Original | Odaily Planet Daily (@OdailyChina)
“With such good liquidity on Polymarket, why would anyone use other prediction market platforms besides farming points for token airdrops?” This is a question I have been pondering while recently experiencing multiple prediction markets.
Indeed, recently Polymarket's parent company Blockratize submitted a trademark application for “POLY” during its token issuance plan, and Polymarket executives have previously made it clear that they plan to launch a native token and conduct an airdrop, leading the market to generally expect that farming Polymarket may be the next “big opportunity.” However, unlike other emerging prediction markets, Polymarket currently does not have a clear points or task incentive system, making it difficult for users to dynamically adjust their “farming” strategies based on feedback from point incentives, which still puts them in a somewhat “blind farming” mode.
In contrast, most of these emerging prediction market platforms have introduced point mechanisms, allowing users to have a clearer “airdrop strategy path” while participating in platform trading. Although many of these prediction market platforms are still in their early stages, with certain bugs that often lead to community complaints, some differentiated highlights that are “different from Polymarket” can indeed be found during the actual experience.
Below, I will introduce several highlights summarized from my recent experiences with different prediction market platforms.
Highlight 1: Funds in positions no longer “sit idle,” and prediction positions can continuously generate returns
In traditional prediction markets (like Polymarket and Kalshi), once users buy YES/NO positions, their funds are usually locked until the event settles. During this time, these funds cannot participate in other strategies and do not generate any returns, essentially representing a “idle fund” with significant opportunity costs.
predict.fun attempts to change this. The platform connects users' collateral funds used for predictions to low-risk earning strategies within the BNB Chain ecosystem, allowing positions to automatically generate stablecoin returns during the holding period. According to the official announcement on January 8, 2026, predict.fun has partnered with Venus Protocol, and the USDT collateral assets locked by users will be automatically deposited into Venus's money market for earning, thus continuously generating additional returns while waiting for event results.
In other words, users' funds continue to “work” on-chain during the holding period, with common stablecoin annualized return levels around 3%–5% (depending on the underlying DeFi strategies and market conditions). More importantly, these returns are independent of the prediction results—regardless of whether the final prediction is successful, the yield generated during the holding period can be claimed separately, effectively adding an extra source of passive income beyond the prediction gains or losses.
From a product mechanism perspective, this design essentially transforms the “dead money” in traditional prediction markets into “live money,” and it is one of the most recognizable differentiating features of predict.fun. The platform has already opened the function for users to claim holding returns, allowing users to collect accumulated holding returns at a fixed time every Tuesday, making long-term holdings have a clearer compound return logic at the strategy level.
predict.fun holding return claim interface
Highlight 2: Short video-style sliding experience makes prediction markets closer to “content consumption products”
Unlike the traditional trading terminal-style interface of prediction markets, some emerging prediction market platforms have begun to significantly align their interaction design with content platforms, attempting to lower participation barriers and increase user retention time. For example, in the mobile interface of predict.fun, one screen corresponds to one prediction event, and users can quickly browse different markets by swiping up and down, making the overall experience more similar to the information flow model of short video platforms. This design allows users to naturally discover interesting prediction targets while continuously “browsing the market,” significantly improving browsing efficiency and participation frequency.
Similarly, Probable adopts a left-right sliding interaction method, making the prediction behavior experience closer to the information matching logic of social products. From a product perspective, the core of this design is not merely to optimize the UI but to transform the prediction market from a “low-frequency trading tool” into a “high-frequency content consumption entry point.”
Interaction method illustrations of two prediction market platforms on mobile (left: predict.fun; right: Probable)
predict.fun founder Dingaling has also mentioned in a Space that he hopes to create a usage scenario for prediction markets similar to short video applications—where users can place bets while browsing trending events and interesting topics, and further enhance community engagement and user stickiness through comments and interactions.
From an experiential perspective, this information flow-style interaction itself is a highly attractive product innovation. Compared to the traditional method of actively searching for markets, swiping allows users to continuously “browse the market” during fragmented time, naturally generating participation behavior while browsing content, making the user experience of prediction markets lighter and smoother.
Highlight 3: “Exclusive event markets” around community hotspots enhance localized participation
In addition to product mechanisms and interaction experiences, some emerging prediction market platforms are also beginning to explore differentiated routes in market content design, no longer simply replicating existing general events on Polymarket or Kalshi, but instead launching more community-specific “exclusive event markets” around topics that are of high interest within the crypto community. For example, predict.fun has launched related prediction events around Binance and community hotspots, including “the change in Bitcoin balance of the Binance SAFU fund wallet on March 1” and “the number of tweets from CZ between February 7 and 14, 2026,” which are more closely aligned with the daily discussions of crypto users.
Exclusive prediction events launched on predict.fun
Compared to traditional macro events or general political and sports markets, these prediction events with clear community attributes are more likely to stimulate user discussion and dissemination, and are also more likely to generate participation enthusiasm within specific user groups. From a platform operation perspective, the continuous launch of exclusive events essentially builds a content supply with platform characteristics, making the prediction market not just a “trading venue,” but gradually becoming a gathering place for community hot sentiments and narratives.
From the above events, it can be seen that predict.fun is consciously differentiating itself at the “event supply” level, rather than simply replicating existing markets on Polymarket or Kalshi. Designing prediction events around CZ, the Binance ecosystem, and community discussion hotspots makes it easier for the platform to generate dissemination and participation within specific user groups, and this content strategy is becoming an important operational direction for some emerging prediction markets.
It is worth noting that among the currently active emerging prediction markets, a significant portion of projects come from the BNB Chain ecosystem, and their user structure is also noticeably more inclined towards the Asian community. In this context, community culture, circle culture, and even more “fan circle” characteristics of participation behavior are gradually becoming important factors influencing the design and dissemination of prediction market events. In this context, for emerging prediction market platforms, the Asian community culture formed around differentiated event design and the more “fan circle” characteristics of participation behavior are becoming a key area of research, and the related impacts will be further discussed in subsequent articles.
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