Mr_Green个

Most storage systems ask you to trust a familiar sentence: “Your file is saved.” It sounds simple because the complexity is hidden behind a company, a contract, and a support ticket. But in Web3, the sentence needs a different shape. If no single operator is in charge, then “saved” cannot be a private promise. It has to become a verifiable fact that other people can check without asking permission.
Walrus is a decentralized storage protocol designed for large, unstructured content called blobs. A blob is simply a file or data object that is not stored like rows in a database table. Walrus supports storing blobs, reading them back, and proving they are still available later. It is built to keep content retrievable even if some storage nodes fail or behave maliciously. This kind of failure, where participants may lie or break in unpredictable ways, is often called a Byzantine fault. Walrus also uses the Sui blockchain for coordination and payments, while keeping the blob content itself off-chain. Only the blob’s metadata is exposed to Sui or its validators.
If you are a developer, a client builder, or someone building DeFi systems that need large amounts of data, the first thing to notice is what Walrus refuses to do. It does not try to store large files inside blockchain objects. Traditional on-chain storage is expensive because it relies on broad replication. Walrus instead treats the chain as a coordination layer. The blob content lives on Walrus storage nodes and optional cache infrastructure, while the chain records the small pieces of truth that matter: ownership of storage capacity, timestamps of responsibility, and events that attest availability.
To understand how Walrus does this, it helps to learn the shape of a blob inside the protocol. When a user wants to store a blob, Walrus does not simply copy it to one node. It erasure-encodes it. Erasure coding is a method of turning one piece of data into many pieces with redundancy so the original can be reconstructed even if some pieces are missing. Walrus uses a construction called RedStuff, based on Reed–Solomon codes. In plain terms, Reed–Solomon codes are a classic family of error-correcting codes that can recover missing pieces as long as enough correct pieces remain. Walrus describes that this encoding expands the size of a blob by a fixed multiple of about 4.5–5×. The point is to pay a predictable price for resilience, without needing full copies everywhere.
After encoding, the blob becomes many smaller parts. Walrus groups encoded symbols into slivers. Slivers are then assigned to shards. A shard is a logical bucket of responsibility. Storage nodes are assigned shards during a storage epoch, and those nodes store and serve the slivers that belong to their assigned shards. An epoch is simply a time window during which committee membership and shard assignments remain stable. On Mainnet, Walrus describes storage epochs lasting two weeks. This is a practical engineering choice. It gives the system a stable map of who is responsible right now, while still allowing the committee to change over time.
The word “committee” here is important. Walrus is operated by a committee of storage nodes that evolves between epochs. Committee membership is tied to delegated proof-of-stake using Walrus’s native token, WAL. WAL can be delegated to storage nodes, and nodes with high stake become part of the epoch committee. WAL is also used for payments for storage, and at the end of each epoch, rewards for selecting storage nodes, storing, and serving blobs are distributed to storage nodes and the people who staked with them. Walrus also defines a subdivision called FROST, where 1 WAL equals 1 billion FROST. This makes it easier to express small payments and precise accounting without awkward decimals.
At this point, a client builder might ask the question that matters most: if the blob is off-chain, how do we know it is really stored and will remain retrievable?
Walrus answers with two linked ideas: an identity for the blob, and a public moment when responsibility begins.
The identity is the blob ID. Walrus computes the blob ID as an authenticator of the set of shard data and metadata. It hashes the sliver representation in each shard, uses those hashes as the leaves of a Merkle tree, and uses the Merkle tree root as the blob hash. A Merkle tree is a structure that lets you commit to many pieces of data under one root hash, while still allowing later verification that a piece belongs to that commitment. In practical terms, the blob ID acts like a seal. If you receive slivers from a node or from a cache, you can check that what you received matches the blob ID’s authenticated structure. This lets clients verify integrity without trusting any single server.
The public moment of responsibility is the Point of Availability, or PoA. For each stored blob ID, Walrus defines PoA as the point when the system takes responsibility for maintaining that blob’s availability. PoA is not a vague statement. It is observable through an event on Sui, along with an availability period that specifies how long the system maintains the blob after PoA. Before PoA, the uploader is responsible for ensuring the blob is actually present and properly uploaded. After PoA, Walrus is responsible for the full availability period. This matters for DeFi and for applications with audits, because it turns “it’s stored” into something checkable from the chain.
The path that creates PoA is built to be verifiable rather than sentimental. A user first acquires storage space on-chain. In Walrus, storage space is represented as a resource on Sui. That resource can be owned, split, merged, and transferred. This is not only convenience. It creates a market-like structure around capacity, which matters when storage has real costs and real lifetimes.
Once the user has the storage resource, they encode the blob and compute its blob ID. They then update the on-chain storage resource to register the blob ID with the desired size and lifetime. This emits an event that storage nodes listen for. After that, the user sends blob metadata to all storage nodes and sends each sliver to the node that manages the corresponding shard.
When a storage node receives a sliver, it checks the sliver against the blob ID. It also checks that there is an on-chain blob resource authorizing storage for that blob ID. If everything matches, the node signs a statement that it holds the sliver and returns the signature to the user. The user collects enough signatures, aggregates them into an availability certificate, and submits that certificate on-chain. When the contract verifies the certificate against the current committee, it emits the availability event for the blob ID. That event marks PoA. At that moment, the system is publicly on record as responsible for availability for the specified period.

