I'm working on a data-driven app, without a database. Instead of primary keys and foreign key constraints, I'm using typing.Literal and Pydantic models. This makes sense to try because the final product is a statically generate website. My Python code does all the content preparation.

So, for example:

```python Category = Literal[ 'Data Formats and Serialization', 'Data Structures', 'Data and Text Processing',

...

]

Topic = Literal[ 'Calculating a date in the future or past', 'Capitalizing a string', 'Checking if a directory exists',

...

]

CATEGORIES_AND_TOPICS: dict[Category, list[Topic]] = { "Getting Started": [ "Starting a new project", ], "Strings": [ "Capitalizing a string", "Concatenating strings",

...

}

class ArticleSpec(BaseModel): """ A specification of a programming article. It consists of a topic, programming language, and natural language. """ model_config = ConfigDict(frozen=True)

human_lang: HumanLang
prog_lang:  ProgLang
topic:      Topic

```

Etc. I can now make complicated data structures from these Literals and I have existence checking right in my IDE. It catches typos right away, etc.

My Python CLI app is idempotent, so when I run it, it only fills in missing data. So for me to add new content and regenerate, it's pretty easy.

Need to revisit my Tetris project

Just released Beta of an Open Source Pure Python Binary Protocol Buffer (Renity), check it out!

Renity @ Github

Volcanic ash plume height detection