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AI Universityby Securafy
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Developer Path · Step 6 of 8

MVP to Production (Developer Edition)

Ship your MVP to production with vertical-slice scoping, TDD build loops, and real deployment hardening — not vague AI hype.

Start Free — 14 Days Included with Pro — no per-course charge

What you’ll walk away with

  • Scope features as vertical slices using YAGNI so you build only what ships, not speculative infrastructure
  • Write technical specs that de-risk the build before you or an agent writes a line of code
  • Run a tight TDD build loop with an AI agent, using spikes, bisection, and rubber-ducking to break through stuck points
  • Deploy to Vercel with a real understanding of environments, build pipelines, and when to use Edge vs Node runtimes
  • Harden production apps with authorization, input validation, rate limiting, and proper security headers
  • Set up monitoring and rollback plans, and complete a capstone that exercises the full MVP-to-production pipeline
Production Developer sample certificate

Where this leads

Production Developer

You can take a feature from spec to production with Claude Code — architected, secured, tested, and shipped.

Questions people ask before they start

Do I need prior experience shipping production apps?

You should already have an MVP or be close to one — this course assumes you can build features and focuses on the scoping, spec-writing, TDD, deployment, and hardening steps that take that MVP to production.

Is this specific to Vercel, or does it cover deployment generally?

The deployment lesson is explicitly built around Vercel — envs, build pipelines, and Edge vs Node tradeoffs — so it's most directly useful if you're deploying there, though the underlying scoping, spec, and TDD lessons apply regardless of host.

How does this course actually use AI agents rather than just teaching AI theory?

Agents show up in the TDD build loop lesson and again in the 'Breaking Through' lesson, where you learn spikes, bisection, and rubber-ducking techniques specifically for getting unstuck while working with an agent — it's hands-on workflow, not conceptual overview.

What does the security portion actually cover?

One full lesson is dedicated to production security: authorization, input validation, rate limiting, and security headers — this is Securafy, so the security content is treated as a core build step, not an afterthought.

Is there a final project?

Yes — the last lesson, Launch Engineering, includes monitoring and rollback setup plus a capstone project that ties together scoping, specs, TDD, deployment, and hardening from earlier modules.

Will this teach me general software engineering, or is it AI-specific?

It's grounded in real engineering practices — vertical slicing, YAGNI, TDD, deployment pipelines, security hardening — applied in the context of building with an AI agent, so the skills transfer even if you change your AI tooling later.

Module 1: Adv · MVP to Production

Quiz locked — finish every lesson in this module first (0/7 done)