AI is becoming more and more integrated into our lives, but a pressing question stands before us: how can we make the reasoning process of AI transparent, auditable, and tamper-proof? This is not just a technical issue; essentially, it's a trust issue.

There's a project called Walrus that offers an interesting approach—building a decentralized memory network. Imagine dispersing all important data across nodes worldwide, then encrypting it with cryptography. This way, data can be stored long-term, and no one can secretly alter it. This concept is somewhat like building an indestructible archive for the digital world.

Technically, there are indeed considerations. Distributed data storage, encryption verification, on-demand retrieval—three-pronged approach. In practical applications? AI can record the entire decision-making process, allowing for playback during audits, tracing back to the source, and establishing a truly reliable trust chain. This is especially valuable in scenarios requiring compliance.

The project is making good progress, having already gained strategic support from industry leaders, and ecosystem development is steadily advancing. As more applications connect, the value of Walrus as infrastructure is beginning to surface.

On the security front, the decentralized architecture and transparent mechanisms work hand in hand. The community collaboratively maintains the network, ensuring data integrity and providing more stable services. Users control their own data and can move it anywhere at any time. This design shows a strong respect for user rights.

Community feedback has been very positive; everyone feels this project is practical and addresses real pain points. Good technology should do just that—work quietly and reliably without causing trouble. I am optimistic about this direction; it truly starts from future needs.