Wizzy204💙 (@Wizzy204_)

@Wizzy204_31 Jul 2025

You are a Web3 OG and you are not part of what @0G_labs is building. Commot for here jhooor!!! What is 0G Labs building? 0G Labs is building a decentralized AI operating system and modular blockchain for scalable, secure, on-chain AI applications. With 0G Labs speed of 50000GB/s you get access to data in real-time, super fast 100x better that other centralized AI operating systems. 0G labs are not just chasing hype with the name, they are building something solid! Pivot to @0G_labs !!!

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Wizzy204💙 · Oct 20, 2026 · 7:37 PM UTC

@Wizzy204_3h

A lot of Web3 systems are very good at verifying the final transaction. The harder part is verifying the information that influenced that transaction in the first place. That difference matters because many real applications still depend on offchain data. A lending protocol may rely on exchange balances. An identity system may rely on credentials from Web2 platforms. An AI agent may rely on external APIs before deciding what action to take. The chain can verify what happened at the end, but it does not automatically know whether the inputs that came before it were authentic. This is where @primus_labs is building with zkTLS. Instead of asking users to reveal complete records, upload screenshots, or rely on a centralized verifier, Primus can make specific claims from ordinary HTTPS interactions cryptographically provable. The goal is not to move all Web2 data onchain. It is to make the important part of that data verifiable when an application actually needs it. Think about a protocol that wants to know whether a user holds more than a certain amount in assets. It does not need to inspect the user’s entire financial history. It does not need every balance across every platform. It only needs reliable proof that one condition is true. That is where selective disclosure becomes powerful. The user proves the fact that matters. The rest of the information stays private. The same idea can apply to social account ownership, payment history, credentials, reputation, and access conditions. In each case, the application gets a trustworthy answer without demanding unnecessary information from the user. The AI-agent side makes this even more important. An agent might say it checked a particular market source, called a specific API, or queried a model before taking an action. But the final output alone does not prove that the process actually happened. That becomes a bigger issue once agents begin moving funds, interacting with protocols, and acting on behalf of users. At that point, “the agent said it did it” is not enough. You need evidence. With zkTLS, those external Web interactions can become verifiable. That means the system can move closer to proving not just the result of an agent’s action, but the process that led to it. Primus also approaches privacy from another direction through FHE. FHE allows computation to happen while the underlying information remains encrypted. That means sensitive data can still be useful without first being exposed in plain form. For financial applications, identity systems, auctions, confidential transfers, and other privacy-sensitive use cases, that opens up a much stronger design space. Then there is zkFHE, which adds another question: If computation happens over encrypted data, how do you know the result was produced correctly? That is where verifiability returns. The goal becomes not only to keep the information private, but also to prove that the computation over that private information was carried out correctly. So the bigger picture with @primus_labs is not simply “privacy infrastructure.” It is about building a system where offchain information can be authentic, private, usable, and verifiable at the same time. That matters because Web3 will not exist in isolation. AI agents will keep calling external APIs. Financial applications will keep depending on private records. Identity systems will keep relying on information that lives outside the blockchain. The challenge is making those interactions trustworthy without forcing every piece of data into the open. That is the layer Primus is building toward.

@Wizzy204_16h

Most privacy products ask you to hide more. Primus is interesting because it asks a different question: what is the minimum amount of information an application actually needs in order to trust you? That distinction matters. A lending app does not need your entire financial life just to know whether you satisfy a collateral or balance requirement. A credential platform does not need every detail attached to your identity just to confirm one qualification. A reputation system does not need your full account history just to prove that you control a particular profile. Good privacy infrastructure should reduce disclosure, not simply move it somewhere else. This is where @primus_labs uses zkTLS. The idea is to take information that already exists behind ordinary HTTPS services and make specific claims about that information verifiable. Instead of uploading a screenshot and saying “trust this,” a user can produce cryptographic evidence tied to the original interaction. What makes that more powerful is the idea of selective proof. The useful output is not necessarily the raw data itself. It can be a statement derived from the data: balance above a threshold, account ownership confirmed, credential valid, condition satisfied. That turns private Web2 information into something applications can actually reason about without demanding unnecessary exposure. Primus is also building a network around this verification process through attestors. That part matters because proof generation should not quietly depend on one centralized server becoming the new trust anchor. The network design pushes verification toward an infrastructure model where attestors participate in zkTLS tasks, while staking, rewards, and execution constraints are used to align the system around correct behaviour. Then there is the AI side. This is where zkTLS starts to feel less like an identity tool and more like infrastructure for machine accountability. Imagine an autonomous agent that is required to check a market source, query an external model, and verify a condition before executing a trade. The transaction onchain only proves the trade happened. It does not prove the agent actually followed those steps. If those API interactions can be attested, the system gains something much more valuable than an output: evidence of process. That could matter a lot as agents become capable of moving funds, interacting with protocols, and making decisions with real consequences. Primus then approaches privacy from another direction with FHE. zkTLS helps prove external data and interactions, while FHE allows encrypted information to remain usable during computation. Add zkFHE research on top of that, and the goal becomes stronger again: not only keeping computation private, but also proving that the encrypted computation was performed correctly. That is why I think the more accurate way to understand @primus_labs is not simply as a privacy project. It is building toward a verification layer for systems that cannot afford to choose between privacy and trust. In a future where Web3 applications, AI agents, financial systems, and Web2 services constantly interact, the winning infrastructure may be the infrastructure that can answer one question cleanly: can this information be trusted without exposing more than necessary?

