Holy crap, that "100% win rate whale" is at it again?! He went long on BTC, ETH, and SOL, dumping a whopping $275 million. Then Powell casually mentioned it in the early morning, and his orders were instantly filled, leaving him with a $2 million unrealized loss. I'd be devastated by now, but he's probably still in a coffee shop watching the market. He made $3 million yesterday, and today he's starting a new venture—this isn't crypto trading, it's like playing a game of Happy Beans! Here's the gossip: However, looking at this market movement, the market's "sentiment algorithm" really does resemble the recent wave sweeping academia and the AI community—both humans and intelligent agents are trying to "understand uncertainty." Recently, the @SentientAGI Foundation has been doing another kind of "hedging": they're bringing the concept of "Open AGI" to campuses—Tsinghua University on October 25th and Shanghai Jiao Tong University on October 28th, both hosting Open AGI Symposiums. On the surface, it's about lecturing, but in reality, it's about building a network that resonates between academia and the community, directly connecting R&D, data, and talent entry points. Siyuan believes that this combination of intensive offline activities and online community collaboration will unleash potential in three ways: 1️⃣ Allowing university labs to directly connect to community computing power and datasets; 2️⃣ Transforming research topics into reusable "intelligent components"; 3️⃣ Upgrading developers from "using models" to "co-building models." Technically, Sentient Labs' ROMA (Recursive Open Meta-Agent) acts as a "scheduling hub" for multi-agent collaboration. Combined with open network GRID and inference strategies, it can break down, route, and re-aggregate complex tasks. For AgentFi and real-time applications, this means an interpretable and composable growth flywheel. Siyuan values its "open-source and verifiable" nature—it can be reviewed by the community and iterated rapidly. Therefore, whether it's the whales' position-based speculation or AGI's academic positioning, the essence is the same: betting on whether the system can self-correct and grow. The difference is that one uses real money for testing, while the other uses code and papers for validation. In the next 2-4 weeks, emotions may be volatile, but the real competitive advantage will be written in the code repository, datasets, and retention curves.