Last December, we were just as excited as with "airdrop farming" to claim test tokens from faucets, painstakingly searching browsers for data blocks that could be deleted. Back then, Walrus was seen by most as just a technical demo—pretty cool, but still far from practical.

A year has passed in the blink of an eye, and this project has undergone a complete transformation. From a conceptual testnet device, it now runs on over 100 real nodes, handling genuine data and bearing real value. This is not just a software update; it’s a qualitative leap from a "toy" to an "engine."

So, what is the core competitive advantage of Walrus? Just two words: coding.

Traditional blockchain storage prevents data loss through the most straightforward method—replication. Making 100 copies of data stored in different locations is disastrous in terms of cost and efficiency. Walrus’s approach is entirely different. It uses mathematical and coding techniques to split a complete dataset into fragments with redundant information. With only 4 to 5 times redundancy, it achieves fault tolerance levels that would require 100 times replication in traditional solutions.

What does this mean? Even if more than two-thirds of the nodes in the network go offline simultaneously, the data can still be fully recovered. This is not just optimization; it’s solving the problem from a different dimension. It fundamentally breaks through the traditional trade-off among cost, efficiency, and security in decentralized storage.