Generating quick programs like "a python script that calculates the mean value of two hex colours, outputting the result as a HTML file displaying the resulting three-color gradient"? Yeah, AI is decent at stupid simple tasks like that, and it's much faster than me writing the script or calculating the values myself. I tend to generate things like these when I'm working on something else, don't want to spend time on things outside the project I'm working on, and can't find a website that does the thing I want.

Touching my actual code? Hell no.

My colleague uses it to generate rambling code, often pointlessly rewriting existing logic to solve all kinds of hallucinated problems, which he doesn't understand a bit, then dumps it on me and acts offended when asked to explain any of it.

I don't.

I played around with it twice, but both times it gave me nonfunctioning code. It seemed stupid to use it when I'd still have to go back and rewrite it anyway.

No

No; I don't use AI at all for programming currently.

No, I don't. I often have to fix the work of my colleague and my boss, who do use it. I often have to gently point out to my boss that just because the chatbot outputs results for things, doesn't mean those results are accurate or helpful.

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 AI as a rubber duck, to compliment the rubber ducks on my desk when they don't give enough feedback. So it's use is mostly conceptual, I find that models that provide "thinking" output perhaps more useful than whatever its actual answer is because it asks questions about edge cases I might not have considered.

As for code generation, I hate it. It outputs garbage, forgets things, hallucinates, and whatever thing it writes I'll have to rewrite anyway to actually make it compile.

As I'm fairly isolated at work I think it makes a good pair programmer partner, so to speak. Offering suggestions that I can take into consideration and research heavily if I think it's a good one.

Ask for oneline problems, that's mostly syntax. Ask for concepts/systems on new knowledge areas To summarise logoutput/debug unknown errortype To add docs and comments, mostly if im in some old code with nothing of the sort. If it absolutely boilerplate, it usually can give fine output.

• I don’t use AI code autocomplete. It was giving me nonsense and interrupts my thought process when I write code. Standard non-AI autocomplete is much better. I tried to use chat to generate medium size logic (up to 100 lines). Mostly it does not work or refining prompt takes more time than writing code myself so I stopped using it for medium sized tasks. I use it for small tasks up to 20 lines where I need an example of how to use specific API. What it does well is generating test cases (not tests themselves). I once tried to summarize set of made up requirements (can elaborate if anyone interested in), it instantly gave me idea of how far we are from AGI as it failed miserably.
• I do not consider for my usage since I use it maybe once-twice a week on average. But generally, I think it’s a huge waste of resources, not only natural but financial and human
• Claude 4 sonnet at work. Mistral for personal curiosity episodes
• I already covered work part. For personal, mostly “searching” random info I couldn’t find via DDG or offloading social rituals such as congratulations

No.

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.

I mostly use it as a code search tool, when dealing with large projects that I'm not very familiar with. Like I can ask "where is this component actually inserted into the web page" and it can sometimes point to a file and function. It doesn't always work of course, but when it does it can save a lot of time.

I don't ever let AI write code for me though

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...

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.

I mostly dislike using AI to code. The one exception I recently got into was when I was fighting with a python script and didn't understand why it was behaving the way it did. I used AI for possible causes and pretty quickly managed to fix it. Sometimes it's just nice to have some possible causes for a bug listed so you can check them out

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.

Mainly use it as a documentation search for APIs I'm not familiar with, or when I'm not sure what options there are to approach a problem. I work with unreal engine a lot, so I'd get a few pointers from an LLM first, then go read the source code of those APIs and inplement the rest myself.

Its sometimes useful for conceptual questions but i dont trust code generated by it.

I use a chat interface as a research tool when there's something I don't know how to do, like write a relationship with custom conditions using sqlalchemy, or I want to clarify my understanding on something. first I do a Kagi search. If I don't find what I'm looking for on Stack Overflow or library docs in a few minutes then I turn to the AI.

I don't use autocompletion - I stick with LSP completions.

I do consider environmental damage. There are a few things I do to try to reduce damage:

1. Search first
2. Search my chat history for a question I've already asked instead of asking it again.
3. Start a new chat thread for each question that doesn't follow a question I've already asked.

