Building an automated content management system for Telegram channels using Go and AI

Running a Telegram channel isn’t just about creativity—it’s a logistical nightmare. Curating content, rewriting posts, and scheduling updates can suck 20+ hours/week from your team. For businesses managing multiple channels, operational costs skyrocket.

Here’s how I automated it using Go, AI, and slashed costs by $400/month per channel—in just 7 days.

My Background: From Mobile Apps to AI Automation

As a mobile app developer specializing in native Android/iOS, Flutter, and Go (see my work here), I thrive on solving problems with new tools. When a client approached me with a Telegram channel struggling to scale, I saw a chance to merge my coding skills with AI—and further enhance my expertise in Go.

The Solution: Architecture of an AI-Powered Bot

Here’s how the system works:

Subreddit Scraper → Notion Database (API) → OpenAI Rewriter → Telegram Scheduler (Bot API)
  1. Subreddit Scraper: Pulls trending posts using Reddit’s API.
  2. Notion Database: Stores curated content via Notion API integration.
  3. OpenAI Rewriter: Refines text for tone and clarity (GPT-4 API).
  4. Telegram Scheduler: Posts content automatically using Telegram Bot API.

Key Features:

  • 💰 $400/month Saved per Channel: Eliminated manual content curation costs.
  • ⚡ Built in Days, Not Months: Leveraged AI tools like Cursor and Claude 3.7 Sonnet for rapid prototyping.
  • 🌐 Scalable for Multiple Channels: Uses Go’s concurrency to manage 5+ channels simultaneously.

The Development Journey: Leveraging Go with AI

Phase 1: Enhancing Go Skills with AI Assistance

With a solid foundation in Go, I utilized Cursor to streamline the development process for:

  • Notion API integration (notion-client-go library)
  • Telegram Bot API setup (telegram-bot-api package)
  • OpenAI GPT-4o text rewriting

Example Early Code Snippet (Simplified):
func fetchSubredditPosts(subreddit string) []Post { ... }

Phase 2: Mastering Control

As the codebase grew, AI helpers like Claude 3.7 Sonnet provided valuable insights, though they occasionally required manual adjustments:

  • ✅ Pros: Rapid iteration for features like scheduled posting.
  • ❌ Cons: Over-optimized code needed careful review to maintain CI/CD pipeline integrity.

Critical Lessons:

  • AI-generated error handling often misses edge cases, which I addressed with Go’s robust error management.
  • Go’s strict typing was leveraged to ensure scalability and performance.

Why This Project Was a Game-Changer

This bot project allowed me to balance AI speed with architectural rigor:

  1. AI ≠ “Go-ish” Code: While AI suggested Python-style try-except blocks, I ensured the use of Go’s error returns for idiomatic code.
  2. Cost Efficiency: Go’s low memory footprint cut server costs by 40% vs. a Python prototype.
  3. Business Impact: A client’s channel grew from 2k to 15k followers by delivering consistently engaging and high-quality AI-refined content that resonated with their audience.

Results That Matter

  • ⏱️ 95% Time Reduction: From 20+ hours/week to 1 hour/week of maintenance.
  • 💵 $2,400 Monthly Savings: For a client with 6 Telegram channels.
  • 🚀 99.8% Uptime: Achieved via Go’s goroutines for background processing.

3 Lessons for AI-Driven Development

  1. AI is a Scaffolding Tool, not a replacement for design.
  2. Review Every Line: AI once “optimized” a function into an infinite loop, which I corrected with my Go expertise.
  3. Document Relentlessly: AI skips comments—add them post-generation.

Let’s Automate Your Workflow

Whether you’re a business owner tired of content costs or a developer wanting to build AI tools:

📩 Contact Me to:

  • Deploy this Telegram bot for your channels.
  • Build custom AI automations (mobile apps, Flutter, Go).