Mass Immersion Approach
Low-Key Anki: Intro
When used with default settings, Anki’s algorithm is horribly inefficient. Although we can never expect Anki’s algorithm to approach the same levels of efficiency as more advanced algorithms such as SuperMemo’s, by modifying Anki’s settings in the right way, we can make significant strides in that direction. Making Anki’s algorithm more efficient means becoming able to learn more things in less time, and in certain situations, remembering what we learn better. Sounds nice, doesn’t it?
The following is an in-depth guide that aims to provide a deep understanding of a specific aspect of Anki optimization: what I call the ease factor problem, and its solution, the Low-Key Anki setup. The Low-Key Anki setup is a series of counterintuitive simplifications one can make to Anki’s algorithm that result in a significant boost in efficiency, as well as a considerable reduction in the stress associated with reviewing cards. The first two sections cover broader aspects of the mechanisms behind spaced repetition, which will provide a foundation for understanding not only the ease factor problem and Low-Key Anki but other aspects of Anki optimization that I will be covering in the future as well, such as optimal retention rate. I recommend watching my guide on the basics of Anki’s algorithm before tackling this guide, as I will be assuming a basic understanding of how Anki’s algorithm works.
If you are not interested in investing the time and effort to fully understand Anki’s algorithm, its problems, and the solutions to those problems, and wish to blindly implement my advice because you trust that I know what I’m talking about, then you can skip to the Summary and Installation section for a TL;DR explanation of the Low-Key Anki setup and instructions for how to implement it.
That said, I highly encourage you to empower yourself by learning how this program you plan on investing so many hours into actually works. To quote Eliezer Yudkowsky:
“A shepherd builds a counting system that works by throwing a pebble into a bucket whenever a sheep leaves the fold, and taking a pebble out whenever a sheep returns. If you, the apprentice, do not understand this system – if it is magic that works for no apparent reason – then you will not know what to do if you accidentally drop an extra pebble into the bucket. That which you cannot make yourself, you cannot remake when the situation calls for it. You cannot go back to the source, tweak one of the parameter settings, and regenerate the output, without the source.”