The oracle track is actually quite interesting. It seems that the threshold is not high; it's just moving off-chain data onto the chain. But doing it well is really difficult. There are reasons why Chainlink has maintained its market dominance for so many years. It has established deep moats in terms of security, reliability, and data coverage.
But this does not mean that latercomers have no opportunity. @APRO-Oracle is entering the market with a differentiated technological route. Today, let's dive deep into what unique aspects the technology architecture of $AT has.
First, we must acknowledge a fact: in the field of general-purpose oracles, it is basically impossible to confront Chainlink head-on. After so many years of development, Chainlink has formed a strong network effect. Most DeFi protocols use its price feeds, the developer documentation is comprehensive, and the integration cost is low. It is very difficult to shake its position.
Therefore, #APRO wisely chose a vertical track, focusing on areas like the Bitcoin ecosystem, RWA tokenization, and AI agents, where Chainlink is insufficient. This is like choosing a character in a game; you can't compare the same skills with others; you have to leverage your strengths.
Specifically, in terms of technical implementation, @APRO-Oracle's biggest difference from traditional oracles lies in its dual-layer architecture. Traditional oracles basically run directly on-chain, with all computations and validations completed in the smart contract. While this approach has high security, it also incurs high costs, and efficiency is limited by the performance of the blockchain itself.
This process has been divided into two layers. The first layer handles off-chain processing, responsible for data collection, cleaning, and preliminary calculations—these computation-intensive tasks. The second layer validates on-chain, focusing only on the core consensus and dispute resolution. The benefit of this design is that it ensures security while significantly reducing costs.
Let's take a look at how the off-chain layer works. #APRO has a protocol called OCMP (Off-Chain Messaging Protocol) specifically designed to coordinate communication between off-chain nodes. Traditional oracle nodes basically work independently, collecting data and directly submitting it on-chain, lacking collaboration.
The approach of OCMP is to let nodes reach a preliminary consensus off-chain first, discussing whether the data is reasonable, whether there are anomalies, and whether certain data sources need to be excluded. Once consensus is reached off-chain, the results are packaged and submitted on-chain, significantly reducing the number of on-chain interactions and lowering Gas fees.
More importantly, OCMP supports complex data processing logic. @APRO-Oracle can run AI models off-chain, perform statistical analysis, and handle unstructured data—tasks that cannot run on-chain. After processing, the results and proofs can be brought on-chain, breaking the limitations of blockchain computing capacity.
The on-chain layer, while lightweight, is very AT. It adopts EigenLayer's AVS architecture for dispute resolution. EigenLayer is a re-staking protocol on Ethereum that allows nodes to use the same staked ETH to participate in the validation of multiple protocols.
By integrating EigenLayer #APRO, you can leverage the security of Ethereum. The cost of node misconduct significantly increases because the tokens they stake, as well as ETH, could be penalized if they are caught cheating.
This design is particularly suitable for startup oracle projects because you do not need to start from scratch to build a large node network. You can directly utilize existing Ethereum validator resources, saving time costs and increasing security.
Let’s talk about the details of data processing. The TVWAP mechanism used by @APRO-Oracle was mentioned in my previous article, but this time I want to delve into why it is better than simple average prices.
Many people might think that price feeds are just about grabbing data from a few exchanges and averaging it. However, there are many issues in actual operation. First is how to allocate the weights of exchanges; should it be equal weights or based on trading volume? If based on trading volume, should data from small exchanges be used?
Then there's the selection of the time window. If the window is too short, it can easily be affected by short-term fluctuations. If it's too long, it won't keep up with market changes. And how to handle outliers? What if a certain exchange's data is incorrect or manipulated?
The solution for TVWAP considers price, trading volume, and time dimensions simultaneously. Exchanges with high trading volumes have higher weights, but it is not a linear relationship; a decay function is used to prevent any single large exchange from dominating pricing. Time-wise, weighting is also applied, with closer data receiving higher weights, but a certain amount of historical data is retained to smooth out short-term fluctuations.
The most critical factor is anomaly detection. #APRO will calculate the price deviation in real-time. If the quotation from a certain data source differs too much from others, the system will automatically reduce its weight or even exclude it directly. This dynamic adjustment mechanism makes price feeds more stable and reliable.
In terms of technology, there is also a very unique aspect: it supports both push and pull modes and can flexibly switch based on application scenarios. The push mode means the oracle actively updates the data, pushing new data on-chain when the time interval is reached or the price change exceeds a threshold.
This model is suitable for scenarios that require continuous monitoring, such as lending protocols needing to track collateral ratios in real-time. Once it falls below the liquidation line, liquidation must be triggered. In this case, you cannot wait for users to pull data; you must actively push it.
The pull mode is on-demand acquisition. The latest price is only requested when the user initiates a transaction, allowing for lower latency. @APRO-Oracle claims to be able to control the latency within 240 milliseconds, which is very important for high-frequency trading and DEX, as in DeFi, a few hundred milliseconds of delay could mean several points of slippage difference.
The combination of the two modes allows #APRO to serve a wider range of application scenarios, unlike some oracles that only support one of them, lacking flexibility.
The design of the node network adopts a hybrid node architecture. Nodes can run on public clouds or locally, with both professional institutional nodes and community nodes welcomed to participate. This diversification of node composition enhances the network's decentralization and resistance to censorship.
The node admission mechanism is also quite interesting. @APRO-Oracle is not completely permissionless, allowing anyone to become a node, nor is it completely permissioned, where only official approval is needed. Instead, it adopts a semi-permissioned model.
Specifically, new nodes need to stake AT tokens. After a trial operation period, the system will evaluate the node's stability, response speed, data accuracy, and other metrics. Nodes that perform well will be incorporated into the formal network, gaining higher weights and rewards, while poorly performing ones will be eliminated and have their stakes confiscated.
This reputation mechanism ensures that nodes have the incentive to provide high-quality services, as your earnings are directly linked to your performance rather than simply distributed based on the amount staked. This avoids the rich-get-richer effect; it's not about who has more money that can control the network.
In terms of security audits, #APRO has not been careless. Smart contracts have been audited by professional security companies like Halborn, and CertiK has also provided a score. Although the score is not particularly high, it at least shows that the team values security and is continuously improving.
Compared to Chainlink, @APRO-Oracle's biggest advantage is cost. The average cost of querying prices is only one-thirtieth of Chainlink's, which is quite significant. This makes it very attractive for small projects that are cost-sensitive.
$AT The advantages are data coverage and flexibility. Pyth mainly focuses on high-frequency crypto price feeds, with limited support for RWA and off-chain data, while #APRO can handle various unstructured data through AI, making its application scenarios broader.
Of course, @APRO Oracle there are still many areas that need improvement, such as the number of nodes being fewer compared to Chainlink. Although more than 200 nodes were used during stress testing, the actual number of nodes in operation is not publicly disclosed. The breadth and depth of data coverage are still expanding. The chosen direction is correct; it does not attempt to surpass Chainlink in all aspects but instead finds its differentiated positioning to establish advantages in emerging fields like the Bitcoin ecosystem, RWA, and AI. This is the correct competitive strategy for a startup project.
Technology ultimately needs to serve applications. #APRO The immediate priority is to quickly land more practical application cases, allowing developers to truly feel its advantages. Only when the user base increases can the network effect be established. Only then can it be said to have truly stabilized its footing in the oracle space.
