Noman_peerzada

There’s a quiet design choice in Falcon that tells you they’re thinking beyond “launch hype” and into “can this survive ugly markets.”
They separate the stable unit USDf from the reward layer sUSDf.
@Falcon Finance , At first glance, that looks like extra complexity. In practice, it’s the opposite: it’s how you keep the system clean when things get messy,when yield turns negative, when redemptions surge, when markets gap, when people start asking hard questions about backing and solvency.
If you want long-term trust, you don’t mix the dollar and the yield in one token and hope nobody notices the risk.
 
1 The simple mental model Falcon is aiming for
Falcon’s split creates two different promises:
• USDf: “This is the synthetic dollar unit. It’s the accounting base.”
• sUSDf: “This is what you hold if you want the reward stream tied to how the system deploys assets.”
That’s a big deal because “stable” and “yielding” are fundamentally different products.
A stable unit is about predictability. A yield unit is about participation (and participation always includes risk, even if it’s managed well).
When those two live inside one token, the protocol ends up in a permanent messaging trap:
• If yield goes up, people treat the token like an investment.
• If something goes wrong, people still demand it behave like cash.
Splitting the layers removes that confusion.
 
2 Why mixing “dollar + yield” is where stablecoins get into trouble
A lot of failures in crypto don’t start with bad code. They start with blurred liability.
If the same token is:
• the main unit used for payments,
• the unit everyone expects to redeem as “a dollar,”
• and the unit exposed to strategy performance,
then every strategy drawdown becomes a direct threat to the perceived stability of the dollar itself.
Even a small event can snowball:
1. yield underperforms (or turns negative for a period),
2. fear spreads that “the stablecoin is losing money,”
3. selling pressure hits the “stable” token,
4. peg stress begins,
5. now you have to defend the stable unit and manage strategy unwind at the same time.
A split token model tries to prevent that chain reaction by making the “stable” claim and the “yield participation” claim different things.
 
3 Cleaner risk separation is not marketing it’s balance-sheet hygiene
Think of USDf as the base liability of the system. It’s the unit the protocol wants to keep clean and widely usable: trading pairs, settlement, collateral, payments, integrations.
Then sUSDf becomes the reward wrapper: it’s what you opt into if you want the yield stream.
This matters because it lets Falcon say something very specific during stress:
• “USDf is the accounting unit and stays governed by collateral rules, buffers, and redemption process.”
• “sUSDf is the yield-bearing position; it reflects the performance of the reward mechanism.”
That separation makes it easier to keep the stable unit socially and mechanically consistent.
And in crypto, social consistency matters more than people admit. Confidence is part of the system’s plumbing.
 
4 Why this design is better for integrations and real usage
If you’re building on top of Falcon, you don’t want to guess what kind of token you’re integrating:
• A “stablecoin” that quietly embeds strategy risk is hard to treat as stable collateral.
• A yield token is fine—as long as it’s labeled and scoped correctly.
By splitting USDf and sUSDf, Falcon makes the integration decision clearer:
• Use USDf when you need a dollar-like unit: LP pairs, collateral, settlement, routing, payments, accounting.
• Use sUSDf when you explicitly want yield exposure: vault strategies, treasuries that can accept time/exit constraints, users choosing to earn.
This doesn’t eliminate risk; it makes risk legible. Legible risk is what institutions and serious builders require.
 
5 Redemptions and stress behavior get simpler when the stable unit is “boring”
Here’s the uncomfortable truth: a stable system is not tested in green weeks. It’s tested when exits spike.
When you separate USDf from sUSDf, you can also separate behavior:
• People who want a clean exit route prioritize USDf redemption mechanics.
• People who want yield accept that yield positions may have constraints (cooldowns, processing windows, strategy unwind realities).
This separation reduces the chance that a redemption wave becomes a full confidence crisis—because the core stable unit isn’t simultaneously trying to be a yield product.
A stable token should feel boring. The yield token can be the one that carries “performance personality.”
 
6 Why this matters for long-term trust (the part most users feel, even if they can’t explain)
Over time, users build trust in stable assets through two things:
1. Clarity: “What am I holding, exactly?”
2. Consistency: “Does it behave the same way across market regimes?”
A single token that tries to be both stable and yield-bearing often fails on both:
• It’s unclear what backs what.
• Behavior changes when market conditions change.
Falcon’s split is basically an attempt to make the system feel consistent:
• USDf is treated as the base unit you can reason about.
• sUSDf is the voluntary layer that says: “I want the return profile.”
That’s what “cleaner risk separation” means in practice: the stable unit doesn’t inherit every emotional reaction to strategy performance.
 
