we got secret missions where u gotta sneak past the shadow fleet and deliver precious cargo to save the world from destruction. but watch out, cuz there be spies and double agents everywhere tryna sabotage ur mission.
if u think u got what it takes to outsmart the russians and save the day, den come join us on dis epic adventure. but be warned, things can get pretty intense on dis server. so buckle up and get ready for a wild ride like no other.
We are a small friendly community. We are a vanilla server with only some minor QOF fixes and admin tools. Join a town or go out and adventure on your own You decide See you soon
silvermc.eu
Reminiscence SMP is a 1.19.3 Fantasy themed survival server, with a wide variety of entertainment to offer!
《 FEATURES 》 » Bedrock Java Crossplay » Proximity Chat » Organised Lore Events » Discord/SMP Cross-Platform Chat » Player Marketplace » Dungeons! » Custom Items & Mobs! » Quests » Friendly & welcoming community » Non P2W! » Awesome cosmetics!
RemiSMP.com
this is a brand-new SMP looking for players. It is a whitelisted server with 4 active players currently.
we run a vanilla+ experience with a few plugins like dynmap, set home, one-player sleep, and a couple more just to help with the vanilla experience. no shop UI’s or any fancy plugins that, (in our opinion it ruins a survival server)
you have more of a chance of getting accepted if you are 15+ and can build decent.
join today through discord!
139.99.68.163
Domi-Craf Network Mexico
Hello Domicraftiano, we are back, with the new version 1.20.2,
No premium
Have fun in survival, NOT suitable for cowards.
Mobs by levels, it doesn’t make it easy at all.
Bosses you must walk very carefully through the world, now you want to sleep at night and you will not travel only through the caves.
Don’t worry, you have PETS and BACKPACK so you don’t lose your items, in addition to BACK, but the mobs will not disappear… They will be waiting for your return. mua ha ha ha ha.
Equip yourself well with CUSTOM CHARMS
Daily, weekly, biweekly and monthly rewards (per month you will have a semi op kit)
We also have a 1.8 style PVP Arena server without coulddown when hitting.
Minigames
What are you waiting for? Invite your friends. (but seriously… invite friends, you’ll be afraid to play alone).
domicraft.pro
A simple Vanilla server where people can build their own civilization
Develop Survive Communicate
d1.minely.pro:25612
[1.19] FruitsCraft is a modern Minecraft: Java Edition server that aims to enhance vanilla gameplay in fun and exciting ways. Explore the vast world of FruitsCraft, from the many unique resource islands of Skyblock, to the difficult Mob Arena of Survival, there’s plenty for you to do here and have fun!
– EVENTS – FULLY CUSTOM SKYBLOCK – UNIQUE SURVIVAL – REGULAR UPDATES – AND MUCH MORE!
Website: https://fruitscraft.com Discord: https://discord.gg/fruitscraft
play.fruitscraft.com
The FRJCraft Network server has a very popular and entertaining minigame like survival and soon bedwars!!!
