The hidden cost of AI coding
(terriblesoftware.org)256 points by Sharpie4679 a day ago
256 points by Sharpie4679 a day ago
> Some people love programming
> Other people see all that as an means to an end
I think it's worth pointing out that most people are both these things at different times.
There's things I care about and want a deep understanding of but there's plenty of tasks I want to just "go away". If I had an junior coder - I'd be delegating these. Instead I use AI when I can.
There's also tasks where I want a jump start. I prefer fixing/improving code over writing from scratch so often a bad AI attempt is still valuable to me.
You likely don’t have a say in the matter, but you should have a junior developer. That’s where senior developers come from.
Why should I have a junior developer who is going to do negative work instead of poaching a mid developer who is probably underpaid since salary compression and inversion are real?
As a manager, say I do hire a junior developer, invest time into them and they level up. I go to the HR department and tell them that they deserve a 30% raise to bring them inline with the other mid level developers.
The HR department is going to say that’s out of policy and then the developer jumps ship.
Many have said that it's useful to delegate writing boilerplate code to an AI so that you can focus on the interesting bits that you do want to write yourself, for the sake of enjoying writing code.
I recognize that and I kind of agree, but I think I don't entirely. Writing the "boring" boilerplate gives me time to think about the hard stuff while still tinkering with something. I think the effect is similar to sleeping on it or taking a walk, but without interrupting the mental cruncing that's going in my brain during a good flow. I piece together something mundane that is as uninteresting as it is mandatory, but at the same time my subconscious is thinking about the real stuff. It's easier that way because the boilerplate does actually, besides being boring, still connect to the real stuff, ultimately.
So, you're kind of working on the same problem even if you're just letting your fingers keep typing something easy. That generates nice waves of intensity for my work. My experience regarding AI tends to break this sea of subconsciousness: you need to focus on getting the AI to do the right thing which, unlike typing it yourself, is ancillary to the original problem. Maybe it's just a matter of practise and at some point I can keep my mind on the domain in question eventhough I'm working an AI instead of typing boilerplate myself.
The first time you write the code to accomplish something you get your highs.
IMHO there's no joy in doing the same thing multiple times. DRY doesn't help with that, you end up doing a lot of menial work to adapt or integrate previous code.
Most of the for-profit coding is very boring.
I've always distilled this down to people who like the "craft" and those who like the "result".
Of course, everything is on a scale so it's not either/or.
But, like you, how I get there matters to me, not just the destination.
Outside the context of music, a project could be super successful but if the journey was littered with unnecessary stress due to preventable reasons, it will still leave a bad taste in my mouth.
> I've always distilled this down to people who like the "craft" and those who like the "result".
I find it very unlikely anyone who only likes the results will ever pick up the craft in the first place
It takes a very specific sort of person to push through learning a craft they dislike (or don't care about) just because they want a result badly enough
What's "the result"? Because I don't like how this divide is being stated (it's pretty common).
Seems to me that "the result" is "the money" and not "the product".
Because I'd argue those that care about the product, the thing being built, the tool, take a lot of pride in their work. They don't cut corners. They'll slog through the tough stuff to get things done.
These things align much more with the "loves coding" group than "the result". Frankly, everyone cares about "the result" and I think we should be clear about what is actually meant
The issue with programming is that it isn't like music or really any other skill where you get feedback right away and operate in a well understood environment. And a lot of patterns are not well designed as they are often based on what a single developer things the behavior ought to be instead of something more deterministic like the laws of physics that influence the cord patterns we use in music.
Nope, your code might look excellent. Why the hell isn't it running though? Three hours later you find you added a b when you closed your editor somewhere in the code in a way your linter didn't pick up and the traceback isn't clear about, maybe you broke some all important regex, it doesn't matter. One second, it's fixed, and you just want to throw the laptop out the window and never work on this project again. So god damned stupid.
And other things are frusterating too. Open a space deliminated python file, god forbid you add a tab without thinking. And what is crazy about that is if the linter is smart enough to say "hey you put a tab here instead of spaces for indent" then why does it even throw the error and not just accept both spaces and tabs? Just another frustration.
Really I would love to just go at it, write code, type, fly, be in the flow state, like one does building something with the hands or making music or doing anything in the physical world. But no. Constant whack a mole. Constantly hitting the brakes. Constant blockers. How long will this take to implement? I have no fucking idea man, could be 5 seconds or 5 weeks and you don't often know until you spend the 5 seconds and see that didn't do it yet.
Some people like to play a musical instrument, others to compose music. Those who play range from classicists, who have limited ability to improvise or interpret, to popular or jazz, or composition, where creativity and subtle expression is the life blood of the work.
Programming is similar to music. (A great many software innovators in the 70s and 80s had musical roots). But AI prunes away all the creativity and stylistic expression from the composition and the performance when designing and building software, reducing the enterprise to mere specification -- as if the libretto of the opera were merely an outline, and even that was based on Cliff Notes.
The case for using AI to code is driven strictly by economics and speed. Stylistically and creatively, AI is a no-brainer.
I think I am somewhere between the two groups you mention
I don't really get any joy from the act of coding, but I also take a lot of pride in doing a good job.
Cutting corners and producing sloppy work is anathema to me, even when I don't really enjoy the work itself
Any work worth doing is worth doing a good job on, even if I don't enjoy the work itself
I think a closer analogy is:
- A singer might learn to play guitar to sing along to it. Guitar is a means to an end; it is simply a tool to them.
- A guitarist learns to play guitar due to love of the instrument.
> On the other hand, I know people that want to jump straight to the end result. They have some melody or idea in their head, and they just want to generate some song that revolves around that idea. I don't really look down on those people, even though the snobs might argue that they're not "real musicians". I don't understand them, but that's not really something I have to understand either.
So if someone generates their music with AI to get their idea to music you don’t look down on it?
Personally I do, if you don’t have the means to get to the end you shouldn’t get to the end and that goes double in a professional setting. If you are just generating for your own enjoyment go off I guess but if you are publishing or working for someone that’ll publish (aka a professional setting) you should be the means to the end, not AI.
Where do you draw that line though?
If you're talking about a person using an LLM, or some other ML system, to help generate their music then the LLM is really just a tool for that person.
I can't run 80 mph but I can drive a car that fast, its my tool to get the job done. Should I not be allowed to do that professionally if I'm not actually the one achieving that speed or carrying capacity?
Personally my concerns with LLMs are more related to the unintended consequences and all the unknowns in play given that we don't really know how they work and aren't spending much effort solving interoperability. If they only ever end up being a tool, that seems a lot more in line with previous technological advancements.
> I can't run 80 mph but I can drive a car that fast, its my tool to get the job done.
Right, but if you use a chess engine to win a chess championship or if you use a motor to win a cycling championship, you would be disqualified because getting the job done is not the point of the exercise.
Art is (or should be) about establishing dialogues and connections between humans. To me, auto-generated art it's like choosing between seeing a phone picture of someone's baby and a stock photo picture of a random one - the second one might "get the job done" much better, but if there's no personal connection then what's the point?
> I can't run 80 mph but I can drive a car that fast
If you drive a car 80mph you don't get to claim you are a good runner
Similarly if you use an LLM to generate 10k lines of code, you don't get to claim you are a good programmer
Regardless of the outcome being the "same"
Why?
