Building an AI Automation System for MyZubster

A developer built an AI automation system for the open-source MyZubster ecosystem that automatically handles GitHub issues, bounties, and Telegram notifications. The system uses local AI models (Gemma, Llama, DeepSeek) via Ollama, with Node.js, Express, MongoDB, and React, and includes a Telegram bot with commands for status, bounties, and AI analysis.

🚀 Building an AI Automation System for MyZubster Introduction MyZubster is an open-source ecosystem for plant mapping, privacy-first payments with Monero, and human-centered AI. In this post, I'll share how I built an AI automation system that automatically handles GitHub issues, bounties, and Telegram notifications. 📋 The Problem Managing an open-source project like MyZubster is complex: Too many issues requiring manual triage Bounties need to be created and managed manually Slow communication with contributors Manual monitoring of GitHub activity 🎯 The Solution I built a modular system that: Monitors GitHub - Automatically detects new issues and PRs Analyzes with AI - Uses local models Gemma, Llama, DeepSeek Creates bounties - Automatically for labeled issues Sends notifications - Real-time alerts on Telegram Orchestrates everything - With automatic fallback between AI models 🏗️ Architecture text ┌─────────────────────────────────────────────────────────────┐ │ MyZubster AI Automation │ ├─────────────────────────────────────────────────────────────┤ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Telegram │ │ GitHub │ │ AI Models │ │ │ │ Bot Handler │ │ Monitor │ │ │ │ │ └──────────────┘ └──────────────┘ └──────────────┘ │ │ │ │ │ │ │ └─────────────────┼──────────────────┘ │ │ │ │ │ ┌────────▼────────┐ │ │ │ Automation │ │ │ │ Orchestrator │ │ │ └─────────────────┘ │ │ │ │ │ ┌─────────────────┼──────────────────┐ │ │ │ │ │ │ │ ┌──────▼──────┐ ┌───────▼───────┐ ┌──────▼──────┐ │ │ │ Database │ │ Backend │ │ Frontend │ │ │ │ MongoDB │ │ Node.js │ │ React │ │ │ └─────────────┘ └───────────────┘ └─────────────┘ │ │ │ └─────────────────────────────────────────────────────────────┘ 🛠️ Technology Stack Core Technologies Node.js - Runtime environment Express - API server MongoDB - Database with Mongoose ODM React - Frontend with Leaflet maps AI Stack Ollama - Local AI model runner Gemma 2B - Google's lightweight AI model default Llama 3.2 - Meta's powerful model fallback DeepSeek R1 - Reasoning model fallback Automation Stack node-telegram-bot-api - Telegram integration Octokit - GitHub API client node-cron - Scheduled tasks systemd - Service management Winston - Logging 🧠 AI Models Setup The AI models run locally using Ollama. Here's how I set them up: Installing Ollama bash curl -fsSL https://ollama.com/install.sh https://ollama.com/install.sh | sh Pulling the Models bash ollama pull gemma:2b ollama pull llama3.2:3b ollama pull deepseek-r1:1.5b Testing the Models bash ollama run gemma:2b "What is MyZubster?" 🤖 The Telegram Bot The bot provides interactive commands for the community: Available Commands Command Description /start Welcome message and guide /status System and services status /github Recent GitHub activity /bounties List of active bounties /analyze AI analysis of an issue /help Show all available commands Bot Configuration javascript class TelegramBotHandler { async start { this.bot = new TelegramBot this.token, { polling: true } ; // Register command handlers this.bot.onText /^\/start/, this.handleStart.bind this ; this.bot.onText /^\/status/, this.handleStatus.bind this ; this.bot.onText /^\/bounties/, this.handleBounties.bind this ; this.bot.onText /^\/github/, this.handleGitHub.bind this ; this.bot.onText /^\/analyze/, this.handleAnalyze.bind this ; this.running = true; this.logger.info 'Telegram bot is ready' ; } } 🐙 GitHub Monitor The GitHub Monitor watches repositories and emits events: javascript const { Octokit } = require '@octokit/rest' ; const EventEmitter = require 'events' ; class GitHubMonitor extends EventEmitter { constructor token, logger { super ; this.token = token; this.logger = logger; this.issues = {}; this.repo = process.env.GITHUB REPO; } async start { this.octokit = new Octokit { auth: this.token, userAgent: 'MyZubster-AI-Automation' } ; this.running = true; await this.checkNewIssues ; this.startPeriodicCheck ; } startPeriodicCheck { setInterval = { this.checkNewIssues .catch error = { this.logger.error 'Periodic check