ZKsync LATAM

El ecosistema de @ZKsync en LATAM 🌎
Conectando instituciones, startups y builders con la Elastic Network.

[ZKsync LATAM @ZKamigos]

Bancos regionales de EE.UU. (Huntington, First Horizon, M&T) están tokenizando depósitos sobre ZKsync.

La SEC ya dijo que zero-knowledge puede resolver compliance sin exponer datos de clientes.

Prividium existe exactamente para esto.

@joseganaya estuvo charlando con @CarlosRomanoYT sobre todo esto y más.
No te lo pierdas!

https://t.co/r8HjdctCFu

[Omar @Ozhar]

Five US banks are now building a tokenized deposit network powered by ZKsync's Prividium, through the Cari Network led by former Comptroller of the Currency Gene Ludwig.

M&T Bank, Huntington, KeyCorp, First Horizon, and Old National are designing infrastructure that enables banks to issue, transfer, and redeem tokenized deposits 24/7, while keeping them on the balance sheet as regulated bank liabilities, FDIC insured, and under existing supervisory oversight.

Banks have been exploring stablecoins to keep pace with demand for always-on money movement. But stablecoins, even under the GENIUS Act, are payment instruments, not banking instruments. They don't support credit creation through fractional reserve lending. They carry issuer counterparty risk. And they don't receive the same balance sheet treatment under Basel III as deposits do.

Until now, what prevented banks from moving to tokenized deposits was a fundamental infrastructure gap. Choose privacy and institutional control, and you lost interoperability. Choose interoperability on public chains, and you lost privacy and control. The combination of both, along with a neutral, open settlement layer, simply did not exist.

Prividium changes that. Anchored to Ethereum, it provides a permissioned environment with full data privacy, regulatory auditability, and interoperability with the broader digital asset ecosystem without compromising institutional controls.

We see stablecoins and tokenized deposits playing complementary roles in every bank's onchain strategy. Tokenized deposits protect and grow the balance sheet within a fully private, bank-controlled environment. Stablecoins serve as the bridge when money needs to move beyond those rails. The Cari Network and its five design partner banks represent the first institutions adopting this infrastructure to enable a new era of 24/7 digital money movement.

[ZKsync LATAM @ZKamigos]

@zksync anunció el lanzamiento de @carinetwork la primera red para depósitos tokenizados co-diseñada con 5 bancos en USA.

Es un paso más en la construcción de la infraestructura financiera on-chain que permite generar productos regulados, con privacidad pero con interoperabilidad DeFi total. 🚀

[ZKsync @zksync]

Today marks a new chapter for U.S. banking.

The Cari Network, developed alongside five regional banks, is building a new platform to bring tokenized deposits onchain.

Secure. Private. Within the regulatory perimeter. Powered by ZKsync’s Prividium.

[ZKsync LATAM @ZKamigos]

Link to event:

https://t.co/K3YboMfKmm

[ZKsync LATAM @ZKamigos]

ZKsync LATAM is in 🇧🇷 !!

Not just for @mmerge_io, we are actively joining the conversation in the financial transformation do Brasil.

Join us today along @PodsFinance @surfcashx and @BitGo to discuss how real world adoption is happening today.

Link below!

[Pods Finance @PodsFinance]

Today, we’re hosting the first edition of Money Legos in our hometown, São Paulo, alongside three partners helping shape the Neo Finance stack: @ZKamigos, @BitGo, and @surfcashx.

From infrastructure to neobanks, the stack is coming together like Money Legos.

That’s the real advantage of what we’re building: it’s composable, it moves at software speed, and it’s built for a new era of finance.

Last call: https://t.co/FLKaZBqF7w

[ZKsync LATAM @ZKamigos]

Transparência não é mostrar tudo.

É provar o que precisa ser provado para quem precisa verificar!

[ZKsync LATAM @ZKamigos]

Transparência não é o mesmo que exposição total de dados. Blockchains públicas foram desenhadas para gerar confiança, mas isso não significa tornar informações sensíveis visíveis para todos.

[ZKsync LATAM @ZKamigos]

A próxima fase da infraestrutura institucional passa por redes que oferecem verificabilidade pública com privacidade configurável.

[ZKsync LATAM @ZKamigos]

Infra privada + liquidación en L1 + ZK proofs =
confianza verificable, no confianza implícita.

El futuro financiero no se aísla.

Se construye sobre Ethereum. ⚡️

[ZKsync @zksync]

The Bank Stack of Ethereum

[ZKsync LATAM @ZKamigos]

ZKsync LATAM es un delegado activo en gobernanza. Si querés delegarnos tu poder de voto, podés hacerlo acá: 👉

https://t.co/YSTqi4zGQ3

[ZKsync LATAM @ZKamigos]

🔼 El target del $ZK Staking Pilot sube de 4% → 5% APR
~180M $ZK ya están delegados y en staking.
La participación sigue creciendo.
La gobernanza se fortalece.

Seguimos impulsando una democracia descentralizada, donde la comunidad no solo opina, decide. 🗳️⚡

[ZKsync @zksync]

📌UPDATE: The target rate for th $ZK Staking Pilot Program has increased from 4% → 5% APR.