What has always held true so far: <new tool x> abstracts challenging parts of a task away. The only people you will outcompete are those, who now add little over <new tool x>.
But: If in the future people are just using <new tool x> to create a product that a lot of people can easily produce with <new tool x>, then, before long, that's not enough to stand out anymore. The floor has risen and the only way to stand out will always be to use <new tool x> in a way that other people don't.
People who can't spin pottery shouldn't be allowed to have bowls, especially mass produced by machine ones.
I understand your point, but I think it is ultimately rooted in a romantic view of the world, rather than the practical truth we live in. We all live a life completely inundated with things we have no expertise in, available to us at almost trivial cost. In fact it is so prevalent that just about everyone takes it for granted.
Sounds like Communist Albania where everybody had to be able to repair the car and take it apart and put it back together to own one
> So if someone generates their music with AI to get their idea to music you don’t look down on it?
It depends entirely on how they're using it. AI is a tool, and it can be used to help produce some wonderful things.
- I don't look down on a photographer because they use a tool to take a beautiful picture (that would have taken a painter longer to paint)
- I don't look down on someone using digital art tools to blur/blend/manipulate their work in interesting ways
- I don't look down on musicians that feed their output through a board to change the way it sounds
AI (and lots of other tools) can be used to replace the creative process, which is not great. But it can also be used to enhance the creative process, which _is_ great.
If they used an algorithm to come up with a cool melody and then did something with it, why look down on it?
Look at popular music for the last 400 years. How is that any different than simply copying the previous generations stuff and putting your own spin on it?
If you heard a CD in 1986 then in 2015 you wrote a song subconsciously inspired by that tune, should I look down on you?
I mean, I'm not a huge fan of electronic music because the vast majority of it sounds the same to me, but I don't argue that they are not "real musicians".
I do think that some genres of music will age better than others, but that's a totally different topic.
I think you don't look down at the product of AI, only the process that created it. Clearly the craft that created the object has become less creative, less innovative. Now it's just a variation on a theme. Does such work really deserve the same level of recognition as befitted Beethoven for his Ninth or Robert Bolt for his "A Man for all Seasons"?
Your company doesn’t care about how you got to the end, they just care about did you get there and meet all of the functional and non functional requirements.
My entire management chain - manager, director and CTO - are all technical and my CTO was a senior dev at BigTech less then two years ago. But when I have a conversation with any of them, they mostly care about whether the project I’m working on/leading is done on time/within budget/meets requirements.
As long as those three goals are met, money appears in my account.
One of the most renown producers in hip hop - Dr. Dre - made a career in reusing old melodies. Are (were) his protégés - Easy-E, Tupac, Snoop, Eminem, 50 cent, Kendrick Lamar, etc - not real musicians?
Have you heard the saying there is too much performance in the practice room? It's the same with programming. Performance is the goal, and practice is how you get there. No one seems to be in danger of practicing too much though.
i mean how far are you willing to take that argument? every decade has just been a new abstraction, imagine people flipping switches or in raw assembly talking about how they don't "understand" you now with your no effort. or even those who don't "understand" why you use your autocomplete and fancy IDE, preferring a simple text editor.
i say this as someone who cut my teeth on this stuff growing up and seeing the evolution, it's both. and at some point it's honestly elitism and gatekeeping. i sort of cringe when it's called a "craft" because it's not like woodworking or something. the process is both full of joy but so is the end result, and the nature of our industry is that the process is ALWAYS changing.
you accumulate a depth of knowledge and watch as it washes away in a few years. that kind of change, and the big kind of change that AI brings scares people so they start clinging to it like it's some kind of centuries old trade lol.
It is not just gatekeeping. It is a stubborn refusal to see that one could be programming something much more sophisticated if they could use these iteration loops efficiently.
Many of these folks would do well to walk over to the intersection of Market, Bush, and Battery Streets in San Francisco and gaze up at the Mechanics Monument.
> It is a stubborn refusal to see that one could be programming something much more sophisticated if they could use these iteration loops efficiently
Programming something more sophisticated with AI? AI is pretty much useless if you're doing anything somewhat novel. What it excels at is vomiting code that has already been written a million times so you can build yet another Electron cross-platform app.
I have actually had some really great flow evenings lately, the likes of which I have not enjoyed in many years, precisely because of AI-assisted coding. The trick is to break the task down in to components that are of moderate complexity so that the AI can handle them (Gemini 2.5 Pro one-shots), and keep your mind on the high-level design which today's AI cannot coordinate.
What helps me is to think of it like I'm a kid again, learning to code full of ideas but without any pre-conceived notions. Rather than the Microsoft QuickBasic manual in my hands, I've got Gemini & Claude Code. I would be gleefully coding up a storm of games, websites, dubious webcrawlers, robots, and lord knows what else. Plenty of flow to be had.
I always wonder what kind of projects are we talking about. I am currently writing a compiler and simulation engine for differential-algebraic equations. I tried few models, hoping they would help me, but they could not provide any help with small details nor with bigger building blocks.
I guess if you code stuff that had been coded a lot in public repos, it is fine, otherwise AI does not help in any way. Actually, I think I wasted more time trying to make it produce the output I wish for than it took me to do this myself.
That's been my experience. If it's been solved a million times, it's helpful. If you're out on the frontier where there's no public code, it's worse than useless.
If you're somewhere in between (where I am now) it's situationally useful for small sub-components but you need to filter it heavily or you'll end up wasting a day or two going down a wrong rabbit-hole either because you don't know the domain well enough to tell when it's bullshitting or going down a wrong path, or don't know the domain well enough to use the right keyword to get it to cough up something useful. I've found domain knowledge essential for deciding when it's doing something obviously wrong instead of saying "I don't know" or "This is the wrong approach to the problem".
For the correct self-contained class or block of code, it is much faster to specify the requirements and go through a round or two of refinement than it is to write it myself. For the wrong block of code it's a complete waste of time. I've experienced both in the last few days.
I don't even think you have to be on the frontier for LLMs to lose most of their effectiveness. Large legacy codebases with deeply ingrained tribal knowledge and loads of idiosyncrasies and inconsistencies will do the trick. Sad how most software projects end in this state.
Obviously LLMs in this situation will still be insanely helpful, but in the same way that Google searches or stack overflow is insanely helpful.
For me it's been toy games built on web languages, which happens to be something I toyed with via my actual raw skills for the past 15 years. LLMs have opened many new doors and options for what I can build because I now technically "know everything" in the world via LLMs. Stuff that I would get stuck wasting hours on is now solved in minutes. But then it ALWAYS reaches a point where the complexity the LLM has generated is too much and the model can no longer iterate on what it's built.
people seem to forget this type of argument from the article was used for stack overflow for years, calling it the destruction of programming. "How can you get into flow when you are just copying and pasting?". Those same people are now all sour grapes for AI assisted development. There will always be detractors saying that the documentation you are using is wrong, the tools that you are using are wrong, and the methodology you are using is wrong.
AI assisted development is no different from managing an engineering team. "How can you trust outsourced developers to do anything right? You won't understand the code when it breaks"... "How can you use an IDE, vim is the only correct tool" etc etc etc.
Nothing has changed besides the process. When people started jumping on object orientation they called procedures the devil itself, just as procedures were once called structured programming and came to banish away the considered harmful goto. Everything is considered harmful when theres something new around the corner that promises to either make development more productive or developers more interchangeable. These are institutional requirements and will never go away.
