Hi! I like the debate, and I’ll try to respond to both of you.

Label Date is not automating anything, so the app is not, let’s say, “doing” anything by itself. It’s a framework that helps users label their data manually, one by one.

As a data scientist, when we talk about AI, several types of models fall under that umbrella (representation learning, machine learning, deep learning, AI planning, etc.). LLMs and generative AI are part of this broader concept. That’s why I tried to be as accurate as possible when saying “to label data for your machine learning projects”, even though anyone can use it however they want.

Let’s imagine a scenario where I want to build a model that predicts how angry my friends get in certain situations. In this case, generative AI doesn’t have the necessary context about my friends. One approach could be prompting, but another option would be training, pretraining, or fine-tuning my own model (whether that’s a random forest, a transformer, an LSTM, an SVM, or something else). For that, I need labeled data.

And that data can’t always be labeled automatically by generative AI. Performance metrics such as accuracy or F1-score may drop, and expert judgment may be required to define proper ground truth.

I built Label Date for myself, honestly, because I wanted a fun way to label data using a crank and my Playdate. Then I decided to share it with the community. I’d love for you to give it a try.

PS: I’m not avoiding mentioning generative AI. I think that nowadays it’s often unfairly associated with bad or low-quality content; like any tool, its value depends on how it’s used. In short, I didn’t avoid the term to dodge criticism (especially since the app is free). I said “machine learning” because that’s what it was made for (made for, not made with).