I've been thinking about this for a while too as an FSRS developer [1].
In general, we can think of a spaced repetition system as being (i) Content-aware vs. Content-agnostic and (ii) Deck-aware vs. Deck-agnostic
Content-aware systems care about what you're studying (language, medecine, etc) while Content-agnostic systems don't care about what you're studying.
Deck-aware systems consider each card in the context of the rest of the cards (the "deck") while Deck-agnostic systems consider each card in pure isolation.
Currently, FSRS is both Content-agnostic as well as Deck-agnostic. This makes it extremely easy to integrate into a spaced repetition system, but this also means the model will underfit a bit.
It it interesting to note that you could in practice optimize seperate FSRS models for each deck covering different topics, which would make it Content-aware in a sense. Additionally, "fuzz" is a somewhat Deck-aware feature of the model in that it exists specifically to reduce interactions between other cards in the deck.
I ran into a question a while ago that I couldn't find a good answer to, and while it's not exactly on topic this seems like a good place to ask it.
I was working in a detail rich context, where there were a lot of items, about which there were a lot of facts that mostly didn't change but only mostly. Getting a snapshot of these details into approximately everyone's head seemed like a job for spaced repetition, and I considered making a shared Anki deck for the company.
What wasn't clear was how to handle those updates. Just changing the deck in place feels wrong, for those who have been using it - they're remembering right, the cards have changed.
Deprecating cards that are no longer accurate but which don't have replacement information was a related question. It might be worth informing people who have been studying that card that it's wrong now, but there's no reason to surface the deprecation to a person who has never seen the card.
Is there an obvious way to use standard SRS features for this? A less obvious way? A system that provides less standard features? Is this an opportunity for a useful feature for a new or existing system? Or is this actually not an issue for some reason I've missed?
Being easy to integrate is an underappreciated feature of FSRS.
Using decks to draw semantic boundaries is likely overly constraining. I think we want to account for finer differences between cards. Decks are coarse and people differ in the ways they use them, some people recommend having just one global deck. Notes are too fine. We explored something in between: a note capturing an idea or concept, plus an associated set of cards. Turns out it's hard to draw idea boundaries. That's why I think it's easier to relate cards by semantic embeddings or more rigid but clearer structures, like the DAG of dependencies suggested elsewhere in this thread.
I’ve been working on https://phrasing.app for a while now, including many iterations of the SRS. It’s been my experience that most of these sorts of improvements are really imperceptible. While I use FSRS as a base, and I’m very happy with the results it provides, it’s really only a few percentage points off of the SM-2 algorithm from the 90s. It’s slightly less stressful, definitely more accurate, but I think only astute users would even notice the difference.
I’ve incorporated many different things into the SRS, from vector embeddings to graph based association to lemma clustering to morpheme linking, and was surprised how much of these I took out.
Most of the unlocks with the SRS have been more in application space. Doing reviews with Anki feels like a chore, and I’m always counting down the reviews left to do. Reviews with Phrasing however are much more addictive, and I routinely spent an extra 30+ minutes in that “ok just one more card” loop.
We will never be able to know with 100% certainty how well you know a card, but FSRS gets us darn close. I think the interesting stuff is less about improving that metric, and more about what can you do with that information.
Thanks to the whole FSRS team btw (I assume y’all will be reading this hn post) <3
There's a lot of UX work to do for SRS. Do you have a sense of how well the ideas behind Humane SRS translate outside of language learning? I imagine the main challenge would be identifying a steady influx of new cards.
I agree that gains in scheduling accuracy are fairly imperceptible for most students. That's why, over the past few years building https://rember.com, we've focused on UX rather than memory models. People who review hundreds of card a day definitely feel the difference, doing 50 fewer reviews per day is liberating. And now that LLMs can generate decent-quality flashcards, people will build larger and larger collections, so scheduler improvements might suddenly become much more important.
Ultimately, though, the biggest advantages is freeing the SRS designer. I'm sure you've grappled with questions like "is the right unit the card, the note, the deck or something else entirely?" or "what happens to the review history if the student edits a card?". You have to consider how review UX, creation/editing flows, and card organization interact. Decoupling the scheduler from these concerns would help a ton.
