Having worked in the crypto space for years, I’ve developed a professional sensitivity whenever I encounter new projects—once I hear phrases like "we’ve solved the trust issue," my mind immediately raises a big question mark. Especially with things like Oracles, their position is painfully awkward: every chain relies on them, yet very few truly understand how they work. When everything runs smoothly, no one thinks about them; but once data errors occur, they become the target of criticism across the entire network.

The emergence of APRO is quite interesting—unlike other projects that boast about curing all ailments, it instead uses a layered architecture to tackle an age-old problem longer than blockchain history itself: how to transform the chaotic information from the real world into a format that automated systems can understand, without overly relying on unreliable human judgment.

The idea is actually straightforward: I don’t make decisions for you, I only assign a confidence score to each piece of "information." Honestly, most Oracle failures aren’t due to malicious intent but because everyone underestimates how complex this issue really is—markets change rapidly, governance mechanisms lag behind, and data sources are still stuck in outdated versions. APRO sees this clearly: relying solely on cryptography, game theory, or a set of algorithms won’t fully solve the trust problem.

It adopts a combination of off-chain processing with on-chain verification: first, it uses AI to sift through the messy real-world content—PDF documents, news reports, IoT data, legal documents, and so on—while tagging each with credibility scores, source tracing, and economic endorsement, then feeds this into smart contracts. This way, the contract doesn’t need to find the absolute correct answer; instead, it learns to make decisions based on probabilities, just like humans, greatly increasing flexibility.

What’s even more worth pondering is the design of these two data operation modes...