I have a solution for that, I just need a small loan of a billion dollars and 5 years. #trustmebro

I hate the simulated intelligence nonsense at least as much as you, but you should probably know about this if you’re saying you can’t 3d print a house: https://youtu.be/vL2KoMNzGTo

You’re making a great analogy with the 3D printing of a house.

However, if we consider the 3D printed house scenario; that skilled craftsman is now able to do things on his own that he would have needed a team for in the past. Most, if not all, of the less skilled members of that team are not getting any experience within the craft at that point. They’re no longer necessary when one skilled person can now do things on their own.

What happens when the skilled and highly experienced craftsmen that use AI as a supplemental tool (and subsequently earn all the work) eventually retire, and there’s been no juniors or mid-levels for a while? No one is really going to be qualified without having had exposure to the trade for several years.

3d printed concrete houses exist. Why can't you 3d print a house? Not the best metaphor lol

Vibe coding

The term for that is actually 'slopping'. Kthx ;-)

I've found them useful, sometimes, but nothing like a fraction of what the hype would suggest.

They're not adequate replacements for code reviewers, but getting an AI code review does let me occasionally fix a couple of blunders before I waste another human's time with them.

I've also had the occasional bit of luck with "why am I getting this error" questions, where it saved me 10 minutes of digging through the code myself.

"Create some test data and a smoke test for this feature" is another good timesaver for what would normally be very tedious drudge work.

What I have given up on is "implement a feature that does X" questions, because it invariably creates more work than it saves. Companies selling "type in your app idea and it'll write the code" solutions are snake-oil salesman.

https://survey.stackoverflow.co/2025/ai/

47% daily use

I have been using it a bit, still can't decide if it is useful or not though... It can occasionally suggest a blatantly obvious couple of lines of code here and there, but along the way I get inundated with annoying suggestions that are useless and I haven't gotten used to ignoring them.

I mostly work with a niche area the LLMs seem broadly clueless about, and prompt driven code is almost always useless except when dealing with a super boilerplate usage of a common library.

I do know some people that deal with amazingly mundane and common functions and they are amazed that it can pretty much do their jobs, but they never really impressed me before anyway and I wondered how they had a job...

I don't use one, and my coworkers that do use them are very loud about it, and worse at their jobs than they were a year ago.

If my coworkers do, they’re very quiet about it.

Gee, guess why. Given the current culture of hate and ostracism I would never outright say IRL that I like it or use it a lot. I would say something like "yeah, I think it can sometimes be useful when used carefully and I sometimes use it too". While in reality it would mean that it actually writes 95% of code under my micromanagement.

It's true, the tech will get better in the future, we just need to believe and trust the plan.

Same thing with crypto and NFT's. They were 99% scam by volume, but who wouldn't love moving their life savings into a digital ecosystem controlled by a handful of rich gambling addicts with no consumer protections? Imagine, you'll never need to handle dirty paper money ever again, we'll just put it all in a digital wallet somewhere controlled by someone else coughmastercardcough.

And another thing, we were too harsh on the Metaverse. Sure, spending 8 hours in VR could make you vomit, and the avatars made ET for the Atari look like Uncharted 4, but it was just in its infancy!

I too want to outsource all my critical thinking to a chatbot controlled by an wealthy insular narcissist who throws Nazi salutes. The technology just needs time to mature. Who knows, maybe it can automate the exile of birthright citizens for us too!

/s

The company I work for has recently mandated that we must start using AI tools in our workflow and is tracking our usage, so I've been experimenting with it a lot lately.

In my experience, it's worse than useless when it comes to debugging code. The class of errors that it can solve is generally simple stuff like typos and syntax errors — the sort of thing that a human would solve in 30 seconds by looking at a stack trace. The much more important class of problem, errors in the business logic, it really really sucks at solving.

For those problems, it very confidently identifies the wrong answer about 95% of the time. And if you're a dev who's desperate enough to ask AI for help debugging something, you probably don't know what's wrong either, so it won't be immediately clear if the AI just gave you garbage or if its suggestion has any real merit. So you go check and manually confirm that the LLM is full of shit which costs you time... then you go back to the LLM with more context and ask it to try again. It's second suggestion will sound even more confident than the first, ("Aha! I see the real cause of the issue now!") but it will still be nonsense. You go waste more time to rule out the second suggestion, then go back to the AI to scold it for being wrong again.

Rinse and repeat this cycle enough times until your manager is happy you've hit the desired usage metrics, then go open your debugging tool of choice and do the actual work.

I have next to zero experience about coding (unless you count a few months of Borland Delphi work back in 00’s, which you shouldn’t). Yet, I’ve managed to create half a dozen really useful tools for my work, and a few more for my hobbies too.

Inflection point for me was Gemini 2.5 Pro. Before that I was only successful with smaller scripts, using ChatGPT mostly. But with Gemini I was able to do Deep Research as the initial step to plan out the overall architecture, interfaces, technologies etc. and finetune the actual coding prompt using that info.

Crucial step after first generated (buggy) version is to copy paste the code and errors to ChatGPT and/or Grok to get their take on it, then feed back those ideas to Gemini again. Some 5-10 iterations of this and I usually have a fully functional application or a component of bigger piece software. Problems at the moment usually arise if any particular file exceeds ~800 lines, and when there are many many iterations. Then LLMs tend to get forgetful, dropping out comments, reintroducing faults from earlier iterations etc. Better to start a new session at that point.

Thinking of LLMs as just a lossy compression algo for all human knowledge, the parallel use of LLMs makes kind of sense: All the companies use approximately the same data in their training, but end up having a bit different looking ”lossy big picture” in the end. But if I ”look at all these pictures” side by side I can perhaps see more detail. Or that some of the pictures are fuzzy on one location but one is much clearer.

LLM’s seem to be very good to at spotting the correct solution when they are given a couple options or hypothesis about an cause of an issue. Most surprising to me is that Grok has been the one to solve majority of the most stubborn bugs that other have gotten stuck to.

With (Edit:) Gemini I just had my first ”hole in one” where it generated a flawless ~500 line web app in the very first try. Just gave it my Git codebase as a zip file and asked for a new module that interfaces the existing stuff. Wild times.

Definitely not good. Sometimes they can solve issues but you gotta point them in the direction of the issue. Other times they write hacky workarounds that do the job for the moment but crash catastrophically with the next major dependency update.

I use it extensively daily.

It cannot step through code right now, so true debugging is not something you use it for. Most of the time the llm will take the junior engineer approach of "guess and check" unless you explicitly give it better guidance.

My process is generally to start with unit tests and type definitions, then a large multipage prompt for every segment of the app the llm will be tasked with. Then I'll make a snapshot of the code, give the tool access to the markdown prompt, and validate its work. When there are failures and the project has extensive unit tests it generally follows the same pattern of "I see that this failure should be added to the unit tests" which it does and then re-executes them during iterative development.

If tests are not available or if it is not something directly accessible to the tool then it will generally rely on logs either directly generated or provided by the user.

My role these days is to provide long well thought out prompts, verify the integrity of the code after every commit, and generally just kind of treat the llm as a reckless junior dev. Sometimes junior devs can surprise you, like yesterday I was very surprised by a one shot result: asking for a mobile rn app for taking my rambling voice recordings and summarize them into prompts, it was immediately remarkably successful and now I've been walking around mic'd up to generate prompts.