Burning BOY

I was wiring identity attestations inside Sign Network and the part that slowed everything down was not storage or gas. It was deciding who gets to be recognized as “real” when the system is under pressure. That question shows up earlier than expected. Before UI, before scaling, before even thinking about cross-chain sync. You hit it the moment multiple issuers start writing identity claims into the same schema. The friction sits at admission.
Sign lets you define schemas for attestations and then different entities can issue claims against them. On paper, that feels flexible enough to model government-style identity. Multiple authorities. Verifiable records. Reusable across services. But once you try to treat those attestations as a gate into something sensitive, like access to benefits or compliance-restricted services, flexibility starts behaving like risk. Because not every issuer should carry the same weight.
In one setup I tested, we allowed three different issuers to write identity attestations under a shared schema. One was a verified institution, the other two were semi-trusted partners. The system technically accepted all three. But downstream applications didn’t treat them equally. Some flows started implicitly prioritizing one issuer over the others, even though nothing in the schema enforced that hierarchy. It emerged through usage. That is where it starts to feel like government systems. Not because of centralization, but because admission quietly becomes policy.
You can keep the schema open and let anyone write attestations, but then you spend your time filtering downstream. Or you tighten admission at the schema level, requiring issuers to meet certain conditions before they can even write. Both paths shift where the burden lives. In the first case, applications absorb the complexity. In the second, the protocol does. One strong line kept coming back while working through this: Identity systems are not built on data, they are built on who is allowed to write it.
Sign exposes that clearly because attestations are composable. Once a claim exists, it can be reused across contexts. That reuse is where things get messy. A single weak issuer can leak into multiple applications if the schema doesn’t gate properly. And once those attestations are referenced elsewhere, cleaning them up is not straightforward.
I tried a stricter admission model next. Issuers had to stake before writing identity attestations. Not a large amount, but enough to make careless issuance expensive. It worked in one sense. Low-quality attestations dropped immediately. The noise reduced. But a different cost appeared. Onboarding slowed down. Smaller institutions hesitated. The system became cleaner, but also quieter. That tradeoff sits right in the middle of the design. You reduce false identities, but you also reduce participation.
Another friction point showed up when syncing identity across services. Even with a shared schema, interpretation drifted. One application treated an attestation as sufficient proof. Another required two independent attestations for the same identity before granting access. Same data, different thresholds. The protocol stayed neutral, but the system as a whole became inconsistent.
If you test this yourself, try issuing a single identity attestation from a trusted source and plug it into two different apps. Watch how each one reacts. One will likely accept it immediately. The other may ask for reinforcement. The difference is not technical. It is policy leaking through implementation. That is where retry behavior becomes visible.
When an attestation is not accepted, users don’t see a schema mismatch. They see failure. They try again. Maybe through a different issuer. Maybe with additional data. Over time, this creates patterns. Certain issuers become default paths not because they are explicitly required, but because they succeed more often. Routing quality turns into hidden privilege. And once that happens, the system is no longer as open as it looks.
There is also the question of permanence. Because Sign anchors references on-chain while allowing data to live off-chain, identity records can be updated without rewriting everything. That helps with corrections and revocations. But it also introduces timing gaps. Between issuance and propagation, different parts of the system can hold slightly different views of the same identity.
If you simulate a revocation event and query immediately from two services, you might get conflicting answers for a short window. Not a bug. Just propagation lag. But in a government-grade context, even small windows like that matter. I’m not fully convinced where the right boundary sits.
Too open, and you spend your time filtering unreliable identity signals. Too closed, and you recreate the same bottlenecks these systems were trying to avoid. Sign does not force either direction. It gives you the surface area to make that decision, which is both useful and uncomfortable.
At some point, the economic layer becomes unavoidable. When you start thinking about issuer reputation, staking requirements, and penalties for bad attestations, the token stops being optional. It becomes the mechanism that holds the admission policy together. Not as an incentive headline, but as a way to price trust. Still feels early.
If you are building identity flows on top of this, try a simple test. Let two issuers with different trust levels write into the same schema and see how your application reacts over time. Then tighten admission and watch what breaks. The difference between those two states says more about your system than any documentation.
I keep coming back to the same unresolved question.
Who should be allowed to write identity, and how expensive should it be to be wrong.
