Sergey Kuzmich

Posted on Apr 2

I open-sourced alternative to ReplyGuy — AI replies on Reddit, Twitter/X & HN for $50/month

#python #webdev #ai #opensource

I built MentionMaster as a paid SaaS, ran it for a year,
and today I'm open-sourcing the full backend.

Here's what it does, how it works, and how to run 600 AI-powered mentions/month for ~$50.

🎬 Demo

The Problem

If you're a founder or indie hacker, you know this loop:

Someone on Reddit asks "what tool helps with X?" — your product is the perfect answer
You never see it
A competitor replies, gets the customer

Manual monitoring doesn't scale. Paid tools like ReplyGuy charge $49–$299/month.
So I built my own and now I'm giving it away.

What It Does

MentionMaster monitors Reddit, Twitter/X, Hacker News, YouTube, and TikTok for
posts matching your product keywords. When it finds a relevant post, it:

Scores it for relevance (saves OpenAI API costs — no point replying to irrelevant posts)
Generates a context-aware reply using GPT-4o-mini
Queues it for your review
Publishes on approval, with auto-translation if your product targets non-English markets

Tech Stack

Layer	Tech
API	FastAPI
Background jobs	Celery + Redis
Database	Firebase Firestore
Auth	Firebase Admin SDK
AI	OpenAI GPT-4o-mini
Reddit	PRAW
Twitter/X	Selenium scraper
Payments	LemonSqueezy
Observability	Prometheus + Grafana + OpenTelemetry
Deploy	Docker Compose

Architecture

FastAPI (REST API)
    │
    ├── Redis (Celery broker)
    │       │
    │       └── Celery Workers
    │               ├── Scrape Reddit every 12h
    │               ├── Scrape Twitter/X every 12h
    │               ├── Scrape HN, YouTube, TikTok
    │               └── Approve queued posts every 2h
    │
    └── Firebase Firestore (users, projects, posts)

The Interesting Parts

Relevance scoring before AI generation

Before calling OpenAI, every scraped post goes through a relevance check:

def is_post_suitable_for_help(text, product_description):
    prompt = (
        f"Given: '{text}', can our product '{product_description}' "
        f"solve this problem? Return only True or False."
    )
    result = client.chat.completions.create(
        model='gpt-4o-mini',
        messages=[{"role": "user", "content": prompt}]
    )
    return result.choices[0].message.content.strip() == "True"

This cuts OpenAI costs by ~60% — you only generate full replies for posts
where your product is actually relevant.

Scheduled scraping with Celery + APScheduler

# Runs every 12 hours in production
scheduler.add_job(create_scrape_reddit_task, CronTrigger(hour='*/12'))
scheduler.add_job(create_scrape_twitter_task, CronTrigger(hour='*/12'))

# Promotes unpublished → approved every 2 hours
scheduler.add_job(create_move_posts_to_approved, CronTrigger(hour='*/2'))

Multi-language support

If your product targets non-English markets, replies are auto-translated
before publishing:

if product_language != 'en':
    ai_response = GoogleTranslator(
        source='auto', 
        target=product_language
    ).translate(ai_response)

💰 How to Run 600 Mentions/Month for $50

If you want to scale beyond one account, here's the setup I used:

Tools needed:

Dolphin{anty} antidetect browser — $10/mo for 20 profiles
Proxy per profile — ~$2/mo each

The math:

20 profiles × 5 social networks × 30 posts/month = 600 mentions/month
Total cost: $10 (Dolphin) + $40 (20 proxies) = $50/month
Cost per mention: ~$0.08

Each profile posts every 4 hours automatically — frequent enough to
cover conversations, slow enough to look natural.

Compare: ReplyGuy charges $49–$299/month for similar volume.

Getting Started

git clone https://github.com/youlast/mentionmaster_backend.git
cd mentionmaster_backend
cp .env.example .env   # fill in your keys
docker compose up --build

You'll need:

OpenAI API key
Firebase project (Firestore + Auth)
Reddit app credentials (free at reddit.com/prefs/apps)

Full setup guide in the README.

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