I'm fascinated by how we build, deploy, and use AI systems in the real world.
My relationship with technology began as a child playing PC video games. To get a competitive edge, I learned to build, upgrade, and overclock my own systems to squeeze out every drop of performance, eventually configuring home servers for friends and designing custom maps with the Valve Hammer Editor.
In 2012, I turned this hobby into my career as an IT Specialist, building corporate networks and managing hardware from scratch. In 2016, I made the leap into software engineering, starting at a startup based at Google Campus Madrid before contributing to specialized agencies and large-scale enterprises across banking, healthcare, and public administration.
Those early years—from hardware hacking to professional software engineering—taught me to understand systems end-to-end: from physical cables, bare metal, and infrastructure all the way up to the final user interface.
Today, I'm investing much of my time learning about AI infrastructure and experimenting with local AI systems. I'm particularly interested in understanding how modern AI models work, how they can be deployed in production, and how engineering workflows can make AI systems more reliable, reproducible, and privacy-friendly.
Most of the repositories you'll find here are experiments, benchmarks, prototypes, or engineering notes documenting what I build and what I learn along the way.
- Local AI & Sovereign AI
- AI Infrastructure
- MLX & Apple Silicon
- Multimodal Models (VLMs)
- Software Architecture
- Performance & Benchmarking
- Java • TypeScript • Rust • Linux
An experimental homelab exploring local AI infrastructure, code quality automation, AST-based analysis, and reproducible engineering workflows.
https://github.com/caballeroluis/neurodoc-ai
A collection of engineering notes, experiments, random ideas, and technical observations.
https://github.com/caballeroluis/ideas-overflow
- Location: Madrid, Spain
- Email:
