How to PM Developer Tools

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Developer products punish generic PM instincts. Your users read the documentation before they see the landing page, adoption happens bottom-up, and the real UI is an API. This course teaches the craft that dev products require: developer experience, documentation treated as product, API design reviews, and adoption funnels. The AI dimension sits at the center, because every developer tool is adding agents and every PM is now asked how AI changes the roadmap. If you present yourself as both an engineer and a product person, this is the structured way to articulate the PM half with evidence behind it.

The outcome

How does this prepare you for a job?

Platform-PM interviews test your judgment about funnels on developer journeys. Define activation for an API is a named FAANG interview question.

Owned developer-product surfaces including API design reviews, SDK ergonomics, CLI workflows, and documentation treated as product.
Instrumented and improved a developer adoption funnel running from signup to first API call to production integration, using the AARRR framework.
Wrote PRDs for AI features inside developer tools, such as agent modes and copilot integrations, with success criteria based on evals.
Ran developer discovery, converting engineer interviews and friction logs into roadmap priorities.
Made platform trade-off calls on breaking changes, deprecations and versioning, each with an analysis of the impact on the ecosystem.
Partnered with DevRel on launches, covering quickstarts and metrics for time to first success.

Tools

AI tools learnt

Be your own first user, which is the fastest way to build judgment about developer products.

Hands-on skills

Define and instrument time to first success for an API or a CLI
Run AARRR against a developer journey, where activation is not signup but the first working call
Review an API design as a PM would, covering ergonomics, versioning and the budget for breaking changes
Treat documentation as product: quickstarts that convert, and references that retain
Interview developers without leading them, and mine friction logs for the blocker that is real
Spec an AI feature for a developer tool with acceptance criteria based on evals
Prioritize platform work such as deprecations and reliability against more visible features, and defend the choice

Syllabus

Modules

Level: Basic

The dev-product mental model

Bottom-up adoption, documentation before landing pages, and who the real buyer is
Map one real tool's journey from end to end
Where generic PM instincts misfire in this context

Funnel and metrics

AARRR applied to developer products, and instrumenting time to first call
Find the activation cliff in a real funnel
Retention for tools, measured by depth of usage rather than logins

Discovery with developers

Interviews and friction logs, run honestly
Turning the findings into a one-page PRD
Saying no with a reason developers respect

Level: Advanced

Platform PM at FAANG scale

API councils, deprecation policy and partner ecosystems at the scale of Google, Microsoft or Stripe
The platform-PM interview case, practiced
Budgets for breaking changes, and the trust ledger they draw on

AI inside the dev tool

Agent modes and copilot features, specced with criteria based on evals
How pricing and packaging interact for AI features
The roadmap answer for when leadership asks how AI changes this

The articulate-both play

Position engineering and product together without splitting the difference
Portfolio artifacts built for platform-PM loops
A mock interview on a real developer-product case

Students can take the full course (both levels) or either level on its own.

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