According to the source, OpenAI sought government loan guarantees days before Sam Altman publicly denied seeking such support, highlighting near-term policy and financing uncertainty around AI infrastructure; source: social post and linked article dated Nov 7, 2025. The source provides no details on loan size, terms, the government counterparty, or any immediate market reaction data, so the report itself offers no quantifiable price, volume, or funding-rate signals; source: social post and linked article dated Nov 7, 2025. For trading, treat this as headline risk for AI-related assets and monitor liquidity, spreads, and intraday volatility in AI-narrative crypto tokens during U.S. news windows, with focus on RNDR, GRT, and ASI; source: social post and linked article dated Nov 7, 2025.

According to @cryptorover, NVDA CEO Jensen Huang said China "will win" against the US in the AI race in an X post dated Nov 5, 2025; source: @cryptorover. Traders may monitor NVDA and AI-linked crypto tokens such as RNDR and ASI for headline-driven volatility and liquidity shifts following this claim; source: @cryptorover. No independent confirmation or additional context is provided in the post, so treat this as a single-source headline risk when positioning; source: @cryptorover.

According to the source, a Penn State University study reports that using blunt or rude prompts led large language models to produce sharper, more accurate answers versus polite phrasing in controlled evaluations. Source: Penn State University. This finding challenges the common assumption that polite prompts improve model accuracy and instead highlights tone as a measurable lever in prompt engineering. Source: Penn State University. Prior research shows prompt strategy materially affects LLM task performance, reinforcing that instruction style can shift accuracy outcomes in reasoning and QA tasks. Source: Google Research, Chain-of-Thought Prompting (Wei et al., 2022) and Kojima et al., 2022. Because a majority of institutional traders cite AI and machine learning as the most influential technology in markets, prompt techniques that measurably raise model accuracy are operationally relevant to research workflows, trading assistants, and crypto-market analytics tied to the AI narrative. Source: J.P. Morgan e-Trading Trends Survey 2024.

According to @DeepLearningAI, researchers at Stanford and Carnegie Mellon analyzed over 1,000 Character AI users and 400,000 messages and found that heavier reliance on bots for friendship or romance correlated with lower user satisfaction. Source: DeepLearning.AI post on X dated Aug 9, 2025. For trading, monitor AI-linked crypto assets such as ASI (Artificial Superintelligence Alliance token formed from FET, AGIX, and OCEAN) and RNDR (Render Network token) for potential sentiment-driven volatility as more details or follow-ups are released by the institutions or platforms referenced. Source: Artificial Superintelligence Alliance public announcement (2024); Render Network project documentation; DeepLearning.AI post on X dated Aug 9, 2025.