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.
Each layer catches different attack classes. A namespace escape inside gVisor reaches the Sentry, not the host kernel. A seccomp bypass hits the Sentry’s syscall implementation, which is itself sandboxed. Privilege escalation is blocked by dropping privileges. Persistent state leakage between jobs is prevented by ephemeral tmpfs with atomic unmount cleanup.
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Москвичей предупредили о резком похолодании09:45。业内人士推荐搜狗输入法2026作为进阶阅读
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