Embrace AIOP (AI oriented programming) to banish copy and paste google driven development which is now considered harmful.
The issue with "AIOP" is that you don't have a litany of others (as is the case with SO) providing counter examples, opinions, updated best practices, etc. People take the AI output as gospel and suffer for it without being exposed so the ambiguity that surrounds implementing things.
Will an engineering team ever be able to craft a thing of wonder, that surprises and delights? I think great software can do that. But I've seen it arise only rarely, and almost always as originating from one enlightened mind, someone who imagined a better way than the well-trod paths taken by so many who went before. I can imagine AI as a means to go only 'where man gas gone before'.
I'm a classic engineer, so lots of experience with systems and breaking down problems, but probably <150 hours programming experience over 15 years. I know how computers work and "think", but I an awful at communicating with them. Anytime I have needed to program something I gotta crash course the language for a few days.
Having LLMs like 2.5 now are total game changers. I can basically flow chart a program and have Gemini manifest it. I can break up the program into modules and keep spinning up new instances when context gets too full.
The program I am currently working on is up to ~5500 LOC, probably across 10ish 2.5 instances. It's basically an inventory and BOM management program that takes in bloated excel BOMs and inventory, and puts it in an SQLite database, and has a nice GUI. Absolutely insane how much faster SQLite is for databases than excel, lol.
I've heard a _lot_ of stories like this. What I haven't heard is stories about the deployment of said applications and the ability of the human-side author to maintain the application. I guess that's because we're in early days for LLM coding, or the people who did this aren't talking (about their presumed failures... people tend to talk about successes publicly, not the failures).
At my day job I have 3 programs written by LLM used in production. One written by GPT-4 (in spring 2023) and recently upgraded by gemini 2.5, and the other two by Claude 3.7
One is a automatic electronics test system that runs tests and collects measurements (50k+ readings across 8-12 channels)(GPT-4, now with a GUI and faster DB thanks to 2.5). One is a QC tool to help quickly make QC reports in our companies standard form (3.7). And the last is a GUI CAD tool for rendering and quickly working through ancient manufacturing automation scripts from the 80's/90's to bring them up to compatibility with modern automation tooling (3.7).
I personally think that there is a large gap between what programs are, and how each end user ultimately uses them. The programs are made with a vast scope, but often used narrowly by individuals. The proprietary CAD program that we were going to use originally for the old files was something like $12k/yr for a license. And it is a very powerful software package. But we just needed to do one relatively simple thing. So rather than buy the entire buffet, buy the entire restaurant, Claude was able to just make simple burger.
Would I put my name on these and sell to other companies? No. Am I confident other LLM junkies could generate similar strongly positive outcomes with bespoke narrow scope programs? Absolutely.
I'm an electrical engineer and work mostly with power electronics.
This is the way. I feel like a kid too again. It's way more fun actually. As a kid I got too frustrated for not being able to install my WAMP stack.
Added joy for me as well mostly by giving me the relevant API calls I need straight away, from publically available documentation, instead of having to read docs myself. "How do I do X in Y"
And if something's not obvious I can always fetch the specifics of any particular calls. But at least I didn't have to find the name of that call in the first place.
I’m right there with you on this.
Thanks for the comment. You articulated how I feel about this situation very well.
There's nothing stopping you from coding if you enjoy it. It's not like they have taken away your keyboard. I have found that AI frees me up to focus on the parts of coding I'm actually interested in, which is maybe 5-10% of the project. The rest is boiler plate, cargo-culted, Dockerfile, build system and bash environment variable passing circle of hell that I really could care less about. I care about certain things that I know will make the product better, and achieve its goals in a clever and satisfying way.
Even when I'm stuck in hell, fighting the latest undocumented change in some obscure library or other grey-bearded creation, the LLM, although not always right, is there for me to talk to, when before I'd often have no one. It doesn't judge or sneer at you, or tell you to "RTFM". It's better than any human help, even if its not always right because its at least always more reliable and you don't have to bother some grey beard who probably hates you anyway.
> The rest is boiler plate, cargo-culted, Dockerfile, build system and bash environment variable passing circle of hell that I really could care less about.
Even more so, I remember making a Chrome extension and feeling intimidated. I knew that I'd be comfortable with most of it given that JS is used but I just didn't know how to start.
With an LLM it is way faster to spin up some default config and get going versus reading a tutorial. What I've noticed in that respect is that I just read what it does and then immediately reason why it's there. "Oh, there's a manifest.json file with permissions and a few other things, fair, makes sense. Oh, so you have the HTML/CSS/JS of the extension, you have the HTML/CSS/JS of the page you're injecting some code into and you have the JS of a background worker. Ah yea, I get that."
And then I just get immediately on coding.
> What I've noticed in that respect is that I just read what it does and then immediately reason why it's there ....
How if it hallucinate and gives you wrong code and explanation? It is better to read documentations and tutorials first.
> How if it hallucinate and gives you wrong code
Then the code won't compile, or more likely your editor/IDE will say that it's invalid code. If you're using something like Cursor in agent mode, if invalid code is generated then it gets detected and the LLM keeps re-running until something is valid.
> It is better to read documentations and tutorials first.
I "trust" LLM's more than tutorials, there's so much garbage out there. For documentation, if the LLM suggests something, you can see the docstrings in your IDE. A lot of the time that's enough. If not, I usually go read the implementation if I _actually_ care about how something works, because you can't always trust documentation either.
Do you mean the laconic and incomplete documentation? And the tutorials that range from "here's how you do a hello world" to "draw the rest of the fucking owl" [0], with nothing in between to actually show you how to organise a code base or file structure for a mid-level project?
Hallucinations are a thing. With a competent human on the other end of the screen, they are not such an issue. And the benefits you can reap from having LLMs as a sometimes-mistaken advisory tool in your personal toolbox are immense.
The kind of documentation you’re looking for is called a tutorial or a guide, and you can always buy a book for it.
Also something are meant to be approached with the correct foundational knowledge (you can’t do 3D without geometry, trigonometry, and matrixes. And a healthy dose of physics). Almost every time I see people strugling with documentation, it was because they lacked domain knowledge.
What do you do if you "hallucinate" and write the wrong code? Or if the docs/tutorial you read is out of date or incorrect or for a different version than you expect?
That's not a jab, but a serious question. We act like people don't "hallucinate" all the time - modern software engineering devops is all about putting in guardrails to detect such "hallucinations".
Fair question. So far I've seen two things:
1. Code doesn't compile. This case is obvious on what to do.
2. Code does compile.
I don't work in Cursor, I read the code quick, to see the intent. And when done with that decide to copy/paste it and test the output.
You can learn a lot by simply reading the code. For example, when I see in polars a `group_by` function call but I didn't know polars could do that, now I know because I know SQL. Then I need to check the output, if the output corresponds to what I expect a group by function to do, then I'll move on.
There comes a point in time where I need more granularity and more precision. That's the moment where I ditch the AI and start to use things such as documentation and my own mind. This happens one to two hours after bootstrapping a project with AI in a language/library/framework I initially knew nothing about. But now I do, I know a few hours worth of it. That's enough to roughly know where everything is and not be in setup hell and similar things. Moreover, by just reading the code, I get a rough idea on how beginner to intermediate programmers think about the problem space the code is written in as there's always a certain style of writing certain code. This points me into the direction on how to think about it. I see it as a hint, not as the definitive answer. I suspect that experts think differently about it, but given that I'm just a "few hours old" in the particular language/lib/framework, I think knowing all of this is already really amazing.
