BullionOX

I have been explaining to some friends lately why @Plasma focus is so heavily on transactional efficiency rather than broad ecosystem expansion, and I wanted to lay it out the same way I do when we're talking face to face based on what I've observed from the network's design, performance metrics, and how it behaves in practice since the mainnet beta launch.
Plasma is a Layer 1 blockchain that went live in mainnet beta on September 25, 2025. The entire project is built around one primary goal: making stablecoin payments, especially USDT, as fast, cheap, and reliable as possible. Unlike many chains that try to be everything to everyone supporting NFTs, gaming, DeFi across dozens of categories, and heavy ecosystem marketing Plasma deliberately narrows its scope to transactional use cases.
The protocol paymaster is the clearest example. For basic USDT send and receive transactions, gas is sponsored at the protocol level. Users pay zero fees, they don't need to hold $XPL or any native token, and they don't have to worry about gas estimation or wallet top ups. This single feature removes one of the biggest barriers to onchain payments the hidden cost and complexity that stops people from using stablecoins for everyday transfers like remittances, payroll, or merchant settlements.
Consensus is optimized purely for speed and predictability. PlasmaBFT, based on Fast HotStuff, delivers sub second block times and deterministic finality. Transactions confirm almost instantly, and the network is capable of more than 1,000 transactions per second. This level of performance is tuned specifically for high frequency, low value stablecoin flows, not for general purpose workloads that would require tradeoffs in speed or cost.
The execution layer uses a modified Reth client in Rust. It maintains full EVM compatibility so developers can bring over existing contracts without rewriting code, but the optimizations are payment centric. State processing is fast and efficient for stablecoin transfers, while custom gas tokens let dApps whitelist stablecoins for fees on more complex interactions. The design avoids unnecessary features that could introduce congestion or higher latency.
Validators stake XPL in Proof of Stake to secure the chain. Rewards come from controlled inflation (starting at 5% annually and tapering to 3%) and fees on non sponsored transactions. This creates a simple economic loop: real payment volume drives fee revenue, which supports validator incentives and network security. There's no heavy emphasis on broad ecosystem incentives, token launches, or marketing campaigns to attract every type of dApp the focus stays on making the core payment layer work exceptionally well.
In conversations with people, I've noticed how this narrow approach stands out. Many chains chase ecosystem expansion by funding dozens of projects, running grant programs for gaming or NFTs, or pushing partnerships across unrelated sectors. Plasma does not do that aggressively. Instead, it concentrates resources on refining transactional efficiency: zero fees for basics, instant finality, high throughput, and EVM compatibility without overcomplicating the stack.
The result is visible in real metrics. Since launch, Plasma has attracted billions in stablecoin TVL, with high utilization in lending and borrowing markets. The chain ranks among top networks for stablecoin deposits and activity, not because of flashy ecosystem campaigns, but because the payment experience is demonstrably better no gas for simple transfers, no waiting, no native token hassle.
This focus on transactional efficiency over ecosystem expansion is intentional. Stablecoins already move trillions monthly globally. The biggest barrier to wider adoption is not lack of dApps or marketing it's friction in the actual movement of money. Plasma solves that friction first, betting that once payments become seamless, developers and users will naturally build and adopt around it.
Validators stake XPL to keep this efficiency secure, and as payment volume grows, so does the demand for staking and network participation. It's a clean, focused model.
If you're following projects in the stablecoin space, Plasma's choice to prioritize transactional efficiency over broad ecosystem expansion is one of the clearest strategic decisions out there. It feels like a deliberate step toward making digital dollars work like digital cash fast, cheap, and without extra steps.
For the technical details on how the paymaster, consensus, and execution layer are optimized for payments, the official documentation from Plasma is straightforward and worth reading.
$XPL #Plasma

