What are the requirements to participate?

Prior experience managing or building digital products—whether as a PM, PO, product leader, founder, or business owner with your own product. You don’t need to know how to code.

Do I need to know how to code?

You don’t need to code like a developer. You do need the willingness to work with technical tools, read plans, review changes, and make decisions. We teach you the minimum terminal, git, branches, and PRs you need to operate autonomously.

What is the attendance requirement?

Attend at least 75% of the sessions for at least 75% of their duration.

How is it different from the AI-First Product Manager (AFPM)?

AI‑First Product Manager (AFPM) covers the full product management cycle executed with AI as an assistant. AI‑First Product Builder (AFPB) focuses specifically on turning product decisions into working experiments: prototypes, flows, automations, and reviewable features. They’re complementary: AFPM provides product judgment; AFPB provides the ability to build what’s been decided.

How is it different from the Certified AI Product Manager (AIPM)?

Certified AI Product Manager (AIPM) teaches you to manage products whose core technology is AI (models, data, observability). AI‑First Product Builder (AFPB) doesn’t manage AI products: it builds both traditional products (landing pages, dashboards, features) and AI products, using AI as the PM’s copilot.

Is it useful if I’m a founder or own my own product?

Yes. For founders, the main value is speeding up tests without relying on a contractor for every experiment. You’ll build your own artifacts and also practice on an existing codebase to prepare for the moment when you need to extend your product, review technical work, or hand off to a dev or contractor.

Does this replace the development team?

No. The program aims for product to show up better to the technical conversation: with evidence, clear scope, visible trade‑offs, and reviewable changes. In some cases you’ll be able to deploy simple experiments; in others, the best outcome will be a PR or plan ready for the technical team to review.

How real is the project I build?

You’ll work on functional artifacts, not just conceptual exercises. Your own product can be tailored to your context, and the final stretch uses an existing codebase to practice a situation closer to day‑to‑day product work.

AI-First Product Builder (AFPB)

Turn product hypotheses into working experiments, without waiting a sprint from the development team for every insight.

Lab

36 hours

En Español

September 9th - November 25th

USD 1000

From USD 295 for Labs subscriptions

Enroll Now

For years, figuring out what to build and actually building it were separate capabilities. Product teams researched, prioritized, and specified. Engineering turned those decisions into software.

A PM wrote a ticket and waited for the next sprint. A founder hired a freelancer for every test. A designer validated ideas with screens that could not capture real behavior. A product leader watched discovery slow down because every experiment had to compete with the roadmap.

With coding agents, that boundary is starting to shift. Teams can turn a hypothesis into something a user can actually try, capture signals, and return to the product decision without turning every learning opportunity into a development project first.

Not because this turns everyone into developers, but because it allows someone with strong product judgment to build just enough to learn sooner: a landing page, a functional flow, an automation, a dashboard, a hidden feature, or a pull request ready for review.

This unique program teaches you, week by week, how to use this new building capability as a clear method and incorporate it into your professional product practice. Not to bypass the engineering team, but to enter the conversation with evidence and make better-informed product decisions.

What changes when you can build

From idea to experiment: You go from describing what should be built to putting a first version in front of users, leads, or stakeholders.

From mockup to signal: You learn to capture data, feedback, and real behavior—not just opinions about a screen.

From ticket to handoff: You turn a product intention into a concrete change that’s documented, testable, and understandable for whoever reviews it.

From dependency to collaboration: You don’t replace the technical team. You show up better prepared: with context, judgment, clear trade‑offs, and less speculative work to delegate.

After finishing the program you'll be able to close the loop between a product belief, a working experiment, and an evidence‑based decision.

Tools

We will use the following tools

Claude Code

Github

Supabase

Vercel

Visual Studio Code

Who is this for?

AI‑First Product Builder (AFPB) is for people who already make decisions about digital products and want to shorten the distance between deciding, building, and learning.

Product Managers / Product Owners

You depend on the development team for every test and want to validate more hypotheses without adding noise to the roadmap.

Founders / Business Owners

You have ideas, customers, or a market, but you don’t want every experiment to depend on hiring or waiting for someone else.

Designers / UX Designers

You already prototype experiences, but you want to move from static screens to functional flows that generate evidence.

Product Leaders / Heads of Product

You need to understand how much real autonomy your PMs can gain with code agents, and where it makes sense to set limits.

Consultants / Product Advisors

You want to show concrete possibilities, not just recommendations, when you help a client decide what to test.

It’s not for you if you want to become a full‑time developer, learn a programming language from scratch, or outsource product judgment to a tool. AI speeds up building; it doesn’t replace judgment about what’s worth building.