If you are building client software, the read side is just as important as the write side. Walrus allows reads either directly from storage nodes or through optional infrastructure like aggregators and caches. An aggregator is a client that reconstructs complete blobs from slivers and serves them over HTTP. A cache is an aggregator with additional caching functionality to reduce latency and reduce load on storage nodes. These are optional because end users can reconstruct blobs directly from storage nodes or run a local aggregator. Walrus emphasizes that caches and publishers are not trusted system components. They may deviate from protocol. What keeps the system honest is that reads can be verified against the blob ID commitments.
When reading directly, a client first gets the metadata for the blob ID from any storage node and authenticates it using the blob ID. Then the client requests slivers from storage nodes for the shards corresponding to that blob ID and waits for enough responses to reconstruct the blob. The system is designed so reconstruction is possible even when some nodes are unavailable or malicious, assuming the protocol’s Byzantine tolerance conditions hold.
Walrus also describes two levels of consistency checks for reads: default and strict. The default check verifies only the portion read, balancing security and performance. The strict check is stronger. It decodes the blob, then fully re-encodes it, recomputes all sliver hashes and the blob ID, and verifies the computed blob ID matches the requested blob ID. The reason strict verification exists is simple: clients are untrusted, and incorrect encoding can create edge cases where some sets of slivers decode to different results. Strict verification removes that ambiguity when stronger guarantees are required.
Walrus also has a clear way of handling incorrectly encoded blobs after PoA. If storage nodes later detect that a blob was inconsistently encoded and cannot be reconstructed consistently, they can produce an inconsistency proof and upload an inconsistency certificate on-chain. After an inconsistent blob event is emitted, reads return None for that blob ID. This may sound harsh, but it preserves a critical property: a blob ID should not silently mean different content to different readers. In DeFi terms, it is better to have a clean “does not resolve” than a world where evidence changes depending on who fetched it.
All of this is coordinated through Sui smart contracts. Walrus describes a system object holding committee information, total available space, and price per unit of storage. Users purchase storage for a duration, storage funds are allocated across epochs, and nodes are paid according to performance, with governance and resource management expressed on-chain. The chain is not carrying the blob content, but it is carrying the lifecycle rules that make availability legible.
Walrus Mainnet was announced as live on March 27, 2025, described as operated by a decentralized network of over 100 storage nodes, with Epoch 1 beginning on March 25, 2025. Alongside stability, the announcement described practical features like improved expiry-time handling in the CLI, the shift of RedStuff’s underlying coding to Reed–Solomon codes, TLS handling for storage nodes to support publicly trusted certificates, and JWT authentication options for publishers to manage real operating costs. These details matter to developers because they show Walrus is not only a paper design. It is shaped around operational reality: monitoring, authentication for paid services, and web compatibility.
If you are a client builder, Walrus is mostly about verifiability at the edges. You can use HTTP delivery without surrendering correctness, because the blob ID and metadata let you verify what you received. If you are a developer, Walrus is a way to store large artifacts and still have smart contracts reason about their availability and duration using on-chain events and objects. If you are building DeFi, Walrus is a storage substrate for the heavy things DeFi often needs but cannot practically put on-chain: audit packs, risk models, historical archives, ZK proofs, and other large evidence that should remain retrievable and verifiable over time.
Walrus is not trying to replace the web’s delivery infrastructure or become a full execution layer. It is trying to make one promise well: large data can live off-chain, but the truth about that data’s identity and availability can still be checked. In a world where “stored” is usually a private claim, that shift alone is a meaningful piece of engineering.
@Walrus 🦭/acc #Walrus $WAL

Institutions rarely ask for magic. They ask for boundaries. A trading desk wants privacy because intention is information. A compliance team wants auditability because rules exist for a reason. An operations team wants fewer moving parts because every extra wrapper becomes a new failure mode. And the technology team wants familiar tooling because time is always the tightest budget.
Dusk is built around those constraints, not in spite of them.
Founded in 2018, Dusk is a Layer 1 blockchain designed for regulated and privacy-focused financial infrastructure. Its core idea is simple to say and hard to execute: privacy and auditability should coexist by design. Not as add-ons. Not as “maybe later.” As defaults.

That mindset becomes clearer when you look at how Dusk is restructuring itself. Dusk is evolving into a three-layer modular stack. Modular just means each layer specializes, instead of forcing one layer to do everything.
At the base is DuskDS, the data and settlement layer. This is where consensus, staking, data availability, settlement, and a native bridge live. Settlement is the moment a transaction becomes final. Data availability means the network keeps the data needed for verification accessible. DuskDS is also described as using a MIPS-powered pre-verifier that checks state transitions before they hit the chain, avoiding a long fault window like the one people associate with some optimistic rollup designs.
Above that sits DuskEVM, the EVM application layer. The EVM is the Ethereum Virtual Machine, the most common environment for smart contracts. Smart contracts are programs that run on-chain and follow rules automatically. Solidity is the language many teams already use to write them. DuskEVM is meant to let developers and institutions deploy standard Solidity contracts with familiar tooling (wallets and developer frameworks), while settling on Dusk’s base layer. Dusk has stated that DuskEVM mainnet is launching in the second week of January.
This matters for institutions because integration is rarely glamorous, but it is often decisive. Many institutional teams will not spend months adapting to bespoke tooling if a standard path exists. Dusk’s own framing is that EVM compatibility reduces friction for integrations with wallets, exchanges, custodians, and service providers, and allows existing EVM applications to migrate with minimal code changes. It also claims that bespoke L1 integrations can take 6–12 months and cost far more than EVM deployments, while EVM integrations can be completed in weeks. Whether you treat those numbers as estimates or benchmarks, the underlying lesson is stable: standards shorten timelines.
The third layer, DuskVM, is described as a forthcoming privacy application layer for full privacy-preserving applications, using the Phoenix output-based transaction model and the Piecrust virtual machine as the privacy stack is extracted into its own layer. The names are less important than the intent: privacy is being treated as something that deserves its own engineering space.
But institutions don’t only ask, “Can it integrate?” They ask, “Can it protect sensitive activity without breaking compliance?”
That is where Hedger fits.
Dusk describes Hedger as a privacy engine built for the EVM execution layer, designed specifically for regulated financial use cases. It aims to enable confidential transactions and private balances while preserving auditability when required. Technically, Dusk says Hedger combines zero-knowledge proofs and homomorphic encryption. Zero-knowledge proofs let you prove correctness without revealing the private inputs. Homomorphic encryption allows computation on encrypted values, so certain operations can happen without exposing the underlying numbers.
This is a different posture from privacy systems that chase invisibility. Dusk’s posture is closer to controlled disclosure: private to the public, verifiable when authorized.