AI helps with quicker bootstrapping by virtue of reading code. And when it gets actually complicated and/or interesting, then I ditch it :)
Most tutorials fail to add meta info like the system they're using and versions of things, that can be a real pain.
I think the fear for those of us who love coding, stability and security, that we are going to be confronted with apples that are rotten on the inside and our work, our love, is going to turn (even more so) into pain. The challenge in computing is that the powers that decide have little overview over the actual quality and longevity of any endeavour.
I work as a consultant assessing other people's code and it's hard not to lose my religion, sort of speak.
So much this. The AI takes care of the tedious line by line what’s-the-name-of-that-stdlib-function parts (and most of the tedious test-writing parts) and lets me focus on the interesting bits like what it is I want to build and how the pieces should fit together. And debugging, which I find satisfying.
Sadly, I find it sorely lacking at dealing with build systems and that particular type of boilerplate, mostly because it seems to mix up different versions of things too much and gives you totally broken setups more often than not. I’d just as soon never deal with the he’ll that is front end build/lint/test config again.
AI generated tests are genuinely fantastic, if you treat them like any other AI generated code and review them thoroughly.
I've been writing Python for 20+ years and I still can't use unittest.mock without looking up the details every time. ChatGPT and Claude are great at that, which means I use it more often because I don't have to deal with the frustration of figuring it out.
Well, maybe they just need X lines of so-called "tests" to satisfy some bullshit-job metrics.
Yes, there are bad places to work. There are also places that require detailed time tracking, do not allow any time to write tests, have very long hours, tons of on-call alerts, etc.
How long until it becomes the rule because of some arbitrary "productivity" metric? Sure, you may not be forced to use it, but you'll be fire for being "unproductive".
That's the case at Shopify already: https://twitter.com/tobi/status/1909251946235437514
> The rest is boiler plate, cargo-culted, Dockerfile, build system and bash environment variable passing
I keep seeing people saying to use an LLM to write boilerplate, but like... do you not just copy that from another project where you already wrote it?
I mean, the training data also has a cutoff date and changed beyond that are not reflected in the code suggestions.
Also, I know that people love to joke on modern software and JS in particular. But if you take react code from 2020 and drop it into a new react codebase it still works. Even class based components work. Yes, if you jumped on the newest framework bandwagon every time stuff will break all the time, but AI won’t be able to help you with that either. If you went for relatively stable frameworks, you can re use boilerplate completely or with relatively minimal adjustments
Ehh most people are good about at least throwing a warning before they break a legacy pattern. And you can also just use old versions of your tools. I'm sure the 2021 tool still does the job. Most people aren't working on the bleeding edge here. Old versions of numpy are fine.
lol, I've been cutting and pasting from the same projects I started in 2010. When you work in vanilla js it doesn't change.
I keep seeing that suggestion as well and the only sensible way I see would be to use one off boilerplate, anything else does not make sense.
If you keep re-using boilerplate once in a while copying it from elsewhere is fine. If you re-use it all the time, just get a macro setup in your editor of choice. IMHO that is way more efficient than asking AI to produce somewhat consistent boilerplate
You know. I have my boilerplate in Rails and it is just a work of art... I simply clone my BP repo, bundle, migrate, run and I have user management, auth, smtp client, sms alerts, and literally everything I need to get started. And it was just this same week I decided to try a code assistant, and my result was shockingly good, once you provide the assistant with a good clean starting point, and if you are very clear on what you want to build, then the results are just too good to be dismissed.
So yes, boilerplate, but also yes, there is definitely something to be gained from using ai assistants.
Like many others writing here, I enjoy coding (well, mostly anyway), especially the when it requires deep thought and patient experimentation to get anywhere. It's also great to preside over finally wiring together the routines (modules, libraries) that bind a project into a coherent whole.
Haven't much used AI to assist. After all, hard enough finding authentic humans capable and willing to voluntarily review/critique one's code. So far AI doesn't consistently provide that kind of help. OTOH seems almost certain over time AI systems will improve in terms of specific and comprehensive "insights" into the particular types of code one is writing.
I think an issue is that human creativity is hard to measure. Likely enough AI is even tougher to assess. Probably AI will increasingly be assigned tasks like constructing project skeletons, assuring parts can be joined together without undue strain, handling "boilerplate" and other routine chores. To be sure the landscape will look different in 50 years, I'm certain we'd be amazed were we able to see what future systems will be doing.
In any case, we shouldn't hesitate to use tools that genuinely boost our creativity. One badly needed role would be enabling development of higher reliability software. Still that's a far cry from the contributions emanating from the best of human originality, talent and motivation.
> doesn't judge or sneer at you, or tell you to "RTFM". It's better than any human help, even if its not always right because its at least always more reliable and you don't have to bother some grey beard who probably hates you anyway.
That’s a lot of trauma you’re dealing with.
That’s only true if you tokenize words rather than characters. Character tokenization generates new content outside the training vocabulary.
All major tokenisers have explicit support for encoding arbitrary byte sequences. There's usually a consecutive range of tokens reserved for 0x00 to 0xFF, and you can encode any novel UTF-8 words or structures with it. Including emoji and characters that weren't a part of the model's initial training, if you show it some examples.
Pretty sure that we’re talking apples and oranges. Yes to the arbitrary byte sequences used by tokenizers, but that is not the topic of discussion. The question is will the tokenizer come up with words not in the training vocabulary. Word tokenizers don’t, but character tokenizers do.
Source: Generative Deep Learning by David Foster, 2nd edition, published in 2023. From “Tokenization” on page 134.
“If you use word tokens: …. willnever be able to predict words outside of the training vocabulary.”
"If you use character tokens: The model may generate sequences of characters that form words outside the training vocabulary."
Source: Generative Deep Learning by David Foster, 2nd edition, published in 2023. From “Tokenization” on page 134.
“If you use word tokens: …. willnever be able to predict words outside of the training vocabulary.”
"If you use character tokens: The model may generate sequences of characters that form words outside the training vocabulary."
I think you're robbing yourself.
Of course, it all depends how you use the LLM. While the same can be true for StackOverflow, the LLMs just scale the issues up.
> The rest is boiler plate, cargo-culted, Dockerfile, build system and bash environment variable passing circle of hell that I really could care less about.
With expertise in anything, I'm sorry, but you also got to do the shit work. To be a great musician you gotta practice boring scales. It's true even if you just want to be a sub par one.
But a little grumpy is good. It drives you to fix things, and frankly, that's our job. The things that are annoying and creating friction don't need be repeated over and over, they need alternative solutions. The scripts you build are valuable. The "useless" knowledge you gain isn't so useless. Those little details add up without you knowing and make you better.
That undocumented code makes you frustrated and reminds you to document your own. You don't want to be a hypocrite. The author of the thing you're using probably thought the same thing: "No one is gonna use this garbage, I'm not going to waste my time documenting it". Yet here we are. Over and over again yet we don't learn the lesson.
I'm not gonna deny there's assholes. There are. But even assholes teach you. At worst, they teach you how not to act.
