I’m a postgraduate student at the University of Cambridge, where I study human-inspired AI with a focus on AI-driven interface development. My passion lies in designing interactive experiences powered by human-in-the-loop intelligent systems, and I’m currently interested in how such technologies can support ecological conservation and the study of natural environments.
Previously, I worked as a Software Engineer at Stellar Cyber in San Jose, California, where I developed AI-driven interfaces for threat hunting [AIM Research, 2024] and human-augmented autonomous cybersecurity operations powered by agentic AI [Business Wire, 2025]. I’m also the creator of GONEXT, a generative AI platform that delivers personalized analytics for League of Legends players.
In my free time, I enjoy being outdoors in nature, going to music festivals, and playing racquet sports.
An AI-powered analytics tool for League of Legends that provides real-time, personalized strategies, matchups, synergies, and builds. It uses the Riot API to gather live game data, which is then processed by language models to deliver tailored, game-specific guidance to players.
This open-source Model Context Protocol (MCP) server empowers LLMs with comprehensive access to League of Legends game data through the Riot Games API. It features over 35 tools and resources for retrieving player statistics, match history, champion information, tournament data, and real-time game monitoring, supporting both stdio and SSE transports for seamless client integration. It is shipped as a Python package and a Docker container. Additionally, a demonstrative MCP client chatbot, leveraging a ReACT agent, is also provided to showcase the server's extensive capabilities and practical usefulness.
docker pull kostadindev/league-mcp
pip install league-mcp
Python package for converting diverse content into a search-engine-friendly knowledge base. It effortlessly ingests files (PDFs, DOCXs, spreadsheets), websites, and GitHub repositories, then leverages LLMs to generate a Markdown knowledge base. Ideal for creating structured and crawlable formats like llms.txt and llms-full.txt, supporting Retrieval-Augmented Generation (RAG) applications, or synthesizing unstructured data from multiple sources. Capable of summarizing an entire github repostory or website in two lines of code.
pip install knowledge-base-builder