Hedger’s design is also positioned as “built for EVM,” not retrofitted to it. Dusk contrasts Hedger with Zedger, which was built for UTXO-based layers. UTXO is a transaction model that works like discrete “notes.” EVM systems are typically account-based, more like balances in accounts. Dusk explicitly notes that the account-based model prevents full anonymity (which it says Zedger still offers), but it positions Hedger as delivering transactional privacy with better EVM compatibility, performance, and architectural simplicity.
For institutional markets, that trade can be attractive. Many institutions do not need the strongest form of anonymity. They need confidentiality of amounts and balances, protection against information leakage, and an audit path that can satisfy oversight.
Dusk also connects Hedger to market structure features that institutions care about. It describes Hedger as laying the groundwork for obfuscated order books, which are meant to reduce market manipulation and protect participants from revealing intent or exposure. It also describes auditable confidential transactions, with holdings, amounts, and balances encrypted end-to-end. And it claims fast in-browser proving, with lightweight circuits enabling client-side proof generation in under two seconds.
Crucially, Dusk is testing this in public. Hedger Alpha is live for public testing on Sepolia testnet. In this alpha, users can create a Hedger wallet, shield test ETH into it, send confidential transfers between Hedger wallets, and unshield back to a standard EVM address. Dusk also states a key detail that sets expectations: sender and receiver are visible on-chain, while amounts and balances remain hidden. That kind of clarity is often what institutional teams look for first. Not perfection. Just a well-defined system boundary.
Institutions also care about whether a network’s “real-world asset” story is anchored to regulated reality. DuskTrade, planned to launch in 2026, is described as Dusk’s first RWA application, built in collaboration with NPEX, a regulated Dutch exchange holding MTF, Broker, and ECSP licenses. DuskTrade is positioned as a compliant trading and investment platform, bringing €300M+ in tokenized securities on-chain, with a waitlist opening in January. The point here isn’t the number as a headline. It’s what it implies: a system designed to sit under regulated issuance and trading workflows, where privacy and auditability are practical requirements.
Finally, institutions value simplicity in how value moves. Dusk describes one DUSK token fueling all three layers: staking, governance, and settlement on DuskDS; gas and fees on DuskEVM; gas for full privacy apps on DuskVM. It also describes a native, trustless bridge run by validators that moves value between layers without wrapped assets or custodians. In institutional operations, fewer representations and fewer intermediaries can mean fewer reconciliation problems and fewer risk questions.
So has Dusk “become the preferred choice” for institutions in a universal sense? That’s not something anyone can claim responsibly without hard, public allocation data. What can be said, based on Dusk’s stated design and roadmap, is that Dusk is deliberately assembling the ingredients institutions usually require: EVM familiarity, privacy that doesn’t abandon auditability, modular separation of responsibilities, a unified token model, and a regulated RWA path through a licensed venue.
In other words, Dusk is not trying to convince institutions to change how they think. It is trying to meet them where they already live: in a world of rules, audits, sensitive information, and tight timelines.
@Dusk #dusk $DUSK

A decentralized web sounds clean in theory. In practice, it gets messy the moment you try to store something real. Not a small transaction payload, not a few bytes tucked into a smart contract, but a heavy artifact that actually matters: a dataset, model weights, a video archive, a game world, an audit trail, a website bundle. These are the things people build with. They are also the things blockchains are not designed to carry in full, because blockchains replicate data broadly and pay for that replication forever.
Walrus is built in the shadow of that reality. It is a decentralized storage protocol for large, unstructured content called blobs. A blob is simply a file or data object that is not stored as rows in a database table. Walrus supports storing blobs, reading them back, and proving they remain available later. It is designed to keep content retrievable even when some storage nodes fail or behave maliciously, the kind of failure engineers call Byzantine faults. Walrus uses the Sui blockchain for coordination, payments, and availability attestations, while keeping blob contents off-chain. Only metadata is exposed to Sui or its validators. That separation is the first quiet clue about what Walrus believes: the chain should be a witness, not a warehouse.

Once you accept that a blockchain is a poor place to store multi-gigabyte files, the next question becomes sharper. If the blob is off-chain, what stops the story from becoming “trust a server” again? Web3 does not gain much if it ends with a different set of gatekeepers. Walrus answers by trying to make two things verifiable: what a blob is, and whether the system has accepted responsibility to keep it retrievable for a defined time window.
To make “what it is” verifiable, Walrus gives each blob an identity called the blob ID. It is not meant to be a casual filename. It is derived from the blob’s encoding and metadata. Walrus describes hashing the stored representations across shards and using an authenticated structure so that readers can verify that the pieces they receive match what was intended. At a human level, the blob ID is a way of saying: this is the exact thing I meant, and you can check that I am not quietly swapping it.
The encoding matters because Walrus is not built on the old instinct of full replication, where you keep complete copies everywhere and hope it feels safe. Full replication is easy to explain, but it becomes expensive quickly, especially when the blobs get large. Walrus uses erasure coding instead. It relies on a construction called RedStuff, based on Reed–Solomon codes. Erasure coding takes a blob and transforms it into many encoded pieces with redundancy. The point is that you can reconstruct the original even if some pieces are missing, which is what you need when some storage nodes are offline, slow, or malicious. Walrus describes an overhead in the range of roughly 4.5–5× the blob size. It is a price paid for survivability, but it is bounded. It does not explode with the number of nodes in the network. That boundedness is what makes “large data on a decentralized network” feel like an engineering decision rather than a fantasy.
Walrus doesn’t scatter random fragments without structure. It groups encoded symbols into units called slivers and assigns slivers to shards. Storage nodes manage one or more shards during a storage epoch. The epoch is important because decentralized systems need stability in order to coordinate. For a window of time, the system has a known committee of storage nodes, and the mapping of shards to nodes is defined. This gives both writers and readers a concrete answer to a basic question: who is responsible right now?
The assumption behind this is not perfection. Walrus expects that some portion of the system can misbehave. It assumes that more than two-thirds of shards are operated by correct storage nodes in a given epoch, and it tolerates up to one-third Byzantine shards. That assumption shapes the rest of the protocol. It shapes how many signatures are required for certification, how many slivers must be requested during reads, and why reconstruction is possible even when not everyone cooperates.

