Angel Alizeh Ali

@Dusk An “auditor-ready” blockchain is a slightly odd phrase, because auditors don’t really want novelty. They want boring things: clear evidence trails, consistent rules, and the ability to reproduce the story of what happened without asking ten different people for screenshots. What’s changing lately is that crypto is being pulled into the same accountability routines that traditional finance has lived with for decades. In Europe, MiCA is now less a talking point and more a set of deadlines, and supervisors have been explicit about what they expect from firms that aren’t authorized: don’t keep operating in the gray—prepare orderly wind-downs where transitional periods have ended. When oversight gets real, the question stops being “is this innovative?” and becomes “can we defend this in an audit?”

That shift is why Dusk Network feels genuinely relevant right now, not just “something to watch.” Most chains still talk like the main goal is speed or fees. Dusk keeps coming back to a different question: can regulated finance actually use a public network without exposing everyone’s business to the world? In its own words, it’s aiming for institutions to issue and manage financial instruments on-chain while building in disclosure, KYC/AML, and reporting rules at the protocol level. That’s not a promise that everything magically becomes compliant, but it’s an honest declaration of priorities. If you’re building for regulated markets, those priorities matter more than clever throughput claims.

A lot of projects still equate “auditability” with “make everything public.” That’s a developer’s answer, not an operator’s. In a real institution, full transparency can be a liability: it exposes counterparties, positions, and customer relationships. Yet full secrecy doesn’t work either, because an audit is controlled scrutiny. The practical target is selective visibility—what needs to be seen can be seen, and what doesn’t need to be seen stays out of the daylight. Dusk’s relevance lives in that middle space, because it’s designed around confidential transactions and smart contracts while still treating rule-following as a first-class requirement rather than a bolt-on.

The tricky bit is doing selective visibility without turning the chain into a surveillance machine. Dusk’s bet is that you can hide sensitive details and still prove the rules were followed, using zero-knowledge techniques to separate “what happened” from “how to verify it.” In plain terms, the chain should let you prove a claim—“this transfer met the eligibility rule,” “this instrument wasn’t sent to a restricted party,” “this limit wasn’t breached”—without broadcasting the underlying business data to everyone with a block explorer. Dusk’s older technical materials explicitly position its architecture around privacy plus compliance needs in tokenization and lifecycle management, which is exactly the stuff auditors end up caring about when real assets, not just memes, are involved.

Under the hood, Dusk makes design choices that support that stance. Its Phoenix transaction model uses a UTXO-style approach where funds exist as discrete “notes,” and transactions consume and create notes rather than constantly rewriting one running balance. That matters because long account histories can turn into a permanent dossier: one address becomes “one life story.” A note-based model gives you cleaner units for evidence. You can prove properties about a note, or a set of notes, without publishing every detail that made that proof possible. It’s not automatically simpler—privacy systems rarely are—but it’s a structure that can be argued about in concrete, testable terms, which is what audits thrive on.

Dusk’s relevance also shows up in its execution details, not just its theory. Audits don’t stop at cryptography. They touch upgrade processes, rollback plans, and whether a network’s behavior is predictable across time. Dusk’s mainnet communications read like staged operations rather than a single big-bang announcement, including a schedule for producing its first immutable block on January 7, 2025. That kind of sequencing is unsexy, but it signals an awareness that “going live” is an operational event with consequences, not a banner moment for social media.

There’s a final reason Dusk keeps popping up in conversations about institutional crypto: it’s trying to make selective disclosure a normal workflow rather than an emergency exception. In my view, that’s the difference between a chain that merely tolerates audits and one that is built to survive them. “Auditor-ready” starts to look less like a slogan and more like a set of habits: predictable controls, explainable outcomes, and privacy that protects legitimate business data without blocking legitimate oversight. If the industry really is moving from “can we do it?” to “can we defend it?”, Dusk’s approach feels relevant because it treats that second question as the main design brief, not an afterthought.

@Dusk #dusk $DUSK #Dusk

@Walrus 🦭/acc There’s a certain rhythm to decentralized storage that you don’t really notice until you try to describe it to someone else. In Walrus, that rhythm is explicit: time is chopped into epochs, and a committee of storage nodes is responsible for keeping data available during each epoch. That “clock-and-committee” framing is a big part of why the protocol has been showing up in more conversations lately—because it turns a messy, always-on problem (people come and go, networks glitch, nodes fail) into something the system can reason about in scheduled steps, without pretending the internet is perfectly reliable.

The “committee” piece is fairly intuitive once you stop thinking about storage as a vague cloud and start thinking about it as a job assignment. Walrus is operated by a delegated proof-of-stake model in which stake influences who ends up in the storage committee for an epoch. In the whitepaper, you can see the protocol’s baseline assumption: epochs have a static set of storage nodes, and a committee is formed based on how storage shards are assigned for that epoch (with the usual Byzantine-fault tolerance framing in the background). The practical consequence is that the network always has a current roster—an accountable group—rather than an undefined crowd.

The “clock” is where it gets more interesting, because time isn’t just for scheduling, it’s for promises. When you store a blob (Walrus’s basic unit of data), you’re not only uploading pieces of it to nodes; you’re also locking in how long that blob should remain available. The protocol leans on Sui as a control plane: metadata and the proof that the blob is available live on-chain, while the blob content itself stays off-chain on storage nodes. The write flow is deliberately ceremony-like: a client encodes the blob into redundant “slivers,” distributes them to the current committee, gathers a quorum of signed acknowledgements, and then publishes that certificate on Sui as a Proof of Availability. That on-chain certificate is the moment the system treats availability as real, not merely hoped for.