And some people are you telling you to RTM and not RTFM. Sure, it has lots of extra information in it that you don't need to get your specific job done, but have you also considered that it has lots of extra information in it? The person that wrote it clearly thought the context was important. Maybe it isn't. In that case, you learned a lesson in how not to write documentation!
What I'm getting at is that there's a lot of learning done all over the place. Trying to take out all the work and only have "the fun" is harming yourself and has a large potential to make less time for the fun stuff[0]. I'd be surprised if I'm alone in this, but a lot of stuff I enjoy now was stuff that originally frustrated me. IME this is pretty common! It's true for every person I know. Similarly, it's also true for things I learned that I thought I'd never use again. It always has a way of coming back.
I'm not going to pretend it's all fun and games. I'm explicitly saying it's not. But I'm confident in the long run it's better. Despite the lack of accuracy, I use LLMs (and Google, and even the TFM) like I would a solution guide the homework problems when I was in school. Try first, then consult. The struggle is an investment in your future. It sucks, but if all the best things in life were easy then we'd all have them. I'm just trying to convince you that it pays off.
I'm completely aware this is all context dependent. There's a time and place for everything. But given the percentages you mention (even taken as exaggeration), something sounds wrong. It's hard to suggest specific solutions without details but I'd be surprised if there weren't better and more rewarding solutions than having the LLM do it for you
[0] That's the big danger and what drives distrust in them. Because you need to work extra hard to find mistakes, increasing workload, not decreasing, because debugging is most of the job!
This.
Frankly I don't want to spend 2 hours reading documentation just to find out some arcane incantation that gets the computer to do what I want it to do.
The interesting part of programming to me is designing the logic. It's the 'this, then that, except when this' flow that I'm really interested in, not the search for some obscure library that has some function that will parse this csv.
Llms are great for that, and let me get away from the pointless grind and into the things that I enjoy and are actually providing value.
The pair programming is also a super good thing. I work best when I can spitball and throw out random ideas and get quick feedback. Llms let me do that without bothering others who have their own work to do.
Most comments here surprise me: I am using Githubs Copilot / ChatGPT 4.0 at work with a code base which is mostly implements a basic CRUD service... and outside of small/trivial example (where the generated code is mostly okay), prompting is more often than not a total waste of time. Now, I wonder if I am just totally unable to write/refine good prompts for the LLM (as it works for smaller samples, I hope I am not too far off) or what could explain the huge discrepancy of experience. (Just for the record: I would totally not mind if the LLM writes the code for the stuff I have to do at work.)
To clarify my questions: - Who here uses LLMs to generate code for bigger projects at work? (>= 20k lines of code) - If you use LLMs for bigger projects: Do you need to change your prompting strategy to get good results? - What programming languages are you using in your code bases? - Are there other people here who experience that LLMs are no help for non trivial problems?
I'm in the same boat. I've largely stopped using these tools other than asking questions about a language that I'm less familiar with or a complex type in typescript for which it can be helpful (sometimes). Otherwise, I felt like I was just wasting my time and becoming lazier/worse as a developer. I do wonder whether LLMs have hit a wall and we're in a hype cycle.
Yes, I have the same feeling about the wall/hype cycle. Most of my time is understanding code and formulating a plan to change code w/o breaking anything... even if LLMs would generate 100% perfect code on the first try, it would not help in a big way.
One thing I forgot to mention is asking LLMs questions from within the IDE instead of doing a web search... this works quite nice, but again, it is not a crazy productivity boost.
Same here. We have a massive codebase with large classes and the LLMs are not very helpful. Frontend stuff is okay sometimes but the backend models are too complex at this point, I guess.
Could you perhaps point me to a youtube video which demonstrates an experienced prompter sculpting code with Cursor/Clause Code?
In my search I just found trivial examples.
My critic so far:
- Examples seem always to be creating a simple application from scratch
- Examples always use super common things (like create a blog / simple website for CRUD)
What I would love to see (see elsewhere): Adding a non trivial feature to a bigger code base. Just a youtube video/demonstration. I don't care about language/framework etc. ...
This morning I made this while sipping coffee, and it solves a real problem for my gf: https://github.com/kirubakaran/xmldiffer Sure it's not enormous, and it was built from scratch, but imho it's not a trivial thing either. It would've taken me at least a day or two of full time work, and I certainly don't have a couple of days to spare on a task like this. Instead, pair programming with AI made it into a fun relaxing activity.
I am happy to read your success story with LLM and thanks for sharing.
Fully agreed, that LLMs/assisted coding is nice for these kind of contained tasks.
You are just bad with prompting or working with very obscure language/framework or bad coding pattern or all of it. I had a talk with a seasoned engineer who has been coding for 50 years and has created many amazing things over lifetime about him having really bad results with AI tools I suggested for him. When I use AI for the same purposes in the same repo he's working on, it works nicely. When he does it, results are always not what he wants. It comes down to a combination of him not understanding how to guide the LLMs to correct direction and using a language/framework (he's not familiar with) he can't judge the LLMs output. It is really important to know what you want, be able to describe it in short points (but important points). Points that you know ai will mess up if you don't specify. And also be able to figure out which direction the ai is heading with the solution and correct it EARLY rather than later. Not overloading context/memory with unnecessary things. Focusing on key areas to improve and much more. I'm using AI to get solutions done that I can definitely do myself but it'll take a certain amount of time to hunt down all documentation, API/lib calls etc. With AI, 1/10th time is enough.
I've had massive success with java, js/TS, html css, go, rust, python, bitbucket pipelines/GitHub actions, cdk, docker compose, SQL, flutter/dart, swift etc.
I've had the same experience as the person to whom you're responding. After reading your post, I have to ask: if you're putting so much effort into prompting it with specific points, correcting it often, etc., why not just write the code yourself? It sounds like you're putting a good deal of effort into prompting it.
Aren't you worried that overtime you'll rely on it too much and your offhand knowledge will get worse?
I have read somewhere, that LLMs are mostly helpful to junior developers.
Is it possible the person claiming success with all these languages/tools/technologies is just on a junior level and is subjectively correct but has no point of reference how fast coding is for seniors and how quality code looks like?
Not OP, it be comes natural and doesn't take a lot of time.
Anyway, if you want to, LLMs can today help with a ton of programming languages and frameworks. If you use any of the top 5 languages and it still doesn't work for you, either you're doing some esoteric work or you're doing it wrong.
Could you point me to a youtube video or a blog post which demonstrates how LLMs help writing code which outperforms a proficient human?
My only conditions:
- It must be demonstrated by adding a feature on a bigger code base (>= 20 LOC)
- The added feature cannot be a leaf feature (means it must integrate with the rest of the system at multiple points)
- The prompting has to be less effort/faster than to type the solution in the programming language
You can chose any programming language/framework that you want. I don't care if it is Java, JavaScript, Typescript, C, Python, ... hell, I am fine with any language with or w/o a framework.
I do not rule out, that I am just very bad with prompting.
It just surprises me, that you write you had massive successes with "java, js/TS, html css, go, rust, python, bitbucket pipelines/GitHub actions, cdk, docker compose, SQL, flutter/dart, swift etc.", if you include the usual libraries/frameworks and the diverse application areas for these technologies, even with LLMs support it seems to me crazy to be able to make meaningful contributions in non trivial code bases.
Concerning SQL I can report another fail with LLMs, in a trivial code base with a handful of entities the LLM cannot come up with basic window functions.