Still, correctness in storage is not only about nodes. It is also about clients. Clients can be buggy. They can be malicious. They can compute encoding incorrectly. Walrus treats that possibility as part of the world, not as an edge case. That is why it describes mechanisms for detecting inconsistencies and for marking a blob as inconsistent if the encoding cannot be reconciled. The idea is strict: if a blob ID does not resolve to a consistent meaning, then the safest outcome is for reads to resolve to None rather than letting the same reference produce different content for different readers. It is harsh, but it preserves the one thing a storage protocol cannot afford to lose: a shared meaning of identity.
If blob ID is about identity, the second big idea in Walrus is about responsibility. Walrus defines the Point of Availability, or PoA. PoA is the moment when the system takes responsibility for maintaining a blob’s availability. Before PoA, the uploader is responsible for ensuring the blob is properly uploaded. After PoA, Walrus is responsible for maintaining it for the availability period, and both PoA and the availability period can be observed through events on Sui. This is where decentralized storage starts to feel different from normal hosting. The promise is not only “I put it somewhere.” The promise becomes “the system publicly accepted responsibility at this moment, and the promise lasts until this time.”
The write path is designed to create that public responsibility in a way that does not depend on trusting the uploader’s word. A user acquires a storage resource on Sui that represents capacity and time. Storage, in this model, is not a vague subscription. It is an owned resource that can be split, merged, and transferred. The user encodes the blob, computes the blob ID, and registers it on-chain, which emits an event storage nodes listen for. The user then uploads metadata and sends slivers to the storage nodes responsible for the corresponding shards. Each storage node checks that what it receives matches the blob ID and checks that an authorized on-chain resource exists. If the checks pass, the node signs a statement that it holds the relevant data for that blob ID. The user aggregates enough signatures into an availability certificate and submits it to the chain. When verified, the chain emits the availability event that marks PoA. A private upload becomes a public, checkable milestone.
Reads in Walrus reflect the same philosophy: do not trust one path, verify what you can. A reader can fetch data directly from storage nodes, collecting enough slivers to reconstruct the blob. Walrus also supports optional infrastructure that speaks the language of the current web. Aggregators can reconstruct blobs and serve them over HTTP. Caches can reduce latency and reduce load on storage nodes by serving popular blobs repeatedly. Publishers can help users store blobs through Web2 technologies like HTTP, performing encoding and coordination steps on their behalf. The key detail is that these intermediaries are optional and not treated as trusted components. They exist to make the system usable, but correctness is grounded in the blob ID and verification rules, not in faith in the cache or the publisher.
This is also where privacy enters in a practical way. Walrus supports storage of any blob, including encrypted blobs. Since blob content stays off-chain, the chain only sees metadata and events, not the payload. But Walrus is careful about a boundary: it is not a key management system. It does not manage encryption or decryption keys. It can store sealed data, but it does not decide who holds the seal. That separation is healthy because key management is its own hard problem, and forcing it into the storage layer often creates new trust bottlenecks. With Walrus, builders can choose how they handle access control and keys, while using Walrus as the storage substrate.
All of this is coordinated through Sui smart contracts. The chain holds the Walrus system object for the epoch, committee metadata, available space, and price per unit of storage. It mediates how storage resources are purchased and how availability events are emitted. Walrus also describes token-based operations through WAL and its subdivision FROST, where delegated stake influences committee selection and WAL is used for storage payments, with rewards distributed across epochs. Regardless of how someone feels about token systems culturally, the functional point is straightforward: a decentralized storage network is an ongoing service, and it needs a mechanism to select operators and pay them to keep serving data over time.
What Walrus does not try to be is part of what makes it believable. It does not reimplement a global low-latency CDN; it aims to remain compatible with CDNs through caches. It does not reimplement a full smart contract execution platform; it relies on Sui for coordination and resource management. It does not pretend to be a complete private storage ecosystem with key infrastructure; it supports encrypted blobs but leaves key management to other systems. The protocol stays focused on a narrower claim: store large blobs in a decentralized way, keep them retrievable under Byzantine conditions, and make identity and availability verifiable through cryptographic commitments and on-chain events.
In the end, Walrus is not trying to make data lighter. It is trying to make data governable. It treats storage as a relationship between identity, time, and responsibility. The blob ID anchors meaning. The Point of Availability anchors obligation. The availability period anchors time. Erasure coding anchors resilience. And the chain anchors public verification without swallowing the payload. It is a design that quietly acknowledges something the web often forgets: the hardest part of storing important things is not copying them. It is keeping the promise that they can still be found.
@Walrus 🦭/acc #Walrus $WAL

In finance, the most expensive leaks are not always security failures. Often, they are coordination failures. Value moves in one system, fees are paid in another, settlement happens somewhere else, and compliance lives in a different universe entirely. The result is a trail of wrappers, intermediaries, and exceptions. Everyone spends time reconciling what should have been simple.
Dusk’s architecture reads like a refusal to accept that fragmentation as normal.
Founded in 2018, Dusk is a Layer 1 blockchain designed for regulated and privacy-focused financial infrastructure. Its goal is not privacy as a rebellion against oversight. It is privacy with auditability built in by design, aimed at institutional-grade finance, compliant DeFi, and tokenized real-world assets. That framing is important, because regulated finance does not need secrecy. It needs controlled visibility.

