VOLT 07

Protocol diagrams are clean. Human behavior isn’t.
Most decentralized storage systems look flawless on paper. Boxes align. Arrows flow. Incentives close neatly. But diagrams don’t panic. Humans do. Diagrams don’t forget. Humans do. Diagrams don’t discover failure too late. Humans do all the time.
The moment storage leaves whitepapers and enters real use, the real design question appears:
Was this system built for diagrams — or for people?
That question fundamentally reframes how Walrus (WAL) should be evaluated.
Humans don’t experience storage as architecture. They experience it as outcomes.
Users never ask:
how many replicas exist,
how shards are distributed,
how incentives are mathematically balanced.
They ask:
Can I get my data back when I need it?
Will I find out something is wrong too late?
Who is responsible if this fails?
Will this cause regret?
Most storage designs optimize for internal coherence, not external consequence.
Protocol diagrams assume rational attention. Humans don’t behave that way.
In diagrams:
nodes behave predictably,
incentives are continuously evaluated,
degradation is instantly detected,
recovery is triggered on time.
In reality:
attention fades,
incentives are misunderstood or ignored,
warning signs are missed,
recovery starts only when urgency hits.
Systems that rely on idealized behavior don’t fail because they’re wrong they fail because they assume people act like diagrams.
Human-centered storage starts with regret, not throughput.
The most painful storage failures are not technical. They are emotional:
“I thought this was safe.”
“I didn’t know this could happen.”
“If I had known earlier, I would have acted.”
These moments don’t show up in benchmarks. They show up in post-mortems and exits.
Walrus designs for these human moments by asking:
When does failure become visible to people?
Is neglect uncomfortable early or only catastrophic later?
Does the system protect users from late discovery?
Why diagram-first storage silently shifts risk onto humans.
When storage is designed around protocol elegance:
degradation is hidden behind abstraction,
responsibility diffuses across components,
failure feels “unexpected” to users,
humans become the final shock absorbers.
From the protocol’s perspective, nothing broke.
From the human’s perspective, trust did.
Walrus rejects this mismatch by designing incentives and visibility around human timelines, not protocol cycles.
Humans need early discomfort, not late explanations.
A system that works “until it doesn’t” is hostile to human decision-making. People need:
early signals,
clear consequences,
time to react,
bounded risk.
Walrus emphasizes:
surfacing degradation before urgency,
making neglect costly upstream,
enforcing responsibility before users are exposed,
ensuring recovery is possible while choices still exist.
This is not just good engineering. It is humane design.
As Web3 matures, human tolerance shrinks.
When storage underwrites:
financial records,
governance legitimacy,
application state,
AI datasets and provenance,
users don’t tolerate surprises. They don’t care that a protocol behaved “as designed.” They care that the design didn’t account for how people discover failure.
Walrus aligns with this maturity by treating humans as first-class constraints, not externalities.
Designing for humans means accepting uncomfortable tradeoffs.
Human-centric systems:
surface problems earlier (and look “worse” short-term),
penalize neglect instead of hiding it,
prioritize recovery over peak efficiency,
trade elegance for resilience.
These choices don’t win diagram beauty contests.
They win trust.
Walrus chooses trust.
I stopped being impressed by clean architectures.
Because clean diagrams don’t explain:
who notices first,
who pays early,
who is protected from late regret.
The systems that endure are the ones that feel boringly reliable to humans because they never let problems grow quietly in the background.
Walrus earns relevance by designing for the way people actually experience failure, not the way protocols describe success.
Designing storage for humans is not softer it’s stricter.
It demands:
earlier accountability,
harsher incentives,
clearer responsibility,
less tolerance for silent decay.
But that strictness is what prevents the moments users never forget the moment they realize too late that a system was never designed with them in mind.
@Walrus 🦭/acc #Walrus $WAL

Value leakage is not a side effect of DeFi. It is a design consequence.
Transparent DeFi promised fairness through openness. In practice, it delivered something else: a permanent extraction layer where those who see first earn most, and those who act last subsidize everyone else.
This leakage is not accidental.
It is the inevitable outcome of making intent visible before execution.
That is why Dusk Network approaches DeFi from a different premise: value leaks because information leaks.
Transparent DeFi turns information into a tradable commodity
On public chains, every transaction broadcasts:
what will happen,
how large it is,
which contracts are involved,
when execution will occur.
