I have always believed that the power of technology is not to make things complicated, but to make complexity something you dare to deliver.

Let’s talk about @RaylsLabs: Among multiple chains and various compliance requirements, what institutions need most is not a faster bridge, but a set of "compliance controllable while being interoperable with DeFi" at the underlying level. #Rayls' approach is a bit like building a chain for banks – EVM compatible, $RLS has also designed private subnet and compliance controls at the protocol layer, allowing traditional finance to retain regulatory/privacy requirements when putting assets on-chain, rather than throwing these issues to external tools for temporary patching. For me, this idea of pre-setting compliance from the bottom level is pragmatic.

Looking at @recallnet again: The biggest problem with AI is not computational power, but whether it can clearly explain its decisions. #Recall treats the "why" of an agent as a first-class citizen: recording, validating, and exchanging knowledge on-chain to form auditable memory and proof. When an automated strategy incurs losses in the trading market, rather than cursing the black box, it can backtrack its reasoning, verify its data sources, and hold accountable — that’s another way of establishing trust. The testnet and toolchain of $RECALL demonstrate that they are turning this concept from paper into a developable and testable product.

Rayls provides a foundation that is more "willing to scale up amounts and compliant on-chain," while Recall offers a methodology that allows intelligent actions to be auditable.

An ideal scenario in the future is for institutions to place assets in compliant networks like Rayls, while simultaneously using mechanisms like Recall to document the reasons for automated decisions. This way, they can perform large-scale financial operations while having verifiable records in case of issues. Both efficiency and auditability are indispensable.

@cookiedotfuncn @cookiedotfun #SNAPS COOKIE

English version：
I've always believed that the power of technology is not in making things more complex, but in making complexity something you're confident enough to deliver.

Let’s start with @RaylsLabs: In a multi-chain world with varying compliance requirements, what institutions need isn’t just a faster bridge; they need a foundational layer that is “compliant, controllable, and interoperable with DeFi.” #Rayls’ approach is a bit like building a blockchain for banks—EVM compatibility, private subnets , and protocol-level compliance controls. This allows traditional finance to bring assets onto the blockchain while maintaining regulatory and privacy requirements, rather than leaving these issues to be pieced together by external tools. For me, this bottom-up approach to pre-setting compliance is pragmatic.

Now, looking at @recallnet: The biggest issue with AI isn’t computational power, but whether the decision-making process can be explained. #Recall treats the "why" of an agent as a first-class citizen: recording, verifying, and exchanging knowledge on-chain to form auditable memories and proofs. When an automated strategy leads to losses in the market, instead of cursing the black box, you can trace its reasoning, verify its data sources, and hold it accountable—that’s another way of building trust. $RECALL ’s testnets and toolchain show they are turning this concept from paper into a developer-friendly, testable product.

Rayls provides a foundation that makes institutions "confident to scale and comply on-chain," while Recall offers a methodology for making smart behaviors auditable.

An ideal future scenario is for institutions to store assets in a compliant network like Rayls, while recording the reasons behind automated decisions with a mechanism like Recall, enabling large-scale financial operations while ensuring auditability in case of issues. Efficiency and auditability are both essential.