After working in the crypto space for so long, I often feel that this ecosystem appears as cold as machines on the surface—trades are precise to the millimeter, code has no warmth, and accounts are impeccable. But in reality, there is an underlying sense of fragility that is hard to describe. On-chain, everything is unclear; we can only passively receive data fed from outside sources. Once this data is contaminated, the consequences are irreversible. Liquidation events and protocol collapses happen frequently, often triggered by a single piece of bad news. Many seasoned players have thus been caught off guard, tossing and turning at night.

This type of oracle project hits exactly this pain point. It doesn’t chase the trend but instead builds a defensive line before an event occurs. It may seem like just a data middleware layer, but in fact, it safeguards all on-chain activities. Looking back at daily operations—placing orders, locking assets, participating in governance, experiencing blockchain games—all of these are supported by off-chain information. Who sets the price? Who confirms the event? Has the randomness been tampered with? Blockchain doesn’t care; once data arrives, it is executed directly, leaving no room for reversal.

The practicality of such projects lies in their direct confrontation with reality: markets can be attacked, data can conflict, humans can deceive, and systems can fail. They are not designed for good weather but are prepared for crises. The dirty and hard work is handled off-chain; on-chain, only settlement, verification, and traceability are involved. While blockchain isn’t good at data cleaning, it acts as a judge—no one is more professional at that.

Regarding data transmission, there are two very practical solutions. The first is proactive push—used for urgent scenarios where “a one-second delay could cause an explosion.” Price drops, positions about to be liquidated, old data can cause disaster within seconds. Real-time updates ensure that the latest information is in hand at critical moments. The second is on-demand pulling—when it’s not urgent, continuous polling isn’t necessary. Data is fetched only when truly needed, saving resources and ensuring timeliness.