I've been immersed in those Layer 1 scaling solutions for too long, and my mind has become a bit dull. It wasn't until tonight when I reopened the white paper on the storage layer, especially reexamining the design philosophy of @Walrus 🦭/acc that I suddenly felt a sense of clarity. This clarity is not the kind of excitement bombarded by marketing jargon, but rather the instinctive shiver of an engineer when seeing a piece of extremely elegant code. We have been discussing the mass adoption of Web3, but every time I see the data synchronization costs of full nodes and the storage fees on Ethereum, I can't help but laugh. We pretend to put data on the chain, but in reality, most JPEGs and front-end assets still lie on AWS's S3. Without a truly cheap, trustless, and most importantly, easy-to-program storage layer, the so-called decentralized network is merely a castle in the air built on sand. In the past few days, I've carefully gone through Walrus's technical documentation, and some thoughts have been swirling in my mind for a long time; I must write them down and organize them.

I have been thinking about the current storage solutions. For example, Filecoin is great, but the computational overhead of that Proof-of-Spacetime is really too high. That is the extra work done to prove that I have stored it. What impressed me about Walrus is that it completely focuses on the modern implementation of erasure coding, which they refer to as Red Stuff. In the past, when building distributed systems, the dumbest way to achieve high availability was to replicate multiple copies—storing one piece of data in three copies at three times the cost. However, in Walrus's design, the logic based on two-dimensional Reed-Solomon coding is simply a mathematical aesthetic of brute force. Imagine that we are no longer simply copying data; we are splitting a Blob binary large object into k original shards, then calculating n minus k parity shards, for a total of n shards. As long as I can find any k shards in the network, I can perfectly reconstruct the original data using Lagrange interpolation or matrix operations. What does this mean? It means that due to Walrus adopting this more advanced two-dimensional coding structure, in the case of extreme network instability where half of the nodes go offline, data recovery no longer requires involving the entire network. I only need to reconstruct data from the remaining fragments at an extremely low bandwidth cost. This is not only a matter of robustness; it is purely a cost efficiency issue. If storage costs cannot be compressed to near the physical hard drive costs at a mathematical level, Web3 storage will forever be just a toy.

At first, I had some doubts about Walrus binding to Sui, thinking it might be an ecosystem bundling. However, after thinking about the decoupling of consensus mechanisms, I realized that this is actually the inevitable choice. Traditional storage public chains attempt to solve three problems—payment, consensus, and storage proof—on the same chain, resulting in an extremely bloated chain and a terribly low TPS. Walrus's idea is quite unique; it delegates complex metadata management and node coordination to a high-performance public chain, namely Sui, while storage nodes are only responsible for storing data. This is like writing a microservices architecture. Sui is that high-performance API Gateway and Service Mesh, handling state changes, Epoch switches, and staking logic, while Walrus's Storage Nodes are like the stateless back-end S3 buckets focused solely on throughput. Because Sui's Move object model is inherently suitable for handling ownership of such resources, when I think about how to build a decentralized Dropbox on Walrus, I find that I do not need to deal with complex storage node handshake protocols. I only need to operate on an Object on Sui that represents the credential for storage resources. This abstraction of storage as an asset is very developer-friendly.

In the past few days, I have been simulating the recovery process of Walrus in my mind, and the more I think about it, the more I feel that the application scenario potential of RaptorQ and its variants is enormous. Suppose I am a malicious node. In the Filecoin network, I can do harm by discarding data while forging zero-knowledge proofs. However, in Walrus, security is not built on computational power but guaranteed by probability theory. When data is encoded and dispersed to hundreds or thousands of nodes, an attacker must simultaneously breach a very high proportion of nodes to destroy a file. As the network scale increases, this attack cost rises exponentially. Moreover, I particularly care about the design of the Fast path and Slow path; when the network condition is good, read and write operations are direct interactions at O(1) level, and only in times of network turbulence does it need to activate the recovery logic of erasure coding. This optimistic execution approach aligns very well with the practical experience of internet engineering, optimizing for the vast majority of normal conditions rather than sacrificing all performance for a very small number of exceptional cases. This leads to the age-old question, where does the incentive for storage nodes come from? If it is permanent storage, you need an extremely complex donation fund model to hedge against future hardware deflation and fiat currency inflation. If it is a leasing model, you need constant renewals and complex staking. The signals that Walrus currently provides make me feel that it is taking a middle road; it is more like a hybrid of decentralized temporary caching and long-term archiving, especially in conjunction with Sui's Tokenomics, where the pricing power of storage space may be entirely handed over to market dynamics.

