The use of spaced repetition memory systems has changed my life over the past couple of years. Here's a few things I've found helpful:

I've memorized about 9,000 cards, over 2 years.

The single biggest change is that memory is no longer a haphazard event, to be left to chance. Rather, I can guarantee I will remember something, with minimal effort: it makes memory a _choice_.

Rule of thumb: if memorizing something will likely save me five minutes in the future, into the spaced repetion system it goes. The expected lifetime review time is less than five minutes, i.e., it takes < 5 minutes to learn something... forever.

I use Anki ( https://apps.ankiweb.net/  ), both desktop and mobile apps. I've no affiliation with them at all.

I mostly enter cards on the desktop app.

I review cards on the mobile app, while going for walks, in line at the coffeeshop, in transit, and so on. I find it meditative. It takes about 20 mins each day.

I tried (and failed) several times to take Anki up.

But what finally made Anki "take" was frustration that I'd never really learned the Unix command line.

For fun, I wondered if it might be possible to use Anki to essentially completely memorize a (short!) book on the command line. It was. I didn't memorize all options, but I did memorize nearly all I could imagine ever using. This was very exciting!

A caveat: there is a difference between remembering facts and
mastering processes. It's one thing to know a command; it's another to
actually type the command.

To really internalize a process, you need to actually do the process. Still, I've found the transfer relatively easy.

(I've experimented with miming the actions while reviewing cards, but it doesn't work so well and is annoying.)

It's best to make cards as atomic as possible.

An example: when learning about soft linking I initially had a question "How to create a soft link?" with answer "ln -s filename linkname".

This was too complicated --- I always stumbled on the order of filename and linkname.

I broke the question in two: "What is the command and option to use to create a soft link?" A: "ln -s". And "What is the ordering of filename and linkname"

This sounds silly and obvious, but the improvement was very considerable: the two cards became trivial.

(There's something quite deep about memory in this example, which I don't understand.)

I use Anki for almost everything.

Learning places and all kinds of facts about my city, from the best things to order at a particular restaurant to demographic statistics (really) to favourite places in parts of the city I don't visit often.

Learning APIs.

Reading papers and books and watching videos. This is especially helpful for building mastery outside your area of expertise. You can (say) read a paper multiple times through, each time just grabbing what is easy, gradually building up an understanding.

For instance, this is how I read the AlphaGo paper (for my article https://www.quantamagazine.org/is-alphago-really-such-a-big-deal-20160329/ ).

I read and reread the paper several times, as well as consulting a lot of adjacent papers, Wikipedia etc.

I added several hundred Anki cards while doing.

The early cards were mostly very simple things: facts about TDGammon (which used a similar approach to beat Backgammon), very basic facts about how Go works, and reinforcement learning and Monte Carlo Tree search.

Later, the facts got more and more complex.

Of course, I didn't master all the literature around the paper. But I think I made pretty rapid progress coming up to speed.

Now, just to write one article that wouldn't necessarily have been a good use of time. But a nice thing about Anki is that the information is retained. When the AlphaGo Zero and AlphaZero papers came out, they were very easy to read.

Verb form: I talk and think of "Ankifying" a paper or book etc.

It's easy to overdo it, especially initially, and waste time Ankifying useless info. Over time, I've found myself cultivating heuristics for how much to Ankify.

E.g., for many papers the answer is 0 to 5 questions. But for papers I want to understand better it might be 20 to 40. And for deep papers that I want to master it may be hundreds.

Lots more to say, but I'll leave it there for now.