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Vibe Coding

Build real apps with AI. Describe, review, debug and ship without writing code from scratch

10 phases. 48 lessons. 48 practice reps. 1 capstone. No coding experience required. Learn to build real working apps by describing what you want to AI tools — Claude Code, Cursor, Lovable, v0, Bolt.new, Replit Agent — through a tool-agnostic workflow that teaches you to plan before building, review AI-generated code, debug when things break, write context files, manage real data and auth, deploy to production, and avoid the security traps that have already taken down real vibe-coded apps. You finish with a deployed capstone project and the judgment to know when to build with AI and when to call an engineer.

10 phases · 48 lessons · 48 practice reps · 1 capstone · Level: Beginner

No prerequisites — this is a starting track.

Outcomes you will have by the end

• A deployed capstone project — A real, live app you built with AI tools — deployed to a real URL, used by at least one real person. Not a tutorial example, not a mockup. Something that exists in the world and does something useful.
• A repeatable build workflow — A documented workflow you can use on any future project: plan, describe, build, review, debug, harden, deploy. You build the muscle memory of shipping with AI tools instead of just watching demos.
• A security sanity-check habit — A concrete checklist you run before shipping any AI-built app — exposed keys, public database rules, missing auth. Grounded in real incidents that took down real vibe-coded apps.
• The director mental model — You think like a director — describe, review, decide — not like a typist. You know when to let AI accelerate and when to slow down and apply judgment. You know the difference between a prototype and production.
• A context file template — A standing context file (CLAUDE.md or .cursorrules) you can adapt for any project, so AI sessions start from a useful place instead of from zero every time.
• Verified Vibe Coding certificate — Issued by DeVenture Academy, tied to your completion record. Lists the tools you used, the projects you shipped, and the skills you developed across 49 practice reps and a capstone.

What you will be able to do

AI-Assisted Building · Plain-English Spec Writing · AI Code Review · Debugging AI Output · Context Engineering · Deployment · Security Sanity Checks · Production Readiness

Every phase, every lesson, every project

• Vibe Coding Fundamentals (5 lessons) — free — What vibe coding actually is (Karpathy's framing, what changed to make it possible), the director-not-typist mental model, what vibe coding is good for and not, the struggle-before-automation principle, your first end-to-end build
  • What Vibe Coding Actually Is
  • The Mental Model: Director, Not Typist
  • What Vibe Coding Is Good For (And Not)
  • Why This Course Makes You Do Some of the Work
  • Your First Build, End-to-End
• The Tool Landscape (5 lessons) — free — Four categories of vibe coding tools (terminal agents, IDE-native, browser app-builders, design-to-code), the decision matrix for choosing a tool, setting up a browser app builder, setting up a terminal or IDE agent, evaluating any new tool in ten minutes
  • Four Categories of Vibe Coding Tools
  • The Decision Matrix
  • Setting Up a Browser App Builder
  • Setting Up a Terminal or IDE Agent
  • Evaluating Any New Tool in Ten Minutes
• Describing What You Want (5 lessons) — Plain English specs that work, plan-first-then-build habit, breaking big ideas into buildable chunks, iterating and giving specific feedback, prompting with images and screenshots
  • Plain English Specs That Work
  • Plan First, Then Build
  • Breaking Big Ideas Into Buildable Chunks
  • Iterating and Giving Specific Feedback
  • Prompting With Images and Screenshots
• Reading & Reviewing AI Code (5 lessons) — Pattern recognition over syntax mastery, spotting red flags in AI output, asking AI to explain itself, git diffs as a safety net, verifying claims the AI makes
  • Pattern Recognition Over Syntax Mastery
  • Spotting Red Flags in AI Output
  • Asking AI to Explain Itself
  • Git Diffs as a Safety Net
  • Verifying Claims the AI Makes
• When Things Break (5 lessons) — The predict-before-reveal habit, pasting errors effectively, the explain-and-retry loop, when to patch vs start over, rollback and version control basics for non-engineers
  • The Predict-Before-Reveal Debugging Habit
  • Reading Error Messages
  • The AI Debugging Loop
  • Escaping Infinite Correction Loops
  • Knowing When to Start Fresh
• Context Engineering (4 lessons) — Why agents forget and drift, writing your first context file (CLAUDE.md/cursorrules), keeping a long build on track, learning from real published context files
  • What the AI Knows and Doesn't Know
  • Writing a Context File
  • Managing Context Across Sessions
  • Docs vs. Chat vs. File References
• Building With Real Data (5 lessons) — From static prototype to real app, adding a database without being a DBA, user accounts and auth basics, secrets and environment variables, the local-vs-deployed gap
  • From Mock Data to Real Databases
  • Setting Up Supabase
  • Authentication Patterns
  • Environment Variables and Secrets
  • Data Patterns That Don't Break Production
• Shipping (4 lessons) — Deploying your first real app, custom domains and going live, what production actually means, having a rollback plan
  • Deployment Platforms Basics
  • Pre-Deployment Checklist
  • Custom Domains and DNS
  • Post-Launch Monitoring
• Security, Privacy & Judgment Traps (5 lessons) — Real vibe-coded security incidents (Moltbook, Lovable, Base44, Orchids, Replit), sanity checks any non-engineer can run, the AI-agrees-with-you problem, privacy and user data responsibility, knowing when to call a real engineer
  • The Security Checklist AI Won't Run
  • Real-World Vibe Coding Failures
  • Privacy and User Data
  • When AI Leads You Astray
  • The Judgment Call: When Not to Ship
• Capstone: Ship a Real Project (5 lessons) — Choosing a real project, planning and building your capstone, reviewing and hardening your capstone, shipping and sharing your capstone, capstone project and what's next
  • Planning Your Project
  • Building with Review Habits
  • The Full Checklist Run
  • Deploying and Sharing
  • Reflection and Next Steps

