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* @param right 右边界（不包含）
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最新・注目の動画配信中の動画を見る天気予報・防災情報天気予報・防災情報を確認する新着ニュースキム総書記の妹 ヨジョン氏が朝鮮労働党「総務部長」に就任 午後3:32水戸女性殺害 車に位置情報特定するタグ取り付けたか 再逮捕へ 午後3:24ペットボトル緑茶 値上げの動き 海外の抹茶ブームも影響か 午後2:56トランプ氏 アンソロピックのAI技術 政府機関使わないよう指示 午後2:23新着ニュース一覧を見る各地のニュース地図から選ぶ
Standing there, I was suddenly transported back to Italy: the smell of tomatoes simmering in the kitchen, the warmth of the sun, the rhythm of daily life centered around simple, beautiful ingredients. Pasta wasn’t complicated. It was pure, intentional and full of flavor. I realized that what was missing in that aisle wasn’t just quality — it was that feeling.，推荐阅读咪咕体育直播在线免费看获取更多信息
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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.