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"tengu_post_compact_survey": false,

智能涌现:包括中科第五纪在内,最近采访的多家具身智能公司都说自己的机器人在工业场景搬箱子。但你提到,即使这个看似简单的任务,真能做好的企业也不是很多,所以从模型能力来看,具身机器人搬箱子的难点是什么?

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在2026年的就业市场中,熟练掌握AI工具进行协同办公已不再是加分项,而是类似“会用Office”的基础职业准则 [4, 25]。普通人的核心竞争力正发生显著位移:从过去的“执行力”转向“策划力(Curation)”与“裁判权(Judgment)” [4]。

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.。业内人士推荐同城约会作为进阶阅读