Hey everyone! Today, I tried using FastText (a local clasification model) as Atlas’ intent engine. It was pretty awesome seeing it actually kinda work! The setup is super easy, you just clone a repo, make a file, then run that file with a few args to load in your training data in a literal .txt file, and just test it there in the command line in literally under five minutes. FastText was looking like it would become Atlas’ intent engine, but unfortunatly, it’s kinda dumb, and I mean that in a very loving way. FastText doesn’t seem to know enough about language to be very confident or accurate in classifying sentances into back-end hooks. Given enough specifically equal training data (because too much data in one hook will make it be the most confident in that hook. Not exactly useful when “Play music” turns into AIRPLANE_ON), it could definitely work out, but for every single feature that i add in the future it would have to gen a GIANT list of examples, and even then, it woudln’t be great at something unpredictable. So I’m going to try flipping the script to some sort of NLU or LM, like MiniLM to use with some sort of ONNX integration so that I can make it work good on not just Android with Kotlin, but also linux, for a raspberry pi hardware project I’m calling Scout, which will basically be like a baofeng radio with a bunch of extra features (I can only work on that once I get my HAM liscense though.) Anyways, FastText proved that the whole pipeline works, and it might work as a ffallback if things go south and i really need something to ship or something for the end of stardance’s prototype. I also tried updating some app UI stuff, but going from HTML + CSS to Jetpack Compose is a wild ride. Anyhow, thanks for reading! ps– in the android studio screenshot, what am I even doing????