When I began researching how AI development could benefit from decentralized infrastructure, Walrus Protocol emerged as a particularly relevant system. The protocol, built by Mysten Labs on the Sui blockchain, is designed to support large scale data storage with properties that align directly with the requirements of trustworthy global data markets in the AI era.
Walrus stores data as blobs arbitrary unstructured files such as training datasets, model weights, inference inputs, or synthetic data generations. These blobs are encoded using RedStuff, a two dimensional erasure coding algorithm. The encoding produces slivers distributed across independent storage nodes, achieving high availability with an effective replication factor of approximately 4x to 5x. This allows reconstruction of the original data even if a significant portion of nodes are offline or unresponsive.
Trustworthiness in data markets depends on provenance and integrity. Walrus provides this through cryptographic commitments embedded in every sliver. During retrieval, these commitments are verified against the blob's root hash, ensuring the data has not been altered since upload. This mechanism prevents tampering and gives buyers confidence that the dataset or model they acquire matches what was originally listed.
Verifiability is enforced by Proof of Availability certificates minted on Sui. These on chain objects confirm that the blob is stored and retrievable at the time of certification. Smart contracts can reference these certificates to validate data existence and integrity without needing to download the full content, enabling trustless transactions in marketplaces.
Security is addressed through the protocol's permissionless yet incentive aligned design. Nodes stake $WAL to participate in storage committees. Rewards are distributed over epochs based on verified performance, while failures to respond to asynchronous challenges result in reduced rewards or slashing of staked WAL. This economic model commits nodes to reliable behavior and protects against malicious storage or data withholding.
Privacy for sensitive datasets is supported through integration with Seal. Seal allows blobs to be encrypted with on chain access policies, where decryption keys are controlled via threshold schemes. This enables selective sharing data can be sold or licensed while remaining confidential until authorized access is granted.
The Sui object model adds programmability. Blobs are represented as Sui objects in Move, allowing smart contracts to manage ownership, extend storage duration, transfer rights, or enforce usage conditions. This makes data a composable asset in decentralized markets, where ownership can be tokenized, royalties enforced, or access gated programmatically.
Global markets require scale and cost efficiency. Walrus's low replication overhead keeps storage affordable for terabyte cale AI datasets. Fees paid upfront in WAL for fixed durations are distributed to nodes over time, creating a sustainable economy tied to actual usage.
Walrus provides a foundation for trustworthy, verifiable, and secure data markets in the AI era by combining efficient erasure coding, on chain proofs, programmable ownership, economic incentives, and privacy tools. This enables scenarios such as decentralized training data marketplaces, secure model sharing, or provenance tracked synthetic data distribution.
@Walrus 🦭/acc $WAL #walrus

When I first tried to grasp how zero knowledge (ZK) algorithms work in blockchains, Dusk Network's implementation provided a clear example through its Rusk protocol. ZK algorithms allow proving knowledge of a fact without revealing the fact itself, and in Dusk, this is core to enabling private yet verifiable financial transactions. Rusk, as Dusk's state transition function, leverages ZK to process contract logic confidentially, making it a practical case study for ZK's core processes.
The first process is setup: ZK algorithms like PLONK (used in Dusk) start with a trusted setup phase to generate parameters for proofs. Rusk optimizes this for efficiency, creating circuits that describe contract rules in a ZK friendly way. This setup ensures proofs are succinct, allowing validators to check validity quickly without heavy computation.
Next comes proving: The prover (e.g., a user submitting a transaction) computes a proof that the state transition follows the rules. In Rusk, this happens for confidential smart contracts inputs like balances are hidden, but Rusk generates a proof showing the output is correct. The process involves polynomial commitments, where the prover commits to values and opens them selectively, containing verification to specific claims.
Verification is the final step: The verifier checks the proof against public parameters. Rusk makes this lightweight, with proofs sized in hundreds of bytes, enabling fast network consensus. For Dusk's institutional focus, this means regulators can verify compliance (e.g., limits met) without seeing data, balancing privacy with accountability.
Rusk's ZK integration supports Dusk's vision: modular execution where privacy is default but verifiable. The token, as the economic layer, covers gas for ZK computations, aligning incentives with secure processes.
From my view, Rusk's ZK walkthrough shows how algorithms like PLONK turn privacy into a tool for trust in finance, not obscurity.
How have ZK proofs changed your blockchain projects?
What core process do you find most challenging?
@Dusk $DUSK #dusk

