You still need to review and verify the code, actually implement it, and improve it if you use AI.
If you just blindly accept it then you’re just lazy to begin with.
That’s the problem. This is one of those things that you gain momentum in, not simply experience. You can lose that momentum.
Tech bros are going to end up enslaving us to this shit.
is this much different than IT guys not knowing how to solder anymore? I started my career learning about individual components and doing math by hand, and shortly after I was told that all we did was swap cards. My job eventually turned into a more or less normal IT job (compared to what it was), and by the time I moved on, we weren’t even using command prompts anymore.
I remember asking one of my instructors about how layer 1 can generate layer 2, and he had an idea, but couldn’t really point at components and give an explanation. One could say that I represent that first step in the death of knowledge due to convenience and optimization, but it hasn’t really negatively affected me outside of curiosity. Even when I’m working on legacy equipment and actually do have to bust out a soldering iron, that’s usually because I’m being cheap and don’t want to buy new cards.
So, this makes me wonder: is it really all that bad if someone can’t sit down and write lines and lines of code, but can understand it well enough to direct AI? I’ve used AI to help me code in some unfamiliar languages and all of the outputs I got were utterly unusable. So, in my anecdote, it didn’t make up for my lack of skill in the slightest.
I say this as someone who taught himself blacksmithing on principle, so it’s not like i’m some techbro or something. Obligatory I think AI is overpromised, but this seems like one of the few things it can actually assist with, assuming the person using it is capable enough to be using it.
When big corpos own the tooling I definitely think it’s a problem.
That’s a good point. I hadn’t thought of that. That’s actually pretty terrifying to think that you’d have to rent your professional skillset.
Software Engineers
Oftentimes I wonder what civil or mechanical engineers think about webdevs-turned-prompt-writers calling themselves “engineers”.
Every real engineer I have ever talked to gets pissed when a key board jockey calls themselves engineer. Regardless of AI or not.
Coders arnt engineers never will be never have been. The engineer title was straight up stolen and misused by corpos and idiots to fluff up their egos. The entire term software engineer is a bullshit title for idiots who have zero respect for actual engineers or are toadies to mega corpos and sold their self respect for a bigger pay check. Prompt engineers are even worse and frankly fuck em all.
They as much engineers as a 3 year old is an engineer when building with Lincoln logs.
Pissed.
Loudly announcing your increasing incompetence to the world seems like a weird career move, maybe consider lying about that?
Solution is simple, learn to code.
ha
Things I’ve realized while working with AI (Claude code):
- It’s fantastic for very small macros and medium length scripts. Think dev ops stuff, pre-commit hooks, transforming data. Keep it small enough to manually review and something you can run without destroying anything important. This can massively boost your codebase QoL. [Double bonus for not wasting tokens to solve the same problem over and over]
- It’s decent-to-good at debugging but not consistent with fixes. It can find some utf encoding edge case that might have taken you 1hr+ but suggest the dumbest bandaid fix you’ve ever seen. Also very good at spinning up unit test suites for basic edge cases.
- Due to obvious training bias, it’s pretty good with common libraries and cloud platform infrastructure. It could probably help with writing a complex cron call, debugging regex or fixing an IaC config. On the flip side it won’t bother to use the latest package version or know your niche/new library.
- It does better with greenfield because exploring your codebase introduces a ton of bias. It might try to fit in an ugly hack when a refactor to simplify everything is way easier.
- It’s absolutely garbage with UI, just throws the most disorganized HTML together that isn’t reactive or reusable. OK enough for ugly internal stuff but God help anyone relying on it for that.
- This is setting up to be the biggest rug pull in history. People that buy into it heavily just to save a couple bucks on engineer payroll are going to be fucked when they start ratcheting up the token price.
All in all it can be useful when used with care but will never be a magic bullet.
This is basically what I discovered as well. I have found that Ai writes code that is complex and “works” (at least most of the time) but it is heavily over engineered and often contains design choices that make expanding functionality effectively impossible without a full refactor.
When I tried having the Ai fix a test failure the Ai would either fix the code, fix the test, or change the test and the code breaking everything else in the chain.
I no longer use vibe coding because it is just faster/better for me to write the code.
But for tiny scripts it is very good.
Yeah, fully agree with all that.
I’ve got some godawful spaghetti code I don’t understand fully, and it’s pretty good at deciphering that and the bizarre labyrinth of code paths leading around it. But it’s absolutely no guarantee of working code, and in any project larger than a simple crud app, you are going to still need programmers who know about things like memory and databases.
It often needs pointing at a solution you want, because as you pointed out, it’s fond of dumb band-aids. Like yesterday when it was trying to hook into mouse wheel events and create separate threads, when all it needed was an event on the dataset I was using to load a sub-dataset.
This is pretty spot on from my experience as well. Also, the gap in quality from the Opus models and say GPT is vast.
100% agree on ui code. Really awful output there regardless of model.
Claude can do some medium complicated sites from scratch relatively quickly. The problem is I’ve seen so many of these at work, not just from non-engineers, but from peers too, that they’re easy to spot. AI sites/apps are going to be the new geocities.
