Many people are first introduced to Walrus, and their initial reaction is "another storage solution," but if you really dissect its design philosophy, you'll find that the focus isn't on capacity at all, but on a deeper aspect—comprehensibility.

What do traditional storage systems care about? It's nothing more than a few things: whether the data fits, whether read/write speeds are fast enough, and whether the cost is acceptable. But Walrus's approach is quite different. It cares about what will happen years down the line—can these data still be understood? Can their source still be traced? Can the process be verified? What you see isn't just a screenshot of a result, but a complete replay of the entire process.

It sounds a bit abstract, but in real systems, this is a solid pain point. Many systems run for years, and their current state is very clear, but explaining "why it became like this" is confusing. What changes were made in the middle, who modified which parameters, what were the starting points of decisions—all become blurry.

Walrus is designed to solve this. From the very beginning, it assumes someone will want to look back—it's not just about freezing a snapshot of the state, but about recording as completely as possible the causal chain behind the state.

Of course, there's no free lunch. This kind of design comes at a cost. It's more complex, demands higher hardware requirements for nodes, and increases the burden of system maintenance. This isn't a lightweight solution; it's a "biased" architecture oriented toward long-term use.

In other words, if you're only looking for cheap, fast, disposable storage, Walrus is probably not your first choice. But if you're building a system that requires long-term traceability and explainability, this approach is truly rare.