In this first module, you’ll get a practical understanding of the top AI tools for writing and maintaining code. We’ll concentrate on code assistants, automated testing tools, and other utilities to help you code faster and with fewer errors. We’ll also briefly introduce two emerging trends—documentation-first and vibe coding—to give you fresh perspectives on how to collaborate with AI.
By the end of this module, you’ll be able to:
Identify the main code assistants and use them within your IDE to speed up coding.
Recognize when and how to leverage AI for automated testing.
Understand basic security and licensing requirements when adopting AI tools.
Each lesson contains:
Video Lecture
Hands-On Coding Session:
Activities & Assignments: Practice exercises to apply your knowledge.
Quiz: Short quiz to test your understanding.
Resources: Links to external documentation and courses for deeper learning.
Quick overview of popular code assistants (Copilot, CodeWhisperer, Tabnine, Cursor, V0).
Practical examples of how to invoke suggestions in different IDEs (VS Code, IntelliJ, etc.).
Local vs. SaaS integration: pros and cons (speed, configuration, cost).
A peek at Meticulous and similar tools for basic automated testing without leaving your coding flow.
Documentation-First:
Generating the structure or specification of a feature (endpoints, classes, functions) before coding.
How having a prior outline helps the AI produce more accurate suggestions.
Vibe Coding:
A more fluid, co-creative development style with AI, where you don’t have rigid specs but iterate rapidly.
Real-world examples: quick prototypes of microservices or functions in an “exploratory” mode.
Security & Confidentiality:
Keeping assistants from exposing credentials or sensitive data.
Privacy and repository setup for production environments.
Costs & Business Models:
Subscriptions vs. pay-per-use (tokens) vs. self-hosted solutions.
Impact on large teams vs. personal projects.
Ethics & Licensing:
Risks of inadvertently copying licensed code.
Internal review policies: always scanning AI output to avoid plagiarism.
Video lectures and downloadable materials are organized in the dashboard.
Hands-on labs and coding environments are integrated (e.g., AWS, Jupyter).