My work is about designing the future of intelligence. While the goal is simplicity, the system underneath is highly advanced. My focus is on building AI systems that learn, adapt, and feel seamless to the user. My cornerstone project, JARVIS V.S., uses a powerful mix of Recurrent Neural Networks (RNNs) and Large Language Models (LLMs). It achieves exceptional accuracy by integrating RAG (Retrieval-Augmented Generation) for real-time data access and using Model Context Protocol (MCP) to perfectly remember every conversation. I engineer for high performance and minimalist precision in every code layer.
Deploying advanced logic into production environments.
ADVANCED NEURO-SYMBOLIC ASSISTANT
An extremely complex, latency-optimized intelligence system. Unlike standard assistants, Jarvis utilizes a hybrid neural architecture fusing Recurrent Neural Networks (RNNs) for sequential logic with Large Language Models (LLMs) for semantic understanding.
The infrastructure integrates Model Context Protocol (MCP) for persistent state management across sessions and utilizes RAG (Retrieval-Augmented Generation) to fetch real-time data, bypassing static knowledge cutoffs.
HEURISTIC SEARCH ALGORITHM VISUALIZER
A grid-based logic engine simulating high-stakes search protocols. Users deploy algorithmic strategies to locate targets within a hidden matrix, demonstrating A* pathfinding concepts and probability density.
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