But the part that makes the clock-and-committee model feel like more than governance theater is what happens at the edges of time—epoch transitions. Most storage networks get awkward here. If you swap out who’s responsible for data, you risk a window where nobody’s clearly responsible, or where reads and writes race each other into confusion. Walrus tackles this with a staged transition approach described in its technical writing: during reconfiguration, writes are pushed to the incoming committee while reads can continue from the outgoing committee, avoiding a single fragile “handover instant” where everything flips at once. It’s one of those designs that sounds mundane until you remember how many outages, “missing file” moments, and half-synced states in distributed systems come from poorly managed transitions.

Why is everyone talking about this now, rather than letting it become one more clever paper people save and forget? It’s a timing thing, more than anything. More on-chain apps are running into the same wall: blockchains are great at agreement, but they’re not built to hold everyone’s photos, models, videos, and datasets forever. At the same time, AI work and media-heavy apps keep raising the bar for what “storage” even means—people want big files handled reliably, with clear rules around access and retention. Walrus lands right in the middle of that tension: keep the logic and guarantees on-chain, keep the heavy data off-chain. So it naturally shows up in conversations about availability, permanence, and storage that behaves more like a system with commitments than a dumb hard drive. Another part is simple momentum: the project has kept publishing concrete technical explanations (like its Proof of Availability mechanism) and rolling out ecosystem activity, which tends to draw builders first, and then the speculation crowd afterward.

I’ll admit I’m usually skeptical of systems that promise to make storage feel “programmable,” because that word can hide a lot of hand-waving. What I find more grounded here is the way Walrus makes obligations legible: a blob exists, it has metadata that’s checkable, it has a stated lifetime, and there’s a known committee responsible right now. If that committee changes, the protocol doesn’t pretend the change is instantaneous; it plans for overlap. In a space that often tries to wish away messy realities—latency, churn, incentives, human operators—there’s something reassuring about a design that says, plainly, “time passes, membership changes, and we’ll run the system accordingly.”

@Walrus 🦭/acc #walrus $WAL #Walrus

@Dusk There’s a quiet contradiction at the heart of bringing finance on-chain: the more valuable the activity gets, the less anyone wants it to be public. Traders don’t want strategies copied in real time. Corporates don’t want payroll and treasury moves mapped by competitors. Banks don’t want client positions broadcast to the world. For a long time, crypto shrugged and said, “That’s the point.” But the closer you get to regulated markets, the more that attitude reads like a category error. Transparency is useful. Total transparency is often incompatible with how real finance is run.

Timing is doing a lot of the work here. In Europe, the Markets in Crypto-Assets Regulation has moved past the “someday” stage and into everyday reality. The rules for e-money tokens and asset-referenced tokens kicked in on 30 June 2024, and the wider framework followed on 30 December 2024. Some firms also have a runway: the transition period for certain existing providers can stretch to 1 July 2026, and ESMA has laid out how different member states are handling that grandfathering window. Meanwhile, ESMA is reviewing the EU’s DLT Pilot Regime and openly discussing whether it should be extended, expanded, amended, or made permanent—language you simply don’t use if you think on-chain market infrastructure is a hobby.

In this environment, “privacy” stops meaning “make it untraceable” and starts meaning something more boring and more important: confidentiality with accountability. Institutions don’t need a chain that hides everything; they need a chain that keeps sensitive terms confidential while still proving compliance, ownership, and settlement outcomes to the parties who are allowed to know. A public ledger that leaks every trade and every position isn’t a neutral design choice for a broker or an issuer. It’s a business risk. You can’t really blame institutions for hesitating when the default architecture turns market activity into a live public feed.

This is exactly where Dusk becomes relevant in a way that many privacy projects don’t. The value is not “privacy” as a slogan, but privacy as market structure. Dusk’s positioning makes more sense when you notice how it behaves. It’s been pointed at regulated finance from the start, which is probably why it pushes for confidentiality that’s built into the network rather than a workaround layered on top. The mainnet rollout followed that same mindset: not a single “we’re live” moment, but a step-by-step process—onramping, genesis prep, cluster deployment—ending with the first immutable block targeted for 7 January 2025. You can’t call that proof of safety, but you can call it a preference for control and structure.

The sharper point is how Dusk tries to make regulated assets behave on-chain. Instead of telling institutions to “wrap” compliance around a token after it exists, Dusk pushes the idea that compliance logic and confidentiality belong in the asset’s rulebook. Its Confidential Security Contract standard, XSC, is positioned for issuing and managing tokenised securities while keeping sensitive terms confidential, and it’s framed in a way that maps to what issuers and intermediaries actually do: corporate actions, auditability, shareholder handling, and the messy reality that securities aren’t just free-floating tokens. I’m not under the illusion that a standard solves regulation, but standards do something quietly powerful: they give lawyers and risk teams something concrete to interrogate, and they give developers a common interface that doesn’t need to be reinvented for every issuance.

