Encodr vs. SuperMemo

SuperMemo is where spaced repetition as we know it started - its SM-2 algorithm is the ancestor most other schedulers, Anki included, trace back to, and later SuperMemo versions have kept pushing the algorithm further (SM-18's neural-network-based interval prediction is genuinely advanced work). Encodr descends from the same idea but takes it in the opposite direction: less configuration, not more.

Depth of algorithm vs. depth of setup

SuperMemo's scheduling is built to be the most accurate predictor of when you'll forget a specific card, and it adjusts based on a rich model of your review history. That accuracy comes with real complexity - understanding what the algorithm is doing, let alone tuning it, is its own learning curve on top of the material you're actually trying to study. Encodr's scheduler runs a fixed interval ladder with no configuration screen at all, trading some of that theoretical precision for something closer to "you never have to think about the algorithm again." See how Encodr's scheduler decides what to show you today for exactly how that ladder works.

Interface era

SuperMemo's interface still reflects its research-software origins more than a modern app's - functional, but not built around the kind of low-friction daily habit a phone-native app aims for. Encodr's feed is a scroll you already know how to use, on the theory that the best algorithm in the world doesn't help if it's attached to an interface you don't open.

Content

SuperMemo is almost entirely a platform for your own material - there's no equivalent to a sourced, curated library of exam-ready decks built in. Encodr's library gives you a starting point (MCAT, licensing exams, book decks) without building anything yourself first.

Where SuperMemo still wins

If you're the kind of learner who wants to understand and tune exactly how your scheduling algorithm works - and some people genuinely do get better results from hand-adjusting difficulty and interval parameters - SuperMemo gives you that control in a way no simplified scheduler can match. It's built for people who see the algorithm as part of the craft, not an implementation detail they'd rather not think about.

The actual question to ask yourself

Do you want to spend some of your study time understanding and adjusting your scheduling algorithm, on the theory that a better-tuned schedule is worth the investment? Or do you want that decision made once, correctly enough, so all your time goes into the material itself? Neither answer is wrong - they're just different bets about where your effort is best spent.