When people talk about a “closed-loop ecosystem,” they often mean something vague. In Dusk’s case, it can be described more plainly: a system where the same native asset powers the network across its layers, where value moves between those layers through a native bridge rather than external custodians, and where privacy features are designed to coexist with compliance needs rather than compete with them. Dusk calls that native asset DUSK, and it explicitly positions DUSK as the single token that fuels its full stack.
Dusk is evolving into a three-layer modular architecture. Modular just means the system is split into layers that each do one job well, instead of forcing one layer to do everything. At the base is DuskDS, the consensus, data-availability, and settlement layer. This is the layer that handles the final record: staking, governance, settlement, and the native bridge. Consensus is how nodes agree on what is true. Data availability is about ensuring the information needed to verify the system is accessible. Settlement is when transactions become final.
On top of that base sits DuskEVM, an EVM-compatible application layer. The EVM is the Ethereum Virtual Machine, the most widely used standard environment for smart contracts. Smart contracts are programs that run on-chain and execute rules automatically. Solidity is the common language used to write them. DuskEVM is designed to let developers and institutions deploy standard Solidity smart contracts while settling on Dusk’s Layer 1. Dusk has stated that DuskEVM mainnet is planned for the second week of January, and it frames this as a way to reduce integration friction and accelerate rollout of compliant DeFi and real-world asset applications using standard Ethereum tooling.
A third layer, DuskVM, is described as a forthcoming privacy application layer for full privacy-preserving applications, using Dusk’s Phoenix output-based transaction model and the Piecrust virtual machine as it is extracted into its own layer. The specific names matter less than the direction: privacy is treated as something worthy of its own dedicated space, rather than being squeezed into a single execution environment.
Now to the “closed loop” part.
Dusk states that a single DUSK token fuels all three layers. On DuskDS, DUSK is used for staking, governance, and settlement. On DuskEVM, DUSK is used as gas and transaction fees for Solidity applications. On DuskVM, DUSK is used as gas for full privacy-preserving applications. Gas, put simply, is the fee users pay to execute transactions and smart contracts.
That one-token model is not just a convenience. It is an architectural choice that tries to reduce the most common kind of complexity in multi-layer systems: token fragmentation. When each layer has its own asset, you often get wrapped representations, external bridges, and custodians that become part of the daily workflow. In regulated contexts, every extra representation can create extra questions: What exactly is this token? Who issues it? What is the redemption mechanism? What risks are introduced by intermediaries? Who is accountable when something breaks?
Dusk’s stated design tries to keep value movement inside the system’s own logic. It describes a validator-run native bridge that moves value between layers without wrapped assets or custodians. In other words, it aims to make cross-layer movement a first-class network function rather than an outsourced dependency. Dusk also states that ERC20 and BEP20 DUSK will be migrated to DuskEVM, with DUSK on DuskEVM becoming the standard for exchanges and users, and that validators and nodes simply run a new release with no action required from stakers while balances remain intact. Even if you treat any migration as a process that needs careful execution, the intention is coherent: unify liquidity and usage around the same token inside the same stack.

If DUSK is the “circulatory system,” then the layers are the organs. But a closed loop needs more than circulation. It needs rules about what can be seen, and by whom.
This is where Dusk’s privacy engine, Hedger, fits into the same loop instead of sitting outside it as a separate product. Dusk describes Hedger as a privacy engine purpose-built for the EVM execution layer, designed to enable privacy-preserving yet auditable transactions on EVM using zero-knowledge proofs and homomorphic encryption, specifically for regulated financial use cases. Zero-knowledge proofs let you prove something is correct without revealing the underlying private data. Homomorphic encryption lets certain computations happen on encrypted values without decrypting them first. The promise is not that nothing can be checked. The promise is that sensitive financial data does not need to be exposed to the public in order for correctness and compliance to exist.
Dusk also distinguishes Hedger from Zedger: Zedger was built for UTXO-based layers, while Hedger is built for full EVM compatibility and integrates directly with standard Ethereum tooling. Dusk is explicit that the EVM’s account-based model prevents full anonymity, which it says Zedger still offers, but it positions Hedger as delivering transactional privacy with better compatibility, performance, and simplicity for DuskEVM. That distinction matters in a compliance-privacy ecosystem, because regulated markets often care less about “disappearing” and more about controlling what is revealed.
Hedger is not just described in theory. Hedger Alpha is live for public testing, deployed on the Sepolia testnet. Sepolia is an Ethereum test network used for experimentation, not real funds. In this alpha, Dusk states that you can shield (deposit) Sepolia ETH into a Hedger wallet, send confidential transactions between Hedger wallets, and unshield (withdraw) back to an EVM address. Dusk also states an important boundary: sender and receiver are visible on-chain, but amounts and balances remain hidden. This is a practical definition of “confidential” in the current testing phase. It is privacy with structure, not privacy as a fog.
And then there is the compliance side of the loop, which Dusk ties to real-world infrastructure rather than abstract ideals.
DuskTrade is planned to launch in 2026 and is described as Dusk’s first real-world asset application, built in collaboration with NPEX, a regulated Dutch exchange holding MTF, Broker, and ECSP licenses. DuskTrade is described as a compliant trading and investment platform bringing €300M+ in tokenized securities on-chain, with a waitlist opening in January. NPEX is described as a licensed stock exchange operating as a Multilateral Trading Facility (MTF) in the Netherlands. An MTF is a regulated trading venue. That matters because it places Dusk’s technology in a context where licensing, auditability, and procedural accountability are part of the environment.
Dusk’s modular stack is also described in a way that attempts to keep compliance scope unified. It states that because NPEX’s licenses apply to the full stack, institutions can issue, trade, and settle real-world assets under one regulatory umbrella, bringing compliant DeFi to market faster. It also describes “one-time KYC across all apps on Dusk” and composability across apps using the same licensed assets. Whether one agrees with the ambition or not, it is clearly aimed at reducing the typical compliance sprawl where identity checks, permissions, and asset status vary from app to app.
This is the essence of the closed loop Dusk is trying to shape:
Value is meant to move through DUSK across the stack without splintering into wrapped stand-ins. Settlement and finality live in DuskDS. Execution and developer access live in DuskEVM, where Solidity and Ethereum tooling can be used. Privacy mechanisms like Hedger are designed to live inside that EVM layer, so confidential transactions can exist in the same place developers already deploy. And the system is built with regulated workflows in mind, including a path toward tokenized securities through DuskTrade and a regulated partner like NPEX.
There is also a philosophical point hiding underneath the engineering. Public blockchains made radical transparency normal. But radical transparency is not automatically fairness. In markets, revealing too much can create manipulation, predatory behavior, and unnecessary exposure. Dusk’s framing suggests a different norm: privacy as a default condition for participants, combined with auditability as an authorized capability. “Private to everyone, yet auditable by regulators” is not only a slogan. It is a design constraint. It forces the system to treat compliance and confidentiality as two sides of the same mechanism.
Of course, none of this removes the need for careful implementation, governance, and operational discipline. Architecture can reduce complexity, but it cannot abolish responsibility. What it can do is decide where responsibility lives.
Dusk is trying to place it inside the network itself: one token, one bridging model, layers that specialize, and privacy tools that are built to coexist with regulated reality rather than deny it. If the effort succeeds, the result is not merely “DeFi with privacy.” It is closer to a compliance-privacy ecosystem where the rails, the rules, and the confidentiality tools are designed to fit together without constant translation.
@Dusk #dusk $DUSK