The most tangible signal that Dusk is not operating in a vacuum is the regulated plumbing it is assembling with real counterparties. In February 2025, Quantoz Payments, NPEX, and Dusk announced EURQ as a MiCAR-compliant digital euro structured as an electronic money token, explicitly aimed at enabling regulated finance activity on Dusk. On its face, that might sound like yet another euro stablecoin headline. The difference is the institutional context: NPEX describes itself as an MTF-licensed venue, and its announcement framed this as the first time such an exchange would use EMTs through a blockchain rail. That doesn’t conjure liquidity overnight, but it does establish something institutions care about more than hype: a credible settlement unit designed for the regulatory perimeter.

Interoperability is where institutional ambitions usually stumble, because institutions rarely want assets trapped on one network, yet they also can’t tolerate ad-hoc bridges that break controls. Here again, Dusk’s relevance is in the choices it has made and the way it has documented them. In November 2025, Dusk and NPEX announced adoption of Chainlink standards: CCIP as the canonical cross-chain interoperability layer for tokenized assets issued by NPEX on Dusk, plus data tooling (including DataLink and Data Streams) to publish official NPEX exchange data on-chain. That combination matters more than it first appears. Cross-chain messaging without trusted market data is awkward for regulated products; trusted data without robust settlement paths is equally limiting. Putting both into the same institutional story—issuance, data, and settlement—moves Dusk from “privacy chain” toward “financial plumbing that happens to be private where it needs to be.”

None of this guarantees Dusk “wins” institutional flow in 2026. Privacy systems are hard to implement correctly, and they can become politically controversial the moment confidentiality is confused with concealment. But if the thesis is that trillions in equities, bonds, and other regulated instruments will gradually be represented on-chain, then radical transparency is a strange end state. Dusk’s case is that confidentiality isn’t a feature you tack on later—it’s the missing layer that lets regulated markets even consider operating on-chain in the first place, and it’s backing that case with mainnet delivery, standards for confidential securities, regulated money rails, and integration choices that look designed for audits rather than applause.

@Dusk #dusk $DUSK #Dusk

@Walrus 🦭/acc There’s a quiet shift happening in Web3 storage, driven by a practical pain: apps now need speed. “Hot storage” is data you fetch constantly—images, game assets, datasets, logs, media libraries. On-chain is great in theory, brutal in cost. So teams shift off-chain, and suddenly the hard part isn’t computation—it’s credibility: who can be trusted, and how do you prove it?

Walrus Protocol feels relevant because it targets that gap between speed and verifiability. Mysten Labs introduced Walrus as a decentralized blob storage and data-availability protocol for large, unstructured files that don’t belong inside a validator’s state. The core move is to encode a file into smaller fragments (often called slivers), distribute them across storage nodes, and make reconstruction possible even if some pieces go missing.
What gives Walrus sharper definition is its encoding engine, Red Stuff. Red Stuff is described as a two-dimensional erasure coding scheme that produces primary and secondary slivers arranged in a matrix, with self-healing recovery when nodes fail. For hot storage, that recovery behavior is the point. A slow read is indistinguishable from downtime, and many decentralized systems buckle when churn forces repairs. Walrus is trying to make repairs look like routine maintenance instead of a network-wide emergency.
The Walrus paper is candid about the trade-offs it wants to escape. Full replication is simple but wasteful, while basic erasure coding can create bandwidth spikes during outages or attacks. The paper claims Red Stuff reaches high security with about a 4.5x replication factor, and it emphasizes storage challenges in asynchronous networks—so a node shouldn’t be able to “look available” by exploiting network delays while not actually storing data. If your application depends on media or datasets being there tomorrow, that kind of adversarial thinking stops being theoretical.
Walrus becomes more relevant when you look at how it plugs into applications. A defining characteristic is that Walrus outsources control-plane functions to Sui, using the chain for coordination, metadata, and verifiable proofs. The Walrus Foundation describes a Proof of Availability recorded on Sui as an on-chain certificate that a blob has been correctly encoded and distributed, creating a public audit trail and marking the official start of the storage service. The docs frame the same idea in simple terms: you can write and read blobs, and anyone can prove a blob was stored and will remain available for later retrieval. Walrus’s own materials also frame this as “programmable storage”: blobs and storage capacity can be represented as objects on Sui, and the protocol positions itself as chain-agnostic through developer tools and SDKs.

There’s also a practical signal behind the concept. By September 2024, Mysten Labs said the Walrus developer preview was already storing over 12 TiB of data, and an event called Breaking the Ice drew more than 200 developers building with decentralized storage. That doesn’t prove durability, but it does suggest Walrus is being shaped by real integration pain, not just theory.
This is why Walrus is getting attention now rather than drifting in “someday” territory. Web3 stacks are becoming more modular, and the center of gravity is shifting toward blobs—video, images, PDFs, datasets, and the cryptographic artifacts modern systems generate. Walrus leans into that reality by framing storage as something that can be coordinated and verified on-chain while keeping the heavy data plane specialized for throughput. The AI part makes this feel way more urgent—not because of hype, but because teams actually need provenance and reproducibility. They want to trace exactly which dataset version trained which model, who touched it, and whether any files quietly changed between runs. A custody proof plus reliable retrieval won’t magically fix governance or licensing, but it does make real accountability possible without dragging everything back into one central chokepoint.
There are early signs Walrus is being tested against real workloads. In late January 2026, Team Liquid announced migrating more than 250TB of historical match footage and brand content to Walrus, described as the largest single dataset entrusted to the protocol to date. Media archives are a brutal proving ground because the files aren’t just stored—they’re constantly accessed, clipped, and repurposed by distributed teams.
None of this magically solves decentralized hot storage. Incentives have to stay aligned so nodes keep serving data, and developer tooling has to feel predictable. But Walrus is relevant precisely because it treats hot storage as it actually exists: big files, frequent reads, node churn, and a need for verifiable guarantees that don’t rely on a single provider’s good behavior.

