Many people initially thought that APRO wanted to be the top player in the oracle space, but that’s a complete misunderstanding. Frankly speaking, the root of this whole thing is quite simple — it’s the anger of a group of coders tormented by reality.

No matter how finely crafted the smart contract, once on-chain data encounters a hiccup, everything is doomed. For example, a sudden blowout on the highway, and you can only watch helplessly as the car becomes scrap. The team composition is quite ordinary: programmers, architects, data pipeline engineers, some coming from traditional finance, some with cloud infrastructure experience, and others who have stumbled multiple times in DeFi. But one thing everyone has realized: blockchain is essentially a deterministic machine, while the real world is a chaotic system. Prices can plummet with a little hype, rules change overnight, and data sources are scarce. APRO was born out of this contradiction.

Those early days were incredibly tough. Off-chain solutions are fast but insecure; on-chain solutions are foolproof but prohibitively expensive. Repeatedly slapped by reality, APRO was gradually refined through failures. Later, the concepts of Data Push and Data Pull modes weren’t just for storytelling — we discovered that different business scenarios have vastly different requirements — some protocols need zero latency, while others only care about the final result. So why insist on a single standard? Let the system adapt to developers’ needs.

The architectural design is quite straightforward: handle speed off-chain, and ultimately delegate decision-making back on-chain. Blockchains shouldn’t be forced to understand this complex world; they only need to make the final call. The most challenging technical problem then arises — how to ensure that data on-chain has not been tampered with? That’s when AI is introduced, but AI isn’t an authoritative judge; it’s just a data inspector, performing multi-source comparisons, anomaly detection, and reducing human error. Ultimately, it also relies on制度设计 and economic incentive mechanisms as safeguards.

Handling randomness is particularly cautious. Use cases like gaming, lotteries…