™ TøBS · Oct 20, 2026 · 6:05 PM UTC

Wizzy204💙 retweeted

™ TøBS

@1Depixel5h

Primus: Web2와 Web3를 더 효과적으로 연결하다 Web3의 한 가지 과제는 유용한 정보가 여러 플랫폼에 흩어져 있다는 것입니다. 거래 기록은 거래소에, 업무 이력은 프리랜서 플랫폼에, 학습 성과는 온라인 교육 사이트에 저장될 수 있습니다. 하지만 탈중앙화 애플리케이션은 이러한 정보에 직접 접근해 활용하기 어려운 경우가 많습니다. Primus는 zkTLS 기술을 통해 이러한 간극을 연결하는 방법을 모색하고 있습니다. zkTLS를 활용하면 지원되는 웹사이트의 특정 정보에 대한 암호학적 증명을 생성할 수 있습니다. 이를 통해 애플리케이션은 온체인 기록에만 의존하지 않고도 관련 웹 데이터를 활용할 가능성을 얻습니다. 그렇다면 이것이 왜 중요할까요? 숙련된 트레이더에게 보상을 제공하거나, 프리랜서의 활동을 인정하거나, 특정 온라인 활동을 완료한 사용자에게 혜택을 제공하는 Web3 플랫폼을 상상해 보세요. 적절한 연동이 이루어진다면 이러한 플랫폼은 자격이나 보상 대상자를 판단할 때 관련 웹 데이터를 활용할 수 있습니다. 물론 실제 활용 여부는 지원되는 데이터 소스와 애플리케이션의 구현 방식에 따라 달라집니다. 이를 통해 온체인 기록이 많지 않더라도 유용한 온라인 활동 이력을 가진 사람들이 Web3 서비스를 이용할 새로운 가능성이 열릴 수 있습니다. 목표는 인터넷 전체를 블록체인으로 옮기는 것이 아닙니다. 기존 플랫폼의 유용한 정보를 탈중앙화 애플리케이션에서도 활용할 수 있도록 연결하는 것입니다. Primus는 Web2에서의 활동이 Web3의 새로운 기회로 이어질 수 있는 미래를 만들어 가고 있습니다. @primus_labs

Lucky_Gold🪙 · Oct 20, 2026 · 5:52 PM UTC

Wizzy204💙 retweeted

Lucky_Gold🪙

@__LuckyGold5h

𝐖𝐡𝐚𝐭 𝐇𝐚𝐩𝐩𝐞𝐧𝐬 𝐖𝐡𝐞𝐧 𝐚 𝐏𝐫𝐢𝐦𝐮𝐬 𝐀𝐭𝐭𝐞𝐬𝐭𝐨𝐫 𝐀𝐜𝐭𝐬 𝐃𝐢𝐬𝐡𝐨𝐧𝐞𝐬𝐭𝐥𝐲? Trust is essential when applications depend on data from the internet. But in a decentralized network, trust cannot rely on promises alone. There must be mechanisms to hold participants accountable. This is why Primus Network incorporates staking, Trusted Execution Environments (TEEs), and a planned penalty mechanism to help protect the integrity of its zkTLS verification process. 𝐖𝐡𝐲 𝐀𝐭𝐭𝐞𝐬𝐭𝐨𝐫 𝐇𝐨𝐧𝐞𝐬𝐭𝐲 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 Attestors are responsible for executing zkTLS tasks with clients and helping generate cryptographic proofs about web data. If an attestor deliberately signs a session without correctly executing the required protocol, it could undermine the reliability of the verification process. For applications relying on those proofs, that is a serious concern. @primus_labs needs a way to establish whether tasks were executed correctly, rather than simply accepting an attestor's claim. 𝐇𝐨𝐰 𝐓𝐄𝐄𝐬 𝐇𝐞𝐥𝐩 𝐃𝐞𝐭𝐞𝐜𝐭 𝐃𝐢𝐬𝐡𝐨𝐧𝐞𝐬𝐭 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 Primus runs attestor software inside a Trusted Execution Environment (TEE). A TEE provides an isolated environment designed to protect code and data during execution. TEE attestation can provide evidence about the execution environment and the software running inside it. This helps the network assess whether the expected operations were performed. The important distinction is that a TEE is not a guarantee against every possible attack. Its effectiveness depends on the implementation, the hardware, and the security assumptions involved. Nevertheless, it provides an additional layer of assurance beyond relying solely on an operator's word. 𝐒𝐭𝐚𝐤𝐢𝐧𝐠 𝐂𝐫𝐞𝐚𝐭𝐞𝐬 𝐄𝐜𝐨𝐧𝐨𝐦𝐢𝐜 𝐀𝐜𝐜𝐨𝐮𝐧𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲 Primus requires attestor operators to stake tokens before officially participating in the network. This creates an economic commitment. Attestors can earn fees from successfully executed tasks, but dishonest conduct can expose them to penalties under the network's applicable rules. The idea is to make honest participation worthwhile while discouraging operators from taking actions that could undermine the service. Staking alone does not prevent misconduct. Its value comes from working alongside execution verification and enforceable penalty rules. 𝐖𝐡𝐚𝐭 𝐀𝐛𝐨𝐮𝐭 𝐅𝐚𝐢𝐥𝐞𝐝 𝐓𝐚𝐬𝐤𝐬 𝐚𝐧𝐝 𝐔𝐧𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐍𝐨𝐝𝐞𝐬? Not every failed task means an attestor is dishonest. A node might experience connectivity issues, software problems, or timeouts even when its operator is acting honestly. Primus therefore describes a threshold-based approach to handling reliability issues. Some failures may be tolerated within a defined time window, while failure rates exceeding the specified threshold can trigger penalties. This distinction matters because a fair network must separate deliberate misconduct from ordinary technical problems as much as its mechanisms allow. 𝐖𝐡𝐲 𝐏𝐞𝐧𝐚𝐥𝐭𝐢𝐞𝐬 𝐀𝐫𝐞 𝐁𝐞𝐢𝐧𝐠 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐞𝐝 𝐆𝐫𝐚𝐝𝐮𝐚𝐥𝐥𝐲 According to the Primus network design, the penalty mechanism will not be active from the first day of mainnet launch. During the initial staging phases, participation is restricted to authorized nodes, and the full penalty mechanism is not yet introduced. This gradual approach gives the network room to establish operating conditions, evaluate reliability, and refine the parameters used to assess failures. Once the mechanism is formally introduced, TEE attestations are intended to help verify task execution and support enforcement of the network's rules. The aim is to avoid punishing honest operators because of poorly calibrated thresholds while still establishing meaningful consequences for unacceptable behavior. 𝐖𝐡𝐲 𝐓𝐡𝐢𝐬 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐄𝐧𝐭𝐢𝐫𝐞 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 Primus connects clients who need verification, developers building applications, and attestors providing the underlying service. If attestors cannot be held accountable, confidence in that service can weaken. A credible security model helps protect clients, gives developers more confidence in the infrastructure they integrate, and encourages operators to maintain reliable systems. The objective is not to assume every attestor will behave dishonestly. It is to make sure the network has mechanisms to respond when participants fail to meet its requirements. A decentralized network becomes more dependable when its participants have clear responsibilities and meaningful accountability. Primus combines staking, TEE-based execution assurance, and a gradually introduced penalty mechanism to address dishonest behavior and persistent reliability problems. The principle is simple: attestors should be rewarded for providing reliable verification, while dishonest execution should carry consequences under clearly defined network rules. That balance is important for building confidence in a network designed to make internet data verifiable and useful across blockchain applications.

