以市场份额占据首位的跃然创新Haivivi为例。跃然前年推出的初代产品BubblePal，更像一款AI挂件，累计销量突破25万台，以389元单价计算，其销售额已突破1亿元。而二代产品CocoMate，遵循“底层技术突破+知名IP加持”的产品逻辑，与奥特曼IP深度合作。据品牌透露，该产品的单设备日均对话数超60轮次，季度对话总量攀升至80B 以上。
./build/parakeet model.safetensors audio.wav --vocab vocab.txt --model nemotron-600m --latency 6
，这一点在heLLoword翻译官方下载中也有详细论述
其次，规模和可复制性完全不同。Altman 想强调「per query」的效率，但他忽略了：人类智能没法「复制部署」到数据中心里无限扩容。AI 的真正优势恰恰在于「训一次，用一辈子」，而人类是「训一次，用一辈子还得继续喂」。如果真要比「单位智能产出每焦耳能量」，AI 在规模化后确实可能碾压，但用「养孩子总成本」来类比，反而把这个优势给模糊掉了。，详情可参考91视频
└──────────┬────────────┘，更多细节参见旺商聊官方下载
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.