@Walrus 🦭/acc #walrus $WAL #Walrus

@Dusk For a long time, “tokenized securities” felt like a concept that could never quite survive contact with regulators. The pitch was simple: put shares, bonds, and funds on a blockchain so they can move faster, settle sooner, and leave a clearer record. The reality was messier. Capital markets run on permissions, liability, and decades of hard-earned caution, and most crypto infrastructure wasn’t built for that world. What’s changing now isn’t just enthusiasm. It’s the slow emergence of venues that are actually licensed to do the job, plus a small set of networks being shaped around the uncomfortable requirements regulated markets bring.

That’s why Dusk Network joining 21X as a launch trade participant matters more than it might sound at first glance. When 21X described Dusk onboarding “at launch,” the initial focus wasn’t on flashy new products. It started with something intentionally plain: routing Dusk-related flows into 21X, beginning with stablecoin treasury investment into tokenized money market funds. If you’re expecting fireworks, you won’t find them there. But if you’re looking for where institutions are genuinely willing to move first, that’s almost the point. Treasurers and finance teams don’t need a revolution; they need a safe place to park liquidity, with clear rules and familiar risk profiles.

The deeper relevance of Dusk is that it isn’t trying to be a generic chain that later gets awkwardly “adapted” for securities. Its whole posture is closer to a financial network with crypto rails than a crypto network hoping to attract finance. The key question it’s trying to answer is simple to say and hard to execute: can regulated assets move on a public network without forcing every participant to publish every balance, trade detail, and counterparty relationship to the world? In markets, confidentiality is not automatically suspicious. It’s often how you protect clients, prevent copycat trading, and keep commercial terms from becoming public ammunition.

That’s where the partnership with 21X starts to look like more than a logo swap. 21X operates within the EU’s DLT Pilot Regime, and it received authorization from Germany’s BaFin as a DLT trading and settlement system in December 2024. That authorization isn’t just a badge. It’s a constraint that forces the venue to behave like market infrastructure, not a tech demo. When a platform is expected to combine trading with settlement under oversight, it needs systems that can support supervision, rules, and recourse without turning the whole marketplace into a glass box where everyone’s business model is exposed.

Dusk’s choices sit right in the middle of that uncomfortable trade-off. Privacy in finance is complicated because it can be abused, and it would be naïve to ignore that. But the real question isn’t philosophical. It’s practical: can you keep sensitive information private while still making the system accountable and the rules enforceable? That’s the direction Dusk has been leaning into, and it’s why its role here feels foundational rather than decorative. If tokenized securities are going to be more than a niche product, the infrastructure has to respect the realities of how regulated participants actually operate.

The talk of deeper technical integration adds weight to that. Both sides have pointed to 21X integrating DuskEVM, which sounds like plumbing until you consider what it enables. Compatibility with the EVM world can reduce adoption friction because teams already know the tooling, the security patterns, and the ways smart contracts can fail. In a regulated setting, familiarity isn’t laziness; it’s risk management. It’s also a way to avoid reinventing every operational wheel before you’ve even proved there’s real volume.

Timing matters too. 21X has been moving from “licensed” to “operating,” and by September 2025 it announced that trading had started. That shift changes the tone around everything. Once trading is live, partnerships stop being aspirational and become operational. Integrations turn into deadlines. Legal reviews become gatekeeping. And credibility becomes less about vision statements and more about whether the system runs, day after day, under supervision.

Dusk’s broader strategy reinforces the same theme. Rather than chasing every trend, it has been building connective tissue for regulated assets, including interoperability work with Chainlink CCIP alongside NPEX. “Interoperability” can sound like a slogan, but in regulated markets it boils down to a blunt requirement: can assets move between environments without losing compliance controls, breaking custody expectations, or creating a reporting mess that nobody wants to own? If Dusk can make cross-network movement feel controlled and accountable, that’s not a side quest. It’s a prerequisite for scale.

Zooming out, the bigger backdrop is that tokenized securities are increasingly treated as a serious agenda item, not a fringe experiment. When mainstream market operators start exploring how tokenized instruments could trade within existing regulatory frameworks, the center of gravity shifts. The “why now?” becomes less about crypto momentum and more about institutions trying to modernize plumbing without compromising safeguards.

There are still risks, and they’re not small. Liquidity remains a chicken-and-egg problem, and a regulated venue can feel like an empty room if instruments are too limited or too bespoke to trade actively. But if tokenization becomes ordinary infrastructure, it will likely arrive through boring wins: shorter settlement cycles, fewer reconciliations, cleaner audit trails, and better control over who can see what. That’s where Dusk’s relevance becomes hard to ignore. Its value isn’t that it makes securities “more crypto.” It’s that it’s trying to make regulated markets on-chain survivable—confidential enough to be usable, structured enough to be supervised, and familiar enough to be built on without reinventing everything from scratch.