Lucky_Gold🪙

@__LuckyGold16h

gPrimus 𝐏𝐫𝐢𝐦𝐮𝐬 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐂𝐫𝐞𝐚𝐭𝐞𝐬 𝐚 𝐌𝐚𝐫𝐤𝐞𝐭 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 Data has become one of the most valuable resources in the digital economy. But having access to information is not enough. Applications also need ways to verify that the information is genuine without unnecessarily exposing sensitive details. @primus_labs is building infrastructure that allows applications to verify information from the internet using zkTLS. By connecting clients, developers, and decentralized attestors, it creates a foundation for turning data verification into a service that applications can access and pay for. 𝟏. 𝐂𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐃𝐞𝐦𝐚𝐧𝐝 𝐟𝐨𝐫 𝐕𝐞𝐫𝐢𝐟𝐢𝐞𝐝 𝐃𝐚𝐭𝐚 Blockchain applications often depend on information that exists outside their networks. A DeFi protocol might need to verify trading activity, while an AI agent may need reliable information from an external website before making a decision. Primus helps address this need by allowing applications to request verification of specific web data. Instead of relying entirely on screenshots or unverified claims, applications can use cryptographic proofs to establish evidence about the information they need. As more applications require trustworthy external data, demand for verification services could grow. 𝟐. 𝐀𝐭𝐭𝐞𝐬𝐭𝐨𝐫𝐬 𝐒𝐮𝐩𝐩𝐥𝐲 𝐭𝐡𝐞 𝐒𝐞𝐫𝐯𝐢𝐜𝐞 For this market to work, someone must perform the verification tasks. Primus uses attestor nodes to execute zkTLS tasks with clients and help generate the associated proofs. Attestors stake tokens to participate and can earn fees from successfully executed tasks. This creates an economic incentive for operators to provide reliable services. The model connects the work performed by attestors with the demand coming from applications. 𝟑. 𝐇𝐨𝐰 𝐭𝐡𝐞 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥 𝐂𝐨𝐨𝐫𝐝𝐢𝐧𝐚𝐭𝐞𝐬 𝐭𝐡𝐞 𝐌𝐚𝐫𝐤𝐞𝐭 Primus system contracts help coordinate task processing, staking, fee settlement, rewards, and penalties. Clients pay task fees in crypto tokens before execution. Once a task is executed, the fees are shared between the participating attestor and the protocol. This creates a clear economic relationship between the people requesting verification, the operators providing it, and the infrastructure coordinating the exchange. 𝟒. 𝐖𝐡𝐲 𝐏𝐫𝐢𝐯𝐚𝐜𝐲 𝐌𝐚𝐤𝐞𝐬 𝐓𝐡𝐢𝐬 𝐌𝐚𝐫𝐤𝐞𝐭 𝐕𝐚𝐥𝐮𝐚𝐛𝐥𝐞 A data verification market must do more than provide access to information. It must also consider privacy. Users may want to prove something about their financial activity or online credentials without revealing their entire records. Primus combines zkTLS with its broader privacy technology stack to help applications verify relevant information while reducing unnecessary data exposure. This could make verified data more useful in areas such as decentralized finance, digital credentials, and AI agents. 𝟓. 𝐖𝐡𝐚𝐭 𝐂𝐨𝐮𝐥𝐝 𝐃𝐫𝐢𝐯𝐞 𝐌𝐚𝐫𝐤𝐞𝐭 𝐆𝐫𝐨𝐰𝐭𝐡? The future of this market will depend on real demand. More developers integrating Primus, more applications requesting verification, and reliable task execution could increase the need for attestor services. A sustainable fee structure will also matter. Clients need affordable verification, attestors need worthwhile compensation, and the protocol needs an economic model that supports continued operation. Growth is therefore not just about producing more proofs. It is about building services that solve problems developers and users are willing to pay for. Primus connects three important parts of the data economy: clients who need verified information, attestors who provide verification, and a protocol that coordinates the service. If adoption grows, this model could help make trustworthy internet data a reusable service rather than a challenge every application must solve independently. The opportunity is to turn data verification into an accessible market where information becomes useful not simply because it exists, but because applications can verify it and use it with greater confidence.