The technologies you will use

Claude Code · Cursor · Lovable · v0 · Bolt.new · Replit Agent · GitHub Copilot · Aider · Vercel · Supabase

Roles this course prepares you for

• Indie App Builder ($500–$10,000+/mo depending on product) — Build and ship small web apps, tools, and prototypes using AI coding tools. You describe what you want, review the output, debug issues, and deploy — without writing code from scratch. Apps can be SaaS tools, internal dashboards, landing pages with functionality, or client projects.
• AI Prototyping Consultant ($50–$200/hr or $1,000–$10,000/project) — Help clients turn ideas into working prototypes fast using vibe coding tools. You take a brief, scope it, build a working demo, and hand it off. Clients use your prototype to validate ideas before investing in engineering resources.
• No-Code Product Manager ($80,000–$160,000/yr) — Build internal tools, dashboards, and customer-facing features yourself using AI coding tools — without waiting for engineering bandwidth. You ship functional prototypes that real users can test, then hand validated builds to engineering for production hardening.
• AI Tool Evaluator ($50–$150/hr) — Evaluate new AI coding tools for companies and teams — testing capabilities, security, output quality, and fit for specific use cases. You produce evaluation reports and recommendations that help teams pick the right tool.
• Vibe Coding Auditor ($75–$200/hr or $500–$5,000/audit) — Audit vibe-coded apps for security issues, broken access control, exposed secrets, and production-readiness before they launch. You run the sanity checks from this course at a professional level and produce reports that founders and teams can act on.

What Vibe Coding actually is

Vibe coding is building real software by describing what you want to AI tools, reviewing the output, and shipping — without writing code from scratch. The term was coined by Andrej Karpathy in February 2025. This course teaches a tool-agnostic workflow: plan, describe, build, review, debug, harden, deploy. You learn durable skills that transfer across any AI coding tool, not just one brand.

What you do every day

You open your AI coding tool and describe what you want built. The AI generates code. You review it — looking for red flags, checking that it actually works, asking the AI to explain anything unclear. You iterate with specific corrections. When things break, you predict what's wrong before asking the AI to fix it. You deploy to a real URL. You run a security sanity check before going live. You update your context file so the next session starts from a useful place.

Why this matters now

AI coding tools have reached a level where a person with no coding background can build a working web app in an afternoon. But the gap between a working prototype and a secure, production-ready app is where most people fail. Real vibe-coded apps have leaked 1.5 million API keys, exposed user data across 170+ apps, and been remotely exploited. This course teaches you to build fast AND ship safely — the combination that most vibe coding education misses entirely.

What this course is not

It is not a passive learning experience — every practice rep requires you to open a real AI tool and build something real. It is not a "just let AI do everything" course — the struggle-before-automation principle means you predict, review, and decide before the AI acts. And it is not a replacement for engineering — it is an on-ramp that teaches you the edge of what's safe to do alone and when to hire someone.

Common questions

What background do I need for Vibe Coding?

None. If you can use a web browser and describe what you want in plain English, you can do this course. No coding experience, no math, no technical background required. Every lesson walks you through exactly what to do in your AI tool of choice.

How is this different from the Prompt Engineering Mastery course?

Prompt Engineering Mastery (c9) teaches you to get better results from any AI — for writing, research, content, and everyday tasks. It has a 4-lesson taste of building with AI in phase 5. This course treats building with AI as the entire subject — 49 lessons on planning, building, reviewing, debugging, managing data, deploying, and securing real apps. If c9-05 was the appetizer, this is the full meal.

How is this different from AI Agent Builder?

AI Agent Builder teaches you to build AI agents — chatbots, automations, voice agents — using no-code tools like n8n, Dify, and Vapi. Vibe Coding teaches you to build web apps and software using AI coding tools like Claude Code, Cursor, Lovable, and Bolt.new. Different tools, different outputs, different skills.

Do I need to pay for AI tools?

You can start free. Most tools have free tiers that let you build your first app. For serious building, expect $20-25/month for one tool subscription. The course teaches tool-agnostic categories so you can pick the one that fits your budget and project.

Will I actually build real things?

Yes. Every practice rep produces a real artifact — a build description, a working app, a debug session log, a context file, a security checklist. By the capstone, you have a deployed app with a live URL that real people can use.

Is vibe coding safe? I've heard about security incidents.

This course directly addresses the security risks. Phase 8 covers real vibe-coded security incidents — Moltbook (1.5M API keys exposed), Lovable (inverted access control, 170+ apps), Base44 (authentication bypass), and others — and teaches you a concrete sanity checklist to run before shipping. The course design is informed by the sharpest critique of AI-assisted learning found in research (Boot.dev's "Vibe Coding Hell" essay) and sequences struggle before automation so you build judgment, not just demos.

What if I want to go deeper after this course?

The capstone lesson includes a roadmap to deeper technical courses: ML & AI Engineering (c1) for full Python and ML mastery, Agentic AI Engineering (c2) for building AI agents in code, and Testing AI-Written Code (c8) for professional-grade testing of AI-generated code. This course is the on-ramp; those courses are the highway.

How long does this course take?

40-60 hours of hands-on content. Most learners complete it in 4-8 weeks at 5-8 hours per week. Phases 0-1 (free) can be completed in a weekend and give you your first working app.

Key terms in this course

Agent

Continue your learning path

AI-Native Product Engineering · Full-Stack Development with AI

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