I don’t use AI when I’m learning a new system, framework or language because I won’t actually learn it.

I don’t use AI when I need to make a small change on a system I know well, because I can make it just as fast and have better insight into how it all works.

I don’t use AI when I’m developing a new system because I want to understand how it works and writing the code helps me refine my ideas.

I don’t use AI when I’m working on something with security or copyright concerns.

Basically, the only time I use AI is when I’m making a quick throw away script in a language I’m not fluent in.

I use continue in VSCode hooked to ollama or mistrial. Sometimes I just ask a chat to “make a script/config that does <my MVP of the project, maybe even less>”.

How much I use depends on how little I am invested. My rule is I try to correct a bad output ONCE. I cannot argue it into fucking getting it right.

I prefer net new code and add this feature. Ironically good refactoring goes a long way. The less it has to adjust the better, and less I have to review the better.

Snippets and architecture design ideas

https://mtg-arena-deck-builder.web.app/ I used 100% Claude for this (im not a programmier)

I tried to use AI to help me code, it only gave me trash.

The only code generation assistance I use is in the form of compilers. For fun I tried to use the free version of Chatgpt to replicate an algorithm I recently designed and after about half-hr I could only get it to produce the same trivial algorithms you find on blog posts even with feeding it much more sophisticated approaches.

Nobody uses “AI” because it doesn’t exist.

Nobody in this thread is talking about any program that’s remotely “intelligent”.

As far as technologies falsely hyped as “AI”, I use google’s search summaries. It’s usually quicker than clicking the actual sources, but I have that option as needed.

I am a data scientist and we use databricks which has copilot (I think) installed by default. So with this we have an autocomplete which I use the most because it can do some of the tedious steps for an analysis if I write good comments which I do anyhow. This is around 50% accurate with it being the most accurate for simple mindless things or getting the name of things correct.

There is code generating block tool that I never use. There is also a something that troubleshoots and diagnosis any error. Those are mostly useless but has been good to finding missing commas and other simple things. Their suggestions sometimes are terrible enough that I mostly ignore this.

We have a Copilot bot as part of our Github (I don’t know is this standard now?) that I actually enjoy and has uses. It writes up great summarizes of what code was commited which has a great format and seems almost 100% accurate for me. Most importantly it has a great spellchecker as part of their suggestions. I am a terrible speller and never double check names so it can fix them both in the notes and in my code (It fixes it everywhere in the code which is nice). The rest of the suggestions are okay. There are some that are useful but some that are way off or overengineered for what I am doing. This I like because it just comes in at the end of my process and I can choose to accept or deny.

-Type in chat, mostly for reference, snippets, help debugging, and questions about libraries or something.

-Not as much as I should

-ChatGPT is my favorite, I have been a user since day one and never tried any other ones.

-As someone who isn’t a leet tier programmer to just help, and write code snippets although I often have to modify it and do things myself as well because something’s the AI will fail at. Also after a certain level of complexity it starts to struggle. Better for snippets and examples but you have to integrate it yourself often times if you are creating something unique that the AI can’t more or less just copy and paste and translate.

I use whatever line completion is built into JetBrains out of the box. Other than that, no AI whatsoever.

Only about 10% of my time at work is actually spent writing code. At least double that time is spent reading code, and the rest is documentation, coordination, and communication work that depends on precise understanding of the code I’m responsible for. If I let AI write code, maybe (doubtfully) that would save a little time out of the 10%, but it would cost me dearly in the other two categories. The code I write by hand is minimal, clear, and easy to understand, and I understand it better because I wrote it myself. I understand all the code around it, too.

If you ask me, AI code generation is based entirely on non-programmers’ incorrect understanding of what programming is.

I use Copilot with mostly Claude Sonnet 4.5. Don’t use the autocomplete because it’s useless and annoying. I mostly chat with it, give it specific instructions for how to implement small changes, carefully review its code, make it fix anything I don’t like, then have it write test scripts with curl calling APIs and other methods to exercise the system in a staging environment and output data so I can manually verify it and make sure all of its changes are working as expected in case I overlooked something in the automated tests.

As far as environmental impact, training is where most of the impact occurs, and inference, RAG, querying vector databases, etc. is fairly minimal AFAIK.

Just intellisense and other language servers. I remember when Microsoft was boasting about how much of their code was generated by intellisense. Now whenever I hear them hype how much ai written code they use I am reminded of it. It’s not an llm but is still a type of ai.

Not a programmer but when I need a more complicated powershell script for something I ask copilot first and then I fix whatever it shits out so that it actually works how I want. It usually saves me some time…

Architect here, not a programmer. I’ve taken Python classes but was never good enough too use it regularly. Using Gemini, I’ve been able to work through creating half a dozen scripts for automating tedious tasks and optimizing models/drawings. I’m hoping to improve myself so I can eventually make use of it for even more useful things, but as a start it’s been awesome. Not perfect, it makes a lot of mistakes, but I’ve been able to work with it to get things right.

Single function text prediction, class boilerplate, some refactoring.

It’s decent when you inherit outrageously bad legacy code and you want better comments and variable names than “A, x, i”, etc.

You do have to do it within an editor that highlights all changes so you can carefully review, though.

Not so much a productivity boost, but rather a bad intern you can delegate boring, easy tasks to. I’d rather review that kind of code than write it, but of you’re the other way around, it’s a punishment.