@SignOfficial #SignDigitalSovereignInfra $SIGN

I ran into this inside Sign Network while trying to wire attestations into a compliance flow that looked simple on paper. A verifier issues a credential, a schema defines what counts, and downstream apps read it. Clean. Until you try to make that system hold up under real-world constraints like revocation, jurisdiction rules, and audit trails that actually need to survive scrutiny. The friction shows up in who gets to verify, not how verification works.
At first I assumed governance here was about token voting or parameter tuning somewhere in the background. It is not. It is about deciding which verifier is allowed to write truth into the system, and what happens when that truth later becomes inconvenient. The moment you connect Sign to anything resembling compliance, verifier authorization stops being a technical detail and starts acting like a liability surface.
The system does not break when data is wrong. It breaks when the wrong party is allowed to make it right.
One setup I worked through used a reusable schema for KYC-style attestations across two apps. Same schema, same format, different verifier sets. In theory that should create portability. In practice it created drift. One verifier was issuing attestations with a 24-hour review window, another with effectively instant issuance. Both valid. Both readable. But downstream, one class of users started passing checks faster simply because their verifier had lower latency and looser review thresholds.
Nothing in the protocol flagged this as inconsistent. From the system’s perspective, both attestations satisfied the schema. From a compliance perspective, they were not equivalent at all. Governance here was not a vote. It was embedded in verifier selection, and that selection quietly shaped who moved faster through the system.
Another case was revocation. Sign allows attestations to be updated or revoked, but the responsibility sits with the original issuer. Sounds reasonable until you hit a real scenario. A verifier goes inactive. Not malicious, just gone. Now you have stale attestations that still pass schema checks but no longer reflect reality. To patch this, we had to introduce a secondary verifier layer with override permissions. That reduced one risk but introduced another. You now have a class of actors who can effectively rewrite trust signals after the fact.
The workflow changed immediately. Instead of asking “is this attestation valid,” we started asking “which verifier issued this, and who can override it.” Two extra steps. More cognitive load. Slower decisions.
There is a tradeoff sitting right in the middle of this. Tightening verifier authorization improves reliability but reduces openness. You can require staking, reputation thresholds, or governance approval before someone can issue attestations, and that does filter out noise. But it also slows onboarding and concentrates power. In one internal test, adding a stake requirement cut low-quality attestations by a noticeable margin, but it also reduced new verifier participation enough that schema coverage became patchy. Some regions simply had no active verifiers.
That is where I start to feel slightly biased. I lean toward stricter verifier gating because the failure modes of loose systems are harder to unwind later. But I also know this biases the network toward fewer, more centralized actors. Not ideal for something that wants to stay composable.
If you want to test this yourself, try mapping three verifiers issuing the same schema and see how quickly their behaviors diverge under load. Or take a schema that depends on off-chain data and simulate what happens when one verifier updates their data source and another does not. The protocol will not stop either of them. It will happily carry both forward.
Another small test. Introduce a delay between attestation issuance and acceptance in your app layer. Even a few minutes. Watch how it changes user expectations and verifier behavior. Some will adapt. Others will drop off. That delay becomes a governance tool, even though it lives outside the protocol.
Eventually the token layer shows up, but not where I expected. It does not just coordinate incentives. It defines who can afford to participate as a verifier and who can absorb the cost of being wrong. If staking is involved, then governance is partially priced. Not in a speculative sense, but in terms of who can lock capital to gain authority. That changes the shape of the verifier set before any vote ever happens.
What surprised me most is how little of this is visible at the surface. From the outside, Sign looks like a clean attestation system with flexible schemas and cheap storage patterns. Underneath, governance is happening through small, compounding decisions about verifier admission, revocation authority, and how much inconsistency the system tolerates before someone intervenes.
I am still not sure where the right balance sits. Too open and you get noisy, uneven trust signals that leak into every downstream app. Too strict and you end up recreating the same gatekeeping structures this was supposed to avoid. The protocol does not resolve this for you. It just makes the tradeoffs programmable.
And once those choices are encoded into schemas and verifier sets, they are harder to unwind than they look.
@SignOfficial #SignDigitalSovereignInfra $SIGN

I ran into this inside *Sign Protocol* while wiring a verifier for a simple credential check. The flow looked clean on paper. Issuer defines schema. User holds credential. Verifier requests proof. Minimal data moves. That part worked. What didn’t settle was who gets to ask for what, and how confidently the system enforces that boundary once things scale beyond a demo.