I would be very interested if you could write up a blog post or could make a youtube video demonstrating your prompting skills... Perhaps demonstrating with a bigger open source project in any of the mentioned languages how to add a non trivial feature with your prompting skills?
> Now, I wonder if I am just totally unable to write/refine good prompts for the LLM (as it works for smaller samples, I hope I am not too far off) or what could explain the huge discrepancy of experience.
Programming language / stack plays plays a big role, I presume.
Fair enough. Still, I was out of luck for some fairly simple SQL statements, were the model knows 100% of the DDL statements.
This comment section really shows the stark divide between people who love coding and thus hate AI, and people who hate coding and thus love AI.
Honestly, I suspect the people who would prefer to have someone or something else do their coding, are probably the devs who are already outputting the worst code right now.
I love coding but I also love AI.
I don't know if I'm a minority but I'd like to think there are a lot of folks like me out there.
You can compare it to someone who is writing assembly code and now they've been introduced to C. They were happy writing assembly but now they're thrilled they can write things more quickly.
Sure, AI could lead us to write buggier code. Sure, AI could make us dumber because we just have AI write things we don't understand. But neither has to be the case.
With better tools, we'll be able to do more ambitious things.
I think there are a lot of us, but the people who dislike AI are much more vocal in online conversations about it.
(The hype merchant, LinkedIn influencer, Twitter thread crowd are super noisy but tend to stick to their own echo chambers, it's rare to have them engage in a forum like Hacker News directly.)
> I don't know if I'm a minority
No, there's plenty of top-class engineers who love coding with AI. e.g. Antirez.
I love AI as a concept.
I hate the reality of our current AI, which is benefitting corporations over workers, being used for surveillance and censorship (nevermind direct social control via misinformation bots), and is copying the work of millions without compensating them in order to do it.
And the push for coders to use it to increase their output, will likely just end up meaning expectations of more LoC and more features faster, for the same pay.
But FOSS, self-hosted LLMs? Awesome!
How is using Claude over Llama benefitting corporations over workers? I work with AI every day and sum total of my token spend across all providers is less than a single NVidia H100 card I'd have to buy (from a pretty big corporation!), at the very least, for comparable purpose?
How are self-hosted LLMs not copying the work of millions without compensating them for it?
How is the push for more productivity through better technology somehow bad?
I am pro FOSS but can't understand this comment.
First of all, Lisp, Fortran and COBOL had been around most of the time when assembly was popular. Assembly was used because of resource constraints.
Secondly, you are not writing anything you get from an LLM. You prompt it and it spits out other people's code, stripped of attribution.
This is what children do: Ask someone to fix something for you without understanding the result.
I very much understand the result of code that it writes. But I have never gotten paid to code. I get paid to use my knowledge of computers and the industry to save the company money or to make the company money.
Do you feel the same way when you delegate assignments to more junior developers and they come back with code?
It’s almost like there’s a big range of different comprehension styles among human beings, and a varying set of preferences that go with those.
It is absolutely possible to enjoy both- I have used LLMs to generate code for ideas about alternate paths to take when I write my code- but prompt generation is not coding, and there are WAY too many people who claim to be coding when they have in fact done nothing of the sort.
> a far higher intensity
I'm not sure what this is supposed to mean. The code that I've gotten is riddled with mistakes and fabrications. If I were to use it directly, it would significantly slow my pace. Likewise, when I use LLMs to offer alternative methods to accomplish something, I have to take the time to sit down and understand what they're proposing, how to actually make it work, and whether that route(s) would be better than my original idea. That is a significant speed reduction.
The only way I can imagine LLMs resulting in "far higher intensity" is if I was just yolo'ing the code into my program, and then doing frantic integration, correction, and bugfix work afterwards.
Sure, that's "higher intensity", but that's just working harder and not smarter.
It is not coding the same way riding a bus is not driving
You may get to the same destination, but it is not the same activity
What if I prefer to have a clone of me doing my coding, and then I throw my clone under the bus and start to (angrily) hyperfocus explore and change every piece to be beautiful? Does this mean I love coding or I hate coding?
It's definitely a personality thing, but that's so much more productive for me, than convincing myself to do all the work from scratch after I had a design.
I guess this means I hate coding, and I only love the dopamine from designing and polishing my work instead of making things work. I'm not sure though, this feels like the opposite of hate coding.
> flawed enough starting premise
That's where we start to disagree what future looks like, then.
It's not there yet, in that the LLM-clone isn't good enough. But amusingly a not nearly good enough clone of me already made me more productive, in that I'm able to deliver more while maintaining the same level of personal satisfaction with my code.
The question of increasing productivity and what that means for us as laborers is another entire can of worms, but that aside, I have never yet found LLM-gen'd code that met my personal standards, and sped up my total code output.
If I want to spend my time refactoring and bugfixing and rewriting and integrating, rather than writing from scratch and bugfixing, I can definitely achieve that by using LLM code, but the overall time has never felt different to me, and in many cases I've thrown out the LLM code after several hours due to either sheer frustration with how it's written, or due to discovering that the structure it's using doesn't work with the rest of the program (see: anything related to threading).
I replaced "code" for "singing" to make a point.
> This comment section really shows the stark divide between people who love singing and thus hate AI-assisted singing, and people who hate singing and thus love AI-assisted singing.
> Honestly, I suspect the people who would prefer to have someone or something else do their singing, are probably the singers who are already outputting the worst singing right now.
The point is: just because you love something, doesn't mean you're good at it. It is of course positively correlated with it. I am in fact a better singer because I love to sing compared to if I never practiced. But I am not a good singer, I am mediocre at best (I chose this example for a reason, I love singing as well as coding! :-D)
And while it is easier to become good at coding than at singing - for professional purposes at least - I believe that the effect still holds.
I think the analogy/ substitution falls apart in that singing is generally not very stable or lucrative (for 99.999% of singers), so it is pretty rare to find someone singing who hates it. Much less uncommon to find people working in IT who hate the specific work of their jobs.
And I think we do tend to (rightfully) look down on e.g. singers who lip-sync concerts or use autotune to sing at pitches they otherwise can't, nevermind how we'd react if one used AI singing instead of themselves.
Yes, loving something is no guarantee of skill at it, but hating something is very likely to correspond to not being good at it, since skills take time and dedication to hone. Being bad at something is the default state.
I have been working in IT for 5 years while being a professional musician for 8 years (in France and touring in Europe). I've never met a single singer who told me they hate singing, on other hand, I can't even count how many of my colleagues told me how much they hate coding.
Another analogy would be with sound engineering. I've met sound engineer who hate their job as they would rather play music. They are also the ones whose jobs are likely to be replaced by AI. And I would argue that the argument stand stills. AI Sound Engineers who hate working on sound are often the bad sound engineers.
> I think the analogy/ substitution falls apart in that singing is generally not very stable or lucrative (for 99.999% of singers), so it is pretty rare to find someone singing who hates it.
I tried to cover this particular case with:
> And while it is easier to become good at coding than at singing - for professional purposes at least - I believe that the effect still holds.
---
> Yes, loving something is no guarantee of skill at it, but hating something is very likely to correspond to not being good at it, since skills take time and dedication to hone. Being bad at something is the default state.
I tried to cover this particular case with:
> It is of course positively correlated with it.
---
> Being bad at something is the default state.
