Coinbase сделала доступной торговлю на DEX в приложении для самостоятельного хранения

cryptonews.ruPublished on 2025-03-18Last updated on 2025-08-19

Криптовалютная биржа Coinbase (Nasdaq: COIN) объявила 8 августа 2025 года о внедрении торговли на децентрализованных биржах (DEX) непосредственно в своем приложении. Это решение предоставит пользователям в США, за исключением жителей Нью-Йорка, доступ к миллионам ончейн-активов через знакомый интерфейс платформы.

Генеральный директор Coinbase Брайан Армстронг сообщил в социальной сети X:

«Мы начинаем внедрение торговли через децентрализованные биржи (DEX) в США, за исключением штата Нью-Йорк. Наша цель — сделать Coinbase самым простым способом доступа к ончейн-рынкам. Вы сможете торговать миллионами активов на базе Base прямо в приложении Coinbase».

В пресс-релизе компания подчеркнула: «Coinbase открывает новую эру доступности, расширяя ассортимент с 300 активов до миллионов в ближайшем будущем, сохраняя при этом привычный и удобный интерфейс». Первоначально интеграция будет поддерживать токены на базе Base, включая Virtuals AI Agents, Reserve Protocol DTF, SoSoValue Indices, Auki Labs и Super Champs, с постепенным добавлением новых активов для обеспечения стабильности платформы.

Как работает интеграция с DEX

Решение включает встроенный кошелек для самостоятельного хранения, упрощая взаимодействие с децентрализованными протоколами. Coinbase берет на себя оплату сетевых комиссий, снижая барьеры для пользователей. Торговля будет осуществляться через DEX, такие как Aerodrome (AERO) и Uniswap, без необходимости покидать приложение Coinbase. Агрегаторы DEX обеспечат поиск лучших цен среди доступных пулов ликвидности.

Для минимизации рисков Coinbase будет блокировать токены, помеченные доверенными поставщиками данных как мошеннические или вредоносные. В ближайшие недели платформа расширит поддержку на все активы Base, затем добавит Solana (SOL) и другие сети, а в перспективе выйдет на международные рынки.

Новые возможности для проектов

Инициатива также меняет подход к распределению токенов. Проекты, запускаемые на Base, смогут подключиться к миллионам пользователей Coinbase уже через час после индексации, минуя длительные процедуры листинга на централизованных биржах.

Хотя некоторые эксперты выражают опасения по поводу рисков, связанных с непроверенными токенами в DEX-среде, сторонники подчеркивают, что быстрый доступ к инновационным проектам укрепляет децентрализованную экосистему и дает преимущества ранним участникам.

NVIDIA's AI Sweeps ARC-AGI-3, Chinese-Led Team Aces All 183 Levels in One Go

NVIDIA's general-purpose coding agent AVO has achieved a perfect 100.00 RHAE score on the ARC-AGI-3 benchmark. It solved all 183 levels across 25 game environments in just 6,624 steps. ARC-AGI-3 is a notoriously difficult test where agents are placed into unfamiliar games with no instructions, forcing them to deduce rules and goals through trial and observation. Top models like Claude Opus 5 typically score only around 30% when acting alone. AVO's breakthrough comes not from improving the underlying AI model (Claude Opus 5), but from adding an intelligent external framework or "harness" around it. This framework addresses three key failure modes: misunderstanding global rules, misapplying familiar mechanics, and failing to learn correctly from success. Two core mechanisms drive AVO's performance: 1. **Persistent Memory:** It stores past attempts, compiler outputs, and reasoning across tasks, preventing the model from resetting and re-exploring dead ends when its context window fills. 2. **Supervisor Agent:** A separate module monitors the main agent's progress, intervening to redirect strategy when it gets stuck or repeats unproductive actions. Notably, AVO processed the visual game environments using a pure 64x64 text grid representation, without any image tokens. Originally developed for GPU kernel optimization, AVO autonomously evolved CUDA code for 7 days, producing attention kernels that outperformed NVIDIA's own cuDNN and the leading open-source implementation, FlashAttention-4. The same core architecture was then successfully applied to ARC-AGI-3, demonstrating its generalizability for long-horizon, autonomous problem-solving. The project features a predominantly Chinese team, including Distinguished Engineer Bing Xu (co-author of the original GAN paper and creator of MXNet) and Tianqi Chen (creator of TVM and XGBoost). NVIDIA's strategy highlights that while AI models are crucial, the surrounding system—managing memory, tools, and feedback—is equally vital for creating capable, long-term autonomous agents. This approach aligns with NVIDIA's focus on providing the essential hardware and software infrastructure for the next generation of AI workloads.

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NVIDIA's AI Sweeps ARC-AGI-3, Chinese-Led Team Aces All 183 Levels in One Go

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Anthropic: AI Has Not Led to an Increase in Unemployment Rate

Anthropic's Chief Economist, Peter McCrory, argues that despite significant growth in AI adoption, it has not yet led to increased unemployment in the US. Current data shows stable unemployment rates, even in occupations highly exposed to AI automation. His analysis, based on Anthropic's research over 18 months, suggests AI is currently a "skill-biased, labor-augmenting" technology. It enhances human capabilities rather than replacing workers because every job contains tasks—like interpersonal coordination or physical interaction—that AI cannot systematically perform. Furthermore, effective use of AI requires high user expertise, and human roles in planning and judgment remain crucial, even as agents handle more delegated tasks. McCrory acknowledges three future scenarios—software, economic, and Coasean singularities—where AI could recursively improve itself or drastically lower transaction costs, potentially disrupting labor markets and organizational structures. However, he posits that "weak links," the indispensable tasks resistant to automation, will act as a buffer, preventing a collapse in labor's share of income. He predicts US unemployment will not rise significantly due to AI within the next 12 months, with key indicators being unemployment in AI-exposed jobs and the return on human expertise in agent-based workflows.

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Goldman Sachs Research Report: China's Semiconductor Self-sufficiency Rate Rises to 70%, 2030 Capital Expenditure Target $82 Billion

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