I just want to be part of the Top 1 percent🤝🎯

Bitcoin has the network, stablecoins have the liquidity, but bringing the two together in a way that works for businesses and everyday financial activity is another challenge entirely. Utexo is working to close that gap by bringing its bridged version of USD₮ to Bitcoin, and its latest announcement marks an important step toward making that happen. On October 22, 2026, @utexocom opened early access to Utexo Mint, a platform designed to help professional operators prepare for the upcoming launch. With testing now available on Bitcoin Signet and a public launch scheduled for November 28, the team is giving exchanges, wallets, market makers, and other financial operators time to prepare their systems before the product becomes publicly available. This is more than a testing phase. It is part of a wider effort to make dollar based liquidity more accessible within the Bitcoin ecosystem and create new opportunities for financial activity on the network. Stablecoins have become an important part of digital finance because they allow people and businesses to hold and transfer value tied to a familiar currency. USD₮, commonly known as USDT, is one of the most widely used stablecoins, and its role in trading and other financial activities has made dollar-based liquidity an important part of the crypto market. However, the availability of stablecoins across different networks does not automatically mean that every ecosystem can benefit from their liquidity in the same way. Bitcoin has its own infrastructure and requirements, and making stablecoin activity practical on the network requires solutions that account for how assets are issued, transferred, and managed. Through Utexo bridged version of USD₮, the project aims to make dollar denominated liquidity available to Bitcoin users and businesses. The goal is to create more room for financial services on Bitcoin, including trading, swaps, and BTC backed lending. For the broader ecosystem, this could give businesses more ways to use Bitcoin infrastructure while accessing the stability and liquidity associated with dollar-based assets. Rather than limiting Bitcoin’s role to holding and transferring BTC, Utexo is working toward an environment where the network can support a wider range of financial activities. Utexo Mint is the platform Utexo has introduced to help professional operators prepare for the upcoming rollout. It allows verified participants to mint and redeem the project’s bridged version of USD₮ while testing the processes they will need to support the asset. The early access environment runs on Bitcoin Signet, a Bitcoin test network that allows developers and operators to test their systems without using the live Bitcoin network for ordinary production transactions. This gives participating businesses an opportunity to check how their integrations work, identify problems, and make necessary adjustments before the public launch. During this period, operators can test minting, confidential transfers, and redemption through RGB, the protocol Utexo uses to issue and transfer assets on Bitcoin. These are important parts of preparing a financial product for real use because businesses need to understand how assets move through their systems and ensure their processes work as expected. Utexo’s approach could create more opportunities for businesses to access dollar based financial services through Bitcoin while protecting sensitive transaction details. For now, Utexo Mint is the next step in making that vision a reality, with early access giving potential partners time to prepare before the public launch.

𝐏𝐫𝐢𝐯𝐚𝐜𝐲 𝐚𝐧𝐝 𝐂𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐭𝐢𝐚𝐥𝐢𝐭𝐲: 𝐓𝐰𝐨 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐒𝐢𝐝𝐞𝐬 𝐨𝐟 𝐭𝐡𝐞 𝐒𝐚𝐦𝐞 𝐆𝐨𝐚𝐥 Privacy and confidentiality are often used interchangeably in blockchain discussions, but they address different parts of the same problem. Privacy is about having control over what information you reveal, who can access it, and what others can learn about you from that information. Confidentiality focuses on ensuring that sensitive information remains protected from people or systems that are not authorized to access it. Although the two concepts are closely connected, they are not identical. Privacy considers the level of control people have over their information, while confidentiality focuses on preventing that information from being exposed to unauthorized parties. This distinction becomes important when we look at how information moves through blockchain networks. Public blockchains are designed to make transactions and network activity verifiable, but that transparency can also reveal details about users and their financial activities. Even when a person’s real identity is not directly attached to a wallet address, transaction records and activity patterns can reveal information they may not want publicly available. The challenge is not to remove transparency entirely, but to give applications better ways to verify information without making every underlying detail accessible to everyone. Confidentiality addresses part of this challenge by protecting sensitive information from unauthorized disclosure. However, keeping information protected does not automatically mean that every aspect of a user’s privacy is preserved. A system might secure the information it stores while still requiring users to reveal more data than necessary to access a service. This is why privacy conscious design also matters. Applications need to consider what information they collect, what they require users to disclose, and whether a particular task can be completed without exposing additional details. Protecting data and limiting unnecessary disclosure are related goals, but both need to be considered. This is where @primus_labs work becomes relevant. Through its focus on ZK technology, zkTLS, and zkFHE, they are exploring cryptographic approaches that can help applications verify information and work with sensitive data while reducing unnecessary exposure. zkTLS focuses on enabling the verification of information obtained from online services without necessarily requiring the full underlying data to be disclosed. Fully homomorphic encryption, on the other hand, allows computations to be performed on encrypted data without first decrypting it. ZK techniques can also support proofs that establish whether certain conditions have been met without revealing all the information behind those proofs. These two addresses different technical challenges, but they contribute to a shared goal: reducing the amount of sensitive information that must be exposed for digital systems to function. Verification remains important because applications need ways to establish that information is valid or that certain conditions have been satisfied. At the same time, users and institutions need stronger ways to protect the information involved in that process. The aim is to make these requirements work together rather than force developers to choose between useful verification and responsible data protection. For Primus, the wider significance lies in making confidentiality part of how applications handle information, rather than treating it as an additional feature. Better cryptographic tools can give developers more flexibility in deciding what needs to be revealed, what can remain encrypted, and what can be verified without disclosing everything behind it. These tools, However, they provide a technical foundation for applications that need to process sensitive information while limiting unnecessary disclosure.

