Devil9

After discussing the matter with the support team, I came to the conclusion that consistently producing high-quality content on a daily basis would lead to better scores. Based on that understanding, I continued putting significant effort into creating quality content, yet I still did not receive the scores I expected.
On the other hand, it appears that some of the campaign's top creators have edited the view counts on their posts, and most of those using this practice are currently ranked at the top. If this is overlooked, it raises concerns about the fairness of the competition and discourages creators who are genuinely putting in honest effort.
As a result, my motivation to continue participating in the campaign has significantly declined. I believe this issue deserves serious attention, and it is essential to ensure fair evaluation and equal opportunities for all participants.
For the past few years, I’ve noticed a pattern that repeats almost every crypto cycle. A new technology captures everyone’s attention, projects quickly attach themselves to the narrative, and discussions become dominated by what the technology could achieve rather than what problems it actually solves.
AI seems to be following that exact path.
Most conversations focus on autonomous agents that can trade, manage portfolios, optimize DeFi strategies, or execute transactions without human intervention. Those possibilities are exciting, but they also raise a question that I think deserves far more attention.
What happens when an AI agent makes the wrong decision?
Not because it was malicious, but because it misunderstood instructions, encountered unexpected market conditions, or even responded to a prompt injection attack.
The more I read about Newton Protocol, the more I realized the project isn’t primarily trying to build smarter AI. Instead, it appears focused on something much less glamorous but potentially more important: defining clear boundaries for autonomous software before it gains more responsibility.
That distinction changed how I looked at the project.
Crypto has spent years removing intermediaries by replacing trust with transparent code. Yet AI introduces a different challenge. Machine learning models don’t operate like traditional smart contracts. They generate outputs based on probabilities, evolving context, and learned behavior rather than deterministic rules.
As AI begins interacting with valuable digital assets, simply assuming agents will always behave correctly feels unrealistic.@NewtonProtocol $NEWT #Newt
Newton’s answer isn’t to prevent AI from making decisions.
It’s to separate decision-making from authorization.
An AI agent may recommend or initiate an action, but execution still depends on policies created by the wallet owner. Spending limits, approved recipient lists, transaction rules, and other predefined conditions act as an independent enforcement layer.
That philosophy feels surprisingly practical.
Security rarely improves by giving software unlimited freedom. Operating systems isolate applications, financial institutions require multiple approval layers, and cloud infrastructure relies heavily on permission management. Constraints are often what allow complex systems to remain dependable.
Crypto, however, has traditionally celebrated unrestricted composability.
Newton seems to explore the opposite direction.
Rather than asking users to completely trust increasingly intelligent agents, it asks whether users should define what those agents are never allowed to do.
That shift may become increasingly valuable as AI adoption grows.
One aspect that reinforced this perspective was Newton’s approach to policy enforcement during agent execution.
Testing environments shared by developers demonstrate that policies can consistently block transactions exceeding spending thresholds, enforce rolling transaction limits, reject transfers to unauthorized wallets, and maintain detailed attestations explaining exactly why an action was denied.
Perhaps even more interesting is what happens during prompt injection scenarios.
Prompt injection remains a model-layer problem. Newton doesn’t prevent an AI from reading manipulated instructions or becoming confused by malicious prompts.
Instead, it provides a final authorization checkpoint.
Even if the AI attempts to perform an unauthorized transfer after being manipulated, the transaction can still be rejected because the wallet policy remains independent of the model’s reasoning.
That distinction is important.
Newton isn’t claiming to make AI perfectly secure.
It’s attempting to make wallet behavior consistently enforceable regardless of how the AI reaches its decisions.
Those are fundamentally different security models.
Another architectural decision I found interesting involves Newton’s risk evaluation framework.
Rather than evaluating transactions using only price data, policies can also reference external risk intelligence before authorizing execution.
However, this design raises an interesting architectural tradeoff.
Current documentation places Credora’s risk intelligence within the broader RedStone Stack instead of treating it as an entirely separate provider.
Operationally, that creates a cleaner and more integrated policy system. Price information and risk assessments follow coordinated infrastructure, reducing synchronization problems between independent services.
At the same time, it introduces a legitimate question about independence.
One benefit of combining multiple external data providers is reducing the likelihood that they fail simultaneously. If both pricing and risk signals ultimately depend on the same underlying infrastructure, then true redundancy may be lower than it initially appears.
Whether this represents acceptable engineering or an unnecessary concentration of dependency isn’t something documentation alone can answer.
Only real-world stress events reveal whether architectural assumptions hold under pressure.
That broader observation applies to Newton as a whole.
Good architecture creates possibilities.It doesn’t guarantee adoption.
Crypto history is filled with technically impressive infrastructure that never achieved meaningful developer activity because solving an engineering problem isn’t always the same as solving a user problem.
Newton still faces difficult questions.
Will developers willingly build within policy-driven environments?
Can sophisticated authorization remain simple enough for everyday users?
Will institutions view programmable permissions as valuable infrastructure rather than additional complexity?
And how will evolving AI regulation influence demand for transparent authorization systems?
Those questions remain unanswered.
Yet I think they’re more interesting than asking whether AI agents can execute trades faster.
As decentralized finance becomes increasingly automated, trust may no longer depend solely on smart contracts or consensus mechanisms.
It may depend on whether autonomous systems can operate within clearly defined, verifiable limits established by the people whose assets they manage.
That’s why I don’t see Newton Protocol primarily as another AI project.
I see it as an attempt to build governance around autonomous execution.
Whether that vision succeeds will depend on adoption, developer participation, usability, and real-world resilience. But by focusing on accountability instead of unrestricted autonomy, Newton shifts the conversation toward a problem the industry will almost certainly face as AI becomes more deeply integrated into blockchain infrastructure.@NewtonProtocol #Newt
Sometimes the most valuable innovation isn’t making autonomous systems more capable.
It’s making sure they remain accountable when capability inevitably expands.$HMSTR $VELVET

