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Recently, while sorting out the scalability path of Layer 2, I found that everyone seems to confuse the concepts of 'Data Availability (DA)' and 'Data Storage.' The much-hyped DA layer addresses the short-term confirmation issue of data publication, but nodes will inevitably prune historical data for performance. So the question arises: where should the pruned historical transaction data be archived? If we still rely on centralized servers, then the immutability of the blockchain has a huge gap. This is why I have been closely monitoring @Walrus 🦭/acc recently. It fills the vacuum between 'short-term DA' and 'permanent storage.' For a truly robust Rollup or full-chain application, having only DA is not enough; there must be a decentralized archiving layer to ensure continuity of storage. The clever design of Walrus lies in its use of Sui's high-performance consensus to handle metadata while sinking large-scale Blob data into a dedicated storage node network. This architecture has led me to think a lot about the 'cost structure.' In the Web2 era, cold storage was extremely cheap, but in Web3 it has always been exorbitantly priced. Walrus, through erasure coding algorithms, is essentially trading computing power for space, minimizing redundancy costs. This means that we can finally keep high-fidelity historical snapshots on-chain, rather than just storing a few hash values. This is a crucial need for building decentralized indexers, block explorers, and even complex state rollbacks for full-chain games. The tech circle can sometimes be easily misled by new terms and overlook the closed loop of system design. If we do not solve the decentralized archiving of historical data, so-called Web3 is just a toy that has memory but no hard disk. Walrus's positioning as a non-intrusive 'data container' focused on the underlying layer is actually the most pragmatic and scarce approach in current infrastructure. It does not need users to perceive its existence, but when we need to trace the source of the data, it must be there. #walrus $WAL

Recently, when reviewing the "decentralized AI" sector, I feel there is a huge gap in the current architectural logic. We are all discussing on-chain reasoning and decentralized computing power, but it seems we intentionally avoid the most awkward question: where should those massive neural network models and training data be stored? If it's still hosted on AWS and only a Hash is stored on-chain, then the so-called decentralization is just self-deception.
When researching @Walrus 🦭/acc , I suddenly realized that this might be the key puzzle piece to fill the gap. We have been building a "world computer" (various L1s), but forgot to equip this computer with a large-capacity "hard drive". The current L1s and even L2s are essentially just CPUs and RAM, extremely expensive and volatile. The emergence of Walrus has shown me the possibility of mounting a persistent storage layer to this computer.
It is particularly noteworthy how it handles Blob data. It does not get entangled in high-frequency read/write of "hot data", but focuses on low-cost, high-reliability storage of "cold data". For the AI sector, model weight files and large-scale datasets are typical examples of this kind of Blob data. Walrus utilizes erasure coding technology to disperse these data slices, which can be in GB or even TB levels, while ensuring data availability (DA) and reducing costs to a level that developers can truly afford.
I wonder, in future Web3 applications, if we cannot break free from reliance on centralized cloud storage, we will never achieve true censorship resistance and ownership. The architecture of Walrus, built on the Sui network, cleverly reuses the existing set of validators, avoiding the redundant overhead of creating a separate consensus just for storage. This engineering principle of "Occam's Razor" is often much more effective than the complex layering of various solutions.
Sometimes I feel that the industry is too restless, as everyone focuses on the narrative at the application layer, while neglecting the physical limitations at the bottom layer. Only when storage protocols like Walrus are truly popularized, capable of carrying massive amounts of unstructured data at extremely low costs, can the full-chain games and fully autonomous AI agents we envision possibly transform from PPT to code. #walrus $WAL

