LinkedIn Post Generator with LLM

#Overview

ArticleGen Pro is a desktop application that combines the power of local AI models with LinkedIn's publishing capabilities. Built with Python and PyQt5, it provides a comprehensive solution for content creators who want to leverage AI assistance while maintaining full control over their content.

#Key Features

#🤖 AI-Powered Content Generation

The application integrates with Ollama, a local AI model runner, providing:

• Content generation assistance without sending data to external servers
• Real-time AI suggestions and improvements
• Complete conversation history for iterative content refinement
• Support for various writing styles and target audiences

#✍️ Rich Text Editor

A full-featured text editor built with PyQt5:

• Complete formatting toolbar (bold, italic, underline, lists, etc.)
• Real-time preview of formatted content
• Markdown support for technical documentation
• Clean, distraction-free writing interface

#📊 Content Management

SQLite-based article management system:

• Create, edit, and delete articles
• Track publication status and dates
• Organize with keywords and metadata
• Search and filter functionality

#🚀 Direct LinkedIn Publishing

Seamless integration with LinkedIn:

• One-click publishing to your LinkedIn profile
• Automatic formatting conversion
• Publication tracking and history
• Draft and scheduled post support

#Technical Architecture

LinkedinPost/
├── main.py              # Application entry point
├── models/              # Data models
│   ├── article.py       # Article model
│   └── database.py      # Database manager
├── ui/                  # PyQt5 interface
│   ├── main_window.py   # Main window
│   ├── article_editor.py # Article editor
│   └── rich_text_editor.py # Rich text editor
├── services/            # External services
│   ├── ollama_service.py # Ollama integration
│   └── linkedin_service.py # LinkedIn integration
└── utils/               # Utilities
    └── markdown_utils.py # Markdown conversion

#Implementation Details

#Database Schema

The application uses a single articles table with comprehensive metadata:

| Field | Description | |-------|-------------| | id | Unique identifier | | title | Article title | | content | Rich text content | | conversation | AI conversation history | | keywords | SEO keywords | | target_audience | Intended readers | | writing_style | Tone and style | | published | Publication status | | published_at | Publication timestamp |

#AI Integration

The Ollama integration provides:

• Local model execution for privacy
• Customizable prompts based on metadata
• Context-aware content suggestions
• Support for multiple language models

#User Interface

Built with PyQt5's Fusion style for a modern, professional appearance:

• Responsive layout that adapts to content
• Intuitive toolbar with common actions
• Status indicators for AI and publication states
• Keyboard shortcuts for power users

#Use Cases

1. Content Creators: Generate engaging LinkedIn posts with AI assistance
2. Marketers: Maintain consistent brand voice across posts
3. Professionals: Share expertise with well-formatted articles
4. Developers: Technical content with code formatting support

#Future Enhancements

• Multi-platform support: Extend to other social networks
• Analytics integration: Track post performance
• Template system: Reusable content structures
• Collaboration features: Team content creation
• Advanced scheduling: Calendar-based publishing

#Getting Started

The application requires:

• Python 3.11+
• Ollama running locally
• LinkedIn account for publishing

Installation is straightforward with pip and the included requirements file. The application creates its database on first run and guides users through LinkedIn authentication.