@Dusk #dusk $DUSK #Dusk

@Walrus 🦭/acc Decentralized storage is having a very practical moment. AI workflows move large files constantly, rollup-style systems need cheap data availability, and more teams are uncomfortable with a “decentralized app” that quietly depends on one cloud bucket. That mix explains why Walrus keeps showing up in benchmarking threads: people want something that feels closer to object storage in day-to-day use, but with independent verifiability and fewer brittle dependencies.

Walrus is a blob storage network built around erasure coding, with coordination and proofs handled through the Sui chain. What stands out in its main performance write-up is the setting. The evaluation looks at a publicly available testnet observed over 60 days and describes a testbed of 105 independently operated storage nodes spread across many regions and hosting providers. Benchmarks done “in the wild” are messy, but they’re also harder to massage into a flattering story.

On upload throughput, Walrus is clear about its ceiling for a single client: write throughput plateaus around 18 MB/s. The reason is architectural, not a tuning problem. A write includes multiple interactions with storage nodes plus a final step that publishes a proof of availability on-chain, so you’re paying for coordination as well as bandwidth. If you need more ingest, the evaluation suggests parallel clients uploading chunks in a fan-out pattern so aggregate throughput scales with parallelism, not with heroic tuning.

Latency is where you feel the design choices most sharply. For small blobs (under 20 MB), the evaluation reports write latency staying under 25 seconds, and it explains that blob size isn’t the main driver at that scale; fixed overhead dominates. Roughly six seconds comes from metadata handling and publishing the proof on-chain. Reads are consistently faster than writes and are described as bounded by network delay, which is a restrained way of saying that geography and routing matter more than protocol overhead once the blob is already placed.

Data recovery is the benchmark that rarely gets celebrated, yet it’s the one that decides whether a storage network survives real life. Walrus’s paper makes two concrete resilience claims: it can reconstruct data even if up to two-thirds of shards are lost, and it can keep accepting writes even if up to one-third of shards are unresponsive. It also argues that efficient repair is essential in erasure-coded systems, because a scheme that can’t heal cheaply tends to accumulate small gaps until the “rebuild” becomes an emergency.

Compared with the usual alternatives, the differences become less philosophical and more operational. IPFS is excellent for content addressing and distribution, but persistence is not automatic; the IPFS docs are blunt that you need to pin content (yourself or via a service) if you want it to stick around and not get garbage-collected. Filecoin adds incentives and a retrieval market; its docs describe clients requesting data from storage providers and paying for retrieval. Arweave takes a different bet: permanent storage as a core feature, backed by its protocol design and “permaweb” framing.

Why is Walrus trending now? In part because it aims directly at the AI-era “blob problem,” and its docs talk openly about making unstructured data reliable and governable. There’s also a maturity signal: coverage describes governance via a Walrus Foundation, which implies the project is trying to outgrow the single-team phase. If you benchmark it, I’d resist the urge to run one upload and declare victory. Test cold reads and warm reads, push parallel uploads, and then do something slightly cruel: drop a slice of nodes and measure how quickly you can still retrieve and reconstitute data. When a system promises both speed and recovery, it deserves to be tested on both.

@Walrus 🦭/acc $WAL #walrus #Walrus

@Dusk For years, the loudest debate in blockchain sounded like a dare: either accept radical transparency, or retreat into closed networks that never quite touch the real economy. That framing is wearing thin. What feels more useful now is a simpler question: how do you run a market where access is controlled—who can trade, who can settle, who can see what—on infrastructure that stays open to anyone who wants to validate and build?

The timing is not accidental. In Europe, MiCA has shifted from “prepare for it” to “operate under it,” with concrete expectations around authorisation, disclosures, and conduct for crypto-asset service providers. Tokenisation is also edging toward central-bank plumbing. The European Central Bank has said the Eurosystem will accept certain marketable assets issued via DLT-based CSD services as eligible collateral from 30 March 2026. And the Bank of England has signaled openness to clarifying and potentially expanding how tokenised assets could qualify as collateral, so long as the risks are properly managed.

Against that backdrop, Dusk is interesting less as a brand and more as a set of patterns that map cleanly onto regulated workflows. In its documentation, Dusk describes itself as a privacy blockchain for regulated finance, designed to give users confidential balances and transfers while giving developers native privacy and compliance primitives, explicitly referencing regimes like MiCA and the EU DLT Pilot Regime. I’m not persuaded by grand narratives about “disruption,” but I am persuaded by systems that admit what regulated finance actually needs: confidentiality for ordinary activity, and controllable visibility when rules demand it.

The first proven pattern is to keep the base layer open while placing permissioning at the edge of each workflow. A network can remain permissionless in the infrastructure sense—anyone can validate, anyone can verify the same state transitions—while a specific market still requires eligibility. Dusk leans on proofs rather than exposure. Its identity framework, Citadel, is documented as a zero-knowledge, self-sovereign identity system where identity rights can be stored privately and users can prove claims about themselves when required. In practice, that’s the difference between “this market is closed” and “this market is open, but participation is conditional.” It’s a subtle distinction, and it’s where permissioned workflows stop feeling like a betrayal of public infrastructure.

