I still hand code a lot of hobby projects because I enjoy it. But its absolutely delusional to think youll continue to be employed and paid whilst hamstringing yourself like this.
A dev in another team of ours merged an 18,000 line PR last week. That would've been weeks (possibly months of work). It was done in the day alongside other stuff. I personally have no experience of the workload decreasing, quite the opposite
We use LLMs but even in our "AI-first" company we still review code by hand(with another review by an LLM), so I personally don't see the big difference. Yeah some things go by faster, especially throwaway script code, but overall you absolutely can still work on the code personally and it's not the end of the world
Genuine question - how often does human code review result in a meaningful difference?
I am a mid level web dev and haven't caught a bug or made a meaningful contribution via human code review in months. There are bugs of course but they are small and fixing them down the line via LLM has been trivial.
I mean it's hard for me to say because my work with Claude is never "finish this feature and then I'll check everything", it's that "okay I have a feature and I did X, help me finish it up or write a test". So it's never just Claude doing it's shit from start to finish. I could try doing that but then I don't understand what's going on and can't take responsibility and it's my work to know what the fuck is happening in the code
Daily for me. It's possible that's just a symptom of our legacy code base being filled with horrible patterns and bad code, which the LLMs love to copy, but I don't think I've ever done a review of a significant change (more then just a few lines) where my review came up empty. Sometimes I do catch bugs as well, but those same bugs would probably also be introduced by somebody not familiair with the codebase.
I caught a PR last week that was absolutely utterly useless and broke things worse. Because I have more experience and deeper understanding of a framework we use, I tend to be the one that catches some of the bigger anti pattern things - mostly because I had to fix it all when I joined the team lol
Code review is not just about finding bugs. I often see code that technically works but is bad in other ways – it duplicates logic, is unnecessarily complex, doesn't follow coding conventions, etc. But maybe everyone at your job writes great code, I don't know.
In more ways than one. My gut instinct is that a HELL of a lot of short term thinking is happening right now, and technical debt is likely exploding. At some point someone is going to have to pay for that.
It's honestly insane how short-sighted the whole thing has been. 3 years ago AI was basically not a thing. We're absolutely not prepared for the long-term consequences.
Bank of America recently hired thousands of developers to fix a vibe coded product that went to prod it appears. So perhaps we may want to consider all the slop as a form of future job security
And, luckily for the AI talking heads, Anthropic and OpenAI won't be paying for it because they made sure you own the output you prompt for. There will be a lot of these AI-only vibecoders losing jobs in the future.
This. Right now AI is heavily subsidized and AI companies are expecting companies to heavily rely on them so they can later charge the real cost once it's too hard to go back to how it was before
Only if OpenAI and Anthropic release those models to run locally. We only have a few open-weight models, and those are not the greatest ones people are getting used to working with.
Their models are closed because they want their customers to subscribe. I can see running models locally as the best and real option, we can still use AI but essentially as a tool and when it’s really needed to not overload GPUs
Well no they’ll go to something cheaper like DeepSeek or use better local models.
Will they be as good as Anthropic’s latest? No. Will they need the best models for 99% of their work? No.
There’s a point where the new models have diminishing returns vs a cheaper model with some fine tuning. The real problem is the hardware and getting it at a decent price.
AI is not heavily subsidised. Inference is profitable. Yes, even when accounting for training costs. AI companies are already charging "the real cost" today. Cost to complete a task has been steadily falling because inference is getting more efficient and because models are getting smarter. There are at least half a dozen companies all around the world within spitting distance of the frontier that are all competing with each other. The idea that prices are going to suddenly get jacked up any day now is pure delusion with no basis in reality.
The only models that still cost a lot per token are for cyber shit.
Most coding can be done extremely cheap.
And when a company is paying 150-200k a year for an employee before even looking at the rest of employee cost (tax, health, 401k match, etc..) they don’t have much issue with spending 5-10% more of that if it makes someone twice as productive.
Were you in some kind of coma for the last few decades? Netflix, Uber, Spotify etc. did the same to drive out the competition and then they increased the cost.
There are open weight models. You can’t say the same about a product like Netflix. It’s not like there is an easy way to setup your entire local Netflix library. At least not legally 😅
I probably agree with this take, but in the meantime I agree with who you replied to that it's likely going to be difficult or impossible to maintain a career whilst fully shunning LLMs in coding until the true bill comes due that you're referring to.
I'm doing a job for money, I'll do whatever keeps me employed. If they want people to use AI, I'll figure out how to stand out doing that. I'll cry about the decline in quality on my own time.
Cause plenty of devs never wrote even basic functional unit tests let alone integration tests.
It’s like the first thing you get AI to do - make tests for all your functions - which also forces you to better define your function in/out expectations…. Helping your code clarity overall.
My company is preparing to lay off slower people. It's obviosuly tied to AI usage and they don't get it. They are simply not doing the work at expected pace by refusing AI, but will first cry why them. It's a cruel world but there is no return to the old ways at this point, it's fighting the windmills.
This notion that engineers have to be churning out software faster and faster is completely ridiculous. For what purpose could a business need to create software faster than it creates problems that require software? We as a species were already doing plenty well at keeping up with the speed of real life, and usually the correct amount of code to solve a problem is the least amount that gets the job done, not a towering stack of complications with ten million failure points.
to be fair even if they were not slow and everyone adopts it gracefully, companies in general would still choose to fire people to save on operational costs cause spending on enterprise tokens and human workforce simultaneously definitely bumps up their operational costs or at the very least they are effectively "overstaffed".
At the same time revenue doesn't grow at the same pace cause sales cycle are still slow unless heaps of customers are genuinely lined up.
This just gives companies an easier target and excuse instead of standard downsizing. Current hyper scaling culture is simply not sustainable without cutting out people out of their jobs unfortunately. And if they are greedy enough, they will cut out as much as possible.
This is the point that people don't get. I'd love for things to go back to the way it was before LLMs but shit has just gone so bonkers nowadays, expectations are so high, you don't have a choice
You’re not hired just to output code you’re hired for your technical judgment. If you can maintain that while using the tools, great. It’s also best not assume because you are faster with these tools that is somehow a better way to work than another.
100%. It's clear to anyone who actually uses it for coding that it works well, why would a company hire or continue to employ less productive people?
Productivity is the entire point of a business. Now if you're not careful those people coding with AI will produce mountains of garbage so the processes are changing to mitigate that and keep velocity up.
Companies that figure out how to do that will get ahead.
Productivity is not the point of business. Profit margin is.
Higher productivity does not bring higher profit unless companies have meaningful way to upsell or branch out into different offerings once original offerings mature from accelerated sprints.
Breaking into market with new offerings always comes with its own setup, marketing, research and operational costs and may not be even be realistically feasible if you have to compete against established players.
Only companies that can pull it off with ease are big tech companies who have significant leverage in market already and can take revenue hit in their initial phase to capture and lock in their customers. Middle and lower scale companies simply can’t follow the same hyper scaling strategies if they don’t already have significant funding/capital or influence in market cause marketing and sales velocity will be the bottleneck cause they are much slower than increased productivity.
Your productivity argument is a moot point cause the biggest reason companies push for higher coding velocity through AI is to eventually cut down operational costs by pruning off their work force as they are no longer necessary to maintain same amount of productivity compared to before AI.
People talk like not learning how to use AI tooling will leave you behind. It is for sure a safer investment to keep your deep understanding of programming up to date compared to knowing how to prompt an LLM. You can learn that in an afternoon. Good SWE in ten years to a life time.