That is an important insight. It is not so much which method gets you to learn more when used for a given amount of time. It is probably more about which method is fun to use, and engages you and thus actually gets used.
Can't help but repeat this old joke: A guy bought a gym-membership for 6 months, and paid $1000. But he was lazy (like most of us are) and never went or very rarely went to the gym, never felt like going there. After 6 months he realized he had wasted $1000. So he thought maybe if he bought the equipment himself he could and would do exercise at home. He bought the equipment for $1000, but then he rarelywent home. Didn't feel like it :-)
This looks incredible, and its obvious that a lot of work has been done, but in exploring it I notice a lot of things that make me hesitate to spend the money!
First, in the section "Expressions are flashcards on steroids", the flavor text on each element (Translations, Audio, etc) is identical.
Next, I look at the pricing and get one idea. Then when I create an account and go to upgrade, I see completely different pricing options. Its not that I care so much about the options, but it kind of worries me!
At one point I swear I saw the phrase "Say something about comprehensible input" instead of an explanation of CI, and the sentence itself was duplicated but now I don't. Maybe you are making this landing page live? It _is_ a nice landing page, to be sure.
Overall, I think it looks really cool and I'm interested in trying it out but just a little nervous at the moment.
On the scheduling end, I'm surprised the article didn't mention https://github.com/fasiha/ebisu which uses Bayesian statistics.
When I was studying Japanese, I was thinking how it's always best to learn words in sentences and that it would be good if the sentences for a particular word were random.
Extending that, the sentences could be picked such that the other words are words scheduled for today meaning much more bang for buck per learning hour.
> When I was studying Japanese, I was thinking how it's always best to learn words in sentences and that it would be good if the sentences for a particular word were random.
>Extending that, the sentences could be picked such that the other words are words scheduled for today meaning much more bang for buck per learning hour.
Just the other day I was thinking about how there’s a good chunk of vocab that could be “mined” from the sentences in my vocab deck.
I think that this idea would work well, but would probably require a whole new SRS program to be able to implement it cleanly. It’s too dynamic for a traditional SRS app like Anki which is pretty static in nature.
I explored memory models for spaced repetition in my master's thesis and later built an SRS product. This post shares my thoughts on content-aware memory models.
I believe this technical shift in how SRS models the student's memory won't just improve scheduling accuracy but, more critically, will unlock better product UX and new types of SRS.
I've been playing with something similar, but far less thought out than what you have.
I have a script for it, but am basically waiting until I can run a powerful enough LLM locally to chug through it with good results.
Basically like the knowledge tree you mention towards the end, but attempt to create a knowledge DAG by asking a LLM "does card (A) imply knowledge of card (B) or vice versa". Then, take that DAG and use it to schedule the cards in a breadth first ordering. So, when reviewing a new deck with a lot of new cards, I'll be sure to get questions like "what was the primary cause of the civil war", before I get questions like "who was the Confederate general who fought at bull run"
I've got a system for learning languages that does some of the things you mention. The goal is to be able to recommend content for a user to read which combines 1) appropriate level of difficulty 2) usefulness for learning. The idea is to have the SRS system build into the system, so you just sit and read what it gives you, and review of old words and learning new words (according to frequency) happens automatically.
Separating the recall model from the teaching model as you say opens up loads of possibilities.
Brief introduction:
1. Identify "language building blocks" for a language; this includes not just pure vocabulary, but the grammar concepts, inflected forms of words, and can even include graphemes and what-not.
2. For each building block, assign a value -- normally this is the frequency of the building block within the corpus.
3. Get a corpus of selections to study. Tag them with the language building blocks. This is similar to Math Academy's approach, but while they have hundreds of math concepts, I have tens of thousands of building blocks.
3. Use a model to estimate the current difficulty of each word. (I'm using "difficulty" here as the inverse of "retrievability", for reasons that will be clear later.)
4. Estimate the delta of difficulty of each building block after being viewed. Multiply this delta by the word value to get the study value of that word.
5. For each selection, calculate the total difficulty, average difficulty, and total study value. (This is why I use "difficulty" rather than "retrievability", so that I can calculate total cognitive load of a selection.)