A friend once told me that the internet is full of ghosts. Not scary ones. Quiet ones. Old links that lead nowhere. Images that used to load. Research files that existed yesterday and became a 404 today. The strange part is that these disappearances rarely feel dramatic. They feel ordinary. We shrug. We move on. And slowly, we accept that the web forgets.
Walrus is built around a different idea: forgetting should not be the default.
Walrus is a decentralized storage protocol for large, unstructured content called blobs. A blob is simply a file or data object that is not stored as rows in a database table. Walrus supports storing blobs, reading them back, and proving they are available later. It is designed to keep content retrievable even when some storage nodes fail or behave maliciously, a class of problems often described as Byzantine faults. Walrus uses the Sui blockchain for coordination, payments, and availability attestations, while keeping blob contents off-chain. Only metadata is exposed to Sui or its validators.

That last part matters more than it first appears to. Many people hear “blockchain” and assume the file itself is going on-chain. Walrus is explicit that it does not do that. The chain is not the warehouse. The chain is the record keeper. It holds the receipts: what was claimed, when responsibility began, and how long the promise is meant to last.
The first time you look at Walrus, it can feel like it is speaking in cold, precise nouns. Blob. Blob ID. Sliver. Shard. Epoch. Committee. But those nouns are doing something human. They are trying to turn a messy world into a world with references.
The blob is the thing you care about: a dataset, a model file, a video, a bundle of web assets. Walrus does not want to break it into tiny on-chain fragments. It wants to keep it whole in meaning, even if it becomes many pieces in storage.
So Walrus encodes the blob. Under the hood it uses erasure coding, via its construction called RedStuff (based on Reed–Solomon codes). Erasure coding is a way of splitting and expanding data so it can be reconstructed even if some parts are missing. The blob becomes many encoded pieces, grouped into slivers, and those slivers are distributed across shards. Storage nodes manage shards during a storage epoch, which is a defined period of time where responsibilities are stable. This is not just for elegance. It’s because stability is what makes coordination possible in a decentralized network. If shard assignments changed constantly, “who should store what” would be a moving target.
In the center of all of this sits a small but powerful concept: identity.
Walrus gives each blob a blob ID, derived from the encoding and metadata. In plain terms, it is meant to be an identity you can verify, not a filename you can rename. Walrus describes using hashes and an authenticated structure (a Merkle tree over shard representations) so that storage nodes and clients can authenticate the pieces they receive. This is the difference between “someone gave me a file” and “someone gave me the file I asked for.”
But identity alone does not stop the web from forgetting. For that you need time, responsibility, and a way to prove both.
Walrus defines the Point of Availability (PoA) for each blob ID. PoA is the moment when the system takes responsibility for maintaining the blob’s availability. Before PoA, the uploader is responsible for getting the blob into the system. After PoA, Walrus is responsible for maintaining it for the availability period. Both PoA and the availability period are visible through events on Sui.
This is where Walrus starts to feel less like “storage” and more like “governance of storage.” It says: we will not pretend availability is a vague feeling. We will make it a recorded fact with boundaries.
The way PoA happens is also shaped like a public promise. A user acquires storage as a resource on Sui. That storage can be owned, split, merged, and transferred. It behaves like an asset with rules, not like a hidden subscription line item. Then the user registers the blob ID and uploads the encoded pieces off-chain to the storage nodes responsible for the relevant shards. Storage nodes verify what they receive and sign statements that they hold the expected slivers. Those signatures can be aggregated into an availability certificate that is submitted on-chain. When verified, the chain emits the availability event that marks PoA.
The result is subtle but important: you can prove availability without moving the whole blob again. You can prove it by referencing events and identities.
Walrus also accepts a truth that many systems try to hide: intermediaries exist. People want HTTP. People want caches. People want fast reads.
So Walrus supports optional infrastructure. Aggregators can reconstruct blobs from slivers and serve them over HTTP. Caches can reduce latency and reduce load on storage nodes by serving hot content repeatedly. Publishers can help users upload blobs through Web2-style flows, receiving a blob over HTTP, encoding it, distributing slivers, collecting signatures, and handling the required on-chain steps.
But Walrus does not treat these helpers as trusted authorities. They are conveniences, not guardians of truth. The design tries to keep verification possible even when the delivery layer is “normal web.”
There is another part of the story that feels almost philosophical: how Walrus treats mistakes.

Walrus treats clients as untrusted, because bugs and malicious behavior both exist. It describes mechanisms that can detect inconsistent encoding and mark a blob as inconsistent, so reads resolve cleanly (returning None) rather than producing a world where the same blob ID quietly yields different content to different readers. This is a harsh rule, but it is also a merciful one. A system that admits “this reference does not resolve” can preserve meaning better than a system that lets ambiguity spread.
At some point, you realize Walrus is not trying to be everything. It does not claim to replace CDNs. It tries to remain compatible with them through caches. It does not claim to be a full smart contract platform. It relies on Sui smart contracts for coordination, resource management, and payments. It supports storing encrypted blobs, but it does not pretend to be a distributed key management system. Those boundaries are part of its credibility. They keep the promise narrow enough to be testable.
And that brings us back to the ghost problem. The web forgets because forgetting is easy. The hard part is building a system where remembering is not accidental.
Walrus tries to make remembering explicit. A blob has an identity. Availability has a timestamp. Storage has a lifetime. Renewal is possible without re-uploading content. Responsibilities are organized into epochs. The chain records the commitments. The network holds the encoded pieces. Optional infrastructure makes it usable. Verification keeps it honest.
My view on Walrus: I see Walrus as a practical attempt to give the web a better memory. Not by claiming “forever,” but by making availability something you can point to, check, and renew. I like that it separates content from coordination, and that it treats time as part of the contract instead of a hidden assumption. If Walrus succeeds, it won’t be because it promised miracles. It will be because it made storage feel less like hope and more like a receipt.
@Walrus 🦭/acc #Walrus $WAL

There is an old lesson in engineering that also applies to people: what you build should fit the world you want to live in. If it doesn’t fit, you can force it for a while. But eventually the world pushes back.
Privacy on blockchains has often worked like that. Many systems were built around a specific transaction model, and then asked everything else to adapt. It can work, but the cost is paid in integration pain, unfamiliar tooling, and awkward compromises that show up later when real users arrive.
Dusk is trying to avoid that kind of friction by building privacy where the industry already builds applications: the EVM.

