Y Combinator. Karpathy's AI Startup School keynote frames LLMs as a new kind of computer — utility, fab, and OS rolled together — and argues for "partial autonomy" products with a human-controlled leash. It is the cleanest articulation of the stack-level mental model the article assumes: that you are picking inference vendors and tooling for a programmable substrate, not a chatbot.
Model names, pricing and capabilities change quickly. Use this for the decision pattern, then verify current model behavior before adopting it.
Evaluate partial-autonomy product ideas where humans keep control over high-risk decisions.
None — watchable cold; it pairs well after the deep dive's training-recipe grounding.
Last reviewed: May 18, 2026
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Hand-picked external courses that go deeper on this topic.
Coursera · DeepLearning.AIAndrew Ng
Real time inside an LLM, learning to prompt deliberately and recognise where generative AI is genuinely useful versus where it's a trap. Calm, no-hype teaching — the perfect bridge from "I've tried ChatGPT once" to "I use it every day with confidence."
Coursera · DeepLearning.AI + AWSAntje Barth · Shelbee Eigenbrode · Mike Chambers · Chris Fregly
When practitioners ask "what should I take if I'm serious about building with LLMs?", this is the answer. Mathematically honest without being a research paper; AWS-flavoured deployment chapters stay useful even if you'll never touch SageMaker.
Coursera · Google CloudGoogle Cloud
Google's own answer to 'what is generative AI, actually' — the non-technical counterpart to Elements of AI, aimed explicitly at sales, HR, marketing, and operations roles rather than engineers. Forty-five minutes to a real mental model of how generative AI differs from classic machine learning, with no code and no jargon.
