𝑹𝒂𝒆𝒆🦉

𝗣𝗥𝗘𝗗𝗜𝗖𝗧𝗜𝗢𝗡 𝗠𝗔𝗥𝗞𝗘𝗧𝗦 𝗔𝗥𝗘 𝗢𝗡𝗟𝗬 𝗔𝗦 𝗚𝗢𝗢𝗗 𝗔𝗦 𝗧𝗛𝗘𝗜𝗥 𝗥𝗘𝗦𝗢𝗟𝗨𝗧𝗜𝗢𝗡. A prediction market can price an outcome correctly and still produce a disputed resolution. Polymarket’s 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 market’s specified timeframe did not qualify. a subsequent UMA token vote upheld the “No” 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. 𝗧𝗪𝗢 𝗗𝗜𝗙𝗙𝗘𝗥𝗘𝗡𝗧 𝗣𝗥𝗢𝗕𝗟𝗘𝗠𝗦 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. 𝗣𝗥𝗜𝗠𝗨𝗦: 𝗣𝗥𝗢𝗩𝗜𝗡𝗚 𝗪𝗛𝗘𝗥𝗘 𝗧𝗛𝗘 𝗘𝗩𝗜𝗗𝗘𝗡𝗖𝗘 𝗖𝗔𝗠𝗘 𝗙𝗥𝗢𝗠 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: • Did the information come from the claimed source? • What exactly did the source report? • 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. 𝗙𝗥𝗢𝗠 𝗩𝗘𝗥𝗜𝗙𝗜𝗘𝗗 𝗗𝗔𝗧𝗔 𝗧𝗢 𝗔𝗖𝗧𝗜𝗢𝗡𝗔𝗕𝗟𝗘 𝗠𝗔𝗥𝗞𝗘𝗧𝗦 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: → Social engagement data from X, Reddit and YouTube is verified using Primus zkTLS. → Brevis's Pico zkVM computes the attention index. → 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. 𝗧𝗛𝗘 𝗡𝗘𝗫𝗧 𝗟𝗔𝗬𝗘𝗥: 𝗘𝗩𝗜𝗗𝗘𝗡𝗖𝗘, 𝗥𝗘𝗔𝗦𝗢𝗡𝗜𝗡𝗚 𝗔𝗡𝗗 𝗝𝗨𝗗𝗚𝗠𝗘𝗡𝗧 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. 𝗟𝗢𝗡𝗚𝗘𝗥 𝗧𝗘𝗥𝗠: 𝗠𝗔𝗥𝗞𝗘𝗧𝗦 𝗕𝗨𝗜𝗟𝗧 𝗔𝗥𝗢𝗨𝗡𝗗 𝗣𝗥𝗜𝗩𝗔𝗧𝗘 𝗗𝗔𝗧𝗔 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. 𝗪𝗛𝗔𝗧 𝗖𝗥𝗬𝗣𝗧𝗢𝗚𝗥𝗔𝗣𝗛𝗜𝗖 𝗣𝗥𝗢𝗢𝗙𝗦 𝗖𝗔𝗡𝗡𝗢𝗧 𝗙𝗜𝗫 It is equally important to understand the limitations. • Ambiguous rules: A valid proof cannot repair a poorly defined market question. • Unreliable sources: zkTLS can establish data provenance, not guarantee that the source itself is truthful. • Timing: A proof establishes what was available at a particular moment. • Human judgment: Resolution still depends on decisions about which sources and conditions count. • 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. 𝗧𝗛𝗘 𝗕𝗜𝗚𝗚𝗘𝗥 𝗣𝗜𝗖𝗧𝗨𝗥𝗘 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. 𝗪𝗛𝗘𝗡 𝗣𝗥𝗢𝗢𝗙 𝗕𝗘𝗖𝗢𝗠𝗘𝗦 𝗖𝗛𝗘𝗔𝗣 𝗘𝗡𝗢𝗨𝗚𝗛 𝗧𝗢 𝗥𝗨𝗡 𝗜𝗡 𝗧𝗛𝗘 𝗕𝗔𝗖𝗞𝗚𝗥𝗢𝗨𝗡𝗗 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. 𝗪𝗛𝗘𝗥𝗘 𝗤𝗨𝗜𝗖𝗞𝗦𝗜𝗟𝗩𝗘𝗥 𝗙𝗜𝗧𝗦 𝗜𝗡 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: → 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. → 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. 𝗪𝗛𝗬 𝗧𝗛𝗘 𝗪𝗢𝗥𝗗 "𝗜𝗡𝗧𝗘𝗥𝗔𝗖𝗧𝗜𝗩𝗘" 𝗠𝗔𝗧𝗧𝗘𝗥𝗦 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. 𝗪𝗛𝗔𝗧 𝗠𝗢𝗥𝗘 𝗘𝗙𝗙𝗜𝗖𝗜𝗘𝗡𝗧 𝗣𝗥𝗢𝗢𝗙𝗦 𝗖𝗢𝗨𝗟𝗗 𝗘𝗡𝗔𝗕𝗟𝗘 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: → Scheduled reserve checks instead of relying exclusively on quarterly reporting. → Real-time policy verification for AI agents. → 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. 𝗔 𝗡𝗢𝗧𝗘 𝗢𝗡 𝗧𝗛𝗘 𝗣𝗘𝗥𝗙𝗢𝗥𝗠𝗔𝗡𝗖𝗘 𝗖𝗟𝗔𝗜𝗠𝗦 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. 𝗪𝗛𝗔𝗧 𝗖𝗢𝗠𝗘𝗦 𝗡𝗘𝗫𝗧? 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.

