Technology

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

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

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

I’m not a “software engineer” but a lot of people that don’t work within tech would probably call me one.

I’m in Cloud Engineering, but came from the sys/network admin and ops side of things rather than starting off in dev or anything like that.

Up until about 5 years ago, I really only knew Powershell and a little bit of bash. I’ve gotten up to speed in a lot of things but never officially learned python, js, go or any other real development language that would be useful to me. I’ve spent way more time focusing on getting good with IaC, and probably more of the SRE type stuff.

In my particular situation, LLMs are incredibly useful. It’s fair to say that I use them daily now. I’ve had it convert bash scripts to python for me very quickly. I don’t know python but now that I’m able to look at a python script next to my bash; I’m picking up on stuff a lot faster. I’m using Lambda way more often as a result.

Also, there’s a lot of mundane filling out forms shit that I delegate to an LLM. I don’t want to spend my time filling out a form that I know no one is actually going to read. F it, I’ll have the AI write a report for an AI. It’s dumb as shit, but that’s the world today.

I've only found two effective uses for them. Every time I tried them otherwise they fell flat and took me longer that it would have to write the code myself.

The first was a greenfield personal project where I let code quality wane since I was the only person maintaining it, and wanted to test LLMs. The other was to write highly repeative data tests where the model can simply type faster than me.

Anything that requires writing code that needs to be maintained by multiple people or systems older than 2 years, it has fallen completely flat. In cases like that I spend more time telling the LLM it is doing it wrong, it would have taken me less time to write the code in the first place. In 95% of cases, I am still faster than an LLM at solving a problem and writing the code.

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.

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.

How are they at debugging? In a silo, they’re shit.

I’ve been using one LLM to debug the other this past week for a personal project, and it can be a bit tedious sometimes, but it eventually does a decent enough job. I’m pretty much vibe coding things that are a bit out of my immediate knowledge and skill set, but I know how they’re supposed to work. For example, I’ve got some python scripts using rekognition to scan photos for porn or other explicit stuff before they get sent to an s3 bucket. After that happens, there’s now a dashboard that’s going to give me results on how many images were scanned and then marked as either acceptable or flagged as inappropriate. After a threshold of too many inappropriate images being sent in, it’ll shadowban them from sending any more dick pics in.

For someone that’s never taken a coding course, I’m relatively happy with the results I’m getting so far. Granted, this may be small potatoes for someone with an actual development background; but as someone that’s been working adjacent to those folks for several years, I’m happy with the output.

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.

They're not good at debugging. The article is pretty spot on, IMO - they're great at doing the work; but you are still the brain. You're still deciding what to do, and maybe 50% of the time how to do it, you're just not executing the lowest level anymore. Similar for debugging - this is not an exercise at the lowest level, and needs you to run it.

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.