Base project ready for Aulafy

We close onboarding by creating a minimal template you can reuse in RAG, agents, MLOps, or local AI courses.

Recommended structure

Terminal

aulafy-project/
  AGENTS.md
  README.md
  .env.example
  .gitignore
  pyproject.toml
  src/
    main.py
  data/
    .gitkeep
  scripts/
    verify.sh
  docker-compose.yml

Minimum README

Terminal

# Aulafy Project

## Requirements
- uv
- Docker Desktop optional
- Ollama optional

## Start
uv venv
source .venv/bin/activate
uv sync

## Verification
bash scripts/verify.sh

Verification script

Terminal

#!/usr/bin/env bash
set -euo pipefail

uv run python -m compileall src
uv run python src/main.py

echo "OK: verifiable project"

Checklist before moving on

The project starts from scratch by following the README.
There are no secrets or private data in Git.
There is a clear command to verify it works.
AGENTS.md explains how AI should work in that repository.

Once you have this, advanced courses stop being a collection of commands and become projects you can maintain.

Docker for AI services Back to course

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 foundationsYou are herePython, 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 automationDesign 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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