Now the teaching algorithm has a lot of things it can do. It can calculate a selection score which balances study value, difficulty, as well as repetitiveness. It can take the word with the highest study value, and then look for words with that word in it. It can take a specific selection that you want to read or listen to, find the most important word in that selection, and then look for things to study which reinforce that word.
You mentioned computational complexity -- calculating all this from scratch certainly takes a lot, but the key thing is that each time you study something, only a handful of things change. This makes it possible to update things very efficiently using an incremental computation [1].
In the language learning world there are some great tools already for adding content-awareness.
AnkiMorphs[1] will analyze the morphemes in your sentences and, taking into account the interval of each card as a sign of how well you know each one, will re-order your new cards to, ideally, present you with cards that have only one unknown word.
It doesn't do anything to affect the FSRS directly—it only changes the order of new, unlearned cards—but in my experience it's so effective at shrinking the time from new card to stable/mature that I'm not sure how much more it would help to have the FSRS intervals being adjusted in this particular domain.
I'm building a SRS language learning app [1] so I've thought about this topic a bit, but I've come to a conclusion that srs algorithms might be just a nerd optimization obsession. My app has "stupid" 1,3,7,15,30 or something like that intervals, and the reality is that if I know a card, I can swipe it within 2 seconds, and if I just barely know it, I can spend 30 seconds on it.
So optimizing the algorithm such that every card comes at the exact right moment might cause all cards to feel too hard or too easy. I think having a mix of difficult and easy cards is actually a feature, not a bug.
Don’t fool yourself into thinking a suboptimal SRS is going to be optimal at the motivational aspect. If a user needs a self-confidence boost during a flash card session, this should be a design choice, not due to poor performance of the core SRS algorithm.
Choose your SRS algorithm to best predict what a user knows and when they’re likely to forget it.
If your application decides that it wants to throw some softballs, that’s an application level decision. If you care about psychology and motivation, build a really good algorithm for that. Then blend SRS with motivation as desired.
> [....] Ignoring the following factors means we are leaving useful information on the table:
> 1. The review histories of related cards. Card semantics allow us to identify related cards. This enables memory models to account for the review histories of all relevant cards when estimating a specific card’s retrievability.
> 2. [...]
I've been thinking that card semantics shouldn't be analyzed at all, and just treated as a black box. You can get so much data off of just a few users of a flashcard deck that you could build your own map of the relationships between cards, just by noticing the ones that get failed or pass together over time. Just package that map with the deck and the scheduler might get a lot smarter.
That map could give you good info on which cards were redundant, too.
edit: this may be interesting to someone, but I've also been trying to flesh out a model where agents buy questions from a market, trade questions with each other, and make bets with each other about whether the user will be able to recall the question when asked. Bankrupt agents are replaced by new agents. Every incentive in the system is parameterized by the user's learning requirements.
SuperMemo's neural network component (implemented in SM-15) already does something similar by tracking correlations between items without semantic analysis, effectively building that "map" of relationships based purely on performance data.
Yes, that reminds me of knowledge tracing and methods like 1PL-IRT.
I think you can do both and get even better results. The main limitation is that the same flashcards must be studied by multiple students, which doesn't generally apply.
I also love the idea of the market, you could even extend it to evaluate/write high-quality flashcards.
You mention that FSRS treats each card independently, even if they derive from the same note. I wonder whether you've tried this Anki plugin, which tries to increase the interval between reviews of 'sibling' cards: https://ankiweb.net/shared/info/759844606
Since in Anki the "note" is the editing unit, that works for some cloze deletions but not for QA cards (only for double-sided QA cards). A content-aware memory model would allow you to apply "disperse siblings" to any set of cards, independently of whether they were created together in the same editing interface.
To help with language learning I tried Anki, didn't like the UX and ended up writing my own SRS, from scratch.
One thing that becomes very obvious very quickly is that all cards derived from the same piece of information should be treated as a group. The last thing you'd want is to see "a cow / ?" quickly followed by "una mucca / ?". This is just pointless.
So while I appreciate the in-depth write-up by the author, I must say that its main insight - that the scheduling needs to account for the inter-card dependencies - lies right there on the surface. The fact that Anki doesn't support this doesn't make it any less obvious.
