# Building a Complete Portfolio Using Only AI: Zero Human Code In February 2026, I achieved something unprecedented in my 3 years of AI-assisted development: I built my entire portfolio—design, architecture, code, content, and deployment—using AI with **zero human code modifications**. Only planning. Not AI-assisted development. **AI-driven development with human orchestration.** This is a technical deep-dive into how I orchestrated multiple AI tools, Model Context Protocol (MCP) servers, and specialized skills to build a production-ready portfolio that scores 96/100 on Lighthouse—without writing a single line of code myself. ## The Unprecedented Constraint After 3 years working with AI tools like GitHub Copilot, ChatGPT, and Claude, I've always written or modified AI-generated code. This time, I set a different challenge: - **Zero lines of human-written code** - **AI generates everything** - **Human only provides direction and planning** **Result**: A production-ready portfolio with 96/100 Lighthouse score, advanced animations, MDX blog with custom components, and comprehensive content—built in 1 day. ## Why Gemini for Design? Before diving into the three-phase pattern, let me explain a critical tool choice: **Google Gemini (website, not CLI)** for the design and architecture phase. **Why Gemini specifically?** Gemini's web search capabilities made it ideal for design research and trend analysis. I needed an AI that could: - **Search the web** for current design trends - **Analyze modern portfolios** to identify patterns - **Gather information** about Neo-Brutalism from design sites - **Brainstorm iteratively** through Q&A sessions ', content: 'Search the web for Neo-Brutalist design trends in 2026. What makes them effective for developer portfolios?', }, { type: 'output', content: 'Searching web for Neo-Brutalist design patterns...\nAnalyzing brutalistwebsites.com, awwwards.com, dribbble.com...', }, { type: 'success', content: 'Found: Thick borders (4-8px), bold color blocks, no gradients', }, { type: 'success', content: 'Found: Heavy drop shadows for depth without blur', }, { type: 'success', content: 'Found: Monospace fonts + bold sans-serif headings', }, { type: 'output', content: 'Neo-Brutalism emphasizes raw, unpolished aesthetics with high contrast and geometric shapes. Perfect for technical portfolios wanting to stand out from minimalist designs.', }, ]} /> Through iterative web research sessions, Gemini helped me: 1. **Discover Neo-Brutalism** as the design language through web trend analysis 2. **Analyze successful portfolios** to identify effective patterns 3. **Define color palettes** based on current design trends 4. **Establish typography systems** used in modern developer sites 5. **Plan component hierarchies** following React best practices found online **Output**: `EXECUTION_PLAN.md` - A comprehensive blueprint with design tokens, component architecture, and technical stack decisions. **Key insight**: Using Gemini's web interface (not CLI) gave me access to real-time design research capabilities that informed every architectural decision. ## Three-Phase Development Pattern ### Phase 1: Design & Architecture with Gemini (Website) I started with **Google Gemini's web interface** to design the system architecture and visual identity through iterative brainstorming. ', content: 'I need a portfolio that doubles as a technical blog. The design should be Neo-Brutalist—bold, geometric, high contrast. Help me define the design system and architecture.', }, { type: 'output', content: 'Analyzing requirements for Neo-Brutalist Code Garden...\nSearching web for design inspiration...\nGenerating comprehensive design system...', }, { type: 'success', content: 'Generated complete design system', }, { type: 'success', content: 'Defined color palette (Cyber Yellow, Circuit Blue, Matrix Green)', }, { type: 'success', content: 'Created component hierarchy (primitives → sections → layouts)', }, { type: 'success', content: 'Planned animation strategy (GSAP + ScrollTrigger)', }, { type: 'success', content: 'Documented technical stack (Next.js 16, Tailwind v4, TypeScript)', }, ]} /> **What Gemini produced**: - Complete design system (colors, typography, spacing tokens) - Neo-Brutalist principles and guidelines (thick borders, heavy shadows, bold colors) - Component architecture (primitives → sections → layouts) - Animation strategy (GSAP + React for scroll-triggered interactions) - Technical stack recommendations (Next.js 16, Tailwind CSS v4, TypeScript, MDX) - **EXECUTION_PLAN.md** - The complete blueprint for implementation **Key insight**: Gemini's ability to search the web for Neo-Brutalist examples, analyze modern portfolio designs, and synthesize trends into a cohesive design system made it invaluable for the architecture phase. It created harmony between portfolio aesthetics and blog functionality through web-informed research. ### Phase 2: Planning with Claude Code I moved Gemini's `EXECUTION_PLAN.md` to **Claude Code CLI** for refinement and implementation planning. ', content: 'Read EXECUTION_PLAN.md and ARCHITECTURE.md. Create a detailed implementation plan with file structure, dependencies, and execution order. Use Sequential Thinking MCP for complex architectural decisions.', }, { type: 'output', content: 'Reading EXECUTION_PLAN.md...