This transforms block production into a competitive intelligence game. Validators, builders, and searchers don’t need to break rules to extract value they simply optimize around what they can already see.
Front-running and MEV are not exploits.
They are rational arbitrage on leaked intent.
Why users always lose first in transparent systems
Value leakage hits users before protocols notice:
swaps execute at worse prices,
liquidations happen earlier than expected,
arbitrage drains upside silently,
execution outcomes vary based on visibility, not logic.
Users experience this as “slippage” or “market conditions.” In reality, it is a structural tax embedded in transparency.
The more valuable the transaction, the more visible and extractable it becomes.
Protocols leak value even when they work as designed
Transparent DeFi doesn’t fail.
It functions perfectly and still leaks value.
Why?
ordering rules are predictable,
state transitions are observable,
execution paths can be simulated in advance.
Even honest validators are incentivized to reorder, delay, or insert transactions if the system allows it. Governance can discourage this, but it cannot eliminate it without changing the information model.
Dusk changes the information model.
How Dusk fixes value leakage at the root
Instead of policing extraction, Dusk removes its raw material.
In Dusk’s architecture:
transaction intent is private,
smart contract execution is confidential,
intermediate state is never exposed,
finality reveals correctness, not strategy.
Validators cannot see what to exploit.
Searchers cannot simulate outcomes.
MEV collapses because there is nothing to extract against.
This is prevention, not mitigation.
Why confidential execution matters more than private balances
Some systems hide balances but expose execution. That still leaks value.
Dusk treats execution itself as sensitive:
logic runs inside a privacy boundary,
proofs verify outcomes without revealing steps,
ordering reveals no economic signal.
This closes the leak where it actually forms during execution, not settlement.
Value preservation restores economic fairness
When extraction disappears:
execution becomes outcome-driven,
pricing becomes predictable,
large orders stop signaling weakness,
users stop subsidizing insiders.
DeFi begins to resemble a real financial system one where returns come from risk and strategy, not from seeing someone else’s move first.
That shift is what attracts serious capital.
Why transparency scales experimentation, not capital
Public DeFi is excellent for:
composability experiments,
rapid iteration,
open research.
It is terrible for:
institutional liquidity,
large position management,
predictable execution under stress.
Institutions don’t avoid DeFi because they dislike decentralization. They avoid it because transparent execution guarantees value leakage.
Dusk resolves that contradiction.
Most chains try to manage leaks. Dusk seals the pipe.
MEV auctions, fair ordering, committees — these are all attempts to redistribute leaked value more politely.
Dusk takes a simpler stance:
If value leaks because intent is visible, remove visibility.
That single decision collapses entire extraction economies.
I stopped asking how DeFi shares value. I started asking where it leaks.
Once you follow the leak upstream, you discover it always starts with information exposure.
Dusk earns relevance by fixing that root cause, not by layering policy on top of it.
Transparent DeFi leaks value because it must.
Confidential DeFi preserves value because it can.
@Dusk #Dusk $DUSK

Liquidity follows predictability not transparency.
Institutional capital does not avoid blockchains because they are decentralized. It avoids them because execution is observable. On public chains, intent leaks before settlement, strategies can be inferred mid-flight, and outcomes depend on who sees what first.
That is not a technology gap.
It is an execution-risk problem.
This is precisely where Dusk Network positions itself: confidential execution as a prerequisite for institutional liquidity.
Why public execution repels serious capital
Institutions price risk long before they price yield. In transparent execution environments, they face risks that do not exist in traditional markets:
pre-trade information leakage,
front-running and MEV exposure,
strategy reconstruction over time,
adversarial inference during stress events.
Even if assets are safe, intent is not. For risk committees, that alone disqualifies most public chains.
Confidential execution changes the economics of participation
Confidential execution ensures that:
transaction parameters are hidden until finality,
smart contract logic executes without revealing intermediate state,
outcomes are provable without exposing decision paths,
validators cannot exploit execution visibility.
This does not just protect users. It reshapes incentives. When intent is unknowable, extraction strategies collapse, and execution becomes outcome-driven rather than information-driven.
Liquidity prefers that environment.
Why institutions care more about execution than settlement
Settlement finality is necessary but insufficient. Institutions ask a harder question:
What can others learn about us before settlement?
On public chains, the answer is: a lot.