They called the police on a 3year old kid because he’s black. It has nothing to do with his talent. If that kid was white, trust me, they would sing him praises and CNN would talk about him. I always tell people that these whites don’t like blacks. Stop putting them on a pedestal!!! Imagine calling the police on a 3 year old…. A 3 year old!!!!!! And the police came to arrest a 3 year old🤦‍♀️ If that isn’t hatred, then I don’t know what it is, cos it’s not normal at all

I no see my area for here ooo…. We’ve not had light for three weeks now😭 Abi na only people on Band A dey important???

𝐃𝐞𝐜𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐈𝐬 𝐍𝐨𝐭 𝐄𝐧𝐨𝐮𝐠𝐡 𝐈𝐟 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥𝐬 𝐒𝐭𝐢𝐥𝐥 𝐍𝐞𝐞𝐝 𝐏𝐞𝐫𝐦𝐢𝐬𝐬𝐢𝐨𝐧 𝐭𝐨 𝐀𝐜𝐭 Decentralization has made it possible for different systems to share information and work together without depending on a single central authority. But there is still a problem that deserves more attention: how do these systems take action without depending on a specific operator or setup? This is basically @fermah_xyz focus Sharing information is one thing. Being able to act on that information without relying on a fixed set of people or tools is another. For decentralized systems to work more freely, they need more than shared state. They also need a way to start actions across different protocols without being tied to one particular setup. This is the problem Fermah is focusing on. 𝑻𝒉𝒆 𝑫𝒊𝒇𝒇𝒆𝒓𝒆𝒏𝒄𝒆 𝑩𝒆𝒕𝒘𝒆𝒆𝒏 𝑺𝒉𝒂𝒓𝒆𝒅 𝑺𝒕𝒂𝒕𝒆 𝒂𝒏𝒅 𝑻𝒂𝒌𝒊𝒏𝒈 𝑨𝒄𝒕𝒊𝒐𝒏 Consensus helps decentralized networks agree on the state of their systems. It allows different participants to work with the same information and follow the same rules. This is an important part of making decentralized systems reliable. However, agreeing on information does not automatically mean a system can act on it. A protocol may have access to the information it needs, but starting an action can still depend on a multisig, a relayer, or a custom system built specifically for that protocol. These extra systems can become points of dependence. Even when a protocol is decentralized in how it manages information, the process of starting an action may still rely on a limited set of people or services. This creates a gap between knowing what needs to happen and being able to make it happen. For decentralized systems to become more flexible, that gap needs to be addressed. When a protocol depends on a multisig, relayer, or specially built operator to initiate actions, that dependency becomes part of how the protocol works. The system may be decentralized in some areas, but it still needs that particular setup to get certain things done. This matters because protocols should be able to do more without being tied to a specific operator. As decentralized systems grow and connect with other protocols, the way they initiate actions becomes just as important as the information they share. The goal is not simply to make protocols communicate. It is to make it easier for them to participate and act across different systems without depending on separate, fixed arrangements for every action. This is one of the ideas behind FERMAH’s execution layer. 𝑯𝒐𝒘 𝑭𝑬𝑹𝑴𝑨𝑯 𝑨𝒑𝒑𝒓𝒐𝒂𝒄𝒉𝒆𝒔 𝒕𝒉𝒆 𝑷𝒓𝒐𝒃𝒍𝒆𝒎 Fermah is focused on making it possible for protocols to initiate actions programmatically across different protocols. In simple terms, this means actions can be started through a programmed process instead of always depending on a specific person or operator to trigger them. This is an important step towards giving protocols more control over how they operate. Rather than treating action initiation as something that must always be handled by a separate setup, FERMAH’s execution layer aims to make it a more flexible part of decentralized systems. The focus is on giving protocols the ability to participate across systems and initiate actions without being tied to the same arrangements that may limit their independence. As decentralized systems continue to develop, this ability could become increasingly important. Protocols need ways to work together, but they also need ways to take action without adding unnecessary points of dependence. The important point is that Fermah is working towards making action initiation more flexible and less dependent on fixed operators.