Zcash’s recent momentum is bringing an important conversation back into focus: What should financial privacy look like as crypto matures? As blockchain adoption grows, so does the amount of financial information exposed through transparent networks. For individual users, that can mean revealing more about their transactions than they intended. For institutions, the implications are even greater. Funds may want to interact with onchain markets without exposing their positions or strategies. Businesses may need to prove financial conditions without publishing sensitive records. Institutions may need to verify information without giving every participant access to the underlying data. This is where the privacy conversation needs to go further. Privacy alone is not enough. Financial systems also need verification. And these two requirements should not have to work against each other. That is the problem space @primus_labs is working on. Primus approaches the challenge through two important capabilities: → zkTLS: Helps verify information from existing Web2 platforms and services without unnecessarily exposing the underlying data. → zkFHE: Enables computation over encrypted data, allowing sensitive information to remain protected while it is being processed. Together with its broader verification and confidentiality infrastructure, Primus is working toward a model where financial information can be useful without being unnecessarily exposed. There is an important distinction here: Zcash brings privacy to blockchain transactions through its shielded transaction system. Primus focuses on confidentiality and verification infrastructure that can work above existing financial rails. Different approaches, but both point toward a broader shift in how we think about privacy. The next stage of onchain finance will not be defined by transparency alone. It will require systems that can protect sensitive information, verify important claims, and still make financial activity useful. Because institutions should not have to choose between keeping their information private and proving what needs to be trusted. That is the balance worth building toward.

