Luna月芽

💗Why does every move you make step on my heart? Are you Beethoven?

Let's go play 🥰

Recently evaluating the potential of @Walrus 🦭/acc in large-scale data distribution scenarios. We have always talked about the interoperability of Web3, but the fragmentation of the storage layer has always been an invisible ceiling. If I were to build a decentralized AI model training market, the biggest bottleneck is actually not the scheduling of on-chain computing power, but the storage costs of massive raw data and the high bandwidth distribution efficiency. The architectural characteristics demonstrated by Walrus give me a glimmer of hope to break this deadlock.
​Its core advantage lies in the data structure design based on 2D Erasure Coding. I am imagining an extreme scenario: when a several hundred GB training set is split into countless blobs and distributed, does the reassembly efficiency on the reading end get affected by fluctuations in network topology? In traditional P2P storage solutions, as long as a few key nodes go offline (Fail-stop failures), the recovery delay of the entire file will soar or even freeze. But Walrus's design seems to intentionally downplay the importance of 'specific nodes.' As long as the total amount of surviving fragments in the network meets the standard, data can be quickly reassembled like fluid. This capability to combat network jitter is decisive for commercial applications.
​This leads to a more interesting technical detail: the role of Sui here. Sui's extremely high TPS ensures that metadata updates are real-time, while Walrus handles the heavy payload. This asynchronous architecture of 'fast and slow separation' actually aligns very well with the design principles of high-performance distributed systems—controlling the flow should be fast, and the data flow should be stable. I am thinking, will the future NFT metadata standard be rewritten because of this? The current HTTP links are too fragile, and centralized pinning services go against the original intention. If Walrus can provide a protocol-level permanent storage commitment based on Blob ID, then the definition of 'digital assets' can be considered truly closed-loop.
​Of course, all these technical assumptions are based on the premise that its incentive layer can withstand the test of game theory. Storage nodes need to be proven (Proof of Storage) to be continuously working, and this proof process cannot consume too much computational resources, otherwise, the gas fees will be passed on to users #walrus $WAL .

While carefully reading the chapter @Walrus 🦭/acc on storage resource management, I realized that my previous understanding of "storage" was still too static. In the context of Web3, simply saving files (like what IPFS does) is just the first step; the real challenge lies in how to let smart contracts on the chain perceive and operate on this data. Walrus seems to be attempting to break down the wall between "storage proof" and "on-chain state" with its deep integration with Sui.
I wonder, in the traditional EVM architecture, contracts only store a Hash, and whether the data behind this Hash is lost or tampered with, the contract is completely "blind." But in the design of Walrus, the availability proof of the Blob is designed as a system-level primitive. This means that a Move contract I write can not only reference data but also confirm at a logical level that this data is "ensured to be alive." This atomic interaction capability is a qualitative difference for building complex decentralized applications (dApps).
Additionally, the rotation mechanism of storage nodes' Epochs is also very interesting. If storage nodes change frequently, will the cost of data migration drag down the network? Walrus seems to leverage the characteristics of the "Red Stuff" algorithm, allowing for changes in the node set without requiring a full data migration, only incremental updates to part of the metadata mapping. This design greatly lowers the operational threshold. After all, if the bandwidth cost of running a node exceeds the storage revenue, the network will inevitably move towards centralization.
Another point that makes me ponder is its storage economic model. The pay-once, store-forever model sounds great, but it must rely on a robust storage fund to combat inflation and currency price fluctuations. This actually tests whether the system's token economics design possesses long-cycle self-regulating capabilities. If this model works, then "data" itself truly becomes a standardized on-chain asset, rather than just an attached item hanging off-chain.
In the next few days, I need to take some time to run its Devnet and see how the delay from the generation of Blob ID to final confirmation (Finality) performs in actual high-concurrency writing scenarios. #walrus $WAL

