Run FSRS optimization for my Mochi cards. Using train_csv.py from fsrs-rs-python. The new weights are below.

[0.26720747351646423, 1.629839539527893, 2.9071414470672607, 10.455878257751465, 6.474127292633057, 0.7291164994239807, 3.0826733112335205, 0.06065294146537781, 1.7888823747634888, 0.3533310890197754, 0.7168195843696594, 1.4715564250946045, 0.03916575014591217, 0.24278202652931213, 1.603986144065857, 0.6014000177383423, 1.8729000091552734, 0.5113826990127563, 0.07216055691242218, 0.09571979939937592, 0.1193043515086174]

I’m using 95% target retention rate for my science deck and I haven’t found it as “hellish” as many people in the SRS community see it. I use 90% for all other decks, so maybe it counterbalances my workload. The algorithm is more cautious with this target — it shows me cards right at the top of the forgetting curve resulting in intervals that grow quite slowly.

I hope these new parameters will optimize the spacing. If not, I may drop the target rate by a few percentage points. The anecdotal evidence is 5% increase in retention can nearly double your review volume.