On confidentiality-first systems, the answer is: only what is strictly necessary.
Dusk’s design treats execution as the sensitive surface not an afterthought aligning on-chain behavior with off-chain financial norms.
MEV is a tax institutions will not pay
MEV is often framed as a validator or protocol issue. Institutions see it as a structural execution tax:
unpredictable slippage,
adversarial ordering,
opaque costs embedded in execution,
disadvantage for non-insiders.
Confidential execution removes the raw material MEV depends on: visibility. Without interpretable intent, front-running becomes guesswork, not strategy. Liquidity flows where execution is fair by construction.
Confidential execution enables selective transparency
Institutions do not want secrecy; they want control over disclosure:
regulators can verify correctness,
auditors can validate compliance,
counterparties cannot infer strategy,
competitors cannot map behavior.
Dusk enables this separation. Proofs attest to correctness without revealing sensitive details, restoring the disclosure boundaries institutions already expect.
Why this unlocks real, durable liquidity
Liquidity that chases yield leaves quickly.
Liquidity that trusts execution stays.
Confidential execution supports:
large order sizes without signaling,
consistent execution under stress,
predictable outcomes independent of observer behavior,
integration with compliance workflows without public exposure.
These properties are not cosmetic. They determine whether capital can scale beyond pilots.
Public transparency scales experimentation. Confidential execution scales balance sheets.
Retail ecosystems thrive on openness. Institutional ecosystems require insulation. Trying to retrofit privacy onto transparent execution is rarely sufficient; inference leaks through the seams.
Dusk’s approach is native: execution is confidential by default, and verification is proof-based. That alignment is what institutions wait for before committing meaningful liquidity.
Why “eventual adoption” won’t happen on public execution
Transparency is not a maturity issue it is a design choice. Institutions know that once strategies are exposed, they cannot be unexposed. They will not “try and see” with public execution and hope MEV doesn’t matter.
They wait for systems where execution risk is structurally constrained.
I stopped asking how much liquidity a chain has. I started asking why it stays.
Sustainable liquidity remains where:
execution is predictable,
information leakage is minimized,
incentives reward correctness over extraction,
trust survives stress events.
Confidential execution is not a feature to attract institutions.
It is the precondition.
Dusk earns relevance by building for that reality not by asking institutions to accept public exposure as the cost of decentralization.

Institutions don’t avoid public blockchains because they don’t understand them. They avoid them because they understand them too well.
From the outside, it looks puzzling. Blockchains offer transparency, auditability, and settlement finality exactly what regulated finance claims to want. Yet large institutions consistently hesitate to deploy meaningful workloads on fully public chains.
The reason is rarely stated plainly.
It isn’t throughput.
It isn’t compliance tooling.
It isn’t even volatility.
It’s uncontrolled information leakage.
That is the context in which Dusk Network becomes relevant.
Public blockchains leak more than transactions. They leak strategy.
On transparent chains, institutions expose:
trading intent before execution,
position sizing in real time,
liquidity management behavior,
internal risk responses during stress.
Even when funds are secure, information is not. For institutions, that is unacceptable. In traditional finance, revealing intent is equivalent to conceding value.
Public blockchains make that concession mandatory.
Transparency is not neutral at institutional scale.
Retail users often view transparency as fairness. Institutions view it as asymmetric risk:
competitors can infer strategies,
counterparties can front-run adjustments,
market makers can price against visible flows,
adversaries can map operational behavior over time.
This isn’t theoretical. It is exactly how sophisticated markets exploit disclosed information everywhere else.
Public blockchains simply automate that leakage.
Why “privacy add-ons” don’t solve the problem
Many chains attempt to patch transparency with:
mixers,
optional privacy layers,
encrypted balances but public execution,
off-chain order flow.
Institutions see through this immediately. Partial privacy still leaks metadata:
timing signals,
execution paths,
interaction graphs,
settlement correlations.
If any layer reveals strategy, the system fails institutional review.
Institutions don’t ask “is it private?” They ask “what can be inferred?”
Risk committees don’t think in terms of features. They think in terms of exposure:
Can someone reconstruct our behavior over time?
Can execution patterns be reverse-engineered?
Can validators or observers extract advantage?
Can this create reputational or regulatory risk?
On most public chains, the honest answer is yes.
That answer ends the conversation.
