GAEL_

The AI systems are not restricted to one prompt or one transaction anymore. They work round the clock, have memory, cause activities and cross services. It is no longer the model capability that is the limiting factor, but infrastructure. The majority of the networks were made to handle discrete transactions and pay the outcomes upon execution. Misbehave That model falls apart when systems need to be able to survive, and recollect context, and holistically complete work over time.

This is the starting point of the architecture of Vanar. It does not presuppose that execution can succeed, but makes it a condition. In this design, we have the execution layer VANRY, in which we implement persistence, accountability and completion on the stack. It is not placed as a story item. It is present to ensure AI long-term operations are reliable.
Perseverance is the issue of perseverance. Smart applications make use of remembered context, dynamic state and repetition. When the workflow is capable of halting in the middle of the process because of congestion, unpredictable charges, or in the case of competing demands, the system is not reliable. Best-effort models of tradition accept such a risk by reconciliation of incentives, post-execution. Vanar does not. Before it starts, it has to be economically devoted to its execution.
This commitment is made possible through $VANRY . Through its demand of initial economic commitment, the network precommits the resources required in a task- compute, storage, coordination and settlement capacity. When implementation begins, the infrastructure is bound to finish it within specified parameters. This makes execution a not-so-probable result into a certain process.
The strategy has a direct bearing to failure handling. In most settings, accountability is not clear in case a task fails. Was it a network error, a validator error or an application error? Vanar minimizes this confusion by tying the accountability in execution to commitment in the front. The failures are not diffuse and opaque but can be detected and identified at the infrastructure level. This is critical when it comes to AI systems that will always have to be there.
It is also important to have predictability. AI applications are not able to sustain unstable costs of execution. Spike in the fees or unpredictable settlement status affects workflow and deteriorates user experience. The solution to this proposed by Vanar is to remove cost complexities out of the application layer. The developers deal with stable execution guarantees, whereas the economic cost of persistence is subdued and the price of keeping your head underwater is paid by $VANRY .
Such design also makes blockchain mechanics unnoticed by end users. There is no necessity of consumers of AI-powered services to learn about gas pricing or validator incentives. The complexity is absorbed in the infrastructure. Applications are not an experimental network but act as a structural component and thus allow for this invisibility to take place through the VANRY operation as a structural component, not as a user-facing interaction.
The focus on implementation justifies the real products and integrated layers that Vanar focuses on. Only the memory, reasoning and automation will be relevant so long as they are capable of being sustained. Semantics memory should be available, context logic should be executable, and automated processes should not be broken to provide continuity throughout the lifecycle of an intelligent system at a low cost.
Notably, this model reinvents the creation of value. Vanar is used to measure reliability, rather than examining the success by metrics of raw throughput or speculative activity. Execution that completes. Memory that persists. Predictable systems. $VANRY is able to sponsor this transition with continuity and not events.
The infrastructure expectations alter as AI systems progress to production instead of being in the pilot phase. Accountability, reliability and persistence become uncompromising. Networks with no guarantees of execution will fail to provide the real-world workloads, despite capacity theory. The architecture of Vanar recognizes this fact at all.
The reason that $VANRY exists is because the presence of persistent AI infrastructure needs enforced implementation. It is the layer which ties resources to responsibility, reduces best-effort processes to reliable systems and allows intelligence to work continuously and not contingently. In the world where AI is becoming more reliant on prolonged context and automation, performance is not incidental. It is foundational.
@Vanarchain
#vanar

@Plasma has Ethereum scaling on a conservative tone. A less ambitious yet more defensible guarantee is offered instead of the uninterrupted execution or the availability that it promises: the users are supposed to be capable of restoring their funds on Ethereum in the event of a failure of off-chain systems. This design decision re-invents the scaling as a question of survival as opposed to performance, and it influences all of the structural decisions that Plasma makes.