As I re-examine the concept of "decentralized storage", I increasingly feel that my previous understanding was too one-sided. We always treat storage as a static warehouse, but in the architecture of @Walrus 🦭/acc , storage has become a programmable dynamic resource. This is a strong shock for those developing dApps.
​The previous dilemma was that if I wanted to create a decentralized Youtube or Instagram, where would the data exist? IPFS is great, but lacks a native incentive layer, making data easy to lose; Arweave is strong, but the one-time buyout model for permanent storage is too rigid in cost for high-frequency iterations of Web2-level applications. Walrus has taken a middle path: using Sui's consensus speed to manage metadata while storing the actual large files (Blobs) through efficient erasure coding.
​I am pondering the deeper logic of this design: it actually acknowledges the physical reality that "computation is expensive, storage is cheap". By separating large volumes of data from the mainnet consensus, Walrus is effectively reducing the burden on L1. The most attractive aspect of this architecture for me is its "separation of storage and access"—writing is subject to strict consensus, but reading can be very fast and cheap, even accelerated through a caching network. It's like equipping the blockchain with an infinitely scalable CDN.
​For the future of Web3, if it only remains at the level of text and transaction records, that would be too boring. What we need is a network capable of carrying video, audio, and even AI model weights. Walrus's ability to directly manipulate Blob data without the need for smart contracts may be the key to breaking the user experience barrier between Web2 and Web3. By not engaging in superficial narratives and only solving the three most fundamental engineering challenges of data "being stored, retrieved quickly, and not lost", this is the attitude that infrastructure should have. #walrus $WAL

I've been reflecting on the current state of the Web3 storage track and feel that everyone is falling into a misunderstanding: overemphasizing 'permanence' while neglecting 'programmability' and 'transfer efficiency.' Recently, while delving into @Walrus 🦭/acc , I found that the logic behind solving pain points is quite direct — not all data requires expensive on-chain state space, nor can all data tolerate the slow retrieval speeds of traditional decentralized storage.
The core of Walrus lies in its engineering mindset towards handling Blob data. The two-dimensional Reed-Solomon encoding (Red Stuff) it employs significantly reduces bandwidth usage while ensuring data can still be recovered even with a large number of node failures. This aspect is crucial in actual system design. If it's about video streaming or large game asset on-chain, bandwidth costs are often more sensitive than storage space costs, and Walrus clearly recognizes this.
Moreover, what I value is its strong binding relationship with Sui. Sui's Object model is naturally suited for handling asset ownership, while Walrus fills in the gaps for the 'content' of these assets. This combination reminds me of the classic architecture of 'compute + object storage' in cloud computing, only this time it's realized in a decentralized environment, achieving true decoupling of storage and execution layers.
Another detail worth pondering is its storage resource management model. This 'stateless' storage node design allows nodes to join or leave at any time without affecting the overall health of the network, which is the robustness that a distributed system should have. Compared to those solutions that require nodes to be online for long periods with extremely high maintenance costs, Walrus's architecture is clearly lighter and more in line with the dynamic characteristics of decentralized networks. The current market is too restless, with everyone focused on coin prices, and few are taking the time to look at the architecture. But if we truly want to build so-called 'full-chain applications,' this kind of infrastructure is the cornerstone that cannot be overlooked. #walrus $WAL

I've been thinking about the 'impossible triangle' of Web3 infrastructure lately, especially regarding storage. Everyone is focused on the TPS of L1, but how do we solve the problem of state bloat if the amount of on-chain data explodes? Simply relying on increasing block size is definitely not a long-term solution. After some research, I feel that the idea of @Walrus 🦭/acc indeed has some merit.
It is not like traditional decentralized storage, which simply fragments files, but instead utilizes the Sui network as a management and coordination layer, truly achieving decoupling of storage and execution. This makes me think that the future blockchain architecture must be modular. The erasure coding technology adopted by Walrus left a deep impression on me; this ability to recover data without a complete replica, especially in unstable network environments, is much 'smarter' than simple multi-replica redundancy and saves node resources.
Many previous NFT projects have metadata that poses the biggest risk. If the image is lost, what significance does the token have? Walrus's solution, optimized for Blob data (large files), seems to be specifically designed to address this issue as well as the high-frequency big data needs of future full-chain games and decentralized social media.
If we don't solve for cheap yet highly available storage, Web3 will forever be stuck in the financial digital games of DeFi. What we need is a container that can carry real content, and Walrus's current architectural design seems to be the closest to that answer. Less hype about concepts and more solutions to actual engineering problems is what the industry should look like. #walrus $WAL

