While 2025 has been the year of the prediction market, the sector suffers from a fundamental data problem: low resolution. Dominant platforms like Polymarket force complex reality into binary "Yes/No" tranches. If a user predicts a temperature of 24°C and the outcome is 25°C, they lose everything to a counterparty who guessed 31°C, simply because the binary threshold was set at 25°C. This binary trap punishes accuracy and rewards directionally correct but imprecise speculation.

Trepa, which launched on Solana Mainnet days before Breakpoint, addresses this by introducing scalar markets. Instead of betting on a threshold, users slide to a specific number—be it exact earnings per share, macro inflation data, or election margins. Payouts are determined by an exponential curve based on proximity to the truth. In a live demonstration at Breakpoint involving an M&M jar count, the market consensus (975) landed within a 0.4% margin of error of the actual count (979), forming a perfect bell curve of crowd sentiment.

For institutional investors, the alpha here is not the betting yield, but the data exhaust. Binary markets only reveal the probability of an event occurring; Trepa reveals the distribution of market expectations. This generates high-fidelity, upstream signals—effectively a crowdsourced probability density function for any metric. A macro hedge fund, for instance, could use Trepa’s "bell curve" data to price derivatives far more accurately than by using simple binary odds.

Co-founded by Jong-Chan Chung and his partner Leon (who met on the Polymarket subreddit), Trepa represents a shift from "gambling" to "forecasting." By leveraging Solana’s low-latency infrastructure, the protocol offers a consumer-grade "slider" interface that abstracts the complexity of the underlying order book, positioning itself to capture the next wave of sophisticated retail and institutional forecasters.