On the high level, Plasma divides execution and enforcement. Off-chain child chains process transactions and change of state and Ethereum is the ultimate authority on ownership and conflict resolution. During normal operation, Ethereum is not involved in any significant way, but it only receives summarized off-chain activity in the form of compact commitments. The bottom layer does not re-implement transactions or keep checking as it should be. Rather, it is on standby to come in when things have gone wrong.
The fact that something is not an edge case is not handled as such. Plasma directly presupposes the possibility of misbehaving of operators. They can either block transactions, stop block production, issue invalid state commitment, or not provide data at all. Plasma embraces these risks as eminent results of off-chain execution as opposed to attempting to design against them. The credibility of the system is determined by its reaction to failure rather than its performance when in the optimal conditions.
The recovery of funds is the main guarantee in this model. Plasma refers to defined exit mechanisms where users can redeem assets of the child chain back to Ethereum bypassing operator co-operation. There are rules that govern these exits, where there are usually challenge periods where invalid claims can be disputed using cryptographic proofs. Unless there is a valid challenge, the exit will finalize and Ethereum would have its ownership enforced.
There are significant implications of this recovery-first position. On-going implementation is not assured. When an operator ceases to make blocks or to share the data on transactions, the users might not be able to make transactions on the child chain. In the architecture of Plasma, it is no longer a protocol failure but rather an indication to enter a signal to take an execution mode to recovery mode. Normal functionality is stalled and the system transitions to an exit state with users working on recovering the money but not the throughput.
@Plasma limits expressibility over ethereum since exits are required to be enforceable. Complex interdependent smart contract logic is not easy to support as exit conditions need to be verifiable with only limited information on the blockchain. A great deal of Plasma construction thus prefer less complicated forms of states, with ownership histories and ways of spending being confirmable with a minimum amount of ambiguity. These limitations are not accidental diametrics, but the cost of making recovery a practicable matter based on adversarial conditions.
Another indication of the priorities of Plasma is the availability of data. The full transaction data is not recorded to Ethereum meaning that users or external watchers need to track the child chain in order to store the information required to assemble the exits and challenges. Without disclosure of data, no fraud evidences might be produced. Plasma is able to react to this threat not by putting more data on-chain, but by allowing users to quit according to the last known valid state. Once again recovery is more important than further execution.
This design philosophy is in contrast to scaling design philosophy which prioritizes continuous application logic and more user friendly user experience. Such systems usually raise on-chain verification or data availability standards in order to minimize user vigilance. Plasma flows in the other way. It reduces overhead on-chain when normal operation is necessary and services more complicated cases when failures occur. What it has created is an efficient framework, which becomes defensible when it fails.
Knowing Plasma in this light makes it easy to understand why it is such an attractive and why Plasma is not. It does not aim at making Ethereum applications look like centralized systems. It is developed to make sure that the off-chain transfer of execution does not undermine the very possibility to ensure the recovery of assets. Plasma takes ownership negotiating-free and execution conditional.
@Plasma explicitly states what is important in the worst-case by making Ethereum scaling design focused on fund recovery instead of the continuity of execution. Increases in performance are good, although they must have a way users can make a clear, enforced route back to the base layer. Plasma in that regard, does not aim at creating failure. It tries to make failure a survivable experience.
#Plasma
$XPL

The issue of technology has never been an isolated problem in regulated finance. Alignment was always the actual challenge. Banking institutions need mechanisms that would have the capability to implement sophisticated agreements and at the same time uphold confidentiality, regulatory and legal responsibility. Part of this equation is only resolved by most public blockchains. They are programmable and transparent, however, at the expense of disclosing information that cannot be disclosed by regulated markets in a structural sense.

That is the issue that @Dusk Foundation was meant to solve in the first place.
Dusk is not a privacy network designed as a general-purpose, or a version of an existing blockchain with some modifications. The architecture is developed with a particular focus on the needs of regulated assets, in which executions need to be verifiable, compliance enforceable and sensitive financial data is by default confidential. These limitations are being assumed as being foundational, rather than being optional.
Confidential execution is in the core of this design. In Dusk, smart contracts are run in a context where inputs of transactions, internal logic and sensitive state transitions cannot be observed by anyone. The network is still able to prove that it is doing the right thing, and that they are acting in accordance with contractual rules, but it does so without sharing financial information with everyone in the system. This distinction of verification and visibility is a key to those institutions that are unable to publically reveal pricing models, allocation strategies, and counterparty relations.
Regulated finance does not however merely require confidential execution. Regulatory risk is always brought about by a system that conceals information but fails to establish any rules. This is the point at which the on-chain compliance approach adopted by Dusk is critical. Transaction validity itself has compliance logic in it. During execution itself, there are eligibility requirements, transfer restrictions and jurisdictional constraints that are imposed. In case a transaction fails to meet these requirements, it will just be unable to be run through the network.
This level of enforcement at the protocol level removes one of the weaknesses that other blockchain systems have. Compliance on most platforms is also off-chain and operated by intermediaries, authorized gateways or legal terms superimposed on execution. These checks by outsiders cause fragmentation. A transaction can be considered valid by the blockchain and be evaluated individually by regulatory systems. Dusk does not face this disconnect by making sure that compliance is not separate to execution.
The other characteristic factor about the design of Dusk is the manner in which it manages regulated asset lifecycles. Issuance, execution and settlement are not considered as distinct steps that need varied environment or assumptions. The levels of confidentiality and compliance remain throughout the lifecycle. Settlement especially does not reintroduce exposure of the people. Change of ownership and finality is done under the same level of confidentiality that regulates execution and they do not expose any sensitive information at a stage where it is subject to legal ramifications.
To institutions, such a single approach makes their operations less complicated. They do not have to transfer assets across private issuance systems, semi-public execution layers and public settlement networks. The risk and cost of every handoff in a fragmented architecture is augmented. Dusk maintains controlled assets in a single protocol environment whereby the behavior can be controlled and enforced throughout.
This design is also beneficial to the builders. Developers are able to model real world financial processes without having to simplify logic to fit transparency-first assumptions. The protocol itself facilitates confidential execution as well as compliance enforcement without adding any significant layers of custom privacy or compliance infrastructure. This enables applications to be developed in a manner that reflects controlled financial operations as opposed to operating around blockchain constraints.
However, in the end, the value of Dusk is the clarity of purpose. It does not seek to instantiate the regulated finance as a permissionless transparency model. Rather, it offers infrastructure in which confidentiality, compliance, and execution are designed as a combination. This conformity ensures that controlled property can be transferred to the blockchain without breaching the law or making sensitive banking details known.
@Dusk is a type of blockchain infrastructure that does not begin with an adaptive approach to regulatory reality but with it. In case of regulated assets, such a distinction is not philosophical. It is foundational.
#dusk
$DUSK