Last night, I revisited the system architecture diagram of @Walrus 🦭/acc and discovered a subtlety I had previously overlooked: it thoroughly implements the decoupling of the "control plane" and "data plane".
In most decentralized storage projects, consensus and storage are often tightly coupled, leading to mutual constraints on scalability. But Walrus's current approach is clever; it uses Sui as an efficient control layer to manage metadata and node states, while offloading heavy data storage to a dedicated storage node network. This design reminds me of the SDN (Software Defined Networking) concept in modern cloud computing architectures—where the control layer is responsible for logic and routing, while the forwarding layer focuses on quickly transporting data.
I have been extrapolating the performance of this architecture in extreme situations. In traditional blockchain storage, writing a massive video file often blocks the consensus protocol, causing TPS to drop. However, in Walrus's logic, the upload and encoding of a Blob do not occupy Sui's consensus bandwidth; Sui only needs to record the proof (Certificate) that "this Blob has been stored and verified." This means that horizontal scaling of storage capacity is theoretically not limited by consensus speed. This is a crucial feature for high-throughput applications—such as decentralized social networks or streaming platforms.
Another point that interests me is its tolerance for "Byzantine faults." Traditional 3-replica backups are actually quite fragile in the Web3 environment because you do not know whether these 3 nodes belong to the same entity. Walrus's erasure coding scheme allows for up to one-third or even more nodes to be offline or malicious while keeping the data readable. This robustness is not achieved by merely stacking hardware, but is guaranteed by algorithms (Red Stuff).
If, in engineering implementation, the client's SDK can encapsulate this complex encoding process in a lightweight manner, then developers will hardly perceive it as "decentralized." This experience is the prerequisite for the large-scale adoption of Web3 storage. There is no need to worry about which node stored my data; only the certificate's presence on the chain matters. This is the correct level of abstraction. #walrus $WAL

Recently, while reading the white paper of @Walrus 🦭/acc , I kept thinking about a question regarding 'network entropy increase'. In traditional distributed systems, maintaining data consistency often means extremely high communication complexity. Once the number of nodes skyrockets due to permissionless mechanisms, synchronizing metadata often becomes a bottleneck for the system. But Walrus provided me with a new perspective: if we accept the 'chaos' of the network at the bottom level, can we instead build a kind of order?
Its core lies in not requiring the entire network to reach strong consensus on the storage location of each data fragment. This 'coordination-free' architecture design is very counterintuitive, yet it aligns perfectly with the decentralized spirit of Web3. The scenario I imagine is this: even if there are many nodes frequently joining and leaving (churn) in the network, relying on its unique two-dimensional erasure code technology, data recovery does not need to consume huge bandwidth like traditional RAID reconstruction. As long as enough erasure code fragments are received, the data can be reconstructed locally on the client side. This approach of distributing computational pressure to the edge, rather than concentrating it on a core coordinator, is the right way to solve the scalability dilemma.
Additionally, its emergence as part of the Sui ecosystem at this point in time is very interesting. Current public chains are all worried about state bloat, and Sui's object model combined with Walrus's external storage is actually achieving extremely thorough modular decoupling. Only asset ownership and very lightweight status are stored on-chain, while heavy assets and multimedia data are handed over to Walrus. This not only saves gas fees, but more importantly, it makes 'full-chain applications' no longer a false proposition.
If we can ensure over 99.99% data availability relying solely on mathematical probability, without expensive enterprise-grade hardware, the potential for reducing storage costs is exponential. Of course, the theoretical elegance still needs the robustness of code implementation to support it. The upcoming stress tests will be crucial, especially when the network is in a state of extreme congestion, whether this coordination-free mechanism can really operate as smoothly as expected still needs time to verify. But in any case, this architecture has brought some truly hardcore engineering innovations to the long-dormant storage track. #walrus $WAL

Recently, I have been reviewing the logical closed loop of the decentralized storage track. I previously studied Arweave's permanent storage and Filecoin's space-time proof. Although each has its merits, I always feel that we are still one step away from large-scale commercial use regarding cost and latency issues in high-frequency interactive scenarios. Over the past few days, I took a deep dive into the architecture design of @Walrus 🦭/acc , especially the erasure coding algorithm referred to as 'Red Stuff.' It indeed addresses the pain points of the current L1 state explosion.
I am pondering a core question: Why is the current L1 still too heavy? Ultimately, data availability (DA) and long-term storage have become confused. Walrus has a clear idea of separating unstructured big data (Blobs). It does not ensure security through simple full node replication—such an approach has too much redundancy and cannot reduce costs—but through mathematical sharding coding, as long as a certain proportion of shards survive in the network, data can be instantly restored. For project parties aiming to create full-chain games or high-fidelity NFTs, this 'external hard drive' solution is indeed a necessity.
If Walrus can reduce storage costs to AWS levels while maintaining decentralized censorship resistance, then the awkward situation of 'Web3 assets existing on Web2 servers' may really come to an end. Especially seeing its positioning in the Sui ecosystem, it is not just cold storage but more like preparation for high-concurrency reading.
Of course, having a self-consistent technical logic is one thing; the incentive model for nodes when landing is another. If nodes can profit from storing fragments without bearing excessive computational pressure, this network will be solid. The current test data looks good, but if this piece of the puzzle can be completed, it may reshape the current landscape of the storage track. Let's observe, as this may be the most interesting technological iteration at the infrastructure level recently. #walrus $WAL

