My work is guided by curiosity, creativity, and a desire to integrate diverse bodies of knowledge. I seek out projects that challenge the boundaries of thought, emotion, and perception—especially those at the intersection of ancient wisdom, artificial intelligence, and consciousness.
One such project is Astro Pema, a system that uses AI not to replicate astrology, but to rethink it. It blends traditional astrological insight with machine learning to explore how meaning, mind, and intelligence emerge—not just in humans, but in the collaborative space between human and machine.
This portfolio isn’t a job application—it’s an offering. It reflects decades of creative work, systems thinking, and philosophical inquiry expressed through code, dialogue, and imagination.
My technical path began in the mid-70s with hands-on training in electronics—working with radios, TVs, and solid-state devices. I worked as an electronics technician for companies in Puerto Rico, including ALCATEL and PR Telephone Company subcontractors. My work covered key systems, EPBX, and assembling Central Offices with Northern Telecom equipment. This foundation taught me to think in systems—from the inside out.
In the mid-80s, I pursued formal education and earned a Bachelor’s in Mathematics and Computer Science with a 3.0 GPA. I learned data structures, algorithms, and system-level thinking—not just to pass exams, but to build, to understand, and to think clearly in the language of machines. That foundation still informs my creative problem-solving.
After college, I moved to Silicon Valley and stepped into the heart of the tech boom. I worked as a network technician for companies like Silicon Graphics and UCSC, wiring infrastructure at a time when the internet was still becoming. I helped build systems for players like SCO in Santa Cruz, gaining firsthand experience in the physical and logical layers of digital communication.
Recently, I've returned to deep technical practice, working intensively with Linux systems as the foundation for modern AI research. I've built a complete machine learning and deep
and Stable-Baselines3. This environment includes support for reinforcement learning, NLP, computer vision, and Jupyter-based experimentation, all hosted locally for full transparency.I'm currently exploring the boundaries of intelligent systems using both small language models (SLMs) running on local hardware, and large language models (LLMs) via internet APIs.