Profile Builder with Local LLM

Why Build a Portfolio with AI?

As a technology professional with experience in cloud computing, data engineering, and analytics, I've increasingly become interested in how AI tools can enhance developer productivity and creativity. I had three main motivations for this project:

• Showcase Professional Growth: Create a comprehensive portfolio that effectively communicates my experience, skills, and accomplishments to potential collaborators.
• Evaluate AI Code Assistance: Personally assess the capabilities and limitations of GitHub Copilot with Claude 3.7 Sonnet for full-scale web application development.
• Develop Prompt Engineering Skills: Strengthen my ability to effectively communicate with AI tools—a skill that's becoming increasingly valuable across the tech industry.

Technology Stack

For this project, I selected a modern, robust stack that would provide excellent performance, developer experience, and deployment flexibility:

AI-Assisted Development Process

This project took a unique approach to development by leveraging GitHub Copilot with Claude 3.7 Sonnet throughout the entire process. Here's how the development workflow unfolded:

1. Project Setup and Architecture

I started by clearly communicating my vision for the portfolio site to GitHub Copilot. Through a series of prompts, I established requirements for a Next.js application with TypeScript support. Claude helped set up the initial project structure, configure Tailwind CSS, and establish the routing architecture.

2. Component Development

For each section of the portfolio, I provided Claude with specific design goals and functionality requirements. The AI suggested component structures, implemented responsive layouts using Tailwind CSS, and even helped create interactive elements like the animated navbar, project cards, and skill visualization.

3. Data Management

Instead of hardcoding content throughout the site, Claude suggested creating a central data file (portfolioData.ts) to manage all portfolio content. This approach made it easy to update information and ensured consistency across the application. The AI helped design appropriate TypeScript interfaces and implemented helper functions for data processing.

4. Interactive Features

Claude assisted in implementing several interactive features that enhanced the user experience:

• Animated transitions between pages
• Auto-scrolling testimonials carousel
• Interactive data visualizations for skills
• Modal windows for project details
• Dynamic content loading from Medium blog API

5. Debugging and Optimization

When issues arose during development, Claude proved particularly valuable. The AI could analyze error messages, suggest potential causes, and provide solutions—often accompanied by explanations that enhanced my understanding of the underlying technologies. Performance optimizations for images, component rendering, and API calls were also implemented with Claude's assistance.

Key Insights from AI-Assisted Development

Strengths of GitHub Copilot with Claude 3.7 Sonnet

• Contextual Understanding: Claude demonstrated an impressive ability to understand the overall project structure and purpose, providing solutions that fit cohesively with existing code.
• TypeScript Excellence: The AI excelled at generating well-typed code and solving type-related challenges.
• Documentation Integration: Claude could effectively draw from Next.js and React documentation to implement best practices.
• Educational Value: Beyond just generating code, the AI provided explanations that enhanced my understanding of complex concepts.

Areas for Improvement

• Occasional Inconsistency: Sometimes Claude would suggest different approaches to similar problems, requiring manual standardization.
• Design Limitations: While Claude could implement designs from descriptions, creating truly unique visual designs still required human creativity and direction.
• Context Window Constraints: For very large components or complex logic, the AI sometimes lost track of earlier requirements or code patterns.

The Power of Effective Prompting

One of the most valuable insights from this project was learning how to effectively communicate with AI tools. The quality of Claude's responses was directly proportional to the clarity and specificity of my prompts.

By applying these practices, I was able to work with Claude to build increasingly complex components and functionality throughout the development process.

Conclusion

This portfolio project demonstrates the transformative potential of AI-assisted development. By leveraging GitHub Copilot with Claude 3.7 Sonnet, I was able to build a comprehensive, responsive, and feature-rich portfolio website in a fraction of the time it would have taken using traditional development methods.

More importantly, the project illustrates how AI tools are not replacing developers but rather augmenting their capabilities—enabling faster prototyping, reducing boilerplate coding, and providing educational insights along the way. Strong prompt engineering skills act as a multiplier for developer productivity when working with these advanced AI tools.

As AI assistance continues to evolve, projects like this one highlight the importance of learning to effectively collaborate with these tools—a skill that will likely become increasingly valuable across the technology landscape in the coming years.