The Android app, Nearby Glasses, alerts you when someone nearby is wearing smart glasses

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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.

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Minimax 推出 MaxClaw2 月 26 日,Minimax 团队发文介绍了 MiniMax Agent Expert 的功能升级,以及全新推出的 AI 助手 MaxClaw。MiniMax 在 Expert 2.0 中进一步优化了专家 Agent 的创建体验。用户不需要考虑 Skill、SubAgent、MCP 的配置,以及提示词的结构编排,只需用自然语言描述任务目标或能力需求,Agent 会根据目标完成 SOP 梳理、工具编排与能力配置。