103.195.101.162:25566
UHC – SCHP Server
85.72.151.150
BEST ANARCHIC SERVER, with beautiful spawn
FairyWorld.mcbe.in:29695
Come mine, sell, buy and most importantly become Minecraft rich!! Mine away make a base have fun be safe enjoy
Play as much as you want when ever you want and do what you want! Apply for admin moderator etc etc Dont read what is below Introduced the idea of using pairs of word-like units extracted in an unsupervised way to provide a noisy top-down signal for representation learning from raw (untranscribed) speech. The learned representations capture phonetic distinctions better than standard (un-learned) features or those learned purely bottom-up. Others later applied this idea cross-lingually (Yuan et al., Interspeech 2016) and used it as a baseline for other approaches (He, Wang, and Livescu, ICLR 2017). This paper focussed on engineering applications, but led to later funding from NSF and ESRC to explore the idea introduced here as a model of perceptual learning in infants.Introduced the idea of using pairs of word-like units extracted in an unsupervised way to provide a noisy top-down signal for representation learning from raw (untranscribed) speech. The learned representations capture phonetic distinctions better than standard (un-learned) features or those learned purely bottom-up. Others later applied this idea cross-lingually (Yuan et al., Interspeech 2016) and used it as a baseline for other approaches (He, Wang, and Livescu, ICLR 2017). This paper focussed on engineering applications, but led to later funding from NSF and ESRC to explore the idea introduced here as a model of perceptual learning in infants.Introduced the idea of using pairs of word-like units extracted in an unsupervised way to provide a noisy top-down signal for representation learning from raw (untranscribed) speech. The learned representations capture phonetic distinctions better than standard (un-learned) features or those learned purely bottom-up. Others later applied this idea cross-lingually (Yuan et al., Interspeech 2016) and used it as a baseline for other approaches (He, Wang, and Livescu, ICLR 2017). This paper focussed on engineering applications, but led to later funding from NSF and ESRC to explore the idea introduced here as a model of perceptual learning in infants.Introduced the idea of using pairs of word-like units extracted in an unsupervised way to provide a noisy top-down signal for representation learning from raw (untranscribed) speech. The learned representations capture phonetic distinctions better than standard (un-learned) features or those learned purely bottom-up. Others later applied this idea cross-lingually (Yuan et al., Interspeech 2016) and used it as a baseline for other approaches (He, Wang, and Livescu, ICLR 2017). This paper focussed on engineering applications, but led to later funding from NSF and ESRC to explore the idea introduced here as a model of perceptual learning in infants.Introduced the idea of using pairs of word-like units extracted in an unsupervised way to provide a noisy top-down signal for representation learning from raw (untranscribed) speech. The learned representations capture phonetic distinctions better than standard (un-learned) features or those learned purely bottom-up. Others later applied this idea cross-lingually (Yuan et al., Interspeech 2016) and used it as a baseline for other approaches (He, Wang, and Livescu, ICLR 2017). This paper focussed on engineering applications, but led to later funding from NSF and ESRC to explore the idea introduced here as a model of perceptual learning in infants.Introduced the idea of using pairs of word-like units extracted in an unsupervised way to provide a noisy top-down signal for representation learning from raw (untranscribed) speech. The learned representations capture phonetic distinctions better than standard (un-learned) features or those learned purely bottom-up. Others later applied this idea cross-lingually (Yuan et al., Interspeech 2016) and used it as a baseline for other approaches (He, Wang, and Livescu, ICLR 2017). This paper focussed on engineering applications, but led to later funding from NSF and ESRC to explore the idea introduced here as a model of perceptual learning in infants.Introduced the idea of using pairs of word-like units extracted in an unsupervised way to provide a noisy top-down signal for representation learning from raw (untranscribed) speech. The learned representations capture phonetic distinctions better than standard (un-learned) features or those learned purely bottom-up. Others later applied this idea cross-lingually (Yuan et al., Interspeech 2016) and used it as a baseline for other approaches (He, Wang, and Livescu, ICLR 2017). This paper focussed on engineering applications, but led to later funding from NSF and ESRC to explore the idea introduced here as a model of perceptual learning in infants.Introduced the idea of using pairs of word-like units extracted in an unsupervised way to provide a noisy top-down signal for representation learning from raw (untranscribed) speech. The learned representations capture phonetic distinctions better than standard (un-learned) features or those learned purely bottom-up. Others later applied this idea cross-lingually (Yuan et al., Interspeech 2016) and used it as a baseline for other approaches (He, Wang, and Livescu, ICLR 2017). This paper focussed on engineering applications, but led to later funding from NSF and ESRC to explore the idea introduced here as a model of perceptual learning in infants.Introduced the idea of using pairs of word-like units extracted in an unsupervised way to provide a noisy top-down signal for representation learning from raw (untranscribed) speech. The learned representations capture phonetic distinctions better than standard (un-learned) features or those learned purely bottom-up. Others later applied this idea cross-lingually (Yuan et al., Interspeech 2016) and used it as a baseline for other approaches (He, Wang, and Livescu, ICLR 2017). This paper focussed on engineering applications, but led to later funding from NSF and ESRC to explore the idea introduced here as a model of perceptual learning in infants.
Hype1mines.minehut.gg