Also, we do not use the database because we have have a lot of data (it's tiny), but because it gives us multi-user serialization.
So many of the previous efforts to "improve things" have been aimed at improving the wrong things. +
As Postscript 2 predicted, AI tools are now here and they can understand the troggle code and all the Django documentation better than our wildest dreams of only a few months ago. So the problem of requiring Django-competent programmers is now substantially reduced. There is now no good reason to abandon Django, and ongoing refactoring is now much easier too. +
We keep the same architecture as now, and incrementally replace modules that use django/SQL with direct object storage of collections using pickle(), shelve() and json(). @@ -219,7 +222,7 @@ So a useful goal, I think, is to make 'troggle2' accessible to a generic python But even 'just Python' is not that easy. Python is a much bigger language now than it used to be, with some increasingly esoteric corners, such as the new asyncio framework.. -
The AI wave is just starting and Option 3 could be just to stick with Django indefinitely and use AI programmer aids to help new coders understand and edit existing troggle. An AI can grok the whole thing.