7 The hidden advantage: it gives Falcon room to evolve yield without rewriting the dollar
Protocols change. Strategies change. Venues change. Risk controls get tighter. Sometimes the best move is to lower risk and accept lower yield for a period.
If yield and “stable unit” are merged, every change becomes controversial because it affects everyone’s primary token.
When you split USDf and sUSDf, you gain flexibility:
• You can upgrade yield logic, reward cadence, or distribution mechanics without forcing the base dollar token to “feel different.”
• You can be conservative on yield when conditions demand it without triggering a stablecoin identity crisis.
That adaptability is underrated. Long-lived systems are the ones that can tighten up without losing legitimacy.
 
8) Where people still get confused (and how Falcon’s model helps)
The main confusion you’ll see in communities is this:
“If sUSDf exists, does that mean USDf is worse?”
No. It means USDf is not pretending to be something it isn’t.
USDf’s job is to be the unit that stays coherent:
• tied to collateral rules,
• buffered by haircuts and reserves,
• designed to remain usable even when markets are chaotic.
sUSDf is the opt-in bet:
• you want yield,
• you accept the system’s reward dynamics,
• you hold the wrapper.
The split is basically consumer protection, but in protocol form.
 
Takeaway
Falcon splitting USDf (stable unit) from sUSDf (yield layer) is not a cosmetic token design. It’s a deliberate choice to keep the system honest:
• Stable money should be clean and predictable.
• Yield should be opt-in and clearly scoped.
• Mixing both into one token tends to create confusion, bad incentives, and harder peg defense during stress.
If Falcon wants USDf to become a real settlement asset over years—not just a short-term farm token—this is exactly the kind of separation you build early.
#FalconFinance $FF