\nReading ARCHITECTURE.md...\nActivating Sequential Thinking MCP...\nAnalyzing architecture...', }, { type: 'success', content: 'File structure defined', }, { type: 'success', content: 'Dependencies identified (Next.js, GSAP, MDX)', }, { type: 'success', content: 'Implementation phases created (design system → primitives → sections → pages)', }, { type: 'success', content: 'Component patterns established (TypeScript types, index exports)', }, ]} /> **Claude Code's strengths**: - Translated architectural vision into actionable implementation tasks - Proposed implementation order (foundation-first: design system → primitives → sections → pages) - Identified potential issues early (import patterns, type safety, responsive design) - Created consistent patterns across codebase (TypeScript interfaces, component structure) - Used **Sequential Thinking MCP** for complex planning scenarios requiring step-by-step problem solving ### Phase 3: Implementation with Claude Code With plans in place, I directed **Claude Code with Claude Sonnet 4.5 and Haiku 4.5** to implement everything. ', content: 'Following the plan, create the design system. Start with Tailwind config and CSS variables for the Neo-Brutalist theme.', }, { type: 'output', content: 'Creating design system with thick borders, heavy shadows, and bold colors...', }, { type: 'success', content: 'tailwind.config.ts updated with Neo-Brutalist tokens', }, { type: 'success', content: 'globals.css with CSS variables (--primary, --secondary, --accent)', }, { type: 'success', content: 'Color palette implemented (Cyber Yellow, Circuit Blue, Matrix Green)', }, { type: 'success', content: 'Typography scale defined (Space Grotesk, JetBrains Mono)', }, ]} /> **The power of zero-code constraint**: When you can't fix AI's output manually, you learn to give better instructions. Every iteration improves your orchestration skills. ## The Secret Weapons: Skills and MCPs I didn't use generic Claude. I used **specialized AI skills** and **Model Context Protocol (MCP) servers** that enhance Claude Code with long-term memory, semantic understanding, and structured problem-solving. ### AI Skills: Industry Best Practices #### 1. Frontend Design Skill ``` https://skills.sh/anthropics/skills/frontend-design ``` This skill transforms generic prompts into production-grade implementations: **Impact**: Generated production-grade components that follow modern UI/UX patterns and implement advanced CSS techniques—not generic Bootstrap clones. #### 2. Vercel React Best Practices ``` https://skills.sh/vercel-labs/agent-skills/vercel-react-best-practices ``` This skill enforces Next.js best practices automatically. For example, it avoids barrel file imports: **Impact**: Direct imports for better tree-shaking, proper code splitting, and Next.js 16 optimization patterns (server components, metadata API, image optimization). ### Model Context Protocol (MCP) Servers MCPs enhance Claude Code with critical capabilities: fetching up-to-date documentation, maintaining context across conversations, and enabling structured reasoning for complex problems. #### Context7 MCP: Up-to-Date Documentation **What it is**: [Context7](https://github.com/upstash/context7) pulls up-to-date, version-specific documentation and code examples straight from the source and places them directly into your prompt. **How I used it**: Add `use context7` to prompts to fetch current documentation and examples: ', content: 'Create GSAP scroll animations with ScrollTrigger for the Hero section. use context7', }, { type: 'output', content: 'Context7 fetching GSAP 3.12 documentation...\nContext7 fetching ScrollTrigger examples...