Just a lonely lover girl!π Web3| Crypto |Tech Contributor @StabilizerFi
Joined August 2022
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One thing I find interesting about @StabilizerFi is how it approaches the backing of its stablecoin, USDZ.
USDZ is designed to be fully backed by stablecoins, meaning every token in circulation must have an equivalent amount of collateral held in the protocol's reserves.
At launch, the backing comes from five supported stablecoins:
β’ USDT
β’ USDC
β’ USDS
β’ USDG
β’ PYUSD
But what makes this model interesting is how Stabilizer handles its reserves.
Instead of using the assets sitting in its trading pools as collateral, Stabilizer keeps the backing reserves separate through dedicated adapter contracts.
Why is this important?
Well, the trading pools and collateral reserves have different responsibilities.
The AMM pools are where users carry out their swaps, while the adapters hold and manage the assets that actually back USDZ.
This means that when someone swaps stablecoins on Stabilizer, it doesn't automatically affect the reserves backing USDZ. Those reserves only change when users mint or redeem USDZ.
Another thing worth looking at is the 1:1 collateral model.
USDZ is minted against an equal amount of collateral. There are no borrowing positions, no liquidation process and no need for users to provide extra collateral above the amount being minted.
In simple terms, the protocol cannot mint more USDZ than its available backing reserves support.
I like that Stabilizer keeps things straightforward here. It separates the assets used for trading from the assets responsible for maintaining the backing of its stablecoin.
Because at the end of the day, having a stablecoin that targets $1 is one thing, but having a clear system for managing its reserves and supporting redemption is just as important.
That's an important part of what makes USDZ worth understanding.
Honestly, choosing just 5 people isn't going to be easy for me.
I have friends here, and I've seen so many people putting in effort and doing great things in this space. I wish I could give everyone a chance, but unfortunately, only 5 spots were available.
The best I can do now is ask them to increase the number to 10 so more people can have the opportunity.
I'll also keep sharing more opportunities from AbiiApp, so I encourage everyone to create an account on abiiapp.com ahead of time.
For this selection, I'll be prioritising ambassadors who already have an AbiiApp account.
More opportunities are coming, and I hope more of you get the chance to benefit.
I will need 5 Ambassadors for @AbiiApp to be paid weekly...
Interested should let me know in the comment section.
Good things has been happening to me lately and All thanks to Allah.
I just earned a special role on @StabilizerFi Discord, and honestly, it means a lot to me.
I've been putting in the work, contributing, testing and doing my best to add value to the community. Seeing that effort finally get recognised feels really special.
Sometimes, consistency pays off in ways you least expect. I'm truly grateful for this one.
Roomey retweeted
First week on DeBank, so Stabi is handing out some cash π
$5 to ten people on the post below.
This one needs a DeBank Web3 ID, with future draws open to more of you and bigger prizes π
debank.com/stream/3596506
Roomey retweeted
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A prediction market can price an outcome correctly and still produce a disputed resolution.
Polymarketβs recent Strategy Bitcoin market is a useful example.
The market asked whether Strategy would sell any Bitcoin by May 31, 2026. An SEC filing published on June 1 disclosed that 32 BTC had been sold between May 26 and May 31.
Polymarket maintained that confirmation outside the marketβs specified timeframe did not qualify. a subsequent UMA token vote upheld the βNoβ outcome, with approximately 98.6% of voting power supporting it.
The dispute was not primarily about fabricated evidence. It exposed something more fundamental: the difference between establishing what happened and deciding whether it satisfies the rules.
That distinction deserves more attention.
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Every resolution process has at least two important questions:
1. What actually happened? This is an evidence and verification problem.
2. Does what happened satisfy the market's rules? This is an interpretation problem.
Clearer market rules help address the second.
Verifiable evidence helps address the first.
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Through zkTLS, @primus_labs enables users to prove that specific information came from a particular web source without exposing their login credentials or handing over the underlying records.
For prediction markets, that capability matters.
A resolution may depend on financial reports, exchange activity, social engagement or other information originating outside the blockchain.
The important questions are:
β’ Did the information come from the claimed source?
β’ What exactly did the source report?
β’ When was that information available?
zkTLS helps establish the provenance of the data. It does not independently decide whether the evidence satisfies the market's rules, but it gives the resolution process something stronger to work with.
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One direction worth watching is the announced collaboration between Primus, Brevis and Trendle on an attention-index market on Monad.
The proposed design brings together three components:
β Social engagement data from X, Reddit and YouTube is verified using Primus zkTLS.
β Brevis's Pico zkVM computes the attention index.
β Traders can take long or short positions based on the resulting index.
It illustrates how verified Web2 information can become an input for onchain financial activity.
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Verification is only one part of the process.
Once evidence has been collected, someone or something still needs to interpret it and determine the outcome.
