Two college sophomores, Avante Price and Eli Taylor-Lemire, started building Posh right in their dorm room. Their vision was straightforward—create a platform that makes event scaling accessible to anyone. By October 2020, they'd launched their first version. The momentum kept building, and by May 2021, both founders decided to fully commit. They dropped out and closed a $1.5M funding round to accelerate growth. What started as a side project became a serious play in the events space, attracting investor confidence in their model and execution speed.
This page may contain third-party content, which is provided for information purposes only (not representations/warranties) and should not be considered as an endorsement of its views by Gate, nor as financial or professional advice. See Disclaimer for details.
8 Likes
Reward
8
4
Repost
Share
Comment
0/400
SchrodingerPrivateKey
· 8h ago
Trading a degree for funding, these two guys really dare to gamble... How long can 1.5 million USD last?
View OriginalReply0
TestnetNomad
· 8h ago
Starting a dorm room startup with a direct 1.5M funding? These two guys are really ruthless, just say dropouts and they drop out.
View OriginalReply0
rekt_but_not_broke
· 8h ago
NGL, these two guys are really strong in execution. Starting from a dorm room to raising 1.5 million in funding, I have to kneel to them.
View OriginalReply0
SadMoneyMeow
· 8h ago
Starting a business in college dorms and raising 1.5M... These two guys are really ruthless, saying drop out and just dropping out.
Two college sophomores, Avante Price and Eli Taylor-Lemire, started building Posh right in their dorm room. Their vision was straightforward—create a platform that makes event scaling accessible to anyone. By October 2020, they'd launched their first version. The momentum kept building, and by May 2021, both founders decided to fully commit. They dropped out and closed a $1.5M funding round to accelerate growth. What started as a side project became a serious play in the events space, attracting investor confidence in their model and execution speed.