AI-Powered Personal Portfolio
Researcher & Implementer
Dynamic portfolio conceived as a proof of concept for coordinating multiple AI agents and collaborative tools in web development.
Overview
Personal project that served as a lab for testing the coordination of multiple AI agents in web development. The goal was not just to build a portfolio, but to understand where each tool adds value, where it falls short and how human oversight remains central throughout the process — from prototyping to content refinement, through accessibility and SEO practices.
Challenge
Develop a responsive, professional portfolio in a short timeframe using AI and no-code/low-code tools while ensuring robustness, scalability and good accessibility and security practices.
Solution
– Orchestration of AI agents and collaborative tools (ChatGPT Agent, Lovable.ai, Manus, Devin, Claude, GitHub Copilot, VS Code Copilot) for development and problem-solving.
– Content refinement with ChatGPT-4, ChatGPT-5, Perplexity, Gemini and Notebook LM.
– Vercel deployment.
– Version control on GitHub.
– Monitoring with Google Analytics.
Results
- Hands-on experience with 10+ AI tools (Claude, ChatGPT, Copilot, Lovable, Devin) in a single workflow
- Understanding of agent differences — each AI has distinct strengths in code generation, review and content
- Iterations in minutes rather than hours, significantly accelerating the development cycle
- Human validation proved essential: AI accelerates but does not replace design and architecture decisions
- Replicable working model for future projects with multi-agent orchestration
Technologies and Methods
Detailed Implementation
Planning & Setup
Defined goals, selected AI tools and prepared the collaborative pipeline.
Accelerated Prototyping
Used agents and no-code/low-code tools to build prototypes and quickly validate flows.
Integrated Development
Built the portfolio with Next.js and Vercel, integrating translations, blog and analytics.
Monitoring & Evolution
Configured metrics with Google Analytics and Lighthouse and performed continuous performance and SEO optimisations.
Impact Quantified
10+
AI tools explored in a real development context
3 phases
Prototyping, development and refinement with distinct agents
Minutes
Iteration cycles that would conventionally take hours
Lessons Learned
- Coordinated use of multiple AI agents accelerates development and raises delivery quality
- Tool orchestration requires clear planning to avoid redundancies
- Different AIs bring complementary perspectives and improve content refinement effectiveness
- Human validation is essential to ensure consistency, compliance and brand alignment
- The experience strengthened skills in UX/UI, API integration and agile deployment
Next Steps
- Test integration with autonomous agents for automatic content updates
- Create a blog section dedicated to project management and enterprise architecture topics, focusing on business architecture
- Integrate the blog with email marketing strategies to increase reach and audience engagement
- Implement automation for content distribution and optimisation to maximise impact and relevance
- Expand to a multilingual version targeting international markets