A practical, 5-hour master lesson on AI-assisted development and DevOps. From rapid app development with Claude Code and Cursor IDE through automated testing and full CI/CD pipelines — bridging AI-assisted coding and production-ready DevOps practices.
This intensive master lesson transforms how development teams leverage AI throughout the entire software development lifecycle. Participants master Claude Code and Cursor IDE for rapid application development, documentation and debugging, then extend these skills into automated testing and CI/CD pipelines.
The course bridges the gap between AI-assisted coding and production-ready DevOps practices — connecting AI-driven development to real-world, automated delivery.
A continuous path from AI pair-programming through testing, security and a complete CI/CD pipeline — every module hands-on.
The modern AI development landscape and the role of AI pair programming. Set up Claude Code and Cursor IDE, configure API keys and workspace, and establish best practices for AI-assisted development.
Focus · Configured AI dev environmentAI-assisted full-stack development — craft effective prompts for complex features, scaffold multi-file projects, and generate REST APIs, database schemas, frontend components and authentication flows.
Focus · Production-quality code generationHarness AI to auto-generate API docs, README files, inline comments, ADRs and user guides — keeping consistent, high-quality, living documentation as code evolves.
Focus · Living documentationAI-assisted debugging and code improvement — quickly identify and fix bugs, interpret error messages and stack traces, detect logic and performance issues, and refactor legacy code.
Focus · Rapid AI debuggingExtend AI with external knowledge via Model Context Protocol — Microsoft Learn Context, Context7, GitHub MCP and database MCPs configured in Claude Code and Cursor for context-aware features.
Focus · MCP-powered contextWhere AI enhances the SDLC — modern DevOps practices, continuous integration fundamentals, the testing pyramid (unit, integration, E2E, security) and GitHub Actions architecture.
Generate high-quality unit tests with AI — test structure, mocking, async handling and coverage — plus edge cases, test data and AI-assisted TDD workflows for robust, maintainable code.
Focus · High-coverage TDDLeverage AI for E2E testing — browser automation, selectors, assertions and organization — generating tests from user stories, handling dynamic content and visual regression across browsers.
Focus · AI-generated E2E suitesAI-driven security and penetration testing — OWASP Top 10, common web vulnerabilities, auth testing and dependency scanning with OWASP ZAP, npm audit and Snyk.
Focus · Automated security testingBuild end-to-end CI/CD pipelines with GitHub Actions — workflow syntax, triggers, jobs, secrets and environments — using AI to automate tests, builds and reporting, add quality gates, caching and parallel jobs.
Focus · Complete automated pipelineThis master lesson is for developers, engineers, QA professionals and DevOps practitioners who want to put AI to work across the full software lifecycle — from writing code to running automated pipelines.
Across five intensive hours you'll move from AI pair-programming to documentation, debugging, testing, security and a complete CI/CD pipeline. If you want practical, production-ready AI-assisted DevOps skills you can apply the next day, this masterclass is for you.
The AI development landscape, Claude Code and Cursor IDE setup, API and workspace configuration.
Generate production-quality full-stack code with AI — APIs, schemas, components and auth flows.
Auto-generate API docs, READMEs, inline comments and ADRs as living documentation.
Identify and fix bugs fast, interpret stack traces and refactor legacy code with AI.
Extend AI with MCP servers — MS Learn Context, Context7, GitHub MCP and database MCPs.
The modern DevOps lifecycle, where AI fits, and an overview of the testing pyramid.
Generate comprehensive unit tests with AI, achieve high coverage and run TDD workflows.
Create E2E tests, generate user-journey tests with AI, and implement visual regression.
Automated security testing — OWASP Top 10, vulnerability detection and pen-test scenarios.
Build complete CI pipelines, automate testing, building and reporting, and implement quality gates.
Missed the masterclass? Catch the sessions on demand, then keep leveling up your AI-assisted DevOps — bring these practices to your team with a corporate program, or skill up on your own with an individual training path.