I am envisioning a scenario in the future where the DA data availability layer in L2 scaling solutions is currently being overshadowed by Celestia. But Celestia only solves the problem of data being published and verifiable in a short time. So where did the L2 transaction data from three months ago go? What about a year ago? Walrus actually fills the huge gap between the DA layer and the permanent archiving layer, which is a cheap, medium to long-term, programmable historical data layer. This is simply a necessity for storing AI training data. The current LLM training sets easily exceed several TB, which is impossible to put on-chain, and storing them on AWS faces the risk of centralized censorship. The Blob-based high throughput design of Walrus is tailor-made for distributing AI model weights and datasets. I just tried to write a piece of pseudocode simulating how to call Walrus to store metadata of an NFT on Sui. In the past, when I did this in Solidity, I had to hardcode the IPFS hash and pray that the IPFS gateway wouldn't go down. Under the Move system of Walrus, the code logic feels more like creating a Blob object, paying storage fees, and obtaining a CertifiedBlob credential, which is then directly embedded into the NFT's Struct. This atomicity is extremely smooth; the ownership of data is strongly bound to the ownership of the NFT on-chain. If you transfer the NFT, you also transfer the management rights to that underlying storage Blob, opening the door for dynamic NFTs or composable NFTs. For example, I can create a game where the character's save file is stored on Walrus, and each time the save is updated, it only generates a new Blob ID and updates the pointer of the Object on-chain. Because Walrus's writing costs are low enough, such high-frequency updates become possible.

Of course, there is no perfect system. I am thinking about several potential bottlenecks of Walrus. For example, the liquidity risk when nodes exit. If a large number of storage nodes go offline simultaneously within an Epoch cycle, although erasure coding can tolerate faults, will the bandwidth pressure of reconstructing data suddenly overwhelm the remaining nodes? This requires large-scale practical stress testing. There's also the issue of garbage data flooding. Since it's cheap, a large amount of garbage data will definitely flow in. How do we distinguish valuable historical data from randomly generated junk? Although for the protocol, they are all bytes, for the overall storage efficiency of the ecosystem, this could trigger the tragedy of the commons. The pricing mechanism must be sensitive enough to automatically deter garbage data through pricing when the load is high. Additionally, the computational burden on clients is also a problem. Although the encoding and decoding of erasure coding are lighter than ZK proofs, if mobile devices need to read large files directly from Walrus, can the CPU consumption of encoding and decoding be accepted? Perhaps in the future, a lightweight caching gateway will still be needed.

Setting aside these technical details, the core reason I am optimistic about Walrus is its positioning. It does not attempt to overturn Amazon, nor does it try to become the eternal home for all data. It pragmatically solves a specific pain point, which is the lack of a native, low-cost, large-file storage layer in the high-performance blockchain Sui ecosystem. Web3 has come to this point due to the lack of such infrastructure, leading us to not only dance with shackles but also to try to fit the entire Internet into a 1.44MB floppy disk. The emergence of Walrus gives me hope for upgrading from a 1.44MB floppy disk to an SSD. I drew that 2D coding matrix diagram on draft paper and stared at the parameters of those rows and columns for a long time. This design allows data recovery without downloading all the data, and even without downloading all the parity blocks. Just a few rows or columns are sufficient for recovery. This extreme saving of bandwidth is the key to decentralized storage surviving in this bandwidth-expensive world. The future DApp frontends will be hosted on Walrus, contracts will run on Sui, and assets will flow across chains; this may be the true Web3 stack. The current market is too restless, everyone is speculating on memes, but I know that only when this underlying pipeline engineering is done well will the real Application Summer arrive. I see this potential in Walrus; it is not sexy or flashy, but it quietly handles those dirtiest and heaviest data bytes, which is what infrastructure should look like. I am ready to run their Devnet nodes; some specific synchronization logic still needs to be checked against the logs to feel assured.