Most developers go in expecting magic. They come out wondering why their app still breaks.
I spent a full month using AI coding assistants as my main workflow tool. The speed on boilerplate code alone made the experiment worthwhile. Tasks like writing utility functions and drafting unit tests used to eat 30-40 minutes. Now they wrap up in under five.
But here's the thing nobody warns you about: the confidence problem. These tools produce code that looks right at a glance. Run it, though, and you might spend longer debugging than if you'd written it yourself. That's the real kicker.
The workflow that actually held up was treating the AI like a junior developer. Give it clear, specific instructions. Smaller, focused tasks work better than vague, open-ended ones. Never accept the first suggestion without reviewing it, and honestly, most people skip that last part.
Knowing where it breaks down matters just as much. Contextual bugs and real architectural decisions still require you to sit down and think it through yourself. No shortcut around that kind of reasoning.
For developers at any skill level, these tools are worth trying. I went in skeptical and came out a convert, sort of. Go in with realistic expectations and you will actually get something out of them.
The confidence problem runs deeper than it looks. AI is optimised for plausibility, not correctness. The code looks structured, compiles fine, variable names make sense. You're reading in review mode, not debugging mode.
"Treat it like a junior dev" is right, but juniors at least understand the system. AI doesn't know your auth context, your state decisions, or the two hacks from six months ago everything quietly depends on. That context gap is where the real risk lives.
It all depends how how you use the ai to code If you plan before hand all possible vulnerabilities the chances of breaking will be very low
Brilliant insights! Honest, practical, and incredibly helpful. Loved this perspective immensely!
This is so accurate 👍
That “confidence problem” you mentioned is real — the code looks perfect, but the hidden bugs cost more time later. Treating AI like a junior dev is honestly the best way to use it.
Totally agree on smaller tasks too — AI shines there, but for bigger decisions, you still need your own thinking.
dolobanko
This matches a lot of what I have seen while building my tool for AI.
The speed boost is real, but the confidence gap is the part people underestimate. AI can get you to looks correct very fast, but that’s not the same as is correct — especially once the task touches real logic, context, or architecture.
Treating it like a junior developer is probably the healthiest mental model I’ve found too: useful, fast, often impressive, but still something you need to review, guide, and verify.