Editing Openai/6932fed8-c6a8-8000-8477-1049d95cb85e (section)

=== User: I’m attaching a JSONL file containing my review stats from my Japanese vocab Anki deck, with: ===
I’m attaching a JSONL file containing my review stats from my Japanese vocab Anki deck, with:
– the text fields (words, translations)
– statistics over days (number of reviews, number of lapses, etc.)

Each line is a JSON object with this compact schema:  
* t: ISO timestamp of the review (string)  
* rt: review type (0=learn, 1=review, 2=relearn, 3=filtered)  
* e: ease button pressed (1=Again, 2=Hard, 3=Good, 4=Easy)  
* i: new interval in days (integer)  
* li: previous interval in days (integer)  
* f: ease factor used by Anki (integer, e.g. 2500 = 2.5x)  
* ms: time spent answering in milliseconds (integer)  
* flds: list of text fields of the note, usually [front, back, extra]  

Analyze this data as a Japanese teacher and a specialist in memory & learning.
Identify:
– the cards that cause me the most problems
– the types of words/kanji that frequently appear in my errors
– the methodological issues (reviewing, overload, etc.) that you detect.

Give me concrete recommendations to:
– understand my learning difficulties
– handle “leech” cards
– adjust my pacing
– and specifically manage difficulties related to kanji (confusion, overload...).

I'm using FSRS (+ only "again" or "good").