When I first compared different AI focused blockchains, I noticed a common emphasis on autonomous agent systems that act independently with minimal oversight. Many projects highlight agent capabilities as the future of decentralized intelligence. Vanar Chain takes a noticeably different path by prioritizing accountability and responsibility in its AI native infrastructure, ensuring that intelligent operations remain transparent, auditable, and verifiable.
Vanar Chain is a modular Layer 1 blockchain that is fully EVM compatible and built specifically for AI workloads. The platform's five layer stack integrates data handling and reasoning in a way that keeps every step traceable on chain. Rather than designing for fully autonomous agents that could operate with limited visibility, Vanar Chain structures its tools so that AI-driven decisions and data flows are always anchored in verifiable records.
The Neutron layer exemplifies this approach. It compresses documents, records, invoices, or compliance files into compact, programmable objects called Seeds. These Seeds are stored directly on the blockchain, carrying cryptographic proofs of origin, ownership, timestamp, and integrity. Any AI process that uses a Seed must reference this on chain record, meaning the source data and its context cannot be altered or obscured. This creates built in accountability: users, auditors, or regulators can always trace back to the original information without relying on external logs or promises.
The Kayon reasoning layer continues this principle. Kayon performs contextual analysis over Seeds in real time, enabling automated validations, compliance checks, or conditional logic. Every reasoning step produces outputs that are tied to the immutable Seeds and the chain's transaction history. This means decisions whether in PayFi payment flows or tokenized real world asset verification are auditable and reproducible. The design discourages opaque “black-box” AI behavior by requiring all inputs and logic to remain transparent and on chain.
This focus on responsibility is reinforced by the platform's economic model. serves as the native gas token for every operation: creating Seeds, querying data, executing reasoning, and securing the network through staking. Gas usage and staking rewards are publicly visible on chain, so network participants can observe how resources are allocated and who is contributing to security. Validators operate under a reputation enhanced Proof of Authority model, where consistent performance and honest behavior directly influence eligibility for rewards, further aligning incentives with accountable conduct.
@Vanarchain provides extensive documentation explaining how Neutron and Kayon maintain transparency and traceability. The platform's carbon neutral operations, achieved through renewable energy sources, also reflect a broader commitment to responsible infrastructure that considers environmental impact alongside technical performance.
By centering accountability rather than unrestricted agent autonomy, Vanar Chain creates a framework where AI capabilities are powerful yet governed by verifiable rules. This design supports applications in regulated domains like finance and real world assets, where responsibility and auditability are prerequisites for trust and adoption.
@Vanarchain $VANRY #vanar

When I first thought about integrating storage into Web3 apps, I expected it would be straight forward upload large files like videos or datasets and assume reliable, low cost access across decentralized networks. But the reality hits differently: high replication costs on chains like Sui, where full validator replication can exceed 100x overhead, make storing unstructured blobs inefficient and expensive for developers building real applications.
That's where @Walrus 🦭/acc changes the equation. As a decentralized storage protocol built on the Sui blockchain by Mysten Labs (now advancing under the Walrus Foundation), Walrus serves as a specialized foundational layer for Web3 development. It focuses on handling large binary objects blobs such as images, videos, AI datasets, NFTs, and even full websites with exceptional efficiency.
The key innovation lies in its architecture: instead of full replication, Walrus employs advanced erasure coding (via the Red Stuff algorithm) to split blobs into slivers distributed across a network of independent storage nodes. This achieves high data availability and robustness with a minimal replication factor of just 4x-5x, far lower than traditional blockchain storage. Sui acts as the secure coordination layer, managing metadata, payments, node incentives, and issuing on chain Proof of Availability certificates to verify blobs remain accessible.
What makes it truly foundational is programmability. Blobs and storage resources become native Sui objects in the Move language, allowing smart contracts to interact directly checking availability, extending storage periods, automating logic based on data presence, or even enabling composable data in dApps. This turns passive storage into an active, programmable component. Real use cases are emerging: projects like Talus AI use Walrus to let agents store, retrieve, and process onchain data scalably, while others leverage it for NFTs, game assets, or AI training datasets.
I slow down on how this bridges the gap between decentralization's ideals and practical developer needs. That low overhead yet resilient design sticks with me, showing a thoughtful evolution beyond brute force replication. I kept wondering if such efficiency could maintain true permissionless security at scale, but the horizontal scaling to thousands of nodes, combined with Byzantine fault tolerance, addresses that convincingly.
I'm left thinking about the broader impact: Walrus could make building data intensive Web3 apps as intuitive and cost effective as centralized alternatives, unlocking more innovation in AI, media, and beyond.
@Walrus 🦭/acc $WAL #walrus

