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Inspiration
I spend way too much time doing repetitive research—opening twenty tabs, summarizing articles, and trying to connect the dots. I realized I didn't just want another chatbot to talk to; I wanted an agent that could actually execute a workflow for me. When I saw the Nebius hackathon, I figured it was the perfect excuse to build a real AI sidekick using Nemotron.
What it does
NexusMind acts like a tireless research analyst. You give it a big, messy goal (like "Analyze the market for AI wearables and draft a 3-point summary"), and it takes over. It uses the massive Nemotron-4-340B model to break your goal down into a logical, step-by-step plan. Then, it executes those steps and synthesizes a final, clean report for you right in the UI.
How I built it
I threw together the frontend using Streamlit because I wanted a clean interface up and running fast. For the backend logic, everything runs on Python and hooks directly into the Nebius Token Factory API. I deliberately routed the heavy reasoning and planning tasks to nemotron-4-340b-instruct because of its crazy high logic capabilities, and used smaller open models on Nebius for the quick summarization steps to keep the app snappy.
Challenges I ran into
The biggest headache was getting the 340B model to consistently output strict, executable plans instead of conversational filler. I spent a lot of time dialing in the system prompts to force it into an "orchestrator" role. Also, chaining multiple API calls in a loop without making the Streamlit UI feel frozen took a bit of trial and error with state management.
What's next for NexusMind
Right now, it's a great proof of concept. Next, I want to integrate live web-scraping and eventually tie the agent into Nebius Serverless Jobs. That way, I can hand it a massive research task, close my laptop, and let it run in the background for hours while I do other things.
Built With
- api
- llm
- nebius
- nebius-token-factory
- nemotron-4-340b
- nvidia-nemotron
- python
- streamlit
