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North Carolina DOJ Seizes $61 Million in USDT Tied to Pig Butchering Scam

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Durov Slams France as "Not Free" After Police Raid X's Paris Office

The U.S. Attorney’s Office for the Eastern District of North Carolina seized more than $61 million in Tether (USDT) linked to money laundering from crypto “pig butchering” investment scams.

Why it matters:

  • Victims of pig butchering scams lose funds to fraudulent platforms showing fake returns, then face demands for “taxes” or “fees” to withdraw. This is a cycle designed to repeatedly extract money.
  • The $61 million seizure ranks among the largest single USDT confiscations tied to romance-based crypto fraud in U.S. history.
  • The case signals direct DOJ and Homeland Security Investigations (HSI) coordination with Tether to freeze and transfer illicit stablecoin holdings.

The details:

  • The U.S. Attorney’s Office for the Eastern District of North Carolina announced the seizure, with the DOJ and HSI leading the operation.
  • Investigators traced the USDT to wallet addresses linked to money laundering tied to crypto investment fraud.
  • Pig butchering scams involve criminals building fake romantic relationships online before steering victims toward fraudulent trading platforms.
  • Tether assisted the DOJ and HSI in facilitating the transfer of the $61 million in seized assets.

The big picture:

  • Pig butchering scams generated billions in global losses in recent years, with U.S. authorities accelerating seizures as stablecoins become the preferred settlement layer for organized fraud networks.
  • The case adds to the DOJ’s growing track record of recovering crypto assets linked to transnational fraud.

The post North Carolina DOJ Seizes $61 Million in USDT Tied to Pig Butchering Scam appeared first on BeInCrypto.

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Crypto World

Tether Unveils AI System to Run Large Models on Smartphones

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Tether Unveils AI System to Run Large Models on Smartphones

Tether, issuer of the world’s largest stablecoin by market cap, USDT, has released a new AI training framework that it says allows large language models to be fine-tuned on consumer hardware, including smartphones and non-Nvidia GPUs.

According to Tuesday’s announcement, the system, part of its QVAC platform, uses Microsoft’s BitNet architecture and LoRA techniques to reduce memory and compute requirements, potentially lowering the cost and hardware barriers to developing AI models.

The framework supports cross-platform training and inference across a range of chips, including AMD, Intel and Apple Silicon, as well as mobile GPUs from Qualcomm and Apple.

Tether said its engineers were able to fine-tune models with up to 1 billion parameters on smartphones in under two hours, and smaller models in minutes, with support extending to models as large as 13 billion parameters on mobile devices.

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