Ask HN: How is AI-assisted coding going for you professionally?

i'm a senior engineer at a mid-size, publicly traded company.

my team has largely avoided AI; our sister team has been quite gungho on it. i recently handed off a project to them that i'd scoped at about one sprint of work. they returned with a project design that involved four microservices, five new database tables, and an entirely new orchestration and observability layer. it took almost a week of back-and-forth to pare things down.

since then, they've spent several sprints delivering PRs that i now have to review. there's lots of things that don't work, don't make sense, or reinvent things we already have from scratch. almost half the code is dedicated to creating 'reusable' and 'modular' classes (read: boilerplate) for a project that was distinctly scoped as a one-off. as a result, this takes hours, and it's cut into my own sprint velocity. i'm doing all the hard work but receiving none of the credit.

management just told me that every engineer is now required to use AI. i'm tired.

FWIW I mainly use Opus 4.6 on the $100/mo Max plan, and rarely run into these issues. They certainly occur with lower-tier models, with increased frequency the cheaper the model is - as for someone using it for a significant portion of their professional and personal work, I don’t really understand why this continues to be a widespread issue. Thoroughly vetting Plan Mode output also seems like an easy resolution to this issue, which most devs should be doing anyways IMO (e.g. `npm install random-auth-package`).

TBH it kinda makes sense why personal projects are where productivity jumps are much larger.

Working on projects within a firm is... messy.

Tools: Claude Code and various VS Code derivatives, and Cursor at work. Generally Opus 4.6 now.

I feel it made me better and other people worse.

GOOD:

I feel that I’m producing more and better code even with unfamiliar and tangled codebases. For my own side projects, it’s brought them from vague ideas to shipped.

I can even do analyses I never could otherwise. On Friday I converted my extensive unit test suite into a textual simulation of what messages it would show in many situations and caught some UX bugs that way.

Cursor’s Bugbot is genuinely helpful, though it can be irritatingly inconsistent. Sometimes on round 3 with Bugbot it suddenly notices something that was there all along. Or because I touch a few lines of a library suddenly all edge cases in that library are my fault.

NOT GOOD:

The effect on my colleagues is not good. They are not reading what they are creating. I get PRs that include custom circular dependency breakers because the LLM introduced a circular dependency, and decided that was the best solution. The ostensible developer has no idea this happened and doesn’t even know what a circular dependency breaker is.

Another colleague does an experiment to prove that something is possible and I am tasked to implement it. The experiment consists of thousands of lines of code. After I dig into it I realize the code is assuming that something magically happened and reports it’s possible.

I was reflecting on this and realized the main difference between me and my current team is that I won’t commit code I don’t understand. So I even use the LLMs to do refactors just for clarity. while sometimes my colleagues are creating 500-line methods.

Meanwhile our leaders are working on the problem of code review because they feel it’s the bottleneck. They want to make some custom tools but I suspect they are going to be vastly inferior to the tools coming from the major LLM providers. Or maybe we’ll close the loop and we won’t even be reviewing code any more.

OP compared AI to interns, and how they need to guide it and instuct it on simple things, like using unit tests. Well, what about when AI is actually more like an ultra talented programmer. What exactly would OP bring to the table apart from being able to ask it to solve a certain problem?

Their comment about people who don't operate like them being out of a job might be true if AI doesn't progress past the current stage but I really don't see progress slowing down, at least in coding models, for quite some time.

So, whatever relevance OPs specific methods have right now will quickly be integrated into the models themselves.