For most decentralized systems the problem of data availability has existed in a unpleasant grey area. Data is uploaded, copied, taken for granted to go back to you, but the point at which the network does become responsible to you is always one that is rarely stated. Builders make assumptions of availability based on the architecture/social guarantees instead of something that the chain can attest to itself. As systems grow to scale and you begin to rely on your data for execution of the system, instead of archival, that ambiguity becomes a risk. Walrus addresses this very gap directly by introducing Point-of-Availability which is a protocol-level concept used to make the availability of data from an assumption to a chain-verifiable contract.

The core idea behind Point-of-Availability is deceptively simple: There is clearly an identifiable moment of responsibility transfer when it comes to a piece of data - from the person uploading the data to the network. Prior to this point, the uploading endures the risk. After it, the network commits to have the data available for a defined period. What makes this powerful is not just the handoff as such but the fact that it is enforced and acted as a signal onchain. Availability is no longer determined by replication depth/replication nodes. It is claimed by protocol events which any participant can also independently verify.
This is important because decentralized storage has always been optimized for persistence rather than accountability. Data could be stored at a wide variety of nodes but no one actor could reference a cryptographic signature and say with certainty, 'the network is now responsible for this data.' In practice, that meant that applications had to be defensive in design. They compressed like crazy, they limited reuse or they discreetly used trusted gateways to cover up uncertainty. Walrus's Point-of-Availability's replacement for this informal contract is a formal one which execution systems can reason about.
Walrus encodes and sends data around the hood using erasure coded indices as well as authenticated identifiers, although the architecture novelty presents itself at the interface. The network sends onchain signals using the Point-of-Availability that helps assert that enough encoded fragments are being maintained and that the window of availability has started. From then on, availability is not a promise that is delivered via documentation, it is a state that is recognised by the chain itself. This makes data something closer to an onchain obligation, as opposed to an offchain convenience.
Using Point-of-Availability as a data contract is not accidental. Contracts- They are about obligations, scope, and duration. Walrus shows the same discipline in terms of data. Availability is limited in time and can be observed in state, and enforced by incentives. Nodes are not simply incentivized to store data indefinitely, but are economically incentivized to harness power to provide it for a time period that the network has committed to providing a service. That following helps to close a gap between the nominal storage capacity, and what is available for execution.
The implications are much more obvious in modular systems. Execution layers ever more outsource data to dedicated availability layers to retrieve near data and loads, and count on numerous independent verifiers to acquire feedback. In those environments, the clean failures are not caused by uncertainty across availability. It causes subtle personality degradation. Verification narrows. Latency spikes. Systems are secretly centralizing around actors who can ensure access. By having the availability explicit and verifiable walrus enables modular execution to need to force dependencies on data without importing these failure modes.
Point-of-Availability also shifts the mind of the developers in terms of lifecycle management. Because the availability windows are explicit, data design can have explicit, greater intent on economic matters. Storage costs, expiration and even burn mechanisms are all part of the application logic instead of an external cost to be concealed. Data is no longer a passive artifact and is no longer managed resource; it occurs responsibility boundary is well defined.
So, and crucially, this approach doesn't involve Walrus over-reaching into execution or settlement. The protocol continues to focus on one problem, which is to make data availability enforceable at the infrastructure level. That is the restraint that gives Point-of-Availability its credibleness. It is not an attempt to solve everything. It resolves the particular issue of assumptions slightly creeping in.
As such, as decentralized systems reach maturity, our industry is discovering that reliability does not come from following your hopes it instead comes from having explicit contracts. Compute has gas. State has ownership. Data until recently did not have. By coming in as a chain verifiable data contract named Point-of-Availability Walrus adds a missing primitive to the stack. It provides builders with a definite answer to a question that has been hard-waved away in the past - when exactly does the network take responsibility for data?
That clarity is not cosmetic. It is structural. And as data continues to migrate from the periphery of blockchain systems into their critical path, structures such as Point-of-Availability are likely to determine which infrastructures are able to scale without compromising their assumptions.
#walrus
@Walrus 🦭/acc
$WAL