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jpo's random neat collection

jpo's random neat collection

A Roblox experience owned by egg's bad game group.

OtherENcollectionuser-generatedsocial

Description (written by the developer; not a statement by this register)

a game made by 2 people who really like neats. all neats made by JPOisPrettyCool all lobbies made by ErkaNoCounteR

Last updated by the developer 2 May 2025, 13:57 UTC.

0
playing (measured 12 Aug 2026, 01:38 UTC)
1,468
lifetime visits
70%
liked (14 👍 / 6 👎)
34
favourites (23.2 per 1k visits)
13 May 2024
created (per Roblox)

Play on Roblox →

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 We do not publish numbers we have not measured.

Measurement log — every players count we have recorded
Measured (UTC)PlayersChange
12 Aug 2026, 01:380first reading

Concurrent players at each of our measurements (insert-on-change).

Badge funnel

Played the game! · welcome649 · 0.00% win
Beat Not Even a Smooth Beginnings · progression426 · 0.00% win
Not Even an Easy Obstacles · other349 · 0.00% win
Beat Not Even a Slanted Climbing · progression299 · 0.00% win
Beat Not Even a Undemanding Difficulty · progression277 · 0.00% win
Beat Not Even a Tightrope Fun · progression151 · 0.00% win
Beat Not Even a Shrinking Pole · progression122 · 0.00% win
Beat Not Even a Pillars · progression102 · 0.00% win
Not Even a Minato Aqua · other90 · 0.00% win
Not Even a Green Green Green · other87 · 0.00% win
Beat Miniscule NEAT · progression79 · 0.00% win
#### ##### NEAT · other74 · 0.00% win
Beat Not Even a Weird Pain · progression71 · 0.00% win
Beat Not Even a One Four One · progression67 · 0.00% win
Beat Not Even a Wacky Catastrophe · progression64 · 0.00% win

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.