AI-generated narration disclosure. The voice-over in this course is synthesized with a text-to-speech model from scripts written and reviewed by the instructor, and the on-screen material (notebooks, code, slides) is the instructor’s own work. This is disclosed in line with Udemy’s policy on AI-generated content.
Jev, released by TypeSafe in September 2026, answers questions without writing a word: you send a message and typed questions, and it returns a probability for every option you gave it. Open models such as Kev and MoJev reproduce the idea. This course builds one from scratch, small enough to read end to end, and then measures whether its probabilities deserve trust.
We start by running Kev locally and making the honest case for and against decision models, using measurements from real benchmarks: when a small trained classifier is the better tool, and when a decision model earns its place. Then we build the design in PyTorch on a 0.6-billion-parameter Qwen base: borrowed special tokens as delimiters, an attention mask that keeps options from seeing each other, shared position ids that make option order irrelevant, and a pointer head that scores each option against a decide token. We train it with LoRA on public data (BANKING77, CC-BY-4.0) plus generated data, for three question types: yes/no, choice and ordered score.