Wizzy204💙 · Oct 20, 2026 · 2:12 PM UTC

@Wizzy204_9h

ZK proving may eventually need something most infrastructure conversations barely mention: price discovery. Today, teams often think about proof generation in technical terms. How much GPU memory does the workload need? How long will proving take? Which proving system is supported? But once proving capacity becomes a marketplace, another question appears: what is this specific workload actually worth to execute right now? Two proof requests can require similar hardware while having completely different economic value because one can wait and the other cannot. That creates an interesting problem for @fermah_xyz and Froben. A marketplace cannot become efficient simply by connecting applications to available machines. It also has to help different kinds of demand compete for limited capacity intelligently. An urgent proof needed for settlement should probably behave differently from a flexible workload that can wait fifteen minutes for cheaper capacity. The hardware might be identical, but the value of immediate access is not. Think about logistics. Shipping the same package overnight costs more than delivering it next week because the customer is paying for more than transportation. They are paying for priority, certainty, and time. Proving infrastructure could develop similar economics. Flexible jobs could absorb unused capacity at lower prices, while applications with strict deadlines could pay for faster routing or reserved resources. That would also give prover operators better incentives. Instead of competing only on who offers the cheapest GPU time, operators could differentiate through availability, completion history, hardware specialization, and their ability to handle urgent workloads reliably. A slightly more expensive operator that consistently finishes difficult jobs on time may ultimately provide more value than cheaper capacity that regularly causes retries. This is why the long term opportunity around @fermah_xyz feels bigger than simply lowering the cost of proofs. Froben can become a place where the network continuously discovers the value of proving capacity under different conditions: different hardware requirements, different deadlines, different levels of demand, and different reliability expectations. And once those signals become strong enough, the marketplace starts allocating resources more intelligently. Flexible workloads move toward cheaper idle capacity. Urgent workloads compete for faster resources. Specialized hardware gets matched with the jobs that actually need it. Operators receive better signals about what capacity the market values most. That is when decentralized proving starts behaving like a real infrastructure market. The future may not be one universal price for compute. It may be a market that understands the difference between a proof that can wait and a proof that cannot.

The real cost of proving infrastructure is not the GPU bill. It is the cost of being wrong about how much capacity you will need tomorrow. A ZK application rarely grows in a perfectly predictable line. Demand can remain stable for weeks, then a launch, incentive campaign, heavy rollup batch, or sudden increase in activity changes the workload almost overnight. At that point, infrastructure planning becomes a financial decision. Build too much capacity and capital sits idle. Build too little and the application discovers its limit at the exact moment users are asking the most from it. That makes proving capacity partly a risk management problem, not only a compute problem. This is where the economics of shared proving infrastructure become much more interesting. Instead of forcing every application to make a large hardware bet on its own future demand, a marketplace can distribute that uncertainty across many workloads and many operators. One application may be experiencing a spike while another is quiet. One prover may have available capacity while another is already saturated. If those differences can be coordinated efficiently, spare capacity stops being stranded and starts becoming useful inventory. That is a more meaningful way to think about @fermah_xyz and Froben. The opportunity is not simply to create a place where somebody requests a proof and somebody else owns a GPU. The harder part is creating enough visibility into hardware compatibility, availability, timing, routing, and completion history that the network can convert fragmented supply into dependable execution. Fermah has recently emphasized that listed machines are not the same as executable capacity, because resources disappear from the usable pool when they fail workload requirements, cannot be routed in time, or cannot successfully complete settlement. Once that exists, proving capacity starts behaving less like hardware ownership and more like a financial hedge against unpredictable demand. A team does not necessarily need to own every machine it could possibly need during its busiest hour. What it needs is confidence that additional capacity can be reached when that hour arrives. That distinction could matter enormously for smaller ZK teams, because infrastructure decisions stop being based entirely on predicting future peaks years in advance. The prover side benefits from the same structure in reverse. An operator carrying expensive hardware also carries utilization risk. Every hour without useful work is capacity that cannot be recovered later. A market that can route different workloads toward that hardware gives operators more opportunities to turn idle time into productive execution. Over time, reliability can become part of that market too: operators that consistently meet requirements, start jobs quickly, and complete them successfully become more valuable than machines that merely exist on a provider list. This is why I think the most interesting future metric for proving infrastructure will not be total GPUs. It will be how effectively the network absorbs uncertainty. How much additional demand can arrive without performance collapsing? How much unused supply can be converted into completed work? How quickly can jobs move toward compatible machines when conditions change? Those questions tell you whether a proving market is actually functioning as infrastructure rather than simply displaying a large amount of hardware. That gives @fermah_xyz a much bigger opportunity than selling cheaper proofs. If Froben can make proving capacity easier to access exactly when applications need it, then it reduces one of the hardest decisions every growing ZK project eventually faces: how much infrastructure should we own for demand that has not happened yet? The strongest proving market may be the one that makes that question less important in the first place.

