We vibe around a lot in our heads and that's great. But it's really refreshing, every so often, to be where the rubber meets the road.

I think you have every right to doubt those telling us that they run 5 agents to generate a new SAAS-product while they are sipping latté in a bar. To work like that I believe you'll have to let go of really digging into the code, which in my experience is needed if want good quality.

Yet I think coding agents can be quite a useful help for some of the trivial, but time consuming chores.

For instance I find them quite good at writing tests. I still have to tweak the tests and make sure that they do as they say, but overall the process is faster IMO.

They are also quite good at brute-forcing some issue with a certain configuration in a dark corner of your android manifest. Just know that they WILL find a solution even if there is none, so keep them on a leash!

Today I used Claude for bringing a project I abandoned 5 years ago up to speed. It's still at work in progress, but the task seemed insurmountable (in my limited spare time) without AI, now it feels like I'm half-way there in 2-3 hours.

Outsourcing this to an LLM is similar to an airplane stall .. I just dip mentally. The stress goes away too, since I assume the LLM will get rid of the "problem" but I have no more incentives to think, create, solve anything.

Still blows my mind how different people approach some fields. I see people at work who are drooling about being able to have code made for them .. but I'm not in that group.

I also use these things to just plan out an approach. You can use plan mode for yourself to get an idea of the steps required and then ask the agent to write it to a file. Pull up the file and then go do it yourself.

Sometimes the AI does weird stuff too. I wrote a texture projection for a nonstandard geometric primitive, the projection used some math that was valid only for local regions… long story. Claude kept on wanting to rewrite the function to what it thought was correct (it was not) even when I directed to non related tasks. Super annoying. I ended up wrapping the function in comments telling it to f#=% off before I would leave it alone.

With AI, the correct approach is to think more like a software architect.

Learning to plan things out in your head upfront without to figure things out while coding requires a mindset shift, but is important to work effectively with the new tools.

To some this comes naturally, for others it is very hard.

How I see it is we've reverted back to a heavier spec type approach, however the turn around time is so fast with agents that it still can feel very iterative simply because the cost of bailing on an approach is so minimal. I treat the spec (and tests when applicable) as the real work now. I front load as much as I can into the spec, but I also iterate constantly. I often completely bail on a feature or the overall approach to a feature as I discover (with the agent) that I'm just not happy with the gotchas that come to light.

AI agents to me are a tool. An accelerator. I think there are people who've figured out a more vibey approach that works for them, but for now at least, my approach is to review and think about everything we're producing, which forms my thoughts as we go.

This is the opinion of someone who has not tried to use Claude Code, in a brand new project with full permissions enabled, and with a model from the last 3 months.

Don't get me wrong, I do think AI coding is pretty dangerous for those without the right expertise to harness it with the right guardrails, and I'm really worried about what it will mean for open source and SWE hiring, but I do think refusing to use AI at this point is a bit like the assembly programmer saying they'll never learn C.

https://news.ycombinator.com/item?id=46827777

“Break things down” is something most of us do instinctively now but it’s something I see less experienced people fail at all the time.

i have written this text by myself except like 2 or 3 sentences which i iterated with an LLM to nail down flow and readability. I would interpret that as completely written by me.

> The suggestions you make are all sensible but maybe a little bit generic and obvious. Asking ChatGPT to generate advice on effectively writing quality code with AI generates a lot of similar suggestions (albeit less well written).

Before i wrote this text, i also asked Gemini Deep Research but for me the results where too technical and not structural or high level as i describe them here. Hence the blogpost to share what i have found works best.

> If not, I'd suggest to augment the post with practical examples or anecdotal experience. At the moment, the target group seems to be novice programmers rather than the typical HN reader.

I have pondered the idea and also wrote a few anecdotal experiences but i deleted them again because i think it is hard to nail the right balance down and it is also highly depended on the project, what renders examples a bit useless.

And i also kind of like the short and lean nature of it the last few days when i worked on the blogpost. I might will make a few more blogposts about that, that will expand a few points.

Thank you for your feedback!

I find rust generally easier to reason about, but can't stand writing it.

The compiler works well with LLMs plenty of good tooling and LSPs.

If I'm happy with the shape of the code and I usually write the function signatures/ Module APIs. And the compiler is happy with it compiling. Usually the errors if any are logical ones I should catch in reviews.

So I focus on function, compiler focuses on correctness and LLM just does the actual writing.