When examining the current Layer 1 competitive landscape, a core contradiction remains unavoidable: the complete transparency of existing public chain architectures (Transparency) fundamentally conflicts with the rigid demands of institutional-level finance for business secrecy (Business Secrecy). Large market makers and hedge funds can never allocate capital on a large scale in an environment where order flow, position data, and trading strategies are exposed. Recently researching the technology stack of @Dusk , its value anchor is very clear— it does not attempt to build another general-purpose EVM-compatible chain, but rather directly addresses the compatibility challenges of "privacy" and "compliance" at the protocol layer.
The core breakthrough of #Dusk lies in its deep integration of zero-knowledge proofs (ZKP), especially in the design of confidential smart contracts (Confidential Smart Contracts). Traditional solutions often involve an external compliance module through a "whitelist" mechanism at the application layer, which is not only inefficient but also carries centralization risks. Dusk adopts a paradigm of "compliance-by-design". Through ZKP technology, the computation of transaction data is completed off-chain, with on-chain nodes only verifying the integrity of the computation (Computational Integrity) and the legality of state transitions.
This design cleverly resolves the legal paradox between the "right to be forgotten" in GDPR and the "immutability" of blockchain ledgers. Personal data has never truly been on-chain; what is on-chain is merely the "proof of data compliance". For tokenization of RWA (Real World Assets), Dusk's XSC standard actually redefines the logic of the clearing layer: compliance is no longer a post-facto regulatory audit but a prerequisite for transactions to occur. This atomic compliance check (Atomic Compliance Check) eliminates the costly reconciliation costs in traditional finance.
From the perspective of technological evolution, this represents a qualitative change in Web3 from a mere "decentralized experiment" to a "programmable compliance financial facility". Future Institutional DeFi does not need to sacrifice privacy for trust, nor does it need to sacrifice decentralization for compliance. Dusk is proving that, through cryptographic means, privacy protection and regulatory access can coexist perfectly on the same ledger. #dusk $DUSK

The lifeline of financial transactions is not just throughput (TPS), but also "Settlement Finality." Recently, while studying the consensus layer logic of @Dusk , I discovered that their Succinct Attestation mechanism addresses a pain point overlooked by the market due to its overly fundamental nature: the contradiction between fork risk and settlement cycles.
Traditional PoW and even some PoS public chains essentially provide "probabilistic finality," requiring you to wait for several block confirmations to reduce rollback risk. This is unacceptable for RWA (real-world assets) such as stocks and bonds—institutions cannot tolerate the possibility of a multi-million-level settlement being subject to "on-chain reorganization" just minutes later. #Dusk employs zero-knowledge proofs to drastically compress the block verification process, allowing nodes to avoid the burden of heavy historical data and achieve consensus by validating only the latest ZK proof.
The direct result of this is "Instant Settlement." This architecture gives decentralized networks a level of certainty and efficiency that only traditional centralized clearinghouses possess. I wonder if future Layer 1 competition might shift from "who runs faster" to "who is more stable." The Piecrust virtual machine, in conjunction with SA consensus, effectively paves a dedicated expressway for high-frequency, high-value compliant transactions. This is not merely a stacking of technologies, but a deep reconstruction of financial clearing logic, aimed at enabling blockchain to truly support legally binding asset settlements. #dusk $DUSK