\nContext7 fetching React integration patterns...', }, { type: 'success', content: 'Retrieved: GSAP 3.12 ScrollTrigger API documentation', }, { type: 'success', content: 'Retrieved: React useGSAP hook patterns', }, { type: 'success', content: 'Retrieved: ScrollTrigger configuration examples', }, { type: 'output', content: 'Creating Hero.tsx with GSAP ScrollTrigger animations...', }, ]} /> **Real example**: When implementing GSAP animations, Context7 fetched: - GSAP 3.12 ScrollTrigger API documentation (not hallucinated APIs) - React `useGSAP` hook integration patterns - Version-specific configuration examples **Value**: No tab-switching, no hallucinated APIs that don't exist, no outdated code generation. Context7 ensures Claude uses the correct, current API documentation for the exact versions in your project. #### Serena MCP: Cross-Conversation Context **What it is**: [Serena](https://github.com/oraios/serena) maintains context, ideas, concepts, and progress across multiple Claude Code conversations—solving the context window reset problem. **How I used it**: The portfolio was built through multiple conversations throughout the day. Serena prevented starting from zero each time: - **Maintaining design decisions**: Neo-Brutalist principles established in conversation 1 carried to conversations 2, 3, 4 - **Preserving architectural choices**: Component hierarchy decided early remained consistent in later conversations - **Remembering progress**: What was built, what remained, without manual re-explanation - **Keeping concepts alive**: Design system tokens, naming conventions, patterns persisted ', content: 'Create the Hero section', }, { type: 'output', content: 'Serena retrieving context from previous conversation...\nLoaded: Neo-Brutalist design system (4px borders, 8px shadows)\nLoaded: Component structure pattern\nLoaded: Color palette and design tokens', }, { type: 'success', content: 'Hero.tsx created matching established design system', }, ]} /> **Real example**: In the afternoon session, when building the blog components, I didn't need to re-explain: - The Neo-Brutalist design language established in the morning - The component file structure pattern (`.tsx`, `.types.ts`, `index.ts`) - The color naming conventions (primary/secondary/accent) - The spacing scale and shadow patterns **Value**: Serena solved the "context window reset" problem. Each new conversation started with full awareness of previous decisions, concepts, and progress. This enabled building a complex portfolio across multiple sessions without repetitive explanations or contradictory implementations. #### Sequential Thinking MCP: Structured Problem-Solving **What it is**: [Sequential Thinking](https://github.com/modelcontextprotocol/servers/tree/main/src/sequentialthinking) is an MCP server that enables Claude Code to break down complex problems into structured, step-by-step reasoning chains. **How I used it**: - **Complex planning scenarios**: Deciding component hierarchy for the design system - **Architecture decisions**: Choosing between animation approaches (CSS vs GSAP) - **Multi-step implementations**: Planning the MDX pipeline with custom components - **Trade-off analysis**: Evaluating build-time vs runtime rendering for blog content **Real example**: When planning the MDX blog pipeline, Sequential Thinking helped Claude Code reason through: 1. **Step 1**: Evaluate MDX processing libraries (next-mdx-remote vs @next/mdx) 2. **Step 2**: Consider build-time vs runtime trade-offs (SSG for blog posts) 3. **Step 3**: Plan custom component registration (Terminal, CodeSnippet, Icon) 4. **Step 4**: Design frontmatter schema for metadata (title, description, tags) 5. **Step 5**: Implement type-safe content collection with Zod validation 6. **Step 6**: Create dynamic routing with `generateStaticParams` for performance ', content: 'Plan the MDX blog implementation. Consider custom components, type safety, and performance.', }, { type: 'output', content: 'Activating Sequential Thinking MCP...\nStep 1: Evaluating MDX libraries...\nStep 2: Analyzing build-time vs runtime trade-offs...\nStep 3: Planning custom component registration...', }, { type: 'success', content: 'Decision: Use next-mdx-remote for runtime flexibility', }, { type: 'success', content: 'Decision: SSG for blog posts (generateStaticParams)', }, { type: 'success', content: 'Decision: Register custom components in mdx-components.tsx', }, { type: 'success', content: 'Implementation plan: 6-step pipeline with type safety', }, ]} /> **Value**: Better decision-making for architectural choices. Sequential Thinking prevented hasty decisions by forcing structured reasoning through multi-step problems. Instead of jumping to implementation, Claude Code evaluated trade-offs, considered alternatives, and documented reasoning—resulting in better architecture. ## Model Strategy: Sonnet 4.5 + Haiku 4.5, No Opus **Key decision**: I never used Opus. Only **Claude Sonnet 4.5** and **Claude Haiku 4.5**. **Why?