AI Systems Architect and Business/System Analyst. I build local-first AI systems that focus on decision quality, context handling, and controllable autonomy.

My projects are practical experiments in one question: How to design AI that is useful in production, but still predictable and auditable?

Main direction:

• Venom: AI control architecture for planning, execution, and decision tracing.
• Rider-Pi: embedded control stack for a self-balancing robot.
• Rider-Pc: companion control server with heavier AI workloads offloaded from the robot.

Compared to many AI repositories focused only on prompts or model wrappers, I focus on:

• Decision flow, not only generation: plan -> act -> verify loops.
• Traceability: system behavior should be inspectable after execution.
• Autonomy boundaries: explicit limits, permission checks, and safety gates.
• Cross-layer design: from embedded robotics to orchestration APIs and governance.

Core goal: build AI systems that can be trusted operationally, not only demoed.

Python FastAPI Docker IoT/Robotics System Architecture

Selected long-form articles on AI assistants, decision systems, and ethics:

• To use AI or not to use AI, that is the question?
• Is AI intelligent?
• The concept of implementing Ethics in AI Assistants
• AI in the Sociological Perspective – 2026
• AI: From Theory to Practice - The Venom v1.5 Case Study