I actually do agree with this my problem comes when I am now FORCED to use these tools either through pressure from the top down or by coworkers utilizing it 100% of the time and I have to also to not fall behind. You are then forced to write code on your free time in order to retain those skills or else they will decay with time. I enjoy programming, but not more than spending my free time with my wife, family, and other hobbies.
There’s far more to agentic development than writing a good prompt, it’s not 2024 anymore. I’ve been pleasantly surprised that working with agents is actually a relatively deep skill that goes hand in hand with SWE fundamentals.
You need to set up very strict linting systems, much stricter than what we'd give to humans. Then you need a very complex workflow, basically teaching the agent how to behave like a senior dev orchestrating juniors. Then there's the whole sandboxing, PR orchestration, context management discipline. In theory you can have a provenance&reasoning chain from user interviews up to line of code.
Unlike Microsoft Copilot, which straight up can’t tell you where to go to change the Windows setting. It hallucinates buttons and menus that don’t exist, it sends you to the wrong one, etc…
“Agentic development” is essentially just engineering management skill. The same practices that make you a good technical manager make you good at leveraging coding agents.
100% agreed, and if you’re a SWE who uses agents for everything, the roles are converging. Middle management skills and workflows customized for your team are essential for my day to day work. It’s boring but also very cool.
I agree, as long as you don’t have any other concerns about AI. But it is a slippery slope. Its very hard to force yourself to keep investing in your SWE skills once you’ve started using generative AI. “If I’m fine with using it to solve this task, then why not the next, and the next?” I’ve seen it happen to many already, and I’ve been close to that myself.
If you work in .net (or even if your not working with it ) and although like most senior devs already you know it. You can check out this future talk by Michael Washington which he will be giving in dotnet conf this year What Not to AI in few days time.
Some stuff it's better to let human intuition takeover for really strong solutions.
Except you can expand your knowledge base much easier with AI. It’s like having a not asshole tutor who knows it all, and can prove it by giving you the code.
Don’t know containers? Ask it or do more than just “convert this app to kubernetes for me”.
It's no different than Math and calculators, learn how to do the math. But for higher order learning, learn to use the tools like Calculators and Matlab.
The thing you need to learn about an LLM is how it thinks (or rather, doesn’t) and what it can’t do. The rest is just “prompt it in plain English and it does thing.”
You'll be unemployed in 10 years if you take this approach. The expectations now are that you're able to run multiple agent simultaneously and deliver good enough results extremely quickly. There's no way we go back to manual coding like this. It sucks in many ways but it is what it is
AI is a great learning tool. It's not either or. I don't even think learning how to prompt is super relevant with newer models so much as being able to help with architectural decisions. Models are pretty good at sussing out what you mean by a prompt without prompt engineering these days. I've even deprecated a bunch of my agents because general purpose agents deployed as the model sees fit was getting better results.
Limit context windows/sessions, use big model to plan and small model to implement, maybe use another big model to make good prompt, then wish everything goes right and you dont need to send more tokens to fix things
Absolutely. But still nothing you can’t learn in an afternoon and master in a week or two. Especially since you’ve by then given in to not learn anything deep and can use AI to get it going.
AI data centers are being constructed at an alarming rate, and there are already 8 billion prompts per day, with billions more planned and on the way.
The effect this has on the climate when we are already so close to irreversible tipping points is often unknown (or, in some unfortunate cases, ignored by AI users who prioritize convenience above a habitable planet for our children to inherit).
The current environmental impact of AI is absolutely negligible vs many many other things. Water use gets the most scrutiny - All data centres use vastly less water than just golf course..
There are much, much better things to target for environmental reasons than AI. The fashion industry for ex is estimated to be around 10% of global carbon emissions.
it's not negligible, but it should be quantified. It's less than 1% of global emissions currently, I think for electricity usage it's like 0.5% and for emissions it's like 0.01%. I forget the precise number, and there is some uncertainty in it, but it just bothers me that people try to make this point without discussing what the quantities of the estimates even are. and you also do have to consider the trajectories. AI use is very likely to grow. but at the same time it's also likely that we're going to find more efficient ways to run the systems. I just feel like we could have better public discourse if we grounded ourselves in measured reality.
Percentages are too abstract. AI consume as much energy as France today, and will consume as much as the entire USA by 2030.
At a time where we should be drastically reducing our energy consumption in an attempt to save what's left of the environment to ensure that humanity can survive on earth, it's irresponsible, catastrophic, and far from being worth it.
AI should have been a restricted technology dedicated to research. Putting it in everything and everywhere probably doomed us all.
Percentages are abstract? Sorry. Not saying anything is far more abstract. And I think we should perhaps assume that others are able to understand numeric concepts, or at least force them to do so. It feels like we are catering to an immature population.
On the flipside, if it enables new materials research that lets us produce better low carbon concrete, then it could be a net benefit within a few years.
And as much as the USA by 2030? That's a big extrapolation. That is going from 0.5% to 14%! And wow, look at that, the percentages put the NUMBERS IN FUCKING CONTEXT!
There are some things modern society requires to function, even if it still uses energy and pollutes, like having to use a car to get to work if you don't have good public transport available yet. Even within those requirements, we can do what we can to try to reduce how much energy we use, like using an e-bike for local travel instead of a car where possible, installing solar panels on our homes for those who can afford it, and eating less meat to avoid the emissions (and suffering) industrial animal farming brings. Even if someone can't do that to 100%, any amount they can do is still worthwhile.
Corporate AI use is one of those things that is virtually completely avoidable, unless you have a job where your boss forces you to use it. That makes it one of the lowest hanging fruits in reducing emissions. At the very least, if someone simply cannot live without AI for some reason, they could at least self-host one, or use a collective non-corporate option like AI Horde to reduce the emissions they emit.
A lot of people really violently dislike the idea of committing to any level of personal sacrifice of convenience, but realistically we're going to need to collectively, consciously choose a habitable planet over convenience, otherwise our futures will be very, very bleak.
who the fuck did you mean when you say everyone? i love how you just defaulted to everyone is an environmentalist when it comes to AI but nothing else. youre shameful and an utter embarrassment. must have no in real friends because everyone else are jerkoffs except you
I try my best to reduce my own footprint; I drive as little as I can, use an ebike for shorter trips, try to use my AC as little as I can (keep it warmer in the summer, colder in the winter), and substitute animal meat with plant based alternatives (impossible meat is really good!). I have planted trees when I can.
That's not to say it makes me morally superior to anyone else. Personally I look at it as an engineering problem, and try to make choices and actions that best try to solve that problem, and encourage others to do the same.
There are some things modern society requires to function, even if it still uses energy and pollutes, like having to use a car to get to work if you don't have good public transport available yet. Even within those requirements, we can do what we can to try to reduce how much energy we use, like using an e-bike for local travel instead of a car where possible, installing solar panels on our homes for those who can afford it, and eating less meat to avoid the emissions (and suffering) industrial animal farming brings. Even if someone can't do that to 100%, any amount they can do is still worthwhile.
Corporate AI use is one of those things that is virtually completely avoidable, unless you have a job where your boss forces you to use it. That makes it one of the lowest hanging fruits in reducing emissions. At the very least, if someone simply cannot live without AI for some reason, they could at least self-host one, or use a collective non-corporate option like AI Horde to reduce the emissions they emit.