I figured tracking network progress for a blockchain like Vanar Chain would be straightforward, mostly boiling down to flashy metrics like skyrocketing TVL or explosive transaction counts. But as I looked closer, it's the quieter, more subtle indicators things like steady integrations and roadmap executions that reveal a network quietly building momentum without the fanfare.
Vanar Chain, spearheaded by @Vanarchain , is an AI native Layer 1 that's EVM compatible and designed for practical, scalable applications. Its native token, VANRY, underpins staking, governance, and now, increasingly, utility in emerging features. One subtle sign of progress is the recent integration with Project Zero, which brings real time data streaming to Vanar's AI native infrastructure. This isn't a headline grabber, but it enhances on chain AI capabilities by providing continuous feeds for tools like Neutron, the semantic memory layer. Developers can now build apps that react to live data without constant off chain pulls, pointing to improved efficiency and reduced latency over time.
Another understated marker is the live rollout of AI integrations in early 2026, including operational on chain intelligence that allows for semantic querying and reasoning via layers like Kayon. Coupled with the upcoming Axon component for automated workflows, this shows Vanar methodically activating its modular stack. The shift to a tool subscription model for AI services is equally telling it's a move toward sustainable economics, where $vanry holders benefit from recurring on chain activity rather than one off hype. Partnerships, such as with Worldpay for agentic payments, and key hires like the new Head of Payments Infrastructure, further indicate institutional buy in, bridging traditional finance with AI driven crypto.
These elements aren't about overnight virality; they're about consistent infrastructure buildup. For instance, Neutron's early access program for on chain projects has quietly attracted builders focused on real world use, like tokenized assets with embedded AI compliance.
I slow down on how these integrations address common blockchain bottlenecks, like data silos, without overpromising. That friction sticks with me because it underscores Vanar's pragmatic approach in a space full of vaporware. I kept wondering if these low key advancements would compound into broader adoption, especially as AI becomes integral to Web3. I'm left thinking about how $VANRY quiet utility growth could signal long term network health, rewarding patient participants.
In essence, Vanar's progress shines through these subtle cues, fostering a resilient ecosystem.
@Vanarchain $VANRY #vanar

I figured sponsoring transaction fees in blockchain networks would be a clunky add on, something tacked onto existing systems with extra layers of complexity and potential points of failure. But diving into @Plasma approach changed that view entirely it's elegantly integrated right into the protocol, making stablecoin transfers feel almost effortless.
At its core, Plasma is a Layer 1 blockchain designed with a focus on stablecoin infrastructure, where the native token powers the network for staking, governance, and general fees. What sets it apart, though, is the Paymaster model, a protocol maintained smart contract that handles sponsored transaction fees, particularly for USDT (USD₮) operations. This isn't just a gimmick; it's a deliberate architectural choice to reduce friction in everyday crypto payments.
Let's break it down technically. In traditional EVM compatible chains, users must hold the native token (like ETH on Ethereum) to pay gas fees for any transaction. Plasma flips this for specific cases by deploying a built in Paymaster contract. This contract acts as a sponsor, covering the gas costs for eligible transactions primarily transfers and approvals on the USDT contract. When a user initiates a USDT transfer, the Paymaster steps in during the transaction validation process. It verifies eligibility (e.g., ensuring it's a standard transfer or approve call on the approved stablecoin contract), then subsidizes the gas from a dedicated reserve pool.
This reserve isn't infinite; it's funded through network mechanisms, including a portion of block rewards or fees from non sponsored transactions. The Paymaster uses account abstraction principles, similar to ERC-4337 on Ethereum, where the sponsor can define rules for what gets covered. In Plasma's case, the rules are hardcoded at the protocol level for consistency and security. Users don't need to interact with it directly the wallet or dApp abstracts it away, so sending USDT feels like a zero fee experience without holding XPL.
I slow down on the eligibility checks, though. That friction sticks with me because it ensures the system isn't abused only USDT related ops qualify, preventing spam or unrelated exploits. For broader transactions, like DeFi interactions or NFT mints, users still pay in XPL or, in some extended cases, directly in the transferred asset via Paymaster extensions. This hybrid keeps the network sustainable while prioritizing stablecoin utility.
Technically, the Paymaster integrates with Plasma's consensus engine, which is a proof of stake variant optimized for high throughput. Validators process sponsored txs without altering the block's fee structure; the sponsorship is settled off chain from the user's perspective but on chain via the contract's balance. This reduces onboarding barriers for merchants or remittance users, who can transact in stablecoins without crypto volatility exposure.
I kept wondering if this model scales under heavy load does the reserve deplete too fast? From what I've seen, Plasma's design includes dynamic adjustments, like fee multipliers during congestion, to maintain the pool. It also encourages xpl staking, where stakers indirectly support the ecosystem by securing the chain and earning rewards that could replenish sponsorship funds.
I'm left thinking about how this could evolve. In a world of fragmented blockchains, Plasma's Paymaster offers a blueprint for user.centric fee models, potentially inspiring cross chain adaptations. It's not revolutionary hype; it's practical engineering solving real pain points in crypto adoption.
For developers building on Plasma, exploring the Paymaster docs is a mustit's open source and verifiable on their GitHub. If you're into stablecoin tech, this is worth a deep dive.
@Plasma $XPL #Plasma