Bruno Jr Talent · Oct 20, 2026 · 12:00 PM UTC

Wizzy204💙 retweeted

@bruno_jr_talent11h

gfermah 𝗙𝗲𝗿𝗺𝗮𝗵 𝗞𝗲𝗿𝗻𝗲𝗹 𝗔𝗻𝗱 𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗢𝗳 𝗔𝗜 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 AI agents are becoming capable of doing more than answering questions. They can interact with APIs, trigger workflows, make payments and coordinate tasks across different systems. But there's a problem people may overlook: an agent can know what to do and still struggle to execute it reliably if the underlying infrastructure keeps changing. This is one of the challenges @fermah_xyz is addressing with Kernel. 𝗧𝗵𝗲 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗦𝗵𝗼𝘂𝗹𝗱𝗻'𝘁 𝗖𝗼𝗻𝗳𝘂𝘀𝗲 𝗧𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 Imagine asking an AI agent to pay for an API using an x402 payment flow. The agent needs to request the service, complete the required payment and receive the expected result. It shouldn't need to understand every internal change to routing, settlement or verification just to repeat that same action. If every infrastructure upgrade forces developers to redesign how agents interact with a service, building reliable autonomous systems becomes much harder. 𝗞𝗲𝗲𝗽 𝗧𝗵𝗲 𝗔𝗰𝘁𝗶𝗼𝗻 𝗦𝘁𝗮𝗯𝗹𝗲 Fermah Kernel approaches this by separating the action an agent requests from the process used to fulfil it. The agent interacts with a consistent interface, while the underlying workflow can evolve as the infrastructure improves. → The agent requests an action. → Kernel executes the defined workflow in a sandboxed environment. → The result can be verified. → Onchain settlement can be handled as part of the workflow. This creates a clearer separation between what the agent wants to accomplish and how the system actually gets it done. Imagine thousands of agents paying for services, requesting data and coordinating tasks without human involvement at every step. At that scale, execution needs to be predictable. Developers need to know what an action is supposed to do, while the infrastructure needs room to improve without constantly breaking the agent's workflow. Kernel's use of deterministic workflows helps make execution more predictable, while sandboxing and verifiable results support a more controlled process. Actual guarantees still depend on the workflow design and implementation. The interesting part of Fermah's approach is that agents shouldn't need to manage every backend detail themselves. A payment route can change. Settlement logic can be upgraded. Verification processes can improve. Yet the action interface can remain consistent, provided the system preserves its defined behaviour. For autonomous software to become genuinely useful, knowing what to do is only half the job. It also needs a dependable way to get things done, even as the infrastructure underneath continues to change. @7wealthh

@bruno_jr_talentOct 18

gfermah 𝗙𝗲𝗿𝗺𝗮𝗵 𝗔𝗻𝗱 𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗢𝗳 𝗥𝗲𝘂𝘀𝗮𝗯𝗹𝗲 𝗖𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻 Imagine a DeFi application where one protocol performs a computation, another uses the result, and a third takes action based on it. That is the kind of workflow @fermah_xyz is exploring ways to support. Instead of developers building a custom connection for every interaction, the goal is to make separate executions easier to connect and reuse. 𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗪𝗶𝘁𝗵 𝗗𝗶𝘀𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗲𝗱 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 Blockchain applications rarely operate in isolation. A single process might need to collect information, perform a calculation, verify a result, and use that result to trigger another action. The difficulty is getting every component to communicate correctly. → One protocol generates an output. → Another protocol needs that output as an input. → Developers must build and maintain the connection between them. As workflows grow, these individual integrations can become more difficult to manage. Composability offers a way to make those connections more reusable. 𝗧𝗵𝗿𝗲𝗲 𝗣𝗮𝗿𝘁𝘀 𝗢𝗳 𝗧𝗵𝗲 𝗙𝗲𝗿𝗺𝗮𝗵 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 Fermah combines several technical components that address different parts of the execution process. → Content-addressed OCI images: These identify program images by their content, providing a consistent reference to the specific program artifact being used. → gVisor's "runsc": This provides a sandboxed runtime environment that helps isolate program execution from the underlying system. → Onchain handoffs: These allow an execution's output to become the input for a subsequent action, helping connect multiple steps into a larger workflow. Each component serves a different purpose. Program identification helps establish what is being executed, sandboxing provides an isolated runtime, and handoffs connect the results to what comes next. Together, these mechanisms offer a foundation for making computation more reusable across connected applications. 𝗪𝗵𝗮𝘁 𝗧𝗵𝗶𝘀 𝗖𝗼𝘂𝗹𝗱 𝗠𝗲𝗮𝗻 𝗙𝗼𝗿 𝗗𝗲𝗙𝗶 Consider a DeFi application that needs to calculate a value before executing another operation. → The first program processes the required information. → Its output is passed to another protocol. → The next step uses that output to determine what should happen. → A follow-up action can proceed when the necessary conditions are met. Instead of treating every operation as a separate task, developers could connect these steps into a coordinated workflow. This could be useful for more complex financial applications where multiple computations and protocol interactions need to happen in the right order. The actual reliability still depends on correct verification, secure handoffs, and the design of each component. AI agents may need to do more than answer questions or complete one isolated task. They may need to gather data, process it, evaluate a result, and initiate another action. A composable execution workflow could help connect those stages. → One component gathers the required information. → Another performs the computation. → The result becomes available for the next step. → The workflow continues according to its defined logic. This creates room for developers to build more structured agentic applications without reinventing every connection from scratch. The good thing is making computation a reusable building block for Web3. When separate components can work together, developers have more flexibility to create applications that go beyond a single protocol or execution. That does not mean every workflow becomes automatically secure or compatible. Programs still need well-defined interfaces, trustworthy verification, and carefully designed execution rules. But the direction is important: instead of rebuilding the same connections for every application, developers could assemble complex workflows from components designed to work together. @7wealthh

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Wizzy204💙 · Oct 20, 2026 · 6:44 AM UTC

Wizzy204💙 retweeted

Most Web3 systems are designed to verify what happens after data reaches the chain. The harder challenge is proving whether the data was trustworthy before it got there. That is a much more important problem than it sounds, because a huge amount of valuable information still lives outside blockchains. Exchange balances, account ownership, financial records, credentials, private APIs, social activity, and AI-agent interactions all sit in environments that smart contracts cannot natively verify. This is where @primus_labs comes in with zkTLS. Instead of asking users to hand over raw information, zkTLS can turn specific claims from normal HTTPS interactions into cryptographic proofs. That means an application can verify the part it actually needs without receiving everything else behind it. Take credit or lending as an example. A protocol may want to know whether a user has enough assets or meets a certain financial condition. The old approach is to reveal account statements, balances, or transaction history. The better approach is to prove the condition directly. The application gets a trustworthy answer, while the user keeps the unnecessary details private. Now extend that logic to identity and reputation. A user may want to prove they control an account, own a credential, completed a specific activity, or meet an eligibility requirement. The interesting part is not simply verifying the data. It is doing that selectively, so verification does not automatically become surveillance. The AI-agent angle makes this even more relevant. An autonomous agent may claim it checked a price feed, queried a model, called an API, or followed a certain sequence before taking an action. But the final action alone does not prove any of that happened. If agents are going to move funds or make decisions for users, then their external interactions need to become verifiable too. That is where Primus starts to look less like “just another privacy project” and more like infrastructure for accountable digital systems. zkTLS can help prove external data and interactions, while FHE allows computation over encrypted data, keeping sensitive information hidden even while it is being used. Then there is zkFHE, which tackles the next question: if computation happens on encrypted data, how do you know the result was produced correctly? Privacy alone is not enough. The computation itself also needs to be verifiable. So the bigger picture with Primus is about combining three things that usually get treated separately: authenticity, privacy, and computation. That matters because the future of Web3 is not going to exist in isolation. It will constantly interact with Web2 services, financial systems, private databases, APIs, and autonomous agents. The challenge is making those connections trustworthy without turning every private interaction into public data. That is the direction @primus_labs is building toward: making offchain information not just accessible, but provable, usable, and private by design.

