AI-Powered Software Engineer

Learn why TDD exists, how it fits into modern software practices, and what types of tests (unit, integration, end-to-end) support a robust testing culture.

Gain hands-on experience using pytest to write simple, test-first Python code. Follow the arrange-act-assert framework for unit testing. Map tests to CRUD operations—create, read, update, delete.

Apply best practices to name, group, and organize tests. Utilize advanced pytest techniques, including fixtures, parameterization, and mocking.

Diagnose failures: read errors, debug with breakpoints, and find root causes. Then go practical—write integration tests against real APIs, track coverage, and use pre-flight checklists before release.

Learn what design patterns are, why they exist, and how they connect to SOLID principles to help you design maintainable, testable, and scalable software.

Apply creational patterns—Singleton, Factory Method, and Builder—to design flexible solutions where object creation should be separated from usage.

Use Adapter, Decorator, and Composite patterns in Python to modify or combine objects without changing their internal logic.

Learn how Observer, Strategy, and Command patterns support delegation, flexible behavior, and decoupled object interactions.

Apply dependency injection to decouple objects, support testing, and enable clean, flexible implementations of modern design patterns.

Define what software architecture is and why it matters. Explore foundational patterns, including MVC, layered, and event-driven architectures, to build intuition for structuring real-world systems.

Turn architectural ideas into clear diagrams. Learn when to use sequence, event flow, and system diagrams—and how to create them with Mermaid and D2.

Design modular systems with microservices. Explore when to split systems apart, how services communicate, and the tradeoffs around resilience, consistency, and performance.

Don't treat the cloud as an afterthought. Design cloud-native systems using 12-factor principles, stateless services, scalable architectures, and cost-efficient patterns.

Design data, machine learning, and AI systems by choosing the right storage, pipelines, and architectures—covering batch vs. streaming data, model serving, orchestration, and observability.

Explore distributed system patterns through CDNs, IoT networks, and blockchain architectures, learning how specialized constraints shape real-world architectural decisions.

Systematically review code—AI or human—by applying the quality pyramid, spotting universal and AI-specific red flags, and practicing critical code review skills for production readiness.

Learn to identify and address five common patterns in AI-generated code, assess code quality, and apply systematic review to ensure reliable AI collaboration in development.

Master proactive, structured prompting to prevent AI code issues. Use templates, clarity, examples, and constraints for higher-quality, efficient, and consistent AI collaboration.

Assess security, ethical, and reliability risks in AI-assisted code, and understand your professional responsibility and limits of trust when approving code for production.

Learn to collaborate with AI throughout the development cycle—planning, coding, testing, refactoring, and documenting—while maintaining engineering control and standards.