I'm a vibecoder who believes in building tools for tomorrow, not just today. I learn by doing—every project teaches me something new. I'm constantly reinventing myself for the future, treating AI as an art form that gets better with practice and close observation.
Multi-agent orchestration platforms and AI-first development workflows. Consolidating 80+ experiments into production-grade tools that push the boundaries of what's possible with AI-assisted development.
Reinforcement learning patterns for development tools, streaming architectures for large-scale data processing, and AI prompt engineering through systematic experimentation.
Practical AI tooling projects, multi-agent system design, and developer workflow optimization. Especially interested in projects that bridge the gap between AI capabilities and real-world productivity.
Scaling multi-agent systems, optimizing RAG performance for large codebases, and building intuitive interfaces for complex AI workflows.
- Multi-agent orchestration and AI development platforms
- Building production-ready AI tools from scratch
- The intersection of computer engineering and AI experimentation
- How to approach AI as an art form rather than just a tool
- GitHub: @pagaldilz
- Open to collaboration on practical AI tooling and developer workflows
I treat AI like a musical instrument—the more you practice, the better you get at coaxing out the right "notes" (responses). It's all about experimentation and close observation!
🚀 NanoGPT
Production-ready, OpenAI-compatible Python client for multiple providers. Built for developers who need reliability, rate limiting, and seamless multi-provider support.
Visual-first code intelligence and insights for faster understanding. Turning complex codebases into intuitive visual representations.
"AI is the new Google search—an art that needs to be perfected by experimentation and close observation."
Core Principles:
- Learn by doing
- Push the limits of your learning
- Reinvent yourself for the future
- Treat AI like an instrument—mastered by practice and attention
What I Do:
- Design and ship practical AI-first tools and workflows
- Explore multi-agent, RAG, and data-intensive patterns when they serve real outcomes
- Favor iterative delivery, measurement, and refinement over theory