On-chain development is most afraid not of performance bottlenecks, but of data architecture deadlocks.

Many projects start off rapidly, but after half a year, they begin to stall. It may not seem like a lack of funds, but rather that the data structures have become rigid—once the business logic is laid out, each iteration requires digging three feet into the ground. Changing a single field might involve modifying the entire application layer, which is a true reflection of how many on-chain projects go from rapid growth to stagnation.

Walrus's approach is quite interesting. It inherently acknowledges a reality: you simply cannot design everything perfectly from the start. Instead of stubbornly sticking to initial ideas, it’s better to keep the data structures flexible and active.

From its technical design perspective, the core is an object-level storage model. Each data object has an independent identity; updates are not patches but natural evolution. Based on testnet performance, the system supports multiple updates to the same object, with individual objects capable of reaching MB levels, and can be maintained collaboratively by multiple nodes to ensure availability.

This leaves developers room to react—there’s no need to predict what will happen three years down the line on day one. When requirements change, data can adapt accordingly. Of course, what’s the cost? Such flexibility can be misused, so application layers must enforce constraints themselves.

But honestly, for real-world software, the ability to correct and adapt is valuable in itself. Compared to being rigidly stuck by architectural decisions, having room for error correction is already a significant step forward.