That's very impressive. Hats off to them! I dont think this is too out of the ordinary either though. I'd guess they started off with a LLM from hugging face, set up some pipeline to ingest code from replit repos to finetune the LLM. The ML aspect of this is not terribly hard given that they probably dont need to train a LLM from scratch. Figuring out how store and serve from replit repos (or publicly available code bases) is not too difficult. From there it's a matter of productionalizing: how to serve the model in real time, figuring out they want the product to look/feel like and I suppose this part of it might take a while. I'd estimate you'd need 1-2 ML engineers, 2 data engineers, 2-3 swes, 1 PM for the team for a minimal viable product.