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#DuoJam - Duolingo 10th Party Land

#DuoJam - Duolingo 10th Party Land

A Roblox experience owned by William Blogs Grupo Oficial.

#DuoJam - Duolingo 10th Party Land is a Roblox experience created on 2022-06-10. The game has accumulated 1,032,937 lifetime visits and 280 favorites. Currently, zero users are playing. The experience holds a 30% like percentage. No specific genre classification is recorded in the available data.

Last updated by the developer 1 Apr 2024, 04:17 UTC.

0
playing (measured 11 Aug 2026, 16:35 UTC)
1,032,937
lifetime visits
30%
liked (162 👍 / 377 👎)
280
favourites (0.3 per 1k visits)
10 June 2022
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 We do not publish numbers we have not measured.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
11 Aug 2026, 16:350first reading

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

Monetization — game passes

Donation Number 4
100 R$
Donation #3
50 R$
Donation #2
10 R$
Donation #1
5 R$

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

Welcome to Duolingo 10th Party Land · other415,294 · 0.00% win
You Found Luis Roblox!!! · collection50,334 · 0.00% win
You Found J_Naruto!!! · collection26,201 · 0.00% win
You Found ByTairon 292!!! · collection18,434 · 0.00% win
You Found William Blogs!!! · collection17,742 · 0.00% win
You Found Benjamin!!! · collection14,795 · 0.00% win
You Found 123MIKIU!!! · collection12,292 · 0.00% win
Encontraste a Blogy! · collection1,825 · 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.