There’s a point where “AI” stops sounding exciting and starts sounding risky.
@APRO Oracle ,Not because AI is useless. It’s because in a financial system, the question is never “can it do the job?” The question is “who is accountable when it’s wrong?” And with oracles, “wrong” isn’t an opinion. Wrong is a liquidation. Wrong is a bad fill. Wrong is a protocol that looks solvent until it isn’t.
That’s why the way APRO frames AI is the part I pay attention to most. The smartest way to use AI inside an oracle network is not as a judge handing down truth. It’s as a verifier, a critic, a pattern-detector that makes the system harder to fool. Helpful intelligence, not absolute authority.
Because the moment an AI model becomes the decision-maker, you’ve basically created a black box that your users cannot properly audit. You can explain it, you can market it, you can even claim it “usually works.” But when the market gets stressed, “usually” is not a comfort. People will ask: why did the oracle output that value? And if the answer is “the model decided,” you’re already losing trust.
So the clean design principle is this: AI should help reduce errors and detect manipulation, but the final truth still needs to be bound to a process that can be verified, reproduced, and challenged.
That’s the line APRO seems to be drawing with its “AI-driven verification” language.
AI as verifier means it plays defense, not offense
In a practical sense, AI inside an oracle network should do a few things very well:
It should compare sources and notice when one source behaves strangely.
It should spot patterns that look like spoofing, thin-liquidity wicks, or coordinated push attempts.
It should flag anomalies early, so the network doesn’t blindly pass toxic inputs into on-chain reality.
These are defensive tasks. They harden the system. They reduce the chance that a single manipulated venue or a single noisy data stream becomes the output.
But none of that requires AI to be “the truth.” It requires AI to be a pressure test.
And this distinction matters more than it sounds because oracles live in the gap between messy off-chain reality and rigid on-chain logic. Off-chain sources are not clean. Exchanges glitch. APIs delay. Indexes diverge. Real-world markets have stale updates. Even “good” data can be wrong for a few minutes. If your oracle system treats every input as equally sacred, you’ll ship mistakes.
So the role of intelligence is to help detect when inputs are unsafe.
The hardest oracle problem is conflict, not collection
A lot of people think oracles fail because they can’t fetch data fast enough. That’s rarely the real failure. The real failure is conflict.
Two sources disagree.
A price spikes on one venue but not others.
A market is thin and a single large trade prints a weird value.
Or worse, multiple venues move because someone is actively trying to push the reference.
In these moments, the oracle isn’t just collecting. It’s choosing.
And choosing is where accountability lives.
If APRO is using AI as part of conflict handling, the most important question is whether AI is used to support a consensus process or whether it replaces it. The first approach is sane. The second approach is fragile.
Sane looks like: the network still relies on multi-source aggregation, signing, verification steps, and clear rules about when something is accepted or rejected. AI adds an extra lens that helps catch abnormal behavior early. In short, AI reduces the chances the system gets tricked.
Fragile looks like: the model becomes the deciding authority. Which might work in calm markets, and then fail in the exact moment you need it most.
Safety boundaries are not optional, they’re the product
If you’re building an oracle network that wants to be trusted by DeFi protocols, RWA products, and AI agents, you need hard boundaries:
Clear rules for what happens when sources disagree
Clear thresholds for rejecting outliers
Clear verification steps for what becomes “truth” on-chain
Clear fallback behavior when data is missing or suspicious
These boundaries are the difference between “fast” and “safe.” And APRO’s pitch, at least in design, is trying to be both: speed where it matters, and verification where it counts.
Now link this back to $AT , because that’s where incentives meet reality.
Why AT matters in an AI-assisted oracle design
The token isn’t just there to be traded. In a real oracle network, the token is supposed to secure behavior.
If APRO is serious about data quality, then AT should help align participants so that delivering correct outputs is rewarded and dishonest behavior is punished. That is the whole point of decentralized oracle economics.
And AI changes the incentive landscape in a subtle way.
AI can catch more anomalies, but it can also be gamed if people learn its blind spots. The only long-term defense is not “better models” alone. It’s a combination: models + multi-source validation + accountable staking/incentives.
That’s what a mature oracle system looks like. It doesn’t trust any single mechanism too much. It layers defenses.
If you’re watching APRO as a project, this is the lens you should use:
Is AI being used to improve verification without becoming the single point of truth?
Are there visible rules, contracts, registries, and verification flows that make outputs defensible?
Are incentives around $AT designed to protect correctness, not just reward activity?
Because if AI is treated like a magic wand, the trust eventually collapses. If AI is treated like an assistant inside a verifiable system, it becomes a real edge.
That’s the difference between “AI oracle” as marketing and “AI oracle” as infrastructure.
#APRO $AT

@APRO Oracle Se gli oracle riguardassero solo la velocità, il problema sarebbe già risolto. Ma poiché DeFi si sta spostando verso i mercati previsionali, RWAs e agenti autonomi, il vero vincolo non è quanto velocemente arrivano i dati. È se quei dati possano ancora essere considerati affidabili sotto pressione. Lo stress ha il modo di esporre i punti deboli: casualità manipolabile, percorsi di verifica costosi e assunzioni che reggono solo quando tutti si comportano onestamente. È qui che l'APRO Oracle si distingue silenziosamente dal pensiero degli oracle convenzionali.

@Falcon Finance USDf non rimane stabile perché le persone “credono” in esso. Rimane stabile se il sistema può assorbire lo stress: riempimenti cattivi, svendite rapide, coperture affollate e onde di riscatto. L'intero motore è fondamentalmente un budget di rischio, suddiviso tra buffer, regole di buzz e controlli operativi.

1 Il primo ancoraggio: il minting è basato sul buzz, non sulla speranza
USDf è coniato contro collaterali depositati. Con le stablecoin, il minting è vicino a 1:1. Con collaterali volatili (BTC/ETH e altre non stabili supportate), il minting non è 1:1. Falcon utilizza un rapporto di sovracollateralizzazione (OCR) in modo che il sistema emetta meno USDf del valore di mercato del collaterale. Quel divario è il primo ammortizzatore.

#XRP , ancora intorno a $1.87, mantenendosi stabile in una liquidità festiva sottile mentre il mercato più ampio si raffredda. Giù di circa il 15% nel mese, ma il modo in cui il prezzo si comporta non sembra indicare che l'interesse stia diminuendo. Sembra che il flusso venga assorbito e bilanciato.

