🚀 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

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🎯 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

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🏗️ 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

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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)

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Automation Stack

node-telegram-bot-api - Telegram integration

Octokit - GitHub API client

node-cron - Scheduled tasks

systemd - Service management

Winston - Logging

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🧠 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 | 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');
}

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}

🐙 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
}

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}

🧠 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',
model: 'gemma:2b'
},
llama: {
url: 'http://localhost:11434/api',
model: 'llama3.2:3b'
},
deepseek: {
url: process.env.DEEPSEEK_API_URL,
key: process.env.DEEPSEEK_API_KEY
}
};
}

async analyzeIssue(issue) {
    const prompt = `

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Analyze this GitHub issue:

Title: ${issue.title}
Description: ${issue.body}
Labels: ${issue.labels.map(l => l.name).join(', ')}

Provide:

  1. Summary (1-2 sentences)
  2. Complexity (Low/Medium/High with reasoning)
  3. Priority (Low/Medium/High with reasoning)
  4. Technical Approach (Step by step)
  5. Estimated Effort (in hours)
  6. Dependencies
  7. Potential Risks

  8. Suggested Bounty Amount
    `;

    // 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

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🔗 Links

Repository: https://github.com/MyZubster-Ecosystem/myzubster

Telegram Bot: @myzubster_bot

Telegram Channel: @myzubster

AI Service: services/ai-automation

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📝 Try It Yourself
bash

Clone the repository

git clone https://github.com/MyZubster-Ecosystem/myzubster.git
cd myzubster/services/ai-automation

Install dependencies

npm install

Configure environment

cp .env.example .env

Add your tokens: TELEGRAM_BOT_TOKEN, GITHUB_TOKEN

Start the system

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

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🙏 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