Let's summarize our Shorts Monster course.

What have we learned?

1. Decomposing tasks, identifying actors and bot classes, creating a class hierarchy
2. Feeling the class, working with inheritance and composition
3. Building the bot's basis, designing the project folder structure
4. Setting up launch logic via console and argparse
5. Storing project settings, securely managing confidential data from environment variables in a .env file
6. Working with text generation (OpenAI API, g4f)
7. Working with AI image generation
8. Automating the browser (Playwright + Bing Image Creator)
9. Generating voiceovers (Elevenlabs API)
10. Working directly with APIs through HTTP requests (Pexels API)
11. Processing images, animating, and generating videos (Pillow, Moviepy, Numpy)

After solving this task, you will have:

1. Strong Python programming skills with a focus on practice
2. A powerful bot that includes modules for text, AI images, voiceovers, and video generation
3. A foundation for building powerful OOP bots for other projects

This is everything I wanted to say and all I wanted to teach you.

You can support my project by subscribing to Patreon or sending some crypto (addresses are on YouTube).

☝️ As part of the Patreon subscription, I've added this course in an easy-to-read PDF version along with some bonuses:

1. The course in PDF format (67 pages)
2. A way for adding captions and transitions
3. A bonus section: abstract classes, interfaces, and contracts using this project as an example
4. I've also separately added a Prompt Builder with hidden columns and ready-made prompts

I also want to express my gratitude to everyone who has already supported the project. I appreciate the support of each one of you. 🖤

How are you?

I often hear questions like these:

• How to build a traffic/account farm?
• How to build a system that runs 24/7?
• What can such a system look like in production?
• What stack and components might it use?

I want to answer these questions here. For some, this will help to see possible directions for developing their own systems further.

What requirements might such a system have? It should run autonomously on a server and provide convenient centralized management.

The tech stack and architecture could look as follows:

1. API layer (FastAPI) — REST endpoints for managing accounts, tasks, schedules, and statuses.

2. Task queue (Celery + Redis) — background workers running 24/7 for generators, uploaders, and registrars.

3. Scheduler — Celery Beat + synchronization of schedules from the database (dynamic periodic tasks).

4. Database — SQLite for local development, PostgreSQL in production (via SQLAlchemy + Alembic).

5. Admin panel (React Admin) — dashboard to manage all entities, runs, statuses, and logs; manual starts and timers.

6. Notifications — simple Telegram wrapper to notify about errors or successes.

7. Logging — JSON logging for API and workers.

Example structure of such a project

Pay attention to the project structure:

Backend — a FastAPI application that handles all the logic: it stores data in the database (accounts, tasks, schedules, media), accepts requests via API routes, validates and processes them (schemas + services), and delegates heavy tasks (image/text generation, post uploads) to background tasks via Celery.

Frontend (admin panel) — a React Admin application that provides a user-friendly interface: you can add accounts, configure modules, create tasks, and view statistics. It’s a convenient dashboard that communicates with the API, while all data and operations come from the backend.

🔄 How everything connects

1. The frontend / admin panel sends a request to the API (e.g., "create a task to post an image").

2. The request goes to the router, where it is validated via schemas.

3. The application logic is executed through services.

4. If something heavy needs to be done (e.g., uploading an image to Pinterest), the service sends a task to Celery, which is processed by a worker in the background.

5. The result is stored in the database.

6. The frontend can fetch the task status or statistics through another route.

Example of running the backend/frontend locally:

• Backend — uvicorn app.main:app --reload

• Frontend — npm run dev

* In production, the system runs in Docker containers or via systemd services.

Implementation approach:

Generate code using Claude/GPT, refine and refactor it, test locally, deploy to the server, and monitor.

That’s all I wanted to share for now.

Ad astra per aspera