5.78 x 2.78 x 0.28 inches
守住纪法底线，确保监督执纪不越位。数字技术只是辅助工具，必须在纪法框架内运行。不管是数据采集还是线索核查，都要严格遵循党章党规和法律法规，不能打着“科技赋能”旗号随意扩大监督范围，更不能用技术手段突破纪法红线。比如，在开展数据核查时，要严格履行审批程序，确保每一个环节都经得起纪法检验，实现政治效果、纪法效果和社会效果有机统一。
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By the 2030s, Sophia hopes to be building larger space data centers out of thousands of TILEs, envisioning a 50-meter-by-50-meter structure delivering 1 MW of computing power. DeMillo argues that attempting to build space data centers with less efficient systems will not be economical and that a single structure rather than a distributed network linked by lasers will be easier to execute.
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.