HiveChronicle dApp

POC dApp which comes pre-installed with every HiveProfile

HiveChronicle is a built-in SocialFi application, enabling on-chain interactions and incentivizing content creation and curation on Sui network and is stored as a dynamic object linked to each user's HiveProfile.


Type of on-chain posts

HiveChronicle supports three distinct post types:

  1. InfusionBuzzes: System-generated posts automatically documenting user contributions during the DegenHive launch phases (airdrop, lockdrop, and infusion).

  2. NoiseBuzzes: Public posts accessible to all HiveProfiles, with support for likes and comments which earns the user BEE tokens.

  3. ChronicleBuzzes: Threaded posts exclusively created by HiveProfiles managing HiveAsset collections.


Entropy points system

The entropy points system prevents gaming of the system for farming BEEs by making it too expensive for bad actors.

As a user performs actions such as like, comment etc, his available entropy reduces by 1 and some of his HIVE balance gets locked in his profile for that epoch, making it unusable for paying subscriptions or updating username etc during that epoch.

The amount of engagement, which comprises likes made to posts or comments, and commenting on posts is limited by available entropy of the HiveProfile, which is calculated based on HIVE balance available with the hiveProfile and its active voting power -

Available entropy = hive_multiplier * (hive_balance + hive_locked_with_unstaked_assets) + power_multiplier * voting_power


BEE token incentives

Each active hiveProfile on the platform earns BEE tokens per epoch according to the formula:

BEE tokens earned by a profile = (engagement earned + engagement curated) / (total engagement earned + total engagement curated)

These BEE token incentives for content curation and creation encourage users to generate more content on the platform. The front-end restricts users to sharing only gen-ai artwork created on the website using community-provided models within their on-chain buzzes. This approach aims to foster the development of improved models to drive the evolution of the degenHive thematic universe over time.

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