New cohort
Welcome: start here before Session 1
Vibe coding means you describe what you want and Lovable builds it. Before the first live session: Lovable account, Maven + WhatsApp, and your capstone one-liner.
How this bootcamp works
This bootcamp is built around vibe coding: you describe what you want, and AI tools build it. Live sessions focus on briefs, decisions, and shipping. Async Deep Dives go deeper on integrations, polish, and mental models. The capstone is one product you grow every week. The rule is simple: ship one thing every week.
What you keep
The four-layer mental model (frontend, backend, data, auth) and a scoped capstone idea before you touch a build tool.
You ship
Lovable account ready, Maven + WhatsApp joined, and a capstone one-liner before the first live session.
Orientation: start here
Step-by-step: what building with AI means, the four-week capstone spine, what Week 1 ships, and what to do before Session 1.
Tool and account setup (do this next)
One Lovable account before Session 1. Join Maven and WhatsApp. No developer tooling required yet.
Seed your capstone
Pick one small, real product - the single thing you will build across all four weeks.
Building in public
Why we share builds openly, where to post (LinkedIn, Substack, share it), and how to talk about the skills you learn.
Pre-session check-in
Post a short intro and your capstone one-liner in Maven or WhatsApp before Session 1 - no build required yet.
Session 1 checklist
What you must have ready before the first live session - and what is fine to finish during or after it.
What you keep
How to go from a written brief to a working full-stack app, and how to write a brief that builds the right thing on the first try.
You ship
A live web app at a real URL, the first version of your capstone.
Watch through (async)
Think Like an AI Engineer (free 10-part series)
- 1How LLM Actually Works (in Plain English)Watch
- 2How to Prompt AI Like a Pro: The 5-Part FormulaWatch
- 3How to Pick the Right AI Model for the JobWatch
- 4Using AI With Your Files, Images and DataWatch
- 5Context Is Everything: The #1 Skill for Using AIWatch
- 6Vibe Coding for Non-Engineers (and Engineers)Watch
- 7What AI Agents Actually Are (Hype vs Reality)Watch
- 8Build AI Workflows With No Code (n8n, Zapier, Make)Watch
- 9AI Evals: How to Know If Your AI Actually WorksWatch
- 10The Fastest Way to Stay Current in AIWatch
The brief is the new spec
The durable skill of the course: write a brief specific enough that the AI builds the right thing first time.
Build your app with Lovable
Turn your brief into a running app, then iterate in the prompt-and-review loop.
Data and auth, handled inside Lovable
Add sign-up, login, and persistence by describing them, no separate backend setup.
Ship to a live URL
Publish a snapshot to a live URL, remove the Lovable watermark, then prove it works for someone who is not you.
Connecting Stripe and other integrations
Three ways Lovable connects to the outside world, app connectors, chat connectors, and any API, with Stripe as your payments example.
AI features for your app
Add live AI to your app with Lovable's built-in connector: chat, summaries, RAG, voice, and more. No API keys required.
Lovable Cloud
Full-stack hosting built in: database, auth, storage, edge functions, and AI backend. No separate Supabase setup.
Design polish for a generated app
Escape the generic AI look: design tokens in your brief, Visual Editor tweaks, external components, and visual references.
Week 1 assignment support: Ship your product v1
Step-by-step Lovable guide: brief, build, auth, AI feature, publish, cohort feedback, and LinkedIn.
Ship your product, version one
Ship a real v1 with auth, persistence, and at least one AI feature, then share for cohort feedback and post on LinkedIn with screenshots.
What you keep
How to direct an AI coder to change a real product safely, how to read a codebase you did not write, and how to hand knowledge work to an AI collaborator.
You ship
A new feature in your product, none of it hand-written.
From "I can't code" to "I direct code"
Plan, change, review the diff, keep or roll back, the safe loop for directing code you did not write.
Extend your app with Claude Code
Run the plan-change-review loop on your capstone and confirm Week 1 still works after the upgrade.
Automate your workflow with Cowork
Hand the work around your product to an AI collaborator, same briefing discipline, new surface.
Ship a new feature
Publish the Week 2 upgrade and test it live, version one plus a directed change.
Lovable, Claude Code, or Cursor: when to use which
Start in Lovable, extend with Claude Code, reach for Cursor when you want to steer file by file.
