wehub-resource-sync
132 lines
3.6 KiB
Markdown
132 lines
3.6 KiB
Markdown
# Social Media Automation workflow using Motia
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A streamlined content generation agent built with [Motia](https://github.com/MotiaDev/motia) that transforms articles into engaging Twitter threads and LinkedIn posts using AI.
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We use the following tech stack:
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- Motia as the unified backend framework
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- Firecrawl to scrape web content
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- Ollama for serving Deepseek-R1 locally
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## 🎯Overview
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**Workflow**
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Our workflow consists of 4 main steps:-
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```
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API → Scrape → Generate → Schedule
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```
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1. **API**: Receives article URL via POST request
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2. **Scrape**: Extracts content using Firecrawl in markdown format
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3. **Generate**: Creates Twitter & LinkedIn content using Deepseek-R1
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4. **Schedule**: Saves content as drafts in Typefully for review
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## 🛠️ Setup
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### Prerequisites
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- Node.js 18+
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- Python 3.x
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- API keys for:
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- Firecrawl
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- Typefully
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### Installation
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1. **Install Ollama:**
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```bash
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# Setting up Ollama on linux
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curl -fsSL https://ollama.com/install.sh | sh
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# Pull the Deepseek-R1 model
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ollama pull deepseek-r1
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```
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2. **Install project dependencies:**
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```bash
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npm install or pnpm install
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```
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3. **Configure environment:**
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```bash
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cp .env.example .env
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# Edit .env with your API keys
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```
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or Create a `.env` file in the root directory with the following variables:
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```bash
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FIRECRAWL_API_KEY=your_firecrawl_api_key
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TYPEFULLY_API_KEY=your_typefully_api_key
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```
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4. **Start the development server:**
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```bash
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npm run dev
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```
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## 🚀 Usage
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### Generate Content
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Send a POST request to trigger content generation:
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```bash
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curl -X POST http://localhost:3000/generate-content \\
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-H "Content-Type: application/json" \\
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-d '{"url": "https://example.com/article"}'
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```
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**Response:**
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```json
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{
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"message": "Content generation started",
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"requestId": "req_123456",
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"url": "https://example.com/article",
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"status": "processing"
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}
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```
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### View Results
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After processing completes:
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1. Visit [Typefully](https://typefully.com/drafts)
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2. Review your generated Twitter thread and LinkedIn post
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3. Edit if needed and publish!
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## 📁 Project Structure
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```
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social-media-automation/
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├── steps/
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│ ├── api.step.py # API endpoint handler
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│ ├── scrape.step.py # Firecrawl integration
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│ ├── generate-linkedin.step.py # Ollama Linkedin generation
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│ ├── generate-twitter.step.py # Ollama Twitter generation
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│ ├── schedule-twitter.step.ts # Twitter Typefully scheduling
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│ └── schedule-linkedin.step.ts # LinkedIn Typefully scheduling
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├── prompts/
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│ ├── twitter-prompt.txt # Twitter generation prompt
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│ └── linkedin-prompt.txt # LinkedIn generation prompt
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├── config/
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│ └── index.js # Configuration management
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├── package.json
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├── motia-workbench.json
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├── requirements.txt
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└── README.md
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```
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## 🔍 Monitoring
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The Motia workbench provides an interactive UI where you can easily deb ug and monitor your flows as interactive diagrams. It runs automatically with the development server.
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## 📬 Stay Updated with Our Newsletter!
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**Get a FREE Data Science eBook** 📖 with 150+ essential lessons in Data Science when you subscribe to our newsletter! Stay in the loop with the latest tutorials, insights, and exclusive resources. [Subscribe now!](https://join.dailydoseofds.com)
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[](https://join.dailydoseofds.com)
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---
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## Contribution
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Contributions are welcome! Please fork the repository and submit a pull request with your improvements.
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