When I first started looking closely at Dusk, what stood out wasn’t the usual promises of speed or scale, but something quieter: how people actually behave when their assets carry real personal stakes. Everyone talks about Dusk as privacy for regulated finance, yet the more interesting thread is in everyday choices players in games who hesitate to expose their rare items, collectors guarding portfolios, or anyone who values discretion over display.
The core mechanics feel deliberate in this light. Zero knowledge proofs, woven natively through the Phoenix model, let confidential assets stay hidden by default amounts, ownership details shielded while still allowing selective auditability when rules demand it. Then there’s the Rusk VM for confidential smart contracts, enabling logic that executes privately without leaking strategy or holdings. And the compliance layer, like Citadel’s privacy preserving identity, bridges the gap so regulated environments don’t force full exposure.
These address real frictions: the guarded instinct in gaming where showing off an asset invites targeting or devaluation, or in broader ownership where repeated small actions like trading in game items build trust only when privacy is reliable, not performative. Institutions pause not over tech, but over unnecessary visibility that disrupts fair play or strategy.
Of course, there’s a tradeoff deliberate constraints on maximal openness mean it’s not the wildest form of decentralization, and rollouts stay measured to preserve that balance.
Stepping back, if this succeeds, most users won’t even notice the blockchain. It fades into the background like reliable infrastructure quiet, present, forgotten until needed.
@Dusk $DUSK #dusk

Balancing the privacy of user data and regulations is no small task in the fast moving world of blockchain, the timing of which regulatory requirements are important. The financial privacy is a key to the adoption of the frameworks such as the MiCA of the EU, which require transparency to reduce risks, but institutional investors cannot give up financial privacy. This is the point at which @Dusk gains its edge, developing a network which entrenches privacy tools that can handle real life regulatory needs without dealing any significant blow to performance.
Dusk Network layer-1 architecture, which was developed since 2018, is a regulated finance modular infrastructure. Its homomorphic encryption, and zero-knowledge proofs also enable transactions to remain secret and create verifiable proofs to be audited. It is not an afterthought, but it is at the heart of the protocol, which allows developers to develop applications that can comply with a strict timeline to roll out compliance. As an example, within the audit requirements of MiCA, the system of Dusk enables the institutions to demonstrate the integrity of transactions without disclosing any information, reducing manual reporting to slow down projects.
This is the case with the Hedger protocol. Live also supports the Hedger Alpha version, which does private computations on the DuskEVM, an EVM compatible layer, supporting regular Solidity contracts. Familiar code can be deployed by developers, on the base layer that is Dusk, making integration easier to integrating with compliant DeFi. This modularity is used to solve time constraints as one can make changes in response to changing regs quickly such as, in the case of new AML standards, having proof mechanisms updated without necessarily having to rewrite the entire stack.
Given practical application in the real world: NPEX is a licensed Dutch exchange and with which DUSK is building DuskTrade. In 2026, this platform will process more than EUR300 million tokenized securities to provide a compliant venue to RWAs. The waitlist was launched in January, where organizations that require privacy in trades were interested in joining but with compliance to the MTF, brokerage, and ECSP licenses. These partnerships indicate that Dusk can transform regulatory challenges into operational opportunities, cutting down the duration between idea and compliant operations.
In the financial aspect, network security is supported by staking and fees on to provide sustainable operations of these privacy oriented tools. It is also connected to the ecosystem but does not take over the story, which helps promote long term functionality in controlled settings.
This is supported by the recent milestones. In January 2026, a significant upgrade to the DuskEVM was the launch of the DuskEVM mainnet, which will improve the accessibility of developers as global regulations become stricter. In the future, roadmap ingredients such as Hedger refinements will enable auditable privacy to more financial instruments.
In the perspective of the observer, the strategy of the organization under scrutiny, DUSK, is based on the feeling of the practical experience gained in the requirements of traditional finance to discreet and still accountable transactions. During a time when privacy accidents can crash efforts, the framework suggested by Dusk is an opportune Web3 infrastructure paradigm, with auditability as a priority to inspire trust. Dusk is not a mere tag; it is an indication of intelligent development in obedient blockchain technology.
@Dusk $DUSK #dusk

