Recently, @infinex has paused the Season 1 activities of Yaprun (Mouth Rollover). The reasons and current situation behind this pause are worth examining objectively. Initially, the design concept of Yaprun was 'Mindshare Attribution,' which rewards users who continuously generate high-quality attention and discussions for Infinex. The team believes that high-quality attention will bring high-quality users, thus forming spontaneous dissemination and discussions within the community. The first season planned to distribute approximately 60 million USD in rewards, aiming to reward genuine, valuable content through data algorithms rather than simple volume manipulation.
In simple terms, Yaprun's original intention was to encourage the community to produce high-quality content and attract more users to participate in product development. However, after the end of Season 1, the team discovered some issues that necessitated a pause for optimization.
Issues
1. Proliferation of low-quality content: A large amount of bot and AI-generated content has mixed in, causing significant noise, drowning out genuine high-quality information.
2. Low conversion rate: Although Kaito brought hundreds of thousands of exposures, the actual number of real users participating in on-chain interactions, recharges, and transactions was only a few thousand, resulting in a very low ROI.
3. Imbalance in reward metrics: The original rewards were more focused on the number of posts and interactions, but this is weakly related to the actual user value for the project side. As a result, AI-generated content and mutual interaction dominated the leaderboard, while truly contributing users may not make the list.
Team Actions
1. Pause Yaprun for algorithm optimization and upgrade of anti-bot measures.
2. Design a brand-new scoring system to better distinguish high-quality content (signal) from low-quality content (noise).
3. Rewards from Season 1 will still be paid out to genuine contributors.
Industry Insights
1. Content mining models are easily impacted by AI and volume manipulation arbitrage; if metrics only consider interactions or post counts, they can easily deviate from real value.
2. The production cycle of high-quality content is long, but it may take time to make it to the rankings in the short term, which also limits opportunities for small accounts.
3. To sustain development, the reward mechanism must anchor on 'genuine usage + long-term contribution,' combining on-chain data and actual behavior to make input-output verifiable.
Objective Evaluation
Objectively speaking, Infinex's product features are very practical: multi-chain one-click Swap, vault management, large cross-chain fund operations, and gas-free withdrawals all have long-term value. Even without relying on airdrops, they are worth using, making it a very high-quality product.
Overall, the pause of Yaprun reflects the typical challenges of incentive activities in the Web3 community: how to maintain content quality and community ecology under reward-driven mechanisms. In the short term, the timeline may be interfered with by noise, but in the long term, if the algorithms and rules are properly optimized, Yaprun has the potential to become a model for high-quality, genuine community interaction. At this stage, observing the adjustments and new rules of Season 2 will determine whether it can truly achieve community-driven growth and the accumulation of high-quality users.
