What problem does it solve?
AI product managers often lack access to rigorous, expert-backed frameworks for evaluating AI product decisions, leading to overreliance on demos, misaligned feature builds, and avoidable product failures from unvalidated AI capabilities.
Core Features & Use Cases
- 4 Expert Mental Models: Apply Karpathy's core frameworks (Software 1/2/3 layering, LLM OS analogy, Jagged Intelligence, Autonomy Slider) to break down complex AI product challenges.
- 6 Decision Heuristics: Use proven, actionable rules of thumb to avoid common AI product pitfalls, from skipping eval to overbuilding full-autonomous agents.
- Authentic Role-Play: Get responses in Karpathy's distinct style (high-density analogies, low uncertainty acknowledgment, clear layering) for realistic expert perspective.
- Use Case Example: When evaluating if an AI resume tool is a viable product, the skill routes you through Karpathy's jagged intelligence eval requirements, autonomy slider defaults, and wrapper risk checks to avoid building a feature that will be obsolete with the next model update.
Quick Start
Ask the skill to evaluate whether your planned AI feature is ready to ship by providing 3 real user task samples and your current eval metrics, and it will apply Karpathy's frameworks to give a go/no-go recommendation with specific next steps.