The first time it felt off was during verifier onboarding. There is a notion of authorization. Some verifiers are supposed to be allowed to request certain claims, others not. In practice, that boundary is softer than it looks. You define schemas with fields, maybe restrict access by contract logic or registry rules, but the moment a verifier is admitted, the system assumes they will behave within intent. It is less about enforcement and more about expectation.
Verifier authorization is not a permission check. It is a trust assumption wearing a technical shape.
That difference shows up quickly in workflow. I had a schema where only age confirmation should be exposed. Boolean, over 18 or not. Instead, the verifier integration was requesting additional fields tied to the same credential context. Not malicious. Just convenient. The marginal cost of asking for more was near zero once the pipe existed. The wallet surfaced the request, yes, but most users do not parse fields line by line. They approve flows, not data structures.
So the system technically preserved selective disclosure. Operationally, it nudged toward over-collection.
A small test I started running. Give two verifier implementations the same schema. One strict, one slightly opportunistic. Watch what they request over time. The second one slowly expands scope. Not because it has to. Because it can. The protocol does not strongly penalize that behavior. It assumes governance somewhere else will. That somewhere else is the weak layer.
Another place this shows up is audit. Sign’s trust fabric emphasizes evidence without moving raw data. That sounds right. You get proofs, not payloads. But verifier authorization determines what gets asked in the first place, which defines what gets logged indirectly. If a verifier is loosely authorized, they can generate a pattern of requests that becomes a behavioral trace. Not raw identity leakage, but interaction leakage.
I tried mapping a user session across three verifiers. Each one only requested minimal proofs individually. Combined, they revealed timing, sequence, and intent. The system did not break privacy rules. It followed them precisely. Still, the composite picture was richer than expected. So now the question shifts. Not what data is shared. What questions are allowed to be asked repeatedly.
There is a real tradeoff here. Tighten verifier authorization too much and onboarding slows to a crawl. Every verifier needs explicit approval, scoped permissions, maybe even stake or reputation before access. That reduces abuse, but it introduces friction at the adoption layer. Teams building on top do not want to wait weeks to get access to a claim type. They will route around it or duplicate logic elsewhere.
Loosen it, and you get velocity. Faster integrations, more usage, quicker feedback loops. But the system starts to behave like a soft gateway instead of a hard boundary. You rely on external governance, legal agreements, or social enforcement to correct behavior later.
In one integration, we tried adding a simple rate constraint. A verifier could only request a certain claim type a fixed number of times per user session. Not perfect, but it introduced a cost to over-requesting. What changed was subtle. The verifier started batching requests more carefully. Fewer redundant calls. Cleaner flows. But it also introduced a new failure mode. If a legitimate retry was needed due to network issues, it sometimes hit the limit and failed. The friction moved from privacy risk to reliability risk. That shift matters. You do not remove friction. You relocate it.
Another mechanical example. Revocation checks tied to verifier authorization. If a verifier is allowed to request a credential, should they always be allowed to check its status? Sounds obvious. But status checks can leak usage patterns if overused. So you gate them. Now the verifier must cache or sync status lists. If their cache is stale, they might accept a revoked credential. If they sync too often, they recreate centralized visibility patterns. The authorization decision directly affects how often they touch the network.
Try this. Run a verifier in a low-connectivity environment. Limit its status sync frequency. Then simulate a revoked credential. Does it catch it in time? Now increase sync frequency and watch network patterns. Somewhere between those two, you pick your risk. I am not fully convinced the current balance is right.
There is also a bias here. I tend to think in terms of adversarial behavior even when most verifiers are benign. Maybe the system works fine for cooperative actors and I am overfitting edge cases. But identity systems rarely fail under normal conditions. They fail at boundaries. And verifier authorization defines those boundaries more than any other layer in this stack.
The token layer eventually enters this conversation, even if indirectly. Incentives can be attached to verifier behavior. Stake to request certain claims. Penalties for misuse. That makes authorization less of a static rule and more of a dynamic posture. But it also adds cost. Not just financial. Cognitive. Builders now have to reason about economics, not just integration.
Another small test worth running. Give verifiers economic skin in the game. Then observe if request patterns become more conservative. Or if they simply pass the cost downstream and continue as before.
What keeps bothering me is how invisible this layer is to most users. Issuers are visible. Credentials are visible. Wallet interactions are visible. Verifier authorization sits underneath, shaping everything, rarely questioned unless something goes wrong.
