2. You want the most forgiving camera system on the market Sure, the Pixel 10 Pro XL may not have all the camera bells and whistles of the Galaxy S26 Ultra, but where it lacks in sensors, it makes up for it in computational tuning and image recognition. The best example is when I tested the Pixel's 100X Pro Res Zoom, which leverages its 48MP telephoto lens and the Tensor G5's ISP to recognize distant subjects and AI-generates lost details. The result, as surveyed by a crowd of media members, showed the Pixel beating the Galaxy's 100X zoom by a long shot.
But when her mission finally took to the skies in 2005, the nightmare scenario happened again. A chunk of foam broke away during launch.
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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.