The first generation of AI coding tools like ChatGPT and V0 created fragmented workflows that required extensive copy-pasting between applications.
Modern AI tools like Cursor and GitHub Copilot Agent are now deeply integrated into IDEs and terminals, eliminating context switching for developers.
The developer workflow has evolved from manually copying code snippets to using AI agents that understand the entire codebase to write, test, and fix code.
Using AI tools introduces new security vulnerabilities and significant data privacy concerns regarding data storage, ownership, and usage that require proactive management.
AI-generated code has improved from unreliable, context-lacking spaghetti code to context-aware output from copilots that understand the entire project.
AI agents can now automate tasks outside the IDE, such as file management and browser control, by integrating with other applications like Slack and Jira.
While AI offers increased speed, developers must remain in control and be wary of vendor lock-in, hidden costs, and the risk of platform shutdowns.
While workflows have shifted from chat windows to terminal agents, core principles like security, data privacy, and strong developer fundamentals remain critical.
A real-world project demonstrates the shift from a frustrating, dependency-heavy process with early AI tools to a streamlined workflow using a modern integrated toolset.
Jobs that call for the skills explored in this talk.