𝐖𝐡𝐲 𝐭𝐡𝐞 𝐔𝐧𝐝𝐞𝐫𝐥𝐲𝐢𝐧𝐠 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 FHE is computationally demanding, so performance is a major part of making it practical. Encrypted operations can require considerably more processing than their ordinary counterparts. A system designed for real financial activity must therefore optimise its cryptographic operations, manage workloads efficiently, and keep transaction processing practical. @primus_labs has highlighted its AutoHoG FHE engine as part of the technology behind confidential on chain finance. The broader challenge is to make encrypted computation efficient enough to support repeated financial operations without compromising the protection that makes the technology valuable. Key management is equally important. Keeping data encrypted during computation does not mean every participant should be able to decrypt the result. A secure implementation needs clear rules governing who can access protected information and under what conditions. The strength of the overall system depends on how well these permissions, cryptographic components, and application level controls are designed. 𝑾𝒉𝒂𝒕 𝑻𝒉𝒊𝒔 𝑴𝒆𝒂𝒏𝒔 𝒇𝒐𝒓 𝑼𝒔𝒆𝒓𝒔 𝒂𝒏𝒅 𝑰𝒏𝒔𝒕𝒊𝒕𝒖𝒕𝒊𝒐𝒏𝒔 For individual users, the benefit is greater control over financial privacy. Public blockchains make it possible to inspect transaction activity, but that visibility can also reveal balances and financial behaviour. Confidential computation can reduce the amount of sensitive information exposed while allowing users to continue interacting with on chain services. For institutions, the implications are even broader. Financial organisations often need to protect portfolio positions, transaction sizes, and operational strategies. Exposing these details on a public blockchain can create commercial risks and make certain activities difficult to conduct. FHE offers a way to process sensitive financial information without making its contents publicly readable by default. Developers also gain a foundation for building applications where privacy is part of the system’s design rather than an additional feature. As the infrastructure matures, confidential computation could support a wider range of financial services that need both blockchain functionality and stronger protection for sensitive data. 𝑷𝒓𝒊𝒎𝒖𝒔 𝑰𝒔 𝑩𝒖𝒊𝒍𝒅𝒊𝒏𝒈 𝑩𝒆𝒚𝒐𝒏𝒅 𝑷𝒖𝒃𝒍𝒊𝒄 𝑩𝒂𝒍𝒂𝒏𝒄𝒆𝒔 The important development here is not simply the ability to hide transaction amounts. It is the ability to keep financial information encrypted while the system continues to use it. Through FHE powered Confidential Finance, Primus Labs is bringing that capability into practical on chain activity, from private asset transfers to yield generation. The technology still has engineering challenges to overcome, particularly around performance, key management, and secure integration. But its direction is clear.

What if a blockchain could process your financial activity without exposing the details behind it? That is the problem Primus Labs is working to solve with Fully Homomorphic Encryption (FHE), a technology that allows data to be processed while it remains encrypted. With its Confidential Finance product now live on BNB Chain, Primus is putting this cryptographic approach to work, bringing private transfers and yield generation to on chain finance. 𝑼𝒏𝒅𝒆𝒓𝒔𝒕𝒂𝒏𝒅𝒊𝒏𝒈 𝑭𝑯𝑬 𝒂𝒏𝒅 𝒁𝑲𝑭𝑯𝑬 Fully Homomorphic Encryption allows computations to happen directly on encrypted data without decrypting it first. Normally, a system needs access to readable data to process it. FHE changes that requirement by allowing mathematical operations to be performed on ciphertext, the encrypted form of the original information. The result remains encrypted until an authorised party decrypts it. The technical challenge is making these operations work securely and efficiently. FHE schemes use mathematical structures that preserve the relationships needed for computation, even after data has been encrypted. Depending on the scheme, these operations can introduce computational overhead and noise that must be managed to keep the results accurate. More advanced schemes support a wider range of computations, including complex operations that require multiple stages of processing. ZKFHE brings this approach together with zero knowledge cryptography. FHE makes computation on encrypted data possible, while zero knowledge proofs can establish that certain statements or operations are valid without revealing the private information behind them. Combining the two can help applications protect sensitive data while providing cryptographic assurance about relevant operations. 𝑯𝒐𝒘 𝑷𝒓𝒊𝒎𝒖𝒔 𝑷𝒖𝒕𝒔 𝑭𝑯𝑬 𝒕𝒐 𝑾𝒐𝒓𝒌 @primus_labs is applying FHE to on chain finance through its Confidential Finance product on BNB Chain. The aim is to let users transfer assets and earn yield while keeping sensitive financial information, including transaction amounts and positions, confidential. The process begins when a user shields supported assets, converting them into encrypted versions that can be used within the confidential system. Once the assets are represented in encrypted form, the system can perform supported operations on their encrypted values without exposing the underlying amounts during every computation. Primus’s Confidential Vault extends this approach to yield generation. Users can put supported assets to work through existing DeFi strategies while keeping their positions private. This is significant because confidentiality should not mean giving up access to financial applications. The goal is to preserve the utility of on chain assets while limiting the exposure of sensitive information. FHE is computationally demanding, so performance is a major part of making it practical. Encrypted operations can require considerably more processing than their ordinary counterparts. A system designed for real financial activity must therefore optimise its cryptographic operations, manage workloads efficiently, and keep transaction processing practical. Blockchains do not have to expose every financial detail to remain useful. Primus is working towards a system where computation can continue, assets can remain productive, and sensitive information does not have to become public by default. That is what makes FHE worth paying attention to: it moves blockchain privacy beyond hiding data and towards making private data usable.