Well, skill-wise yes. But being talented at something can happen, even when you hate something.
> And I think we do tend to (rightfully) look down on e.g. singers who lip-sync concerts or use autotune to sing at pitches they otherwise can't, nevermind how we'd react if one used AI singing instead of themselves.
Autotune is de rigueur for popular music.
In general, I'm not sure that I agree with looking down on people.
I love coding - but I am not very good at it. I can describe what I want in great detail, with great specificity. But I am not personally very good at turning that detailed specification into the proper syntax and incantations.
AI is like jet fuel for me. It’s the translation layer between specs and code I’ve always wanted. It’s a great advisor for implementation strategies. It’s a way to try new strategies in code quickly.
I don’t need to get anyone else to review my code. Most of this is for personal projects.
I don’t really write professionally, so I don’t have a ton of need for it to manage realities of software engineering (large codebases, peer reviews, black box internal systems, etc). That being said - I do a reasonable amount of embedded Linux work, and AI understands the Linux kernel and device drivers very well.
To extend your metaphor: AI is like a magic microphone that makes all of my singing sound like Tony Rice, my personal favorite singer. I’ve always wanted to sound like him - but I never will. I don’t have the range or the training. But AI allows my coding level to get to that corresponding level with writing software.
I absolutely love it.
This is really interesting to me.
Do you love coding, or do you love creating programs?
It seems like the latter given your metaphor being a microphone to make you seem like you could sing well, i.e. wanting the end state itself rather than the achievement via the process.
"wanted to sound like him" vs "wanted to sing like him"
Who write boilerplate this day? I just lift the code from the examples in the docs (especially css frameworks). And I love looking at functions docs, because after doing it a few times, you develop an holistic understanding of the library and your speed increases. Kinda like learning a foreign language. You can use an app to translate everything, or asks for the correct word when the needs arises. The latter is a bit frustrating at the beginning, but that’s the only way to become fluent.
Seriously, I see this claim thrown around as though everyone writes the same starting template 50 times a week. Like, if you've got a piece of "boilerplate" code you're constantly rewriting... Save It! Put it in a repo or a snippet somewhere that you can just copy-paste when you need it.
You don't need a multi-million dollar LLM to give you slightly different boilerplate snippets when you already have a text editor on your computer to save them.
Every PR I have to review with an obviously LLM-generated title stuffed with adjectives, and a useless description containing an inaccurate summary of the code changes pushes me a little bit more into trying to make my side projects profitable in the hope that one takes off. It usually only gets worse from there.
Documentation needs to be by humans for humans, it's not a box that's there to be filled with slop.
> The actual documentation needs to be by humans for humans.
This is true for producing the documentation but if there is an LLM that can take said documentation and answer questions about it is a great tool. I think I get the answer far quicker with LLM than sifting through documentation when looking for existence of a function in a library or a property on an object.
The documentation are for answering your questions, it’s not a puzzle to be solved. Using the reference docs assumes that you already have an understanding about the thing that is being documented and you’re looking for specificity or details. If not, the correct move is to go through a book, a tutorial, or the user guide. Aka the introductory materials.
seeing a lot of `const thing = doThing(); // add this line` showing up lately too.
I think that comment is conflating 2 different things: 1) people like you and I who use LLMs for exploring alternative methods to our own, and 2) people who treat LLMs like Stack Overflow answers they don't understand but trust because it's on SO.
Yes, there are tasks or parts of the code that I'm less interested in, and would happily either delegate or negotiate-off to someone else, but I wouldn't give those to a writer who happens to be able to write in a way that approximates program code, I'd give them to another dev on the team. A junior dev gets junior dev tasks, not tasks that they don't have the skills to actually perform, and LLMs aren't even at an actual junior dev level, imhe.
I noted in another comment that I've also used LLMs to get ideas for alternate ways to implement something, or to as you said "jump start" new files or programs. I'm usually not actually pasting that code into my IDE, though- I've tried that, and the work to make LLM-generated code fit into my programs is way more than just writing out the bits I need, where I need. That is clearly not the case for a lot of people using LLMs, though.
I've seen devs submitting PRs with giant blocks of clearly LLM-gen'd code, that they've tried to monkey-wrench into working with the rest of the program (often breaking conventions or secure coding standards). And these aren't junior devs, they're devs that have been working here for years and years.
When you force them to do a code review, they know it's not up to par, but there is a weird attitude that LLM-gen'd code is more acceptable to be submitted with issues than personally-written code. As though it's the LLM's fault or job to fix, even though they prompted and copied and badly-integrated and PR'd it.
And that's where I think there is a stark divide. I think you're on my side of the divide (at least, I didn't get the impression that you hate coding), it just sounds like you haven't really seen the other side.
My personal dime-store psych theory is that it's the same mental mechanism that non-technical computer users fall into of improperly trusting/ believing computers to produce correct information, but now happening to otherwise technical folks too because "AI" is still a black box technology to most of us, like computers in general are to non-techies.
LLMs are really really cool, and really really impressive to me, and I've had 'wow' moments where they did something that makes you forget what they are and how they work, but you can't let that emotional reaction towards it override the part that knows it's just a token chain. When you do, people end up (obviously on the very extreme end) 'dating' them, letting them make consequential "decisions", or just over-trusting their output/code.
I like solving problems but I hate coding. Wasting 20 minutes because you forgot a semicolon or something is not fun. AI let's me focus on the problem and not bother with the tedious coding bit.
I write code to solve problems for my own use or for my hobby electronics projects. Asking chatgpt to write a script is faster than reading the documentation of some python library.
Just last week it wrote me a whole application and gui to open a webpage at a specific time. Yeah it breaks after the first trigger but it works for what I need.
And that's OK! I'm not trying to gatekeeping anyone from the title of coder or programmer. But what is fine for quick small scripts and throwaway code can be quite bad even for smallish projects. If you're trying to solve a problem in a systematic way, there's a lot of concerns that pertain to the durability of the solution.
There's a lot of literature about these concerns and a lot of methodologies to alleviate them. I (and others) are judging LLMs in light of those concerns. Mostly because speed was never an issue for us in prototypes and scripts (and it can be relaxing to learn about something while scripting it). The issue is always reliability (can it do what I want) and maintainability (can I change it later). Performance can also be a key issue.
Aside: I don't know the exact problem you were solving, but based on the description, that could have been done with systemd timers (macOS services are more of a pain to write). Yes, there's more to learn, but time triggering some command is a problem solved (and systemd has a lot more triggers).
This doesn't even make sense, forgetting a semicolon is immediately caught by the compiler. What positive benefits does AI provide here?
I started “coding” in 1986 in assembly on an Apple //e and by the time I graduated from college, I had experience with 4 different processor families - 65C02, 68K, PPC and x86. I spent the first 15 years of my career programming in C and C++ along with other languages.
Coding is just a means to an end - creating enough business value to convince the company I’m working for to give me money that I can exchange for food and shelter.
If AI can help me do that faster, I’m going to use it. Neither do I want to spend months procuring hardware and managing building out a server room (been there done that) when I can just submit some yaml/HCL and have it done for me in a few minutes.
I love coding and don't love questioning AI and checking responses.
But the simple fact is I'm much more productive with AI and I believe this is likely true for most programmers once they get adjusted.
