Ask HN: How are you automating your coding work?

For my real work? It has not been helpful so far.

For side projects? It's been a 10x+ multiplier.

I have similar observations. The time saved is things like going to some library I wrote to find the exact order of parameters, or looking up some API on the internet and adjusting my code to it. Inevitably if I did that the old way, I would screw up something trivial and get annoyed.

I rarely let it run for over 10 minutes unattended, but the benefits are not just pure time.

Being able to change the code without getting bogged down allows you to try more things. If I have to wait half an hour between iterations, I'm going to run into bedtime quite fast.

On top of this, I'm finding that the thing that takes the deepest attention is often, amazingly, trivial things. Fiddling with a regex takes attention, but it doesn't often decide the success of the project.

By contrast, the actual work, which is making technical decisions, is something I can do without concentrating in the same way. It's strange that the higher value thing feels less stressful.

Put these together and I'm more productive. I can string together a bunch of little things, and not have to be at my sharpest. Work can be scheduled for whenever, which means days are more flexible. More gets done, with less attention.

ML Engineer here. For coding, I mostly use Cursor/Claude Code as a fast pair. I'll detail what I want at a high level, let it draft, then I incrementally make changes.

Where I've automated more aggressively is everywhere around the code. My main challenge was running experiments repeatedly across different systems and keeping track of the various models I ran and their metrics, etc. I started using Skyportal.ai as an ops-side agent. For me, it's mostly: take the training code I just iterated on, automatically install and configure the system with the right ML stack, run experiments via prompt, and see my model metrics from there.

I haven't automated anything to be honest, but LLMs are invaluable in connecting dots in repositories or exploring dependencies source codes.

The first saves me days of work/month by sparing me endless paper pages of notes trying to figure out why things work in a certain way in legacy work codebases. The second spares me from having to dig too much in partially outdated or lacking documentation or having to melt my brain understanding the architecture of every different dependency.

So I just put major deps in my projects in a `_vendor` directory that contains the source code of the dependencies and if I have doubts LLMs dig into it and their test to shed light.

What I haven't seen anybody yet accomplish is produce quality software by having AI write them. I'm not saying they can't help here, but the bottleneck is still reviewing and as soon as you get sloppy, codebase quality goes south, and the product quality follows soon after.