And when something does go wrong, it does not look like a bug. It looks like a normal flow that asked slightly more than it should have.
I keep coming back to that moment during integration. Everything technically correct. Nothing obviously broken. Still a sense that the system trusted the verifier a bit too early.
Not enough to fail. Enough to matter.
@SignOfficial #SignDigitalSovereignInfra $SIGN

Identity doesn’t move. It gets re-proven, every time.
I ran into this inside Sign Protocol while trying to reuse a credential across two chains. Same wallet. Same schema. Same user context. Still had to re-attest, or at least it felt that way.
At first I assumed it was a sync issue. Maybe indexing lag. Maybe I pushed too quickly. But after a few iterations, it became clear the system wasn’t failing. It was being strict. The attestation existed, but on the second chain it wasn’t treated as the same object. It was treated as a claim that needed to be re-validated in that environment.
That’s where Sign’s model starts to feel different. It doesn’t try to unify identity across chains. It standardizes how claims about identity are structured and verified. You’re not moving identity. You’re moving evidence.
I tested this using a simple credential stored off-chain on IPFS, referenced on Ethereum through a schema. When I reused it on another chain, I didn’t rewrite the data. Just referenced the same CID. The gas cost dropped significantly. In one case, writing a full on-chain payload would have cost 5 to 10 times more than just anchoring a reference. But the bigger shift wasn’t cost. It was behavior.
Before, identity fragmentation showed up quietly. Same user, slightly different state across chains, nothing obviously broken. After using Sign, the failure becomes visible. Either the reference resolves correctly under the schema or it doesn’t. No silent drift. That removes one class of risk. But it introduces another.
Second test. I pushed around 60 attestations across two chains, all tied to the same schema and off-chain data. About 20 of them didn’t resolve cleanly on the receiving side the first time. Not because the data was wrong, but because the schema interpretation differed slightly between environments. The system didn’t corrupt. It just refused to accept.
That pause is doing real work. It shifts the burden from execution to design. If your schema is loose, cross-chain identity becomes unpredictable. If it’s tight, things start behaving more consistently, almost like a shared verification layer rather than separate identity silos. But you pay for that upfront.
Designing schemas that survive across chains is not forgiving. You need to anticipate how different systems will read the same fields before anything goes live. That slows iteration. It adds friction early. Feels unnecessary until you try scaling identity across three or four environments and realize the alternative is constant duplication. I’m not fully convinced most teams will get this right early on.
There’s also a deeper shift in where the cost sits. Off-chain storage reduces gas usage, especially when you’re dealing with larger payloads. But now coordination becomes the expensive part. Every system consuming that attestation needs to agree on how to interpret it. And that agreement is not enforced by the chain.
It’s enforced socially. Through shared schemas. Through conventions. Through teams deciding, often implicitly, what a field actually means.
Try this. Take one attestation and reuse it across two chains, but change how one field is interpreted on the receiving side. Not the structure, just the meaning. Watch what happens a few steps later when something depends on it. The break won’t be immediate. It’ll show up downstream, where identity is assumed to be consistent.
Or go the other way. Lock the schema tightly. Enforce strict validation everywhere. You’ll get cleaner cross-chain behavior, but you’ll also feel the slowdown in development. More planning. More coordination. Less flexibility.
Somewhere in the middle of this, I stopped thinking about attestations as identity and started seeing them as checkpoints. Not who someone is, but what has been accepted about them at a specific moment, under a specific schema.
That framing made things easier to reason about. You’re not syncing identity across chains. You’re syncing agreement.
Which is why the token starts to make sense later, even if you ignore it at first. You need some way to coordinate participants around shared schemas, resolve disputes in interpretation, and maintain consistency over time. Without that, the system drifts back toward fragmentation, just at a higher layer. Still, I have doubts.
If two applications use the same schema but assign slightly different meaning to one field, is the identity actually shared? Or have we just moved fragmentation from infrastructure into interpretation?
And what happens when scale increases. Not hundreds, but thousands of attestations moving across chains daily. Does schema discipline hold, or does it loosen under pressure?
Try pushing 50 to 100 attestations across multiple chains and watch where alignment starts slipping. It doesn’t break immediately. It degrades slowly. That’s the part I’m still watching.
Sign doesn’t eliminate fragmentation. It makes it visible, structured, and harder to ignore. It replaces silent divergence with explicit verification. But it also introduces a new dependency on coordination that doesn’t live on-chain. And that coordination might end up being the real system.
