent storage in localStorage
  • Conditional script loading for analytics
  • Logging to Supabase for audit trail

This was a complex feature with many edge cases. Working with Claude, I could think through requirements while it handled implementation details.

Key moment: When I described the geo-detection requirement, Claude suggested using Vercel's x-vercel-ip-country header—something I didn't know existed. AI doesn't just execute; it brings knowledge to the table.

Phase 4: SEO Optimization (Days 5-6)

Starting SEO score: 95/100. Ending SEO score: 100/100.

What was missing:

  • OpenGraph images
  • Structured data (JSON-LD)
  • Article tags and keywords
  • Enhanced author schema

The process:

I asked Claude to audit the site against SEO best practices. It identified specific gaps.

Then, for each gap:

Me: "Implement dynamic OpenGraph image generation using @vercel/og"
Claude: [Created the API route, showed how to use it in metadata]

Me: "Add BlogPosting schema to blog posts with enhanced author information"
Claude: [Added the JSON-LD, explained what each field does for SEO]

Lesson: AI is great at implementing established patterns. SEO has well-documented standards (schema.org, OpenGraph spec), so Claude could generate correct implementations without much back-and-forth.

Phase 5: Responsive Design (Day 7)

Making everything work on mobile was the final piece.

The challenge: The site had inline styles everywhere, which made responsive design hard.

The approach:

Me: "The blog post layout has ~600 lines of CSS-in-JS causing flash of unstyled content. Migrate it to globals.css with proper responsive breakpoints."

Claude: [Moved all styles to CSS, added media queries, organized by component]

This was pure grunt work—exactly what AI handles well. Tedious transformations with clear rules.

What Worked Well

1. Iterative Refinement

Instead of trying to specify everything upfront, I described the general shape and refined based on what Claude produced. This felt more like pair programming than specification writing.

2. Learning While Building

Claude explained why it made certain choices. I learned about Next.js 14 patterns, Vercel-specific features, and modern CSS approaches while building something real.

3. Handling the Boring Parts

Writing TypeScript interfaces, creating similar components, migrating styles—tasks that require attention but not creativity. AI handled these while I focused on decisions.

4. Quick Debugging

Instead of searching Stack Overflow for error messages, I could paste errors directly and get contextual explanations. Claude understood my codebase, so suggestions actually worked.

What Didn't Work

1. Complex State Logic

The cookie consent system went through several iterations. AI's first attempts had race conditions and edge cases. Getting the logic right required careful back-and-forth.

Lesson: For complex logic, AI is a collaborator, not a replacement for thinking.

2. Design Decisions

AI could implement any design, but it couldn't tell me what the design should be. Visual aesthetics and UX decisions still required human judgment.

3. Business Logic Assumptions

Sometimes Claude assumed things about how the business should work. I had to be explicit about requirements that seemed "obvious" but weren't.

The Numbers

| Metric | With AI | Without AI (Estimate) | |--------|---------|----------------------| | Time to MVP | 1 week | 4-6 weeks | | Lines of code | ~15,000 | Same | | Major features | 12 | Same | | Components created | 25+ | Same | | Blog posts migrated | 25 | Same |

The code quality is comparable to what I'd write myself—because I reviewed everything and made Claude match my patterns.

The Mental Model Shift

Before AI-assisted development, I'd think: "This feature will take 3 days. Do I really need it?"

Now I think: "Can I describe this clearly enough for AI to implement? Then it takes hours, not days."

This changes what's worth building. Features that seemed like "nice to have" become achievable because the implementation cost dropped dramatically.

Tips for Your Own AI-Assisted Project

1. Start with Structure

Get the project skeleton right first. Framework, routing, styling approach. Everything else builds on this.

2. Work in Focused Sessions

Each conversation should tackle one feature or problem. Context stays fresh, and you can course-correct quickly.

3. Provide Examples

Show Claude your existing code. "Match the pattern in this file" works better than describing the pattern.

4. Review Everything

AI-generated code can look right and be subtly wrong. Test it. Read it. Understand it.

5. Use CLAUDE.md

Keep a project context file that tells Claude about your codebase. This site has a comprehensive CLAUDE.md that explains structure, conventions, and current state.

6. Commit Often

When something works, commit it. If the next change breaks things, you can rollback cleanly.

What This Means for Solo Founders

The traditional advice was: "Solo founders should do less. Focus on one thing."

AI changes that equation. With AI assistance, a solo founder can:

  • Build more sophisticated products
  • Maintain higher quality standards
  • Move faster without cutting corners

The constraint isn't implementation anymore—it's clarity of vision. If you can describe what you want precisely, you can build it.

The Site Today

KuduTek.com is now:

  • ✅ 100/100 Lighthouse SEO score
  • ✅ GDPR-compliant with proper consent management
  • ✅ Fully responsive across all devices
  • ✅ Fast (Next.js static generation)
  • ✅ Maintained by one person with AI assistance

It's not a demo or proof of concept. It's a real business website that serves real visitors.

And yes, Claude helped write this blog post too.

Related Reading

This post is part of the AI & Automation series. The best way to learn AI-assisted development is to try it on a real project.