** With proper planning, skills, and MCPs, Sonnet 4.5 delivers Opus-level quality at significantly lower cost and faster speed. **My model selection strategy**: - **Claude Sonnet 4.5**: Component implementation, complex logic, architecture decisions, MDX content generation - **Claude Haiku 4.5**: File operations, simple edits, repetitive tasks, type file creation - **Google Gemini (Website)**: Initial design research, system architecture through web search ', content: 'Create Hero section with GSAP scroll animations and responsive design', }, { type: 'divider', content: '', }, { type: 'comment', content: 'Simple type definition → Haiku 4.5', }, { type: 'input', prompt: '$', content: 'claude --model haiku', }, { type: 'input', prompt: 'claude>', content: 'Create TypeScript types for Hero component props', }, ]} /> **Result**: - **Lower cost** (Sonnet 4.5 is significantly cheaper than Opus) - **Faster responses** (Sonnet 4.5 is quicker than Opus) - **Same quality** (with proper planning, skills, and MCPs) **The secret**: Well-structured plans and specialized skills eliminate the need for the "smartest" model. **Sonnet 4.5 with good direction, frontend-design skill, and Context7 MCP outperforms Opus with vague prompts.** Cost savings: ~60% compared to using Opus for all tasks, with zero quality degradation. ## The Iterative Workflow Here's a typical development session showing the **Explore → Implement → Refine → Verify** pattern: ', content: "Explore the components folder and tell me what patterns we're using", }, { type: 'output', content: 'Reading components/ui/primitives...\nSerena analyzing patterns...\nContext7 retrieving design decisions...', }, { type: 'output', content: 'Found primitives pattern: Button, Card, Badge, NavLink\nConsistent TypeScript types and index exports\nNeo-Brutalist styling: 4px borders, 8px shadows\nDesign tokens: --primary, --secondary, --accent', }, { type: 'divider', content: '', }, { type: 'comment', content: 'Step 2: Implementation (Frontend Design Skill)', }, { type: 'input', prompt: 'claude>', content: 'Using frontend-design skill, create a FilterBar component following the same pattern', }, { type: 'output', content: 'Activating frontend-design skill...\nCreating FilterBar with Neo-Brutalist styling and accessibility...', }, { type: 'success', content: 'FilterBar.tsx created (responsive, accessible)', }, { type: 'success', content: 'FilterBar.types.ts created (TypeScript interfaces)', }, { type: 'success', content: 'index.ts created (named exports)', }, { type: 'success', content: 'Added to sections/index.ts', }, { type: 'divider', content: '', }, { type: 'comment', content: 'Step 3: Refinement (Context7 verification)', }, { type: 'input', prompt: 'claude>', content: "The spacing doesn't match our design system", }, { type: 'output', content: 'Context7 checking design system spacing tokens...\nFound: Space scale (4, 8, 12, 16, 24, 32, 48, 64)\nUpdating FilterBar spacing to match...', }, { type: 'success', content: 'Spacing updated: gap-6 (24px), padding-4 (16px)', }, { type: 'success', content: 'Verified against design system tokens', }, ]} /> **Pattern**: Explore → Implement → Refine → Verify This workflow leveraged: - **Serena MCP** for maintaining context across conversations (preserving design decisions from earlier sessions) - **Context7 MCP** for fetching up-to-date documentation (`use context7`) - **Frontend Design Skill** for production-grade implementation - **Sequential Thinking MCP** for multi-step architectural reasoning ## What AI Excelled At ### 1. Design System Implementation Created complete design system with: - CSS variables for theming (`--primary`, `--secondary`, `--accent`) - Tailwind configuration with custom tokens - Consistent spacing scale (4px increments: 4, 8, 12, 16, 24, 32, 48, 64) - Typography scales (Space Grotesk for headings, JetBrains Mono for code) - Color palette with semantic naming (primary, secondary, accent vs yellow, blue, green) ### 2. Component Architecture Built modular component hierarchy: ### 3. Advanced Features Implemented complex functionality: - GSAP scroll animations with ScrollTrigger and custom React hooks - MDX content pipeline with custom component registration - Type-safe content collections with Zod validation - Dynamic routing with `generateStaticParams` for SSG - Responsive design with mobile-first Tailwind utilities - Dark mode support with CSS variables and theme switching ### 4. Content Generation Created comprehensive content: - 5+ technical blog posts with code examples and Terminal components - 4 experience pages with professional narratives and achievement bullets - About page with storytelling and technical background - All with proper MDX structure, SEO metadata, and Open Graph images ## What Required Human Guidance ### 1. Aesthetic Judgment AI can implement design systems but needs guidance on "what looks good" in context. Specific measurements and Tailwind classes guide AI to the exact aesthetic you want. ### 2. Strategic Direction Deciding **what to build and why** remains human responsibility: - Which features to prioritize (blog vs projects vs experience) - Content strategy (technical deep-dives vs quick tips) - Target audience (developers vs recruiters vs both) - Branding decisions (Neo-Brutalist vs minimalist vs playful) ### 3. Quality Gates Reviewing generated code for: - **Accessibility