On the third point, my understanding is that when you write a message in an LLM chat all previous messages in the thread are processed by the LLM again so it has context to respond to the new message. (It's possible some providers are caching that context instead of replaying chat history, but I'm not counting on that.) My thinking is that by starting new threads I'm saving resources that would have been used replaying a long chat history.

I use Claude 4.5.

I ask general questions about how to do things. It's most helpful with languages and libraries I don't have a lot of experience with. I usually either check docs to verify what the LLM tells me, or verify by testing. Sometimes I ask for narrowly scoped code reviews, like "does this refactored function behave equivalently to the original" or "how could I rewrite this snippet to do this other thing" (with the relevant functions and types pasted into the chat).

My company also uses Code Rabbit AI for code reviews. It doesn't replace human reviewers, and my employer doesn't expect it to. But it is quite helpful, especially with languages and libraries that I don't have a lot of experience with. But it probably consumes a lot more tokens than my chat thread research does.

As for actual coding, I use ChatGPT sometimes to write SDK glue boilerplate or learn about API semantics. For this kind of stuff it can be much more productive than scanning API docs trying to piece together how to write something simple. Like for example, writing a function to check if an S3 bucket is publicly accessible. That would have taken me a lot longer without ChatGPT.

In short: it basically replaced google and stack overflow in my workflow, at least as my first information source. I still have to fall back to a real search engine sometimes.

I do not give LLMs access to my source code tree.

Sometimes I'll use it for ideas on how to write specific SQL queries, but I've found you have to be extremely careful with this use case because ChatGPT hallucinates some pretty bad SQL sometimes.

I am still relatively inexperienced and only embedded. (Electronics by trade) I am working on an embedded project with Zephyr now.

If I run into a problem I kind of do this method (e.g. trying to figure out when to use mutexes vs semaphores vs library header file booleans for checking ):

• first look in the zephyr docs at mutexes and see if that clears it up

• second search ecosia/ddg for things like "Zephyr when to use global boolean vs mutex in thread syncing"

• if none of those work, I will ask AI, and then it often gives enough context that I can see if it is logical or not (in this case, it was better to use a semi-global boolean to check if a specific thread had seen the next message in the queue, and protect the boolean with a mutex to know if that thread was currently busy processing the data), but then it also gave options like using a gate check instead of a mutex, which is dumb because it doesn't exist in zephyr.

For new topics if I can't find a video or application note that doesn't assume too much knowledge or use jargon I am not yet familiar with, I will use AI to become familiar with the basic concept in the terms so that I can then go on to other, better resources.

In engineering and programming, jargon is constant and makes topic introduction quite difficult if they don't explain it in the beginning.

I never use it for code with the exception of codebases that are ingested but with no documentation on all of the keys available, or like in zephyr where macro magic is very difficult to navigate to what it actually does and isn't often documented at all.

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.

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

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.

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.

Sparingly. I use chatgpt to help with syntax and idioms when learning new languages. And sometimes I use it to help determine the best algorithm to use for a general problem. Other times I feed in working code and ask for improvements like a mini code review

The only time I had it code something from scratch for me was when I wanted some Vimscript and I didn't want to learn it. I tried the same thing with jq and it failed and I had to learn me some jq.

I hate popups in editors in general (no intellisense for me), so I lothe AI trying to auto complete my code.

I use AI as a sort of junior developer, I know the problem domain but a bit to lazy to write all the code. I develop on a remote Linux VM with tmux, nvim and opencode. Have the ai tmux session and my development session on a different project. Make sure to have a clean git tree and then I detach from one session to the ai session and check the progress.

The ai makes mistakes so a cautious review of all the code is needed.

I mostly use Claude and I can NOT recommend any kimi k2 model. If you need something okish and cheap use open router gpt-oss 120.

AI is a power tool if you don’t know what are you doing you get burned.

I have used AI to give me the syntax and function names I need, then researched those functions and found better ones instead.

I once asked AI to show me how to do something and it gave me a 20 line script. After 2 hours working with it, I finally got it to work. Another 30 minutes of optimizing and got it down to 3 lines. A bit more research and I discovered that what I wanted was actually a language feature, and I just needed to call a single function with a single argument.

AI occasionally saves me time, and usually causes a significant time waste.