The biggest misconception in the RWA (Real World Assets) track is that everyone is focused on the action of "asset on-chain" while ignoring the adaptability of the on-chain container. Although the general ERC-20 standard has good liquidity, it fundamentally cannot meet the complex full lifecycle management needs of securities-type assets. In this dimension, the XSC (Confidential Security Contract) standard proposed by @Dusk is actually trying to define the underlying paradigm of compliant digital assets in the future.
When I was pondering the technical logic of XSC, I found that its core breakthrough lies in the fact that compliance rules are directly "hard-coded" into the token standard, rather than being hung on the outer DApp logic. This means that regardless of how assets circulate in the secondary market, compliance checks based on zero-knowledge proofs will automatically execute at the underlying level. For institutions, this kind of certainty of "protocol is compliance" is much more critical than simply having low Gas fees. #Dusk's approach to privacy is very pragmatic; it is not aimed at pursuing geek-style absolute anonymity, but rather at constructing "audit-level privacy" to meet regulatory requirements such as the Markets in Financial Instruments Directive (MiFID II).
Additionally, Dusk's consensus mechanism, Succinct Attestation, is clearly a specific optimization designed to accommodate the high computational demands of privacy transactions. If Layer 1 cannot maintain instant settlement while handling heavy ZK verification, then institutional-level applications would be mere talk. The future competition among financial public chains will not simply be a linear comparison of throughput (TPS), but rather about who can perfectly replicate the privacy levels and compliance granularity that traditional finance must possess on this decentralized ledger. #dusk $DUSK

I have always felt that the "complete transparency" promoted by blockchain is actually a huge obstacle when it comes to commercial implementation. No market maker or fund is willing to broadcast their holdings and trading strategies to the entire world in real-time. In this respect, @Dusk defines privacy very precisely: it does not offer the "anonymity" of evading regulation, but rather the "confidentiality" that protects business logic.
When researching the privacy architecture of Dusk, I found that it aims to solve the chronic issues of MEV (Maximum Extractable Value) and front-running in the Ethereum ecosystem. If transaction data is transparent before verification, large trades are bound to be arbitraged. #Dusk achieves encryption of transaction data at the protocol level through zero-knowledge proofs (ZKPs), allowing verification nodes to confirm transaction legality without knowing the specific amounts, sender, or receiver. This state of being "verifiable but invisible" is the prerequisite for financial institutions to dare to go on-chain.
From a technical perspective, the PLONK algorithm they use has advantages over older generations of ZK technology in terms of versatility and setup. The current market logic is very clear: the future privacy track will inevitably diversify: one type is purely geek anonymity, while the other type is commercial privacy compatible with regulations like Dusk. For the Web3 financial infrastructure being built, privacy is not only a right but also a moat to prevent being consumed by predatory trading (掠夺性交易). #dusk $DUSK

Recently, while reviewing several key pain points in the RWA (Real World Assets) sector, I found that the biggest obstacle for institutions entering the market is not liquidity, but the fundamental contradiction between the native transparency of public chains and financial privacy compliance. In this regard, the technology choice of @Dusk caught my attention, as they are following an extremely vertical RegDeFi (compliant decentralized finance) route.
I am contemplating why general Layer 1 solutions struggle to support true institutional-level trading. This is because banks and asset management institutions cannot accept the complete exposure of on-chain holdings and counterparty information. The core value of #Dusk lies in its approach to privacy not through simple mixing logic, but by embedding zero-knowledge proofs (ZKPs) at the protocol layer. Particularly, their Citadel protocol, which is a ZK-based KYC/AML solution, effectively addresses a long-standing paradox: how to prove to on-chain validators that a user has passed compliance checks without exposing the user's specific identity.
From a technical architecture perspective, Dusk has abandoned the traditional EVM-compatible path and instead developed the Piecrust VM. While this may seem to increase the migration costs for developers, upon closer examination, it is reasonable. Due to the high resource consumption of ZK operations, general virtual machines struggle to achieve finality in seconds. Piecrust has performed underlying optimizations for the generation and verification of ZK proofs, which is essential for financial applications requiring high-frequency settlement. If it continues to run on the old architecture, the gas fees and confirmation times for compliant privacy transactions will not support commercial implementation.
Current market logic is shifting from "complete anonymity" to "selective disclosure." #Dusk is precisely at this turning point. If future on-chain finance needs to comply with regulations such as the EU's MiCA, then this natively compliant Layer 1 will no longer be an option but a necessary infrastructure. Rather than focusing on short-term price fluctuations, it is better to pay attention to the pilot data from traditional financial institutions after its mainnet launch, as that is the only standard for validating whether this technology stack is operational. #dusk $DUSK

