Analyzed automatically. Assessed 2026-10-05 from 694 user reviews, the store listing and its privacy label. We have not scanned the app's package, so nothing on this page was read from inside the app itself.
App Store
This analysis describes version 1.17.3 of the app, analyzed 2026-10-05 . The app may have changed since.
The store currently lists version 1.17.3, updated 2026-09-25.
Google Play
This analysis describes version 1.17.2 of the app, analyzed 2026-10-04 . The app may have changed since.
The store currently lists version 1.17.2, updated 2026-09-30.
What the analysis found
Positive evidence 1
Things this app does right, published at the same strengths as the catch list.
One source only — shown, not confirmed 2
A single kind of evidence. We publish these because withholding them would be the bigger distortion, and we do not treat any of them as a finding we stand behind.
What reviewers said
Verbatim excerpts from public store reviews, shown as corroborating user reports. They are not Appworthy audit evidence, and a quotation is not proof of the finding it sits under.
Aggressive ad load
Awesome landscapes, some absolutely beautiful, and you don't drive me nuts with your moderate amount of Ads like some Apps do with tons and tons of Ads, and thank you for that. Hope it's ok to say that. I don't mean to disparage anyone or…
Tons of ads after every single puzzle (past the first couple ones), requires data to run so you can't do it offline. Uses AI images, so it's a terrible game overall!
I had high hopes for this app & at level 20 I left 5 stars because at that point it had kept its word advertising 'low amount of ads'. However after I left my review the ads were after every single game. I'm so tired of ads that lie &…
What this app sells
From the store listing. Billing terms are read from each purchase’s name — the stores don’t publish them — so treat them as a strong hint, not a disclosure.
Show 1 more purchase we couldn’t categorise
These are really on sale; we just can’t tell from the name whether they recur.
How the automated analysis works
This assessment is produced automatically. Three independent signals are read — user reviews, the store listing and the store privacy disclosure — and each candidate finding is filed under the sources that support it.
No charge stands on one kind of evidence — with one published exception. A finding is either a measured fact that anyone can reproduce (an ad SDK is in the package; every subscription bills weekly; the privacy label omits a tracker we can see), or it is an interpretation — and an interpretation needs independent sources to agree before it reaches the catch list. The exception is a harm only users can see: a charge may publish from reviews alone, as “Reported by N users”, once enough reviewers raise it to clear that charge’s own published bar. That agreement rule is what replaced the human reviewer, not a model’s confidence.
There is no overall score, on purpose. A 0–99 number was published here until 2026-09-15. It was withdrawn because it discriminated too weakly to be the instrument — 44.7% of every pair of apps tied — and because a single number is a verdict where a count is an observation. What publishes instead is a row of measurements, each of which says what was counted and, where we could not look, says that instead.
Findings under One source only are real reports we do not stand behind as findings. They are shown because withholding them entirely would hide a genuine complaint, and labelled because publishing them as charges would be an accusation off a single source.
Several measurements above say “Not assessed” because no artefact we hold can answer them. A human review is what resolves those — and it can remove a favourable band as easily as add one.
Hands-on inspections are not yet open.