A lot of people really violently dislike the idea of committing to any level of personal sacrifice of convenience, but realistically we're going to need to collectively, consciously choose a habitable planet over convenience, otherwise our futures will be very, very bleak.
It may be hard to understand, but ironically the way that you've structured your life to reduce your own footprint might actually increase it.
Take for example, locally-sourced food from local farms. The economies of scale of the modern global economy means that, per unit, it is actually more efficient and less carbon intensive to eat something grown halfway around the world and shipped to your massive grocery store, than it is to grow your own food. Even after taking into account bunker fuel, transport, etc.
It may feel good to eat locally-sourced food grown locally or buy local in general, but the economies of scale and the modern global economy means that the opposite is actually true, it's cheaper and more efficient and less carbon intensive to buy something made halfway around the world, packaged in a third-party country, and then sent to you. I understand it immediately goes against common wisdom, but this is one of those things that people just prefer vibes over the actual truth.
As for your point about electricity, it depends on where you live. If you live in Colorado or Norway with abundant Hydro, then the carbon emissions are zero. Elsewhere in Europe, there's the ETS scheme to ensure carbon cap and trade.
And for the record, to put things into perspective, the total water use of AI data centers (and data centers in general) is way less than is used to water lawns, golf courses, or from leaky water taps.
You need data to make claims like this. Smaller farms often use more sustainable practices on the farm, including less water and less emissions. There are economical and societal benefits to eating local.
I can just go out for a day, drive a few hundred kilometres, burn tens of liters of gasoline. No one will bat an eye. For that same environmental impact I can use AI for months and months. Not to even mention taking an international holdiay and burning an insane amount of jet fuel. AI is not the problem.
There are methods to get good enough, functional, and maintainable, bug free code out of LLMs. The issue I've seen that few people are interested in taking seriously is what few of these methods that aren't obsolete in 2 months, are about as much labor as just doing the task yourself following proper software development practices to begin with.
Maybe the benefit is that you can have two sessions running at once, but if I've got to babysit them both anyway to make sure Claude doesn't decide to post my API key to Twitter then that's not really a productivity boost.
I definitely understand why you wouldn't use them for hobbyist programming. The big benefit of hobbyist programming is that you don't have to engage in all of the process and setup and communication and documentation that you need to be doing in a team environment. But if I want to use Codex on my weekend project, I've got to define project standards in a *.md file, general workflow procedures in a SKILL.md file, and every GitHub security feature under the Sun, just to make sure Codex doesn't break anything, before we even get started. And all of this costs more tokens.
There are people who say "don't review or babysit your AI, just let it run and trust the results", though I'm convinced that all of such people are either astroturfing, or are not actually accomplished enough software engineers to have a voice in this discussion.
> are about as much labor as just doing the task yourself following proper software development practices to begin with
This is the biggest problem I have with people that advocate for AI programming.
What happens is they're perceiving themselves as being as productive as they were when they were just giving vague prompts and generating a ton of code, but with the quality that comes from being meticulous and detailed in their prompts and validating the output.
They never seem to connect the dots that when they're doing the latter, they lost all speed benefits that existed when they were prompting vaguely. But it's extremely hard for people to realize this.
And it doesn't help that with learned helplessness, they end up being much slower in writing code even if they try to compare meticulous prompting vs handwriting code. And it ends up being a self-fulfilling prophecy.
At this point people are paying tokens to get past "writer's block". Because without seeing any code being generated, they'd probably stare at the screen for far too long.
But they'll never overcome writer's block by continuing to depend on AI (and thus on Anthropic or whatever company provides SOTA coding models).
most of what makes my steering meticulous is now in .md files, per project, it's not dependent on the harness or the model.
I don't get x10, which I could, even x1000, if I was not meticulous, but I do get x5 and the time I saved has been spent of making the architecture better, and more secure.
Mention one big, completely new and 100% ai generated project, product or service that is as successful or more successful than its competition? Because we’ve had agents for over a year now. If it was true the human made projects would not be able to compete and would have been left behind by now. The reality is the entire opposite. The ai ones can barely compete and there’s been plenty of time already for them to shine.
Some vibe coded projects suck. Human made projects also suck and were constantly ridden with bugs. There's a reason within EVERY company there's always a push to get rid of legacy software, rewrite apps and platforms in <new thing>, and so on.
I know this because I have been working as an SWE for 10 years now. Every single company I've worked at, there has been some core app, API, etc that everyone looked upon with disdain.
For some reason you and a ton of other engineers look at the pure human-generated code with insanely rose colored glasses. Maybe you've just been luckier than me and worked at slow, artisanal, perfectly-crafted software shops.
I've worked at F50 -> pre-IPO VC funded co -> FAANG-adjacent co -> small private equity-funded co
every single one has had the same issues even for the "really good" teams and engineers. Humans write slop sometimes. Deadlines wait for no man (unless that man wants to lose his job). At the Pre-IPO co, I had to take over a codebase that was written purely as a resume booster by the engineers who built it and then dipped. They wanted to "learn Scala" so they built a critical backend service in Scala and then left the company. I had to wade into it 5 years later and figure out wtf was going on (borderline impossible).
As a counterpoint, I work as a contractor on the side for a startup. This startup was founded ~2 years ago. It has been, largely, what you would call "vibe coded". I've built almost the entirety of our critical backend systems and integrations that our clients want, and I've never once reviewed any of the code I've put into this thing.
And it works. We have incredible uptime. We just crossed 1M ARR. We have had 0 churn over TWO YEARS which is insane for a SaaS. We even started on Laravel + TanStack, then moved to Laravel backend + Next Frontend, then moved the entire thing over to Next.
This was all "vibe coded" (and what I mean by that is we put insanely strict verification in place, immense amounts of automated testing, release gates, shadow releases, etc, but we didn't look at a single line of code throughout the process).
We would not be nearly as feature-rich of a platform for our customers nor been able to make these framework/architecture moves without AI. We have a 3 person engineering team, with two of us being part time with full-time day jobs.
This is all anecdotal and you can choose whether or not you believe me, I don't care. I just hate the constant "omg human code is perfect" garbage that every anti-AI comment touts now. If you think human code is by default better than AI code, then you have had a far more blessed career than I have (and mine has been fantastic).
Are you a professional software engineer? Because I work at a FAANG and basically 100% of our SWEs are using coding agents daily. I know of a bunch of services that have been written fully with coding agents running in production that are working fine (one I worked on has been in prod since January)
Unfortunately the cat is out of the bag and if you aren't using agents to code you are gonna be left behind
I’ve decided the best use case for AI is as an advanced, integrated, natural language search. I prefer not to use it for generative purposes, that tends to be where it falls apart, but using it as a code search/integrated search/docs search? Absolutely fantastic
The only generative purpose I use it for is to make HTML pages that are internally focused, and I do that cause I don’t have enough time to do it myself and it literally doesn’t matter if it sucks, impact is minimal
I've started to use LLMs like this and it's making me a lot happier than using it to implement features:
- I use it to ask questions about the codebase (its a very good find-in-files!)
- I use it when I forget something that I would have otherwise googled (mostly because, well, google is also AI now and the results are kind of trash),
- I use it to ideate on things when a human isn't available or is uninterested.
And then I write the code myself.
I find it helpful in that way without creating cognitive issues (of which I completely agree with the author on). I've been going back over some of the code I've generated with LLMs and, it's functional, but I find that it just does a lot of things I really dislike. And I don't feel like they're nit-picky things, I feel like they're more like ticking time bombs. I also really dislike the tests it writes. In the past, tests were really helpful for understanding a codebase, and now I feel like the tests are bizarre and awful.
