3. 5#楼天井操作平台未按要求编制审批危大方案，且连墙件间距偏大、无剪刀撑；5#楼爬架高度14米，局部两道支座， 顶部悬高超6米无临时拉结措施；且爬架与结构外墙间隙大于15cm，四周全高范围上下贯通无中部翻板。（违反《房屋市政工程生产安全重大事故隐患判定标准（2024版）》第四条第四款、第九条第四款，属于重大事故隐患。）
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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.