Dusk, founded in 2018, is a Layer 1 blockchain designed for regulated and privacy-focused financial infrastructure. It is positioned for institutional-grade financial applications, compliant DeFi, and tokenized real-world assets, with privacy and auditability built in by design. As Dusk evolves into a modular architecture, it introduces Hedger, a privacy engine purpose-built for the EVM execution layer.
To understand why this matters, it helps to define the environment Hedger is built for.
The EVM, or Ethereum Virtual Machine, is the most widely used standard for running smart contracts. Smart contracts are programs that execute on-chain and follow preset rules automatically. Solidity is the most common language used to write them. Around the EVM, a huge ecosystem of tools exists: developer frameworks, wallets, audit practices, and exchange integration habits. This ecosystem is not perfect, but it is familiar, and familiarity matters when real money and regulated instruments are involved.
Dusk’s EVM-compatible layer is called DuskEVM, described as an execution layer that settles on Dusk’s Layer 1, which in the modular stack is DuskDS. Dusk has stated that DuskEVM mainnet is planned to launch in the second week of January. The goal, as described, is to remove friction for integrations and unlock compliant DeFi and real-world asset applications by using standard Ethereum tooling.
Hedger is meant to live inside that world.
Dusk describes Hedger as enabling privacy-preserving yet auditable transactions on EVM using a combination of zero-knowledge proofs and homomorphic encryption, designed specifically for regulated financial use cases. Zero-knowledge proofs allow a party to prove something is correct without revealing the underlying private data. Homomorphic encryption allows computation on encrypted values without first revealing them.
In plain terms, Hedger is not just trying to hide information. It is trying to hide the right information while still allowing the system to prove correctness, and still allowing auditability when required.
Dusk also makes a clear distinction between Hedger and Zedger. Zedger was built for UTXO-based layers. Hedger is built for full EVM compatibility.
That difference is not a branding detail. It is a transaction-model detail.
A UTXO model represents value as discrete outputs, like digital “notes.” You spend outputs and create new ones. Many privacy systems that aim for full anonymity work naturally in that model because the transaction structure is already built around outputs that can be mixed and reassembled.
The EVM, on the other hand, is typically account-based. It looks more like balances in accounts. That model has different constraints. Dusk even acknowledges one implication: the EVM’s account-based model prevents full anonymity, which it says Zedger still offers. But Dusk’s message is that Hedger can still deliver complete transactional privacy in a way that is scalable, auditable, and easy to adopt from day one because it integrates with standard Ethereum tooling.
This is the trade Dusk is choosing: privacy that fits the dominant execution environment, rather than privacy that requires abandoning it.

Dusk’s description of Hedger’s design highlights a layered cryptographic approach. Many DeFi privacy systems rely solely on zero-knowledge proofs. Hedger is described as combining multiple cryptographic techniques to balance privacy, performance, and compliance, including homomorphic encryption and zero-knowledge proofs, and a hybrid UTXO/account model that supports cross-layer composability and integration with real-world financial systems.
That word “composability” often gets used loosely. Here, it is practical. It means the ability for applications and financial primitives to connect and interact without breaking the rules of the system. In financial infrastructure, composability can help reduce operational overhead. But it can also increase risk if it is not controlled. Dusk’s framing is that composability should exist in a licensed and compliance-aware environment, not in a vacuum.
Hedger is also presented as a foundation for specific market features. Dusk states it lays the groundwork for obfuscated order books, which can be important for institutional trading because they prevent market participants from revealing intent or exposure in ways that can lead to manipulation. It also states that transactions are auditable by design, and that holdings, amounts, and balances remain encrypted end-to-end while transactions stay auditable. And it claims fast in-browser proving, with client-side proof generation in under two seconds using lightweight circuits.
These are strong technical claims, and Dusk made the system testable through Hedger Alpha.
Hedger Alpha is now live for public testing and is deployed on the Sepolia testnet. Sepolia is an Ethereum test network used for experimentation. Dusk’s description of the alpha says it enables confidential transactions and private balances: private to everyone, yet auditable by regulators. It also includes an important limitation that grounds expectations: sender and receiver are visible on-chain, but the amounts and balances remain hidden.
In other words, the alpha does not attempt to erase traceability. It attempts to remove unnecessary exposure of financial details, while keeping a path for verification where required.
If you step back, the philosophical difference between Hedger and many privacy narratives becomes clearer. Some privacy systems start from the question, “How do we disappear?” Hedger starts from a different question: “How do we keep sensitive financial data confidential while still proving we followed the rules?”
That second question is the one regulated markets live with every day.
Dusk’s broader direction includes DuskTrade, planned for 2026, built with NPEX, a regulated Dutch exchange. If you imagine a world where tokenized securities are issued and traded on-chain, then privacy cannot mean chaos. It must mean confidentiality with structure. Hedger’s “built for EVM” approach is Dusk’s attempt to make that structure compatible with the tooling and workflows the industry already uses.
Privacy, in that sense, becomes less like a mask and more like a window shade. It is not there to deny reality. It is there to control what is exposed, so markets can function without turning every participant into a public target.
@Dusk #Dusk #dusk $DUSK

