The team said that the upcoming feature would be made available in the app’s next update but is yet to reveal a timeframe.
Decentralized social network Damus teased an upcoming feature in its app that would allow users to earn satoshis — the smallest fraction of Bitcoin — based on post engagement on the platform.
In a tweet, the Damus team highlighted that there will be a feature that allows users to earn satoshis in the next version that’s “coming soon.” The team did not provide any details after the announcement.
Damus describes itself as a social network controlled by users and does not rely on centralized companies. The application is built on Nostr, or “Notes and Other Stuff Transmitted by Relays,” a decentralized network that enables end-to-end private messaging. There are no servers within its network. Instead, the protocol uses decentralized relays to distribute messages.
Various community members expressed their excitement about the new Damus feature, with some even going as far as describing Nostr as “the future of monetization.”
Former Twitter CEO Jack Dorsey has also been expressing support for Nostr by providing funds to the developers of the project. On Dec. 16, Dorsey said that he donated 14 BTC, which is around $250,000 at the time, to support the development of the decentralized social network.
Cointelegraph reached out to a Damus developer for comments but did not get a response yet.
On Feb. 1, Damus went live on the Apple App Store and became available for download for iPhone users. Following this, Jack Dorsey also shared the news through his Twitter account and described the update as a new “milestone” for open-source protocols.
The former Twitter CEO also pushed for the creation of a decentralized Twitter alternative back on Dec. 14. This followed the release of an internal investigation led by Elon Musk that highlighted censorship-related issues on Twitter. Dorsey highlighted potential solutions to the issues like resilience from corporate or government control, leaving the right to remove content to the authors and implementing algorithmic moderation.