》What separates SERA-Crypto from typical AI models: Most AI systems handle crypto questions by pattern matching. They recognize a token name, recall common narratives, and return a polished explanation. This works for learning basics, but it falls short when someone needs to evaluate real risk. SERA-Crypto approaches the problem from another angle. When tested by @SentientAGI against models like GPT-5 and Gemini 3 Pro, SERA showed its advantage on complex evaluations. Take a question about SOL, for example. Instead of describing the ecosystem or repeating market sentiment, SERA treats the query as a risk assessment task. It begins by clarifying intent: the user is not asking “What is Solana?” but “What are the risks of exposure to $SOL?” From there, SERA selects analysis frameworks built for financial decision-making rather than general conversation. Multiple dimensions are examined at the same time: - network reliability and technical design - token issuance and supply dynamics - market liquidity and trading behavior - governance and validator concentration - narrative dependence and sentiment cycles Each category is assessed separately and then linked to show how risks either reinforce or offset each other. Short-term price volatility is clearly distinguished from long-term structural or governance risks. The final output is concise yet actionable. Instead of overwhelming users with information, it provides a structured view of where the real risks lie and why they matter. This represents a broader shift in AI research tools, from merely summarizing information to actively supporting judgment. SERA isn’t trying to sound insightful; it's designed to help users think more clearly about crypto decisions, which is what makes it stand out. @SentientAGI is building, and $SENT is close!