Interesting to see the stats here. My total active library size is about the same as the author's (~50k cards), yet I performed less than 100k reviews this past year. That said, my overall retention is a good bit lower (~83%). Wouldn't have expected a 6% difference to make for a 3x higher review load!
1. My algorithm is probably inefficient, and a big Q1 2026 goal is to figure out where the inefficiencies are and (better) to get a better system for addressing and remediating them in an automated way.
2. A lot of my cards were also made in 2025 (and 2024), so I'm probably much farther to the left of you on the learning curve, on average.
Comments
Interesting to see the stats here. My total active library size is about the same as the author's (~50k cards), yet I performed less than 100k reviews this past year. That said, my overall retention is a good bit lower (~83%). Wouldn't have expected a 6% difference to make for a 3x higher review load!
What are your cards about? The author seems to be learning for the sake of learning.
1. My algorithm is probably inefficient, and a big Q1 2026 goal is to figure out where the inefficiencies are and (better) to get a better system for addressing and remediating them in an automated way.
2. A lot of my cards were also made in 2025 (and 2024), so I'm probably much farther to the left of you on the learning curve, on average.