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CyberFeather, an open-source platform, fosters collaborative problem-solving and knowledge sharing, mirroring the essence of Stack Overflow. It's designed to empower users to discuss, solve problems, and contribute across various topics.

JavaScript 0.22% CSS 4.80% TypeScript 94.98%

cyberfeather's Introduction

CyberFeather

CyberFeather is a community-based software on GitHub, resembling Stack OverFlow in its functionality.

https://cyber-feather-koart-warlences-projects.vercel.app/

Installation

Install dependencies:
```
npm install
```

Set up environment variables: Create a .env.local file in the project root and configure the following environment variables:

 NEXT_PUBLIC_CLERK_PUBLISHABLE_KEY=YOUR_PUBLIC_CLERK_PUBLISHABLE_KEY
 CLERK_SECRET_KEY=YOUR_CLERK_SECRET_KEY

 NEXT_PUBLIC_CLERK_SIGN_IN_URL=/sign-in
 NEXT_PUBLIC_CLERK_SIGN_UP_URL=/sign-up
 NEXT_PUBLIC_CLERK_AFTER_SIGN_IN_URL=/
 NEXT_PUBLIC_CLERK_AFTER_SIGN_UP_URL=/

 NEXT_CLERK_WEBHOOK_SECRET=YOUR_WEBHOOK_SECRET

 NEXT_PUBLIC_TINY_EDITOR_API_KEY=YOUR_PUBLIC_TINY_EDITOR_API_KEY

 MONGODB_URL=YOUR_MONGODB_CONNECTION_STRING

 NEXT_PUBLIC_SERVER_URL=http://localhost:3000
 OPENAI_API_KEY=YOUR_OPENAI_API_KEY

Make sure to replace these values with the respective actual values.

Start the project:
```
npm run dev
```
The project will run at http://localhost:3000.

Features

Day/Night/System Mode:
- Switch between different color schemes based on user preferences.
Third-party Authentication Support:
- Provides authentication through third-party platforms.
Special Functionality:
- Evaluates community engagement based on user participation, including question-asking, answering, and ratings received from others.
Comprehensive Search:
- Enables search across all pages, including tag questions and answers.
Markdown Code Blocks:
- Allows the creation of Markdown code blocks using an editor.
Save Favorite Threads:
- Users can bookmark discussions they like.
Custom Tag Categories:
- Offers various custom tag classifications.
Flexible Sorting Options:
- Newest / Recommended / Frequent / Unanswered.
- Recommended suggests relevant results based on the user's past exploration of similar questions.