«Стакан апельсинового фреша 250 мл это 100–130 ккал. Облепиховый напиток с медом 300–400 миллиграмм это 150–250 килокалорий. Смузи из банана, ягод, йогурта и меда это 200–350 килокалорий. Модный боул с фруктами, гранолой, орехами и медом легко тянет на 400–700 килокалорий. И все это поверх обычной еды, а не вместо», — сказал эксперт.
Израиль нанес удар по Ирану09:28。搜狗输入法2026对此有专业解读
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8点1氪丨玛莎拉蒂母公司全年净亏损1800亿元人民币；男童发育不良新药引爆股价，长春高新回应；德国总理默茨参访宇树科技，详情可参考雷电模拟器官方版本下载
中國國家主席習近平近日罕見地公開提及一場導致國家最高將領被撤職的清洗行動。
Many people reading this will call bullshit on the performance improvement metrics, and honestly, fair. I too thought the agents would stumble in hilarious ways trying, but they did not. To demonstrate that I am not bullshitting, I also decided to release a more simple Rust-with-Python-bindings project today: nndex, an in-memory vector “store” that is designed to retrieve the exact nearest neighbors as fast as possible (and has fast approximate NN too), and is now available open-sourced on GitHub. This leverages the dot product which is one of the simplest matrix ops and is therefore heavily optimized by existing libraries such as Python’s numpy…and yet after a few optimization passes, it tied numpy even though numpy leverages BLAS libraries for maximum mathematical performance. Naturally, I instructed Opus to also add support for BLAS with more optimization passes and it now is 1-5x numpy’s speed in the single-query case and much faster with batch prediction. 3 It’s so fast that even though I also added GPU support for testing, it’s mostly ineffective below 100k rows due to the GPU dispatch overhead being greater than the actual retrieval speed.