Real😅 Have a great weekend❤️

Building a product is one thing. Letting people in to try it before the public launch is another. @fermah_xyz has been building for years now, and they’ve successfully built lots of amazing products and now they are ready to let more people try what they’ve been building. Fermah is now taking that next step, giving its community a chance to get early access to the products the team has been working on. @vanishree_rao Fermah’s co-founder, recently announced that access through the waitlist is being opened in phases. The first 10,000 people are already in, and the next tens of thousands will be admitted in smaller groups. So, if you have been following Fermah, this is your chance to get closer to what the team has been building. 𝑾𝒉𝒂𝒕 𝑰𝒔 𝒕𝒉𝒆 𝑾𝒂𝒊𝒕𝒍𝒊𝒔𝒕 𝑨𝒃𝒐𝒖𝒕? The waitlist is for people who want to get the opportunity to test Fermah’s products while they are still in beta. Fermah has been working on products around ZK proofs and compute, with the aim of making proof generation easier and more accessible. Now, as the team begins opening access in phases, the waitlist gives interested users a way to get in line for a chance to try its products. 𝑾𝒉𝒚 𝑺𝒉𝒐𝒖𝒍𝒅 𝒀𝒐𝒖 𝑱𝒐𝒊𝒏 𝑬𝒂𝒓𝒍𝒚? Getting involved early gives you a chance to be among the people who experience Fermah’s products before a wider launch. It also puts you in a position to follow the project more closely as the team continues to develop and improve its products. Since the products are still in beta, there is more to come, and the testing phase is part of that process. It has been confirmed that the first 10,000 people have already been admitted, but more spots are opening. So, there is still an opportunity to join the waitlist. Joining is straightforward. Head over to joinwaitlist.xyz and sign up. According to Vanishree’s announcement, people will be admitted in quick cohorts, meaning access will be rolled out to groups over time rather than everyone getting in at once. Fermah is moving from building behind the scenes to letting people experience its products firsthand. If you want a chance to test Fermah’s products before a wider release, don’t wait until everyone is talking about them. Get on the waitlist and follow the journey from here.

On my previous post, I talked about what Conviction on XO represents. You can go through the quote tweet to read about it. Now, I will be exploring how it works. 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐇𝐨𝐰 𝐂𝐨𝐧𝐯𝐢𝐜𝐭𝐢𝐨𝐧𝐬 𝐖𝐨𝐫𝐤 Convictions are at the centre of XO Market’s approach. When someone creates a Conviction, they are introducing a market that other users can discover and trade in. The creator sets the opening odds, while activity from participants helps the market develop. However, creating a market is only one part of the process. @xomarket also gives creators a reason to take their role seriously by allowing them to earn a share of the trading fees generated by their markets. Creators receive 20% of all lifetime trading fees from their Convictions. This is an important part of the model because it connects market creation with the activity that follows. A creator is not only responsible for bringing an idea to the platform; they can also benefit when that market continues to attract trading activity. It gives users an additional reason to think about what they create and whether their ideas can attract genuine interest. More importantly, it introduces a different way for users to participate in the prediction market economy. Instead of benefiting only from trading outcomes, they can also earn through creating markets that other people want to engage with. 𝑭𝒓𝒐𝒎 𝑪𝒐𝒏𝒗𝒊𝒄𝒕𝒊𝒐𝒏 𝒕𝒐 𝒂 𝑭𝒖𝒍𝒍 𝑷𝒓𝒆𝒅𝒊𝒄𝒕𝒊𝒐𝒏 𝑴𝒂𝒓𝒌𝒆𝒕 Not every Conviction will immediately become a full prediction market, and that distinction matters. XO uses a process called Graduation. A Conviction must reach a certain level of traction before it graduates into a full Prediction Market. Once it reaches that threshold, the market moves to the main Markets tab, where it receives greater visibility and access to deeper liquidity. This creates a distinction between launching a market and growing one. Anyone can introduce an idea, but its progress depends on whether it gains enough traction to meet the platform’s graduation requirements. This gives newly created markets an opportunity to develop while creating a pathway for those that attract sufficient interest to reach a broader audience. It also means that market creation does not have to begin at the same level for everyone. A user can introduce a Conviction and allow it to find its audience before it reaches the main prediction market section. The idea is to make room for more markets without treating every new market as though it already has the same level of demand. For creators, this creates an opportunity to bring their ideas forward and build interest around them. For participants, it expands the range of markets they can discover as the platform develops.