Puoi percepire la divisione: le istituzioni continuano ad allocare, mentre i detentori più grandi e le posizioni in derivati rimangono più difensive. Quella tensione è la ragione principale per cui XRP continua a mantenere questo intervallo.
Calma dopo un ritracciamento
XRP non sta scendendo. Si sta restringendo. La volatilità si è attenuata e il seguito al ribasso è stato limitato anche con la liquidità ridotta. Questo è solitamente ciò che si osserva quando il mercato è bilanciato, non morto.

@Falcon Finance , Most synthetic dollars aren’t designed for how people actually hold assets. They’re designed for a clean balance sheet: one collateral type, one risk profile, one predictable user. Show up with a real trader’s bag, and the protocol’s message is basically, “tidy it up first, then we’ll talk.”
USDf is built on a different assumption. The market will not tidy itself up. People will keep holding a mix of stables, majors like BTC and ETH, selected higher-beta assets, and eventually more tokenized real-world exposure. So instead of forcing everyone into one collateral lane, the synthetic dollar has to handle multiple lanes without turning into a fragile promise.
That’s what “universal collateralization” is trying to be. Not a slogan about accepting more assets, but a system that can mint the same dollar unit from different balance sheets while staying conservatively backed. The moment you accept that goal, you stop building a mint and start building a risk engine that has to survive ugly weeks, not just average days.
The behavior problem USDf is targeting is straightforward. People want dollar liquidity, but they don’t want to close the positions they believe in just to get it. Selling into stables is clean, but it’s also a psychological and financial reset button. You lose exposure, you lose optionality, and you often re-enter later at worse prices because markets don’t wait for your comfort. A synthetic dollar that scales has to offer another option: unlock dollars while keeping core holdings intact.
Universal collateral only works if minting is disciplined at the entry point. If you price risk lazily, the system looks healthy until a fast drawdown turns that hidden looseness into a scramble. So the real story is the minting paths, because those paths reveal how the protocol thinks about users.
The first path is what most people expect. Deposit collateral, mint USDf, manage your position, unwind when you want. It feels simple, but the important detail is that the rules change depending on what you deposit, because pretending a stablecoin and BTC carry the same risk is how synthetic dollars break.
When someone deposits stablecoins, the mental model can be close to 1:1. It’s not because stables are perfect, but because the day-to-day volatility risk is lower and the accounting is cleaner. This is the boring lane, and boring is a feature. It’s the lane that lets USDf behave like a usable dollar unit for payments, routing, and portfolio management. It’s also the lane that lets supply grow without dragging in unnecessary volatility.
The second lane is where the design either earns trust or loses it. Volatile collateral changes the entire problem. If BTC or ETH can move fast, the protocol must mint conservatively and keep a cushion that can absorb price swings, slippage, and unwind costs. You will often see this expressed as an overcollateralization ratio with an explicit buffer. The exact threshold can vary by collateral type and market conditions, but the intent stays the same: mint less than the collateral value and keep room for bad candles.
That buffer is not a reward. It’s not a bonus. It’s an insurance layer that sits inside the position. If the market behaves, you may end up reclaiming most of it when you unwind. If the market turns violent, the buffer does its job quietly so the system doesn’t have to socialize losses.
This is also where users mix up redemption and collateral recovery. They sound similar, but they’re different actions with different consequences. Redeeming USDf is about turning the stable unit back into supported stable assets. Closing a collateral-backed position is about unwinding the specific risk you opened when you minted against volatile collateral. In most setups, you close that position by returning the USDf you minted, then you reclaim the collateral net of whatever happened inside the buffer. That separation matters because it keeps the system honest. It tells users, very clearly, that minting against volatility is not the same as swapping stables.
The second mint path is built for a different mindset. It’s for people who can lock collateral for a defined term and prefer clear outcomes over constant flexibility. Instead of behaving like an always-open position, the contract behaves more like a fixed-term deal. You mint USDf upfront, your collateral is committed for a period measured in months, and the end result depends on where price ends relative to predefined levels.
The easiest way to understand this is to think in conditional outcomes. If price falls far enough during the term, collateral can be liquidated to protect system health while the user keeps the USDf they minted upfront. If price finishes in a middle band, the user can typically reclaim collateral by returning the original minted USDf within a maturity window. If price finishes strong above a strike-like threshold, there can be an additional USDf payout based on the terms. It’s not magic yield. It’s a trade: immediate liquidity now, and a more defined payoff profile later, with the user accepting constraints.
Why include a fixed-term lane in a synthetic dollar design. Because scale rarely comes from one type of depositor. Some users want flexibility and will pay for it by minting conservatively. Others want capital efficiency and are willing to accept a lock and clear boundaries. Multiple lanes widen the intake without forcing the entire system to loosen risk standards just to grow.
This is the real reason universal collateralization can scale if it’s executed with discipline. It widens the funnel without telling the market to become simpler. Stablecoin holders can participate. BTC and ETH holders can participate. Higher-beta holders can participate if eligibility is carefully controlled. Tokenized real-world exposure can eventually participate if it meets liquidity and risk criteria. That matters because market seasons rotate. A synthetic dollar that depends on one collateral class tends to stall when that class falls out of favor.
The second reason it scales is that it prices risk at the mint, not later when it becomes panic. The mint is the moment where you decide whether future stress is survivable. If you mint too generously, you create a quiet debt that only appears when volatility hits. If you mint with buffers and conservative ratios, you buy time and reduce the odds of a cascade.
The third reason is operational realism. Big systems need pressure valves. The fantasy is instant redemption at infinite scale while collateral is actively deployed and markets are stressed. Real markets do not behave like that. Timing controls, cooldowns, and structured unwind routes can feel annoying in calm periods, but they exist for the weeks when everyone wants out at once. The protocols that survive are usually the ones that admit this early instead of learning it publicly.
If you’re evaluating USDf, the questions worth asking aren’t about how broad the collateral menu looks on a poster. They’re about discipline and behavior under stress. How conservative the buffers remain during fast crashes. How unwind routes behave when liquidity vanishes where you expected depth. How dependent operations become on a small set of rails. How large the backstop capacity is relative to system size, and how often it would realistically need to engage. And whether the protocol keeps its standards when growth pressure arrives, because that’s the moment most systems quietly weaken themselves.
Universal collateralization doesn’t make a synthetic dollar risk-free. It makes scaling possible without pretending the peg is held together by optimism. If USDf works the way it’s meant to, the big change isn’t a new stable unit. It’s the idea that dollar liquidity can be unlocked from the portfolios people actually have, without forcing them to sell first and regret later.
#FalconFinance $FF