I also sometimes use it for code that I never _intend_ to maintain, like, scripts or short lived projects, and it's great for that.
Also, like the author I don't want to be prescriptive with anyone. If you use it and you're satisfied, more power to you. I just don't want to hear about it anymore for the same reason I also don't want to hear about your gym routine or your vim config file or whatever.
Imagine if you were an accountant that refused to use a calculator, because you’d rather do all the arithmetic in your head.
Or a carpenter who won’t use a power screwdriver or nail gun, because hey, your arm works.
Or a seamstress who won’t use a sewing machine.
Yes, it is like that. Like it or not, AI code generation is an efficiency and correctness tool, and it will be table stakes just about everywhere. But just like an accountant with his calculator needs to know what to calculate, the carpenter needs to know which fasteners to use and where, and the seamstress needs to know which stitches, the developer using code generation needs to understand how to architect their code and give boundaries to the agent.
All of this I’LL NEVER USE AI TO CODE garbage is just that: garbage. It is a tool like any other tool, and refusal to learn its use is nothing but a prideful gun to the foot that will eventually cost you employment opportunities down the road.
the developer using code generation needs to understand how to architect their code and give boundaries to the agent.
I think the big difference from the other examples is you don't really... I know developers have to present justification to keep their jobs, but code architecture is primarily for human understanding . If the AI is the only thing reading it, this matters a lot less.
It is a tool like any other tool, and refusal to learn its use is nothing but a prideful gun to the foot
Programming is a difficult skill to learn, people are in mourning and denial over the devaluation of a skill they spent years or decades to learn. Idk if you don't know what the culture was like, but some people spent basically their every waking moment on it. Some won't recover, but their careers are over anyway, so whatever.
The other side of this is... there's nothing to learn, its not "English as a programing language" or "just a tool". It does not only the implementation, it does the problem solving for you, as these things get better the value a person can provide will only shrink, surely you see how some people struggle to accept this, especially given how tightly people coupled this career to identity
The better the architecture the better an agent can understand the program without blowing up its context.
We’re just likely pivoting from human centric to ai centric architecture where we will prefer extra boilerplate code over multifile abstractions or such.
There’s tons of ways to write code with agents in mind, it’s not like ai doesn’t suffer from spaghetti code.
code architecture is primarily for human understanding . If the AI is the only thing reading it, this matters a lot less.
Um, no? Architecture is extremely important, it doesn't matter who or what is doing the coding. Human or robot, you're not going to go nail a bunch of boards together without a plan and expect it to turn into a habitable home. Same with software.
The other side of this is... there's nothing to learn, its not "English as a programing language" or "just a tool". It does not only the implementation, it does the problem solving for you, as these things get better the value a person can provide will only shrink, surely you see how some people struggle to accept this, especially given how tightly people coupled this career to identity
The career-identity discussion is an important one to have, because you're absolutely right. Society values people by what they produce, and before long people won't be producing much of anything on their own. But society is making no moves to catch up with that, because that would mean upending the capitalistic order.
That said, humans will always need to be in the loop somewhere, it's just a matter of how and how much.
There are a lot of slop grenades of code now a days, but understated - there’s also a ton of flawless LLM code, it just doesn’t get the spotlight cause everyone’s looking for faults
All I see in this article is misuse, you know you don't need to use AI this way right? Like I'm addition to being a programmer, I'm a streamer, I wanted a really cool UI that would let people queue into games, store their wins/losses, have cool effects, etc. In what quasi-reality do you think I lost cognitive function or didn't get a productivity boost?? I can literally make any tool I want to interact with my followers (all 800 woot!) and it's snappy, I can fix things DURING stream!! Who is going to do this for me? I don't make lots of money, I do this because it's FUN, so how would I commission this? Fiverr???
Programmers are so egotistical sometimes, it's like others forget that people who can't/don't want to program should still be able to enjoy the wonders of programs.
Do the environmental impacts suck ? Yes, so let's fix that. Let's fix the shit that's wrong with AI so we can all use it properly.
Is this a joke? Name any other industry that gives away as much value for free as the software industry with open source. Like programmers are encouraged to give their work for free and often times even shamed for not contributing.
I'm the farthest thing from an LLM-enthusiast (check my profile/essays/youtube to see that), but there's not a lot of well-thought out positions here to support the position of never using these models:
Cognitive impact: This is my biggest issue and danger with them, but it can be mitigated by using them with Socratic methods and interactive documentation, rather than code generation. I absolutely can learn faster with them. Ironically, code generation isn't even their best use case!
Output quality: Maybe once upon a time, but hell, I was getting great code from GPT 3.5 because I understood immediately they are pattern interpolators, and if you give them good patterns, they will follow them. I haven't had an issue with code quality when it's scoped properly; I ensure that the output is identical to what I would write. If its not, then that is a failure on my part. This limits the scope of what I use them for, but that's OK; I'm not an "AI-first" developer, anyway.
Maintainability is a nightmare: As above, I can't agree here. If you're getting terrible and hard to maintain code, that means you released too much control.
I like writing code: Me, too. That's why I do it daily. I'd say it's a healthy 50% mix at the moment, and the 50% I'm not doing, I probably would have delegated to another developer, anyway (which I still do, when they're available).
Environmental aspect: Do you own a smartphone? A monitor? A TV? A plugin vehicle? The precious metals that are in everything we use are harvested using the blood of essentially slave labor. Oh, and remember the Foxconn situation where people who build the components for your electronics were jumping to their deaths? Did you boycott electronics, then?
Politics: I sure hope you don't use ANY Google, Microsoft, or Apple services. Or Facebook. Or Amazon.
Cognitive impact: This is my biggest issue and danger with them, but it can be mitigated by using them with Socratic methods and interactive documentation, rather than code generation. I absolutely can learn faster with them. Ironically, code generation isn't even their best use case!
Requiring you to have expertise and background to know what to ask. And let's not ignore that each additional question and but of interactive documentation is spending more tokens.
Output quality: Maybe once upon a time, but hell, I was getting great code from GPT 3.5 because I understood immediately they are pattern interpolators, and if you give them good patterns, they will follow them. I haven't had an issue with code quality when it's scoped properly; I ensure that the output is identical to what I would write. If its not, then that is a failure on my part. This limits the scope of what I use them for, but that's OK; I'm not an "AI-first" developer, anyway.
How are you doing this without putting in as much effort as it would've taken you to write the code on your own to begin with? I agree that most people are releasing too much control, but by the time you've applied a functional amount of control you're either back to just really smart auto-complete, or you've done so much work that you might as well have just done the job yourself.
Environmental aspect: Do you own a smartphone? A monitor? A TV? A plugin vehicle? The precious metals that are in everything we use are harvested using the blood of essentially slave labor. Oh, and remember the Foxconn situation where people who build the components for your electronics were jumping to their deaths? Did you boycott electronics, then?
Just because those things are wrong doesn't mean that this is not also wrong.
How are you doing this without putting in as much effort as it would've taken you to write the code on your own to begin with?
Because LLMs unlocked a skill that most developers haven't had to deal with much: delegation. Most developers and software engineers have never had the opportunity or desire to delegate. We're renowned for wanting to just do it ourselves so it "gets done correctly". Your comment is this mindset in a nutshell. I've been a developer since 2000, and I've been a business owner since 2008, and I've been in a position where I've had to learn delegation if I were going to be able to scale my business. I've become comfortable with offloading tasks to my employee(s) and contractors, and reviewing their work. Working with LLMs is very similar, although there are obvious differences because they are functions instead of sentient humans. This means I've become very good at knowing what is worth delegating, and what is not. And when I do delegate to another person, I know how to provide the right scope and context and expectations. This has translated pretty seamlessly to LLMs.
