Ask HN: How do you use AI for software engineering?

This is a common phenomenon with humans, FWIW. The same sort of thing happens with traditional information sources. For example, when a media outlet reports on things we don't know much about personally, we believe them. Then they cover something where we have direct domain expertise and find all manner of misunderstandings and errors trivially, but instead of suspecting that they're likely just as wrong about a lot of other things as well, we assume it's a special case where they just got our special knowledge domain wrong.

In any case, to answer the OP... I use these new AI tools to generate content where the details don't matter and the cost of being wrong is near zero. Such as, graphics for slides, market/product/pitch blurb pablum, etc.

I use them to compensate for my limited artistic/graphic design skills and to overcome my propensity to tediously labor over copy despite that copy being basically throwaway.

The context sensitive stuff like highlighting a line of code and asking questions about it and the completions are okay too but the chat is the most useful part imo.

Also for refactoring/creating tests and writing/fixing docs

About the second point, yes. Sometimes the LLM will create "almost correct" code but you definitely need to understand it and know why it is wrong.

I'm currently dealing with immense legacy tooling (90s to 2010s) and codebases (C, Cpp, C#, Python, JS) in a central team that fell apart a year before I joined. They had 15+ people with diverse profiles who made snippets and undocumented solutions everywhere, and pretty much all quit at the same time due to working conditions at that time. Needless to say it's a daunting task to try and understand everything, even more so when you discover it all as it breaks down and prevents 500 employees from using a functionality you didn't even know existed.

While I can survive and navigate just fine the situation, it's been very helpful to use LLMs to run a parallel effort that helps me understand just why the hell somebody wrote a self-altering stored procedure in a 800GB MsSQL DB which fails due to poor design and high recursion. Sometimes it is just about fixing some syntax, but often enough it is about managing expectations and offering replacement solutions which create less technical debt - with minimal interruption to the entire company's workflow.

It isn't abnormal for my day to require reverse engineering and fixing a 500-line stored procedure, a Go CLI, multi-CI imbricated pipelines, IIS websites, cronjobs, and even c-shell scripts (!). That's when I'll occasionally throw a big pile into chatgpt as a bit of a glorified rubber ducky. Also tried local LLMs but my 3070ti can't sustain any worthwhile model.

Many of us here are in a very unique position of having seen the entire stack - from quantum tunneling all the way up to your CI badly parsing a groovy script or why an endlessly long JS program is stumbling over itself because its writer didn't factor in any estimate of runtime complexity. For me this is a beautiful and lucky thing to see through all those orders of magnitude with a certain equanimity, and I kinda wish I could find it in my own colleagues as much as I'm already seeing it in the illusion of competency created by LLMs.

Yeah, it'll be interesting to see how that changes things.

I think even pre-AI there's already a big difference in old-school programmers and the current generations of graduates.

People like myself who grew up programming in the 8-bit era are used to having to design things from scratch, out of necessity, whether that's custom algorithms or data structures, or something like a parser or editor, or whatever. It seems that current generations are more used to assembling things out of lego blocks - more system integration than what I would consider as programming. I guess this is fine when it works - when you can find the pieces you need, but if that's the only skill you have then there is nothing to fall back on if you need to create something complex truly from scratch.

On one hand, it makes me really think about my first job - when we automated a lot of things, but the people who did it knew how things really worked. After 2 years, all the newcomers only knew if the indicator is red or green without any understanding of what's really going in - it went downhill from here.

On the other hand, if I understand your last paragraph correctly, the real question is do we really need to know all those details ? Would it just be better to have it automated, and the AI agent just ask you questions relating to the desired outcome and outlines the limitations and impacts of your choices, as well as the options available.