But when you want to move beyond the basic thing that impresses the c suites for some reason, it hits a pretty big wall in speed to output and needs a lot more hand holding.
I fear that the c suites don’t really care about quality, just speed and saving money. So while I’m a much better developer than Claude (which is imo the best at the moment), I don’t think that makes my job secure. I have to use the AI, and it’s getting silly/scary religious here about it. We have to talk about how we used AI and how it’s making things better. And to make things worse, I don’t see a company that’s not drinking the Flavor Aid.
It can be useful, and used right, you can do a lot of things faster. But the expectations from the top don’t align with the reality of the product, and us developers are being blamed for the gap.
Man, I disagree with all of this. The frontier models are actually good, and basically everyone in my F500 company has been using it. The codebases i work on are super-legacy java, where it does great despite us having like 75 different patterns for each task, and a massive front-end web repo where it thrives because we’ve been extremely strict in typing and patterns leading up to this. It even does pretty well across repo boundaries, despite having significantly lower context for those situations.
I genuinely will never understand the people saying they suck. Are the worth the price? I have no idea, I’ve never used them for personal project. But they are at least as good as a dev with 3-5 years of experience, at this point. Our career is boned.
I don’t doubt it’s possible to get better consistency but the juice is really not worth the squeeze for me. You end up churning through huge expensive models, orchestrating sub agents, writing out boilerplate hand-holding instructions (“please don’t break this, stop trying to commit to main, please lint ffs…”).
I don’t use it for Java but that would make sense with rigid enterprise patterns and
VeryVerboseNamesThatAreEasierForAModelThanAHumanFactoryClazz {...I don’t think our career is boned, moreso that all juniors trying to get in are boned. Everyone who knows what going on transition to a more hands-off architect role.
But like I said, our tokens are heavily subsidized right now. When they pull the rug, code monkey jobs will start to get listed again (with lower salaries of course).
Oh no… who could have… possibly… foreseen this…
We use it at work and I now have disabled it for all the typeahead stuff. Far too many times it guesses what I am doing incorrectly and it made using my TAB key (which inserts the propper two spaces) impossible.
The only place I still use it is for reading and identifying compiler errors. Even then it is only about 50% correct as most times it falls into the “Oh you are right, X isn’t the solution. Have you tried X?” I have had few bad interns and even they were smart enough to not forget what they said in their previous sentence.
This is why I’ve never tossed any of the developer bookmarks
I’ve been training new hires how to look stuff up on stack and dictionaries to fix code that went wrong after AI mucked it up. They aren’t even being trained to parachute in school.
What a sad time line we are in.
Nah, AI isn’t that good. When you don’t properly review every single line twice, you get the most absurd bullshit you’ve ever seen.
I use Claude Code Opus daily btw.That’s the funnest part. You loose your ability to code, and you do it by using thing that isn’t even that good, and you don’t get anything out of it. Isn’t that great?
You forgot that you’ll work for less salary because “work has become much simpler, every intern can do it now!/s”
You speak for yourself, I’m flying through this killer sudoku book…
I’ve worked on a cloded codebase. It’s not… uh, good.
I weap for the environment and our future water and electricity availability.
weep*
That you captain autocorrect.
I want to become a software entomologist, you know, so I can study all their bugs.
It feels like relying on GPS while driving around. If you know the roads well and just want some help with live traffic or somewhere you haven’t been before, it’s a decent tool.
If you rely on it because you don’t want to think and just want to press the easy button, you’re going to have a bad time sooner or later.
Back to software, I think there are a lot of people introducing concepts they don’t understand or can’t maintain (either from poor quality slop or it is just too advanced for their current level of understanding). You can do a few turns like this, until you’re stuck burning tokens in a loop without moving forward in a meaningful way.
I try to avoid taking the easy route myself unless I’ve burnt too much time stuck on some small detail. Ultimately I feel it is super important to understand what you are delivering. Whether it is writing it yourself, copying a stack overflow post, or using an LLM. Once you commit and push to prod you’ve got to deal with that crap.
Agree completely but I wanted to add: you can also get into an incomprehensible mess without vibing. Just follow the serverless flask tutorial, start writing raw SQL, and away you go!
I asked Claude today about why a coworker was getting errors and it almost exploded.
Lol! Losers. I’ve been programming for almost two decades and extensive use of AI hasn’t compromised my skills AT ALL! These slop machines can’t hope to compete with the quantity and magnitude of subtle bugs I write. My code was terrible long before I made bots have mental breakdowns trying to work with it.
AI also gives you the benefits of a middle manager. If everything works as intended you take the credit but if something breaks that’s not your fault, AI made the mistake. If they try to put the blame on you just say you have 6 agents working on 6 different domains all cross-reviewing their commits and you can’t be expected to review every single line of code yourself. Time to play corporate like a damned fiddle!
It really is like having your own personal trainee.
If it only could make coffee.
See this is why we need smart appliances, so AI could make you coffee.
Saved me a paragraph there.