@APRO Oracle , People keep calling everything “next-gen” until the words stop meaning anything. So when I hear Oracle 3.0, I don’t think version numbers. I think failure modes. What exactly broke in 1.0 and 2.0, and what has to be true for the next step to be worth building on.
Oracle 1.0 was basically delivery. Get a price on-chain. Make it available. The core risk was obvious: if you can corrupt the feed, you can corrupt the protocol. Oracle 2.0 improved the economics and decentralization around that delivery, but it still lived in a narrow world. Mostly prices. Mostly scheduled updates. Mostly numeric data that’s easy to define and hard to verify at the edge.
Oracle 3.0, at least the version builders care about, is not “more feeds.” It’s a change in what the oracle is responsible for. The oracle becomes a verification layer, not just a publishing layer. It’s expected to deliver data fast, but also to prove that the data deserves to be trusted at the moment value moves.
That difference matters because modern DeFi isn’t waiting politely for a heartbeat update. Liquidations happen in seconds. Perps funding shifts constantly. Vaults rebalance around tight thresholds. RWA protocols depend on reference values that may be stable most days and suddenly sensitive when stress hits. Agents query data repeatedly, not because they love data, but because they make decisions continuously. In all those cases, “stale but cheap” is not a neutral trade. It’s a hidden risk multiplier.
So what does Oracle 3.0 mean in practical terms.
It means separating data retrieval from data finality. Retrieval can be fast, messy, and frequent. Finality has to be strict. If you compress both into one step, you either get slow truth or fast guesses. Oracle 3.0 tries to keep speed without letting speed become trust.
For builders, that usually shows up as a two-mode mindset. One mode is push, where updates are published on a schedule or when certain thresholds are hit. The other mode is pull, where the application asks for the latest value at the moment it needs it, and the oracle provides a value along with the proof path that makes it safe to act on. In practice, this changes your architecture. You stop designing around “the feed updates every X seconds” and start designing around “the feed is verifiable when my contract needs it.”
Speed plus verification matters most in three places.
The first is liquidation logic. If your risk engine triggers based on a price, your whole protocol is a race between market movement and data freshness. A fast oracle without verification lets manipulation slip through. A verified oracle that is too slow causes bad debt because positions aren’t closed in time. Oracle 3.0 tries to narrow that gap by letting you request data on demand while still keeping the acceptance criteria strict.
The second is RWA settlement. Real-world assets introduce a different kind of fragility. Prices can be stable, but they can also be discontinuous. Market hours, corporate actions, reporting delays, and fragmented venues all complicate “truth.” Builders need more than a number. They need timestamps, confidence, and an audit trail that can survive disputes. Oracle 3.0 fits this better because it treats “verification” as a first-class requirement rather than assuming the oracle is trusted by default.
The third is agent-based systems. Agents don’t just consume data. They iterate on it. They poll, compare, update, and act. If your oracle is slow or expensive, agents adapt by caching or using heuristics, and that’s where errors creep in. If your oracle is fast but weak, agents become attack surfaces because they react instantly to poisoned inputs. Oracle 3.0 is basically acknowledging that agents raise the frequency of truth demands, and frequency without verification becomes an exploit factory.
One of the most useful ways to think about APRO’s Oracle 3.0 angle is that it treats the oracle as part of the application’s security boundary. In older models, the oracle was “outside” the app. You trusted it, then built your app logic inside that trust. In a verification-first model, the oracle becomes a component you can reason about, because the app can validate what it receives rather than swallowing it whole.
That shifts the builder workflow. You don’t only ask “what price do I get.” You ask “what do I get that proves the price is acceptable.” That is a different integration story and it forces cleaner design.
There are tradeoffs, and they’re worth naming plainly. Verification has cost. Even if parts are optimized, nothing is free. If your protocol pulls frequently, you need to design so you’re not paying verification overhead on every trivial action. This is where caching layers, threshold triggers, and risk-based frequency scheduling matter. The best integrations treat oracle calls like risk operations, not like UI refreshes.
Another tradeoff is complexity. Developers love simple interfaces. But the reality is that oracles have become more complex because applications became more complex. You can hide that complexity with abstraction, but you can’t remove it without giving up either speed or safety. Oracle 3.0 is basically choosing to expose just enough of the complexity that builders can make good decisions.
If you zoom out, this fits DeFi, RWA, and agents for the same reason. All three are about moving value based on external truth. DeFi is fast truth. RWA is contested truth. Agents are frequent truth. The common denominator is that the oracle is no longer a price pipe. It’s a decision surface.
The line I’d leave you with is this. Oracle 3.0 isn’t an upgrade because it’s newer. It’s an upgrade because it admits what builders already learned the hard way: speed without verification is a liability, and verification without speed is a bottleneck.
#APRO $AT

