CFPB исключает криптокошельки из правила «Больших участников»

cryptonews.ru2024-05-22 tarihinde yayınlandı2024-11-22 tarihinde güncellendi

Бюро по защите прав потребителей в сфере финансов (CFPB), финансовый регулятор США, завершило разработку своих правил, регулирующих критерии «Большие участники» для цифровых платежных платформ, и исключило передачу криптоактивов из правила.

Согласно окончательному правилу, цифровые кошельки, такие как Apple Pay, и централизованные «одноранговые» платежные сервисы по-прежнему будут подчиняться правилу, которое охватывает только транзакции, номинированные в долларах США. Правительственное агентство написало:

«Окончательное правило ограничивает определение «годового объема покрываемых потребительских платежных транзакций» транзакциями, номинированными в долларах США. С этим разъяснением и соответствующим изменением пункта (b)(3)(i) тест на крупных участников в этом окончательном правиле исключает передачу цифровых активов, включая криптоактивы, такие как биткоины и стейблкоины».

Участники отрасли, такие как исследовательская инвестиционная фирма Paradigm и некоммерческие группы, выступающие за криптовалюту, успешно выступили против первоначальной версии правила CFPB, которая включала транзакции с цифровыми активами.


Обложка окончательного правила CFPB.

CFPB фокусируется на цифровых платежных сервисах

CFPB начал фокусироваться на цифровых платежных сервисах, таких как Apple Pay, Google Pay, и «одноранговых» платежных платформах, таких как Venmo, в сентябре 2023 года. В то время агентство ссылалось на потенциальные монополистические проблемы крупных технологических компаний, вытесняющих более мелкие компании в этой сфере.

В то время Рохит Чопра, директор CFPB, также ссылался на монетизацию данных потребителей, предоставленных этим компаниям, как на еще одну область беспокойства.

После первоначального объявления CFPB предложил контролировать поставщиков криптовалютных кошельков; однако расширение надзора столкнулось с сопротивлением со стороны криптоиндустрии и законодателей.

В январе 2024 года законодатели США направили письмо в CFPB, в котором выступили против правила из-за его потенциального влияния на криптовалюты.

«Одноранговые транзакции через «самостоятельные кошельки» являются основным компонентом экосистемы цифровых активов, поскольку они устраняют риск третьих лиц», — написали законодатели.

Несмотря на противодействие, CFPB, похоже, удвоил усилия в апреле 2024 года, нацелившись на видеоигры на блокчейне из-за возможности торговли внутриигровыми токенами активов за пределами игровой экосистемы на электронных биржах.

İlgili Okumalar

$100M Annual Revenue, Two Berkeley Roommates in Their 20s Build the Most Profitable AI Business

Arena, the AI model ranking platform, has become a $100 million annual revenue business just eight months after launching its commercial service. Originally a UC Berkeley open-source research project called Chatbot Arena, it created a "battle arena" where users blind-test and vote on anonymous AI model responses. This has generated a highly trusted, community-driven leaderboard based on over 10 million user evaluations and 82 million votes. Major AI companies like OpenAI, Google, and Anthropic submit their flagship models to be ranked. The core monetization strategy is its AI Evaluations service, where model developers and large enterprises pay for in-depth performance analysis from Arena's massive user community. This provides real-world feedback on model strengths, weaknesses, and hallucinations—a critical service as models become more complex. The company, spun out from Berkeley in early 2025, quickly raised $100 million in seed funding at a $600 million valuation and later secured a $150 million Series A at a $1.7 billion valuation. The founding team includes CEO Anastasios Angelopoulos, a mathematician focused on rigorous model evaluation; CTO Wei-Lin Chiang, creator of the popular Vicuna chatbot; and co-founder Ion Stoica, a renowned Berkeley professor. Arena is now expanding beyond chat benchmarks into "Agent Mode," evaluating AI agents on complex, multi-step tasks like coding and research. The company's success illustrates the growing value and cost of independent, real-world AI model evaluation as the industry intensifies.

marsbit7 dk önce

$100M Annual Revenue, Two Berkeley Roommates in Their 20s Build the Most Profitable AI Business

marsbit7 dk önce

Racking Up 24,000 Stars: With One Command, AI Can Now Find Its Own Skills

Vercel, known for its developer tools like Next.js, has launched 'skills', a package manager for AI coding agents, garnering 24,000 GitHub stars. It allows developers to add specialized capabilities, such as React best practices, to AI assistants like Claude Code or Cursor with a single command: `npx skills add <package>`. Skills are shareable, reusable modules that define an AI agent's behavior for specific tasks, moving beyond one-off prompt engineering towards standardized 'capability engineering'. A key innovation is the 'find-skills' skill, which acts as an internal search engine, allowing an agent to autonomously find and install the right skill for a user's request. This lowers the barrier for non-developers to leverage advanced AI coding assistance. However, this 'npm moment' for AI brings significant security risks. Security audits of thousands of skills on platforms like skills.sh and ClawHub found over 30% contained security flaws, with about 13% classified as severe. Threats include malicious scripts that can access local files and credentials, and prompt injection hidden within skill documentation. Unlike traditional code packages, skills blend instructions, code, and system access, posing a direct risk to user machines and data. Experts advise treating skills like code—reviewing them carefully before installation, especially their scripts, and being wary of excessive permissions. Ultimately, Vercel's initiative represents a major shift towards modular, reusable AI capabilities, but its rapid adoption requires developers to bring the same caution used in managing traditional software dependencies.