@SignOfficial #SignDigitalSovereignInfra $SIGN

I noticed it the second time I tried to run something through Midnight Network, not the first. The first attempt felt smooth enough, almost suspiciously so. The second one is where the friction showed up. Not failure, but hesitation. A kind of quiet check before anything moved forward. That’s where it starts to feel different.
Most privacy systems I’ve touched before either pretend everything is invisible or push the complexity somewhere you don’t immediately see. Midnight doesn’t do that. It doesn’t hide the boundary. It makes you feel it.
The core difference shows up in how admission actually works when you try to interact with a contract that splits public and private logic. You don’t just send data and expect it to be handled. You structure it differently. Some parts are explicitly exposed. Others are locked behind proofs that take time to generate and verify. That split sounds simple until you actually run it.
The system is not asking “is this private?” It is asking “what exactly are you allowed to hide right now, and who still needs to see something to let this through?” That changes how you think about every interaction.
In one case, I tried pushing a transaction where the private section carried more context than strictly needed. It didn’t fail outright. It stalled. The verification step took noticeably longer, and the feedback wasn’t an error message. It was just slower confirmation. You feel it in the workflow. Not dramatic, but enough to make you rethink what you’re sending.
Another case was simpler but more revealing. A contract where the public side handled eligibility and the private side handled the actual values. If the public eligibility check passed, everything moved cleanly. If it didn’t, the private logic never even had a chance to execute. That sounds obvious, but it means failure happens earlier, and more deterministically. You don’t waste cycles proving something that will be rejected anyway.
The risk reduced here is wasted computation and blind trust in hidden logic. The failure mode that becomes harder is submitting something that technically validates but should never have been allowed in the first place. The cost, though, is upfront discipline. You have to design your interaction carefully. The friction shifts from execution to preparation.
Midnight isn’t selling “everything is hidden.” It’s enforcing “only what should be hidden survives the process.” That line keeps coming back.
There’s a tradeoff sitting right in the middle of this. You lose the illusion of seamless privacy. It stops feeling like a black box and starts feeling like a system that constantly asks for justification. It can slow you down, especially when you’re prototyping or trying to move quickly. You don’t get to ignore structure. You have to respect it. I’m not entirely convinced yet that most developers will enjoy that.
But it does something subtle to reliability. Once you adjust, interactions become more predictable. You stop guessing what might fail later because the system forces you to resolve ambiguity early. That changes your workflow. Less trial and error. More upfront thinking. Slightly annoying, but also stabilizing.
If this actually works at scale, you’d expect a few patterns to repeat. Developers would start minimizing private payloads not for gas reasons, but to reduce verification overhead. Contracts would lean heavily on public gating logic. Users might not even notice the privacy layer directly, but they would feel the difference in consistency. Fewer weird edge cases. Fewer “it worked yesterday but not today” moments.
If adoption doesn’t happen, the opposite shows up quickly. People start bypassing the structure. Overloading private sections. Treating the system like a generic execution layer. That’s where things would degrade. Slower confirmations. More unpredictable behavior. The same old pattern, just with extra steps.
There’s also a coordination layer that quietly emerges once you start seeing how interactions depend on this structure. The token eventually makes sense here, not as an incentive in the usual sense, but as a way to price the verification work and align how much complexity you push into the system. If you overload private logic, you pay for it. If you keep things lean, the system rewards that behavior indirectly through smoother execution. Not in a flashy way. More like a feedback loop.
I’ve started catching myself before sending certain transactions. Trimming things down. Splitting logic more cleanly. That’s not something I ever did on other privacy systems. There, the goal was to hide more. Here, the goal is to hide only what survives scrutiny. A small shift, but it compounds.
If this is going to hold, I’d watch for how people actually design contracts over time. Do they default to heavy private logic, or do they start pushing more into the public layer for efficiency? Do verification times stay stable as usage increases, or do they creep up in a way that discourages more complex interactions?
And maybe more quietly, whether people stop talking about privacy as a feature and start treating it as a constraint they work around. I’m still not sure which way that goes.
@MidnightNetwork #night $NIGHT

I kept running into this inside Sign’s TokenTable while trying to map eligibility flows across a Gulf-based distribution pilot. Not the token logic itself. The identity layer. Specifically, who gets recognized as “valid” when multiple issuers, jurisdictions, and attestations start colliding in the same table. Sign looks clean from the outside, but once you’re inside the attestation graph, admission stops feeling neutral. Identity stops being a record and becomes a filter.