The second pattern is selective disclosure, and it matters emotionally as well as technically. Most people accept that regulated markets require checks. What they do not accept is the idea that compliance must equal permanent exposure. Dusk has been explicit about using privacy-preserving zero-knowledge proofs to support KYC and AML processes while keeping sensitive user information protected and under the user’s control. The practical payoff is that an issuer can enforce an eligibility rule without learning a participant’s entire profile, and a participant can satisfy a requirement without leaving a permanent trail of personal data behind. In a world where data leaks are routine and “just trust us” has worn out, that starts to feel less like a technical flourish and more like basic hygiene.

The third pattern is auditability with a narrow aperture. Traditional finance works because you can be inspected, not because every action is broadcast to strangers forever. And the compliance world is tightening around information-sharing expectations such as the travel rule. Financial Action Task Force reporting shows travel rule implementation is spreading, but also that implementation remains uneven across jurisdictions. In that reality, “reveal everything” is overkill and often counterproductive, while “reveal nothing” is a dead end. The healthier target is a controlled aperture: clear roles, clear triggers, and a way to produce the right evidence to the right party at the right time.

What makes these patterns feel timely is that market infrastructure is moving in public. DTCC has said it anticipates beginning to roll out a tokenisation service for Depository Trust Company-custodied assets in the second half of 2026, explicitly in a controlled production environment under SEC staff no-action relief. When the biggest pipes start experimenting, the bar rises for everyone else. “Interesting tech” is no longer enough; you need workflows that survive compliance reviews, operational risk committees, and the boring-but-deadly details of settlement.

Dusk’s documentation ties these ideas to settlement mechanics too, highlighting a proof-of-stake consensus it calls Succinct Attestation, aimed at fast, final settlement. I’m wary of treating any single chain as destiny, but the direction is clear: permissioned workflows are coming on-chain, and permissionless networks that want institutional relevance must make governance and confidentiality feel like normal features, not awkward exceptions. Dusk’s patterns are one grounded attempt to do exactly that.

@Dusk #dusk $DUSK #Dusk

@Walrus 🦭/acc If you talk to people building serious AI systems right now, the conversation eventually turns into an unglamorous question: where does all the stuff go? Not the polished demo, but the training sets, the endless logs, the checkpoints, the evaluation reports, and the “we swear this is the same dataset as last week” receipts. Storage has quietly become a bottleneck for trust as much as for capacity. As models get larger and teams get more distributed, it’s harder to answer basic questions like which dataset version produced a surprising behavior, whether a “fixed” bug actually changed the data pipeline, or which artifact is safe to ship when everything is moving fast.

That’s one reason Walrus has started showing up in the AI data conversation. Walrus describes itself as a decentralized storage and data availability protocol built for large binary files—“blobs”—with a control plane integrated with the Sui blockchain. In plain terms, it aims to keep unstructured content available even when some storage nodes fail or behave maliciously, while giving developers a structured way to reference and manage that content. The Walrus docs frame this as making data “reliable, valuable, and governable,” and Mysten Labs originally positioned the project as a storage layer for blockchain apps and autonomous agents, which is an interesting overlap with today’s agent-heavy AI tooling.

It’s trending now for the same reason backups used to feel boring… until you lose something you can’t recreate. AI teams are iterating nonstop, and every iteration produces more prompts, traces, datasets, and checkpoint files that matter later—especially when something behaves oddly. Agents make it even more obvious: if your assistant is “always on,” then its logs and intermediate work can’t be treated like disposable scraps. And governance is becoming less optional. More orgs are saying, “Show me what changed,” and expecting a real answer, not a guess. Whether it’s internal model risk reviews or customers asking for stronger provenance, the bar is rising for “show me what you trained on” and “show me what changed.” Walrus leans into exactly that itch by treating stored data as something you can reason about, not just something you can dump in a bucket.

There’s also real technical work behind Walrus’s story. One of the core ideas is “Red Stuff,” a two-dimensional erasure coding approach meant to keep decentralized storage efficient while staying resilient and fast to recover. The associated research frames a target called “complete data storage,” with properties like write completeness and consistent reads even with Byzantine behavior by some participants. In everyday language, the system is designed so you don’t need full copies everywhere to feel confident you can reconstruct what you wrote and serve it later. This is the kind of progress that matters because it addresses the classic complaint about decentralized storage: reliability is great until costs explode or recovery is slow.

What makes this especially relevant for ML teams is how directly Walrus connects to familiar artifacts. The project’s own objectives and use-case list calls out clean training datasets with verified provenance, model files and weights, and even proofs of correct training, plus ensuring the availability of model outputs. I’m skeptical that formal proofs become everyday practice overnight, but the need underneath them is obvious. When something goes wrong, the hardest part is often reconstructing what happened: which data went in, what code touched it, which weights came out, and which evaluation you trusted when you shouldn’t have. A storage layer that’s explicitly designed to preserve and reference those pieces, and to make their availability verifiable, is at least pointed at the right problem.

Another subtle angle is collaboration across boundaries. Many storage setups assume everyone is inside one organization with one permission system. In practice, AI projects now span vendors, contractors, open-source communities, and partners who need limited, auditable access. Walrus emphasizes programmability by representing stored blobs as objects that onchain logic can manage, which opens the door to automated lifecycle rules, verifiable references, and clearer “who can do what” mechanics around shared artifacts. But none of this makes Walrus a universal answer. Decentralized systems add operational overhead, and adoption friction is real; even sympathetic commentary points out that complexity and developer ergonomics often decide what survives. Walrus’s own SDK notes that reading and writing blobs can involve many requests, which is exactly the kind of detail that matters when you’re building pipelines you need to trust at 2 a.m. Still, the broader trend is hard to ignore: as AI systems move from prototypes to infrastructure, the boring parts—storage, logs, lineage, and artifacts—are starting to look like the real battleground for reliability.

