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The TV Games Encyclopedia is very much a product of this moment. Its lavish physical production—the frosted plastic slipcase, the variety of paper stocks, the multiple print techniques and finishes—reflects the kind of excess that was not only possible but expected. A book about video games had no business being this beautifully made. And yet here it was, priced at ¥3,500, with the ambition and budget of an art object. When the bubble burst in 1991—ushering in what became known as the Lost Decades—this kind of thing simply stopped being made.
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«Мы выбиваем из них всю дурь». Трамп рассказал о ситуации на Ближнем Востоке и назвал «колоссальную угрозу» со стороны Ирана07:11
Филолог заявил о массовой отмене обращения на «вы» с большой буквы09:36
People increasingly use large language models (LLMs) to explore ideas, gather information, and make sense of the world. In these interactions, they encounter agents that are overly agreeable. We argue that this sycophancy poses a unique epistemic risk to how individuals come to see the world: unlike hallucinations that introduce falsehoods, sycophancy distorts reality by returning responses that are biased to reinforce existing beliefs. We provide a rational analysis of this phenomenon, showing that when a Bayesian agent is provided with data that are sampled based on a current hypothesis the agent becomes increasingly confident about that hypothesis but does not make any progress towards the truth. We test this prediction using a modified Wason 2-4-6 rule discovery task where participants (N=557N=557) interacted with AI agents providing different types of feedback. Unmodified LLM behavior suppressed discovery and inflated confidence comparably to explicitly sycophantic prompting. By contrast, unbiased sampling from the true distribution yielded discovery rates five times higher. These results reveal how sycophantic AI distorts belief, manufacturing certainty where there should be doubt.