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I found an easier way that Works For Me (TM). I describe the problem to LLM and ask it to solve it step by step, but strictly in the Ask mode, not Agent. Then I copy or even type the linws to the code. If I wouldn't write the line myself, it doesn't go in, and I iterate some more.

I do allow it to write the tests (lots of typing there), but I break them manually to see how they fail. And I do think about what the tests should cover before asking LLM to tell me (it does come up with some great ideas, but it also doesn't cover all the aspects I find important).

Great tool, but it is very easy to be led astray if you are not careful.

I wonder at the end of this if it's the still worth the risk?

A lot of how I form my thoughts is driven by writing code, and seeing it on screen, running into its limitations.

Maybe it's the kind of work I'm doing, or maybe I just suck, but the code to me is a forcing mechanism into ironing out the details, and I don't get that when I'm writing a specification.

The real value that AI provides is the speed at which it works, and its almost human-like ability to “get it” and reasonably handle ambiguity. Almost like tasking a fellow engineer. That’s the value.

By the time you do everything outlined here you’ve basically recreated waterfall and lost all speed advantage. Might as well write the code yourself and just use AI as first-pass peer review on the code you’ve written.

A lot of the things the writer points out also feel like safeguards against the pitfalls of older models.

I do agree with their 12th point. The smaller your task the easier to verify that the model hasn’t lost the plot. It’s better to go fast with smaller updates that can be validated, and the combination of those small updates gives you your final result. That is still agile without going full “specifications document” waterfall.

Remember having to write detailed specs before coding? Then folks realized it was faster and easier to skip the specs and write the code? So now are we back to where we were?

One of the problems with writing detailed specs is it means you understand the problem, but often the problem is not understand - but you learn to understand it through coding and testing.

So where are we now?

How to write good code with AI -> put in as much effort as you did before on 20% more code than you used to work with.

Some pattern I found from my hobby project.

1. Keep things small and review everything AI written, or 2. Keep things bloated and let AI do whatever it wants within the designated interface.

Initially I drew this line for API service / UI components, but it later expanded to other domains. e.g. For my hobby rust project I try to keep "trait"s to be single responsible, never overlap, easy to understand etc etc. but I never look at AI generated "impl"s as long as it passes some sensible tests and conforming the traits.

Every engineering org should be pleading devs to not let AI write tests. They're awful and sometimes they literally don't even assert the code that was generated and instead assert the code in tests.

First article about writing code with AI i can get behind 100%. Stuff i already do, stuff i've thought about doing, and at ideas i've never thought doing ("Mark code review levels" especially is a _great_ idea)

Sounds like an awful lot of work and nannying just to avoid writing code yourself. Coding used to be fun and enjoyable once...

Too bad that software developers are carrying water for those who hate them and mock them for being obsolete in 6-12 months, while they are eating caviar (probably evading sanctions) and clink the champagne glasses in Davos:

https://xcancel.com/hamptonism/status/2019434933178306971

And all that after stealing everyone's output.

Hi i5heu. Given that you seem to use AI tools for generating images and audio versions of your posts, I hope it is not too rude to ask: how much of the post was drafted, written or edited with AI?

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).

If this was written with help of AI, I'd personally appreciate a small notice above the blog post. 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.

> Use strict linting and formatting rules to ensure code quality and consistency. This will help you and your AI to find issues early.

I've always advocated for using a linter and consistent formatting. But now I'm not so sure. What's the point? If nobody is going to bother reading the code anymore I feel like linting does not matter. I think in 10 years a software application will be very obfuscated implementation code with thousands of very solidly documented test cases and, much like compiled code, how the underlying implementation code looks or is organized won't really matter

That sounds like the advice of someone who doesn't actually write high-quality code. Perhaps a better title would be "how to get something better than pure slop when letting a chatbot code for you" - and then it's not bad advice I suppose. I would still avoid such code if I can help it at all.

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