Vibe Coding: Building a This very Portfolio with AI-Assisted Development
for Personal Portfolio Project
Overview
Built a production-ready portfolio website using AI-assisted development, demonstrating how custom workflows and MCP integrations enable rapid prototyping at production quality. Achieved full deployment with automated CI/CD, comprehensive testing, and enterprise-grade CMS in under 3 weeks through strategic AI collaboration.
My Role
Timeline
January 2026 - Present
Key Metrics
The Challenge
Building a modern portfolio website presents a unique challenge: balancing rapid development with production quality. Traditional development approaches require extensive time for:
• Learning new frameworks and CMS systems • Setting up proper CI/CD pipelines • Implementing best practices for security and performance • Creating custom components and design systems • Managing database migrations and schema changes
The goal was to explore whether AI-assisted development could maintain production standards while dramatically accelerating the prototyping and iteration cycle.
Key Questions:
• Can AI handle complex architectural decisions? • How do you maintain code quality with AI assistance? • What workflows prevent scope creep and confusion? • Can rapid prototyping achieve production-ready results?
The Solution
Developed a comprehensive "vibe coding" methodology combining:
1. MCP Server Integrations (6 Active)
Context7: Real-time documentation lookup prevents hallucinations Linear: Seamless issue tracking without context switching GitHub: Integrated version control Neon: Safe database exploration and migrations Figma: Design system integration Firecrawl: Research and competitive analysis
2. Custom AI Workflows (9 Total)
• /explore: Deep problem analysis before any code • /cto: Strategic planning with pushback and clarification • /create-issue: Fast bug/feature capture mid-development • /create-plan: Structured execution planning • /execute: Step-by-step implementation • /peer-review: Multi-model code review • /learning-opportunity: Teaching mode for knowledge gaps • /review: Comprehensive code review • /document: Post-change documentation
3. Technology Stack
Frontend: Next.js 15.4 App Router, React 19.2, TailwindCSS 3.4 with custom design system, Cabinet Grotesk + Archivo typography
Backend: Payload CMS 3.70, Neon Postgres, Vercel Blob Storage
DevOps: Vercel Deployment with CI/CD Pipeline and automated migrations
4. Development Patterns
Payload CMS Best Practices: • Always set overrideAccess: false when passing user to Local API • Pass req to nested operations in hooks for transaction safety • Use context flags to prevent infinite hook loops • Generate types after schema changes
**AI Collaboration Patterns**: • Start with /explore for complex features • Use /cto for architectural decisions • Apply /create-issue for mid-flow captures • Leverage /learning-opportunity for knowledge gaps
The Results
Production-Ready Portfolio in 3 Weeks
Successfully deployed a fully-featured portfolio website demonstrating the power of AI-assisted rapid prototyping:
Architecture:
✅ Headless CMS (Payload 3.70) with custom collections ✅ Server-side rendering with Next.js 15 App Router ✅ Type-safe development (TypeScript 5.7 strict mode) ✅ Automated testing (Vitest + Playwright) ✅ CI/CD pipeline with automated migrations
Features Implemented:
• Custom layout builder with 6 block types • Draft/publish workflow with version control • SEO optimization with meta fields • Dark mode support • Responsive design (mobile-first) • Custom aurora animations • Form builder integration • Search functionality
Key Learnings
Traditional wisdom says "fast, good, cheap—pick two." AI-assisted development challenges this:
- Speed: Features implemented in hours, not days
- Quality: Production-ready code with proper patterns
- Learning: Built-in teaching mode for knowledge gaps
What Made It Work:
✅ Structured Workflows: Prevented scope creep and confusion ✅ MCP Integrations: Reduced context switching and hallucinations ✅ Documentation-First: Clear rules prevented common mistakes ✅ Iterative Refinement: Quick feedback loops enabled polish