failed:', error ; } ; }, 300000 ; // 5 minutes } } 🧠 AI Orchestrator The AI Orchestrator is the brain of the system. It uses multiple models with fallback strategies: javascript class AIOrchestrator { constructor logger { this.logger = logger; this.models = { gemma: { url: ' http://localhost:11434/api http://localhost:11434/api ', model: 'gemma:2b' }, llama: { url: ' http://localhost:11434/api http://localhost:11434/api ', model: 'llama3.2:3b' }, deepseek: { url: process.env.DEEPSEEK API URL, key: process.env.DEEPSEEK API KEY } }; } js async analyzeIssue issue { const prompt = Analyze this GitHub issue: Title: ${issue.title} Description: ${issue.body} Labels: ${issue.labels.map l = l.name .join ', ' } Provide: Suggested Bounty Amount ; js // Try models in order with fallback const models = { name: 'gemma', config: this.models.gemma, method: 'ollama' }, { name: 'llama', config: this.models.llama, method: 'ollama' }, { name: 'deepseek', config: this.models.deepseek, method: 'api' } ; for const model of models { try { return await this.analyzeWithModel model, prompt ; } catch error { this.logger.warn ${model.name} failed, trying next... ; } } return this.getDefaultAnalysis issue ; } } 🔧 Systemd Service Management For production deployment, I created a systemd service: ini Unit Description=MyZubster AI Automation Service After=network.target mongod.service Service Type=simple User=root WorkingDirectory=/root/myzubster/myzubster-merged/services/ai-automation ExecStart=/usr/bin/node /root/myzubster/myzubster-merged/services/ai-automation/index.js Restart=always RestartSec=10 Environment=NODE ENV=production Environment=PORT=5678 Install WantedBy=multi-user.target 📊 Real Example Analysis Here's a real analysis from the system on a Monero payment issue: text 📊 AI Analysis Results: 1. Summary: The issue proposes adding Monero XMR payment support for bounties. 2. Complexity: High. The integration of a new cryptocurrency like XMR involves technical complexities related to API integration, smart contract development, and potential compatibility issues with existing systems. 3. Priority: High. Integrating XMR payments would attract crypto-focused users. 4. Technical Approach: • Develop an API integration with the Monero blockchain • Create smart contracts for managing bounties • Integrate with the bounty platform • Implement user interfaces 5. Estimated Effort: 3-4 weeks 120-160 hours 6. Suggested Bounty: 0.01 XMR per bounty ~$2-5 USD 🚀 Results After deploying the system, I saw immediate benefits: Metric Before After Issue response time 24-48 hours < 5 minutes Bounty creation Manual 2-3 days Automatic 5 minutes Contributor notifications Manual emails Automatic Telegram Time spent on triage 5+ hours/week 0 hours automated 📁 Project Structure text services/ai-automation/ ├── src/ │ ├── telegram/ │ │ └── bot.js Telegram bot handler │ ├── github/ │ │ └── monitor.js GitHub monitor │ ├── ai/ │ │ └── orchestrator.js AI orchestrator │ └── orchestrator/ │ └── index.js Main orchestrator ├── logs/ ├── scripts/ ├── .env.example ├── index.js ├── package.json └── README.md 💡 Key Lessons Learned Local AI is Powerful - Running models locally saves API costs and keeps data private Fallback Strategies Matter - Multiple models ensure reliability Event-Driven Architecture - Makes the system extensible and maintainable Mock Mode - Enables testing without external dependencies Systemd - Essential for production deployment 🔗 Links Repository: https://github.com/MyZubster-Ecosystem/myzubster Telegram Bot: @myzubster bot Telegram Channel: @myzubster AI Service: services/ai-automation 📝 Try It Yourself bash git clone https://github.com/MyZubster-Ecosystem/myzubster.git https://github.com/MyZubster-Ecosystem/myzubster.git cd myzubster/services/ai-automation npm install cp .env.example .env npm start 🎯 What's Next? I'm planning to add: Web Dashboard - Visual monitoring of all services Slack Integration - Alternative notification channel Auto-Reply - AI-powered responses to common issues Sentiment Analysis - Understanding contributor sentiment Multi-Repo Support - Monitor multiple repositories 🙏 Conclusion Building this AI-powered automation system has transformed how I manage MyZubster. What started as a simple idea grew into a complete ecosystem that handles complex tasks automatically. The best part? It's all open source and you can use it for your own projects too Built with ❤️ by the MyZubster Team