I don't mess with code autocomplete, Cursor, agents or any of that stuff. I've got subscriptions to 2 platforms that give me access to a bunch of different models and I just ask whatever model I need directly, copy/paste the context it needs. On that note, AI search engines like Perplexity genuinely bring zero value to my workflow. I'd rather do the searching myself and feed it the relevant context, feels like it misleads me more often than it helps. I actually have a Perplexity sub (got it free) and haven't touched their web search in like 4 months.

I've thought about the environmental impact and taken steps to minimize my usage. That's actually one reason I avoid Cursor, agents, and AI web search - feels super wasteful and I'm not convinced it's sustainable long-term. I guess I just like being in control, you know? I also try using smaller open source models when I can, even if they're not as powerful.

My go-to models right now for daily use (easiest to hardest tasks): Llama 4 Scout -> DeepSeek v3.1 -> DeepSeek v3.1 (thinking) -> Gemini 2.5 Pro / Claude 4 Sonnet (thinking) -> GPT 5 (thinking). Sometimes I'll throw in other models like Gemini 2.5 Flash but mostly stick to these.

By the way I would recommend trying out t3.chat ( that's one of the platforms that I use). Cost 8 USD / month and is made by Theo pretty happy with it for the price. The UI is honestly its strongest point.

For how I actually use AI, I wrote a more detailed answer in another thread about AI usage. Have a read

I'm using it for some side projects. I used it as an assistant for setting up services in Kubernetes - also used it a lot for debugging and creating kubectl commands.

another side project is to write a full web app in F# SAFE stack, which uses Fable to transpile to JavaScript and React, so I'm learning several things at once.

At work I didn't use it as much, but it got used to generate tests, since no one really cared enough about them. I also did some cool stuff where someone wrote a guide on how to migrate something from a V1 of a thing to a V2 of a thing, I hooked up MCP to link the doc, asked it to migrate one, and it did it perfectly.

I used it a lot to generate Mongo queries, but they put that feature in MongoDB Compass.

We used Claude Sonnet pretty much exclusively. for my side projects I often use Auto since Sonnet burns through the monthly budget pretty quickly. It definitely isn't as good, but it tends to be favorable for template-y things, debugging why some strange thing is happening in F# or react.

For the side projects, I find I'm using it less as I learn more. It's good for getting over a quick hump if you have a sense of how things generally should be.

I've considered the lakes I've burned because I didn't copy paste those kubectl commands to a file.

I prefer Sonnet. Anything less isn't that great, which is one reason I think people hate it.

I tend to use it for crufty things. And certain front end things. It's been a long time since I've done web UI.

Nope, nothing. I wanna have the ability to pinpoint what part of my code is causing what error and offshoring my work off AI would make it both more unfun and harder

Never used it, never will

Yeah, I use Claude/ChatGPT sometimes for:

• Throwaway scripts: "write me a bash script to delete all merged git branches starting with 'foo'"
• Writing functions that are tedious to look up but I can fairly easily evaluate for correctness: "write a C function to spawn a process and capture stdout and stderr merged"
• Doing stuff in systems I'm not very familiar with: "write an OCaml function to copy a file"

I haven't got around to setting up any of that agentic stuff yet. Based on my experience of the chat stuff I'm a bit skeptical it will be good enough to be useful on anything of the complexity I work on. Find for CRUD apps but it's not going to understand niche compiler internals or do stuff with WASM runtimes that nobody has ever done before.

Sometimes it is helpful to summarize large unfamiliar codebases relatively quickly, provide a high level overview, quickly understanding the layout and structure and help me locate the particular areas I'm interested in but I don't really use it to write or modify code directly. It can be good at analyzing logs and datafiles to find problems or patterns or areas that need closer (human) investigation. Even the documentation it produces can sometimes be tolerably decent, at least in comparison to my own which is sometimes intolerably bad or missing completely.

