The Short Version
Neurako now lets you tune your own FSRS scheduler, not just use a one-size-fits-all default.
That means you can:
- Change your desired retention to trade off recall vs. review volume.
- Set a maximum interval for how long a mastered card can stay away.
- Toggle fuzz to smooth out review spikes.
- Optimize your FSRS weights from your own review history once you have enough data.
For learners coming from Anki, this closes one of the biggest remaining gaps: serious control over the scheduler without having to babysit a desktop-first workflow.
Why This Matters
Many flashcard apps say they "use FSRS" when what they really mean is:
- they run the default algorithm,
- with the default parameters,
- the same way for every user,
- with no way to adjust the workload once your goals change.
That is better than legacy interval systems, but it is not the full promise of FSRS.
Real FSRS power comes from the fact that it is a parameterized memory model. Different learners tolerate different workloads. A medical student preparing for exams often wants higher recall and is willing to accept more reviews. A language learner building vocabulary over the long term may want fewer daily reviews and accept slightly lower retention.
Neurako now supports both.
What You Can Personalize in Neurako
Desired retention
Desired retention tells FSRS what probability of recall to aim for when it schedules the next review.
- Lower retention such as 0.80 to 0.84 means fewer reviews, but more cards will feel rusty when they come back.
- Mid-range retention such as 0.85 to 0.89 is a balanced option if you want to reduce workload.
- 0.90 remains the default and is still the right choice for most learners.
- Higher retention such as 0.91 to 0.94 means more frequent reviews and stronger recall, which is useful for high-stakes study.
- Very high retention such as 0.95+ is possible, but you should expect significantly heavier daily queues.
The important idea is simple: raising retention buys certainty with more work; lowering retention buys time with more forgetting.
Maximum interval
Some learners are comfortable letting a mature card disappear for years. Others want a hard cap so important material resurfaces sooner.
Neurako now lets you define that cap directly with maximum interval. If you never want even a strong card to go beyond a certain number of days, you can enforce it.
Fuzz
Fuzz adds a small amount of randomness to due dates so your queue does not become overly synchronized. In practice, it helps avoid the feeling that entire batches of cards come back on the exact same day.
If you want more deterministic scheduling, you can turn it off.
Before and After: What the Change Feels Like
Before personalization
Before this release, every learner effectively used the same scheduler defaults. FSRS was already stronger than old interval logic, but the experience was still generic:
- the interval preview was fixed to the shared defaults,
- the study workload could not be tuned from the app,
- and optimization was not exposed as a product feature.
After personalization
Now your study settings can directly shape future interval previews and future scheduling decisions:
- lowering retention makes the next intervals shorter and more conservative,
- raising retention lengthens the recall target but increases review pressure,
- and optimized weights let the scheduler better fit your own memory patterns once you have enough history.
The practical result is not just "more control." It is a study loop that feels more obviously yours.
Why This Is Better Than a Typical Power-User Setup
Historically, personalizable scheduling has mostly lived in tools that assume you are comfortable:
- digging through advanced settings,
- reading long forum threads,
- and manually tuning a workflow across desktop, plugins, and sync.
Neurako takes a different approach.
You still get serious FSRS controls, but they live inside a modern product flow:
- mobile and web both use the same settings,
- interval previews immediately reflect your preferences,
- and the optimization path is built into the product instead of hidden behind community scripts.
For many learners, that is the right middle ground: Anki-level scheduling seriousness with less setup friction.
Who Should Change Desired Retention?
You should consider adjusting it if:
- your daily review load feels too heavy even though the cards are not especially hard,
- your queue feels too light and you want stronger recall before exams,
- or you are switching study modes, such as moving from casual retention to exam preparation.
You probably should not change it every day. Give the scheduler time to accumulate enough reviews for your settings to actually mean something.
When Optimization Helps
Neurako’s optimizer is meant for learners with enough review history to make personalization meaningful.
Optimization is most useful when:
- you have already accumulated substantial review data,
- your cards cover a reasonably consistent domain,
- and you want the scheduler to better match your actual forgetting curve.
It does not rewrite your historical reviews, and it does not rewrite old card states retroactively. It improves how future reviews are scheduled.
The Bigger Picture
This release matters because it shifts Neurako from "an app that uses modern scheduling" to "an app that gives serious learners real ownership over modern scheduling."
That is the standard Anki users have expected for years. The difference is that Neurako now delivers it in a cleaner cross-platform product experience:
- capture cards quickly,
- study with FSRS by default,
- tune the scheduler when your goals change,
- and optimize it when your review history is rich enough.
That is why we think Neurako now offers the best personalizable FSRS experience outside of Anki.
Want the fundamentals first? Read FSRS Explained. Want to compare workflows? See Neurako vs Anki. Ready to use it? Upgrade to Pro or open your study settings in Neurako.