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[UPD] Build a GIANT DICE!

[UPD] Build a GIANT DICE!

A Roblox experience owned by PeakBits.

SimulatorENbuildingco-optraining

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

🎲 Welcome to Build a Giant DICE! Collect blocks, stack them up and build a GIANT DICE with the whole server! 🏗️ 🧱 Collect as many blocks as you can 🏗️ Stack them up and build the dice piece by piece 🤝 Team up with everyone in your server ⚡ Train your Speed to move faster 💪 Train your Strength to carry more blocks 🏊 Finish a dice to unlock a HUGE training bonus in the pool 💎 Earn Gems and get stronger 👥 Bring your friends! Each friend in your server gives you +10% Speed, Strength and Gems! 📱 Play on Desktop, Console, Mobile and Tablet! 👍 Like & ⭐ favorite for more updates!

Last updated by the developer 18 Oct 2026, 20:01 UTC.

3
playing (measured 19 Oct 2026, 07:45 UTC)
28,776
lifetime visits
71%
liked (15 👍 / 6 👎)
962
favourites (33.4 per 1k visits)
17 September 2026
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 We do not publish numbers we have not measured.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
19 Oct 2026, 07:453first reading

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

Monetization — game passes

Triple Hatching
399 R$
VIP
299 R$
+3 Pet Equip
249 R$
DOUBLE GEMS
199 R$
+1 Pet Equip
99 R$
Fast Hatch
99 R$
Auto Collect
99 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 Build a GIANT DICE! · welcome19,920 · 100.0% 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.