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hi (𝜏) · Oct 20, 2026 · 8:03 AM UTC

Wizzy204💙 retweeted

hi (𝜏)

@whitey_xyz15h

gm traders a moving man will always meet his luck right? Well, one man just met his luck just by copy trading on @copyfomo 🙂‍↔️ top catches on copyfomo right now: 4.7x $GLOAM 4.3x $FrunkStore 4.2x $ORACLE7 3.6x $KIBSHI all copied from traders, not guessed. one held 9 min, one held almost 3 days. same bot. link to start copying in the CS

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Xabi Alonso · May 8, 2014 · 7:11 PM UTC

Wizzy204💙 retweeted

Xabi Alonso

@XabiAlonso8 May 2014

#BringBackOurGirls

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An0i🍁 · Oct 20, 2026 · 7:03 AM UTC

Wizzy204💙 retweeted

An0i🍁

@anoi_exe16h

think about how much information we share and use on the internet every day. your bank balance, trading history, online purchases, social media activity, and even your identity all exist somewhere online now imagine you need to prove that some of this information is real to another platform how would you do it? you could take a screenshot, share your account details, or provide a document as proof but here's the problem - how does the other person know that the screenshot hasn't been edited? - how can they confirm that the information is genuine? - and do you really need to expose your personal details just to prove one small thing? these are some of the challenges that come with using internet data as Web3 continues to grow, more applications need information from outside the blockchain to work properly. for example, a lending platform might need to verify certain financial details. a rewards platform might need to confirm that a user completed a task. another application might need to check information from an existing website. the challenge is finding a reliable way to verify that information without making users share more than necessary. this is where Primus @primus_labs comes into the picture primus is working on ways to make internet data verifiable while helping protect users' privacy one of its key technologies is zkTLS, which can help users prove information from websites without having to reveal all the details behind it. yhink of it this way. instead of asking someone to trust a screenshot or simply believe what you say, there can be a way to verify the information using cryptographic proofs. that changes how applications could handle data of course, not every type of information can be verified in the same way. It depends on the data, the technology being used, and how the application is built. but the idea is, we need better ways to know when information is genuine, especially when applications depend on that information to make decisions. and this goes beyond crypto. the internet is full of useful information, but having access to it isn't the same as being able to prove that it's authentic as more services connect with blockchain applications, reliable data verification will become even more important. for me, this is one of the interesting things about what Primus is building. it's not just about putting more information on-chain. It's about making information from the internet more useful and verifiable without ignoring privacy. because in the end, data is only as useful as our ability to trust and verify it.

An0i🍁

@anoi_exeOct 19

what is primus, and what problem is it trying to solve? one thing i've noticed in web3 is that we have a lot of data, but proving that some of that data is real and trustworthy can be a challenge think about it what if you want to prove your trading volume on an exchange, show your account history, or confirm certain information from a website to a blockchain application? gow do you prove that the information is real without sharing more personal data than necessary? this is one of the problems @primus_labs is working to solve primus is building technology that helps applications verify information from the internet while protecting users' privacy one of the key technologies behind this is called zkTLS it helps you prove that certain information from a website is valid without having to reveal everything about your account or expose all your personal information for example, imagine you're trying to use a lending platform that needs to check some information about your financial activity jnstead of simply trusting what you say or asking you to share screenshots, the application could use cryptographic proofs to verify the information. you get a way to prove your claim, while keeping unnecessary details private that's an important difference because privacy shouldn't mean that nobody can verify your information. and verification shouldn't mean that you have to reveal everything about yourself primus is working to make it easier for applications to use internet data in a way that can be verified and still respect users' privacy this could open up interesting possibilities for DeFi, digital identity, financial services, and other applications that need reliable information from outside the blockchain of course, the exact use cases depend on how the technology is implemented by each application but the bigger idea is worth paying attention to

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Vicky · Oct 20, 2026 · 4:56 AM UTC

Wizzy204💙 retweeted

Vicky

@vicki_ranking18h

you really should stop being average and do better. a few tips: - do a thousand replies every day - farm perp dexes (and expect to get 4 figs back some day) - make 4 useless posts every day - quit college - vibecode one tool every day, that no one is going to use - gamble with your salary they don’t teach this easily but i gave you for free.

