I built NEXORA an "On-Device AI Workspace", a fully offline, private local-first AI assistant and document intelligence workspace created for the Hacktoberfest Open-Source AI Challenge.
As a mechanical engineering student at NIT Trichy, I wanted a tool that could instantly parse dense research papers, textbook chapters, and codebases without sending any data over the internet or risking privacy leaks. NEXORA combines local model inference through Ollama, semantic vector document search using PyMuPDF and NumPy embeddings, mathematical tool execution, and global capital weather forecasting into a single lightning-fast Streamlit dashboard.
Demo
Here is a video link of NEXORA in action locally:
Functioning snapshots:
Partner Technologies
For this project, I integrated several open-source technologies and developer tools:
Ollama & Open-Weight Models: Used Ollama running locally on port 11434 to power local inference using open-weight models (localmind) and embedding generation (nomic-embed-text) with zero cloud latency or API billing.
Streamlit & PyMuPDF: Built the reactive desktop interface using Streamlit and handled local PDF document parsing, text chunking, and semantic vector indexing using PyMuPDF and NumPy.
MLH & DEV Ecosystem: Connected my Major League Hacking account profile via DevRelay CLI to track open-source contributions and share this build with the global community.
Hackathon Experience
Building NEXORA was an incredible journey combining systems engineering and practical AI deployment. Working from NIT Trichy, the atmosphere was charged with late-night coding sprints, debugging vector embeddings, and optimizing local model memory footprints. Collaborating with fellow hackers in the MLH community pushed me to focus heavily on practical developer utility, data privacy, and clean, high-performance UI design.
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