首頁GXN新聞STEPN GO & Adidas Elevate the Move-to-Earn Experience with Exclusive Genesis NFT Sneakers

STEPN GO & Adidas Elevate the Move-to-Earn Experience with Exclusive Genesis NFT Sneakers

2024-09-23
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STEPN GO & Adidas Elevate the Move-to-Earn Experience with Exclusive Genesis NFT Sneakers

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The new Genesis collection will consist of 1,000 unique NFTs, available through a raffle mint on MOOAR, FSL’s NFT marketplace. Each raffle entry costs 10,000 GMT, with refunds given to those who don’t win a sneaker.

Since STEPN GO’s on September 4, 2024, the platform’s Haus System has gained popularity quickly. This system lets users lend their sneakers to friends, share Energy, and earn rewards together, making it a truly innovative feature.

It also broadens the platform’s reach by allowing users new to crypto to join in using simple Activation Codes. By encouraging group activity and shared rewards, STEPN GO has opened its doors to a wider audience, further strengthening its position in the move-to-earn market.

Beyond the Haus System, STEPN GO offers several other features to reward users for staying active. The Fitness Level feature boosts earnings for consistent activity, while the platform’s native GO GAME TOKEN (GGT) can be earned through walking, jogging, or running.

These tokens can be used within the app for upgrading Sneakers and minting Shoeboxes. Plus, the platform focuses on sustainability by requiring users to burn Sneakers to gain Energy, which helps maintain a balanced in-app economy and encourages long-term engagement.

In early 2022, Adidas Prada to launch an NFT project promoting Adidas for the Prada Re-Nylon collection. FSL’s co-founder, Yawn Rong, expressed excitement about the continued collaboration, noting how this next phase of their partnership with Adidas strengthens the connection between digital assets and fitness rewards.

He emphasized that the companies are pushing the limits of what’s possible in the Web3 lifestyle space, further developing their vision of move-to-earn as a vibrant and sustainable model.

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