A developer at Hugging Face is using an AI agent called ML-Intern to rapidly build custom machine learning models at a fraction of traditional costs. The agent, integrated into HuggingChat, autonomously plans training workflows, enforces budget constraints, runs baseline tests, and publishes models to the Hugging Face Hub. In just a few days, the developer created six specialized models—including a citrus disease detector, a character illustration generator, and a camera-angle LoRA—each costing between $1.90 and $16 in compute resources. The approach solves a common problem in machine learning: when an official model is too large or expensive for a specific task, developers can now fine-tune existing models on curated datasets without extensive technical work. The citrus disease model, for example, improved accuracy from 14.9% to 52.8% after training on agricultural images. ML-Intern enforces strict budget controls, asks for permission before spending money, and runs smoke tests before expensive training runs, ensuring efficient resource allocation. The developer's detailed prompting methodology—including verified facts sections and baseline measurements—guides the agent's work effectively. This workflow suggests a shift toward AI agents automating the technical work of model development, potentially democratizing access to specialized models for developers without deep machine learning expertise. Models built this way run efficiently on consumer hardware despite being highly specialized.