MeetnGreetMe (WEL)
摘要
类型: | ICO | |
类别: | 旅游 | |
驗證: | 否 | |
白名单: | 否 | |
KYC: | 否 | |
筹资目标 (软上限): | 7 500 000 USD | |
筹资目标 (硬上限): | 7 500 000 USD | |
代币量: | 75 000 000 WEL | |
代币价格: | 1 WEL = 0.35 USD | |
最低購買量: | 0.10 WEL | |
空投计划: | 否 | |
赏金计划: | 否 | |
托管支付: | 是 (自己代理) | |
产品原型: | 是 | |
白皮书: | 打开 | |
货币: | ETH, BTC | |
平台: | Ethereum | |
地点: | 爱沙尼亚 | |
网站: | https://ico.meetngreetme.com | |
链接: |
描述
MeetnGreetMe is a global platform for delivering personalized travel services from locals to travelers.
Adapting the blockchain technology allows MeetnGreetMe to have its own means - WelcomeCoins (WELs) - to incentivize all the stakeholders to cooperate and radically change a way people plan and arrange their travel-related tasks and activities.
资金信息
● | Crowdsale | 50.00% |
● | Reserve pool for ecosystem development | 25.50% |
● | Team and future hires | 7.50% |
● | Founders | 7.50% |
● | Early stage backers and advisors | 7.00% |
● | Bounty, referrals, contributors | 2.00% |
● | Bounty manager | 0.50% |
● | Untill 1 160 ETH: | 9% |
● | Untill 2 900 ETH: | 6% |
● | Untill 5 800 ETH: | 4% |
● | Untill 8 800 ETH: | 0% |
● | Additional bonuses to each contributor if after ICO the following milestone achieved: 1160 ETH: | +2% |
● | Additional bonuses to each contributor if after ICO the following milestone achieved: 2900 ETH: | +4% |
● | Additional bonuses to each contributor if after ICO the following milestone achieved: 5800 ETH: | +6% |
● | Additional bonuses to each contributor if after ICO the following milestone achieved: 8800 ETH: | +8% |
媒体
路线图
11.02.2014 MeetnGreetMe Idea; proof of concept
22.03.2016 Incorporation in Estonia
25.05.2016 Fully operational platform with payment system
29.06.2016 MeetnGreetMe first customers; getting first revenue
12.04.2017 UN recognition in Travel and Hospitality category
21.12.2017 MeetnGreetMe Pre-ICO
12.02.2018 Community development: decentralized curation strategy
27.06.2018 Platform growth. Partnership and expansion in new cities
08.11.2018 Smart contracts for decentralized curation integrated
04.01.2019 Scaling and machine learning: big data preparation
团队
统计
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