In the process of upgrading the Web3 ecosystem from 'scenario building' to 'experience deepening,' Bubblemaps completely breaks the traditional model of 'separation of data tools and scenarios,' focusing on 'native integration of scenarios' to construct a native integration hub for on-chain data and scenarios. By deeply embedding on-chain data into the 'core processes' of DeFi, NFT, GameFi, and other scenarios, rather than acting as 'additional query tools,' Bubblemaps transforms data from 'external references' into 'internal elements of scenario operations,' addressing the core pain points of Web3 users who 'need to switch to check data when using scenarios' and 'disconnect between data and operations.'
First, core architecture: a breakthrough from 'externalized data' to 'native embedding in scenarios.'
The underlying innovation of Bubblemaps lies in a three-layer native integration architecture that achieves 'seamless coupling' of on-chain data and scenario processes, rather than simple 'functional stacking.'
• Native data layer for scenarios: customized development of 'data-scenario' binding modules for the core processes of different scenarios, allowing data to directly serve scenario operations. For example, in the DeFi staking process, the module converts 'historical volatility data of staked assets,' 'staking rate changes of similar assets,' and 'performance records of associated addresses' into 'real-time staking rate suggestions' (for example, 'current market volatility is high; it is recommended to lower the staking rate to 60% to reduce liquidation risk'), embedded in the staking confirmation interface; in the NFT minting process, the module transforms 'post-mint circulation data of similar NFTs' and 'on-chain preferences of potential collectors' into 'pricing and quantity suggestions for minting' (for example, 'when the pricing of NFTs in the same series is 0.1 ETH, they sell out in 3 days; it is recommended to price this minting at 0.08-0.1 ETH, controlling the minting quantity within 500 pieces'), presented in real-time on the minting parameters setting page. This native embedding allows users to receive data-supported decision suggestions without leaving the scenario.
• Dynamic interaction layer: achieving real-time linkage of 'user operations-data feedback-scenario adjustments,' allowing data to dynamically respond to user behavior. For example, when a user chooses 'item staking to earn rewards' in GameFi, the interaction layer will synchronously update the item's 'historical staking yield fluctuations' and 'current saturation of the staking market'—if market saturation exceeds 80%, it automatically prompts 'current staking competition is fierce, yields may decline by 15%, it is recommended to prioritize choosing another item'; after the user adjusts the staking duration, the data immediately updates 'expected yields for the corresponding duration' and 'penalty rates for early unlocking,' ensuring there is no delay between operation and data feedback. After the integration of a certain GameFi project, the 'hesitation rate' for users in item staking dropped by 50%, and the unlocking rate after 7 days of staking decreased by 35%.
• Value symbiosis layer: establishing a closed loop of 'scenario participation-data optimization-rights feedback,' allowing all roles participating in the scenario (users, developers, project parties) to benefit from data integration. Users submit 'data feedback' (for example, 'deviation in DeFi staking yield predictions') in the scenario, and after verification, they can receive 'exclusive rights' within the scenario (such as reduced staking fees or priority unlocking rights); developers who add 'native data modules' for the scenario (for example, adding 'item power-asset value mapping' for GameFi) can receive 'long-term revenue sharing' from scenario earnings (for example, 5% of transaction revenue generated after the module is applied); project parties, after optimizing scenario experience through data, will see increased user engagement leading to revenue, which will in turn feedback into the iteration of the data module, forming a positive cycle of 'scenario-data-rights.'
Second, scenario implementation: allowing native data integration to adapt to the experience upgrade needs of different fields.
The value of Bubblemaps lies in addressing the 'core operational pain points' across various Web3 fields by embedding data into key processes, directly enhancing user experience and scenario efficiency, rather than providing additional data query functionalities.
• DeFi field: 'data-driven risk prevention' in staking liquidation.
In response to the issues of 'difficulty in predicting liquidation risks and untimely operational adjustments in DeFi staking,' Bubblemaps developed the 'native protection module for staking liquidation,' embedding it throughout the staking process: during staking, the module generates 'safe staking amount suggestions' based on 'historical fluctuations of user assets' and 'overall market leverage ratios' (for example, 'your ETH asset has fluctuated 12% in the last 7 days; it is recommended that the maximum staking amount does not exceed 70% of your holdings'); during staking, it monitors 'price trends of staked assets' and 'distance to the liquidation line' in real-time, automatically pushing 'adjustment suggestions' (for example, 'you can add 0.5 ETH to staking or partially unlock to reduce leverage') when the distance to the liquidation line is only 5%, and providing a 'one-click supplement to staking' entry; if the user does not operate in time, the module can trigger 'automatic partial unlocking' (with prior user authorization required) to avoid liquidation losses. After the integration of a certain DeFi platform, the user liquidation rate dropped from 8% to 2%, with a 60% increase in user satisfaction.