Reading a codebase without fear
Ask for a map, trace one feature end to end, and use read-only mode to explore safely.
Claude Code stack: Skills, Subagents, and MCP
Setup, CLAUDE.md, Skills, Subagents, MCP, and Cowork. The repeatable layer beneath the plan-build-debug loop.
Week 2 homework: Ship your second Pennywise feature
Plan-build-debug loop, /new-feature skill, Cowork PRD, WhatsApp before/after, and a published LinkedIn post.
Ship a new feature with Claude Code
One feature live, a /new-feature skill you used, PRD in Cowork, and a published LinkedIn post.
What you keep
How to build an automation that runs without you, the real difference between a workflow and an agent, and what an agent actually is, learned by operating one.
You ship
A multi-step automation on real triggers, and a live agent you operate.
Automate with n8n
Trigger, actions, connections, build a real automation wired to your product on the canvas.
Workflow versus agent: the line that matters
Workflows follow your script; agents decide their own steps, use the simplest thing that does the job.
Operate your own agent with Hermes
Configure a persistent agent, connect it to WhatsApp, and watch it decide and act.
A peek under the hood (and where to go deeper)
A model using tools in a loop, and the honest bridge to the Engineering Bootcamp.
n8n automation patterns
Trigger-do-notify, enrich with an LLM, human in the loop, recognise the shape before you build.
Hermes skills authoring for non-coders
A skill is a described ability, write when to use it clearly, then test that it fires when it should.
The agent loop, explained without code
Think, act, observe, repeat, and why early wrong turns compound when an agent misbehaves.
Add an automation or a live agent to your product
Something useful on a real trigger, and you can say in one line whether it is a workflow or an agent.
What you keep
How to evaluate an AI feature like a leader, using TRACE. Error analysis is product work, you own the front of this loop.
You ship
Your product, evaluated, with a clear read on where it stands before the sprint.
The vibe-check trap
"It looked good when I tried it" is not evaluation, and vibes do not survive change.
TRACE: Trace and Read (error analysis is your job)
Capture real interactions, read them one by one, and journal what went wrong, product work, not engineering.
TRACE: Analyze (decide what matters, fix the obvious)
Cluster failures by frequency, fix the cheap ones, and judge pass/fail, not vague scores.
What good evals tooling looks like (so you can lead it)
Codify and Enforce are engineering, but you can recognise good checks and hold a team to them.
Build a simple must-pass checklist for your product
Turn top failures into binary pass/fail cases, and re-run the list every time you change the product.
How to brief an engineer to build the evals you need
Hand traces and must-pass cases, not a request for generic quality metrics.
Run TRACE on your product
Traces read, failures ranked, must-pass checklist built, at least one fix shipped.
Build-support office hours
Working time with help on hand. Bring a concrete blocker, not a vague "how's it looking."
Final product brief and ship checklist
One-page brief, three-minute Demo Day walkthrough, and a non-negotiable ship checklist.
Demo Day
Submit your live URL, present opt-in in three minutes, what it does, how you built it, what evals surfaced.
Course resources
- Full series: Think Like an AI Engineer (10 parts)
- Part 1: How LLM Actually Works (in Plain English)
- Part 2: How to Prompt AI Like a Pro: The 5-Part Formula
- Part 3: How to Pick the Right AI Model for the Job
- Part 4: Using AI With Your Files, Images and Data
- Part 5: Context Is Everything: The #1 Skill for Using AI
- Part 6: Vibe Coding for Non-Engineers (and Engineers)
- Part 7: What AI Agents Actually Are (Hype vs Reality)
- Part 8: Build AI Workflows With No Code (n8n, Zapier, Make)
- Part 9: AI Evals: How to Know If Your AI Actually Works
- Part 10: The Fastest Way to Stay Current in AI
- Welcome and your first project
- Prompting best practices (the brief-that-builds playbook)
- Publish your project
- Set up a custom domain
- Lovable integrations (app connectors, MCP, APIs)
- Stripe app connector
- AI features for your app
- Lovable Cloud
- Best UI with Lovable, ShadCN, React (video)
- Lovable UI polish walkthrough (video)
AI Product Management learning resources
A curated reference for students. Every link is a primary source or a practitioner-written guide, favouring free material where possible.
- The Ultimate AI PM Learning Roadmap (Paweł Huryn)
Best single free reference: templates, step-by-step guides, core concepts without deep stats or Python.
