Fair Pricing - Price feeds can be a problem. Data providers are fragmented, and the high cost of data and the need for powerful computing leads to class separation among market participants. The sources of digital asset prices are also fragmented. Centralized exchanges, automated market makers (\"AMMs\"), and oracles offer price feeds with different strengths and weaknesses. AMMs can be fooled, especially with thinly traded assets like we saw in this week's Mango manipulation. Similarly, oracle attacks can lead to cascading liquidations and collateral theft in decentralized applications downstream. And centralized digital asset exchanges replicate the issues in more traditional markets. Pyth, a price feed protocol on Solana, offers a new way of doing things. Over 70 data providers, now including the CBOE, feed the application with continuous estimates for digital assets, equity, foreign exchange, and commodity prices. These estimates require both a price and a confidence interval -- your predicted range where the consensus price will land. If you submit a price with a tighter range, your submission receives higher weighting in the global average. The cardinal sin is to be confidently wrong, a behavior that carries the greatest penalty. Better to widen the range and lower your certainty. Math combines the data into a final price with a crowd-sourced confidence score. An accuracy estimate that outlines implicit market microstructure. Pyth's price data is currently limited to a small set of assets as the protocol focuses on adding providers and improving quality, but the tracks are laid. Though it seems decentralization is fetishized in blockchain applications, perhaps a decentralized system is better equipped to determine a global price than the current fragmented model. Directly compensating providers for accuracy has merit. Decentralized data protocols like Pyth present a growing threat to incumbents, as evidenced by the CBOE's participation. It is a real use case. Competition makes us better.