When seeking truly effective Web3 projects that address real pain points, the storage sector's Walrus has indeed exceeded many expectations. Let's focus on a specific application scenario: AI teams handling training small sample storage. A long-standing dilemma in this process has been—either storage costs skyrocket or data retrieval speeds can't keep up with training iteration cycles, creating a catch-22.

After switching to Walrus, this dilemma has seen a substantial breakthrough. Data shows that storage costs have decreased by 30%, retrieval latency has been significantly reduced, and the overall training process efficiency has been perceptibly improved. These improvements are not just marketing narratives; they are backed by solid technological support.

The core lies in the clever collaboration of two technologies. Red Stuff 2D Erasure Code finds the optimal balance between data security and storage costs, avoiding the all-or-nothing dilemma. Meanwhile, Quilt batch storage mechanism is specifically optimized for small file scenarios. The combined use of these two technologies precisely addresses the real needs of high-frequency small file storage in AI training small samples and NFT metadata. This is not simply stacking technologies but accurately grasping application scenarios—an hallmark of excellent Web3 projects.

Looking at the tokenomics design, WAL's value anchoring is quite clear: storage consumption and node staking constitute real and ongoing demand scenarios, providing tangible support for token value rather than floating above narratives. Notably, community allocation exceeds 60%, indicating that ecosystem co-creation is not just a slogan but backed by a concrete profit-sharing mechanism.

In contrast, Walrus does not waste effort on conceptual packaging or market hype. Its full focus is on technological implementation and ecosystem expansion. This pragmatic approach, directly addressing core issues, makes it a project worth long-term observation and tracking.