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Recently, I have been reviewing the underlying logic of the Web3 payment track and increasingly feel that merely high TPS is not the cure; the disconnection in user experience is the core issue. I have delved into the technical architecture of @Plasma , and several design details have sparked deep thoughts, worth noting.
First is the Paymaster mechanism. It can achieve zero Gas fees for stablecoin transfers; although this is not an entirely new concept technically, very few can truly implement it. This directly smooths out the differences in payment experiences between Web3 and Web2, addressing the biggest pain point for large-scale adoption. Coupled with its full compatibility with EVM and support for mainstream tools like Hardhat and Foundry, it means that developer migration costs are extremely low, and the threshold for infrastructure has been minimized. Interestingly, its security layer design—periodically anchoring the state to the Bitcoin network—seems very pragmatic and robust in the current fiercely competitive Layer 2 landscape.
Looking at ecological data, this is where I felt surprised. The TVL of the SyrupUSDT lending pool on Maple has surprisingly reached 1.1 billion USD, a scale that ranks among the top on the network, indicating a high level of recognition from institutional funds. In terms of payment implementation, the integration of Rain cards and Oobit directly covers millions of merchants globally, even connecting to the Visa network. Additionally, with the integration of the euro stablecoin EURØP that complies with the MiCA framework, it shows that they are deeply involved in compliance and institutional fund introduction, engaging in real business rather than just issuing tokens.
Of course, we must face the price performance of XPL. The decline from the peak is nearly 90%, and there is indeed significant selling pressure. The current validator network is controlled by the team, with limited decentralization; ecological applications are mainly concentrated on transfer lending, which is relatively singular. But conversely, does this decline mean that the bubble has been completely "squeezed dry"?
In the industry, it is often said that "good technology does not equal good assets," but when prices deviate significantly from fundamental value, it is often an opportunity for correction. The current low price might be washing out speculators, leaving opportunities for true builders. If we consider the current price as a "golden pit," then the logic of value return is valid. #plasma $XPL

Technical Architecture Analysis and Reflections on @Vanarchain

In this extremely crowded L1 public chain market, I've been pondering a new variable: when AI data processing demands truly go on-chain, can the existing infrastructure handle it? I've recently spent considerable time studying Vanar Chain's white paper and technical documentation, attempting to strip away the marketing noise and understand its logic from an architectural perspective.

What first caught my attention was the layered design of the Vanar Stack. This isn't a simple modular stack, but rather a specialization for AI workloads. In particular, the introduction of the Neutron layer (Semantic Memory) and the Kayon layer (Contextual AI Reasoning) made me realize that the core pain point it's trying to solve isn't simply TPS, but rather the "semanticization" of data. Traditional EVM chains store hashes and raw bytes, while Neutron attempts to transform unstructured data (such as PDFs, invoices, and legal documents) into on-chain queryable smart objects (Seeds). This design is extremely valuable in PayFi and RWA (Real-World Asset) scenarios because compliance documents are no longer external IPFS links, but rather part of the on-chain logic. In analyzing its consensus mechanism, I discovered it employs a 3-second block time and a 30 million gas limit per block. This high-throughput parameter setting is clearly designed to handle high-frequency transactions, but its fee model is even more interesting. Unlike Ethereum's auction-based gas mechanism, Vanar uses a fixed-fee model, locking the cost of each transaction at an extremely low level. For developers building large-scale consumer apps, cost predictability is far more important than simply low cost.

Of course, as an EVM-compatible chain, how to build a true moat with extremely low migration costs remains a question for me. However, from the perspective of the completeness of its technology stack, it builds more than just a ledger; it's a closed-loop system encompassing computation, storage, and inference. For analysts focusing on the high-performance L1 and AI convergence track,#Vanarprovides a very worthwhile example to analyze. I will continue to track its mainnet's stability performance in real-world high-concurrency environments.#vanar $VANRY