A controlled deorbit requires the use of several core systems, including those for communications, power, and avionics. Some of the ISS machinery was not specifically certified to perform in a depressurized environment. (NASA believes that critical systems would remain operable, based on technical analyses, and emphasizes that many of these systems are already used in vacuum.) Another thing to worry about: the ISS losing control over its orientation in space. The spacecraft could start tumbling, flipping the station’s solar arrays away from the sun, taking the primary source of power with it.
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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