compliance**: ARIA labels, keyboard navigation, semantic HTML - **Performance implications**: Bundle size, code splitting, image optimization - **Security vulnerabilities**: XSS prevention, safe content rendering - **Edge cases**: Loading states, error handling, empty states ### 4. MCP Orchestration Knowing when to leverage each MCP: - **Context7**: Add `use context7` to prompts when you need current, version-specific documentation - **Serena**: Maintains context automatically across conversations—no action needed - **Sequential Thinking**: Use for complex architectural decisions requiring step-by-step reasoning ## Results That Speak ### Development Velocity - **Traditional estimate**: 2-3 weeks (solo developer) - **AI-driven actual**: 1 day (with zero code written) - **Speed multiplier**: **10-15x faster** ### Quality Metrics - **Lighthouse**: 96/100 (Performance: 100, Accessibility: 95, Best Practices: 100, SEO: 100) - **TypeScript**: Strict mode, full type safety, zero `any` types - **Accessibility**: WCAG AA compliant, keyboard navigation, ARIA labels - **Architecture**: Clean component hierarchy, maintainable patterns, scalable structure ### Lines of Code Generated - **By me**: 0 (only planning, direction, and orchestration) - **By AI**: ~15,000+ lines (components, styles, content, configuration) - **Human modifications**: 0 (constraint maintained throughout) ## The Learning Paradox **Counterintuitive finding**: I learned MORE by not writing code. Why? Because I spent my time: - **Reading and understanding** AI-generated patterns and best practices - **Analyzing architectural decisions** and their trade-offs - **Discovering new approaches** (Repository Pattern for content, GSAP hooks, MDX component registration) - **Reviewing production-quality code** with detailed explanations and documentation **Traditional coding**: You write code → Maybe learn something new (if you research first) **AI-driven coding**: AI writes code → You review and learn constantly (forced to understand every decision) ## My Recommendations for AI-Driven Development ### 1. Start With a Plan (Use Gemini for Research) Spending time on planning before coding accelerates implementation and prevents rework: 1. **Use Gemini (web)** for design research and architecture 2. **Leverage web search** to analyze design trends and competitors 3. **Synthesize patterns** into a comprehensive plan 4. **Then implement** with Claude Code ### 2. Use Specialized Skills (Not Generic AI) Don't use generic AI. Use skills that follow industry best practices: - **frontend-design** for production-grade UI components - **vercel-react-best-practices** for Next.js optimization patterns Skills embed domain expertise that vanilla Claude lacks. ### 3. Leverage MCPs for Context (Essential for Multi-Session Work) Use MCPs to enhance Claude's capabilities: - **Context7 MCP**: Fetch up-to-date documentation and code examples (`use context7`) - **Serena MCP**: Maintain context, ideas, and progress across conversations - **Sequential Thinking MCP**: Structured reasoning for complex architectural decisions Without MCPs, each session starts from zero and uses potentially outdated information. With MCPs, Claude has current documentation and remembers your project context. ### 4. Choose the Right Model (Cost vs Capability) Not every task needs the most powerful model: - **Planning & Research**: Gemini (web search) or Claude Opus - **Implementation**: Claude Sonnet 4.5 (with skills and MCPs) - **Simple edits**: Claude Haiku 4.5 (types, config, simple components) **Rule**: Use the smallest model that can accomplish the task with proper context and skills. ### 5. Iterate Transparently (Specific Feedback) Be precise about measurements, class names, and desired outcomes. Specific, actionable feedback teaches AI what you want **and** improves your own design articulation skills. ## The Future of Development This portfolio proves AI can: - Design system architecture (with web research) - Implement complex features (animations, MDX pipeline, dynamic routing) - Write production-quality code (TypeScript, accessibility, performance) - Create comprehensive content (technical writing, narratives) - Optimize for performance (Lighthouse 96/100) But it still needs: - **Human judgment** for aesthetics and branding - **Human oversight** for correctness and security - **Human direction** for priorities and strategy - **Human orchestration** of tools, MCPs, and skills **The future isn't AI replacing developers.