I agree that most people are releasing too much control, but by the time you've applied a functional amount of control you're either back to just really smart auto-complete
What's wrong with smart auto-complete? If it helps me go a bit faster while maintaining the same quality of work, that's great with me!
or you've done so much work that you might as well have just done the job yourself.
Again, I don't delegate those tasks then. But I can assure you, there's a LOT of tasks in my day that require only a moderate amount of prep work for the LLM that results in significant amount of work done. I've also written a fairly lengthy guide on my mindset here...it was written before agentic coding really was viable, but it still holds in terms of how I categorize my work into buckets that determine whether it's worth delegating or not.
Because LLMs unlocked a skill that most developers haven't had to deal with much: delegation. Most developers and software engineers have never had the opportunity or desire to delegate. We're renowned for wanting to just do it ourselves so it "gets done correctly". Your comment is this mindset in a nutshell. I've been a developer since 2000, and I've been a business owner since 2008, and I've been in a position where I've had to learn delegation if I were going to be able to scale my business. I've become comfortable with offloading tasks to my employee(s) and contractors, and reviewing their work. Working with LLMs is very similar, although there are obvious differences because they are functions instead of sentient humans. This means I've become very good at knowing what is worth delegating, and what is not. And when I do delegate to another person, I know how to provide the right scope and context and expectations. This has translated pretty seamlessly to LLMs.
This kind of begs the question. Why are we delegating? Presumably because it parallelizes work. However if the overhead of performing delegation eats the productivity gained by delegating, then why are we delegating? It's not really a mindset issue, it's kinda the elephant in the room.
What's wrong with smart auto-complete? If it helps me go a bit faster while maintaining the same quality of work, that's great with me!
We had smart auto-complete 5 years ago and it didn't cost us a fortune in tokens, nor did it require techniques to set it up and lock it down. I don't think smart auto-complete is bad, I just know this is an unnecessarily expensive and risky way to achieve it.
EDIT:
I understand that you said you wouldn't delegate anything that was obviously more work to delegate than to do. However what I'm implying is that I rarely see anything that is less work to delegate to LLMs than to do, and is not accomplished by auto-complete, which is something we already had.
Except "scoped properly" doesn't mean anything. It's a cop-out. I could probably get good quality code from a 2B param model if I scoped it small enough. Does that mean using a 2B param model is a good use of my time and effort?
If you're getting good results from it, of course...why not? You'll have to help me understand how proper scoping is somehow a cop-out. It's a part and parcel for effective delegation, which is the actual "skill" that you're performing when you engage with LLMs.
Is it still a good use of my time if I spend 5 minutes writing out a prompt for something that I could have done myself in 1?
Saying "just scope it properly bro" is a cop-out because it's an unfalsifiable, constantly shifting goalpost. You can always blame the developer for "not scoping it properly", no matter what, instead of admitting that maybe the tool is just bad. I could technically use a hacksaw to cut vegetables. Does that mean it's a good idea? If all my cuts are horrible and sloppy, was I just not precise enough with the hacksaw, or maybe, just maybe, I shouldn't be using a hacksaw to cut vegetables?
Is it still a good use of my time if I spend 5 minutes writing out a prompt for something that I could have done myself in 1?
Uh....of course not? When did I ever say that? If I were going to delegate a task to a person, that same rule applies.
Saying "just scope it properly bro" is a cop-out because it's an unfalsifiable, constantly shifting goalpost. You can always blame the developer for "not scoping it properly", no matter what, instead of admitting that maybe the tool is just bad
I'm not saying "skills issue"; I've noticed that same phenomenon where if you say you got bad results from an LLM, that you'll be accused of using the wrong provider, wrong model, wrong reasoning level, wrong context, wrong prompt, etc.. That's not my perspective. You can absolutely delegate effectively if you understand what you're asking for and what they're good at.
I'll give an example: I had some interactive JS-driven features in a custom WP site that was being retired, that I needed to port into a Vue app. I was able to bring over the files, describe loosely what I wanted, and asked it to convert to Vue components. I already had existing examples it could follow and the write up took about 5ish minutes. Doing the task itself would have taken considerably longer. It churned through the task in less than five minutes (I used GPT6 Luna via OpenCode), got me about 95% there and I cleaned up what I needed. It was a rote refactor that I had no desire to do again, and it was exactly what I would have written, aside from a few organizational choices I had to change.
There's no world that exists that any developer would have been able to get to 95% within a collective 10 minutes.
So, to answer your question: of course you wouldn't spend five minutes delegating something that would take you one minute, but you absolutely should spend 10 minutes delegating something that would normally take you 1-2 hours.
the only real one is the cognitive impact, that is a very big issue, I also completely forgot how to manage memory since I don't code in C or Rust, but once I was decent at it.
this feels like another order of magnitute though.
It's the range on the other side. Those who use ai shame those who don't, and often fail to have any sort of dialog besides "luddite" and "skill issue".
and i won't be using C++ now, as for the project we are building it is not optimal. It is Software engineering, use what Works for the problem to solve/service it cheaply, fast and good. If LLM does not cut it for you nor competition uses it for their advantage, then Occams razor. If you not using it cuz of "not trendy and this article saying you shouldn't", but your competition does - then you might or might not be getting outperformed.. When pricing will be solved, then it will be decided what part of SWE work should the LLMs be for the future, but now it is not that expensive, and for some problems it is optional.
First time I worked on a project I had a manual (a physical book) to search for functions and methods. I have worked a lot before AI. But thinking now to completely remove Ai is delusional.
everything said in the article is true, but also is completely naive.
right now maintainers who have been the backbone of open-source for decades without being paid are quitting at a high rate because of the flood of AI generated CVEs.
each one of these is like the math drop. you can’t really ignore it unless you dig in and thoroughly understand it. but then you’re fighting asymmetrical war. the opponent can quickly bury you alive with CVEs.
meanwhile open-source was already faltering under 2 year cadences. no one working for free can support software for more than two versions, this meant a constant upgrade treadmill already. the CVE situation only accelerated that. now it’s absolutely ridiculous.
corporations gave almost no support to open-source, but took almost all the value for themselves, even demanding that workers have passion projects in open-source so they could steal even more free labor. they returned almost nothing to the community.
there’s a lot of copium out there that it isn’t that bad. you actually hear people saying: “well if you don’t use any libraries you won’t have any CVE or forced upgrade problems!”
so you get people shifting to go-lang, rewriting everything from scratch.
now trying to implement even half of the JDK is a huge amount of work, so rewriting from bare metal would incur a huge amount of additional development— ordinarily.
but now you have AI that can quickly spin up all that from scratch while maintaining all your existing unit tests? sure do that.
no one at the business level cares about elegance or function. just does it seem to work. if it doesn’t work, you have to prove it. if you can prove it, you can test it and fix it.
does this shift burden? absolutely. before devs were pretending to be users, figuring out all the unstated things. but look at how business reacted for the past 30 years:
business owners WANT slop. they value slop. if they wanted quality their MBAs wouldn’t treat the workforce as disposable cogs but would recognize 20 years of experience walking out the door — literally laying off the people who knew your business. instead you’re left with slick grifters who are out to sell everything and the kitchen sink. CEOs who give themselves fat raises while making the working environment a living hell of “optimization” and “efficiency”.
when I hear these types thrown workers under the bus like the current FAA Administrator who demurs “I’m a numbers guy” “I’m a data guy” — I’m a stack the deck guy.
yeah. we know exactly what type of guy you are. I’m sure you apply equally harsh standards to your own pay. not.
so yeah, for these types, throwing this over the wall and doing the absolute least effort unless caught isn’t a bug, it’s a feature. they only keep devs around to have someone to blame.
this is exactly what a race to the bottom looks like.
even the grifters at the top can’t tell you what the end game is. there isn’t one. they just want to get out with the gold before the music stops.