Writing zero-knowledge proof circuits was a torment in the past, whether it was Circom or ZoKrates, the non-Turing complete logic made it very difficult to bring complex business logic onto the chain. Recently, I delved into the technology stack of @Dusk and found that their Piecrust virtual machine might be severely underestimated by the market. Most people only focus on privacy and overlook the innovation at the execution layer. Piecrust is actually based on WASM and is specifically optimized for ZK.
The technical ambition behind this is significant. #Dusk clearly realizes a problem: if privacy development requires developers to be PhDs in cryptography, then the ecosystem will never take off. By building a ZK-friendly abstraction layer that allows developers to write contracts in general-purpose languages like Rust or C++, while automatically generating the proofs required for state transitions, this is the key to breaking down barriers. This kind of "Merkelized" memory model design maintains a very high throughput while ensuring data privacy, and this balance is very rare in the current Layer 1 competition.
When I looked at their self-proving logic, I felt a kind of architectural cleanliness. It is not just about running code; it is continuously generating "computation fingerprints" for the computational process. This means that future financial applications will no longer need to manually write cumbersome proof logic, as this has already been taken care of at the virtual machine level.
If EVM defined the 1.0 era of smart contracts, then this ZK-VM may open the 2.0 era of "generalized privacy computing". For financial institutions that hold a large number of Web2 codebases, Dusk's architecture, which can reuse existing logic and seamlessly integrate privacy compliance, is much more attractive than those public chains that require rewriting all the code. This is not just a simple technical showcase; it is a profound insight into the nature of developers. #dusk $DUSK

After reviewing several high-performance public blockchain node attack incidents, I once again realize the foresight of @Dusk in consensus layer design. The biggest hidden danger of traditional PoS (Proof of Stake) does not actually lie in the 'rich get richer', but in the premature exposure of validator identities. Once an attacker knows who the next block-producing node is, DDoS attacks become inevitable. The most interesting aspect of Dusk's SBA (Separated Byzantine Agreement) is that it incorporates the 'lottery' process into zero-knowledge proofs. This 'Private Sortition' mechanism turns the election process of validating nodes into a black box. When I looked at the code logic, I found that nodes do not need to expose specific staking amounts or identities to prove their eligibility for consensus; they only need to submit a proof. This means that attackers have no idea who to attack before the block is finalized. For a chain aimed at supporting financial settlement, this ability to resist censorship and attacks is a baseline, not an elective course. Additionally, I am impressed by #Dusk's obsession with 'Immediate Finality'. The most feared thing during financial settlements is chain forks. If an asset transaction is rolled back a few minutes later due to the long-chain principle, it can be devastating to the credibility of the financial system. The SBA mechanism ensures that as long as a block is certified by the committee, it is irreversible. This design sacrifices probabilistic eventual consistency in pursuit of absolute settlement certainty. This is technically much more challenging than simply stacking high TPS because it requires extremely efficient network communication. If Dusk can truly maintain such high consensus efficiency on its mainnet, it offers traditional financial institutions not just a ledger but an execution environment with legally binding settlement certainty. This is what an 'institutional-level public blockchain' should look like. #dusk $DUSK

I've been pondering the Citadel protocol of @Dusk , which addresses a problem I find quite bothersome but have to face: KYC (Know Your Customer). In the current Web3 ecosystem, compliance often means running naked—having to submit passport information to centralized entities and then praying their databases aren't hacked. This completely contradicts the essence of decentralization. The handling logic here at Dusk is quite interesting; they have turned KYC into a non-interactive zero-knowledge proof service.
​The core logic of Citadel lies in the separation of 'permissions' and 'identity information.' I don't need to show my ID to every DApp; I just need to present a mathematical proof that 'I have been verified.' This 'principle of minimal disclosure' is architecturally very elegant. It not only protects user privacy but also safeguards institutions—because institutions don't want to hold large amounts of sensitive user data, which means huge compliance costs and security liabilities.
​#Dusk embeds this identity layer directly onto Layer 1, rather than creating an external smart contract, which is quite a bold move. This means that future asset issuers can directly set trading thresholds at the protocol level (for example, 'only for certified qualified investors') without needing to establish a cumbersome whitelist system themselves. This native support for compliance in the underlying infrastructure is irresistibly attractive to traditional giants looking to issue bonds or securities.
​From a technical perspective, this is essentially redefining what an 'account' is. An account is no longer just an address; it is a container that includes compliance attributes. If the Dusk mainnet can successfully run this process, it will solve not just privacy issues but also break down the high wall built between the fiat world and the crypto world due to 'compliance fears.' This is precisely what infrastructure is meant to do.#dusk $DUSK

