makesite VS datasette.io

Thank you for sharing the link to the source code. My simple site generator is based on my wife's project makesite.py[1]. In fact, I used her site generator for a few years before I went all in on Common Lisp for my personal projects. Then I reimplemented makesite.py in Common Lisp.

[1]: https://github.com/sunainapai/makesite/

I ended up using the developer-focused https://github.com/sunainapai/makesite , but I heard Hugo and Jekyll have plenty of themes that I assume can be dropped in. Good luck!

I've been using makesite.py, which is ~200 lines of python, dumping the picture in a pics folder and then hand editing the markdown.

Maybe a few lines of code and template work would let you add a custom template that would automatically add a folder worth of images.

https://github.com/sunainapai/makesite/blob/master/makesite....

Opened an issue exploring alternatives here: https://github.com/simonw/datasette.io/issues/109

I decided to just drop "any size" but keep "any shape".

Here's an example from one of my projects: https://github.com/simonw/datasette.io/blob/main/.github/workflows/deploy.yml

I've come around to almost the opposite approach.

I pull all of the data I can get my hands on (from Twitter, GitHub, Swarm, Apple Health, Pocket, Apple Photos and more) into SQLite database tables that match the schema of the system that they are imported from.

For my own personal Dogsheep (https://simonwillison.net/2020/Nov/14/personal-data-warehous...) that's 119 tables right now.

Then I use SQL queries against those tables to extract and combine data in ways that are useful to me.

If the schema of the systems I am importing from changes, I can update my queries to compensate for the change.

This protects me from having to solve for a standard schema up front - I take whatever those systems give me. But it lets me combine and search across all of the data from disparate systems essentially at runtime.

I even have a search engine for this, which is populated by SQL queries against the different source tables. You can see an example of how that works at https://github.com/simonw/datasette.io/blob/main/templates/d... - which powers the search interface at https://datasette.io/-/beta

I've been exploring this technique more over the past year and I really like it - https://datasette.io (code at https://github.com/simonw/datasette.io ) is a more recent and much more complicated example.

Extracting links from markdown and using them to populate some additional columns or tables at build time would be pretty straight forward.

I run SQLite in serverless environments (Cloud Run, Vercel, Heroku) for dozens of projects... but the trick is that they all treat the database as a read-only asset.

If I want to deploy updated data, I build a brand new image and deploy the application bundled with the data. I tend to run the deploys for these (including the database build) in GitHub Actions workflows.

This works really well, but only for applications that don't need to apply constant updates more than a few times an hour! If you have a constant stream of updates I still think you're better off using a hosted database like Heroku PostgreSQL or Google Cloud SQL.

One example of a site I deploy like that is https://datasette.io/ - it's built and deployed by this GitHub Actions workflow here: https://github.com/simonw/datasette.io/blob/main/.github/wor...