@Walrus 🦭/acc #walrus $WAL #Walrus

@Walrus 🦭/acc For years, “put it on-chain” has been a nervous joke in Web3 circles. Everyone knows the punchline: you can’t, at least not for most of the data that makes an application feel real. The moment your project needs images, audio, video, model files, or even a chunky set of logs, a base chain becomes the wrong place to park those bytes. Fees jump, blocks bloat, and you end up treating the blockchain like a hard drive it was never meant to be. That mismatch is why “blobs” keep resurfacing in serious conversations, not as a meme but as a practical boundary line between what chains do well and what they shouldn’t be forced to do.

A blob is just a binary large object: a chunk of bytes stored and moved as a unit. In Web3 today, that simple idea shows up in two very different ways. Ethereum’s EIP-4844 introduced blob-carrying transactions so rollups can publish data more cheaply than permanent calldata, but with a deliberate tradeoff: blob data is temporary. The spec and explainer ecosystem are consistent on the core point—blobs are pruned after roughly 4,096 epochs, commonly summarized as about 18 days—because the network can’t afford for every scaling step to become permanent historical baggage.

That design is doing real work. After EIP-4844 went live (March 2024), blobs quickly became the normal lane for rollup data posting, and you can see the shift show up in how people talk about data availability costs and throughput as operational realities rather than research concepts. I’ve noticed the tone change in builder conversations, too. The question is no longer “can we scale?” but “what kind of data are we talking about, and how long do we actually need it to stick around?”

Because the moment you say “18 days,” a different category of needs pops up. A lot of applications want data that persists: NFT media that shouldn’t disappear, game assets that need to load next month, datasets that an AI workflow might reference repeatedly, or an audit trail you’ll want to re-check long after the launch excitement fades. The old pattern—upload somewhere centralized, pin a hash on-chain, hope the link behaves—can be fine until it isn’t. The chain can attest that a file once existed, but it can’t force anyone to keep serving it, and it can’t make storage feel like a first-class part of your app logic.

This is where Walrus earns the title “blob store” in a way that’s more literal than it sounds. Walrus was introduced by Mysten Labs as a decentralized storage and data availability protocol designed specifically to handle unstructured data blobs by encoding them into smaller pieces (“slivers”) distributed across storage nodes. The key promise is pragmatic resilience: the original blob can be reconstructed from a subset of those slivers, even when a large fraction are missing.

That’s not just a nice-to-have detail. It’s the entire point of treating blobs as a native object type rather than as an awkward attachment to a smart contract. Walrus leans on erasure coding so it doesn’t need naive full replication everywhere, and the technical paper frames this as a response to the cost and recovery inefficiencies that show up when nodes churn or partial outages are the norm rather than the exception. If you’ve ever tried to make a decentralized app feel “normal” to users—fast, predictable, and not mysteriously broken—this is the kind of engineering choice that matters more than branding.

What makes Walrus feel especially relevant to Web3, though, is how it ties storage to onchain state without pretending storage is the chain. In the Walrus documentation, storage space is represented as a resource on Sui, and stored blobs are represented as objects on Sui as well. That means smart contracts can check whether a blob is available and for how long, extend its lifetime, or optionally delete it, while the heavy bytes live in the storage network. The Walrus blog describes Sui as a control plane for metadata and for publishing an onchain proof-of-availability certificate that attests the blob is actually stored.

Those lifecycle details are where Walrus stops being an abstract “decentralized storage” idea and turns into something an application can design around. Blobs are stored for a chosen number of epochs specified at write time, and the docs explicitly note that mainnet uses an epoch duration of two weeks. In other words, storage is not a vague promise; it’s a lease with explicit time boundaries that your code can reason about. And if you need the blob to stick around longer, the client tooling supports extending blob lifetimes as long as the blob isn’t expired, with clear ownership rules around who can extend what.

Even the “boring” constraints are refreshingly explicit. The docs state a current maximum blob size of 13.3 GB, with the straightforward guidance to chunk bigger data if needed. That kind of specificity tends to be what separates a protocol you can actually build on from a concept you only talk about on panels.

So why is this trending now, beyond the usual cycle of new protocol launches? Because the industry is being forced to mature its mental model of data. Ethereum blobs made it socially acceptable to say, out loud, that some data only needs to be available temporarily for verification windows—and that optimizing for that window is a feature, not a compromise. At the same time, the apps people try to use are getting heavier: wallets that render galleries, games that stream assets, and AI-adjacent workflows that move model artifacts and provenance records around like it’s normal.

Walrus fits into that moment by focusing on the other side of the blob story: not the temporary “post it for settlement” blob, but the durable “store it, prove it’s there, and let my app manage its lifetime” blob. I’m not saying it removes the hard questions—storage networks live and die by incentives, latency, and what happens when reliability drifts over time. But it does something important: it treats blob storage as part of the core architecture instead of a side quest every team has to reinvent. That’s why Walrus feels strongly relevant to the title—and why “blobs” finally feel like they belong in the main plot, not in the footnotes.

