obsankipy VS free-spaced-repetition-scheduler

Introducing Obsankipy: I am open sourcing Obsankipy, a Python-based program designed to seamlessly sync your notes from Obsidian to Anki. Inspired by the obsidian_to_anki plugin, Obsankipy allows you to run the sync process without having Obsidian running, and even automate it! While it's still a work in progress, Obsankipy supports key features such as basic and reversed cards, cloze cards, images, audio, math formulas, code blocks, links, and more. Key Features:

... https://github.com/open-spaced-repetition/fsrs4anki/wiki/The... ...

I'm not sure I believe we understand our own learning/memory anything like enough for this not to be total pseudoscience? Reminds me of A Beautiful Mind.

Libraries: https://github.com/open-spaced-repetition/free-spaced-repeti...

All are MIT licensed I believe, Anki is primarily AGPL

Just read the wiki ¯\_(ツ)_/¯

The model used by FSRS: https://github.com/open-spaced-repetition/fsrs4anki/wiki/Free-Spaced-Repetition-Scheduler

Also, sorry for nitpicking, but I just checked the code here, and I saw that you changed the formula for post-lapse stability, but you didn't update the formula in the description here.

I have another one, also about difficulty. Look at the long "Stability updating formula after successful review" formula, there is a term D-b. This seems very counter-intuitive. A large value of D corresponds to an easy card, but thanks to that formula it will produce a very small change in stability. A small value of D corresponds to a difficult card, but according to that formula it will change stability a lot.

So I checked this and it seems that there's a whole bunch of different parameters (initial difficulty, initial stability, etc), and a lot of them are constant. So the next step would be to use some kind of optimization algorithm, like gradient descent, to optimize those parameters based on user's review history, right?