A playground where I explore AI systems , autonomous agents , and intelligent developer tools.
AI Lab is my experimental space where I build and test AI-powered systems. I explore large language models, autonomous agents, and developer tools that automate workflows and enhance productivity.

A modular Retrieval-Augmented Generation (RAG) system built from scratch to explore document indexing, semantic retrieval, prompt engineering, streaming responses, and production-ready AI architectures.
Most AI experiments in this lab follow a layered architecture combining interfaces, backend orchestration, AI models, and scalable infrastructure.
User-facing interfaces where AI tools, chat systems, and experiments are interacted with in real-time.
Future explorations planned for the AI Lab. These ideas push the boundaries of intelligent systems and automation.

Building a character-level Multi-Layer Perceptron (MLP) language model from scratch in PyTorch. This project extends the Bigram model by learning character embeddings and predicting the next character using multiple previous characters as context.

Exploring language modeling from first principles by implementing both a statistical Bigram model and a neural-network-based Bigram model in PyTorch to learn how machines generate text one character at a time.
Handles request flow, prompt construction, API calls, and coordination between different AI components.
Core intelligence powered by LLMs for reasoning, generation, and tool-based workflows.
Manages state, caching, and scalability for AI interactions and real-time systems.