anki-ultimate-geography VS free-spaced-repetition-scheduler

For those who want to learn map, capital, and flag: https://github.com/anki-geo/ultimate-geography

There is a shared Anki deck about this https://ankiweb.net/shared/info/2109889812

hello! i made a new US Presidents Deck because the ones currently uploaded on Anki didn't have all the information/ individual cards i wanted. i used the card style/code from the Ultimate Geography deck, and all the information on the cards is from here. hope you all enjoy! :D

If Anki is a life long journey, then the most important is to create good note types. Take Ultimate geography as an example. Each type of information has its own field. It makes it very easy to manage and expand the deck. You won't have the issue of recognizing cards by their shape, you won't have duplicates notes.

Use Anki. It is a flashcard program, and I believe it has some cool geography sets that also includes flags and capitals. Try this one to start out with: https://ankiweb.net/shared/info/2109889812.

Are you saying that you are looking for a different resource than the one used in the Ultimate Geography in the share deck?

11 cards from Ultimate Geography

https://github.com/anki-geo/ultimate-geography/releases - scroll down to the Assets section.

... 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?