The dataset problem nobody talks about.. and the API that quietly solves it.
Everyone has an opinion on which model to fine-tune.
Nobody talks about where the training data actually comes from.
Ask any ML engineer who has built something on scientific literature and you'll hear the same story: the model took two weeks. The dataset took two months. The dataset was the hard part.
I've been there. Cobbling together CSVs from PubMed exports, writing scrapers that broke every time a journal sneezed, hand-cleaning PDF extractions that looked like someone ran a blender through a research paper. It's unglamorous, it's slow, and it's the reason a lot of genuinely good AI projects never ship.