marsbit8 dk önce

Racking Up 24,000 Stars: With One Command, AI Can Now Find Its Own Skills

marsbit8 dk önce

Token Devours One-Third of Payroll, Silicon Valley's AI Bill is Spinning Out of Control

The article discusses the dual reality of AI token costs in Silicon Valley. While research firm SemiAnalysis reports spending 30% of its employee salary budget on internal LLM tokens—translating to massive productivity gains like converting complex Excel models in minutes—other giants are struggling with ballooning, uncontrolled AI bills. Uber exhausted its annual AI budget in months after rapid engineer adoption, and Microsoft is cutting third-party AI tools due to high costs. NVIDIA's CEO argues tokens are becoming "means of production" and plans substantial AI budgets per engineer. Despite current cost concerns, the analysis emphasizes that cost collapse is just beginning. Through software optimizations (like 14x throughput boosts) and next-gen hardware (e.g., GB300 NVL72 with 17-32x H100 performance), real token costs can fall far below list prices. Anthropic's gross margins reportedly soared as token prices dropped. Gartner predicts a >90% inference cost drop by 2030. The piece highlights a split: massive AI capex ($740B announced) contrasts with tech layoffs and minimal measured economic impact so far. The transition mirrors past infrastructure shifts—investment precedes widespread productivity. For early adopters like SemiAnalysis, tokens already deliver high leverage; for others, the choice is to adopt now or risk falling behind.

marsbit12 dk önce

Token Devours One-Third of Payroll, Silicon Valley's AI Bill is Spinning Out of Control

marsbit12 dk önce

Claude Engineer Finally Unveils Fable 5's Ultimate Strategy, Teaching You How to Bridge the Information Gap with AI Models

This article, titled "Claude Engineer Finally Releases Fable 5 'Skill-Burning' Guide, Teaching How to Bridge the Information Gap with Models," details a blog post by Claude Code engineer Thariq Shihipar. The core concept is the "information gap" or "unknowns"—the disconnect between a user's instructions (the "map") and the actual task requirements (the "territory"). The article argues that with powerful models like Claude Fable 5, work quality depends on the user's ability to identify and clarify these unknowns. Shihipar categorizes unknowns into four types: Known Knowns (explicit instructions), Known Unknowns (awareness of gaps), Unknown Knowns (implicit, unstated knowledge), and Unknown Unknowns (unforeseen issues). The blog provides a framework for addressing these gaps throughout the workflow: * **Before Implementation:** Techniques include "Blindspot Scanning" to uncover Unknown Unknowns, brainstorming/prototyping for visual or complex tasks, having Claude ask clarifying questions, using reference code/examples, and creating implementation plans. * **During Implementation:** Maintaining an "implementation notes" file for Claude to document deviations and decisions made due to encountered edge cases. * **After Implementation:** Creating summary documents for review and having Claude generate quizzes to ensure the user fully understands the completed changes. The article concludes that as models become more capable, the key to success is systematically discovering and defining these unknowns through low-cost methods like prototyping and planning, allowing for more effective collaboration.

marsbit13 dk önce

Claude Engineer Finally Unveils Fable 5's Ultimate Strategy, Teaching You How to Bridge the Information Gap with AI Models

marsbit13 dk önce

GPT-5.5 Suffers Secret Intelligence Downgrade, Crashes at Thought Token 516, Stumbles Harder on Tougher Tasks

The article reports a concerning anomaly discovered in OpenAI's GPT-5.5 model. Developers analyzing backend metadata found a disproportionate number of its reasoning processes abruptly terminating precisely at 516 tokens, with similar spikes at multiples like 1034. Statistical analysis revealed that while GPT-5.5 handled only 19.3% of total responses, it was responsible for over 80% of these "exact 516" stoppages. This pattern is drastically more pronounced in GPT-5.5 compared to other models, suggesting a possible hidden "reasoning budget" cap or truncation mechanism. Concurrently, users report the model has become overly formulaic, persistently using bullet points, offering unsolicited corrections, and providing excessive options, making interactions feel less helpful. The core concern is that GPT-5.5 may be prioritizing structured, truncated responses over genuine, in-depth problem-solving, especially for complex tasks.

marsbit29 dk önce

GPT-5.5 Suffers Secret Intelligence Downgrade, Crashes at Thought Token 516, Stumbles Harder on Tougher Tasks

marsbit29 dk önce

İşlemler

Spot
活动图片