Tether and Opera Partner to Expand USDT and Tether Gold Access Through MiniPay Wallet

TheNewsCryptoОпубліковано о 2026-02-03Востаннє оновлено о 2026-02-03

Анотація

Tether has partnered with Opera to integrate its stablecoin USDT and tokenized gold product, Tether Gold (XAUT), into Opera's self-custody MiniPay wallet. This integration allows users in mobile-first regions to send, receive, and hold USDT, as well as convert balances into gold-backed XAUT to protect savings against inflation. The MiniPay wallet, built on the Celo blockchain, already had over 7 million active USDT wallets by December 2025, facilitating significant peer-to-peer transaction volumes. Following the announcement, Opera's stock surged 18% intraday. Tether's CEO emphasized the goal of making stable money accessible for everyday use, while Opera highlighted the growing adoption of digital dollars.

Tether has partnered with Opera to expand its Stablecoin USDT and its tokenized gold XAUT through Opera’s Mini wallet. After this announcement, the Opera’s stock has sharply risen to 18%.

With the new integration, Mini users can now send, receive, and hold USDT. They can also convert part of their balance into Tether Gold, which allows people to save digital dollars and protect the value using gold during high inflation. Tether said this helps people who need stable money for everyday use.

What is Minipay Wallet

Minipay is the self-custody wallet built by Opera. It runs on the Celo blockchain and is designed for a mobile-first region. By December 2025, over 7 million phone-verified USDT wallets were active, and the users processed more than 96 million USDT transfers in one month. Over 3.5 million peer-to-peer payments were completed. The Firm has introduced the Pockets features where the users can swap between USDT, USDC AND cUSD easily.

Users can now protect savings from inflation and have an alternative to holding only local currency with this update. They get access to gold without needing banks. Tether said that this option is especially useful in countries where people struggle to protect the value of their savings.

Paolo Ardoino, CEO of Tether, said that the goal is to make stable money simple and useful for people to send money and save in dollars. Jorgen Arnesen, EVP of mobile at Opera, said that millions of people are now using digital dollars for the first time. After the announcement, the Opera stock rose 18% intraday and closed the day up 3.5%.

Highlighted Crypto News:

Trump Says He Was Not Involved in $500M Abu Dhabi WLFI Deal

TagsMinipayOperaTetherUSDT

Пов'язані питання

QWhat is the main purpose of the partnership between Tether and Opera?

AThe partnership aims to expand access to Tether's stablecoin USDT and tokenized gold XAUT through Opera's MiniPay wallet, allowing users to send, receive, hold, and convert between these digital assets.

QWhich blockchain does the MiniPay wallet operate on?

AMiniPay wallet runs on the Celo blockchain and is designed for mobile-first regions.

QWhat significant financial impact did Opera experience after this partnership announcement?

AOpera's stock sharply rose 18% intraday and closed the day up 3.5% following the partnership announcement.

QHow does Tether Gold (XAUT) help users according to the article?

ATether Gold allows people to save digital dollars and protect the value of their savings using gold during periods of high inflation, providing an alternative to holding only local currency.

QWhat are some key usage statistics mentioned for MiniPay wallet?

ABy December 2025, over 7 million phone-verified USDT wallets were active, users processed more than 96 million USDT transfers in one month, and over 3.5 million peer-to-peer payments were completed.

Пов'язані матеріали

Upbit Rebalances 864B SHIB In Internal Wallet Move

South Korean cryptocurrency exchange Upbit moved a total of 864 billion SHIB (worth approximately $4 million) between its internal wallet addresses. The activity, involving transfers of 384 billion SHIB outbound and 480 billion SHIB inbound, occurred following a 36% SHIB price rally. On-chain data indicates this was a routine internal wallet rebalancing operation by the exchange, not a sell-off or liquidation event. The article cautions that while large on-chain transfers are highly visible, they require proper context and wallet labeling to interpret correctly. In this case, the movement between known Upbit addresses does not inherently signal a change in market supply or selling pressure for SHIB.

