Mojo-Lings: A Hands-On Mojo Tutorial Inspired by Ziglings

Welcome to Mojo-Lings! This tutorial is modeled after the Ziglings approach: you’ll learn Mojo by fixing and extending small, focused code snippets. Each exercise targets a core language feature, with broken or incomplete code and detailed comments. Your job is to read, repair, and run each one—gaining practical Mojo mastery as you go.

Keep in mind the problem might be code only valid in previous versions of Mojo. This will help you learn what the most recent syntax and conventions.

Assumptions

• You have a decent understanding of Python.
• You have read about Mojo and have a general understanding of what it is and why you would use it.
• You know how to get to the Modular docs and Mojo Manual
• You have tried and succesfully ran the Get Started with Mojo section of the docs.
• I used pixi as recomended in the Mojo documentation. I expect that you can use this to setup the environment.

How to Use This Tutorial

• Read the intro and comments for each exercise.
• Fix or complete the code as instructed.
• Run your solution and check the output.
• Reflect on what you learned before moving to the next exercise.

Getting Started

Prerequisites

Before you begin the exercises, make sure you have:

• Mojo installed - Follow the Get Started with Mojo guide
• Git installed - For cloning the repository
• Basic Python knowledge - Understanding of Python syntax and concepts

Step-by-Step Setup Instructions

1. Install Pixi (If Not Already Installed)

Pixi is the recommended package manager for Mojo projects. Install it using one of these methods: On macOS/Linux:

curl -fsSL https://pixi.sh/install.sh | sh

On Windows (PowerShell):

iwr -useb https://pixi.sh/install.ps1 | iex

Alternative package manager installations:

# macOS
brew install pixi

# Windows
winget install prefix-dev.pixi

After installation, restart your terminal or run the source command as instructed.

2. Set Up the Environment (Optional but Recommended)

If you want to use pixi for environment management (recommended by Mojo docs):

Assumes you have pixi installed!

# Clone the repository
git clone https://github.com/borbert/Mojolings.git

# Navigate directly to the mojolings subdirectory
cd Mojolings/mojolings

# Initialize pixi in the current directory
pixi init . -c https://conda.modular.com/max-nightly/ -c conda-forge

# Install Mojo
pixi add modular

# Start the pixi shell
pixi shell

3. Start Learning

# View tutorial options
mojo exercises/main.mojo

# Start with foundation exercises (recommended)
mojo exercises/main.mojo modern

# Begin with your first exercise
mojo exercises/exercise_001.mojo

# You'll see compilation errors - this is expected!
# Your job is to fix the code marked with ????

4. Work Through the Exercises

For each exercise:

1. Read the exercise description - Each file has detailed comments explaining the concepts
2. Find the TODOs - Look for lines marked with ???? that need to be fixed
3. Fix the code - Replace ???? with the correct Mojo syntax
4. Test your solution - Run the exercise again to see if it works
5. Move to the next exercise - Progress through exercises in order

# Example workflow:
mojo exercises/exercise_001.mojo  # See the errors
# Edit the file to fix the ????
mojo exercises/exercise_001.mojo  # Test your fix
mojo exercises/exercise_002.mojo  # Move to next exercise

6. Track Your Progress (Optional)

If you want to save your progress:

# Fork the repository first (on GitHub), then:
git remote set-url origin https://github.com/YOUR_USERNAME/Mojo-lings.git

# Commit your changes as you progress
git add .
git commit -m "Complete exercise_001: Hello Mojo"
git push origin main

Exercise Structure

• Foundation Exercises (001-010): Core language concepts

  • Variables, functions, structs, traits
  • Ownership, design patterns, Python interop
  • Basic performance optimization

• Advanced Exercises (011-023): High-performance programming

  • SIMD vectorization, parallelization
  • GPU programming, memory optimization
  • Machine learning, cryptography applications

Running Individual Exercises

# From the project root directory:
mojo exercises/exercise_001.mojo
mojo exercises/exercise_002.mojo
# ... and so on

# Or navigate to exercises directory:
cd exercises
mojo exercise_001.mojo
mojo exercise_002.mojo

Need Help?

• Exercise stuck? Check the comments and hints in each file
• Mojo syntax questions? Refer to the Mojo Manual
• Environment issues? Review the Mojo installation guide
• Tutorial navigation? Run mojo exercises/main.mojo help

Exercises

• Exercise 001: Hello, Mojo! - Your first Mojo program and basic syntax
• Exercise 002: Variables and Types - Static vs dynamic typing with var and let
• Exercise 003: Functions - Understanding def vs fn functions
• Exercise 004: Structs and Initialization - Object-oriented programming basics
• Exercise 005: Traits - Interfaces and polymorphism
• Exercise 006: Ownership and Borrowing - Memory safety fundamentals
• Exercise 007: Design Patterns - Singleton pattern implementation
• Exercise 008: Strategy Pattern - Polymorphic algorithms and sorting
• Exercise 009: Python Interoperability - Using Python libraries in Mojo
• Exercise 010: Basic Performance Optimization - def vs fn performance comparison
• Exercise 011: Parallelism - Concurrent computation with parallelize
• Exercise 012: SIMD Vectorization - CPU acceleration fundamentals
• Exercise 013: Vectorize - Automatic SIMD optimization
• Exercise 014: Parallelization Redux - Multi-core processing power
• Exercise 015: Tensor Operations - AI/ML accelerated mathematics
• Exercise 016: GPU Kernel Programming - GPU programming with @kernel
• Exercise 017: Memory Optimization Patterns - Advanced memory management
• Exercise 018: Advanced SIMD Algorithms - Complex SIMD operations
• Exercise 019: Heterogeneous Computing - CPU + GPU coordination
• Exercise 020: Performance Benchmarking - Measuring and optimizing performance
• Exercise 021: Real-World AI Application - High-performance matrix multiplication
• Exercise 022: Neural Network Layer - Complete AI/ML implementation
• Exercise 023: Cryptography and Blockchain - SHA-256 and Merkle trees

Contributing

We welcome contributions to Mojo-lings! Here's how you can help improve this tutorial:

Ways to Contribute

• Fix bugs or errors in existing exercises
• Improve exercise explanations and comments
• Add new exercises covering additional Mojo features
• Update exercises for newer Mojo versions
• Enhance documentation and setup instructions
• Report issues with exercises or setup process