@Walrus 🦭/acc $WAL #walrus #Walrus

@Dusk Tokenizing real-world assets used to feel like a clever demo: take something slow and paperwork-heavy, wrap it in code, and call it progress. Lately it’s started to look more like financial plumbing—less about proving a point, more about building a workflow that can survive auditors, counterparties, and real money. One clear signal is the growth of tokenized money market funds holding U.S. Treasuries. Chainalysis pointed out that assets under management for tokenized Treasury money market funds rose above $8 billion in December 2025.

That kind of number doesn’t matter because it’s huge compared to traditional markets (it isn’t). It matters because it’s “big enough to be annoying.” Once the market is visible, the conversation stops being dreamy and becomes operational: who is the record keeper, what happens at redemption, what counts as final settlement, and how do you prove compliance without leaking everyone’s business to the public internet? You can see the same shift in institutional experiments, like the collaboration between Goldman Sachs and BNY to create digital tokens that mirror shares of money market funds—an attempt to make buying and selling fund shares feel more “real-time,” but still inside familiar control points.

Regulation is trending because it forces the “okay, what are we doing?” moment. In Europe, MiCA came with hard dates: stablecoin-related rules started applying June 30, 2024, and the broader rulebook landed December 30, 2024. You can argue with parts of it, sure—but at least it’s a target compliance can design around and prove out with evidence. Meanwhile, regulators worldwide are spelling out the failure modes more bluntly than before. IOSCO has warned that tokenization can introduce or amplify risks, including confusion about whether an investor truly owns the underlying asset or merely holds a token representing it, plus new counterparty risks created by token issuers and intermediaries.

This is where privacy stops being a philosophical debate and becomes a practical requirement. Traditional markets are not built on radical transparency. Banks don’t broadcast balances. Funds don’t publish positions in real time. Traders don’t advertise intent unless they’re required to. Yet most public blockchains make transaction flows easy to trace, which can expose strategies, relationships, and even business health. In a regulated setting, that’s not “nice to have” privacy; it’s often basic commercial discretion.

The core pitch of Dusk Network is that regulated assets shouldn’t be forced into an awkward choice between compliance and confidentiality. Its documentation describes a “privacy by design, transparent when needed” approach, with the ability to reveal information to authorized parties when required. What makes that more than a slogan is that Dusk explicitly separates two transaction models that match how regulated finance actually behaves. Moonlight is designed to be transparent—accounts have visible balances and transfers show sender, recipient, and amount—so it can support flows that must be observable, including reporting-heavy scenarios. Phoenix, by contrast, is built for confidentiality, using a UTXO-style model intended for obfuscated transactions and confidential smart contract execution.

That split matters because real markets mix public and private needs all the time. Some activity must be visible by design: attestations, disclosures, certain settlement rails, some treasury operations. Other activity should not be visible to everyone: who holds what, how positions change, who is trading with whom, or how a company’s cap table evolves day by day. Dusk’s approach is essentially to treat privacy as a normal operating mode, not an exotic add-on you bolt on later and pray regulators will tolerate.

The second piece is the asset wrapper itself. For tokenized RWAs, especially securities-like instruments, the token standard is where rules either exist or don’t. Dusk’s Confidential Security Contract (XSC) is presented as a security-token standard meant to support the lifecycle controls regulated instruments demand—things like corporate actions, voting, and auditability—while aiming to avoid the “everything is public forever” problem that makes institutions flinch. The point isn’t magic compliance. It’s that compliance logic can be native to the instrument, rather than scattered across spreadsheets, custodians, transfer agents, and ad-hoc middleware that all need to agree at the same time.

Identity is the part everyone wants to solve without saying it out loud. Markets can’t function without eligibility checks, but they also shouldn’t turn into permanent dossiers. Dusk’s Citadel is positioned as privacy-preserving verification, built to let users prove they qualify without broadcasting personal data across every venue and integration. I find this framing more honest than most: it doesn’t pretend KYC disappears; it tries to reduce the blast radius of KYC data when systems interconnect.

The “strong relevance” test, though, isn’t whether the architecture reads well. It’s whether it shows up in concrete rails with recognizable constraints. One visible step is the relationship with NPEX, aimed at bringing regulated issuance and trading closer to an on-chain workflow rather than treating tokenization as a sidecar. And because tokenized assets rarely live on a single island, Dusk and NPEX have tied interoperability to an industry-standard messaging and transfer layer by integrating Chainlink CCIP as a canonical cross-chain solution for tokenized assets issued in that setup. It’s not just “move the tokens.” It’s move them in a way that still honors the rules—who’s allowed to see what, who’s allowed to do what, and how you prove it later when audit season shows up.

If you zoom out, the title basically says: regulated assets + built-in confidentiality. And that’s the core of the Dusk Network angle: privacy isn’t the villain—opacity is. The goal is selective disclosure: keep everyday market activity private by default, but still be able to prove (and reveal) exactly what regulators, auditors, and approved counterparties need to verify. In 2026, that “middle lane” feels less optional and more inevitable. Pure confidentiality with zero accountability goes nowhere. Pure accountability without confidentiality turns into surveillance. The hard (and interesting) work is building both, without either side feeling played.

@Dusk #dusk $DUSK #Dusk