But as far as generating code? I've found the autocomplete largely useless and random. As for chat, where I can direct it more carefully, it might be able to accurately provide a well-known algorithm for something but then will use a mess of variables and inputs that interact with that algorithm in the stupidest ways possible, the more code you ask it to generate the worse it gets, getting painfully overengineered in some aspects and horribly lacking in others. If it even compiles and runs at all. Even for relatively simple find this/replace it with this refactoring I find I cannot fully trust it and rely on the results, so I don't. I'm proficient enough with regex and scripting that I don't find it any faster to walk a generative AI to the result I want while analyzing the fuzzy logic it uses to get there than it is to just write a perfectly deterministic script to do it instead.

As a general rule, I find it is sometimes better at quickly communicating particular things to my manager or other developers than I am, but I am almost always better and quicker at communicating things to computers than it is. That is, after all, my job. Which I happen to think I'm pretty good at.

As for the environmental aspect, that's why I don't use it in my personal life basically at all if I can avoid it. Only at work, and only because they judge my usage of it as part of my performance. I would be just as happy not using it at all for anything. And when I do use it for personal use, which is a point I haven't really reached except for a bit of experimentation and learning, I am never willingly going to use a datacenter-hosted model/service/subscription, I will run it on my own hardware where I pay the bills so I am at least aware of the consequences and in control of the choices it's making.

When I use it, I use it to create single functions that have known inputs and outputs.

If absolutely needed, I use it to refactor old shitty scripts that need to look better and be used by someone else.

I always do a line-by-line analysis of what the AI is suggesting.

Any time I have leveraged AI to build out a full script with all desired functions all at once, I end up deleting most of the generated code. Context and "reasoning" can actually ruin the result I am trying to achieve. (Some models just love to add command line switch handling for no reason. That can fundamental change how an app is structured and not always desired.)

Sometimes. There are some cases where LLMs are better than duckduckgo, like when you're looking for something very specific but 100% of results are about similar but different topic. In 95% of cases search is still faster and more reliable.

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 it as a search engine but not as my only source. It's really good at regurgitating the most relevant Stack Overflow answer I might find, which may or may not actually be applicable to my situation. As a rule I never copy paste code directly, I always rewrite it "in my own words", even in cases where it's basically the same. If the code it provides is more than 5 lines or so I can almost always think of a better way. I feel like I'd still be better off with a really solid reference manual though, and a recipe book. But they go out of date too fast these days.

I use the Jetbrains AI Chat with Claude and the AI autocomplete. I mostly use the AI as a rubber duck when I need to work through a problem. I don't trust the AI to write my code, but I find it very useful for bouncing ideas off of and getting suggestions on things I might have missed. I've also found it useful for checking my code quality but it's important to not just accept everything it tells you.

I use the JetBrain AI like a search engine when the web has no obvious answer. Most of time it gives me a good starting point, and the answer is adjusted to the existing content.

It can also translate snippets from one language or framework to another. For (a fake) example, translating from Unity in Python to Vulkan in C++.

I also use it to analyze shitty code from people who left the company a long time ago. Refactoring and cleaning obscure stuff like deeply hidden variables or things that would take days to analyze can be done in minutes.

I use it once a day at most.

Being able to interrogate a codebase on how it works is incredible

I use the generator function in Databricks for Python to save me a lot of typing but autocomplete functions drive me crazy

Ive used it when I’ve found myself completely stumped with a problem, and I don’t know exactly how to search for the solution. I’m building a macOS app, and unfortunately a lot of the search results are for iOS — even if I exclude iOS from the results (e.g. how to build a window with tabs (like safari tabs), but all results comes up for iOS’ TabView).

My company has internally hosted AI. I use the web interface to copy/past info between it and my IDE. So far I have gotten best results from uploading the official Python documentation and the documentation for the framework I am using. I then specify my requirements, review the output, and either use the code or request a new revision with information on what I want it to correct. I generally focus on requesting smaller focused bits of code. Though that may be for my benefit so I can make sure I understand what everything is doing.

I’ve tried chat prompt coding two ways. One, with a language I know well. It didn’t go well; it insisted that an api existed that had been deprecated since before and removed around 2020, but I didn’t know that and I lost a lot of time. I also lost a lot of time because the code was generally good, but it wasn’t mine so I didn’t have a great understanding of how it flowed. I’m not a professional dev so I’m not really used to reading and expanding on others’ code. However, the real problem is that it did some stuff that was just not real, and it wasn’t obvious. I got it to write tests (something I have been meaning to learn to do) and every test failed; I’m not sure if it’s the test or the code because the priority for me at the time was getting code out, not the tests. I know, I should be better.

