Legacy Labs

Ship Your First AI Feature Without Breaking Your Codebase

Guru

Clockwise

Girl Develop It

Georgia Tech

Mansi Pathak

Built enterprise products at VC-backed startups. Now advising the founders.

Identify the right AI use case for your stage and ship a working feature in days, not months.

A framework for scoping your first LLM feature

See exactly where to place LLM logic so it's testable, swappable, and won't take down your app.

Keep AI logic cleanly separated from your core product

Real examples: cost blowouts, prompt drift, latency spikes, and the decisions that prevent them.

The 5 failure modes in rushed AI integrations

Own the first mile — prototype real AI features using Lovable, Supabase, vector search, and n8n. No dev queues, just shipped demos.

Feb '26 Epoch I

AI Founder | Google AI Accelerator Alum

Google

Amazon Web Services

NVIDIA

OpenAI

Aki Wijesundara, PhD

AI Founder | Educator | Google AI Accelerator Alum

AI Advisor | Co-Founder & CEO at Krybe 

Manu Jayawardana

AI Advisor | Co-Founder & CEO at Krybe | Co-Founder of Snapdrum

Jan '26 Epoch I

Cohort 7 [Upcoming]

Every PM and founder knows AI is reshaping their industry. Most have a product idea. Almost none know where to start and the sheer volume of new tools, frameworks, and hype makes it harder every week, not easier.You've watched the demos. You've read the threads. You've tried a tool or two. But you still haven't shipped anything you'd show a user, a hiring manager, or an investor.That changes here.In 4 weeks you'll go from overwhelmed observer to someone who has built and deployed a real agentic product. An app with live data, AI features, and an autonomous agent that works without you in the loop. No coding background required.<ul><li>Ship a full-stack AI app in Week 1 using Lovable</li><li>Extend and own your codebase with Cursor and Claude Code</li><li>Run AI Evals on your product like a senior PM; with evidence, not instinct</li><li>Deploy a live autonomous agent connected to WhatsApp, Slack, or Telegram</li></ul>This isn't a theory course. Every week ends with something deployed and shareable.5,000+ students trained. Alumni from OpenAI, Google, Meta, McKinsey, and BCG. Built by practitioners who ship agentic products for real clients every week.The tools will keep changing. The ability to build won't.

Learn AI PM the way it actually works - by building and deploying actual agents!

Ship a full-stack AI app from a prompt using Lovable

Write briefs and specs that actually ship

Own your codebase without being an engineer

Evaluate AI features like a senior PM Evaluate AI features like a senior PM

Ship a live autonomous agent

Comfortable with basic web tools (Notion, Figma, etc.)

Basic familiarity with digital tools

Eager to explore AI tools and turn ideas into real demos — no prior experience needed.

Curiosity to build and experiment

Light familiarity with Python or JavaScript helps, but all key snippets and templates are provided.

Optional coding awareness

The PM or Project LeadExpected to lead AI initiatives but never built one. Time to change that.

The Founder or Builder Has an AI product idea. Tired of waiting for an engineer to build it.

The Career Changer Breaking into AI product roles and needs a portfolio, not just a certificate.

Ship production features 2x faster by using coding agents across research, planning, implementation, testing, and review workflows

Cohort 2

Creator of Stanford's AI Coding Course

Amazon

Stanford University

Y Combinator

Mihail Eric

Created Stanford's first AI coding class. Former YC founder and Amazon AI lead. 

Cohort 1

Cohort 3

Today many developers use AI, but few are maximally productive with it. I created Stanford's first AI software development class, and after building a YC-backed coding company and leading AI at Amazon, I've seen how top engineers integrate AI into production workflows. My techniques have been used to train 200+ Stanford engineers and industry professionals.My goal is simple: make you dramatically more productive writing software with AI than without it.In this course, you'll learn practical, end-to-end workflows for using AI in real-world development, not toy examples.We'll cover:<ul><li>Building production features with AI agents using the research → plan → implement → test workflow</li><li>Configuring an optimal AI native dev environment for your specific tech stack (IDE, code review, tool integrations, and beyond)</li><li>Setting up review and CI processes that catch AI errors, hallucinations, and slop before production</li><li>Enabling multiple agents to work together on the same codebase without conflict, accelerating software delivery and throughput</li></ul>If you're ready to write better code, ship to production faster, and stay in control of your coding agents, let's get started.