Rather than relying on an embedding space, my approach is to have the cards themselves be grammars that can define the relationships between concepts explicitly. Then the problem becomes what specific sampling of all the possible outputs is optimal for a learner to see at any given time, given their knowledge state.
This is awesome. I've been using Bunpro for a while, which has great content, but I find myself memorizing the sentences rather than the grammar. Randomly generating cards based on the grammar points and vocab makes a ton of sense.
Some questions / comments / suggestions:
1. Is there a way to import vocab / kanji from Wanikani? WK is quite popular and has a good API. Bunpro integrates nicely with it, where it will or won't show furigana for kanji in the example sentences based on whether you've already learned the word in Wanikani. I'm guessing in your case you'd just want to import all the vocab. Even though I did the placement test, Grsly is still trying to teach me basic vocab like uta and obaasan. This is slowing down my progress through the grammar points.
2. Similar to question 1, is there a way to import grammar progress from Bunpro? Or even just click a button and have it assume I know everything from N5. The placement test only seemed to test a handful of basic grammar points.
3. Some of the sentences it has generated are quite awkward, like "ironna musume" ("all kinds of my daughter"). I guess that's grammatically correct, but it seems pretty unlikely to show up anywhere in real life. Have you considered using a local/small LLM to score or bias the example sentence generation? It's possible to constrain an LLM to only generate output that matches a grammar. You could construct such a grammar for each nontrivial element in your deck, with the vocab currently available for use. I guess you'd have to change the answer in your FAQ if you started using AI.
Amazing work! In https://rember.com the main unit is a note representing a concept or idea, plus some flashcards associated to it, hsrs would fit perfectly! I'll look more deeply into it.
| The main challenge in building content-aware memory models is lack of data. To my knowledge, no publicly available dataset exists that contains real-world usage data with both card textual content and review histories.
I wonder if the author has ever considered reaching out to makers of Anki decks used by premeds and medical students like the AnKing [1]. They create Anki decks for users studying the MCAT and various Med School curricula, so have a) relatively stable deck content (which is very well annotated and contains lots of key words that would make semantic grouping quite easy) b) probably contains loads of statistics on user reviews (since they have an Anki addon that sends telemetry to their team to make the decks better IIRC), and c) contains incredibly disparate information (all the way from high-school physics to neurochemistry).
The one thing I would want from a content aware scheduler would be to not put similar together.
What ends up happening is I have two similar cards mixed up. For the first card I take a 50/50 and get it right. Then for the second card I get it correct by process of information instead of having to take another 50/50. This results in the system incorrectly thinking I knew the second card that came up.
joshdavham|7 months ago
In general, we can think of a spaced repetition system as being (i) Content-aware vs. Content-agnostic and (ii) Deck-aware vs. Deck-agnostic
Content-aware systems care about what you're studying (language, medecine, etc) while Content-agnostic systems don't care about what you're studying.
Deck-aware systems consider each card in the context of the rest of the cards (the "deck") while Deck-agnostic systems consider each card in pure isolation.
Currently, FSRS is both Content-agnostic as well as Deck-agnostic. This makes it extremely easy to integrate into a spaced repetition system, but this also means the model will underfit a bit.
It it interesting to note that you could in practice optimize seperate FSRS models for each deck covering different topics, which would make it Content-aware in a sense. Additionally, "fuzz" is a somewhat Deck-aware feature of the model in that it exists specifically to reduce interactions between other cards in the deck.
[1] https://github.com/open-spaced-repetition/py-fsrs
dllthomas|7 months ago
I was working in a detail rich context, where there were a lot of items, about which there were a lot of facts that mostly didn't change but only mostly. Getting a snapshot of these details into approximately everyone's head seemed like a job for spaced repetition, and I considered making a shared Anki deck for the company.
What wasn't clear was how to handle those updates. Just changing the deck in place feels wrong, for those who have been using it - they're remembering right, the cards have changed.
Deprecating cards that are no longer accurate but which don't have replacement information was a related question. It might be worth informing people who have been studying that card that it's wrong now, but there's no reason to surface the deprecation to a person who has never seen the card.