Dusk addresses the real blocker: inference, not secrecy
Dusk does not aim to hide activity in an otherwise transparent system. It removes transparency from the surfaces where it becomes dangerous:
private transaction contents,
confidential smart contract execution,
shielded state transitions,
opaque validator participation.
The goal is not anonymity for its own sake.
It is non-inferability.
Institutions can transact, settle, and comply without broadcasting strategy to the market.
Why this aligns with real-world financial norms
In traditional finance:
order books are protected,
execution details are confidential,
settlement does not reveal intent,
regulators see more than competitors.
Public blockchains invert this model everyone sees everything. That inversion is exactly why institutions stay away.
Dusk restores the familiar separation:
correctness is provable,
compliance is enforceable,
strategy remains private.
That is not anti-transparency. It is selective transparency the only kind institutions accept.
MEV is a symptom, not the disease
Front-running and MEV are often cited as technical issues. Institutions see them as evidence of a deeper flaw:
If someone can see intent before execution, extraction is inevitable.
Privacy-first design removes the condition that makes MEV possible. This is not mitigation. It is prevention.
Dusk’s architecture treats this as a first-order requirement, not an optimization.
Why institutions won’t wait for public chains to “mature”
Transparency is not a maturity issue. It is a design choice.
And once baked in, it cannot be undone without breaking everything built on top of it.
Institutions know this. That’s why they don’t experiment lightly on public chains and “see how it goes.” The downside is permanent.
They wait for architectures that were private by design.
This is the hidden reason adoption stalls at scale
It’s not that institutions dislike decentralization.
It’s that they cannot justify strategic self-exposure to competitors, counterparties, and adversaries.
Until blockchains stop forcing that exposure, adoption will remain shallow and cautious.
Dusk exists precisely to remove that blocker.
I stopped asking why institutions aren’t coming faster. I started asking what they see that others ignore.
What they see is simple:
Transparency without boundaries is not trustless it is reckless.
Dusk earns relevance by acknowledging that reality and designing around it, rather than pretending institutions will eventually accept public exposure as a virtue.
@Dusk #Dusk $DUSK

“Decentralized” feels reassuring right up until it isn’t.
For years, decentralization has been treated as a proxy for safety. Fewer single points of failure. More resilience. Less reliance on trust. The word itself became a comfort blanket: if something is decentralized, it must be safer than the alternative.
That illusion holds only until the day something goes wrong and no one is clearly responsible.
That is the day “decentralized” stops being a comfort word and becomes a question.
This is the moment where Walrus (WAL) starts to matter.
Comfort words work until reality demands answers.
Decentralization is easy to celebrate during normal operation:
nodes are online,
incentives are aligned,
redundancy looks healthy,
dashboards stay green.
In those conditions, decentralization feels like protection.
But when incentives weaken, participation thins, or recovery is needed urgently, the questions change:
Who is accountable now?
Who is obligated to act?
Who absorbs loss first?
Who notices before users do?
Comfort words don’t answer these questions. Design does.
Decentralization doesn’t remove responsibility it redistributes it.
When something fails in a centralized system, blame is clear. When it fails in a decentralized one, blame often fragments:
validators point to incentives,
protocols point to design intent,
applications point to probabilistic guarantees,
users are left holding irrecoverable loss.
Nothing is technically “wrong.”
Everything worked as designed.
And that is exactly why decentralization stops feeling comforting.
The real stress test is not censorship resistance it’s neglect resistance.
Most people associate decentralization with protection against attacks or control. In practice, the more common threat is neglect:
low-demand data is deprioritized,
repairs are postponed rationally,
redundancy decays quietly,
failure arrives late and politely.
Decentralization does not automatically protect against this. In some cases, it makes neglect easier to excuse.
Walrus treats neglect as the primary adversary, not a secondary inconvenience.
When decentralization becomes a shield for inaction, users lose.
A system that answers every failure with:
“That’s just how decentralized networks work”
is not being transparent it is avoiding responsibility.
The day decentralization stops being comforting is the day users realize:
guarantees were social, not enforceable,
incentives weakened silently,
recovery depended on goodwill,
accountability dissolved exactly when it was needed.
That realization is usually irreversible.
Walrus is built for the moment comfort evaporates.
Walrus does not sell decentralization as a guarantee. It treats it as a constraint that must be designed around.