bitcoinist48 хв тому

Upbit Rebalances 864B SHIB In Internal Wallet Move

bitcoinist48 хв тому

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

This article introduces a novel training paradigm for generative models called Explorative Modeling (XM), which enables true end-to-end training. Traditionally, powerful generative models like autoregressive and diffusion models are not trained end-to-end. They are trained to predict a single small step but require iterative multi-step sampling for inference. This "exposure bias" leads to error accumulation and limits performance. The core challenge XM addresses is "mode blurring." In generative tasks, a single input (e.g., "generate a dog") corresponds to many valid outputs (multiple modes). Standard training objectives like reconstruction loss force the model to average these modes, producing unrealistic, blurry outputs. To avoid this, existing models break generation into many small, almost deterministic steps, sacrificing end-to-end training. XM tackles this by restructuring the training loop itself. Its key insight is to amplify "generative expressivity." For each training input, instead of generating one sample, the model generates K candidate outputs. Only the candidate closest to the real data is used for computing the loss and updating the model via backpropagation. This simple "best-of-K" mechanism is implemented as a short for-loop. By exploring multiple possibilities, the model learns to distribute its guesses across different modes rather than collapsing to their uninformative average. The paper demonstrates that "exploration" acts as a new, powerful scaling axis. Gains from XM increase with model size, data scale, and compute. Experiments show improvements in FID scores for image generation and significant efficiency gains, sometimes outperforming larger models without exploration. When pushed to the limit, XM enables fully single-step, end-to-end generative models. In robotics tasks, an "Explorative Policy" matched the performance of a 100-step Diffusion Policy with a single forward pass, drastically improving inference speed. While the best-of-K concept is not entirely new, the authors' contribution lies in formally understanding it as a direct method to boost generative expressivity without fragmenting the generation process. This work suggests that as models scale, enhancing exploration during training may become crucial for overcoming fundamental performance bottlenecks.

marsbit48 хв тому

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

marsbit48 хв тому

A Maximum of 20 Submissions Per Person? DeepMind Researcher Satirically 'Petitions' Against ICLR's New Rule

The International Conference on Learning Representations (ICLR) 2027 has introduced a new submission rule limiting authors to a maximum of 20 papers per conference. This policy, announced due to a 68% surge in submissions for ICLR 2026 that strained the review system, aims to ensure fair and informative peer review. Google DeepMind researcher Dan Roy responded with a sarcastic petition on X, calling the 20-paper cap "absurd" and claiming it would slow AI progress. His post mockingly argued that AI agents can now produce incremental research and that large language models (LLMs) already handle all reviewing, so output should be maximized. This satire critiques the broader trend of AI's role in academia, referencing a 2025 study that found about 21% of ICLR 2026 reviews were likely fully AI-generated. Roy's underlying point questions whether simply capping submissions addresses the systemic issue of AI potentially flooding conferences with agent-written papers and AI-assisted reviews. The debate highlights tensions between managing submission volume and maintaining research quality as AI tools become pervasive.

marsbit53 хв тому

A Maximum of 20 Submissions Per Person? DeepMind Researcher Satirically 'Petitions' Against ICLR's New Rule

marsbit53 хв тому

Coldcard Vulnerability Leads to $89 Million Theft, Sparking Largest On-Chain Migration Since FTX Collapse

A security vulnerability in Coldcard hardware wallets led to the theft of 1,367.05 BTC (approx. $89 million) from 4,585 addresses, according to Galaxy Research. The flaw, caused by insufficient randomness in seed phrase generation for specific firmware versions, triggered a massive migration of funds as users moved assets to new, secure wallets. This event caused the largest on-chain movement of small Bitcoin transactions since the FTX collapse, distorting key market indicators like active addresses and exchange deposits, and significantly dampening market sentiment. The incident also highlighted challenges in cybersecurity investigations, as researchers reported that safety guardrails on mainstream U.S. AI models hindered the analysis of attack patterns, forcing them to use an open-source Chinese AI model for forensic work. This underscores a dilemma in balancing AI security restrictions with the need for rapid response in tracing stolen crypto assets.

marsbit1 год тому

Coldcard Vulnerability Leads to $89 Million Theft, Sparking Largest On-Chain Migration Since FTX Collapse

marsbit1 год тому

Annual Salary of Millions Competing for Electricians, Meta Rushes to Open Its Own Technical School

The AI boom is facing an unexpected bottleneck: a severe shortage of skilled construction workers and electricians. As tech giants like Meta, OpenAI, and Alphabet race to build massive data centers—such as OpenAI's $16 billion "Stargate" project—they are hitting a critical labor wall. The U.S. needs an estimated 130,000 more electricians, 240,000 construction workers, and 150,000 supervisors by 2030 for AI infrastructure alone, but tens of thousands of electrician jobs go unfilled each year. While AI companies offer high premiums, with electricians earning up to $280,000 annually, worker scarcity still causes massive losses—delays on a single project can cost $14.2 million per month. The complexity of building AI data centers, which require immense power (equivalent to powering hundreds of thousands of homes), sophisticated electrical systems, and advanced liquid cooling solutions, demands highly skilled technicians who are in short supply. To combat this, companies are investing heavily in training. Meta has committed $115 million to a free training school offering tuition, housing, and stipends, targeting 5,000 new workers. OpenAI is partnering with unions to secure skilled labor. These efforts are paying off, with a significant rise in Gen Z interest in trade schools over college. However, the power demands are staggering. AI data centers are driving a rapid surge in electricity consumption, projected to account for up to 12% of U.S. power use by 2028 and raising costs for consumers. Furthermore, the construction boom is project-based, leading to a potential future glut of trained workers once building peaks, which could depress wages industry-wide. The race for AI supremacy now depends as much on skilled hands as on advanced chips.

marsbit2 год тому

Annual Salary of Millions Competing for Electricians, Meta Rushes to Open Its Own Technical School

marsbit2 год тому

Торгівля

Спот
活动图片