Le marché des cryptomonnaies n’est pas étranger aux nouveaux récits. Il y a quelques mois, les memecoins dominaient la conversation. Puis les projets d’IA ont pris le devant de la scène, suivis par les réseaux de couche 2, les actifs du monde réel (RWA), et l’infrastructure DeFi. Ces changements sont si rapides que de nombreux investisseurs finissent par courir après les tendances au lieu d’évaluer si un projet résout un problème concret et significatif. C’est pourquoi beaucoup de projets liés à l’IA peuvent sembler presque identiques au premier coup d’œil.
Cependant, passer du temps à étudier un projet révèle souvent un tableau bien différent. Le protocole Newton est un exemple parmi d’autres. Plutôt que de se concentrer sur ce que l’IA peut faire, il semble s’intéresser à une question plus fondamentale : comment exécuter de manière sécurisée des actions pilotées par l’IA, comment les vérifier de façon transparente et comment s’y fier lorsque de la vraie valeur est en jeu ? Cette perspective donne au projet un angle différent de celui de nombreux récits sur l’IA qui circulent actuellement sur le marché.

Une hypothèse a discrètement façonné ma façon de penser à Newton pendant des mois.
Si une transaction se mettait en pause pendant l’autorisation, j’attribuais instinctivement la faute au réseau d’opérateurs. Plus d’opérateurs devrait signifier un débit plus élevé. Une meilleure décentralisation devrait signifier moins de délais. Cela semblait être une explication simple, car la plupart des conversations sur la blockchain nous apprennent à d’abord regarder les validateurs dès qu’une question de performance se pose.@NewtonProtocol $NEWT #Newt
Plus j’étudiais l’architecture de Newton, moins j’étais convaincu que la disponibilité des opérateurs constitue la vraie contrainte.

Auparavant, il semblait que les scores de CreatorPad reposaient principalement sur la qualité du contenu. Désormais, le système semble davantage se concentrer sur les indicateurs d’audience et sur les vues.
Parallèlement, nous avons constaté que certains créateurs modifiaient leurs publications après les avoir publiées ou utilisaient des tactiques de clickbait pour augmenter l’engagement. Malgré cela, il semble qu’ils obtiennent des classements plus élevés et de meilleurs scores. Cela a un impact direct sur des créateurs comme nous qui suivent les règles de CreatorPad et créent du contenu de manière équitable.$BTW
J’espère que CreatorPad continuera à faire respecter ses propres directives et à évaluer les créateurs au moyen d’un système juste et transparent, afin que ceux qui travaillent honnêtement soient récompensés en conséquence.

Newton s’appuie sur des autorisations programmables grâce à des technologies telles que l’Abstraction de compte, zkPermissions et les clés de session.
Plutôt que de donner aux applications une autorité permanente, les utilisateurs définissent précisément ce qu’une application est autorisée à faire.
Cela ramène le contrôle vers l’utilisateur.
Au lieu que le logiciel détermine ses propres limites, le propriétaire fixe ces limites d’abord.
Cette différence compte.
L’automatisation devient quelque chose que vous surveillez plutôt que quelque chose que vous abandonnez.@NewtonProtocol $NEWT #Newt
La confidentialité est un autre élément du puzzle qui reçoit souvent moins d’attention qu’elle ne le mérite.

Depuis longtemps, j’ai le sentiment que la cryptographie résout le mauvais problème.
Chaque cycle introduit des chaînes plus rapides, des transactions moins coûteuses, de meilleures interfaces utilisateur et des outils d’IA de plus en plus sophistiqués. Pourtant, un problème revient sans cesse, quelle que soit la sophistication de la technologie : la confiance. Pas la confiance dans les blockchains elles-mêmes, mais la confiance dans ce que l’on autorise les logiciels à faire à notre place.
Plus j’ai exploré le protocole Newton, plus j’ai réalisé que ce n’est pas simplement un autre projet d’infrastructure d’IA. Il s’agit d’essayer de répondre à une question beaucoup plus fondamentale.