A lot of people know Primus Labs for privacy, but how many really know the story behind them, the investors backing it, and what they are actually building? Let’s go back to the beginning. @primus_labs formerly known as PADO, is building infrastructure that allows data to be verified and used without exposing sensitive information. Its goal goes beyond simply keeping information private. It wants to make privacy and verification work together, especially in Web3, DeFi, and institutional finance. Let’s Talk About the Funding. In February 2025, Primus announced that they had raised $6.5 million in a combined pre seed and seed funding round. The round was led by three major names: Dispersion Capital, Symbolic Capital, and VanEck. Other investors included Samsung Next, Alchemy, Maelstrom, Amber Group, SNZ Holding, Hash Global, Modular Capital, Initial Ventures, Mozaik Capital, Karatage, Welara, Blockchain Builders Fund, Arweave SCP Ventures, and Legend Star. But the story doesn’t end there. In August 2026, Primus announced a $500k investment from YZi Labs, formerly Binance Labs. Bringing their total raise to $7M So, What Exactly Is Primus Labs Building? Primus describes itself as the confidential infrastructure layer for institutional on chain finance. Public blockchains offer transparency, which is one of their biggest strengths. Transactions can be verified, and activities can be tracked. But when it comes to financial institutions, businesses, and serious on chain finance, not every piece of information should be visible to everyone. Financial positions, transaction details, and sensitive business data may need to remain private, even when the underlying activity must be verified. This is why Primus is important. Using technologies such as zero knowledge Transport Layer Security (zkTLS) and Fully Homomorphic Encryption (FHE), Primus is working to make it possible to verify information and perform computations without unnecessarily exposing the data behind them. In simple terms, you can prove that something is true without revealing everything that proves it. Why Does This Matter for Institutional On Chain Finance? Institutions need more than the ability to move money on chain. They also need ways to protect sensitive information while meeting the requirements of the systems they operate in. Without suitable privacy infrastructure, putting financial activities on a public blockchain can create concerns around confidentiality and exposure. Primus approach could support financial applications where sensitive data remains protected while authorised parties can still verify the information they need. This is an important part of making on chain finance more suitable for institutions without giving up the benefits of blockchain technology. Primus isn’t simply trying to hide transactions. It’s working towards infrastructure that allows privacy, verification, and on chain financial activity to coexist. When you look at Primus Labs, don’t stop at its funding or the names behind it. Those details are part of the story, but the technology is what makes the project worth following. The challenge is clear: how do we make information useful without making it unnecessarily exposed? Primus is building towards an answer. Because the future of on chain finance won’t be about transparency alone. It will also be about knowing what to reveal, what to protect, and how to prove what’s necessary without exposing everything else. And that’s where confidential infrastructure becomes important.

𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐚𝐧 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐟𝐨𝐫 𝐏𝐡𝐲𝐬𝐢𝐜𝐚𝐥 𝐀𝐈 One of the central challenges facing embodied AI is producing enough high quality data to support development at scale. Collecting physical interaction data is not as straightforward as gathering information from digital sources. It requires capturing real interactions, maintaining data quality, and ensuring that the information is useful for training AI systems. This is why @4Dlabs_Official is positioning their work around infrastructure rather than data collection alone. Its approach combines embodied data production with a Web3 protocol designed to support a data production network at scale. The aim is to establish a system that can support the creation and supply of physical interaction data as demand for embodied AI grows. The broader vision follows three key stages: 𝒄𝒐𝒍𝒍𝒆𝒄𝒕, 𝒕𝒓𝒂𝒊𝒏, 𝒂𝒏𝒅 𝒅𝒆𝒑𝒍𝒐𝒚. Data is collected from physical interactions and other relevant sources. That data supports the training and improvement of AI models. The resulting intelligence can then be deployed into physical environments, where it can perform tasks and interact with the world. As the demand for physical AI increases, the infrastructure supporting this process will become increasingly important. 4D Labs is working towards that underlying layer, connecting physical data production with the development and deployment of embodied intelligence.

USDT on Bitcoin is not a completely new idea. It has been around before, so the big question is not just about bringing it back. The real question is, what will Utexo do differently to make it work this time? During a recent live call, Utexo co founder @VIhnatiuk had with @Gate he was asked this very question. What has changed, and what is the team doing differently from what was done in the past? His answer came down to two important things: 𝙨𝙥𝙚𝙚𝙙 𝙖𝙣𝙙 𝙩𝙧𝙖𝙣𝙨𝙖𝙘𝙩𝙞𝙤𝙣 𝙛𝙚𝙚𝙨｡ Utexo wants to make USDT transfers on Bitcoin settle in as little as 50–100 milliseconds, while pushing transaction fees towards zero. And these two goals explain a lot about what @utexocom is trying to achieve. 𝙎𝙥𝙚𝙚𝙙 𝙄𝙨 𝙖 𝘽𝙞𝙜 𝙋𝙖𝙧𝙩 𝙤𝙛 𝙩𝙝𝙚 𝙋𝙡𝙖𝙣 When people send USDT, they want their transactions to be fast. Nobody wants to wait longer than necessary to know that their money has been sent and received. Bitcoin is known for its security, but settling transactions on the network can take time. This creates a challenge when trying to make USDT transfers feel fast and convenient for everyday use. According to Viktor, the team is working towards settlement finality in the range of 50–100 milliseconds. In simple terms, finality is about reaching the point where a transaction is considered settled and cannot simply be reversed. That target is important because Utexo wants to make the process of sending USDT on Bitcoin much faster, without losing sight of the need for secure settlement. 𝙏𝙧𝙖𝙣𝙨𝙖𝙘𝙩𝙞𝙤𝙣 𝙁𝙚𝙚𝙨 𝙉𝙚𝙚𝙙 𝙩𝙤 𝘽𝙚 𝙇𝙤𝙬 𝙏𝙤𝙤 Speed is one side of the story. The other is cost. People want to move their money without having to worry about paying high fees every time they make a transaction. If sending USDT becomes too expensive, it can discourage people from using the network, especially when they need to make frequent transfers. Utexo is aiming to push transaction fees towards zero. This is an important part of the plan because bringing USDT back to Bitcoin is not just about making it possible to send stablecoins on the network. It is also about making the experience affordable enough for people to use. A fast transaction is good, but when the cost of sending money is also low, the experience becomes more attractive. 𝙎𝙤 𝙒𝙝𝙖𝙩 𝙄𝙨 𝙐𝙩𝙚𝙭𝙤 𝘿𝙤𝙞𝙣𝙜 𝘿𝙞𝙛𝙛𝙚𝙧𝙚𝙣𝙩𝙡𝙮? USDT has existed on Bitcoin before, but bringing it back does not automatically solve the challenges that affected its earlier use. A successful return needs more than simply making the asset available again. Utexo is focusing on the experience around the transfer itself: how quickly transactions settle and how much users have to pay. These are important factors because people need a network that works for their daily financial activities, not just one that supports a particular asset. Bitcoin already has a strong position as a settlement network. Utexo wants to build on that foundation by making USDT transfers faster and cheaper. Utexo’s plan is about more than bringing USDT back to Bitcoin. It is about addressing two practical concerns that affect how people move money: 𝘸𝘢𝘪𝘵𝘪𝘯𝘨 𝘵𝘪𝘮𝘦 𝘢𝘯𝘥 𝘵𝘳𝘢𝘯𝘴𝘢𝘤𝘵𝘪𝘰𝘯 𝘤𝘰𝘴𝘵𝘴.

𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐍𝐞𝐞𝐝 𝐏𝐫𝐨𝐨𝐟, 𝐍𝐨𝐭 𝐉𝐮𝐬𝐭 𝐓𝐫𝐮𝐬𝐭 AI agents are moving beyond answering questions. They are beginning to manage wallets, handle portfolios, and make financial decisions. But as we give these agents more control over our money, one question becomes increasingly important: how do we protect our data while verifying what these agents do? How do we know they are making decisions based on the right information? An AI agent can execute a transaction in seconds, but speed means nothing if the information behind that decision cannot be verified. The agent might report that everything went as planned, but how do we know the data it relied on was accurate or that it actually did what it claims? Here comes a problem @primus_labs is working to solve. Through zkTLS, Primus is exploring how to make the information behind AI agents’ decisions verifiable. The idea goes beyond checking whether an agent completed a task. It is about establishing where its information came from, what state it relied on, and whether the actions it performed can be verified. 𝘛𝘩𝘦 𝘨𝘰𝘢𝘭 𝘪𝘴 𝘵𝘰 𝘮𝘰𝘷𝘦 𝘧𝘳𝘰𝘮 𝘴𝘪𝘮𝘱𝘭𝘺 𝘵𝘳𝘶𝘴𝘵𝘪𝘯𝘨 𝘢𝘯 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵 𝘵𝘰 𝘩𝘢𝘷𝘪𝘯𝘨 𝘢 𝘸𝘢𝘺 𝘵𝘰 𝘤𝘩𝘦𝘤𝘬 𝘪𝘵𝘴 𝘸𝘰𝘳𝘬. Consider an AI agent managing your portfolio. You instruct it to buy ETH only when the price drops below $1000. The agent checks an exchange, sees a price that meets your condition, and places the order. But how can you confirm that the price it used was genuine or that the order was actually sent to the exchange? With a suitable zkTLS verification system, the agent could provide proof of the relevant exchange data and its interaction with the service, allowing the information to be checked without exposing your private credentials. This is where Primus becomes relevant. Agents do not operate on blockchain data alone. They also depend on information from exchanges, APIs, and other Web2 services. If that information cannot be verified, users have limited ways to check what influenced an agent’s decisions. Primus approach brings verification into this process. With zkTLS, information from Web2 services can be proven without exposing all the underlying data. This creates a way to establish that information came from a genuine source while keeping sensitive details private. But the bigger picture goes beyond data. Verification also matters for account states, permissions, transactions, and the actions an agent performs. As this infrastructure develops, it could help users and protocols check more of an agent’s activity instead of relying entirely on its own reports. Privacy is just as important here. Giving an AI agent access to financial information should not mean exposing an entire account or handing over sensitive credentials unnecessarily. Primus is working toward a model where information can be verified while limiting what needs to be revealed. Of course, verification alone cannot guarantee that an AI agent will always make the right decision. But it can help users understand what the agent relied on and establish greater accountability around its actions. As AI agents take on more responsibility in Web3, trust cannot depend on what an agent says it has done. There needs to be a way to verify it. Primus is working toward that future: where AI agents can act, their information can be checked, and privacy does not have to be sacrificed along the way. Because when real money is involved, an agent’s word should not be the only proof.

