Alongside industrial work, things emerge here that I simply find interesting. No consulting slides, no client projects — just code, experiments, and things that are fun to build.
A hands-on course that shows engineers and technicians how to apply AI methods in industrial settings — without math overload, but with runnable code. From data preparation to productive models.
Topics: ML fundamentals, time series analysis, predictive maintenance with Python, LLM basics & RAG patterns, model integration in OT environments.
A real-time dashboard for monitoring and trading cryptocurrencies. Runs on my own infrastructure (Synology NAS + Docker), fetches market data via REST/WebSocket, and visualizes positions, risk exposure, and performance metrics.
Tech stack: Python, FastAPI, InfluxDB, React, Docker. Full closed-loop operated in-house — from order execution to dashboard visualization.
Various open-source tools and scripts created over time: deploy automation, data migration helpers, COMOS API playgrounds, and monitoring setups. Many of them run productively on my own stack.
Whether the AI course, a specific project, or just a technical exchange — I'm open to conversations.
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