Dev Environment

Your tools shape your thinking. Set them up once, set them up right.

Type: Build Languages: Python, Node.js, Rust Prerequisites: None Time: ~45 minutes

Learning Objectives

• Set up Python 3.11+, Node.js 20+, and Rust toolchains from scratch
• Configure virtual environments and package managers for reproducible builds
• Verify GPU access with CUDA/MPS and run a test tensor operation
• Understand the four-layer stack: system, packages, runtimes, AI libraries

The Problem

You're about to learn AI engineering across 200+ lessons using Python, TypeScript, Rust, and Julia. If your environment is broken, every single lesson becomes a fight against tooling instead of learning.

Most people skip environment setup. Then they spend hours debugging import errors, version conflicts, and missing CUDA drivers. We're going to do this once, properly.

The Concept

An AI engineering environment has four layers:

graph TD
    A["4. AI/ML Libraries\nPyTorch, JAX, transformers, etc."] --> B["3. Language Runtimes\nPython 3.11+, Node 20+, Rust, Julia"]
    B --> C["2. Package Managers\nuv, pnpm, cargo, juliaup"]
    C --> D["1. System Foundation\nOS, shell, git, editor, GPU drivers"]

We install bottom-up. Each layer depends on the one below it.

s0-env-stack

Build It

Step 1: System Foundation

Check your system and install the basics.

# macOS
xcode-select --install
brew install git curl wget

# Ubuntu/Debian
sudo apt update && sudo apt install -y build-essential git curl wget

# Windows (use WSL2)
wsl --install -d Ubuntu-24.04

Step 2: Python with uv

We use uv — it's 10-100x faster than pip and handles virtual environments automatically.

curl -LsSf https://astral.sh/uv/install.sh | sh

uv python install 3.12

uv venv
source .venv/bin/activate  # or .venv\Scripts\activate on Windows

uv pip install numpy matplotlib jupyter

Verify:

import sys
print(f"Python {sys.version}")

import numpy as np
print(f"NumPy {np.__version__}")
a = np.array([1, 2, 3])
print(f"Vector: {a}, dot product with itself: {np.dot(a, a)}")

Step 3: Node.js with pnpm

For TypeScript lessons (agents, MCP servers, web apps).

curl -fsSL https://fnm.vercel.app/install | bash
fnm install 22
fnm use 22

npm install -g pnpm

node -e "console.log('Node', process.version)"

macOS / Apple Silicon (M1/M2/M3/M4): If the installer stops with Error: Cannot install under Rosetta 2 in ARM default prefix (/opt/homebrew), your terminal is running under Rosetta 2 (arch prints i386) while Homebrew is a native arm64 build. Install fnm forcing arm64, wire it into your shell, then rerun the commands above from fnm install 22:

arch -arm64 brew install fnm
echo 'eval "$(fnm env --use-on-cd)"' >> ~/.zshrc
source ~/.zshrc

Step 4: Rust

For performance-critical lessons (inference, systems).

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

rustc --version
cargo --version

Step 5: Julia (Optional)

For math-heavy lessons where Julia shines.

curl -fsSL https://install.julialang.org | sh

julia -e 'println("Julia ", VERSION)'

Step 6: GPU Setup (If You Have One)

NVIDIA (Linux / Windows):

nvidia-smi

# Install PyTorch with CUDA
uv pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124

macOS / Apple Silicon (M1/M2/M3/M4): There is no CUDA on a Mac — that's expected, not a failure. Do not pass --index-url .../cuXXX (those wheels are Linux/Windows only, so the install fails). Install the plain build, which includes Apple's MPS (Metal) GPU backend:

uv pip install torch torchvision torchaudio

Verify (works on any platform):

import torch
print(f"CUDA available: {torch.cuda.is_available()}")           # False on macOS — expected
print(f"MPS available:  {torch.backends.mps.is_available()}")   # True on Apple Silicon
if torch.cuda.is_available():
    print(f"GPU: {torch.cuda.get_device_name(0)}")

No GPU? No problem. Most lessons work on CPU. For training-heavy lessons, use Google Colab or cloud GPUs.

Step 7: Verify Everything

Run the verification script:

python phases/00-setup-and-tooling/01-dev-environment/code/verify.py

Use It

Your environment is now ready for every lesson in this course. Here's what you'll use where:

Ship It

This lesson produces a verification script that anyone can run to check their setup.

See outputs/prompt-env-check.md for a prompt that helps AI assistants diagnose environment issues.

Exercises

1. Run the verification script and fix any failures
2. Create a Python virtual environment for this course and install PyTorch
3. Write a "hello world" in all four languages and run each one