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Detail Analysis

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Date:

Status:

Count:

Contributor:

March 21, 2022

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Verified

1

zerofriction.io

Loss Amount:

2,200,000

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Recovered Amount:

-

Currency:

Dollars

KYC By:

Audit By:

None

None

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Website:

Twitter:

Discord:

No data

Telegram:

No data

Medium:

No data

Github:

No data

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Key Indicators

Platform:

Type:

Category:

Method:

Solana

NFTs

NFTs

Scams

Extended Method:

Rug Pull

Data Sources:

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NeoNexus was a metaverse NFT project that raised about 25,000 SOL (worth around $2.2 million today; previously worth $3.5 to $4.5 million). The project had sold various "property NFTs", and had plans to create other NFTs representing things like characters and vehicles.

On March 21, the project's founder Jack Shi wrote on Twitter, "It is with a heavy heart that we must inform you that we can no longer continue healthy development of the NEONEXUS project. We would like to hand over the project to our community, or a community-selected party for takeover if that's feasible / possible." Going into more detail on Discord, he said the project had run out of money, which he blamed on waning interest in Solana NFTs.

The reaction to the announcement was overwhelmingly negative, particularly given the project's founder's apparent habit of bragging about his luxury cars. Many users described the abrupt shutdown as a rug pull, and one user even mentioned looking into a class action suit against the project team.

https://web3isgoinggreat.com/?id=2022-03-21-3

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DISCLAIMER: While Zero Friction LLC has used the best efforts in aggregating and maintaining this database, Zero Friction LLC makes no representations or warranties with respect to the accuracy or completeness, and specifically disclaim any implied warranties of merchantability or fitness for any particular purpose. 

Under no circumstances, shall Zero Friction LLC be liable for any loss of profit or funds, any regulatory or governmental penalties, any legal costs, or any other commercial and non-commercial damages, including but not limited to special, incidental, consequential, or other damages from any or all usage of the dataset or information derived from our database.