** The future is developers **with AI moving 5-10x faster** while learning more, building better systems, and focusing on what humans do best: strategy, creativity, and judgment. ## Resources ### AI Tools Used - **[Claude Code CLI](https://claude.ai/download)** - AI-powered development environment with MCP support - **[Google Gemini](https://gemini.google.com)** - Design brainstorming and web research (used website, not CLI) ### Model Context Protocol (MCP) Servers - **[Context7](https://github.com/upstash/context7)** - Fetches up-to-date, version-specific documentation and code examples straight from the source - **[Serena](https://github.com/oraios/serena)** - Maintains context, ideas, concepts, and progress across multiple Claude Code conversations - **[Sequential Thinking](https://github.com/modelcontextprotocol/servers/tree/main/src/sequentialthinking)** - Structured problem-solving framework for complex architectural decisions ### Claude Skills - **[Frontend Design](https://skills.sh/anthropics/skills/frontend-design)** - Production-grade UI component generation with accessibility and responsive design - **[Vercel React Best Practices](https://skills.sh/vercel-labs/agent-skills/vercel-react-best-practices)** - Next.js optimization patterns, code splitting, and performance best practices ### Technical Stack - **[Next.js 16](https://nextjs.org/)** - React framework with App Router, server components, and metadata API - **[Tailwind CSS v4](https://tailwindcss.com/)** - Utility-first CSS framework with custom design tokens - **[GSAP 3.12](https://greensock.com/gsap/)** - Animation library with ScrollTrigger for scroll-driven interactions - **[MDX](https://mdxjs.com/)** - Markdown with JSX components for rich content - **[next-mdx-remote](https://github.com/hashicorp/next-mdx-remote)** - Runtime MDX processing with custom component registration - **[Vercel](https://vercel.com/)** - Deployment platform with Edge CDN and automatic optimizations ### Design Inspiration - **[Neo-Brutalism in Web Design](https://brutalistwebsites.com/)** - Brutalist web design collection and examples - **[Neubrutalism UI](https://www.neubrutalism.dev/)** - Neo-Brutalist design system patterns and components ### Source Code - **[Portfolio Repository](https://github.com/Codestz/portfolio)** - Full source code of this AI-built portfolio (every component, animation, and line of AI-generated code) ## Technical Stack What AI built with zero human code modifications: - **Framework**: Next.js 16 (App Router) + React 19 - **Language**: TypeScript (strict mode, zero `any` types) - **Styling**: Tailwind CSS v4 with custom Neo-Brutalist design system - **Animation**: GSAP 3.12 with ScrollTrigger + custom React hooks - **Content**: MDX with next-mdx-remote and custom component registration - **Deployment**: Vercel with Edge CDN, automatic image optimization, and route caching **Development Tools**: - **Claude Code (CLI)** with Claude Sonnet 4.5 & Haiku 4.5 - Main implementation tool - **Google Gemini (Website)** - Design research, architecture, and web-based brainstorming - **AI Skills** - frontend-design, vercel-react-best-practices - **MCPs** - Context7 (memory), Serena (patterns), Sequential Thinking (reasoning) ## Open Source This entire portfolio is open source. Every component, every animation, every line of AI-generated code is available for you to study: **[GitHub Repository](https://github.com/Codestz/portfolio)** Clone it, explore it, learn from it. See what's possible when you trust AI with proper direction, specialized skills, and context-aware MCPs. ## Conclusion After 3 years of AI-assisted development, I've learned: - **2023**: AI helps me write code faster (GitHub Copilot autocomplete) - **2024**: AI helps me write better code (ChatGPT architecture advice) - **2026**: AI writes all the code—I orchestrate (Claude Code + MCPs + Skills) The shift from **assistant to driver** is profound. Instead of: - "How do I implement this animation?" I now ask: - "What's the right animation strategy for my users?" Instead of: - "How do I structure these components?" I now ask: - "What architecture will scale for 50+ blog posts?" **AI handles the "how." You focus on the "what" and "why."** That's the unlock. That's the future. And you don't need Opus. You need: - **A good plan** (Gemini for research) - **The right skills** (frontend-design, vercel-react-best-practices) - **Context-aware tools** (Context7, Serena, Sequential Thinking MCPs) - **The discipline to orchestrate** instead of code --- **Final Stats:** - **Timeline**: 1 day from concept to deployment - **Human code written**: 0 lines - **AI-generated code**: 15,000+ lines - **Quality**: Production-ready (Lighthouse 96/100) - **Models used**: Claude Sonnet 4.5, Claude Haiku 4.5, Google Gemini (Website) - **MCPs leveraged**: Context7 (documentation), Serena (cross-conversation context), Sequential Thinking (structured reasoning) - **Skills used**: Frontend Design, Vercel React Best Practices **Built with** **AI orchestration**, **MCP-powered context**, and **strategic planning.**

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Building a Complete Portfolio Using Only AI: Zero Human Code