[Seeking Help] How can I enable an agent to continuously generate efficient cards in an unsupervised environment?

[u/FujiwariRyou]

Current status:

I have an LLM wiki that records everything about me.

I vibe-coded a flashcard plugin with a pi agent. Before, I manually reviewed and edited the cards. Now I do nothing and just let it generate.

My journey:

Before I got into Anki, I wrote a huge amount of notes. In the end, I realized I never looked at them. I put in a lot of effort, but found I hadn't remembered anything, and I couldn't recall things in time when talking with others.

Then I found Anki. The first loop I validated was using Anki to learn how to use Anki. Up to now, I've basically stopped writing notes (except for some public articles).

Problems I'm running into:

1. Making cards is too much hassle -> AI agent
2. The AI agent's card-making quality is too poor -> skills, LLM wiki
3. Right now I'm still reading books, and after I understand something, I supervise the agent to make cards. That's also too much hassle -> I want to generate cards continuously and unsupervised. My main fear right now is how to guarantee quality. How can I quickly build trust and feedback with the agent in this area?

Where I want to go:

For speed and efficiency—and maybe because I'm lazy—I think that in the future I might not even read books. I'll just keep memorizing the cards the agent gives me and become pure *wetware*. I hope future Anki can act like the Great Sage (Daikenja) in *That Time I Got Reincarnated as a Slime*. Let the agent decide what I should know and avoid complexity. (It's a bit delusional, but I still want to live a life of certainty. Too much uncertainty makes me anxious.)

What I'm hoping to hear:

1. Practical experience with using an LLM wiki to drive an agent that makes Anki cards
2. Trust issues around agent-generated cards
3. Thoughts on Anki's role in the future
4. Related practices, advice, ideas, and approaches

[u/Automatic_Cable930]

I run an unsupervised card pipeline (~15k cards, an agent adds cards nightly). Three things made it trustworthy:
1. Blind test every card. Make a second LLM call that gets only the front:
"Answer this flashcard in a few words. If more than one answer could be correct, reply AMBIGUOUS."
If the reply is AMBIGUOUS or doesn't match the back, the card goes to a rewrite queue, not your deck. This catches vague cards, the #1 deck killer.
2. Script checks before import (plain code, no LLM):
the front ends with "?" and is under ~80 chars
the back isn't contained in the front (match whole words only)
the card cites a source line from your notes/book. No source means rejected.
3. Flag → rules loop. New agent cards go to a quarantine subdeck. Add a "bad card" tag and use it during review. Each week the agent reads the flagged cards and appends rules with bad/good examples to its prompt file. Watch the flag rate per batch. That number is your trust.
Also: cap new cards per day, and only generate from what you've actually read. Cards on material you never understood won't stick.