Mochi has great native apps on macOS and iOS (and maybe more?), the cards are formatted in markdown so I can generate them with LLMs with a custom system prompt, and I just found out today they have an API so I might try my hand at getting an LLM to push new cards on its own via. an MCP server.

1. https://mochi.cards/

- it has a clunky and complicated UI: interactions with blocks/line was clunky on mobile and web.

- The table UI for showing your cards, can also be very limiting.

- Converting blocks to cards by adding `<==>` is an ingenious idea.

- The use of "AI" is really over-rated.

* Open Source

* Cross-platform

* $0 except on iOS

* Popular enough to have a community and ecosystem around it

A lot of it down to the effort this guy has put into making the deck. https://www.asiteaboutnothing.net/w_ultimate_spanish_conjuga...

Seeing this did make me wonder how I might be able to get better at memorising important parts of iso/iec standards at work, but I can't see how that maps to flashcards

Anki is set up with a long review cycle (1 day, 1 week, 1 month, then automated) and I sit down to do my reviews about once a week. In that process, I usually end up having new ideas to make notes about based on either the randomized order the notes show up in or spotting a connection between the review note and something I've been working on lately.

[1] In practice, I let many/most of these go unrecorded - I probably average about one new note per day, but in bursts.

* Learning biology, memorize terms like "anabolic reaction" or "reverse transcriptase"

* Learning algebra, memorize major groups like S_n or GL_n

* Learning statistics, memorize the major probability distributions, their means, and standard deviations

* Preparing for math contests, remember things like "Chinese remainder theorem"

That's a tiny part of learning, but it dramatically accelerates the other parts. At that point, when you're working through texts, you'll understand what you're reading without looking things up or thinking about it. And when you're engage in complex problemsolving, you'll have the knowledge ready.

Do this either on or before the first (surface learning) pass, and once they're memorized, use them in more advanced contexts (e.g. reading research papers, teaching, complex problemsolving, etc.).

All this stuff interconnects, and SR gives a fast, cheap way to start building out the simpler parts of the knowledge network.