Neural net and GPU enthusiast
ML Engineer with a decade of experience training — and sometimes coercing — machine learning models to solve industry problems in computer vision, NLP, and recommendation systems. Motivated by solving hard, high-impact problems with elegant ML solutions that scale.
Written contributions and insights from Michael.
Experimenting with GraphRAG: Adding Knowledge Graphs to RAG Pipelines
Blending knowledge graphs with RAG pipelines unlocks richer, scalable insights—bridging the gap between granular retrieval and holistic understanding.
Self-Learning LLM Agents: A Fractal Approach to Domain-Specific Knowledge
Exploring how agents can autonomously build and evolve their own domain expertise—scaling from generic LLMs to self-learning, specialized assistants.
Team Spirit Matters: How Collaborative Context Boosts Multi-Agent LLM Performance
Adding social accountability to multi-agent workflows boosts the depth, coherence, and empathy of LLM responses—mirroring real-world teamwork dynamics.