I Finally Sat Down With AI. Here's What I Think So Far

I'll be honest, I was late to this. While the tech world has been losing its collective mind over large language models, I was heads-down building things the way I've always built them: my IDE, a terminal, and twenty years of muscle memory. I'd used ChatGPT a handful of times for the kind of things everyone uses it for: rewriting an email, asking a question I didn't feel like Googling, but I'd never seriously explored what these tools could do for my actual work.

So I finally decided to sit down and really dig in. Not casually, deliberately. I wanted to understand what AI could and couldn't do for someone like me: a senior developer building real software, not a hobbyist asking it to write a todo app.

What prompted the deep dive

Partly curiosity, partly pragmatism. I kept hearing developers I respect talk about AI not as a novelty but as a genuine part of their workflow. Not "AI wrote my app for me" hype, but quieter observations; that it was saving them real time on real tasks. I figured I owed it to myself to form my own opinion rather than relying on secondhand impressions.

What I found

I tried several tools, but the one that caught my attention was Claude from Anthropic. What stood out wasn't just the quality of the output it was the way it handled context. I could describe a problem in detail, including constraints and tradeoffs, and get back a response that actually engaged with the nuance rather than pattern-matching to the most common answer. It felt less like talking to an autocomplete engine and more like thinking out loud with a knowledgeable colleague.

A few things genuinely surprised me:

It understands Laravel. Not in a surface-level "here's a basic route" way. I threw real architectural questions at it: service container bindings, job batching patterns, Eloquent relationship edge cases and got responses that were not only correct but well-reasoned. It understood why you'd choose one approach over another, not just how to implement it.

It's useful for the boring parts. Writing tests for existing code, drafting documentation, generating boilerplate, scaffolding database migrations from a description, the tasks that eat time without requiring creative thought. This is where the productivity gain is most immediately obvious.

It doesn't replace judgment. The times it was most wrong were when the answer required context it didn't have such as business constraints, team dynamics, the history of a particular codebase. It generates plausible solutions, not necessarily appropriate ones. The developer still needs to evaluate, and that evaluation is the hard part.

Where I'm headed next

I've started looking into Claude Code, Anthropic's command-line tool for agentic coding tasks. The idea of delegating implementation work directly from the terminal while staying in my normal development flow is compelling. I haven't formed a strong opinion yet because I'm still early in exploring it, but the concept aligns with how I actually want to work: not in a chat window, but in my editor and terminal.

I'm not ready to make sweeping claims about AI replacing developers or transforming the industry overnight. I've been around long enough to have seen a few "everything changes now" moments that turned out to be incremental at best. But I will say this: after a month of serious exploration, I'm more interested than skeptical. The potential here is real, and I plan to keep pulling on this thread.

More thoughts to come as I dig deeper.