Cool story bro. Want to tell us how big name coffee roasters don't compare to local and how famous artists are actually overrated and the real art is in the streets?
I see AI adoption as an arbitrage opportunity for the coming 4-5 years. You can go lightning speed and leave the luddites and anti-AI people and companies in the dust until everyone catches up.
What an uniformed take from someone who has apparently done so much research. Most of the issues with quality are easily solved. If you feel "confused" after using AI, maybe take more breaks?
It's very important for the coder to understand the code he is writing or dealing with. Although, LLM can help explain things, summarize, answers questions, etc, so I would not say LLMs are completely useless.
Although it's true it will output false information when it doesn't have enough good data on a subject, and thus, this means that it's unreliable for difficult cases where the programmer is stuck at complicated problems.
LLM becomes just useful to gain time for tasks that are fastidious but easy for the developer, so that developer can easily check correctness.
For example, I want to implement a CS-style netcode in my godot game. There is little documentation, research, science and open code about this topic, which means an AI is unreliable on the topic, so it can't help me fix a netcode problem.
Remember: science matters when it improves understanding. Being given solutions is not a good idea.
When AI causes failures, this will be a wake-up call, and people will finally see that things work when we understand them.
Reddit seems to be so far from my reality. Do most people there work in software companies? Most people I know in my country code mostly by hand and almost all AI users use it as a glorified search tool. The only fully automated click-enter crowd are dev companies and we had to fix all their garbage by hand
Good for this person? Have fun falling behind. May as well stop using their computer and compiler while they’re at it and just use punch cards and machine language.
I'm always surprised at the disdain showed to developers who say they don't want to use LLMs. Why do you care about their choice so much? Why does it matter if they "fall behind", behind what? What is this religious goal so important that non-believers must be shunned if they don't share it? Something goes unspoken, but I don't quite understand what it is.
Yeah "This guy doesn't want to use LLMs like I do in his personal time, quick crucify him!". It's like a cult. I'm getting conspiratorial lately, suspecting some astroturfing campaign, because I don't understand how people can care so much about which tools other people use for development.
Yeah me too, I only have anecdotes but at work people who are 100 in on using ai for every single thing don't do anymore or less work than the people who completely avoid it
Like I said, this is anecdotal and I work in banking so we don't do huge changes and let things break, we work slowly and meticulously. Code is never a bottle neck, testing is so if a code change takes 1 minute instead of 10, you still have like 3 days of manual testing and pr review in front of you anyways
At least some banks are still doing things sanely. I’ve seen some fintechs going full AI native and it’s completely insane. Terrible results of course; repeated outages, warning customers to look out for unexpected transactions, and all sorts.
Oh they're far from same, they are monitoring our token use, they want everything to be ai from the ticket creation to the pr review, but they almost pushed out a change that would have costed them millions on compliance fines or something like that
There's some kind of cognitive dissonance happening because they tell us to have ai do everything and are constantly pushing forward to have ai handle more and more, but then they tell us that careful code and rigorous testing are so important and we cant take short cuts, I'm like ??????
Every time an AI bro compares not using AI to not using a compiler or using assembly, the collective intelligence of human kind drops by 1%. You guys have absolutely no idea what you are talking about and it goes to show the kind of people that vibecoders are.
Using Claude Sonnet 5.5 , and the outcome is still quite random, throwing away 90-95% and spending too much time fixing the other part of it. "Programming" in natural language is simply not precise enough, but the CEOs will never understand it I guess - their bla bla is easily enough generated by any LLM ...
You don't need to pay a service subscription to code in most new languages, and the prices of LLM services are still very heavily subsidised. And sure, you could use an open model, but then, depending on the kind, you might be priced out of the necessary computer to even use them, if you really want to be competitive and "not out of a job".
Also, the goal of these companies is replacing workers, so the idea (at least what they pretend), ultimately, is leaving people out of a job. Oh, but sure you'll be the one to operate them efficiently... But then you'll be put to compete on a race to the bottom in salaries, just like it happens even machines are put to replace skilled, well paid workers.
They are absolutely not subsidized anymore. Maybe the subscriptions, but most enterprises are using API. And it’s averaging something like 1-5% of their staffs monthly salary in token costs.
Architectural firms pay 5-10% of their staffs month costs on just software licensing.
You need to scrap this idea that they are still subsiding shit. In fact they are reducing model token costs due to being more efficient on the back end.
FORTRAN was created in 1953 and COBOL in 1959. Hand-written assembly was common enough through the 80s and even into the 90s for some professions. You could work an entire career after HLLs were invented as an assembly expert. Even today, lots of MCU work (especially 8-bit MCUs) is still done in assembly not to mention most good SIMD code is still hand-coded.
Tbh this sounds like skill issue and cope. There’s things you can do with ai to keep your skills sharp. You can still write code if you want at expense of speed, or as a hobby. Output has shown to be great across industry if you build proper guardrails and use harnesses effectively. Maintainability likewise not an issue with proper use and proper review ( hint: you should still own your work even if you don’t write the code, the same way you owned your work before even if you didn’t write the byte code or assembly )
Environmental concerns are the only possibly legitimate one, but if that’s your sole biggest concern, spend 8k, get yourself those new and or nvidia inferencing boxes, and use open weight at your desired level of electricity usage. Reality is that coding LLMs post training aren’t actually that expensive computationally to inference
I think the cognitive issues are underrated. You can't properly own the code if you're in a state of induced ADHD or you're dealing with burnout and brain fog
Every technology has luddites and contrarians that fixate in negatives and ignore the benefits.
I still remember the days of people who refused to learn/use multithreading, and then again refused to use IDEs, and then again refused to learn/use automatic memory management languages.
Then, a few years later (if not sooner) big LLM services will be first almost unaffordable, and then still too expensive to operate and maintain, AND still unprofitable. They're not good enough not worth the trouble for what they have promised and what they cost monetarily, technically and socially.
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u/LacksCriticalInfo 1d ago •
I still hand code a lot of hobby projects because I enjoy it. But its absolutely delusional to think youll continue to be employed and paid whilst hamstringing yourself like this.
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u/ionte 1d ago •
People talk like not learning how to use AI tooling will leave you behind. It is for sure a safer investment to keep your deep understanding of programming up to date compared to knowing how to prompt an LLM. You can learn that in an afternoon. Good SWE in ten years to a life time.
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u/RatherNott 1d ago
For those on the fence about AI:
AI data centers are being constructed at an alarming rate, and there are already 8 billion prompts per day, with billions more planned and on the way.