Its focus is not:
how decentralized the system appears, but
how it behaves when decentralization creates ambiguity.
That means:
making neglect economically irrational,
surfacing degradation early,
enforcing responsibility upstream,
ensuring recovery paths exist before trust is tested.
This is decentralization without the comfort language.
Why maturity begins when comfort words lose power.
Every infrastructure stack goes through the same phase:
New ideas are reassuring slogans.
Real-world stress exposes their limits.
As a source of trust design takes the place of language.
Web3 storage is entering phase three.
At this stage, “decentralized” no longer means “safe by default.” It means:
Show me how failure is handled when no one is watching.
Walrus aligns with this maturity by answering that question directly.
Users don’t abandon systems because they aren’t decentralized enough.
They leave because:
failure surprised them,
responsibility was unclear,
explanations arrived after recovery was impossible.
At that point, decentralization is no longer comforting it’s frustrating.
Walrus is designed to prevent that moment by making failure behavior explicit, bounded, and enforced long before users are affected.
I stopped trusting comfort words. I started trusting consequence design.
Because comfort fades faster than incentives, and slogans don’t survive audits, disputes, or bad timing.
The systems that endure are not the ones that repeat decentralization the loudest they are the ones that explain exactly what happens when decentralization stops being reassuring.
Walrus earns relevance not by leaning on the word, but by designing for the day it stops working.
@Walrus 🦭/acc #Walrus $WAL

Every storage system makes a choice even when it claims neutrality.
Decentralized storage is often described as neutral infrastructure: data goes in, data comes out, rules apply equally to everyone. But neutrality is a comforting myth. When conditions deteriorate, storage systems don’t fail evenly.
They decide implicitly or explicitly who absorbs pain first.
That decision is where real design intent lives, and it is the lens through which Walrus (WAL) should be evaluated.
Failure always has a direction.
When incentives weaken or nodes churn, something has to give:
retrieval latency increases,
repair work is delayed,
costs rise,
availability degrades.
The critical question is not whether this happens, but where the damage lands first.
Does it land on:
validators, through penalties and reduced upside?
the protocol, through enforced repair costs?
applications, through degraded guarantees?
users, through silent data loss discovered too late?
Claiming neutrality avoids answering this question. Systems still choose they just choose invisibly.
“Best-effort” storage quietly pushes pain downstream.
In many designs, early failure is absorbed by:
applications compensating for missing data,
users retrying requests,
builders adding redundancy off-chain,
trust eroding silently before metrics change.
Validators continue acting rationally.
The protocol continues “operating as designed.”
The cost accumulates where it is least visible and hardest to reverse.
That is not neutral design.
That is downstream pain allocation.
Walrus starts by deciding who should suffer first and why.
Walrus does not pretend that failure can be avoided indefinitely. It assumes stress will arrive and asks a more honest question:
When something has to break, who should pay early so users don’t pay late?
Its architecture emphasizes:
penalizing neglect before users are affected,
surfacing degradation while recovery is still possible,
making abandonment costly rather than convenient,
forcing responsibility to appear upstream, not downstream.
This is not harsh design. It is preventative design.
Why upstream pain is safer than downstream pain.
When pain is felt early:
behavior changes quickly,
recovery remains economically rational,
damage is bounded,
trust is preserved.
When pain is deferred:
degradation compounds silently,
accountability blurs,
recovery becomes expensive or impossible,
users discover failure only when it’s final.
Walrus deliberately shifts pain toward enforceable actors and away from end users.
Storage neutrality collapses under real-world stress.
As Web3 storage increasingly underwrites:
financial records and proofs,
governance legitimacy,
application state,
AI datasets and provenance,
the cost of hidden pain allocation becomes unacceptable. Users don’t care that the system was “neutral.” They care that they were the last to find out and the only ones who couldn’t recover.
Walrus treats neutrality claims as a liability and replaces them with explicit consequence design.
Designing pain distribution is unavoidable avoiding it is the mistake.
Every storage protocol embeds answers to:
Who pays when incentives weaken?
Who notices first?
Who can still act?
Who is left explaining after it’s too late?
The only difference is whether those answers are:
explicit and enforced, or
implicit and socially assumed.
Walrus earns relevance by making these answers explicit even when they are uncomfortable.
I stopped believing storage could be neutral.
