Welcome to F97.BE!

Hello, I’m Erik Schmidt, a lifelong tech tinkerer, systems thinker, and builder of things that run smarter. Based in Eynatten, Belgium, I combine a formal education in Mathematics, Informatics, and Political Sciences with more than three decades of hands-on experience in creating resilient, elegant, and efficient solutions across both hardware and software domains.

Over the past 30 years in the IT world, I’ve architected systems that people rely on in situations where failure simply isn’t an option. This includes work in critical infrastructure and intelligent automation. One of my proudest collaborations was with my late business partner Dr. Rein Scheele. Together, we developed the AT Mobimeter for the Dutch Government, a visionary model for usage-based vehicle taxation that was far ahead of its time.

My online journey began in 1989, long before the web as we know it existed. While studying in Aachen, Germany, I became one of the minds behind the German FidoNet. We helped engineer one of the earliest decentralized communication networks, where messages were passed from node to node in a world still running on dial-up and DOS.

Throughout my career, I have worked at the intersection of sales and software development, translating agile business processes into robust technical solutions. I have authored several publications across Europe, sharing expertise in network security, monitoring technologies, and risk assessment.

I speak and work fluently in English, German, and Dutch, and I am comfortable in French as well. This has enabled me to collaborate and teach internationally, whether writing code, documenting systems, or leading training sessions.

Currently, I am deeply involved with CAM-AI Ingenieurbüro für intelligente Sicherheitstechnik, where I focus on artificial intelligence for camera systems. In my primary role, I serve as a software trainer for an English company, helping employees effectively use an internally developed ERP system.

In addition to that, I design and build tailored software and hardware systems for solar forecasting, intelligent EV charging, and home automation. My work connects code and circuitry in practical ways, from soldering sensors to writing Python scripts that interface with solar inverters, wallboxes, and weather APIs.

I work extensively with REST APIs, machine learning, and real-time data pipelines to improve energy efficiency and forecast solar yields. My backend logic runs on Linux-based systems such as Raspberry Pi and Ubuntu, and my user interfaces are built with clean, responsive HTML/CSS, optimized for display on any screen, including full-screen information panels in the living room.

Whether I am adjusting inverters, fine-tuning charging algorithms, or wiring relays that respond to incoming weather data, I enjoy turning smart energy concepts into systems that are practical and dependable.

I am old enough to have written real-world code in Assembler, Fortran, and Pascal. I began my journey on iconic machines like the Commodore 64, Z80-based systems, and eventually the legendary PDP-11. These platforms may seem like relics today, but working with them was a masterclass in constraints, discipline, and precision. When every byte mattered and nothing was abstracted away, you had to understand systems from the ground up, starting with memory layout and reaching all the way to timing behavior.

That experience ingrained in me a deep respect for structure, logic, and efficiency. It taught me how to write lean, reliable code, how to debug by instinct, and how to build systems that do exactly what they’re supposed to do — no more, no less. Those lessons are still the foundation of how I approach modern software, whether I’m training machine learning models or wiring up edge devices.

More recently, I have immersed myself in the world of artificial intelligence. While AI is often treated as a purely theoretical or academic discipline, I see it as something deeply practical and hands-on. I train models that predict solar energy production, optimize the charging behavior of electric vehicles, and guide smarter decisions in home automation based on live environmental data. Whether I am refining a Random Forest model to improve predictive accuracy or deploying neural networks on embedded hardware, I build systems where intelligence runs close to the physical layer.

In working with AI, I have also found something unexpected. The process of designing intelligent systems has sharpened my own reasoning. Training a model often feels like holding a mirror to my own decision-making, forcing me to think clearly about patterns, exceptions, and causes. It has changed how I approach software architecture. Instead of simply instructing the machine what to do, I now think in terms of goals, constraints, and decision strategies.

This shift opens up fascinating possibilities. I am no longer just coding instructions, but planning systems that can adapt, evaluate outcomes, and improve themselves over time. It encourages a more modular and forward-looking way of designing programs, especially in areas like automation, control systems, and predictive analytics. The process of learning AI has become a way of reasoning with the machine as a partner in thought, which has deeply influenced how I design and build systems.

There’s something truly magical about uniting code, circuits, and artificial intelligence. It allows me to turn raw data into coherent narratives—stories that don’t just explain the world, but help change it. The more I collaborate with intelligent systems, the more it feels like a shared reasoning process. This interplay opens up entirely new perspectives on how I design, build, and evolve software.

On a personal note, I live in a solar-powered home with my artist wife and our daughter, surrounded by light, creativity, and purpose. Together, we are part of local energy cooperatives and support Ecolo, Belgium’s green party, working toward a future that is as thoughtful as it is sustainable. In our driveway, three quiet electric cars stand as everyday symbols of the life we choose to lead.

And when I am not coding, troubleshooting devices, or soldering together some new prototype, I often turn to quieter pursuits. You will likely find me browsing through antique shops, leafing through dusty bookstalls, or tracking down vintage postcards that capture forgotten places and stories. These moments of stillness balance the technical intensity of my work and remind me that history, like code, is full of hidden patterns waiting to be uncovered.