• NFT field: 'data-driven efficiency' of the creator economy.
In response to the challenges faced by NFT creators, such as 'difficulty in pricing, unclear audience targeting, and low transfer efficiency,' Bubblemaps launched the 'native data module for NFT creators,' embedding it throughout the 'minting-release-transfer' process: during the minting stage, the module analyzes 'on-chain circulation data of past works by creators' and 'market heat of NFTs in similar themes,' generating 'pricing ranges and suggestions for minting quantities'; during the release stage, based on 'on-chain preferences of potential collectors' (for example, 'among users interested in this theme, 60% prefer the price range of 0.05-0.1 ETH and are active in Discord communities'), it pushes 'precise suggestions for release channels'; during the transfer stage, it synchronously updates 'changes in market demand for collectibles' (for example, 'in the past 24 hours, inquiries for this series of collectibles increased by 30%, suggesting the listing price can be adjusted upward by 5%'). One NFT creator using this module shortened the release time from 7 days to 2 days, increased transfer rates by 45%, and expanded premium space by 20%.
• GameFi field: 'data-driven asset appreciation' during asset transfer.
In response to GameFi's issues of 'difficulty in valuing items and low asset transfer efficiency,' Bubblemaps developed the 'GameFi native data module,' embedding the 'item acquisition-staking-trading' process: after acquiring an item, the module generates 'real-time asset value scoring' based on 'the item's in-game power,' 'historical transaction prices,' and 'scarcity of similar items' (for example, 'this weapon has a power score of 85, a scarcity of 90, and a current market value of about 1.2 ETH'); during staking, it synchronizes 'supply and demand data of the staking market' (for example, 'currently, there is strong demand for staking this type of weapon, with a staking yield of up to 15%/month'); during trading, it provides 'price trend forecasting' (for example, 'in the last 7 days, the price of this type of weapon has risen by 20%, and demand is still increasing, suggesting to hold for another week before trading'). After the integration of a certain GameFi project, the trading frequency of items increased by 50%, and user asset appreciation satisfaction reached 75%.
Three, future evolution: moving towards 'cross-scenario native data linkage.'
Following the trend of 'scenario integration and experience unification' in Web3, the next phase of innovation for Bubblemaps will focus on three core directions:
First, cross-scenario native data linkage breaks down the data barriers of individual scenarios, achieving 'native circulation of data across multiple scenarios.' For example, the 'staking credit data' of ETH assets staked by users in DeFi can be natively synchronized to the NFT scenario—high-credit users can enjoy '0 down payment installment minting' rights when minting NFTs; in the GameFi scenario, this credit data can be converted into 'higher limits for item staking,' allowing data to directly serve user operations across different scenarios without secondary querying, achieving a 'one-stop data experience.'
Second, the AI-driven native generation of scenario data introduces large models to automatically generate 'personalized data services for adapted scenarios.' For example, for novice users, AI simplifies the data presentation dimensions (such as only displaying 'core returns' and 'risk levels') and matches expressions that are easy for novices to understand; for experienced users, AI generates 'in-depth data insights' (such as 'correlation analysis of DeFi staking yield volatility with a certain index'); it can even automatically adjust data update frequency (such as changing from 1-hour updates to 10-minute updates) based on the user's 'operating habits' (like preference for short-term trading), making data services more aligned with personalized user needs.
Third, the construction of a native compliance system for scenario data, collaborating with Web3 compliance institutions to establish 'compliance standards for natively embedded scenario data'—clearly defining the 'displayable dimensions of data' in scenarios, 'scope requiring user authorization,' and 'security requirements for data storage.' For example, in DeFi scenarios, user asset data can only display 'personal holdings' and must not disclose 'asset distribution of similar users'; in NFT scenarios, user preference data must be used for recommendations only after user authorization to avoid data abuse. This standard will help project parties operate compliant in 'native data integration,' while ensuring user data security.
In the long run, Bubblemaps' core value lies in reconstructing the 'relationship logic' between Web3 data and scenarios—when data is no longer an 'additional tool outside the scenario,' but an 'indivisible core element within the scenario process,' Web3 can truly achieve 'integrated experience, efficient operations, and value symbiosis.' Bubblemaps is promoting this transformation with the positioning of a 'native integration hub for scenarios,' allowing every piece of on-chain data to 'actively exert value' within the scenario, helping the Web3 ecosystem transition from 'fully functional' to a new stage of 'excellent experience.'@Bubblemaps.io #Bubblemaps $BMT
