In 2015, there were 11 companies with co-CEOs in the Russell 3000 group of the biggest public companies in the US, while in 2024, this had more than doubled to 24, according to an analysis by public company intelligence firm MyLogIQ.
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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.
为官一任、施政一方，如持卷应答，惟有认真审题、科学破题，“坚持具体问题具体分析，‘入山问樵、入水问渔’，一切以时间、地点、条件为转移”，才能“真正把情况摸清、把问题找准、把对策提实”，做到“一把钥匙开一把锁”。