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Nebula

Nebula: AI tool that analyzes GitHub repos to auto-generate documentation. Extracts code structure, creates README files, and identifies tech stacks using Gemini, GROQ LLM. Uses MongoDB vector search.

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Description

Nebula is an AI-powered documentation generation tool that automatically transforms complex code repositories into professional documentation. By analyzing GitHub repositories, Nebula creates comprehensive documentation in various formats, saving developers significant time and effort.

Core Functionality

  • Analyzes GitHub repositories using AI to understand code structure and extract insights

  • Automatically generates professional READMEs, technical articles, and social media content

  • Organizes documentation by type and purpose with section-based editing features

  • Provides preview mode to see rendered markdown during editing

Technical Implementation

The project uses a modern tech stack with:

  1. Frontend: Next.js, React, TailwindCSS

  2. Backend: Node.js, Express

  3. Database: MongoDB

  4. Authentication: Clerk

  5. Deployment: Vercel (frontend), Railway (backend)

  6. Web3 Integration

Nebula incorporates blockchain technology through its Nebula-Dapp component, which enables:

Publishing user-generated documentation articles to the blockchain

Smart contract integration via Solidity (in the Nebula-contracts repository)

Web3 framework implementation through the third-web framework

Decentralized storage of documentation, likely providing ownership verification and permanence

The project consists of multiple repositories that work together:

Main Nebula repository (frontend)

Nebula-backend (API and processing)

Nebula-contracts (Solidity smart contracts)

Nebula-dapp (decentralized application interface)

This architecture allows users to not only generate documentation automatically but also publish and own their technical content on the blockchain through the dapp component.

Progress During Hackathon

1. Project Ideation, architecture/workflow designed & Project initialized. 2. Landing Page added. 3. Authentication Page. 4. set up LLM Responses. 5. Authentication (Clerk) 6. Pages added. 7. models, prompts (backend) 8. Utils, database operations. 9. Pages added (UI). 10. Formats added. 11. Integrations of frontend and backend. 12. Some Bug fixes. 13. More routes added. 14. Web3 integrated.

Tech Stack

Next
Python
MongoDB
Node
GenAI
Vector Search
Langchain
Solidity
Sector
AIOtherDeFi