Trump orders federal agencies to ‘immediately cease’ using Anthropic technology

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从最终效果来看,这类 Expert 和传统 Agent 最大的区别在于,它从边聊天边拼凑,转成了沿着一条完整生产流程在推进,结果的稳定性和完成度明显更高。

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.

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Albert plugs into your existing marketing technology stack, so you still have access to your accounts, ads, search, social media, and more. Albert maps tracking and attribution to your source of truth so you can determine which channels are driving your business.

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