Is there an obvious way to use standard SRS features for this? A less obvious way? A system that provides less standard features? Is this an opportunity for a useful feature for a new or existing system? Or is this actually not an issue for some reason I've missed?
ran3000|7 months ago
Using decks to draw semantic boundaries is likely overly constraining. I think we want to account for finer differences between cards. Decks are coarse and people differ in the ways they use them, some people recommend having just one global deck. Notes are too fine. We explored something in between: a note capturing an idea or concept, plus an associated set of cards. Turns out it's hard to draw idea boundaries. That's why I think it's easier to relate cards by semantic embeddings or more rigid but clearer structures, like the DAG of dependencies suggested elsewhere in this thread.
barrell|6 months ago
I’ve incorporated many different things into the SRS, from vector embeddings to graph based association to lemma clustering to morpheme linking, and was surprised how much of these I took out.
Most of the unlocks with the SRS have been more in application space. Doing reviews with Anki feels like a chore, and I’m always counting down the reviews left to do. Reviews with Phrasing however are much more addictive, and I routinely spent an extra 30+ minutes in that “ok just one more card” loop.
We will never be able to know with 100% certainty how well you know a card, but FSRS gets us darn close. I think the interesting stuff is less about improving that metric, and more about what can you do with that information.
Thanks to the whole FSRS team btw (I assume y’all will be reading this hn post) <3
And if anyone is curious I wrote up a bit about my SRS here: https://phrasing.app/blog/humane-srs
ran3000|6 months ago
There's a lot of UX work to do for SRS. Do you have a sense of how well the ideas behind Humane SRS translate outside of language learning? I imagine the main challenge would be identifying a steady influx of new cards.
I agree that gains in scheduling accuracy are fairly imperceptible for most students. That's why, over the past few years building https://rember.com, we've focused on UX rather than memory models. People who review hundreds of card a day definitely feel the difference, doing 50 fewer reviews per day is liberating. And now that LLMs can generate decent-quality flashcards, people will build larger and larger collections, so scheduler improvements might suddenly become much more important.
Ultimately, though, the biggest advantages is freeing the SRS designer. I'm sure you've grappled with questions like "is the right unit the card, the note, the deck or something else entirely?" or "what happens to the review history if the student edits a card?". You have to consider how review UX, creation/editing flows, and card organization interact. Decoupling the scheduler from these concerns would help a ton.
galaxyLogic|6 months ago
Can't help but repeat this old joke: A guy bought a gym-membership for 6 months, and paid $1000. But he was lazy (like most of us are) and never went or very rarely went to the gym, never felt like going there. After 6 months he realized he had wasted $1000. So he thought maybe if he bought the equipment himself he could and would do exercise at home. He bought the equipment for $1000, but then he rarelywent home. Didn't feel like it :-)
echo_time|6 months ago
First, in the section "Expressions are flashcards on steroids", the flavor text on each element (Translations, Audio, etc) is identical.
Next, I look at the pricing and get one idea. Then when I create an account and go to upgrade, I see completely different pricing options. Its not that I care so much about the options, but it kind of worries me!
At one point I swear I saw the phrase "Say something about comprehensible input" instead of an explanation of CI, and the sentence itself was duplicated but now I don't. Maybe you are making this landing page live? It _is_ a nice landing page, to be sure.
Overall, I think it looks really cool and I'm interested in trying it out but just a little nervous at the moment.
jarrett-ye|6 months ago
https://github.com/open-spaced-repetition/awesome-fsrs
absoluteunit1|6 months ago
Just a friendly heads up, I’m on mobile and I noticed that the burger menu doesn’t work.
(iPhone 13)
Otherwise - awesome work
Revisional_Sin|6 months ago
mabster|7 months ago
When I was studying Japanese, I was thinking how it's always best to learn words in sentences and that it would be good if the sentences for a particular word were random.
Extending that, the sentences could be picked such that the other words are words scheduled for today meaning much more bang for buck per learning hour.
jarrett-ye|6 months ago
cosmic_cheese|7 months ago
>Extending that, the sentences could be picked such that the other words are words scheduled for today meaning much more bang for buck per learning hour.
