Project: 24/7 agent with inbox

The final project is a homegrown agent that receives tasks, classifies them, executes only what's safe, and leaves delicate items pending human approval.

Set up a simple architecture with inbox, worker, and logs.
Separate automatic responses, drafts, and actions requiring approval.
Control costs, loops, secrets, and permissions in an always-on agent.

Terminal

Telegram or Discord
  -> webhook / inbox
  -> Redis or SQLite queue
  -> agent worker
  -> allowed tools
  -> logs + summary
  -> human approval if there is risk

auto_actions:
  - summarize
  - search documents
  - prepare draft
actions_requiring_permission:
  - send email
  - publish
  - touch production
  - modify customer data

Recommended stack

Mini PC or Mac: best if you need local files or Ollama.
Small VPS: best for webhooks and constant availability.
Telegram or Discord: simple entry point for tasks and approvals.
SQLite: sufficient for small queue, auditing, and state.
Ollama or cloud provider: decide based on privacy, cost, and quality.

GitHub Actions and Routines 24/7 offline agents on real hardware

Complete Aulafy mapSee how this lesson fits without leaving your path.

Complete Aulafy map

How all courses connect

This is not a checklist. Start with the foundation, choose an outcome, and go deeper only when your project needs more control.

1Understand
2Apply or build
3Operate with confidence

Choose an application

Turn the foundation into a visible outcome: a website, a business improvement, media, or an interactive experience.

AI for small businesses and freelancersUse AI where it improves real work.Beginner to intermediate · 20 lessonsView course Generative AI: image, voice and videoCreate production-ready media with AI pipelines.Beginner to intermediate · 7 lessonsView course Games, 3D and simulations with AIPrototype interactive worlds with AI-assisted tools.Beginner to intermediate · 6 lessonsView course

Continue into the technical branch when you need to maintain code, data, or infrastructure.

Build with code

Prepare your environment, work with coding agents, and run models while keeping control of your projects.

Aulafy foundationsPython, Git, terminal, and Docker for learning AI smoothly.Beginner · 6 lessonsView course Codex for programmersBuild, test, and ship changes with OpenAI Codex.Beginner to advanced · 10 lessonsView course Claude Code, from zero to proBuild real software by talking to AI in your terminal.Beginner to advanced · 24 lessonsView course Claude Code + Local AIPrivate AI tools that run on your machine.Intermediate · 34 lessonsView course

This branch prepares you to design and operate reliable AI systems.

Take systems to production

Combine retrieval, agents, evaluation, security, deployment, and model adaptation when the problem requires it.

Advanced and secure RAGBuild retrieval systems that answer better and leak less.Intermediate · 14 lessonsView course Agents and automationYou are hereDesign agents that can work, recover and stay under control.Intermediate to advanced · 20 lessonsView course Production agents with LangGraph and n8nTake agent prototypes into reliable operations.Intermediate to advanced · 9 lessonsView course Self-hosted AI automation for small businessesRun AI workflows on your own infrastructure.Intermediate · 10 lessonsView course AI Router and content systemRoute local and frontier models with quality, cost, and privacy controls.Intermediate to advanced · 6 lessonsView course AI security and evaluationMeasure, test and harden AI systems before trusting them.Intermediate · 7 lessonsView course Local MLOps and model deploymentServe, monitor and operate models from your own stack.Intermediate to advanced · 10 lessonsView course Fine-tuning and post-training for LLMsTrain useful adapters without losing control.Intermediate to advanced · 8 lessonsView course

You do not need every course: choose the component your system needs and return as it grows.

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