In the Web3 ecosystem, on-chain analysis has long faced the issue of 'collaborative disconnection'—the capabilities and resources of professional analysts, ordinary users, and security institutions are difficult to link. It either relies on a few experts to output conclusions or falls into the inefficiency of 'each fighting their own battles'. Bubblemaps moves beyond the positioning of a 'single tool' and focuses on 'intelligent collaboration', building a decentralized analysis network of 'user contributions + AI integration + institutional verification', allowing dispersed on-chain insights to converge into credible conclusions, enabling individual analytical capabilities to gain ecological recognition, and allowing institutional professional resources to serve all. This model of 'everyone can participate, conclusions can be verified, and values can be shared' not only enhances the efficiency and credibility of on-chain analysis but also reshapes the production and circulation logic of Web3 data insights.
I. Distributed Insight Collection: Allowing Every User to Become a 'On-Chain Analyst'
Traditionally, the discourse power of on-chain analysis is concentrated in professional institutions or KOLs, making it difficult for ordinary users' observations and discoveries to be valued. Bubblemaps activates the dispersed insights of a massive number of users through 'micro insight submission + incentive mechanism', allowing every participant to contribute value to on-chain analysis.
Users can submit key information through the platform's 'Micro Insight' feature when they discover any on-chain anomalies in their daily use (e.g., a wallet suddenly receiving a large amount of tokens, or a project's holding structure changing). No professional knowledge is required—users can upload screenshots, mark anomalies, and provide a simple description of their observations (e.g., 'The top address of XX token transferred to the exchange 3 times in the last hour'). These dispersed 'micro insights' will enter the platform database in real time, and the AI system will automatically cluster similar insights (e.g., categorizing 'multiple abnormal transfer insights of the same token' into one category), forming initial analysis clues.
To incentivize user participation, the platform has established a 'Insight Contribution Value' system: submitted micro insights will be included in the clue database by the AI, enabling users to earn basic contribution values; if later verified as valid information by institutions, they can also receive additional BMT rewards. A regular user once submitted a micro insight about 'multiple new wallets of a certain Solana meme coin concentrating on receiving large amounts of tokens', which was verified by a security institution as 'potential pump preparation'. This user not only received a high amount of BMT, but their insight was also included in the platform's risk warning database, helping other users avoid losses. This model of 'everyone can contribute, small insights have great value' breaks down the barriers between professionals and non-professionals, making on-chain analysis a participatory ecological behavior for all.
II. AI Smart Integration: Gathering Dispersed Insights into 'Credible Conclusions'
The micro insights submitted by a massive number of users are often scattered and fragmented, making it difficult to directly form effective conclusions. Bubblemaps' 'AI Integration Engine' connects dispersed information into logically complete and evidence-rich on-chain conclusions through multi-dimensional analysis, addressing the pain point of 'information overload but insufficient insights'.
The AI engine will process micro insights from three dimensions: first is 'Relevance Analysis', determining whether different users' submitted micro insights point to the same event (e.g., whether 'Wallet A transfer' and 'Exchange B receiving' refer to the same transaction) through address association and timeline matching; second is 'Evidence Chain Completion', where if a certain insight lacks key data (e.g., 'only finding the transfer but not specifying the token usage'), the AI will automatically call the platform's bubble chart data and historical transaction records to supplement the evidence; finally, 'Credibility Rating', combining the historical contribution value of the submitting user and the sufficiency of evidence for the integrated conclusion (e.g., '★★★★☆ Supported by 5 micro insights, and matches on-chain data' '★★☆☆☆ Only 1 insight, lacking associative evidence').
The integrated conclusions will be presented in the form of 'visual reports', containing not only the core viewpoints but also all original micro insights and on-chain links to supplementary data, allowing users to trace and verify at any time. The conclusion of 'potential liquidity risk' for a certain DeFi project was formed by AI integrating 23 user micro insights, combined with data on mining pool fund flows. After the report was released, the project party promptly supplemented liquidity, avoiding a small-scale run. This model of 'distributed contributions + AI integration' ensures both breadth of analysis and the credibility of conclusions through technical means, making on-chain insights no longer reliant on a single source.
III. Institutional Collaborative Verification: Letting Professional Resources Endorse and Enhance Trust in the Ecology
The analysis conclusions generated by users and AI still require verification from professional institutions to enhance authority—especially in key scenarios involving risk warnings, compliance judgments, etc. Bubblemaps collaborates with security institutions, auditing firms, and industry associations to build an 'Institutional Verification Network', providing professional endorsement for the integrated conclusions while allowing institutional resources to efficiently serve ordinary users.
The platform will push the high-value conclusions (e.g., 'There is a contract vulnerability risk in a certain project' 'There are anomalies in the distribution of a certain token') integrated by AI to cooperating institutions, who can conduct in-depth verification based on their professional capabilities (e.g., code auditing, on-chain tracing). Verified conclusions will be labeled with the institution's name and verification report, synchronously updating the credibility rating (e.g., upgrading from '★★★★☆' to '★★★★★ Verified by XX security institution'); if verification reveals deviations in conclusions, institutions will also submit correction opinions to help the AI optimize integration logic.
In addition, institutions can also initiate 'special investigations' based on user micro insights: when a certain type of micro insight appears in concentration (e.g., 'multiple users reporting anomalies in a certain NFT project's whitelist address'), cooperating institutions will actively intervene, carry out in-depth on-chain tracking, and form a more comprehensive investigation report. A certain security institution once investigated the 'abnormal fund flow' of a cross-chain project based on 17 user micro insights, ultimately discovering it was 'project party testing transfers', and promptly released a clarification report to avoid community panic. This collaborative model of 'users providing clues, institutions verifying professionally' not only allows institutional resources to reach more ordinary users, but also enables on-chain analysis conclusions to possess both breadth and depth, achieving an organic combination of 'universal participation' and 'professional authority'.
Summary
From activating the potential of all citizens through 'distributed insight collection', to gathering effective conclusions through 'AI smart integration', and then improving authoritative credibility through 'institutional collaborative verification', the core value of Bubblemaps lies in 'building a decentralized collaborative network for on-chain analysis'. It is no longer just a single analytical tool but an 'intelligent collaborative hub' connecting individuals, technology, and institutions—ensuring that every user's small discovery is valued, transforming scattered information into credible insights, and allowing professional resources to serve the entire ecology. With the expansion of the collaborative network and the upgrade of AI integration capabilities, Bubblemaps is expected to become the 'trustworthy infrastructure' for on-chain analysis in Web3, driving the industry from 'relying on experts' to 'ecological co-creation', and making on-chain transparency truly a consensus of participation and benefit for all.