Prediction markets don't just need better predictions. they need better ways to verify the truth behind them. Prediction markets are built around uncertainty. Will an election result go a certain way? Will a company hit its target? Will a particular event happen before a deadline? People commit capital based on what they believe will happen. but when the event is over, one question becomes critical: What evidence determines the outcome and can that evidence be trusted? This is why @primus_labs make a meaningful difference. Many prediction markets depend on information from outside the blockchain: official announcements, financial data, public records, social platforms, APIs and other Web2 sources. The challenge is not simply accessing that information. It is establishing that the information is authentic before using it to resolve a market. Primus approaches this through zkTLS. Instead of asking users or applications to expose sensitive source data, zkTLS can generate cryptographic attestations that help verify where information came from while preserving the privacy of the underlying data. That introduces an important improvement to the prediction market process: → Retrieve relevant information from an external source. → Verify its authenticity through zkTLS. → Pass the verified evidence into the market's resolution process. → Allow the application or its reasoning layer to interpret the evidence. → Make the resulting decision more transparent and auditable. The distinction matters: Primus helps establish whether the evidence is authentic; the market's resolution logic still determines what that evidence means. And this is not just a theoretical direction. Primus and @CournotProtocol are exploring this relationship: Primus verifies the authenticity of off-chain evidence, while Cournot interprets that evidence and turns it into structured, auditable judgments. But the opportunity extends beyond market resolution. What could this look like in the future? Imagine prediction markets where participants can prove eligibility or relevant account conditions without exposing their entire financial history. Imagine markets that rely on verified exchange data, official records or social activity, with stronger guarantees about the origin of that information. Imagine confidential trading environments where sensitive positions and strategies can remain protected while the system still performs the necessary checks. This is where Primus broader work in fully homomorphic encryption (FHE) could become relevant. FHE enables computation on encrypted information, creating possibilities for applications that need to use sensitive data without first exposing it. These capabilities are distinct, but together they point toward a more capable financial infrastructure: zkTLS helps verify the evidence. Confidential computation helps protect sensitive information. The application uses verified inputs to make decisions. For prediction markets, that could mean stronger resolution processes, more privacy for participants and greater confidence in the information behind market outcomes. The future of prediction markets should not be built on the assumption that everything must be public to be trusted. It should be built around a better principle: Verify what matters. Protect what is private. Make every important decision accountable. That is the direction in which infrastructure like Primus could help move the ecosystem.

Good morning CT. AlphaNet is @primus_labs reputation campaign. Instead of asking what wallet you hold, it asks what you can prove, using zkTLS to verify facts from your web accounts without exposing your passwords or raw records. Think of it as three tracks. Do them in this order and the dashboard becomes easy to read. 𝗕𝗘𝗙𝗢𝗥𝗘 𝗬𝗢𝗨 𝗦𝗧𝗔𝗥𝗧 - Open the dashboard - Connect an EVM wallet. - Add the Primus browser extension, which creates the proofs. 𝗧𝗥𝗔𝗖𝗞 𝟭: 𝗣𝗥𝗢𝗩𝗘 𝗪𝗛𝗢 𝗬𝗢𝗨 𝗔𝗥𝗘 Identity proofs link your accounts to your profile. the task list covers platforms such as X, GitHub, Google, LinkedIn, Luma, Spotify, TikTok and Naver. Each task checks one fact, like a username, follower count, email or contribution history. Start here: these are usually the quickest to finish. 𝗧𝗥𝗔𝗖𝗞 𝟮: 𝗣𝗥𝗢𝗩𝗘 𝗪𝗛𝗔𝗧 𝗬𝗢𝗨 𝗗𝗢 Activity proofs go deeper. They include verified followers on X, events you attended on Luma and your last year of GitHub contributions. the newer agent-themed set covers proofs such as Proof of PnL, Proof of Nomad Life, Proof of AI Affinity and Proof of Trade. Pick the ones that genuinely describe you. a proof means more when it is true. 𝗧𝗥𝗔𝗖𝗞 𝟯: 𝗣𝗥𝗢𝗩𝗘 𝗪𝗛𝗔𝗧 𝗬𝗢𝗨 𝗛𝗢𝗟𝗗 The Confidential Vault campaign lets you shield assets to earn encrypted yield. Your score scales with your effective shield balance, so size matters here. The vault is an early product, so shield only what you are comfortable locking in. 𝗞𝗘𝗘𝗣 𝗜𝗧 𝗚𝗢𝗜𝗡𝗚 - Check the dashboard now and then. New tasks appear over time. - Share Primus with your community the campaign offers referral benefits. What zkTLS does for you in all of this: you prove the fact and the platform never sees your login.