Because neutrality disappears the moment conditions change. What remains is design deciding who bears cost early and who is protected from irreversible loss.
Under that framing, Walrus is not just a storage protocol. It is a statement about responsibility: that users should not be the shock absorbers of infrastructure decay.
Storage always chooses who suffers first.
The only question is whether that choice is deliberate or hidden behind comforting language.
Walrus chooses to surface pain early, enforce responsibility upstream, and preserve trust where it matters most. That is not neutrality. That is maturity.
@Walrus 🦭/acc #Walrus $WAL

Every protocol has an incentive everyone believes in until someone actually tries to trace it.
In decentralized storage, the most common belief is simple: participants are incentivized to keep data available. It’s repeated so often that it feels foundational. But beliefs are not mechanisms, and repetition is not enforcement.
So I did something deliberately uncomfortable.
I tried to find the exact incentive that guarantees long-term care not during hype cycles, not during high demand, but after attention fades.
That search reframes how Walrus (WAL) should be understood.
The assumed incentive is usually phrased, not specified.
Most storage narratives rely on a familiar structure:
nodes are rewarded for storing data,
penalties exist for misbehavior,
redundancy reduces risk,
market forces keep things balanced.
What’s often missing is precision:
Rewarded how long?
Penalized for which exact failure?
Who detects degradation?
When does neglect become irrational rather than merely suboptimal?
The incentive exists in language not always in binding outcomes.
The gap appears when demand goes quiet.
In early stages, incentives feel obvious because:
demand is high,
rewards are fresh,
participation is active,
everyone is watching.
But incentives don’t fail loudly. They thin out:
repair work yields less return,
low-demand data stops justifying attention,
validators reprioritize rationally,
“later” becomes “eventually.”
This is where the assumed incentive is supposed to activate and where many designs fall silent.
I wasn’t looking for motivation. I was looking for enforcement.
Motivation depends on mood and markets. Enforcement survives boredom.
The incentive everyone assumes usually amounts to:
It’s probably still worth it for someone to take care of the data.
That is not an incentive.
That is a hope expressed as economics.
Why this assumption is dangerous in decentralized storage.
Because storage doesn’t fail when incentives vanish completely.
It fails when they weaken just enough:
enough that neglect is cheaper than maintenance,
enough that redundancy masks early decay,
enough that no single actor feels responsible.
At that point, data doesn’t disappear.
It becomes unsafe and no one is clearly at fault.
Walrus starts from the absence, not the assumption.
Walrus does not assume that long-term care is naturally incentivized. It asks:
What happens when repair yields minimal upside?
Is neglect penalized or merely tolerated?
Does degradation surface before users are affected?
Can responsibility be deferred indefinitely?
Its architecture is built around making the absence of care expensive not just making care attractive when conditions are favorable.
The real incentive is not reward it’s consequence.
In systems that actually hold:
abandonment has a cost,
silent decay is punished,
repair remains rational even when demand is low,
responsibility cannot evaporate into “the network.”
Walrus emphasizes consequence-driven incentives over optimism-driven participation. This is less exciting in early narratives and far more durable over time.
Why “someone will do it” is the weakest incentive of all.
Decentralization multiplies participants.
It also multiplies the temptation to defer responsibility.
When everyone assumes an incentive exists, no one checks whether it still binds under stress. That is how:
repair queues lengthen unnoticed,
availability degrades politely,
recovery becomes uneconomic,
users discover failure too late.
Walrus treats this diffusion of responsibility as a design flaw, not an emergent property to accept.
As Web3 matures, assumed incentives become unacceptable.
When storage underwrites:
financial records,
governance legitimacy,
application state,
AI datasets and provenance,
“probably incentivized” is not good enough. Serious systems must demonstrate:
when incentives activate,
what happens when they weaken,
who pays before users do,
how neglect is surfaced early.
Walrus aligns with this maturity by making incentives auditable in outcomes, not inferred from intent.
I didn’t find the incentive everyone assumes and that was the point.
Because when incentives are truly present, they are hard to miss. They show up as:
immediate consequences,
predictable behavior under stress,
early recovery instead of late explanations.
The absence of hand-waving is the signal.
Walrus earns relevance by refusing to rely on the incentive everyone assumes exists and by building around the uncomfortable possibility that it doesn’t.
@Walrus 🦭/acc #Walrus $WAL