OBSERVE [HORROR]
A Roblox experience owned by Wiremesh Studios™.
HorrorENpsychological horrorobservation dutyanomaly hunting
Description (written by the developer; not a statement by this register)
Leave a thumbs up if you enjoy the idea, or the game! :) You are a Private Anomaly Investigator (P.A.I.), assigned to cases involving anomalous environments. Can you identify over 100 unique anomalies? ⚠️ Psychological Horror 🎧 Headphones Recommended ⭐ Max Graphics Recommended This game contains JUMPSCARES and FLASHING LIGHTS. [NOTES] • This game is inspired by Exit 8 and Observation Duty Games. • Unlike traditional horror games, failure shouldn’t frustrate; it should teach you to observe more carefully. • Developed as a solo project, if you encounter any bugs or errors, press F9 to view the console log and report them on the group wall! 📱🕹️💻 Fully supported on Mobile, Console, and Desktop. Tags (ignore): anomaly game, psychological horror, observation game, spot the difference, jumpscare game, creepy, solo dev, beta release, exit 8 inspired, observation duty, start survey forest, start survey 2, creepy, dark, alone, solo, tense, tension, horror, scary, jumpscare, jumpscare
Last updated by the developer 6 Apr 2025, 13:19 UTC.
Live players over time
Growth
Growth history is still building. We hold one measurement for this entry. A trend needs at least two, taken days apart — so this will fill in on its own rather than being estimated. We do not publish numbers we have not measured.
| Measured (UTC) | Players | Change |
|---|---|---|
| 10 Aug 2026, 18:31 | 0 | first reading |
Concurrent players at each of our measurements (insert-on-change).
Monetization — game passes
Game passes for sale and their prices, measured from the Roblox passes API. Developer products bought inside the game aren’t publicly listable.
Badge funnel
How many players have ever earned each badge — a proxy for how far players get before they stop. Steep drop-offs mark where a game loses people. Win rate is the past-day share of players who earned it; categories are assigned by a language model from each badge’s own name and description.