I’ve also used it with a language I don’t know well to accomplish a simple task - basically vibe coding. That went OK as far as functionality but based on my other experience it is illegible, questionably written, and not very stable code.

The idea that it’ll replace coders in a meaningful way is not realistic in the current level. My understanding of how LLMs work is incomplete, but i don’t think the hallucinations are easily overcome.

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'm an electrical engineer that has become a proprietary cloud-tool admin. I occasionally use an LLM (chatGPT web) to write VBA code to do various API calls and transform excel/Jason/XML/CSV data from one format to another for various import/export tasks that would otherwise eat up my time.

I just use the chat, and copy/paste the code.

I spend an hour to meticulously describe the process I need the code to do, and then another hour or two testing, debugging and polishing, and get a result that would take me days to produce by myself. I then document the code (I try to use lots of sub-modules that can be reused) so that I can use the LLM less in the future.

I don't feel great about the environment impact, which is why I try to limit the usage, and do debugging and improvements by myself. I'm also trying to push management to invest in a lean LLM that runs on the companies servers. I'm also looking into getting a better PC privately, which I could also run a local LLM on and use for work.

I use autocomplete, asking chat, and full agentic code generation with Cline & Claude.

I don't consider environmental damage by AI because it is a negligible source of such damage compared to other vastly more wasteful industries.

I am primarily interested in text generation. The only use I have for generated pictures, voices, video or music is to fuck around. I think I generated my D&D character portrait. My last portrait was a stick man.

What I ask it to do? My ChatGPT history is vast and covers everything from "how is this word used" to "what drinks can I mix given what's in my liquor cabinet" to "analyze code for me" to "my doctor ordered some labs and some came back abnormal, what does this mean? Does this test have a high rate I'd false positive?" to "someone wrote this at me on the internet, help me understand their point" to "someone wrote this at me in the internet, how can I tell them to fuck the fuck off.... nicely?" And I write atrocious fiction.

Oh I use it a lot to analyze articles and identify bias, reliability, look up other related articles, things that sound bad but really don't mean anything and point out gaps in the journalistic process (I.e. shoddy reporting).

I also have written a discord dungeon master bot. It works poorly due to aggressive censorship and slop on open AI.

Visual Studio provides some kind of AI even without Copilot.

Inline (single line) completions - I not always but regularly find quite useful

Repeated edits continuation - I haven't seen them in a while, but have use them on maybe two or three occasions. I am very selective about these because they're not deterministic like refractorings and quick actions, which I can be confident in correctness even when doing those across many files and lines. For example invert if changes many line indents; if an LLM does that change you can't be sure it didn't change any of those lines.

Multi-line completions/suggestions - I disabled those because it offsets/moves away the code and context I want to see around it, as well as noisy movement, for - in my limited experience - marginal if any use[fulness].

In my company we're still in selective testing phase regarding customer agreements and then source code integration into AI providers. My team is not part of that yet. So I don't have practical experience regarding any analysis, generating, or chat functionality with project context. I'm skeptical but somewhat interested.

I did do private projects, I guess one, a Nushell plugin in Rust, which is largely unfamiliar to me, and tried to make use of Copilot generating methods for me etc. It felt very messy and confusing. Generated code was often not correct or sound.

I use Phind and more recently more ChatGPT for research/search queries. I'm mindful of the type of queries I use and which provider or service I use. In general, I'm a friend of ref docs, which is the only definite source after all. I'm aware of and mindful of the environmental impact of indirectly costly free AI search/chat. Often, AI can have a quicker response to my questions than searching via search ending and on and in upstream docs. Especially when I am familiar with the tech, and can relatively quickly be reminded, or guide the AI when it responds bullshit or suboptimal or questionable stuff, or also relatively quickly disregard the entire AI when it doesn't seem capable to respond to what I am looking for.

I sometimes use a chatbot as a search engine for poorly documented or otherwise hard to find functionality. Asking for how to do x in y usually points me to the right direction.

Do you consider environment damage that use of AIs can cause?

My use is so little that no. For AI bullshit in general, yes.