Just the other day I was thinking about how there’s a good chunk of vocab that could be “mined” from the sentences in my vocab deck.
I think that this idea would work well, but would probably require a whole new SRS program to be able to implement it cleanly. It’s too dynamic for a traditional SRS app like Anki which is pretty static in nature.
ran3000|7 months ago
I believe this technical shift in how SRS models the student's memory won't just improve scheduling accuracy but, more critically, will unlock better product UX and new types of SRS.
IncreasePosts|7 months ago
I have a script for it, but am basically waiting until I can run a powerful enough LLM locally to chug through it with good results.
Basically like the knowledge tree you mention towards the end, but attempt to create a knowledge DAG by asking a LLM "does card (A) imply knowledge of card (B) or vice versa". Then, take that DAG and use it to schedule the cards in a breadth first ordering. So, when reviewing a new deck with a lot of new cards, I'll be sure to get questions like "what was the primary cause of the civil war", before I get questions like "who was the Confederate general who fought at bull run"
unknown|7 months ago
[deleted]
gwd|6 months ago
I've got a system for learning languages that does some of the things you mention. The goal is to be able to recommend content for a user to read which combines 1) appropriate level of difficulty 2) usefulness for learning. The idea is to have the SRS system build into the system, so you just sit and read what it gives you, and review of old words and learning new words (according to frequency) happens automatically.
Separating the recall model from the teaching model as you say opens up loads of possibilities.
Brief introduction:
1. Identify "language building blocks" for a language; this includes not just pure vocabulary, but the grammar concepts, inflected forms of words, and can even include graphemes and what-not.
2. For each building block, assign a value -- normally this is the frequency of the building block within the corpus.
3. Get a corpus of selections to study. Tag them with the language building blocks. This is similar to Math Academy's approach, but while they have hundreds of math concepts, I have tens of thousands of building blocks.
3. Use a model to estimate the current difficulty of each word. (I'm using "difficulty" here as the inverse of "retrievability", for reasons that will be clear later.)
4. Estimate the delta of difficulty of each building block after being viewed. Multiply this delta by the word value to get the study value of that word.
5. For each selection, calculate the total difficulty, average difficulty, and total study value. (This is why I use "difficulty" rather than "retrievability", so that I can calculate total cognitive load of a selection.)
Now the teaching algorithm has a lot of things it can do. It can calculate a selection score which balances study value, difficulty, as well as repetitiveness. It can take the word with the highest study value, and then look for words with that word in it. It can take a specific selection that you want to read or listen to, find the most important word in that selection, and then look for things to study which reinforce that word.
You mentioned computational complexity -- calculating all this from scratch certainly takes a lot, but the key thing is that each time you study something, only a handful of things change. This makes it possible to update things very efficiently using an incremental computation [1].
But that does make the code quite complicated.
[1] https://en.wikipedia.org/wiki/Incremental_computing
jeffalyanak|7 months ago
AnkiMorphs[1] will analyze the morphemes in your sentences and, taking into account the interval of each card as a sign of how well you know each one, will re-order your new cards to, ideally, present you with cards that have only one unknown word.
It doesn't do anything to affect the FSRS directly—it only changes the order of new, unlearned cards—but in my experience it's so effective at shrinking the time from new card to stable/mature that I'm not sure how much more it would help to have the FSRS intervals being adjusted in this particular domain.
1: https://mortii.github.io/anki-morphs/intro.html
ran3000|6 months ago
kebsup|7 months ago
So optimizing the algorithm such that every card comes at the exact right moment might cause all cards to feel too hard or too easy. I think having a mix of difficult and easy cards is actually a feature, not a bug.
[1] https://vocabuo.com
xpe|6 months ago
Choose your SRS algorithm to best predict what a user knows and when they’re likely to forget it.
If your application decides that it wants to throw some softballs, that’s an application level decision. If you care about psychology and motivation, build a really good algorithm for that. Then blend SRS with motivation as desired.
emsixteen|7 months ago
pessimizer|7 months ago
> 1. The review histories of related cards. Card semantics allow us to identify related cards. This enables memory models to account for the review histories of all relevant cards when estimating a specific card’s retrievability.
> 2. [...]