If you’re taking your first steps in managing AI‑based products, we recommend starting with AIPM. If you want to specialize in agents and multi‑agent architectures, AAPM is your next step. And if you’re looking for a program that covers the full product cycle executed with AI, AFPM is the best fit.

By the end of the program you will be able to

Turn a hypothesis into a runnable experiment: translate a product belief into a scope, a metric, an exposure method, and a decision criterion.

Build prototypes that capture signals: go from a screen or idea to a landing page, flow, or dashboard with persistent data.

Work with Claude Code without losing control: install, configure, plan, run, and verify long sessions with managed context.

Build with a basic full‑stack anatomy: integrate authentication, database, forms, dashboards, and simple automations without becoming a developer.

Encode your work rituals as skills: turn repetitive procedures into reusable tools for research, review, documentation, or artifact generation.

Enter someone else’s codebase with judgment: understand what a product you didn’t write does, identify where to make changes, and anticipate risks before editing.

Leave a change ready for review: open a branch, document the change, request assisted review with sub‑agents, and prepare a readable PR for the technical team.

Close the learning loop: read signals, decide whether to proceed, adjust, or discard, and communicate the decision with evidence.

Curriculum

First artifact

You install Claude Code, understand how it works, and leave with your first piece running. The goal is simple: for the tool to stop being an abstract promise and start producing something verifiable on your machine.

Visual iteration cycle

You learn the loop *task → do → verify*: ask, review, fix, and run again. Speed doesn’t come from nailing it on the first try, but from quickly closing the gap between what you imagined and what actually got built.

First digital product

You build a first version with real pieces: interface, forms, persistent data, and visualizations. You begin to see the technical anatomy of a digital product from a product point of view.

Skills: Your personal toolkit

You package the procedures you repeat every week into tools the agent can execute on demand. You leave with the first building block of a personal toolkit you can carry from one project to another.

The project manifesto

You build the operating brief a new collaborator would need to avoid breaking anything: what the project is, how work gets done, what decisions are already made, and what lines shouldn’t be crossed.

Context management

You learn to manage the hidden cost of long sessions: context, memory, compaction, tools, and quality degradation. The goal is to work longer without losing control.

Sub-agents and parallelization

You stop operating a single conversation as your only work channel. You delegate research, review, documentation, or testing to specialized sub‑agents and assess when it’s worth parallelizing.

Connecting your stack

You connect the agent to the tools where your work already lives: repositories, tickets, knowledge bases, browser, messaging, or data. The question isn’t what can be integrated, but which integration reduces real friction.

Onboarding to an external codebase

You practice a core skill of the modern builder: stepping into a product you didn’t write, understanding how it works, identifying where to make changes, and opening a working branch without reading line by line.

Hypothesis and exposure plan

You go from “I want to build this” to “I’m going to test this belief with these users, measuring this, and presenting it this way.” You draft a plan that product and engineering can discuss before building.

Building and reviewing the change

You build a functional change and prepare it for review. A reviewer, a doc‑writer, and a test‑writer audit the work so the PR is understandable, testable, and easy to inherit.

Closing the product loop

You close the full cycle: you present the hypothesis, the experiment you built, the signal captured, and the decision you would make based on that evidence.

Program specs

A learning path

24 live hours (12 sessions of 2 hours) combined with applied work between sessions.

Three progressive pillars

The program covers three stages that increase in complexity: building your own artifacts, operating Claude Code with judgment, and stepping into someone else’s codebase to hand off to the technical team or perform a controlled deployment.

Expected outcome

You leave with a complete practice of AI‑assisted product building: from a hypothesis and your own artifact to a reviewable feature in an existing product.

Frequently Asked Questions

Everything you need to know about this course

Your Instructor

Martin Alaimo

Since 2009, he has worked with more than 200 organizations and supported over 8,000 professionals in their career development journeys.

His approach is situational and hands-on, delivering immersive learning through innovative experiences that enable practical, immediately applicable outcomes—especially in areas often overlooked by traditional academia.

He has spoken at more than 30 conferences across the United States and 14 countries in Latin America and Europe, and is the author of six books on product and digital innovation.

His most recent book, AI Strategy Workshop, provides tools to move beyond the “feature factory” mindset and integrate artificial intelligence with strategic intent and real business impact.

He is the founder of Verica, a platform for running evaluations (Evals) on LLM-based products, designed for organizations that need to measure the quality of the outputs generated by their AI products.

As part of his commitment to innovation, he is an organizing member of Product Tank, the world’s largest Product Management community.

He is one of the few experts to hold the highest-level certifications in Agile practices: Certified Scrum Trainer (CST), Certified Enterprise Coach (CEC), Certified Team Coach (CTC), Certified Agile Leadership Educator (CAL Educator), and Path to CSP Educator.

Visit his complete professional profile and thought leadership activities on LinkedIn.