Baked Data

The The Baked Data architectural pattern describes this approach, which is key to taking full advantage of SQLite and Datasette.

I like to build my databases in GitHub Actions.

Niche Museums and TILs

• https://www.niche-museums.com/ is published from the https://github.com/simonw/museums repository - one big YAML file for the content.

• https://til.simonwillison.net/ is published https://github.com/simonw/til - separate Markdown files for each item.

Both of these sites have Atom feeds that are defined using a Datasette canned query, in conjunction with the datasette-atom plugin.

• https://www.niche-museums.com/browse/feed

• https://til.simonwillison.net/tils/feed

Generating a newsletter with an Observable notebook

I wrote about this in Semi-automating a Substack newsletter with an Observable notebook:

• https://datasette.simonwillison.net/simonwillisonblog is a Datasette/SQLite copy of my Django blog, created using db-to-sqlite by my https://github.com/simonw/simonwillisonblog-backup GitHub repository.

• https://observablehq.com/@simonw/blog-to-newsletter is my Observable notebook that assembles a newsletter from that data.

• https://simonw.substack.com/ is the Substack newsletter that I copy that content into.