I've been thinking that card semantics shouldn't be analyzed at all, and just treated as a black box. You can get so much data off of just a few users of a flashcard deck that you could build your own map of the relationships between cards, just by noticing the ones that get failed or pass together over time. Just package that map with the deck and the scheduler might get a lot smarter.
That map could give you good info on which cards were redundant, too.
edit: this may be interesting to someone, but I've also been trying to flesh out a model where agents buy questions from a market, trade questions with each other, and make bets with each other about whether the user will be able to recall the question when asked. Bankrupt agents are replaced by new agents. Every incentive in the system is parameterized by the user's learning requirements.
ethan_smith|6 months ago
ran3000|7 months ago
I think you can do both and get even better results. The main limitation is that the same flashcards must be studied by multiple students, which doesn't generally apply.
I also love the idea of the market, you could even extend it to evaluate/write high-quality flashcards.
rahimnathwani|7 months ago
ran3000|7 months ago
Since in Anki the "note" is the editing unit, that works for some cloze deletions but not for QA cards (only for double-sided QA cards). A content-aware memory model would allow you to apply "disperse siblings" to any set of cards, independently of whether they were created together in the same editing interface.
huhtenberg|6 months ago
One thing that becomes very obvious very quickly is that all cards derived from the same piece of information should be treated as a group. The last thing you'd want is to see "a cow / ?" quickly followed by "una mucca / ?". This is just pointless.
So while I appreciate the in-depth write-up by the author, I must say that its main insight - that the scheduling needs to account for the inter-card dependencies - lies right there on the surface. The fact that Anki doesn't support this doesn't make it any less obvious.
absoluteunit1|6 months ago
I put together and built a Raycast extension for Anki for this reason: https://www.raycast.com/anton-suprun/anki
Using Raycast keyboard friendly UI makes Anki a lot more fun and friction-less.
If you use a Mac - give it a go.
Apologies if some features are missing - I’ve been procrastinating on patching it with some requested features
vhrrsrivr|6 months ago
See how it's applied to Japanese learning here: https://elldev.com/feed/grsly
meatmanek|6 months ago
Some questions / comments / suggestions:
1. Is there a way to import vocab / kanji from Wanikani? WK is quite popular and has a good API. Bunpro integrates nicely with it, where it will or won't show furigana for kanji in the example sentences based on whether you've already learned the word in Wanikani. I'm guessing in your case you'd just want to import all the vocab. Even though I did the placement test, Grsly is still trying to teach me basic vocab like uta and obaasan. This is slowing down my progress through the grammar points.
2. Similar to question 1, is there a way to import grammar progress from Bunpro? Or even just click a button and have it assume I know everything from N5. The placement test only seemed to test a handful of basic grammar points.
3. Some of the sentences it has generated are quite awkward, like "ironna musume" ("all kinds of my daughter"). I guess that's grammatically correct, but it seems pretty unlikely to show up anywhere in real life. Have you considered using a local/small LLM to score or bias the example sentence generation? It's possible to constrain an LLM to only generate output that matches a grammar. You could construct such a grammar for each nontrivial element in your deck, with the vocab currently available for use. I guess you'd have to change the answer in your FAQ if you started using AI.
ran3000|6 months ago
HDMI_Cable|6 months ago
I wonder if the author has ever considered reaching out to makers of Anki decks used by premeds and medical students like the AnKing [1]. They create Anki decks for users studying the MCAT and various Med School curricula, so have a) relatively stable deck content (which is very well annotated and contains lots of key words that would make semantic grouping quite easy) b) probably contains loads of statistics on user reviews (since they have an Anki addon that sends telemetry to their team to make the decks better IIRC), and c) contains incredibly disparate information (all the way from high-school physics to neurochemistry).
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[1]: https://www.theanking.com
ran3000|6 months ago
charcircuit|6 months ago
What ends up happening is I have two similar cards mixed up. For the first card I take a 50/50 and get it right. Then for the second card I get it correct by process of information instead of having to take another 50/50. This results in the system incorrectly thinking I knew the second card that came up.
daft_pink|7 months ago
I love Anki and used it before when I needed to memorize things, but would love to know what other options on the market exist.
unknown|7 months ago
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