Mr_Green个

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

Some things in life are easiest to value when they are visible. A full pantry. A bright streetlight. A sturdy bridge. Storage is stranger. It is mostly invisible. We notice it only when it fails, like memory itself. Yet decentralized storage forces us to count it, because no single institution can silently absorb the cost. In a network of independent operators, the invisible must become measurable, or the promises become poetry with no budget.
Walrus is a decentralized storage protocol built for large files and other unstructured data, which it calls blobs. A blob is simply data that is not stored like rows in a table. With Walrus, you can store blobs, read them back, and prove they are still available later.

Walrus is designed to keep data retrievable even if some storage nodes go offline or act maliciously. This kind of failure is often called a Byzantine fault. Walrus uses the Sui blockchain to coordinate the system, handle payments, and record availability proofs. The actual blob content stays off-chain on Walrus storage nodes. Only the blob’s metadata is ever exposed to Sui or its validators.
If you want to understand Walrus deeply, you can start with a quiet question: what does the system choose to measure?
Walrus measures storage not as a vague “plan,” but as a resource with a lifetime. Storage space is represented as Sui objects that can be acquired, owned, split, merged, and transferred. This makes storage feel less like a cloud subscription and more like a held promise. It also means that time becomes part of the arithmetic. You are not only paying for bytes. You are paying for the right for those bytes to remain retrievable through an availability period.
Walrus also measures availability as a public event. It defines a Point of Availability (PoA), the moment when Walrus takes responsibility for maintaining a blob’s availability. Before PoA, the uploader is responsible. After PoA, Walrus is responsible for the availability period, and both PoA and the availability period are observable through events on Sui. This is a moral shift as much as a technical one. It replaces “trust me” with “you can check.”

But measurement is not only about responsibility. It is also about redundancy. Walrus uses erasure coding through RedStuff, based on Reed–Solomon codes, and describes an expansion of blob size by about 4.5–5×. This overhead is the price paid for resilience: storing enough encoded pieces across shards so the blob can be reconstructed even when some storage nodes are down or malicious. In other words, Walrus chooses to measure the cost of survivability directly, not hide it.
The system also measures participation through epochs and committees. Storage epochs organize the network’s committee of storage nodes and shard assignments over time. Walrus assumes more than two-thirds of shards are operated by correct storage nodes within each epoch, tolerating up to one-third Byzantine shards. It uses WAL for delegated stake and for payments, and it describes rewards distributed each epoch to storage nodes and stakers, mediated by Sui smart contracts. These are not merely “tokenomics.” They are a measurement system for ongoing work: storing, serving, and staying accountable in a world where nobody is obligated to be kind.
Seen this way, Walrus is not trying to make storage magical. It is trying to make storage governable. Governance requires measurement. Measurement requires humility. You cannot promise availability without counting what it costs, who bears it, and how long the promise lasts.
And perhaps this is where the philosophy sharpens. We often treat data as weightless, because copying feels effortless. But copying is not the same as keeping. Keeping requires space. Keeping requires electricity. Keeping requires operators who do not walk away. Walrus is a reminder that “memory” is a physical and economic act, even when it lives in the cloud.
There is also an ethical edge in the way Walrus separates what the chain sees from what the chain does not. Walrus keeps blob contents off-chain and exposes only metadata on Sui. This respects scale and privacy possibilities, but it also implies a discipline: the chain should record commitments and accountability, not swallow the entire world. The chain becomes a witness, not a warehouse.
In a future where data markets grow louder, it may be tempting to chase speed and spectacle. Walrus instead leans into the quieter work: proving what was stored, showing when responsibility begins, pricing storage as a timed resource, and distributing encoded pieces so that a blob’s existence does not depend on any single keeper. It is the difference between shouting “this will last” and building a system where lasting can be renewed, measured, and verified.
Because in the end, the honest question is not “can we store everything.” The honest question is “what can we keep, and what are we willing to pay, together, to keep it?”
@Walrus 🦭/acc #Walrus $WAL