Transform 2+ hours of daily LinkedIn content creation into 5 minutes of approval time while maintaining authentic personal voice and professional credibility.
A complete AI-powered LinkedIn automation platform built specifically for developers, founders, and tech professionals who want to maintain a consistent, authentic LinkedIn presence without spending hours on content creation.
- Time Drain: Creating LinkedIn content takes 2-3 hours daily
- Inconsistency: Posting sporadically hurts professional visibility
- Authenticity: Generic content doesn't reflect your unique voice
- Trend Awareness: Missing relevant industry conversations and opportunities
- AI Trend Discovery: Automatically finds relevant topics from HackerNews, Reddit, GitHub
- Voice-Matched Content: Generates posts that authentically sound like you
- Smart Scheduling: Posts at optimal times for maximum engagement
- Performance Analytics: Tracks results and continuously improves
- 4-Agent AI System: TrendScout → IdeaGenerator → ContentWriter → ApprovalManager
- Real-time Trend Discovery: HackerNews, Reddit (r/programming, r/webdev), GitHub trending
- Voice Learning: Analyzes your writing style for authentic content generation
- Quality Scoring: Multi-dimensional content assessment (hook strength, authenticity, engagement potential)
- Performance Tracking: Real LinkedIn engagement metrics and ROI analysis
- A/B Testing: Automated testing of hooks, content types, and posting strategies
- Growth Analytics: Profile development, audience insights, influence scoring
- Continuous Optimization: System learns from your feedback and improves over time
- Intelligent Scheduling: Learns optimal posting times for your audience
- Content Calendar: Visual scheduling with conflict detection and bulk operations
- Publishing Queue: Automated posting with retry logic and success verification
- Multi-method Posting: Copy-paste, browser automation, or LinkedIn API integration
- Real-time Progress: Live pipeline execution with agent-by-agent updates
- Modern UI: Clean, responsive interface with skeleton loading and smooth transitions
- Multi-user Support: Secure authentication with data isolation via Supabase
- Comprehensive Settings: Fine-tune automation, personalization, and safety controls
Frontend: React 18 + TypeScript + Tailwind CSS + Shadcn/UI
Backend: Supabase (PostgreSQL + Edge Functions + Real-time)
AI Services: Groq API (Primary) + OpenAI (Fallback)
APIs: HackerNews, Reddit, GitHub, LinkedIn (Future)
Deployment: Vercel (Frontend) + Supabase (Backend)
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Trend Sources │───▶│ AI Agents │───▶│ User Interface │
│ │ │ │ │ │
│ • HackerNews │ │ • TrendScout │ │ • Dashboard │
│ • Reddit │ │ • IdeaGenerator │ │ • Approval │
│ • GitHub │ │ • ContentWriter │ │ • Analytics │
│ • Dev.to │ │ • QualityScorer │ │ • Calendar │
└─────────────────┘ └──────────────────┘ └─────────────────┘
│
┌──────────────────┐
│ Supabase │
│ │
│ • Database │
│ • Authentication │
│ • Real-time │
│ • Edge Functions │
└──────────────────┘
- Node.js 18+
- npm or yarn
- Supabase account
- API keys (Groq, Reddit, GitHub)
# Database & Authentication
VITE_SUPABASE_URL=your_supabase_url
VITE_SUPABASE_ANON_KEY=your_supabase_anon_key
# AI Generation
VITE_GROQ_API_KEY=gsk_...
VITE_OPENAI_API_KEY=sk_... # Fallback
# Trend Discovery
VITE_REDDIT_CLIENT_ID=your_reddit_client_id
VITE_REDDIT_CLIENT_SECRET=your_reddit_secret
VITE_GITHUB_TOKEN=ghp_...
