Bubblemaps has recently been gaining more attention in the field of on-chain analysis. It uses bubble charts to visually present complex wallet relationships and token distributions in a very intuitive way. For ordinary users, the raw data in block explorers may seem like an alien language, but Bubblemaps transforms them into visual maps that anyone can understand—this is quite practical.
It currently supports mainstream chains like Ethereum, BNB Chain, Polygon, Avalanche, and Arbitrum, and is expanding into the Layer 2 ecosystem. Multi-chain coverage means a broader data source, making analysis more precise. Whether it's new tokens that have just launched or the fund movements of large holders, Bubblemaps can help you discern some insights.
Interestingly, Bubblemaps has also created a feature called Intel Desk, allowing users to submit analysis proposals for suspicious projects. Once approved by community votes, the platform mobilizes resources for in-depth investigations. If the clues you provide are verified, you can also earn BMT rewards—this is essentially a community crowdsourcing model that gathers dispersed analytical capabilities, making on-chain supervision more efficient.
Speaking of BMT, it is not a meme coin, but the core token of the entire ecosystem. In addition to incentivizing community investigations, it may also be used for premium feature subscriptions, API calls, and even profit-sharing from on-chain data services in the future. This actually provides a clear value capture path for BMT.
In practical use cases, Bubblemaps is particularly suitable for risk assessment before investment. For example, for a new project, you can directly pull up its bubble chart to see whether the top addresses are highly correlated and if there are issues with early centralized allocation. Many projects that appear decentralized actually reveal their true nature when visualized.
I've also noticed some cases, such as a certain celebrity coin YZY that surged and then plummeted upon launch, resulting in most people losing money while only a few addresses made over a million. If this type of project had been analyzed in advance with Bubblemaps regarding the distribution of holdings, many people might not have entered the market at all.
Additionally, they are continuously iterating on the product, such as adding a Time Travel feature to trace historical holding states or using Magic Nodes to cluster and display related addresses—these are not just superficial features; they can genuinely help users identify manipulation traces.
In the long run, the value of Bubblemaps will grow with the market's increasing demand for transparency. Especially as more institutional funds enter this space, there will be a stronger need for on-chain data visualization and compliance review. If combined with AI for predictive analysis or identity tagging in the future, its ceiling will be even higher.
Overall, Bubblemaps is not just a tool; it feels more like building a foundational infrastructure for on-chain transparency. It allows users to make decisions based on data rather than blindly rushing into projects. In an increasingly complex crypto market, this capability will become more and more important.