What I do
I am a software engineer and independent AI researcher focused on the design of systems that preserve context, coordinate decisions, and remain understandable as they evolve.
My work combines software architecture, artificial intelligence, memory systems, and long-term agent behavior. I am particularly interested in the challenges that emerge when intelligent systems must retain useful knowledge, distinguish relevant information from noise, and adapt without losing coherence.
Research direction
My research explores memory architectures for artificial intelligence, persistent context, semantic stability, cognitive governance, and the interaction between memory and decision-making in intelligent agents.
Rather than treating memory as simple information storage, I investigate it as a dynamic process involving selection, consolidation, influence, revision, and forgetting. This perspective guides projects such as DREAM, an experimental architecture for persistent and adaptive memory in intelligent agents.
Software engineering
Alongside my research, I design and build software systems with an emphasis on maintainability, clear boundaries, practical automation, and architectural longevity.
I work primarily with backend development, system architecture, data processing, and AI experimentation. My projects range from business applications and developer tools to experimental architectures such as DREAM, MQ-AGI, LFD, and HGC-Net.
Current work
My current work focuses on evaluating memory mechanisms, conceptual representations, and decision processes in intelligent systems. I develop prototypes and controlled experiments to understand not only whether an architecture works, but also where it fails, how it behaves over time, and which assumptions still need to be challenged.
Through CodeByMP, I document these projects, publish technical reflections, and share the ideas and experiments behind my research.