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Bruno Jr Talent · Oct 20, 2026 · 6:13 AM UTC

Wizzy204💙 retweeted

@bruno_jr_talent17h

gm 𝗖𝗮𝗻 𝗗𝗲𝗙𝗶 𝗣𝗿𝗼𝘁𝗲𝗰𝘁 𝗜𝗻𝘀𝘁𝗶𝘁𝘂𝘁𝗶𝗼𝗻𝗮𝗹 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗗𝗮𝘁𝗮 One reason traditional financial institutions have to think carefully about using DeFi is that public blockchains expose a lot of transaction information. Anyone can potentially inspect wallet activity, track fund movements and study transaction patterns. That transparency is useful for verification, but it can also reveal details that businesses would rather keep confidential. This is where privacy by design becomes important, and why the work @primus_labs is exploring around confidential data verification is worth understanding. 𝗪𝗵𝗮𝘁 𝗣𝘂𝗯𝗹𝗶𝗰 𝗧𝗿𝗮𝗻𝘀𝗮𝗰𝘁𝗶𝗼𝗻𝘀 𝗖𝗮𝗻 𝗥𝗲𝘃𝗲𝗮𝗹 Imagine a company managing millions of dollars in treasury assets. It wants to use DeFi to lend capital, trade assets or move liquidity between protocols. However, every visible transaction could reveal information about its financial decisions. Competitors might study its wallet balances, identify recurring transfers or infer when the company is preparing to enter or exit a position. For an institution, this isn't just a matter of personal preference. Exposing sensitive financial activity can create business risks. 𝗣𝗿𝗶𝘃𝗮𝗰𝘆 𝗠𝘂𝘀𝘁 𝗕𝗲 𝗣𝗮𝗿𝘁 𝗢𝗳 𝗧𝗵𝗲 𝗗𝗲𝘀𝗶𝗴𝗻 Adding privacy after a financial application has already been built can be difficult. The system needs to consider confidentiality from the beginning. √ Which transaction details should remain private? √ Who should have permission to access sensitive information? √ What information must be revealed to verify compliance or enforce rules? √ How can applications verify claims without collecting unnecessary data? These questions affect how institutional financial applications are built, not just how users interact with them. 𝗧𝗵𝗲 𝗕𝗮𝗹𝗮𝗻𝗰𝗲 𝗕𝗲𝘁𝘄𝗲𝗲𝗻 𝗣𝗿𝗶𝘃𝗮𝗰𝘆 𝗔𝗻𝗱 𝗧𝗿𝘂𝘀𝘁 Privacy doesn't mean making every transaction impossible to verify. Financial systems still need reliable checks, access controls and ways to demonstrate that certain requirements have been met. Cryptographic techniques can help separate these two needs. An application may be able to verify a particular claim without exposing all the information behind it, depending on the technology and implementation. This is an important distinction: confidentiality limits unnecessary exposure, while verification helps establish that the required conditions have been satisfied. 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 𝗙𝗼𝗿 𝗜𝗻𝘀𝘁𝗶𝘁𝘂𝘁𝗶𝗼𝗻𝗮𝗹 𝗗𝗲𝗙𝗶 Liquidity and transaction speed alone may not be enough to bring more institutions onchain. Organizations also need infrastructure that respects the sensitivity of their financial information while allowing necessary verification to take place. Primus Labs is exploring privacy technologies that can support this direction. The goal isn't to remove transparency completely. It's to make transparency more selective, so financial activity can remain verifiable without exposing every sensitive detail to everyone. For institutional DeFi to mature, privacy cannot be an afterthought. It needs to be considered from the start.

@bruno_jr_talentOct 19

gPrim 𝗧𝗵𝗲 𝗣𝗿𝗶𝘃𝗮𝗰𝘆 𝗚𝗮𝗽 𝗕𝗲𝘁𝘄𝗲𝗲𝗻 𝗪𝗲𝗯𝟮 𝗔𝗻𝗱 𝗪𝗲𝗯𝟯 Blockchains are good at verifying what happens onchain. But some of the information that Web3 applications need comes from somewhere else entirely. Think about exchange balances, bank records, website activity or data returned by an API. Smart contracts cannot automatically access and verify these details by themselves. So, how can a Web3 application trust information from the internet without asking users to expose more personal data than necessary? This is the problem @primus_labs is working to address with zkTLS. 𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗪𝗶𝘁𝗵 𝗣𝗿𝗼𝘃𝗶𝗻𝗴 𝗢𝗻𝗹𝗶𝗻𝗲 𝗗𝗮𝘁𝗮 Imagine a lending protocol needs to confirm that a user has a certain balance on a cryptocurrency exchange. The application needs evidence of that balance, not the user's entire transaction history, account activity or other private details. Revealing everything just to prove one fact creates an unnecessary privacy problem. This is where selective data verification becomes useful. Instead of sharing all the information, users should be able to prove the specific claim an application needs to check. 𝗛𝗼𝘄 𝗭𝗸𝗧𝗟𝗦 𝗛𝗲𝗹𝗽𝘀 zkTLS aims to connect information exchanged through existing internet services with blockchain applications using cryptographic proofs. In simple terms, it can help an application verify a claim about online data without necessarily receiving the underlying private information. That could make it easier to use Web2 information in Web3 without treating privacy as something users must sacrifice to participate. There isn't just one way to build zkTLS. 1. MPC-based approaches use cooperation between multiple parties to help establish a proof. 2. Proxy-based approaches use an intermediary in the data-verification process. Each method comes with different trade-offs involving trust, performance and verification. It's also important to understand that cryptographic proofs don't magically make every source trustworthy. The protocol must still establish that the data came from the claimed source and was verified correctly. If implemented effectively, this approach could support several useful applications: • Lending platforms checking financial claims. • Web3 apps verifying account-related information. • Users proving eligibility without revealing unnecessary personal details. • AI agents providing evidence for information retrieved from external services. These are potential applications, not guarantees that every use case is already supported. Web3 doesn't need every piece of internet data to become public before that data can be useful onchain. What matters is giving applications a reliable way to verify the information they need while limiting unnecessary disclosure. That's the direction Primus Labs is exploring with zkTLS: making internet data more useful to Web3 while giving privacy a place in the verification process.

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Wizzy204💙 · Oct 20, 2026 · 6:44 AM UTC

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Wizzy204💙 · Oct 20, 2026 · 5:39 AM UTC

@Wizzy204_17h

GM to my 15 active mutuals. Another beautiful day to show up, stay consistent, and chase bigger wins.

