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.
The Free Software Foundation (FSF) fsf.org🇺🇸
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第七十条 非法安装、使用、提供窃听、窃照专用器材的，处五日以下拘留或者一千元以上三千元以下罚款；情节较重的，处五日以上十日以下拘留，并处三千元以上五千元以下罚款。
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Despite an ultimatum from Defense Secretary Pete Hegseth, Anthropic said that it can't "in good conscience" comply with a Pentagon edict to remove guardrails on its AI, CEO Dario Amodei wrote in a blog post. The Department of Defense had threatened to cancel a $200 million contract and label Anthropic a "supply chain risk" if it didn't agree to remove safeguards over mass surveillance and autonomous weapons.
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