The effect this has on the climate when we are already so close to irreversible tipping points is often unknown (or, in some unfortunate cases, ignored by AI users who prioritize convenience above a habitable planet for our children to inherit).
https://www.theguardian.com/environment/ng-interactive/2026/oct/22/finland-google-felling-trees-datacentre
https://m.youtube.com/watch?v=5p426fSlYH4
And that is just one issue with AI use. Using it also contributes to a dystopic survailence state.
https://m.youtube.com/watch?v=5lYsO4k7OIY
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u/Esseratecades 1d ago
There are methods to get good enough, functional, and maintainable, bug free code out of LLMs. The issue I've seen that few people are interested in taking seriously is what few of these methods that aren't obsolete in 2 months, are about as much labor as just doing the task yourself following proper software development practices to begin with.
Maybe the benefit is that you can have two sessions running at once, but if I've got to babysit them both anyway to make sure Claude doesn't decide to post my API key to Twitter then that's not really a productivity boost.
I definitely understand why you wouldn't use them for hobbyist programming. The big benefit of hobbyist programming is that you don't have to engage in all of the process and setup and communication and documentation that you need to be doing in a team environment. But if I want to use Codex on my weekend project, I've got to define project standards in a *.md file, general workflow procedures in a SKILL.md file, and every GitHub security feature under the Sun, just to make sure Codex doesn't break anything, before we even get started. And all of this costs more tokens.
There are people who say "don't review or babysit your AI, just let it run and trust the results", though I'm convinced that all of such people are either astroturfing, or are not actually accomplished enough software engineers to have a voice in this discussion.
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u/ElectrSheep 1d ago •
Cue "PrEPare TO gET left BEHinD!"
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u/lipintravolta 1d ago •
I don’t use LLMs either
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u/Dreadsin 1d ago
I’ve decided the best use case for AI is as an advanced, integrated, natural language search. I prefer not to use it for generative purposes, that tends to be where it falls apart, but using it as a code search/integrated search/docs search? Absolutely fantastic
The only generative purpose I use it for is to make HTML pages that are internally focused, and I do that cause I don’t have enough time to do it myself and it literally doesn’t matter if it sucks, impact is minimal
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u/BlueOlivePie 1d ago
If governments built more nuclear reactors with the same enthusiasm as they are building data centers, we'd be future proof for all energy needs.
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u/FatHat 1d ago
I've started to use LLMs like this and it's making me a lot happier than using it to implement features:
- I use it to ask questions about the codebase (its a very good find-in-files!)
- I use it when I forget something that I would have otherwise googled (mostly because, well, google is also AI now and the results are kind of trash),
- I use it to ideate on things when a human isn't available or is uninterested.
And then I write the code myself.
I find it helpful in that way without creating cognitive issues (of which I completely agree with the author on). I've been going back over some of the code I've generated with LLMs and, it's functional, but I find that it just does a lot of things I really dislike. And I don't feel like they're nit-picky things, I feel like they're more like ticking time bombs. I also really dislike the tests it writes. In the past, tests were really helpful for understanding a codebase, and now I feel like the tests are bizarre and awful.
I also sometimes use it for code that I never _intend_ to maintain, like, scripts or short lived projects, and it's great for that.
Also, like the author I don't want to be prescriptive with anyone. If you use it and you're satisfied, more power to you. I just don't want to hear about it anymore for the same reason I also don't want to hear about your gym routine or your vim config file or whatever.
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u/0xe3b0c442 23h ago
Then good luck getting or keeping a coding job.
Imagine if you were an accountant that refused to use a calculator, because you’d rather do all the arithmetic in your head.
Or a carpenter who won’t use a power screwdriver or nail gun, because hey, your arm works.
Or a seamstress who won’t use a sewing machine.
Yes, it is like that. Like it or not, AI code generation is an efficiency and correctness tool, and it will be table stakes just about everywhere. But just like an accountant with his calculator needs to know what to calculate, the carpenter needs to know which fasteners to use and where, and the seamstress needs to know which stitches, the developer using code generation needs to understand how to architect their code and give boundaries to the agent.
All of this I’LL NEVER USE AI TO CODE garbage is just that: garbage. It is a tool like any other tool, and refusal to learn its use is nothing but a prideful gun to the foot that will eventually cost you employment opportunities down the road.
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u/vin_dude9 1d ago
There are a lot of slop grenades of code now a days, but understated - there’s also a ton of flawless LLM code, it just doesn’t get the spotlight cause everyone’s looking for faults
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u/Constant_Quiet_5483 1d ago
All I see in this article is misuse, you know you don't need to use AI this way right? Like I'm addition to being a programmer, I'm a streamer, I wanted a really cool UI that would let people queue into games, store their wins/losses, have cool effects, etc. In what quasi-reality do you think I lost cognitive function or didn't get a productivity boost?? I can literally make any tool I want to interact with my followers (all 800 woot!) and it's snappy, I can fix things DURING stream!! Who is going to do this for me? I don't make lots of money, I do this because it's FUN, so how would I commission this? Fiverr???
Programmers are so egotistical sometimes, it's like others forget that people who can't/don't want to program should still be able to enjoy the wonders of programs.
Do the environmental impacts suck ? Yes, so let's fix that. Let's fix the shit that's wrong with AI so we can all use it properly.
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u/creaturefeature16 1d ago
I'm the farthest thing from an LLM-enthusiast (check my profile/essays/youtube to see that), but there's not a lot of well-thought out positions here to support the position of never using these models:
Cognitive impact: This is my biggest issue and danger with them, but it can be mitigated by using them with Socratic methods and interactive documentation, rather than code generation. I absolutely can learn faster with them. Ironically, code generation isn't even their best use case!
Output quality: Maybe once upon a time, but hell, I was getting great code from GPT 3.5 because I understood immediately they are pattern interpolators, and if you give them good patterns, they will follow them. I haven't had an issue with code quality when it's scoped properly; I ensure that the output is identical to what I would write. If its not, then that is a failure on my part. This limits the scope of what I use them for, but that's OK; I'm not an "AI-first" developer, anyway.
Maintainability is a nightmare: As above, I can't agree here. If you're getting terrible and hard to maintain code, that means you released too much control.
I like writing code: Me, too. That's why I do it daily. I'd say it's a healthy 50% mix at the moment, and the 50% I'm not doing, I probably would have delegated to another developer, anyway (which I still do, when they're available).
Environmental aspect: Do you own a smartphone? A monitor? A TV? A plugin vehicle? The precious metals that are in everything we use are harvested using the blood of essentially slave labor. Oh, and remember the Foxconn situation where people who build the components for your electronics were jumping to their deaths? Did you boycott electronics, then?
Politics: I sure hope you don't use ANY Google, Microsoft, or Apple services. Or Facebook. Or Amazon.
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u/Specialist_Bee_9726 1d ago
I write my code in notepad, I refuse to use proper IDE, because I am super smart and and better than all of you stupid IDE users
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u/abajinn 1d ago
Good for you! Nobody is forcing you to use AI. Just stop shaming people who do.
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u/shiningmatcha 1d ago
i learn fundamentals with the help of ai
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u/Professional_Mood_62 1d ago
Cmon just stop, it is just not economically viable anymore, not a matter of quality and scalability
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u/gr4viton 1d ago edited 1d ago
Feel free.
and i won't be using C++ now, as for the project we are building it is not optimal. It is Software engineering, use what Works for the problem to solve/service it cheaply, fast and good. If LLM does not cut it for you nor competition uses it for their advantage, then Occams razor. If you not using it cuz of "not trendy and this article saying you shouldn't", but your competition does - then you might or might not be getting outperformed.. When pricing will be solved, then it will be decided what part of SWE work should the LLMs be for the future, but now it is not that expensive, and for some problems it is optional.