New week, new energy. Let's get to work and get those bags.💰 Gm CT

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Wizzy204💙 · Oct 17, 2026 · 2:41 PM UTC

Wizzy204💙 retweeted

@Wizzy204_Oct 17

The problem with offchain data isn’t that it’s unavailable. It’s that blockchains have no native reason to trust it. A lot of important information still lives outside blockchains: exchange balances, bank records, credentials, payment history, social identities, private APIs, and increasingly, the actions taken by AI agents. The moment an onchain application needs any of that information, trust usually has to be reintroduced somewhere. You either trust a screenshot, trust a centralized verifier, trust an intermediary, or ask the user to reveal far more information than the application actually needs. None of those options are ideal, especially for systems that are supposed to be verifiable by design. This is where @primus_labs uses zkTLS. The idea is to turn data from normal HTTPS interactions into cryptographic proofs, so an application can verify a claim without requiring the full underlying dataset. A protocol might only need to know whether a user holds enough assets to qualify for a product. It does not need the exact balance, every transaction, or the user’s complete financial history. That distinction between proving a fact and revealing the data behind that fact is one of the strongest parts of the model. The same logic can apply to proving ownership of a social account, validating a credential, confirming access to a service, or showing that a specific condition has been met. The AI-agent side makes this even more important. An agent can claim it checked a specific API, queried a model, or retrieved market data before making a decision, but the final action alone does not prove that process actually happened. As agents become more autonomous and start executing actions on behalf of users, that lack of verifiability becomes a serious trust issue. With zkTLS, those external interactions can become provable too. Instead of simply trusting that an AI agent followed the correct process, another system can verify that the required interaction actually took place. That moves us away from “trust the machine” toward something much stronger: prove what the machine did. Primus also extends beyond data verification with FHE. Fully Homomorphic Encryption allows computation to happen while the underlying data remains encrypted. That means sensitive information can still be processed without first being exposed in plain form, which is useful for finance, identity, auctions, confidential transfers, and other applications where privacy matters. Then there is zkFHE, which adds another layer to the problem. Keeping data encrypted is useful, but you still need confidence that the encrypted computation was performed correctly. Primus' work in this area brings privacy and verifiability closer together by focusing on proving the correctness of computation over encrypted data. So when you zoom out, the Primus stack is really bringing together three important ideas: authentic data, private data, and verifiable computation. That combination is what makes the architecture interesting. Because the future of Web3 will not live entirely onchain. AI will keep interacting with external APIs. Financial applications will keep depending on private data. Identity systems will still rely on information from outside the blockchain. The real challenge is making those interactions trustworthy without forcing everything into the open. That is the direction @primus_labs is building toward: infrastructure where offchain information can remain private, still be useful, and still be proven when it matters.

@Wizzy204_Oct 15

The more Web3 depends on information from outside the blockchain, the more one question keeps coming up: how do you prove offchain data without exposing everything behind it? That is the kind of problem @primus_labs is building around. A lending protocol may need proof that a user has enough assets. A reputation system may need proof that someone controls a social account. An identity product may need to verify a credential. An AI agent may need to prove it checked a required API before making a decision. In every case, the useful information exists somewhere outside the chain. That is where trust usually comes back into the system. You can rely on screenshots, centralized databases, trusted intermediaries, or simply ask users to reveal the full underlying data. But each option creates a weakness somewhere, whether that is poor verifiability, extra trust assumptions, or unnecessary exposure of sensitive information. Primus approaches this with zkTLS, which can turn claims from normal HTTPS interactions into cryptographic proofs. Instead of handing over the entire dataset, a user can prove the specific fact an application actually needs. A protocol may only need to know that your balance is above a certain threshold, not your exact balance, full transaction history, or every asset you hold. That same model becomes even more important with AI agents. As agents start executing trades, calling APIs, moving funds, and acting on behalf of users, it will not be enough for them to simply say they followed the right process. If an agent claims it checked a particular price source or queried a specific model before acting, that interaction should be verifiable too. Primus also extends this idea with FHE, where computation can happen while the underlying data remains encrypted. Then zkFHE pushes the model further by focusing on whether those encrypted computations can also be proven correct. That brings privacy and verifiability together instead of forcing applications to choose between them. So the bigger Primus story is not just about hiding data. It is about making information private, useful, and verifiable at the same time. As Web3 keeps interacting with Web2 services, AI agents, financial systems, and private APIs, that kind of trust layer could become increasingly valuable.

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Wizzy204💙 · Oct 19, 2026 · 5:22 AM UTC

Wizzy204💙 retweeted

Billycoins@BillycoinsxOct 19

New week, new energy. Let's get to work and get those bags.💰 Gm CT

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Xabi Alonso · May 19, 2012 · 9:33 PM UTC

Wizzy204💙 retweeted

Xabi Alonso

@XabiAlonso19 May 2012

Football drama II. Congrats Chelsea. Sorry for Bayern.

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Billycoins · Oct 19, 2026 · 2:43 PM UTC

Wizzy204💙 retweeted

Spenda morning with me Woke up stressed,then suddenly started craving shawarma… only to realize I had nothing but crypto. That’s when I tried @MySpenda 2.0 Watch till the end 🎥 #MySpenda

Spenda Africa

@MySpendaSep 24

₦15,000,000 up for grabs! 💰 For 150 creators across X, Instagram, and TikTok (50 winners per platform). To win: - Make a video talking about your favorite Spenda 2.0 feature(s) - Post on X, Instagram, or TikTok - Tag and follow @myspenda ( @ spenda_app on IG) and use the hashtag #MySpenda P.S: Your video could be funny, educational, creative, a day-in-the-life, a feature demo, a skit, a hot take — anything goes! Submission ends: 20th October

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Wizzy204💙 · Oct 19, 2026 · 1:18 PM UTC

Wizzy204💙 retweeted

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Wizzy204💙 · Oct 19, 2026 · 3:25 PM UTC