The friction shows up the moment you try to reuse an attestation across contexts. A KYC badge issued for a fintech sandbox in the UAE does not behave the same when referenced inside a Saudi distribution workflow, even if both technically resolve on-chain. Sign lets you anchor both, but TokenTable forces a decision: which issuer carries weight here? That sounds like governance. It’s actually routing.
One mechanical example. We tested a scenario where two issuers provided equivalent credentials for the same participant, one from a regional bank and another from a local regulator-backed platform. Both were valid. Both cryptographically verifiable. But when TokenTable evaluated eligibility, only one path propagated cleanly through the allocation logic. The other stalled, not rejected outright, just not prioritized. Same identity. Different route. That changed who received allocation in the first pass.
So the risk that got reduced is obvious: you avoid low-quality or spoofed attestations flooding the system. But the failure mode that becomes harder is more subtle. It becomes difficult to detect when a legitimate identity is being underweighted simply because its issuer sits outside the dominant routing path. Nothing breaks loudly. It just… doesn’t include.
And the cost shows up as redundancy. Teams start collecting multiple attestations for the same user, not because they need stronger proof, but because they need compatibility with whatever routing layer TokenTable implicitly favors. Identity inflation. You feel it in onboarding time.
Second example. Retry behavior under load. When eligibility queries spike, especially during allocation windows, the system doesn’t just slow down uniformly. Some attestations resolve faster because their issuers are already cached or prioritized within the evaluation graph. Others require deeper resolution, more hops, more checks. In practice, this means two identical users can experience different inclusion timing based purely on how “close” their issuer is to the active routing layer.
What changed in workflow is small but persistent. You stop asking “is this identity valid?” and start asking “will this identity resolve fast enough when it matters?” That’s a different question.
There’s a tradeoff here that doesn’t sit comfortably. By tightening which attestations flow smoothly, Sign reduces noise and coordination overhead. But it also creates a quiet gradient of privilege based on issuer proximity. Not intentional, maybe. But operationally real.
I’m not fully convinced this stabilizes at scale. It might. Or it might just formalize a new kind of gatekeeping that looks decentralized on paper.
If this system were to extend into broader Middle East economic workflows, say subsidy distribution, licensing, or cross-border workforce verification, this routing bias would not stay abstract. It would show up as repeated behavior. Certain identities consistently clearing faster. Certain issuers becoming default bridges. Others fading out, even if technically sound. You’d start seeing patterns like:
• Teams pre-selecting issuers before onboarding users
• Participants re-attesting through “accepted” channels just to avoid delays
• Allocation cycles clustering around specific identity providers
And if adoption doesn’t happen evenly, the system doesn’t fail outright. It fragments. Different sectors or jurisdictions would anchor to different issuer clusters, and cross-context portability, which Sign is supposed to enable, becomes conditional again.
The token only starts to make sense once you feel this coordination pressure. $SIGN isn’t just sitting there for incentives. It quietly underwrites which interactions get prioritized, how issuers align, and how verification costs are absorbed. Not in price terms. In behavior. Who stakes, who validates, who becomes part of the fast path. You can almost treat it as a gravity layer for identity routing.
There are small signals already. Developers defaulting to a narrow set of issuers when building flows. Repeated integrations with the same attestation providers. Less experimentation than you’d expect in an open system. That could be early-stage caution. Or it could be the beginning of convergence around a few “safe” identity routes. Two open tests I keep coming back to:
If you onboard a new issuer with strong credentials but no existing routing weight, does it naturally gain usage, or does it stay peripheral?
If you deliberately route through a less common attestation path, do you see consistent delays or exclusions during peak allocation?
And one more, harder to simulate without scale: when multiple jurisdictions start interacting through the same TokenTable, does one identity standard quietly dominate?
What I’m watching is not throughput or transaction count. It’s repetition. Which identities keep showing up in successful allocations, and which ones slowly disappear from the graph even though they never technically failed.
If Sign’s identity layer is going to act as infrastructure in these systems, the real question isn’t whether identities can be verified.
It’s whether they can move without friction across contexts that don’t share the same default trust paths. I’m not sure yet that they can.
@SignOfficial #signdigitalsovereigninfra $SIGN