Recently, I've been looking at address profiling again, with labels like "smart money," "institutions," and "whales"—once you slap those tags on, it seems convincing... But honestly, labels are just shadows of historical behavior, not IDs. An address can be split into multiple aliases, and these can be shared among teams or bots. No matter how clever the clustering algorithm is, it will still mistake "similar" for "the same."

Now I trust the flow of funds more: which chain, which bridge, which pools it entered, how long it stayed—these paths are more reliable than "who it is."

AI agents and automated trading are also quite interesting: narrative folks are calling for freeing up hands, while security folks are focused on permissions, private key custody, and contract upgrade rights... Anyway, whenever I see a new bot, I first treat it as a "colleague prone to mistakes."

When the noise is too high, my denoising strategy is simple: look less at label rankings, focus more on the back-and-forth movement of the same funds, and only draw conclusions after three consistent steps.