On a late evening, a small team finishes a dataset they are proud of. It is not glamorous data. It is the kind that takes patience. Clean labels. Clear provenance notes. Fewer duplicates. The sort of work people only notice when it is missing. The team wants to share it with builders, and maybe charge for access later. But first, they want one simple thing: the dataset should not vanish behind a dead link.
That is how they end up thinking about storage as a promise, not a folder.
Walrus is a decentralized storage protocol built for large, unstructured content called blobs. A blob is simply a file or data object that is not stored like rows in a database table. Walrus supports storing blobs, reading them back, and proving they remain available later. It is designed to keep content retrievable even if some storage nodes fail or act maliciously. That kind of failure is often called a Byzantine fault. Walrus also uses the Sui blockchain for coordination, payments, and availability attestations, while keeping blob contents off-chain. Walrus is explicit that only metadata is exposed to Sui or its validators.
The team’s first surprise is that Walrus does not treat storage like an eternal vow. It treats it like a contract with dates. In Walrus, storage space is represented as a resource on Sui that can be owned, split, merged, and transferred. In plain language, storage is something you can hold and move, like a ticket that grants capacity for a period of time. That feels closer to reality. Disks are real. Bandwidth is real. Time is real.
They upload the dataset as a blob, but the upload is not only an upload. Walrus encodes the blob using erasure coding. Walrus uses a construction called RedStuff, based on Reed–Solomon codes. Erasure coding is a way to add redundancy without storing full copies everywhere. It turns one big file into many encoded pieces so the original can be reconstructed even if some pieces are missing. Walrus describes the storage overhead as roughly 4.5–5× the original blob size. It is a fixed multiple. It does not balloon just because there are many nodes.

The team reads that and pauses. It is not “free,” but it is not magical either. It is a measured cost for survivability. They find comfort in that honesty.
Then comes the part that feels almost philosophical: Walrus defines a moment when the system takes responsibility. Walrus calls it the Point of Availability (PoA). Before PoA, the uploader is responsible for getting the blob into the system. After PoA, Walrus is responsible for maintaining availability for the availability period, and both PoA and the availability period can be observed through events on Sui. In other words, availability becomes a public receipt, not a private claim.
This is where the story changes from “I uploaded a file” to “I can prove it is supposed to be retrievable.” If a partner asks, “how do I know the dataset is really there?” the team can point to what Walrus records on-chain: the event that marks PoA and the lifetime window attached to it. They do not need to send the whole dataset just to prove it exists.
When the team digs deeper, they learn why Walrus can make this promise without trusting one server. Walrus runs with a committee of storage nodes that changes over time in epochs. On Mainnet, Walrus has been described as operated by a decentralized network of over 100 storage nodes, with Epoch 1 beginning on March 25, 2025, and the Mainnet announcement dated March 27, 2025.
The storage nodes hold encoded pieces of blobs and serve them when asked. Walrus is designed to tolerate Byzantine behavior under its assumptions, so no single node gets to be “the truth.” The data is spread. The system is meant to keep working even when parts misbehave.

To make the web feel normal, Walrus also allows optional infrastructure. Aggregators can reconstruct full blobs and serve them over HTTP. Caches can keep popular blobs close to users and reduce load on storage nodes. Publishers can help upload blobs using Web2 methods like HTTP and handle the steps of encoding, distributing pieces, collecting signatures, and submitting the right on-chain actions. The important detail is that these intermediaries are optional, and correctness can still be verified by clients. This is how Walrus tries to stay familiar without becoming a new centralized gatekeeper.
At some point, the team asks a practical question: “What does it cost to store this blob?” On Mainnet, storing uses the network’s token, WAL. WAL is also used for delegated stake to storage nodes, which influences committee selection, and for distributing rewards to nodes and stakers each epoch. That is the economic engine behind the availability promise.
And because they are human, they check the price.
As of January 10, 2026 (Dhaka time), WAL was trading around $0.144 per WAL. The exact number moves constantly, and different sites show slightly different snapshots. For example, Binance’s price page showed WAL around the mid-$0.14 range, along with an estimated market cap and 24-hour volume that also change in real time. CoinMarketCap and CoinGecko list similar live prices around the mid-$0.14 range as well.
The team does not treat this as an investment signal. They treat it as a budgeting input. If storage is a timed resource, then price is part of planning. They also notice that Walrus describes FROST as a subdivision of WAL, where 1 WAL = 1,000,000,000 FROST, which makes it easier to price tiny storage actions without awkward decimals.
They run a small experiment. They store a smaller sample blob first. They watch the process like a ritual: acquire storage space as a Sui resource, compute the blob’s identity, upload the encoded pieces, collect signatures from storage nodes, then submit a certificate so the chain can emit the event that marks PoA. The moment PoA arrives, something shifts. It feels less like “my file is on a server somewhere” and more like “the system has accepted responsibility for this reference.”
A week later, a teammate tries to fetch the blob through a cache. It arrives quickly over HTTP, like an ordinary download. But the team keeps a habit: they verify. Walrus uses cryptographic commitments (Merkle-tree-based authentication for sliver hashes) so clients can check that what they retrieved matches the intended blob identity. Verification is the quiet discipline that keeps convenience from turning back into blind trust.
They also learn about the system’s way of handling mistakes. Walrus treats clients as untrusted, because bugs and malicious behavior are both possible. If a blob is incorrectly encoded, Walrus describes mechanisms for detecting inconsistencies and marking a blob as inconsistent, so reads can resolve to None rather than producing chaotic, conflicting results. It is not sentimental about bad data. It prefers a clear outcome to a confusing one.
By the end of the month, the team has a new understanding of what it means to “publish” data. Publishing is not only making bytes reachable. It is making responsibility legible. It is putting a timestamp on the promise. It is allowing third parties to verify, without asking permission from the publisher.
That is why Walrus keeps returning to the phrase “data markets.” Markets need more than storage. They need identity, receipts, time bounds, and enforceable expectations. Walrus tries to provide the storage layer where those expectations can be checked: blob identity anchored in cryptography, availability anchored in on-chain events, and ongoing service anchored in periodic payments.
The team’s dataset is still just a dataset. It did not become holy because it lives on a decentralized network. But it became easier to talk about honestly. It has a reference. It has a visible availability window. It has a renewal path. It has a clear cost model. In a world where data often disappears quietly, that kind of honesty feels like progress.
And maybe that is the most human part of the protocol. Walrus does not promise forever. It promises a system where “keeping” is a real act, with receipts, incentives, and time you can point to. Like a lighthouse that does not guarantee calm seas, but does guarantee you can still see the shore.
@Walrus 🦭/acc #Walrus $WAL