The AI-Augmented Developer - Mindset and Ecosystem

• How AI is reshaping software development: From IDE helpers to autonomous code agents.

• Overview of the AI dev landscape (Copilot, Gemini, Cursor, Claude, Continue.dev).

• AI coding assistants vs. AI pair programmers vs. AI code agents

• Realistic expectations - what AI can and cannot do (yet).

• Ethics, privacy, and IP considerations in AI-assisted coding.

AI-Assisted Development in Practice

• Integrating AI tools into your daily workflow (IDE plugins, terminals, documentation, tests).

• Prompting for developers — writing effective technical prompts.

• Pair-programming with AI: debugging, refactoring, generating unit tests, code comments, documentation, and API clients.

• Best practices: review discipline, context awareness, data leakage prevention.

• Demo: Building a small REST API using AI assistance only.

• Hands-On:

  • Build and test a CRUD app.

  • Use AI to generate and fix unit tests.

  • Prompt AI to explain legacy code and generate documentation.

“Vibe Coding” — Natural-Language Driven Development

• What is vibe coding and why it’s emerging.

• From Copilot to Devin-style workflows — conversational coding agents.

• Setting up a local vibe coding environment (e.g., Continue.dev, OpenHands).

• When to use vibe coding vs. when to stick to traditional dev workflows.

• Managing the “AI handoff problem”: maintaining generated code, version control, and re-generation strategies.

• Pitfalls: hallucinated logic, security flaws, incomplete edge case handling.

• Hands-On: Use a vibe coding tool to build a simple end-to-end project (e.g., a task tracker). Then, manually review and extend the generated code — showing the “beyond the AI” principle.

The AI-Enhanced Product Lifecycle

• How AI transforms each stage of the SDLC:

• Requirements: AI for user story drafting, acceptance criteria generation, backlog refinement.

• Design: AI-generated architecture diagrams, sequence diagrams, and UX mockups.

• Development: collaborative coding with AI.

• Testing: AI-assisted test plan creation, test data generation, QA automation.

• DevOps: AI in CI/CD pipelines, monitoring, and deployment scripting.

• Maintenance: AI for bug triage, ticket summarization, changelog generation.

Security & Quality Code in AI-Generated Code

• Common vulnerabilities in AI-generated code (injection, missing validation, hardcoded secrets).

• How to perform security audits and static analysis.

• Tools

• Demo (optional):

  • Use AI to generate a login API, then analyze vulnerabilities manually and with a scanner.

  • Use AI again to fix security issues — discuss what it missed.

Advanced Workflows & Automation with AI Agents

• Beyond code generation: agents that can plan, test, refactor, and deploy.

• Using AI to orchestrate workflows (LangChain, LlamaIndex, CrewAI, Agent Builder).

• Building internal AI developer tools and assistants.

• Connecting AI tools to issue trackers (Jira, GitHub), docs, databases, etc.

• Integrating AI with CI/CD pipelines.

• Demo: building an AI Agent workflow

Emerging Trends & The Road Ahead

• From copilots to autonomous dev agents — how fast is it moving?

• Evolution of local LLMs, open-source models, and on-device dev tools.

• Future of IDEs — integrated multimodal assistants (voice, sketch-to-code, screen understanding).

• AI and software architecture evolution.

• Impact on developer roles, hiring, and education.

Final Project

• Pick a small real-world software feature or product concept.

• Use AI tools across the lifecycle: requirements → design → implementation → test → doc → deployment (optional).

• Present outcomes, what worked, what didn’t, and lessons learned (optional).