This is a Next.js project bootstrapped with create-next-app.

Project Template

This is a pre-configured template for new projects. Follow the steps below to get started:

Features

• tailwindcss
• axios
• zustand
• eslint
• prettier
• less
• PWA
• CI/CD
• SEO

Getting Started

First, run the development server:

npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev

Open http://localhost:3000 with your browser to see the result.

You can start editing the page by modifying app/page.tsx. The page auto-updates as you edit the file.

This project uses next/font to automatically optimize and load Geist, a new font family for Vercel.

Devin.cursorrules

Transform your $20 Cursor/Windsurf into a Devin-like experience in one minute! This repository contains configuration files and tools that enhance your Cursor or Windsurf IDE with advanced agentic AI capabilities similar to Devin, including:

• Process planning and self-evolution
• Extended tool usage (web browsing, search, LLM-powered analysis)
• Automated execution (for Windsurf in Docker containers)

Usage

1. Copy all files from this repository to your project folder
2. For Cursor users: The .cursorrules file will be automatically loaded
3. For Windsurf users: Use both .windsurfrules and scratchpad.md for similar functionality

Setup

1. Create Python virtual environment: ```bash

   Create a virtual environment in ./venv

   python3 -m venv venv

Activate the virtual environment

On Unix/macOS:

source venv/bin/activate

On Windows:

.\venv\Scripts\activate

2. Configure environment variables:
```bash
# Copy the example environment file
cp .env.example .env

# Edit .env with your API keys and configurations

3. Install dependencies: ```bash

   Install required packages

   pip install -r requirements.txt

Install Playwright's Chromium browser (required for web scraping)

python -m playwright install chromium

## Tools Included

- Web scraping with JavaScript support (using Playwright)
- Search engine integration (DuckDuckGo)
- LLM-powered text analysis
- Process planning and self-reflection capabilities

## Testing

The project includes comprehensive unit tests for all tools. To run the tests:

```bash
# Make sure you're in the virtual environment
source venv/bin/activate  # On Windows: .\venv\Scripts\activate

# Run all tests
PYTHONPATH=. python -m unittest discover tests/

The test suite includes:

• Search engine tests (DuckDuckGo integration)
• Web scraper tests (Playwright-based scraping)
• LLM API tests (OpenAI integration)

Background

For detailed information about the motivation and technical details behind this project, check out the blog post: Turning $20 into $500 - Transforming Cursor into Devin in One Hour

License

MIT License