~180M $ZK is already delegated and staked.

[ZKsync LATAM @ZKamigos]

Seguridad ≠ más fricción.

Es reducir la brecha entre intención y ejecución, con capas que se validan entre sí.
En entornos institucionales, #Prividium aplica este principio: privacidad, cumplimiento y verificación sobre Ethereum, sin depender de un único “punto de verdad”.

[vitalik.eth @VitalikButerin]

How I think about "security":

The goal is to minimize the divergence between the user's intent, and the actual behavior of the system.

"User experience" can also be defined in this way. Thus, "user experience" and "security" are thus not separate fields. However, "security" focuses on tail risk situations (where downside of divergence is large), and specifically tail risk situations that come about as a result of adversarial behavior.

One thing that becomes immediately obvious from the above definition, is that "perfect security" is impossible. Not because machines are "flawed", or even because humans designing the machines are "flawed", but because "the user's intent" is fundamentally an extremely complex object that the user themselves does not have easy access to.

Suppose the user's intent is "I want to send 1 ETH to Bob". But "Bob" is itself a complicated meatspace entity that cannot be easily mathematically defined. You could "represent" Bob with some public key or hash, but then the possibility that the public key or hash is not actually Bob becomes part of the threat model. The possibility that there is a contentious hard fork, and so the question of which chain represents "ETH" is subjective. In reality, the user has a well-formed picture about these topics, which gets summarized by the umbrella term "common sense", but these things are not easily mathematically defined.

Once you get into more complicated user goals - take, for example, the goal of "preserving the user's privacy" - it becomes even more complicated. Many people intuitively think that encrypting messages is enough, but the reality is that the metadata pattern of who talks to whom, and the timing pattern between messages, etc, can leak a huge amount of information. What is a "trivial" privacy loss, versus a "catastrophic" loss?

If you're familiar with early Yudkowskian thinking about AI safety, and how simply specifying goals robustly is one of the hardest parts of the problem, you will recognize that this is the same problem.

Now, what do "good security solutions" look like?

This applies for:

* Ethereum wallets
* Operating systems
* Formal verification of smart contracts or clients or any computer programs
* Hardware
* ...

The fundamental constraint is: anything that the user can input into the system is fundamentally far too low-complexity to fully encode their intent. I would argue that the common trait of a good solution is: the user is specifying their intention in multiple, overlapping ways, and the system only acts when these specifications are aligned with each other.

Examples:

* Type systems in programming: the programmer first specifies *what the program does* (the code itself), but then also specifies *what "shape" each data structure has at every step of the computation*. If the two diverge, the program fails to compile.
* Formal verification: the programmer specifies what the program does (the code itself), and then also specifies mathematical properties that the program satisfies
* Transaction simulations: the user specifies first what action they want to take, and then clicks "OK" or "Cancel" after seeing a simulation of the onchain consequences of that action
* Post-assertions in transactions: the transaction specifies both the action and its expected effects, and both have to match for the transaction to take effect
* Multisig / social recovery: the user specifies multiple keys that represent their authority
* Spending limits, new-address confirmations, etc: the user specifies first what action they want to take, and then, if that action is "unusual" or "high-risk" in some sense, the user has to re-specify "yes, I know I am doing something unusual / high-risk"

In all cases, the pattern is the same: there is no perfection, there is only risk reduction through redundancy. And you want the different redundant specifications to "approach the user's intent" from different "angles": eg. action, and expected consequences, expected level of significance, economic bound on downside, etc

This way of thinking also hints at the right way to use LLMs. LLMs done right are themselves a simulation of intent. A generic LLM is (among other things) like a "shadow" of the concept of human common sense. A user-fine-tuned LLM is like a "shadow" of that user themselves, and can identify in a more fine-grained way what is normal vs unusual.

LLMs should under no circumstances be relied on as a sole determiner of intent. But they are one "angle" from which a user's intent can be approximated. It's an angle very different from traditional, explicit, ways of encoding intent, and that difference itself maximizes the likelihood that the redundancy will prove useful.

One other corollary is that "security" does NOT mean "make the user do more clicks for everything". Rather, security should mean: it should be easy (if not automated) to do low-risk things, and hard to do dangerous things. Getting this balance right is the challenge.

[ZKsync LATAM @ZKamigos]

O maior valor do zero-knowledge não é no que ele esconde, mas no que ele permite construir:
infraestrutura mais cuidadosa com os dados, mais compatível com o mundo real e preparada para crescer sem expor demais.

[ZKsync LATAM @ZKamigos]

Zero-knowledge parece complexo.
Mas a ideia é simples. É a possibilidade de provar que algo é verdadeiro sem revelar a informação sensível por trás.
Nada mais, nada menos.

[ZKsync LATAM @ZKamigos]

Zero-knowledge não é mágica.
Não elimina a confiança de um dia para o outro.
Não resolve tudo.
Mas reduz quanto precisamos “acreditar” e aumenta quanto podemos verificar.