Zero-Knowledge Proofs' Computational Commodification
Recently, I've been researching the consensus mechanism of @Dusk , particularly how they handle the generation load issue of Zero-Knowledge Proofs (ZKP). Most privacy public chains share a common problem: clients generate proofs too slowly, or the verification cost is too high, leading to an inability to achieve TPS. When I looked at the documentation for Dusk, the idea of 'separating computation from consensus' really made me think for a long time.
They introduced a market mechanism similar to 'blind computation'. Rather than having each user painfully generate complex ZK proofs on their own devices, it's better to outsource this task, provided that data content is not leaked. This actually creates a new role at the protocol level—Provisioners. This design turns privacy computing into a tradable commodity rather than just a system consumption.
For enterprises looking to issue Security Token Offerings (STO), this is the pain point. We used to worry that privacy computing would slow down settlement speed, leading to excessive slippage in transactions, making it impossible to support high-frequency trading. However, Dusk's architecture is essentially using computational power to exchange for compliant privacy space. If the future financial market is on-chain, then this capability of 'instant settlement + absolute privacy' is not just a nice-to-have but a ticket to entry.
Looking at the current RWA (Real World Assets) track, everyone is hyping the concept of asset on-chain, yet very few are addressing the performance bottlenecks of compliant privacy at the underlying level. Dusk has indeed honed its tech stack in this regard. If the Piecrust virtual machine can be launched as scheduled, this permissionless environment that meets compliance requirements could fundamentally change our understanding of the boundaries between 'private chains' and 'public chains'. #dusk $DUSK

Recently, while reviewing the technical architecture of the privacy track, I increasingly feel that the contradiction between 'complete anonymity' and 'institutional compliance' is the core deadlock hindering the large-scale on-chain of RWA (real-world assets). The mixers on Ethereum or some L2 privacy solutions essentially avoid regulation rather than solve it. These days, I revisited the underlying logic of @Dusk and found that their optimization of the Piecrust virtual machine hits a rather tricky but necessary pain point: how to prove that data complies with regulatory requirements without exposing specific data.
​This is not just a simple stacking of ZK-SNARKs, but an attempt to write compliance into the consensus layer. I used to think that Layer 1 should be as lightweight as possible, leaving complex logic to contracts; however, when it comes to security token offerings (STOs), this lightweight approach actually becomes a compliance loophole. #Dusk has chosen a difficult path—directly integrating privacy compliance standards at the L1 level. This means that validators do not need to know 'who you are'; they only need to confirm 'you are qualified' through zero-knowledge proofs.
​From a technical aesthetic perspective, this kind of 'programmable privacy' is much more appealing than simply hiding transaction amounts. If traditional financial institutions really want to enter public chains, they absolutely cannot accept completely transparent ledgers, nor can they accept uncontrolled black boxes. Dusk's 'regulatory compliant privacy' (RegDeFi) architecture is likely to be the only solution for financial infrastructure in the coming years. Rather than patching at the application layer, it is better to set the rules at the protocol layer, which aligns with the rigor of financial engineering. #dusk $DUSK

Recently, I have been examining the competitive landscape of Layer 1. The market is too noisy, everyone is chasing high-throughput data games, yet it seems to have forgotten the most original and attractive application scenario of blockchain— the circulation of currency itself.
When I re-examined the architecture of @Plasma , I realized that the term "infrastructure" has been misused. True infrastructure should be invisible. In the past, to transfer USDT, we had to first buy ETH or other native tokens for Gas, which is logically inhumane. Plasma uses the Paymaster mechanism as a core component to achieve zero Gas transfer of USDT, which is not just a technical optimization but a restoration of the act of "payment". Users should not care about what happens on-chain; they only care whether the money arrives intact.
This is how Fintech should be. It is no longer a simple Crypto casino but a true track that carries the flow of value.
I value its positioning as a "native chain for stablecoins"; this is much smarter than creating a large and comprehensive general-purpose chain. Especially with its design of anchoring the Bitcoin network (Bitcoin-anchored security) at the settlement layer, this hybrid architecture ensures flexibility while maintaining EVM compatibility and leverages the finality of BTC. At this point in 2026, institutions need this kind of trustless sense of security, not the illusory TPS competition.
When the prosperity fades, those networks that allow funds to flow with as little friction as information will be the final winners. This is why I focus on #plasma — it is doing subtraction, removing noise, and retaining only the purest value transfer.
This is not only a victory of technology but also a return to commercial logic. #plasma $XPL