This is where Cournot enters the picture.
Cournot is described as the AI-native resolution layer for the 42 event-trading protocol. Its Proof of Reasoning framework is designed to expose the evidence, sources and reasoning behind an outcome.
The potential relationship is straightforward:
Primus helps authenticate the information going into the process.
Cournot focuses on the reasoning used to reach the outcome.
Together, they point toward a more inspectable path from external evidence to a final market resolution.
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There is another opportunity beyond publicly available information.
Some valuable market questions depend on sensitive financial data: verified income, credit metrics, account performance or other private records.
Publishing that information should not be the price of participating in an onchain market.
Combining zkTLS with confidential computation could support applications that verify relevant financial information and process it without unnecessarily exposing the underlying data.
Primus has identified private credit as an area of exploration. It is a direction worth watching, rather than a claim that a finished product is already available.
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It is equally important to understand the limitations.
β’ Ambiguous rules: A valid proof cannot repair a poorly defined market question.
β’ Unreliable sources: zkTLS can establish data provenance, not guarantee that the source itself is truthful.
β’ Timing: A proof establishes what was available at a particular moment.
β’ Human judgment: Resolution still depends on decisions about which sources and conditions count.
β’ AI accountability: Automated reasoning needs transparent processes and meaningful ways to challenge disputed outcomes.
Technology can strengthen the evidence layer, but it cannot eliminate the need for sound market design.
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The next generation of prediction markets should not be judged by pricing accuracy alone.
It should also be judged by how clearly outcomes are defined, how reliably evidence is verified, and how transparently decisions are reached.
Clear rules determine what counts.
Verifiable evidence establishes what happened.
Transparent reasoning explains the final decision.
Primus is working on an important part of that foundation: making external information verifiable while protecting what should remain private.
And as onchain markets mature, that foundation could matter far beyond prediction markets.
Good morning, CT.
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A proof that nobody can afford to generate is little more than a research achievement.
But when generating that proof becomes efficient enough to happen quietly, repeatedly and at scale, it starts to look like infrastructure.
That is one of the important ideas behind zkTLS and a key part of Primus approach is QuickSilver.
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zkTLS aims to prove that an authentic TLS session with a real website took place without exposing the sensitive information exchanged during that session.
@primus_labs uses QuickSilver, a lightweight interactive zero-knowledge proof system, in both of its zkTLS modes:
β MPC Mode: The user and attestors collaboratively establish the TLS session. this helps prevent the user from modifying the data before the attestor contributes its share of the session key.
β Proxy Mode: An attestor relays the TLS traffic and records the encrypted exchange. At the end, the user proves knowledge of the plaintext behind the recorded ciphertexts.
Different approaches, but the same objective: make data from existing web services verifiable without unnecessarily exposing it.
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Traditional proof systems can involve substantial computation and communication overhead.
QuickSilver takes an interactive approach: instead of producing one large standalone proof, the prover and verifier exchange a sequence of smaller messages.
Primus describes its design as requiring very little memory and relatively small amounts of communication at each step.
Think of it as replacing one enormous exchange with a stream of smaller, coordinated interactions.
There is an important trade-off, however.
The verifier remains part of the process. In Primus architecture, that role is handled by an attestor, which produces an attestation that applications can subsequently verify.
The efficiency comes with a trust model that developers must understand when choosing how to use the system.
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When proof generation becomes more practical, verification no longer has to be reserved for occasional demonstrations or periodic checks.
It opens the door to applications such as:
β Scheduled reserve checks instead of relying exclusively on quarterly reporting.
β Real-time policy verification for AI agents.
β Credential verification integrated into everyday sign-in experiences.
The bigger opportunity is making verification part of normal application workflows rather than treating it as an expensive extra step.
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Primus has reported performance improvements of roughly an order of magnitude over certain earlier approaches, based on its own reporting and cited analysis.
Reproducible, independently verified measurements would make those performance claims even stronger.
And one distinction remains essential: zkTLS can help establish where data came from and whether it was altered. It does not automatically prove that the information represents the complete picture.
Proxy mode also carries network assumptions that developers need to consider when selecting an implementation.
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Primus research extends beyond verifying data.
Its zkFHE work, including HasteBoots, explores how to prove that computations performed on encrypted data were executed correctly. this remains research rather than a shipped product.
Put these research directions side by side and a broader vision starts to emerge:
Verify that the data is authentic.
Protect the information that must remain private.
Prove that the computation was performed correctly.
That is a much bigger conversation than simply making proofs faster.
Because when verification becomes efficient enough to fit into everyday digital activity, the question is no longer just:
"Can we prove it?"
It becomes:
"What should we be able to prove without having to reveal everything?"
That is the direction of privacy-preserving verification worth paying attention to.
Sorry for the delay.
Check your @AbiiApp
@Maidatacrypto
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