- Interactive RAG simulator (Product Compass)
Linked from Huryn's roadmap - see retrieval in action.
- The Complete AI PM Transition Guide, 2025 (Aakash Gupta)
12-week plan: customer interviews, evals, LLM-as-Judge, agents, first prototype and portfolio.
- AI Learning Roadmap for PMs (Product School)
Strategic layer: 6-12 month path and writing an AI charter before tools.
Start here if AI terminology still feels fuzzy. Short, free, and written for non-engineers.
- Generative AI for Everyone (DeepLearning.AI)
~3 hours. What gen AI can and cannot do, tools landscape, responsible use. No code.
- AI for Everyone (Andrew Ng, Coursera)
Business-friendly mental models: ML vs AI, data, what teams actually need to ship.
- Elements of AI
Free two-part course on how AI works, ethics, and societal impact. Good first week if you are completely new.
- AI Foundations for Product Leaders (Nikhil Kumar)
Deep but PM-native: transformers, RAG, agents, evals, and RLHF explained without asking you to code.
How to decide what to ship, how much autonomy to give AI, and why design beats raw model accuracy.
- The CAIR framework: Confidence in AI Results (LangChain)
Value / (Risk x Correction). Reframe PM work from "make the model smarter" to "design so users trust the output."
The topic not to skip. Error analysis and the right product metrics matter more than architecture.
- Evals FAQ (Hamel Husain)
Large free reference - every AI PM should understand evals in depth.
- Your AI product needs evals (Hamel Husain)
Why vibes are not a release strategy, and what a minimum eval loop looks like for PMs.
- Why error analysis matters in LLM evals (Hamel Husain)
The TRACE "Read" step in practitioner form - read traces, cluster failures, fix what repeats.
- IBM AI Product Manager Professional Certificate (Coursera)
Free to enroll, ~3 months, no prior experience. PM fundamentals plus gen AI and prompt engineering.
- AI for Product Management (Pendo, free with badge)
2-3 hours, self-paced. AI across the product lifecycle, building AI-powered features, product-led org strategy.
- AI Product Management Specialization (Duke, Coursera)
Three-course series, no programming required. ML intuition, leading ML projects, privacy and ethics.
- 12 Best AI PM Courses (Aakash Gupta)
Comparison of paid options by career goal.
- AI Evaluations for Product Managers (The AI Internship, Maven)
Live intensive: gold sets, ship/hold gates, and a weekly quality cadence PMs can own.
Keep learning after the bootcamp. These update faster than any syllabus.
- Product Growth (Aakash Gupta)
Weekly AI PM tactics, career moves, and teardowns. Start with the AI PM learning roadmap issue.
- The Product Compass (Paweł Huryn)
Roadmaps, templates, and free PM resources. Huryn also maintains the best consolidated AI PM link list.
- Lenny's Newsletter
Search "AI PM" for playbooks on AI product discovery, evals, and working with eng on LLM features.
- Product Growth 2025 review: best AI PM podcasts (Aakash Gupta)
Standouts: Aman Khan's AI PM crash course and Tanguy Crusson on AI product discovery.
Read these first.
- Building Effective Agents (Anthropic)
Workflows vs agents; find the simplest solution and add complexity only when needed.
- Building Effective AI Agents: Architecture Patterns (PDF)
Longer version with case studies from Coinbase, Intercom, and Thomson Reuters.
- Writing Tools for AI Agents (Anthropic)
How to design and prompt-engineer tools your agents call.
- Effective Context Engineering for AI Agents (Anthropic)
Smallest set of high-signal tokens that maximise the desired outcome.
- Agentic AI Frameworks (Uvik)
15 frameworks vs cost, latency, efficacy, assurance, reliability - graph, role, handoff, hierarchical styles.
- Best AI Agent Frameworks 2026 (Alice Labs)
Practical shortlist from real deployments; Claude Agent SDK = same architecture as Claude Code.
- LangGraph
Stateful graph workflows with human-in-the-loop. LangChain Academy course is free.
- CrewAI
Fast role-based multi-agent prototypes.
- Hugging Face agents-course (GitHub)
Multi-framework view across smolagents, LlamaIndex, and LangGraph.
Teach MCP as a standard, not a vendor feature - JSON Schema protocol (Anthropic 2024, Linux Foundation 2025). Write a tool once, use it in every framework.