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Choose a course built by PL for a specific professional outcome.

Turn fuzzy product intent into a buildable object model and artifact set.

Choose and defend a pricing move under uncertainty.

Build a personal learning filter for AI and product skill changes.

Read AI claims without getting pushed around by launch theater.

Select the smallest viable model and design evals tied to user value.

Write prompts as executable product specifications instead of vibes.

Explain what is actually running when an agent works for hours.

Design tool and memory surfaces that raise agent reliability.

Build eval harnesses that catch agent failure before users do.

Decide when multiple agents multiply capability and when they add tax.

Plan observability, recovery, and cost controls for production agent systems.

Map role fit, company fit, and career tradeoffs before committing.