𝐅𝐞𝐫𝐦𝐚𝐡 𝐈𝐬 𝐌𝐚𝐤𝐢𝐧𝐠 𝐎𝐧𝐜𝐡𝐚𝐢𝐧 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 𝐌𝐨𝐫𝐞 𝐂𝐨𝐦𝐩𝐨𝐬𝐚𝐛𝐥𝐞 What if protocols could run programs, receive their results, and use those results to trigger the next action onchain without building a new integration for every connection? This is the idea behind @fermah_xyz approach to composable execution. Protocols need reliable ways to run programs and use their outputs. But when every integration depends on a specific endpoint, connecting different systems can become difficult to maintain. Fermah is working towards a more flexible approach, where execution results can move directly into the next step of an onchain process. 𝙂𝙞𝙫𝙞𝙣𝙜 𝙋𝙧𝙤𝙜𝙧𝙖𝙢𝙨 𝙖 𝙇𝙖𝙨𝙩𝙞𝙣𝙜 𝙄𝙙𝙚𝙣𝙩𝙞𝙩𝙮 One part of this approach is the use of content addressed OCI images. These give each program a unique identity based on its content, making it easier to identify the exact program being executed. This matters because protocols need a reliable way to reference the programs they depend on. Instead of relying on bespoke endpoints that tie callers to specific integrations, content addressed images provide a more consistent way to identify programs. The result is a setup that can support better coordination between protocols and the programs they need to run. 𝙏𝙝𝙚 𝙍𝙤𝙡𝙚 𝙤𝙛 𝙜𝙑𝙞𝙨𝙤𝙧 𝙧𝙪𝙣𝙨𝙘 Fermah also uses gVisor’s runsc to provide the runtime environment for execution. The runtime is an important part of the process because programs need an environment in which they can run. By including this component in its execution setup, Fermah establishes the context needed to carry out program execution as part of its broader system. Together, the program’s identity and its runtime environment form important parts of the execution process. 𝙏𝙪𝙧𝙣𝙞𝙣𝙜 𝙍𝙚𝙨𝙪𝙡𝙩𝙨 𝙄𝙣𝙩𝙤 𝙊𝙣𝙘𝙝𝙖𝙞𝙣 𝘼𝙘𝙩𝙞𝙤𝙣𝙨 The next part is where composability becomes especially important. Once a program produces an output, that result can be handed off onchain and used as the input for another action. This creates a connection between execution and what happens next, allowing protocols to build on existing results rather than treating every operation as a separate process. It also reduces the need to rebuild integrations whenever protocols need to connect different actions. Instead, execution becomes part of a process that can continue from one step to another. 𝙒𝙝𝙮 𝙏𝙝𝙞𝙨 𝙈𝙖𝙩𝙩𝙚𝙧𝙨 Composable execution gives protocols more flexibility in how they build and connect their operations. Program identity, runtime execution, and onchain handoff each play a role in making that possible. Fermah’s approach brings these pieces together so protocols can initiate execution, consume the results, and use those results to move their onchain processes forward. The bigger picture is a system where execution does not end when a program returns its output. That output can become the starting point for the next action, making it easier for protocols to build connected workflows without constantly rebuilding their integrations.

𝐖𝐡𝐚𝐭 𝐃𝐨𝐞𝐬 𝐆𝐚𝐭𝐞 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐌𝐞𝐚𝐧 𝐟𝐨𝐫 𝐔𝐬𝐞𝐫𝐬? We need to understand that people use USDT for different reasons and for that, the benefits also differs. The benefits become clearer when we look at how people actually use USDT. 𝘍𝘰𝘳 𝘵𝘳𝘢𝘥𝘦𝘳𝘴, 𝘪𝘵 𝘤𝘳𝘦𝘢𝘵𝘦𝘴 𝘢𝘯𝘰𝘵𝘩𝘦𝘳 𝘰𝘱𝘵𝘪𝘰𝘯 𝘧𝘰𝘳 𝘮𝘢𝘯𝘢𝘨𝘪𝘯𝘨 𝘧𝘶𝘯𝘥𝘴. USDT is widely used across crypto markets because it allows traders to hold dollar linked value without being exposed to the same price movements as assets like BTC. With USDT on Bitcoin coming to Gate and GateDEX, users could have another way to interact with USDT within the Bitcoin ecosystem. The specific options available will depend on the trading pairs and deposit, withdrawal, and swapping features supported by the platforms. So if you use Gate for all your crypto trades, be it spot trading, swaps or Futures, you now have access to a better option for all your USDT transfers. 𝘍𝘰𝘳 𝘱𝘦𝘰𝘱𝘭𝘦 𝘴𝘦𝘯𝘥𝘪𝘯𝘨 𝘮𝘰𝘯𝘦𝘺, 𝘪𝘵 𝘤𝘰𝘶𝘭𝘥 𝘰𝘧𝘧𝘦𝘳 𝘢𝘯𝘰𝘵𝘩𝘦𝘳 𝘳𝘰𝘶𝘵𝘦 𝘧𝘰𝘳 𝘵𝘳𝘢𝘯𝘴𝘧𝘦𝘳𝘳𝘪𝘯𝘨 𝘷𝘢𝘭𝘶𝘦. Cross border transfers can involve multiple service providers and fees that reduce the amount a recipient receives. Stablecoins offer a digital way to move dollar linked value, and Utexo’s approach could make USDT transfers over Bitcoin useful for people sending money between supported wallets and services. The real benefit will depend on the total transaction cost, speed, and ease of use. With USDT becoming available on Bitcoin network, transaction cost will be lower, settlement will be fast. These are the factors that matter when someone needs to send money regularly, whether to family, friends, or business partners. 𝘍𝘰𝘳 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴𝘦𝘴, 𝘪𝘵 𝘰𝘱𝘦𝘯𝘴 𝘶𝘱 𝘢𝘯𝘰𝘵𝘩𝘦𝘳 𝘱𝘰𝘴𝘴𝘪𝘣𝘪𝘭𝘪𝘵𝘺 𝘧𝘰𝘳 𝘱𝘢𝘺𝘮𝘦𝘯𝘵𝘴. Businesses working with international customers, remote workers, and suppliers often need reliable ways to send and receive money across borders. Stablecoins can help them manage dollar linked payments without relying entirely on traditional banking processes. By supporting USDT transfers on Bitcoin, Utexo is working towards another option for businesses that want to use stablecoins within the Bitcoin ecosystem. For these businesses, the important questions are practical: How much does a payment cost? How quickly does it settle? Can the recipient receive and use the funds without unnecessary complications. @utexocom already laid out tools to tackle the above questions. Tools like RGB and Lightning, Liquidity routers and many more are already been put in place to make user experience swift. The goal is clear: make USDT payments on Bitcoin something people can actually use in their everyday lives.

