US Federal Reserve Removes Restrictions on Bank Crypto Use

TheCryptoTimesОпубліковано о 2025-04-25Востаннє оновлено о 2025-04-25

The US Federal Reserve has taken a major step towards embracing crypto by scrapping key guidance that restricted banks from engaging in crypto and stablecoin activities.  

In a statement on April 24, Fed announced that it will no longer issue the 2022 and 2023 supervisory letters. These letters earlier compelled state member banks to seek permission from the Fed for engaging in crypto services and capped the operations of stablecoins. 

Thus, any crypto activity will be just supervised as a part of the regular supervisory process of the Fed. This is a positive signal of making the country more open and friendly towards cryptocurrencies, which has already been initiated under the Trump administration.  

Michael Saylor, co-founder of Strategy and a long-time Bitcoin supporter, reacted on X, saying, “Banks are now free to begin supporting Bitcoin.”  

The Fed also withdrew joint statements made with the FDIC and OCC that warned banks about fraud risks linked to crypto firms. These past warnings had raised concerns over consumer protection, financial stability, and crypto’s role in money laundering.  

The policy shift comes after the SEC revoked a rule in January that required banks to list crypto holdings as liabilities — a rule many felt slowed crypto adoption in the banking sector.  

With these changes, the US banking system may increase its involvement in crypto, particularly in stablecoins and Bitcoin. It can be said that it can spur innovation in the crypto space while allowing users to access cryptocurrencies through traditional financial institutions, which are regulated.  

This could be a potential start for the development of crypto banking in the United States.

Also Read: Fed Chair Powell Urges Legal Framework for Stablecoins



Пов'язані матеріали

Embodied Intelligence 'Gaokao' is Insanely Hard, Humans Score 100, Best Model Only 12.8

Embodied AI Faces a Daunting "Everest": New Benchmark Reveals Huge Gap Between Models and Humans A comprehensive new benchmark for robotic manipulation, RoboDojo, has been released, painting a stark picture of the current state of embodied AI. It serves as a unified evaluation platform covering both simulation and real-world robot tasks. The benchmark assesses five core capabilities: Generalization (adapting to new scenes/objects), Memory, Precision manipulation, Long-Horizon multi-step tasks, and Open semantic understanding. It includes 42 simulation tasks and 18 standardized real-world tasks across three dual-arm robot platforms. The results are sobering. In simulation, the best-performing generalist robot policy achieved an average success rate of only 8.80%. Performance in the real world was slightly higher but still low, with the top model succeeding 12.8% of the time on average. In stark contrast, human experts scored 76.03% in simulation and 100% in real-world tests. The benchmark highlights significant, uneven gaps in current models' abilities. While some excel in specific areas like visual recognition or simple actions, they struggle with reliability, especially in long-horizon tasks where errors accumulate and in open-ended semantic instructions. The low scores, particularly in real-world deployment with physical uncertainties like camera noise and contact dynamics, underscore that today's models are far from being robust, general-purpose operational robots. RoboDojo is more than just a ranking; it's an infrastructure designed for fair, reproducible comparison. Its companion system, XPolicyLab, standardizes the interface for different models to be evaluated. Maintained by an academic consortium without commercial ties, it aims to provide a community-wide "altitude meter" to track genuine progress toward reliable and generalizable robot manipulation.

marsbit1 год тому

Embodied Intelligence 'Gaokao' is Insanely Hard, Humans Score 100, Best Model Only 12.8

marsbit1 год тому

Zuckerberg's 'Mango' Image Generation Model Trails Only GPT Image 2, It Learned to Revise Prompts on Its Own

Meta's MSL has launched Muse Image, an advanced image generation model nicknamed "Mango," which ranks second globally in text-to-image benchmarks, closely trailing OpenAI's GPT Image 2. Its key innovation is agent-like behavior: it searches for factual information, writes code for charts, and, most notably, has developed self-correction abilities through reinforcement learning, allowing it to revise its own outputs without explicit programming. This shift emphasizes reasoning over immediate generation. Integrated with Meta's ecosystem, Mango connects with the Muse Spark language model for complex tasks and features a unique "@" function that can incorporate public Instagram photos into generated images—raising privacy concerns as it's enabled by default. The model is directly accessible in Meta AI, Instagram, and WhatsApp, leveraging Meta's vast user base for distribution rather than competing solely on image quality. Accompanying Mango is the preview of Muse Video, a video generation model with integrated audio, currently ranked third in its category. All Mango-generated images include an invisible, persistent watermark (Content Seal) for AI identification, alongside a public detection tool. While Mango advances "thinking" image models, its use of social data poses new ethical questions about consent and digital boundaries.

marsbit2 год тому

Zuckerberg's 'Mango' Image Generation Model Trails Only GPT Image 2, It Learned to Revise Prompts on Its Own

marsbit2 год тому

Торгівля

Спот
活动图片