I'll be honest. A year ago, if you told me I'd be having full conversations with an AI while building features at work, I would have laughed. Not because I didn't believe the tech was coming, but because I didn't think it would actually be useful in the messy, context-heavy, "why is this CSS not working" reality of day-to-day development.
I was wrong.
I now use AI tools almost every day. Not as a replacement for thinking, but as something closer to a really fast colleague who never gets annoyed when I ask dumb questions at 11 PM. But I've also learned where it falls apart, where it confidently leads you off a cliff, and where I personally choose to not use it at all.
What Actually Works
Let me start with the stuff that has genuinely changed how I work. Not in a "this is the future" hype way, but in a "this saved me 45 minutes today" way.
Debugging Partner
This is the single biggest win. When I'm staring at an error message that makes no sense, or a component that renders fine locally but breaks in production, explaining the problem to an AI often gets me to the answer faster than StackOverflow ever did.
We had a page that got progressively slower the longer a user kept it open. No errors, no warnings, just gradual performance degradation. I described the component tree and the observable patterns we were using, and Claude caught that a switchMap inside a nested subscription wasn't completing when the parent component destroyed, because the outer observable was tied to a shared service that lived outside the component lifecycle. The subscription kept piling up silently. Not something you'd catch in a code review unless you were specifically looking for it.
The key here is that AI doesn't just search for your error message. It reasons about the interaction between different parts of your code. That's the difference.
Boilerplate and Repetitive Code
I write a lot of Angular components at work and React components for my personal projects. The amount of boilerplate involved in setting up a new component, a service, a route configuration, a form with validation, is significant. AI handles this extremely well.
I describe what I need in plain English. "Create an Angular component that takes a list of items and displays them in a table with sorting and a tooltip on each status column." And I get back something that's 80-90% correct. The remaining 10-20% is where my actual expertise comes in, adjusting it to fit our codebase, our styling conventions, our state management patterns.
That last bit is important. AI gives you a starting point. Your job is to shape it into something that belongs in your project.
Learning New Concepts
When I was exploring machine learning, I worked through algorithms like logistic regression, KNN, and Naive Bayes. AI was incredibly helpful here, not to write the code for me, but to explain the intuition behind the math.
"Why does KNN struggle with high-dimensional data?" is the kind of question where a textbook gives you a formal answer and AI gives you an analogy that actually clicks. Both are useful, but when you're learning something new and just need to build intuition, the conversational explanation is faster.
Same thing happened when I was setting up my blog with Next.js and MDX. I had questions about static generation, dynamic routes, metadata APIs. Instead of reading through three different docs pages and piecing it together, I could ask one question and get a focused answer with context.
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