Handling data in streams is fundamental to how we build applications. To make streaming work everywhere, the WHATWG Streams Standard (informally known as "Web streams") was designed to establish a common API to work across browsers and servers. It shipped in browsers, was adopted by Cloudflare Workers, Node.js, Deno, and Bun, and became the foundation for APIs like fetch(). It's a significant undertaking, and the people who designed it were solving hard problems with the constraints and tools they had at the time.
I remember wondering if something like the Goldtouch Elite Adjustable existed when I first started testing ergonomic keyboards. It didn’t at the time, as far as I could tell, but now a connected yet adjustable split board is indeed a product you can buy. It’s a solidly-built board and the ball joint connecting the two halves feels like it will put up with a lot of use. A squeeze of the lever at the top of the keys lets you set the board just how you like, adjusting both the vertical tenting and the angle between the two halves. There’s no programming to speak of, just the ability to swap a few function keys like print screen and home.
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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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