Ship It¶
Deployment in Your Environment¶
Your Environment comes with a deployment pipeline already configured. You don't need to set up servers, configure infrastructure, or understand cloud architecture. The hard part is already done.
Here's how the pipeline works. Your AI assistant commits and pushes your changes to the main branch. Every push to main triggers the pipeline, which runs your tests, security scans, and any other checks configured for your project. When the pipeline passes, deployment happens automatically. You don't need to run a deploy command; pushing to main IS deploying.
If something fails, your AI assistant can help you debug. You can ask it things like:
Did my tests pass in the pipeline?
How is my pipeline looking?
In Your AI Assistant
Your workspace has a pre-configured deployment pipeline. Your AI assistant can help you check pipeline status and debug failures, but deployment itself is automatic on every push to main.
Deploy Your Application
Mob Session | ~3 minutes total | One person drives, everyone else navigates.
Rotate the driver. Pick someone who hasn't been at the keyboard recently.
Step 1: Make sure your tests pass. Ask your AI coding assistant:
Run all the tests.
If anything fails, fix it first. Tests gate deployment. Nothing ships with failing tests.
Step 2: Run Save & Sync:
Save my progress and sync it.
Save & Sync pushes your work to the main branch. Every push to main triggers the deployment pipeline, which runs automated checks (tests, security scans) before anything goes live. If the pipeline passes, your application deploys automatically.
The pipeline can take several minutes to run. Don't wait for it. Continue on through the curriculum and check on it when you start your Challenge. If the pipeline failed, ask your AI assistant:
How is my pipeline looking? Did anything fail? Help me fix it.
When the pipeline passes, click Production on your dashboard to see it live. Walk through your Dark Vessel Risk Assessment Tool as a team: scan the traffic display, check the sanctions flags, pull up a vessel's details, investigate a gap history. Exercise the analytical workflow you built across Challenges 1 and 2.
The Journey So Far
Team Discussion | ~2 minutes total
You just shipped a live, tested application.
Discuss: Trace the path of a single acceptance criterion through the lessons. It started as part of a delegation contract (Lesson 1). It became a manual checklist item (Lesson 2). It became an automated test (Lesson 3). And it just gated the deployment of a live application. How does each step build on the last?
What's Next¶
You've come a long way since Lesson 1. You started with the fundamentals: how AI thinks, how to make clear requests, how to write delegation contracts. In Lesson 2, you leveled up: decomposing big goals, encoding processes as skills, and reviewing output against acceptance criteria. Now in Lesson 3, you've automated verification and shipped a live application.
But notice what's still happening: you're working through your backlog one piece at a time. Write a story. Delegate. Wait. Verify. Move on to the next one. AI is faster than you at building, but you're still the bottleneck because you can only focus on one workstream at a time. There's so much more you want to build.
In Lesson 4, you'll break that bottleneck.
Key Insight
Tests gate deployment. They give you the confidence to ship, not just the hope that it works. Your acceptance criteria started as delegation contracts in Lesson 1, became manual checklists in Lesson 2, became automated tests in Lesson 3, and now gate the deployment of a live application. But the real test is whether the people you built it for actually use it.