Privacy and transparency should not have to compete with each other. In a digital world where almost everything involves sharing data, the real challenge is knowing how to prove something is true without revealing more information than necessary. @primus_labs is working to make privacy and verification work together across Web2 and Web3. For a long time, verification has often meant exposing information. When a platform needs to confirm that certain data is correct, sensitive details may also become visible in the process. This creates a problem because people and businesses need to establish trust without giving up control over their private information. Primus is working to change this through zero knowledge Transport Layer Security (zkTLS) and zero knowledge Fully Homomorphic Encryption (zkFHE). The goal is to make it possible to verify information while keeping sensitive data confidential. The idea is simple: information should be verifiable without making everything public. With zkTLS, Primus helps connect information from Web2 platforms to Web3 applications while preserving the privacy of the underlying data. This makes it possible to verify information from existing online services without simply exposing all the details behind it. It also creates opportunities for applications to rely on information from outside the blockchain while maintaining stronger privacy protections. zkFHE takes this further by supporting computation on encrypted data. Instead of requiring sensitive information to be exposed before it can be processed, this technology allows certain operations to be performed while the data remains encrypted. This is important for applications that need to use information without unnecessarily revealing its contents. Together, they contribute to a more private and verifiable digital environment, where access to information does not automatically mean access to everything that information contains. But privacy alone is not enough. Verification matters just as much. Keeping information confidential is important, but digital systems also need ways to establish whether the information they receive can be trusted. Without verification, privacy can protect data without necessarily proving that the data is reliable. At the same time, verification that exposes sensitive information creates a different problem. Primus is addressing this balance by bringing privacy and verification together. The aim is to make digital interactions more trustworthy without requiring users to sacrifice confidentiality to establish credibility. This becomes increasingly relevant as Web3 continues to connect with existing internet services, financial applications, and AI powered systems. These environments need reliable information to function properly, but they also need better ways to protect the data moving between them. This is what makes Primus worth paying attention to. It is not simply about hiding information. It is about changing how information can be used, verified, and trusted in the first place. Primus is working towards building a balance for a more trustworthy digital future. Proof without exposure. Privacy without losing the ability to verify.

If you study very well about Quantum Physics, you’ll start to see life more differently and you won’t attribute your whole existence and consciousness to a god Study! Study!! Study!!!

USDT on Bitcoin is coming to @Gate and @GateDEX marking another step forward for Utexo and its work to bring USDT payments to Bitcoin network. Gate has over 59 million users and reports more than $16 billion in daily spot trading volume. With a user base of this size, the integration puts USDT on Bitcoin in front of a large audience of crypto users across the world. This is a big step for @utexocom. The goal has always been to make USDT transfers on Bitcoin practical for real people, and bringing it to major crypto platforms is part of making that happen. Now is the time to pay attention to what Utexo is building. Gate integration is just the beginning. Soon we will see more exchanges and wallets integrating USDT on Bitcoin network.

𝐅𝐫𝐨𝐦 𝐃𝐚𝐭𝐚 𝐭𝐨 𝐄𝐦𝐛𝐨𝐝𝐢𝐞𝐝 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 4D Labs describes its vision through three connected stages: data, embodiment, and artificial general intelligence. ☑️ The first stage is the data layer. This is where AI systems gain access to information about physical interactions. High quality data helps models learn how the world behaves and how their actions affect their surroundings. ☑️ The second stage is embodiment. At this level, intelligence moves beyond digital understanding and becomes capable of operating through physical systems. Models must be able to translate what they have learned into actions that work in real environments. This also requires adapting intelligence across different forms of embodiment. A model’s learned capabilities must be translated appropriately to the physical system carrying out a task. ☑️ The third stage is the development of more general intelligence that can function in the physical world. As AI becomes better at understanding physical conditions and interacting with its surroundings, it can develop the foundations needed to perform a wider range of tasks. These stages are closely connected. Better physical interaction data can support better training, while better trained models can improve how AI systems operate in real environments. @4Dlabs_Official vision is built around this progression, with data serving as the foundation on which the other stages depend.