Anita Conner @conneranit5118 - Twitter Profile

4 months ago

Happy Lunar New Year from #Binance 🧧 Gallop into the year with an abundance of prosperity and good health. 🐴 Gong Xi Fa Cai! 🍊

442

1K

227

39

171K

ConnerAnit5118 retweeted

BNB Chain

@BNBCHAIN

5 months ago

In The Mood For BNB. Hong Kong. Feb 10. Show up and hear directly from institutions and builders discuss tokenization and institutional adoption in practice. Register below 👇 https://t.co/3v2i5lcOhC

75

175

43

7

23K

ConnerAnit5118 retweeted

Leo

@Leozayaat

9 months ago

The next ByteDance is a prediction market The media has profoundly changed. News consumption evolved from passive curation to active engagement. Today, 54% of the U.S. population accessed news via social, and AI is playing an increasing role in our daily information. LLMs paired with a powerful and pervasive “For You” pattern increases convenience, but exacerbate echo chambers: Narrower, more confirmatory, and more polarized. In a world of infinite mirrors, markets remain the most powerful compass of consensus. An effective prediction market design focuses on matching information providers with varying degrees of knowledge at lowest possible cost. It crowdsources private knowledge and turns them into common information. While prediction market metrics are growing at unprecedented rates, demand remains constrained and unevenly distributed. Polymarket recorded approximately 1.16 billion dollars in monthly volume in June 2025. Activity spikes clustering around elections and major events. Outside these peaks, most markets have low participation with open interest below six figures. This happens when distribution fails to reach the right long-tail audience and fails to scale. To achieve social consensus at scale, a prediction market feed should personalize, evolve, and measure itself. Retrieval should surface questions relevant to users' identity, location, and demonstrated expertise. Ranking should prioritize expected information gain per interaction. Exploration should direct attention where uncertainty and user fit are high. Learning should happen online AND onchain. ByteDance's key lesson was precisely matching long-tail content with the right consumers. Liquidity concentration in headline topics isn't an outcome of natural selection of market topics, but also structural. The Conditional Token Framework is clean and composable but relies on external market makers and loss-bearing liquidity providers. Unlike standardized markets such as perps or tokens, each topic needs to be modeled by subsidized market makers. This approach is expensive, explaining why liquidity bootstrapping remains challenging. If every topic requires over a million dollars in working capital for proper probability discovery, prediction markets will remain limited to select topics, and the future of media will devolve into just another glorified sportsbook. The core mechanism needs to be liquidity-agnostic, such that a $1000 market feels as engaging and fun as a $100 million market. Without protocol-level innovation, scaling liquidity for new markets will repeatedly face the same costly issue. We are working on all these big problems at 42. We are building a prediction‑market protocol that localizes, personalizes, and evolves. By pairing an innovative suite of liquidity‑agnostic mechanisms with identity‑aware distribution, we aim to unlock market consensus on any topic. Long-tail topics aren't niche. They're where most tacit, local knowledge resides. This is where prediction markets truly shine. Come work with us to build the next generation of media. Break free from the world of infinite mirrors. https://t.co/ylmeoHTlKb