The Walrus Protocol is unique in the decentralized storage field because it was well designed and efficient in terms of its approach to balancing efficiency, security, and scalability. The system at its simplest is based on a system of storage nodes which do the actual data persistence. These nodes are the building blocks of Walrus as they provide a stable way of storing large binary objects, or blobs, in a distributed installation.
Walrus storage nodes are autonomous nodes. Both of them have to handle certain fragments of data at specific points in time known as storage epochs. The storage epoch can take between two weeks on the mainnet and this gives it a fixed time frame in which it can operate.

The process starts with erasure coding when a user wishes to store a blob. It is a method of dividing data into smaller sections known as slivers. Erasure coding introduces redundancy, and thus the system can restore the original blob even in case some slivers are lost or not available.
These slivers are further scattered over the shards. Walrus is configured with a fixed number of logical shards of approximately 1000 in the existing configuration. The shards are assigned to storage nodes out of coordination by the Sui blockchain, the control plane of the whole protocol.
In this case, the Sui blockchain is essential. It processes metadata, payment, and shard assignment. Storage nodes constantly check blockchain events to be aware of the shards they are in charge of and to keep up with the network.
This division allows there to be no single node that contains the whole blob which makes it more secure and fault tolerant. Walrus is designed to have a tolerance of up to one third of the bad or malicious nodes by assuming that more than two thirds of the shards are operated by honest nodes.
Storage nodes do not only store data, they also offer signatures that data slivers have been received. These signatures are gathered and accumulated to a certificate which has to be recorded to the Sui blockchain and used as evidence of storage.
Going upwards along the storage layer, the next layer is the aggregation layers. Aggregators are optional nodes of the Walrus architecture, although they also bring a great deal of usability value.
The primary task of an aggregator is to reassemble the complete blob using the slivers transmitted to it. On request of data, the aggregator makes a query to the storage node required thus retrieving the slivers and reassembling them into the original form.
This reconstruction is on demand and the aggregators will be able to support the blob on a regular web protocol such as HTTP. This renders Walrus more usable in applications that are not closely tied to blockchain technology.
Aggregators can have caches overlaid upon them which serve as content delivery networks. To minimize latency and to alleviate storage node pressure on popular data they store reconstructed blobs on a temporary basis.
The publishers also belong to this ecosystem of aggregation. They are optional aids which facilitate the first upload. Through a web interface, a publisher can send a blob, encode the same, divide the slivers to the storage nodes, and manage the on chain certification.
The only thing that connects all this is the fact that the system is permissionless. Any person or organization can operate a storage node, aggregator, or publisher, provided he/she adheres to the protocol provisions imposed by the Sui blockchain.
Security is integrated on all levels. As an example, the outputs of aggregators may be checked by users comparing them with the blob ID, or independently rebuilt in case necessary.
Its efficiency is due to the innovations such as the RedStuff that is a custom two dimensional erasure coding scheme of Walrus. It has high durability and has low replication factor of about 4.5 times that is more efficient compared to traditional methods.
This implies that storage nodes do not incur unnecessary redundancy and costs are kept low by users.
As a practical arrangement, this layout facilitates practical applications, such as the storage of large media files in Sui as part of the decentralized apps.
The seamlessly flowing nodes to aggregators allow developers that write on Walrus to concentrate on their applications without the complexity of underlying storage.
This system has been structured horizontally (up to thousands of nodes) by walrusprotocol to be able to handle increasing demands.
The $WAL token is a part of this economy, where it can be paid and rewarded in the network.
On the whole, the learning of Walrus as relating storage nodes to aggregation layers provides an insight of a protocol, that is not merely theoretical but it is designed practically to suit decentralized data requirements.
@Walrus 🦭/acc $WAL #walrus