Using the fewest words, get the most complete answer, @ dappOS_com's @ xUbble_ai Recently, there have been more and more Binance Alpha projects, and I casually asked AI a very simple question: Are there any projects worth participating in on Binance Alpha? ” Previously, for this type of problem, most AI methods would first help me spread out the information: which projects had high popularity, which had just been launched, which had airdrops, and which had risen sharply. These pieces of information are certainly useful, but fundamentally they are still answering: What's happening now The real trouble lies in the next step, which ones are worth continuing to look at, which ones are just short-term emotions, and where to start verifying them. These usually need to be reorganized on their own. Later, I threw the same question to xUbble, which did not stop at the step of "list me a few projects", but automatically matched a set of Crypto Research SOPs, and the output result was roughly like this: It first provides a judgment framework: in the current Alpha ecosystem, projects that combine the dual popularity of "Binance Alpha+Binance Contract" will have greater overall flexibility. Then proceed with the specific project and path following this framework. For example, when it comes to MYX and ZORA, their short-term performance will be exaggerated after Alpha adds contract heat, with one approaching 14 times and the other about 7 times. Going further down, it didn't just stop at 'looking at what project', but also supplemented the participation methods together. For those with smaller funds, they tend to lean towards the points route: daily login, completing test network tasks, and gradually accumulating up to 230 points to draw Alpha Box. For those with larger funds, they can directly pledge BNB to Launchpool and distribute new coins. The difference between these two paths is actually quite significant, but they are discussed within the same framework rather than listed separately. Additionally, it also added some risk information. For example, in historical data, about 40% of the tokens launched by Alpha will experience a pullback of over 50% in the short term. This data may not be new, but placing it within the entire structure will make the matter of whether or not to participate more complete. I found that this may be where it differs from many AI tools. Many times, users do not know how to ask questions, let alone what tools to call or what research process to follow. And what xUbble does is to complete the middle path for users when they only use simple prompt words to propose goals. It is quite meaningful to use SOP system to ensure high-quality output with low threshold input. Ordinary people should not be blocked from AI productivity by the increasing cost of learning. To truly lower this threshold, it's not just about making the model stronger that can solve it, but also about AI learning how to use AI on its own.