Study reveals whistling secret of horses’ whinny

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As part of Apple’s deal with F1, Apple TV subscribers get F1 TV Premium for the 2026 season. This gives you access to things like onboard cameras, team radios and live telemetry in addition to live coverage of the entire grand prix weekend. So, you can watch races on Apple TV or F1 TV, depending on your app preferences, or use the additional features of F1 TV Premium as a second (or third, etc.) screen setup. Netflix will also broadcast the Canadian Grand Prix in May as part of the deal that brought Drive To Survive to Apple TV.

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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.