web application/case study
CodeLens
AI-powered GitHub code exploration tool with semantic search, automated documentation, and natural language codebase querying.
Project Overview
A developer productivity tool that understands large repositories using LLMs, pgvector, and type-safe APIs.
Developers spend hours jumping across files, reading unfamiliar code, and manually searching large repositories. Traditional search is keyword-based and doesn’t understand code semantics, relationships, or intent.
Built an AI-powered analysis engine that lets developers query codebases in natural language, generate documentation, and perform semantic code search using pgvector embeddings and LLM reasoning.
I built CodeLens because I constantly deal with large, messy codebases in real-world projects, and context-switching kills productivity. This tool reduces research time from hours to minutes by making the codebase itself ‘talk back.’
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