Increase developer productivity with AI-first, production-ready workflows

Configure optimal AI-first developer environments to improve your coding output

Learn how to navigate the ecosystem of cutting-edge AI developer tools 

Exercise effective strategies for prompting and building with coding agents

Reduce hallucinations, software errors, and AI slop all while shipping faster

Become a manager of coding agents so you can keep many agents productive 

Develop a deep intuition for how AI coding platforms work under-the-hood

Engineers who want to increase their productivity with AI-generated code while making it actually production grade.

Engineering managers who want to ensure their teams aren't being left behind when it comes to the new way of developing software.

Those confused about how to develop with AI the right way (how to maintain context and manage multiple agents without compromising code)

By the end of 4 weeks, you'll have the skills, the methodology, the tools and the hands-on experience to lead AI initiatives at your company

Co-founder @ Lovelaice, Product Leader

Madalina Turlea

Co-founder @Lovelaice, 10+ years in Product

Founder @Lovelaice | 14+ years in Tech

Catalina Turlea

Founder @Lovelaice, 14+ years leading Tech

February 2026

March 2026

A 4-week hands-on course where Product Managers learn to test prompts, benchmark models, write evals and build business cases for AI features using real data on your real use-case.Most PMs are stuck between two extremes: either waiting for engineering to figure it out, or shipping features that fail in production because they only tested on two happy cases.There's a better way.This course teaches you a systematic methodology for building AI features that actually work - before you write a single line of production code.You'll learn how to test prompts across multiple models, evaluate results like a pro, and make data-backed decisions about what to ship.This isn't theory.You'll run real experiments on your own product ideas using the same approach that's helped teams catch critical failures before their customers did, discover that cheaper models often outperform expensive ones, and ship AI features with confidence instead of crossed fingers.By the end of 4 weeks, you'll have the skills, the methodology, the tools and the hands-on experience to lead AI initiatives at your company - not just participate in them.

Stop guessing. Start testing. Ship AI with confidence.

How to integrate AI into your product

Build a eval pipeline for your AI features

You want to be in the top 5% of PMs who actually understand how to build with AI

You're tired of waiting for engineering to lead AI initiatives.You feel pressure to "do something with AI" but don't know where to start

You've shipped an AI feature but aren't confident it's working well (or your customers are finding the bugs)

Lead AI initiatives with clear goals, defensible evaluation, explicit risk decisions, and exec updates that drive alignment and investment.

CPO, GM xBraintrust, Nextdoor, OpenTable

Braintrust 🧠

Nextdoor

LiquidSpace I Work from where it works.

OpenTable

BlackRock

Elena Luneva

f-CPO. Product executive (Braintrust, Nextdoor, OpenTable, LiquidSpace)

Ex-Alexa AI Principal PM

Elsevier

Schneider Electric

Polly Allen

Ex-Alexa AI PM turned founder at AI Career Boost

Magic

AI increases what your ICs can do. It also breaks the management systems most teams rely on.Prototypes multiply. Quality becomes subjective. Risk ships quietly. You end up in every review because no one is sure what "good" looks like — or who's accountable for evals, failure modes, and rollout decisions.If you're a Director, Head of Product, or VP with multiple AI initiatives in flight: you're managing something you didn't build a system for yet. Execs keep asking "How do we know it's good?" and "What's the ROI?" — and you're the one fielding it.This two-day workshop gives you the operating system. Not theory. Not frameworks. The actual playbooks, artifacts, and rituals high-performing, AI-native teams use — built during the workshop so you leave with real deliverables, not homework.Leave with:1. A 90-day AI portfolio — clear bets, owners, and decision gates so nothing gets orphaned2. A repeatable risk review process your team can run without pulling you in3. A shared quality bar — examples, acceptance criteria, and review gates so "is this good enough?" stops being a judgment call4. Reusable operating artifacts built during the workshop — actual deliverables, not slides

Get the operating system used by high-performing AI-native teams

Install an AI-native operating cadence that keeps speed and alignment

Run AI risk reviews that drive decisions and confidence

Own and drive evaluation metrics and process, before and after launch

Implement playbooks that let your team move with you, not wait for you

Director/Head of Product: Leads AI initiatives; turns prototypes into production with clear quality, gates, and exec updates.

Eng leader (Sr Mgr/Dir/VP): Ships AI fast while managing reliability, security, and cost with consistent eval + risk reviews.

Data/ML leader (Dir/Head): Owns evals and performance; aligns product and execs with shared language and decision-ready tradeoffs.