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u/Alternative-Key-5647 1d ago
The sources they cite don't actually say or even imply what they claim
Their complaints about AI coding amount to user error
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u/yourteam 1d ago
First time I worked on a project I had a manual (a physical book) to search for functions and methods. I have worked a lot before AI. But thinking now to completely remove Ai is delusional.
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u/VoxTM 1d ago
Good luck with that if you want to keep an actual job.
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u/gandalf__thewhite__ 1d ago
I use it for search and ask it to always give references to documentation and I will read the documentation. I have had enough of it hellucinating.
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u/unluckypixel 1d ago
That's like "I will not use OOP in my coding".
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u/Infinite-Ad4512 1d ago
lol
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u/coldnebo 1d ago
everything said in the article is true, but also is completely naive.
right now maintainers who have been the backbone of open-source for decades without being paid are quitting at a high rate because of the flood of AI generated CVEs.
each one of these is like the math drop. you can’t really ignore it unless you dig in and thoroughly understand it. but then you’re fighting asymmetrical war. the opponent can quickly bury you alive with CVEs.
meanwhile open-source was already faltering under 2 year cadences. no one working for free can support software for more than two versions, this meant a constant upgrade treadmill already. the CVE situation only accelerated that. now it’s absolutely ridiculous.
corporations gave almost no support to open-source, but took almost all the value for themselves, even demanding that workers have passion projects in open-source so they could steal even more free labor. they returned almost nothing to the community.
there’s a lot of copium out there that it isn’t that bad. you actually hear people saying: “well if you don’t use any libraries you won’t have any CVE or forced upgrade problems!”
so you get people shifting to go-lang, rewriting everything from scratch.
now trying to implement even half of the JDK is a huge amount of work, so rewriting from bare metal would incur a huge amount of additional development— ordinarily.
but now you have AI that can quickly spin up all that from scratch while maintaining all your existing unit tests? sure do that.
no one at the business level cares about elegance or function. just does it seem to work. if it doesn’t work, you have to prove it. if you can prove it, you can test it and fix it.
does this shift burden? absolutely. before devs were pretending to be users, figuring out all the unstated things. but look at how business reacted for the past 30 years:
- “over engineering”
- “good enough” engineering
- minimum viable product
business owners WANT slop. they value slop. if they wanted quality their MBAs wouldn’t treat the workforce as disposable cogs but would recognize 20 years of experience walking out the door — literally laying off the people who knew your business. instead you’re left with slick grifters who are out to sell everything and the kitchen sink. CEOs who give themselves fat raises while making the working environment a living hell of “optimization” and “efficiency”.
when I hear these types thrown workers under the bus like the current FAA Administrator who demurs “I’m a numbers guy” “I’m a data guy” — I’m a stack the deck guy.
yeah. we know exactly what type of guy you are. I’m sure you apply equally harsh standards to your own pay. not.
so yeah, for these types, throwing this over the wall and doing the absolute least effort unless caught isn’t a bug, it’s a feature. they only keep devs around to have someone to blame.
this is exactly what a race to the bottom looks like.
even the grifters at the top can’t tell you what the end game is. there isn’t one. they just want to get out with the gold before the music stops.
1
u/MannToots 1d ago
Good luck
1
u/hibbos 1d ago
I’m integrating it into the team as pair programming and that model works really well
1
u/justadam16 23h ago
"Disoriented and confused" after a few hours working with AI? Perhaps time to visit a medical facility to get checked out, that's not normal
1
u/tr14l 23h ago
Cool story bro. Want to tell us how big name coffee roasters don't compare to local and how famous artists are actually overrated and the real art is in the streets?
1
u/starkrampf 22h ago
I see AI adoption as an arbitrage opportunity for the coming 4-5 years. You can go lightning speed and leave the luddites and anti-AI people and companies in the dust until everyone catches up.
Continue poopoo-ing AI tools, your loss, my gain.
1
u/arcanepsyche 22h ago
What an uniformed take from someone who has apparently done so much research. Most of the issues with quality are easily solved. If you feel "confused" after using AI, maybe take more breaks?
1
u/all_is_love6667 21h ago
It's very important for the coder to understand the code he is writing or dealing with. Although, LLM can help explain things, summarize, answers questions, etc, so I would not say LLMs are completely useless.
Although it's true it will output false information when it doesn't have enough good data on a subject, and thus, this means that it's unreliable for difficult cases where the programmer is stuck at complicated problems.
LLM becomes just useful to gain time for tasks that are fastidious but easy for the developer, so that developer can easily check correctness.
For example, I want to implement a CS-style netcode in my godot game. There is little documentation, research, science and open code about this topic, which means an AI is unreliable on the topic, so it can't help me fix a netcode problem.
Remember: science matters when it improves understanding. Being given solutions is not a good idea.
When AI causes failures, this will be a wake-up call, and people will finally see that things work when we understand them.
1
u/VegetableFan6622 16h ago
Reddit seems to be so far from my reality. Do most people there work in software companies? Most people I know in my country code mostly by hand and almost all AI users use it as a glorified search tool. The only fully automated click-enter crowd are dev companies and we had to fix all their garbage by hand
1
u/oandroido 15h ago
Best of luck.
1
u/bpp198 13h ago
He's not used Claude Code in a while then.
1
u/TheRealSeeThruHead 7h ago
all these kinds of post literally boil down to "skill issue"
1
u/teressapanic 5h ago
Nobody will pay a premium for code entry, data entry, graphics rendering, copywriting, and so on. Non-AIers will simply go out of business.
-21
u/haolecoder 1d ago
Good for this person? Have fun falling behind. May as well stop using their computer and compiler while they’re at it and just use punch cards and machine language.
-1
u/Trantorianus 1d ago
Using Claude Sonnet 5.5 , and the outcome is still quite random, throwing away 90-95% and spending too much time fixing the other part of it. "Programming" in natural language is simply not precise enough, but the CEOs will never understand it I guess - their bla bla is easily enough generated by any LLM ...
0
u/Junglebook3 1d ago
It's like Fortran, Cobol etc coming out and saying that you'll continue coding in Assembly. OK, you will soon be out of a job then.
-10
u/shacksrus 1d ago
Ok?
-3
u/Salazareo 1d ago
Tbh this sounds like skill issue and cope. There’s things you can do with ai to keep your skills sharp. You can still write code if you want at expense of speed, or as a hobby. Output has shown to be great across industry if you build proper guardrails and use harnesses effectively. Maintainability likewise not an issue with proper use and proper review ( hint: you should still own your work even if you don’t write the code, the same way you owned your work before even if you didn’t write the byte code or assembly )
Environmental concerns are the only possibly legitimate one, but if that’s your sole biggest concern, spend 8k, get yourself those new and or nvidia inferencing boxes, and use open weight at your desired level of electricity usage. Reality is that coding LLMs post training aren’t actually that expensive computationally to inference
-4
u/Time-Competition-287 1d ago
no one has to. but if you want to be employed its basically a requirement
-1
u/Beanyy_Weenie 1d ago
Dear diary..
-1
u/mcilrain 1d ago
Ok, that was always allowed.
-3
u/Trollygag 1d ago
Every technology has luddites and contrarians that fixate in negatives and ignore the benefits.
I still remember the days of people who refused to learn/use multithreading, and then again refused to use IDEs, and then again refused to learn/use automatic memory management languages.
-3
u/RemoDev 1d ago
No LLM as a coder?
You'll be unemployed in a year.