• Si sta formando un disallineamento strutturale
Si sta formando un disallineamento silenzioso tra come è stata progettata l'infrastruttura blockchain e come l'intelligenza sta iniziando a operare.
@KITE AI ,La maggior parte delle catene sono state costruite su un'assunzione che è rimasta valida per oltre un decennio: l'attore economico è umano. Un portafoglio corrisponde a una persona o a un'organizzazione. Una transazione rappresenta un momento esplicito di intento. La governance presuppone deliberazione, responsabilità e tempi di reazione misurati in minuti o giorni. Anche l'automazione, dove esiste, è inquadrata come delega sotto stretta sorveglianza—bot che eseguono strategie ristrette, script che seguono regole deterministiche, sistemi che possono essere messi in pausa o incolpati quando qualcosa va storto.

@KITE AI , Se costruisci un'economia che paga per il contributo, non attiri solo costruttori. Attiri fabbriche. La prima seria minaccia non è qualcuno che ruba fondi. È qualcuno che produce “utilità” su larga scala, riciclando crediti attraverso migliaia di identità usa e getta fino a quando il lavoro reale viene escluso dal prezzo. Questo è il momento in cui l'attribuzione smette di essere una bella idea e diventa un problema di sicurezza.
PoAI si trova proprio nella zona di esplosione di quel problema perché sta cercando di trasformare l'attività degli agenti in valore misurabile. Una volta che il valore diventa misurabile, diventa manipolabile. Il più economico attacco Sybil in un'economia di agenti non è rompere il consenso. È allagare il livello di punteggio con attività che sembrano abbastanza legittime da passare, poi raccogliere ricompense come una tassa su tutto il sistema.