03 — Python as a development tool

Time: 2–4 h. Milestone: create a fresh environment, run a script, read/write JSON, and commit a change.

Sources

MIT Missing Semester — the shell, tools, Git, debugging.
Python venv documentation.
Git book.

macOS Terminal + VS Code lab

First complete the hands-on Mac workbench. Use native Terminal.app and VS Code, not Linux/Windows instructions, and practise Control-A start, Control-E end, Control-P/N previous/next history, Control-R reverse search. Command-C/V copy/paste on macOS; Control-C interrupts. Use code . to open the current folder.

The following commands work in zsh on Apple Silicon:

mkdir -p ~/Projects/prompt-library
cd ~/Projects/prompt-library
python3 -m venv .venv
source .venv/bin/activate
python -m pip --version
code .
python -c 'print("Environment is ready")'

macOS uses python3 before activating a virtual environment and generally python inside it. Never use sudo pip install for this project. After unzipping course material, use ~/Projects/py-ai-studyplan as the working directory.

Optional shell enhancement: Zsh for Humans

zsh4humans works on macOS and adds completion, autosuggestions and fuzzy history; its upstream tips include macOS key variations. But its maintainer warns of very limited support, with most bugs unfixed. The installer changes zsh startup files after offering backups. Teach built-in macOS zsh first, then discuss whether an optional shell modification is worthwhile. It is never required to graduate, and remote install scripts should not run automatically. See workbench tradeoffs.

Exercise: fetch Markdown and edit it

The course's web links now open rendered HTML for comfortable reading. The raw .md sources stay downloadable for editing in VS Code. After learning shell paths, try:

cd ~/Projects/python-ai-playground
curl --fail --location --show-error \
  --output lesson.md \
  https://study.fkr.dev/docs/01-ai-intuition.md
head -n 12 lesson.md
code lesson.md

Explain curl (fetch), --output (save), and code (open in an editor). Compare with the formatted lesson in the browser. Do not execute downloaded scripts.

JSON exercise

Save a file such as:

{"prompts": [{"name": "friendly", "text": "Explain this simply."}]}

Write Python to load, list, and append prompts using json.load / json.dump. Handle missing files with a friendly error.

Git basics

From ~/Projects/prompt-library (your exercise project, separate from the course download), create .gitignore in the editor before staging:

.venv/
__pycache__/
.env

Then save prompts.json and your reader as main.py:

git init
git status
git add .gitignore main.py prompts.json
git diff --cached
git commit -m "Add prompt library"

Before commit: keep .venv/, caches, API keys and generated files out of Git (see .gitignore). Explain the distinction between source code, data and dependencies.

Debugging lesson

Create a deliberate TypeError, read the last traceback line, inspect the offending variable, then repair the code. Use print(repr(value)) before introducing an interactive debugger.

Mastery checkpoint

In the exercise project, deactivate and rename .venv to .venv-backup, create a new .venv, and run main.py again. Do not run the renamed environment; virtual environments should be recreated at their intended path. Describe what Git tracks and what it should not track. Keep the backup until the fresh environment works.

Next: neural networks