What AI Coding Tools Are Good (and Bad) At

AI changed the job. It didn't remove it.

AI coding assistants (chat tools, editor autocomplete, coding agents) are now part of most developers' daily work. They can write a working function in seconds. So why learn to code?

Because someone has to decide what to build, check that the code is right, and fix it when it isn't. That person gets hired.

What AI is great at

• Boilerplate and common patterns (forms, API calls, tests)
• Explaining unfamiliar code or error messages
• Suggesting approaches and alternatives
• Translating between languages
• Speeding up the boring parts so you can focus on the hard parts

Where AI gets it wrong

• Subtle bugs: off-by-one errors, missed edge cases, wrong boundary conditions
• Out-of-date APIs: code for library versions that no longer exist
• Confident mistakes: plausible code that doesn't do what you asked
• Missing context: it doesn't know your product, users or constraints unless you say so
• Security: unsafe patterns such as SQL injection or leaked secrets

The developer's job now

Before	Now
Type every line	Direct, review and verify
Remember syntax	Understand concepts deeply enough to judge
Write code	Own outcomes: correctness, security, maintainability

Everything you learn in this program (fundamentals, debugging, testing, Git) is what lets you use AI safely and effectively. That's exactly what interviewers look for in a junior developer today.

How we use AI here: The AI tutor in your exercises gives hints instead of answers. Learning happens when you do the thinking. Use AI to unblock yourself, not to skip the struggle.