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

I feel bad saying this because so many folks have not had the best of luck, but it's changed the game for me.

I'm building out large multi-repo features in a 60 repo microservice system for my day job. The AI is very good at exploring all the repos and creating plans that cut across them to build the new feature or service. I've built out legacy features and also completely new web systems, and also done refactoring. Most things I make involve 6-8 repos. Everything goes through code review and QA. Code being created is not slop. High quality code and passes reviews as such. Any pushback I get goes back in to the docs and next time round those mistakes aren't made.

I did a demo of how I work in AI to the dev team at Math Academy who were complete skeptics before the call 2 hours later they were converts.

Around a year ago I started a new position at a very large tech company that I won't name, working on a pre-existing web project there. The code base isn't terrible - though not very good either, by-and-large - but it's absolutely massive, often over-engineered, pretty unorthodox, and definitely has some questionable design decisions; even after more than a year of working with it I still feel like a beginner much of the time.

This year I grudgingly bit the bullet and began using AI tools, and to my dismay they've been a pretty big boon for me, in this case. Not just for code generation - they're really good at probing the monolith and answering questions I have about how it works. Before I'd spend days pouring over code before starting work to figure out the right way to build something or where to break in, pinging people over in India or eastern Europe with questions and hoping they reply to me overnight. AI's totally replaced that, and it works shockingly well.

When I do fall back on it for code generation, it's mostly just to mitigate the tedium of writing boilerplate. The code it produces tends to be pretty poor - both in terms of style and robustness - and I'll usually need to take at least a couple of passes over it to get it up to snuff. I do find this faster than writing everything out by hand in the end, but not by a lot.

For my personal projects I don't find it adds much, but I do enjoy rubber ducking with ChatGPT.

I have good success using Copilot to analyze problems for me, and I have used it in some narrow professional projects to do implementation. It's still a bit scary how off track the models can go without vigilance.

I have a lot of worry that I will end up having to eventually trudge through AI generated nightmares since the major projects at work are implemented in Java and Typescript.

I have very little confidence in the models' abilities to generate good code in these or most languages without a lot of oversight, and even less confidence in many people I see who are happy to hand over all control to them.

In my personal projects, however, I have been able to get what feels like a huge amount of work done very quickly. I just treat the model as an abstracted keyboard-- telling it what to write, or more importantly, what to rewrite and build out, for me, while I revise the design plans or test things myself. It feels like a proper force multiplier.

The main benefit is actually parallelizing the process of creating the code, NOT coming up with any ideas about how the code should be made or really any ideas at all. I instruct them like a real micro-manager giving very specific and narrow tasks all the time.

Sounds like tech debt as a service. If the code review is automated, how can you be sure the code isn't full of security or maintanability issues?

It’s not really that useful for what people tell me it will be useful for, most of the time. For context, I am a senior engineer that works in fintech, mostly doing backend work on APIs and payment rail microservices.

I find the most use from it as a search engine the same way I’d google “x problem stackoverflow”.

When I was first tasked with evaluating it for programming assistance, I thought it was a good “rubber duck” - but my opinion has since changed. I found that if I documented my goals and steps, using it as a rubber duck tended to lead me away from my goals rather than refine them.

Outside of my role they can be a bit more useful and generally impressive when it comes to prompting small proof of concept applications or tools.

My general take on the current state of LLMs for programming in my role is that they are like having a junior engineer that does not learn and has a severe memory disorder.