Сможет ли TRX повторить рост XRP?

cryptonews.ruPubblicato 2023-04-04Pubblicato ultima volta 2024-12-04

Токен TRX вчера нарисовал «свечу Бога». Это предполагает рост аж на 104%, что совпало с достижением нового исторического максимума.

Рост с минимумов 5 ноября составляет примерно 140%.

С ростом цены совпадает и рыночная активность. Сегодня ежедневный объем спотовых торгов превысил $14,6 млрд. Для сравнения, это на 510% больше, чем вчера.

Настолько импульсивный рост вызвал сомнения в устойчивости восходящего тренда. Внутри крипто-сообщества стали возникать мысли, что рынок перегрет.

TRX возвращается в топ-10

Вместе с ростом цены выросла и рыночная капитализация. Как результат, TRX смог вернуться в топ-10 крипто-активов по рыночной капитализации.

Вчерашний рост на 65% также дарит оптимизм на 2025 год.

Ближайшим преследователем TRX является Avalanche. Рыночная капитализация компании составляет $22 млрд.

Джастин Сан, основатель Tron, прокомментировал успех TRX. Он заявил:

изменилось все, кроме одного: TRON остается в топ-10

Открытый интерес к TRX растет

Параллельно с ростом цены вырос и открытый интерес (OI) TRX. Показатель поднялся до 3,5-летнего максимума в $438.3 млн.

Помимо этого, растет и спрос на фьючерсные лонг-позиции по TRX. Ставки финансирования крайне перегреты.

Тем не менее, это значит, что трейдеры настроены крайне оптимистично. Такой оптимизм может повлечь продолжение восходящего тренда.

Повторит ли TRX успех XRP?

Подобные перфомансы, которые возникают все чаще на крипторынке, указывают на начало альтсезона. Рост XRP действительно многих шокировал, однако от TRX теперь ждут то же самое. И в этом есть логика, потому что токен обновил ATH, чего не смог сделать в 2021 году, также как XRP.

Технический анализ допускает, что TRX может достичь отметки $1.8, согласно месячному графику. Да, это не произойдет за один день, но подобная вероятность присутствует.

Однако важно сохранять контроль над собой. Несмотря на перспективы роста, TRX может начать коррекцию к отметке $0.33.

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DeepSeek has updated its DeepSeek V4 model with the DSpark speculative decoding framework, achieving a significant 60-85% speedup in generation for Flash models and 57-78% for Pro models while maintaining the same overall throughput. This engineering-focused update, rather than a core architectural change, introduces DSpark to address latency and throughput bottlenecks in high-concurrency production environments. DSpark combines high-throughput parallel generation with adaptive load-aware verification. Its key innovations include a semi-autoregressive generation architecture to model dependencies within token blocks and a hardware-aware confidence-scheduled verification system. This system uses a confidence head to predict token acceptance probabilities, allowing it to dynamically optimize verification length per request and allocate compute only to tokens with the highest expected payoff. The asynchronous scheduler is designed for real-world deployment, ensuring zero-overhead scheduling and continuous CUDA graph replay while preserving the target model's output distribution. In tests across mathematical reasoning, code generation, and daily dialogue, DSpark outperformed state-of-the-art models like Eagle3 and DFlash, increasing average acceptance length by 26.7%-30.9% and 16.3%-18.4% respectively on Qwen3 target models. DeepSeek also open-sourced DeepSpec, a full-stack codebase for training and evaluating speculative decoding draft models, providing a standardized toolkit that includes data preparation tools, model implementations, training code, and evaluation scripts.

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Andrej Karpathy, a prominent figure in AI, has reportedly joined Anthropic, leading to a noticeable decrease in his open-source contributions and social media activity. A document claiming to be his personal "CLAUDE.md" file—a set of instructions for the Claude AI to follow within a specific codebase—has been circulating online. While its authenticity is unverified, the content aligns closely with Karpathy's publicly shared principles on effective AI-assisted programming. The document outlines key rules for AI coding assistants, emphasizing the importance of reading existing code thoroughly before writing new code to maintain consistency. It advises against over-engineering, advocating for simple, surgical modifications that match the project's existing style. Other guidelines include clarifying assumptions upfront, writing meaningful tests, thoughtful debugging, and carefully considering dependencies. The core message is that these principles help prevent common AI coding failures, such as introducing unnecessary abstractions, style drift, or making invisible architectural decisions. The community has noted that even experts like Karpathy require detailed instructions to guide AI effectively, akin to managing a junior developer. A related GitHub repository, "andrej-karpathy-skills," which encapsulates these ideas, is reported to significantly reduce Claude's code error rate. Ultimately, the advice stresses that the best CLAUDE.md is tailored to one's own tech stack and coding practices.

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