Technology

Hamster Kombat hints at NFTs, web app after airdrop

Hamster Kombat hints at NFTs, web app after airdrop



Less than a day before its airdrop, the Hamster Kombat team shared an updated roadmap with new planned features extending through mid-2025.

While users speculated on Hamster Kombat’s (HMSTR) listing price and unhappy users threatened to boycott the viral Telegram mini-game, the project’s developers announced an expansion of its roadmap. According to Hamster Kombat’s website, the team plans to launch non-fungible token support and a progressive web app targeting desktop and mobile devices, including Apple’s iPhone.

More attuned to HMSTR’s potential price action is the token buyback blueprint. The team intends to use advertising revenue to acquire supply and redistribute tokens across future airdrop seasons.

Hamster Kombat airdrop debacle

User perception of the HMSTR airdrop appeared unaffected by Hamster Kombat’s expanded roadmap. The token distribution, scheduled for Sept. 26 by noon UTC, was promised to be one of the largest airdrops in cryptocurrency history.

However, hopes that the team would meet expectations were dashed earlier this week with the HMSTR allocations. Many users felt shortchanged by the coin rewards, claiming that influencers and promoters were unfairly prioritized.

Over 300 million people played the Telegram-based game powered by The Open Network (TON). The team disqualified approximately 2.3 million users for cheating and reported that 131 million users were eligible for the airdrop.

Several cryptocurrency exchanges, including Binance, were set to list HMSTR, which could boost the token in open markets—or not. According to the team, HMSTR is expected to surpass previous game airdrops like Notcoin. At the time, Notcoin distributed over 80 million tokens worth $1 billion to its community and participants.

Elsewhere, one of the project’s co-founders reportedly launched a hard fork called Hamster Kombat. Hard forks are independent imitations of an existing protocol, usually aimed at improving upon previous iterations.





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