Lately, I've been repeatedly pondering the various possibilities within the blockchain space. Recently, I came across the Walrus project, so I decided to stop and learn more about it. I've been secretly observing the $WAL project for several months, not just following the trend, but more like watching a newcomer gradually make a name for themselves.
To be honest, I wasn't particularly interested in the decentralized storage sector at first. The veterans like Filecoin and Arweave have already carved out most of the market, making it quite difficult for new projects to stand out. However, when Walrus appeared, I sensed something different—like the project was gearing up for a major move.
I've been experimenting within the Sui ecosystem for a while, and the high-speed parallel processing architecture really gives a sense of addiction. But on-chain storage has always been a concern, especially when dealing with large files. Previously, uploading videos or AI models to the chain required splitting them into many fragments first, which was time-consuming and costly in gas fees, making the whole process quite uncomfortable.
Walrus's approach is very targeted—specifically addressing the pain point of large blob storage. It uses erasure coding technology to split data into small fragments and distribute them across various nodes in the network. When data needs to be recovered, only a subset of fragments is required; even if two-thirds of the nodes fail, the data can still be reconstructed completely. This design logic is like a puzzle assembled in childhood—losing a few pieces still leaves the overall picture clear.
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GasFeeVictim
· 12h ago
The WAL approach is indeed brilliant; finally, someone is using erasure coding to address storage issues on Sui.
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CrashHotline
· 13h ago
Erasure coding is indeed impressive, but it only counts once it is truly implemented.
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blockBoy
· 13h ago
The Sui ecosystem has indeed been holding back for too long, just waiting for something like Walrus to blow off steam. Erasure coding looks simple, but actually implementing it on the chain is a different story. Looking forward to it making some waves.
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PaperHandSister
· 13h ago
Wow, Walrus really has something this time. The erasure coding logic is truly excellent.
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WalletWhisperer
· 13h ago
erasure coding angle is interesting but ngl the real signal here is wallet clustering patterns around $WAL accumulation... three months of silent observation screams statistically significant positioning before any major announcement hits
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TrustlessMaximalist
· 14h ago
Walrus is indeed quite interesting; the erasure coding scheme is definitely much smarter than just piling up hard drives.
Lately, I've been repeatedly pondering the various possibilities within the blockchain space. Recently, I came across the Walrus project, so I decided to stop and learn more about it. I've been secretly observing the $WAL project for several months, not just following the trend, but more like watching a newcomer gradually make a name for themselves.
To be honest, I wasn't particularly interested in the decentralized storage sector at first. The veterans like Filecoin and Arweave have already carved out most of the market, making it quite difficult for new projects to stand out. However, when Walrus appeared, I sensed something different—like the project was gearing up for a major move.
I've been experimenting within the Sui ecosystem for a while, and the high-speed parallel processing architecture really gives a sense of addiction. But on-chain storage has always been a concern, especially when dealing with large files. Previously, uploading videos or AI models to the chain required splitting them into many fragments first, which was time-consuming and costly in gas fees, making the whole process quite uncomfortable.
Walrus's approach is very targeted—specifically addressing the pain point of large blob storage. It uses erasure coding technology to split data into small fragments and distribute them across various nodes in the network. When data needs to be recovered, only a subset of fragments is required; even if two-thirds of the nodes fail, the data can still be reconstructed completely. This design logic is like a puzzle assembled in childhood—losing a few pieces still leaves the overall picture clear.