Deep technical explorations into Large Language Models, AI architectures, and the evolution of data systems through comprehensive academic research.
A comprehensive technical guide that demystifies Large Language Models by building on familiar database concepts. This paper bridges the gap between traditional data structures and modern AI architectures, making complex neural network concepts accessible to educated professionals who want to understand how AI actually works at a fundamental level.
A groundbreaking technical paper proposing DNS-inspired hierarchical architectures for scalable LLM deployment. This research demonstrates an 85% reduction in communication overhead and 47% cost savings through intelligent semantic routing and specialized inference engines. The paper presents a novel approach to distributed AI systems that mirrors the proven scalability patterns of internet infrastructure.