Karpathy Zero to Hero wins LLM sprint

Hot take: if you only have 10 hours, optimize for a clean mental model, not completeness. Karpathy is the best spine, Raschka the best reference, and StatQuest the best prerequisite.

• –Karpathy's curriculum is sequenced from micrograd to GPT/tokenizer and is roughly 13.5 hours total, which makes it the closest thing to a single-track crash course that still reaches modern LLM plumbing: https://karpathy.ai/zero-to-hero.html
• –Raschka's book and repo are the best next step when you want to code along, understand pretraining/finetuning, and revisit implementation details: https://www.manning.com/books/build-a-large-language-model-from-scratch and https://github.com/rasbt/LLMs-from-scratch
• –StatQuest's neural-net series is excellent for intuition on backprop, attention, and transformers, but it is broader deep-learning grounding rather than an LLM-first path: https://statquest.org/video_index.html
• –A smart 10-day plan is Karpathy for the storyline, a few Raschka chapters/code notebooks for practice, and StatQuest only when a concept feels fuzzy.
• –If you want one extra warmup before the technical track, Karpathy's "Deep Dive into LLMs like ChatGPT" is the fastest orientation: https://karpathy.ai/blog/zero-to-hero.html