# Optional: Direct LinkedIn Integration
VITE_LINKEDIN_CLIENT_ID=your_linkedin_client_id
VITE_LINKEDIN_CLIENT_SECRET=your_linkedin_secret# Clone the repository
git clone https://github.com/yourusername/linkedin-ai-automation.git
cd linkedin-ai-automation
# Install dependencies
npm install
# Set up environment variables
cp .env.example .env
# Edit .env with your API keys
# Start development server
npm run dev- Create a new Supabase project
- Run the database migrations:
-- Run the SQL schema from /database/schema.sql
-- This creates all necessary tables with proper relationships and security- Configure Row Level Security (RLS) policies
- Set up Edge Functions for background processing
- Sign up with email or social login
- Complete onboarding: Set expertise areas, writing preferences
- Train your voice: Upload 3-5 existing LinkedIn posts
- Configure automation: Set posting frequency and content types
6 AM → System discovers trending topics
7 AM → Generates content ideas from trends
8 AM → Creates LinkedIn-ready posts
9 AM → Sends approval notification
→ You review and approve (5 minutes)
→ System schedules and posts automatically
- Upload existing posts: Paste text, upload files, or import from URLs
- Analysis: System extracts vocabulary, style, and tone patterns
- Confidence building: More training data = better voice matching
- Continuous improvement: Learns from your approvals and edits
- Review generated content: Quality scores and trend context
- Edit or approve: One-click approval or inline editing
- Schedule optimization: System finds optimal posting times
- Performance tracking: Real engagement metrics and insights
interface ContentPreferences {
favoriteTopics: string[]; // React, SaaS, AI, etc.
avoidTopics: string[]; // Topics to filter out
contentTypes: ContentType[]; // hot-take, dev-tip, build-story
postingFrequency: number; // Posts per week
technicalDepth: number; // 1-10 scale
}interface AutomationSettings {
autoApproveHighQuality: boolean; // Auto-approve 9+ rated content
schedulingEnabled: boolean; // Auto-schedule approved content
weekendPosting: boolean; // Include weekends
optimalTimingOnly: boolean; // Only post at peak times
}- Rate limiting: Maximum posts per day/week
- Content review: Human-in-the-loop approval required
- Account safety: Monitor for unusual activity
- API usage tracking: Cost monitoring and alerts
- Engagement: Likes, comments, shares, impressions
- Growth: Profile views, connection requests, followers
- Content: Top-performing posts, optimal content types
- ROI: Time saved, engagement improvement, opportunity generation
- Approval patterns: What content you consistently approve/reject
- Voice confidence: How well the system matches your style
- Trend relevance: Success rate of trend-to-content pipeline
- Optimization impact: Measured improvements over time
- Audience analysis: Demographics, engagement patterns
- Competitive benchmarking: Industry performance comparisons
- Growth opportunities: Content gaps and trending topics
- Thought leadership: Influence scoring and recognition tracking
- Row Level Security: Complete data isolation between users
- Encrypted storage: All sensitive data encrypted at rest
- API key management: Secure credential storage and rotation
- Audit logging: Complete activity tracking for security
- Terms of Service: Multiple compliance options (copy-paste, API, automation)
- Rate limiting: Respectful platform usage with human-like behavior
- Account safety: Monitoring and alerts for suspicious activity
- User education: Clear guidance on automation best practices
- AI agent pipeline with real trend discovery
- Voice learning and content personalization
- Multi-user authentication and data isolation
- Smart scheduling and publishing pipeline
- Comprehensive analytics and A/B testing
- Performance optimization and strategic insights
- Direct LinkedIn posting via multiple methods
- Real-time performance metrics collection
- Audience insights and engagement analytics
- Multi-platform support (Twitter, Medium, Dev.to)
- Team collaboration and agency features
- Advanced AI models and personalization
- Enterprise security and compliance
- Subscription tiers and payment processing
- Usage-based billing and optimization
- Partner integrations and white-labeling
- Marketplace for content strategies
We welcome contributions! Please see our Contributing Guide for details.
# Fork the repository
# Create feature branch
git checkout -b feature/amazing-feature
# Make changes and test
npm run test
npm run lint
# Commit with conventional commits
git commit -m "feat: add amazing feature"
# Push and create pull request
git push origin feature/amazing-feature- AI-First: Every feature designed around intelligent automation
- User Control: Human-in-the-loop for authenticity and safety
- Performance: Real-time updates and efficient API usage
- Scalability: Multi-tenant architecture from day one
This project is licensed under the MIT License - see the LICENSE file for details.
- Groq for fast, reliable AI inference
- Supabase for excellent backend-as-a-service
- Shadcn/UI for beautiful, accessible components
- The developer community for inspiration and feedback
- Documentation: docs.yourapp.com
- Issues: GitHub Issues
- Discussions: GitHub Discussions
- Email: support@yourapp.com
- Twitter: @yourapp
Built with ❤️ for the developer community
Transform your LinkedIn presence from time-consuming obligation to automated growth engine.