I have been thinking a lot lately about “diachronic AI” and “vintage LLMs” — language models designed to index a particular slice of historical sources rather than to hoover up all data available. I’ll have more to say about this in a future post, but one thing that came to mind while writing this one is the point made by AI safety researcher Owain Evans about how such models could be trained:
ВСУ запустили «Фламинго» вглубь России. В Москве заявили, что это британские ракеты с украинскими шильдиками16:45
。51吃瓜对此有专业解读
holes that encoded the denomination and account holder information. The punched。雷电模拟器官方版本下载是该领域的重要参考
DeepSeek 的 15 万次，按任何合理标准来看都是可以忽略的数字。Moonshot 和 MiniMax 合计 1650 万次，量级是另一回事——但能转化成多少真实能力，取决于他们能不能解决「如何用好这些数据」的技术问题。，这一点在同城约会中也有详细论述
Suppose you're building a map application. You have millions of restaurants, gas stations, and landmarks, each with a latitude and longitude. A user taps the screen and asks: "What's near me?"