Engineer and CEO of OpenHives AI, where I build infrastructure for AI and systems — from models to execution — with a strong commitment to digital sovereignty.

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Through this engineering work, I conduct applied research in machine learning and deep learning, with a focus on the inference architecture of large language models: how computational state is produced, transported, persisted, and recomposed across calls, sessions, and devices.

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My work is structured around three areas: theoretical formalization, experimental measurement, and practical implementation. I publish research papers, critical reading notes on the literature, analyses of existing system architectures, and essays at the intersection of technology and epistemology.

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Inference is not a passive channel. It is where the model exists, where computation becomes prediction, and where the main constraints converge — memory, cost, continuity, and sovereignty. That is where I work.