This story doesn't have a fancy or lofty opening; it's just a common gripe among a group of coders.

Whether it's DeFi, gaming, or building Web3 applications, everyone ends up stuck in the same pit: data. It's not that there's no data; it's that the data is fundamentally unreliable. Sometimes the connection is painfully slow, sometimes the data itself is fake, or it's so expensive that no one can afford to use it. No matter how awesome your smart contract is, if the input is garbage, the output will only be a disaster.

So the core question boils down to: how can blockchain trust information from the real world?

Initially, the people working on this had little fame. Engineers, data scientists—some from big companies, others long involved in the crypto space. Everyone shared the same view—without a reliable data layer, decentralization is just talk. They spent a lot of time studying past failures: attacks, delays, misaligned incentives. Funds were tight, iterations slow, but these experiences laid the foundation for them: no rush, validation is key, and never casually say "trust."

The first product was actually quite rough. Early versions could only push price information and often made mistakes. Off-chain data didn't match on-chain data, and verifying on-chain was too expensive to be practical. Latency issues were also a headache. The team didn't hide from problems; they faced them head-on. They experimented with hybrid off-chain computation and on-chain verification, developing two approaches: "push mode" and "pull mode." One prioritized speed, the other accuracy.

Later, they realized that this "impurity" actually provided greater flexibility.

As they progressed, relying solely on cryptography and mathematics was no longer enough. They incorporated AI-driven verification mechanisms—not to sound flashy, but to add an extra layer of defense. AI models could detect anomalies in real-time, compare multiple sources, and significantly improve fault tolerance.