So for production, what I love the most doesn't really matter, otherwise I'd be growing tomatoes and guiding river rafting expeditions. I'm resigned to the fact the age of manually writing "for loops" is largely over, at least in my case.
If devs would learn how to document their work properly then there'd be much less use for AI and more people who enjoyed coding.
If you're using those things to do *the core function* of the program you're writing, that's an issue.
SDKs and libraries are there to provide common (as in, used repeatedly, by many) functions that serve as BUILDING BLOCKS.
If you import a library and now your program is complete, then you didn't actually make a useful program, you just made a likely less efficient interface for the library.
BUT ALSO-
SDKs and libraries are *vetted* code. The advantage you are getting isn't just about it having been written for you, it's about the hundreds of hours of human code review, iteration, and thought, that goes into those libraries.
LLM code doesn't have that, so it's not about you benefitting from the knowledge and experience of others, it's purely about reducing personally-typed LoC.
And yes, if you're wholesale copy-pasting major portions of your program from stack overflow, I'd say that's about as bad as copy-pasting from ChatGPT.
This belies the way that LLM code is being used.
People aren't taking LLM code and then thoughtfully refactoring and improving it, they're using it to *avoid* doing that, by treating the generated code as though it's already had that done.
That's why the pro-LLM-code people in this very thread are talking about using it to automate away the parts of the coding they don't like. You really think they're then going to go back and improve on the code past it minimally working?
There will be no advancement from that, just mediocre or bad code going unreviewed and ignored until it breaks.
After all, if we lose the joy in our craft, what exactly are we optimizing for?
Solving problems for real people. Isn't the answer here kind of obvious?
Our field has a whole ethos of open-source side projects people do for love and enjoyment. In the same way that you might spend your weekends in a basement woodworking shop without furnishing your entire house by hand, I think the craft of programming will be just fine.
Same as when higher-level languages replaced assembly for a lot of use cases. And btw, at least in places I've worked, better traditional tooling would replace a lot more headcount than AI would.
Not even close, those were all deterministic, this is probabilistic.
So what? I know most compilers are deterministic, but it really only matters for reproducible builds, not that you're actually going to reason about the output. And the language makes few guarantees about the resulting instructions.
I already see this happening with low code, SaaS and MACH architectures.
What used to be a project doing a CMS backend, now is spent doing configurations on a SaaS product, and if we are lucky, a few containers/serveless for integrations.
There are already AI based products that can automate those integrations if given enough data samples.
Many believe AI will keep using current programming languages as translation step, just like those Assembly developers thought compiling via Assembly text generation and feeding into an Assembly would still be around.
No, only primitive UNIX toolchains still do this, most modern compilers generate machine code directly, without having to generate Assembly text files and executing the Assembler process on it.
You can naturally revert to old ways, by asking for the Assembly manually, and call the Assembler yourself.
> Solving problems for real people. Isn't the answer here kind of obvious?
No. There are a thousand other ways of solving problems for real people, so that doesn't explain why some choose software development as their preferred method.
Presumably, the reason for choosing software development as the method of solving problems for people is because software development itself brings joy. Different people find joy in different aspects even of that, though.
For my part, the stuff that AI is promising to automate away is much of the stuff that I enjoy about software development. If I don't get to do that, that would turn my career into miserable drudgery.
Perhaps that's the future, though. I hope not, but if it is, then I need to face up to the truth that there is no role for me in the industry anymore. That would pretty much be a life crisis, as I'd have to find and train for something else.
"There are a thousand other ways of solving problems for real people, so that doesn't explain why some choose software development as their preferred method."
Software development is almost unique in the scale that it operates at. I can write code once and have it solve problems for dozens, hundreds, thousands or even millions of people.
If you want your work to solve problems for large numbers of people I have trouble thinking of any other form of work that's this accessible but allows you to help this many others.
Fields like civil engineering are a lot harder to break into!
> That would pretty much be a life crisis, as I'd have to find and train for something else.
There's inertia in the industry. It's not like what you're describing could happen in the blink of an eye. You may well be at the end of your career when this prophecy is fulfilled, if it ever comes true. I sure will be at the end of mine and I'll probably work for at least another 20 years.
The inertia argument is real, and I would compare it to the mistaken believe of some at IBM in the 1970s that SQL would be used by managers to query relational databases directly, so no programming was needed anymore.
And what happened? Programmers make the queries and embed them into code that creates dashboards that managers look at. Or managers ask analysts who have to interpret the dashboards for them... It rather created a need for more programmers.
Compare embedded SQL with prompts - SQL queries compared to assembler or FORTRAN code is closer to English prose for sure. Did it take some fun away? Perhaps, if manually traversing a network database is fun to anyone, instead of declaratively specifying what set of data to retrieve. But it sure gave new fun to people who wanted to see results faster (let's call them "designers" rather than "coders"), and it made programming more elegant due to the declarativity of SQL queries (although that is cancelled out again by the ugliness of mixing two languages in the code).
Maybe the question is: Does LLM-based coding enable a new kind of higher level "design flow" to replace "coding flow"? (Maybe it will make a slightly different group of people happy?)
This echoes my sentiment that LLMs are higher level programming languages. And, as every layer of abstraction, they add assumptions that may or may not fit the use case. The same way we optimize SQL queries by knowing how the database makes a query plan, we need to optimize LLM outputs, specially when the assumptions given are not ideal.
> No. There are a thousand other ways of solving problems for real people, so that doesn't explain why some choose software development as their preferred method.
I don't see why we should seek an explanation if there are thousands of ways to be useful to people. Is being a lawyer particularly better than being an accountant?
I'm probably just not as smart or creative as you, but say my problem is I have a ski cabin that I want to rent it to strangers for money. Nevermind a thousand, What are 100 ways without using software that I could do something about that, vs listing it on Airbnb?
solving real problems is the core of it, but for a lot of people the joy and meaning come from how they solve them too. the shift to AI tools might feel like outsourcing the interesting part, even if the outcome is still useful. side projects will stick around for sure, but i think it's fair to ask what the day-to-day feels like when more of it becomes reviewing and prompting rather than building.
> Solving problems for real people. Isn't the answer here kind of obvious?
Look at the majority of the tech sector for the last ten years or so and tell me this answer again.
Like I guess this is kind of true, if "problems for real people" equals "compensating for inefficiencies in our system for people with money" and "solutions" equals "making a poor person do it for them and paying them as little as legally possible."
Those of us who write software professionally are literally in a field premised on automating other people's jobs away. There is no profession with less claim to the moral high ground of worker rights than ours.
I often think about the savage job-destroying nature of the open source community: hundreds of thousands of developers working tirelessly to unemploy as many of their peers as possible by giving away the code they've written for free.
(Interesting how people talk about AI destroying programming jobs all the time, but rarely mention the impact of billions of dollars of code being given away.)
> Those of us who write software professionally are literally in a field premised on automating other people's jobs away.
How true that is depends on what sort of software you write. Very little of what I've accomplished in my career can be fairly described as "automating other people's jobs away".
You're automating the 1's and 0's. There could be millions of people in an assembly like line of buttons, being paid minimum wage to press either the 1 or 0 button to eventually trigger the next operation.
Now all those jobs are gone because of you.
Bit of a tangent but...
Haven't we been automating jobs away since the industrial revolution? I know AI may be an exception to this trend, but at least with classical programming, demand goes up, GDP per capita goes up, and new industries are born.
I mean, there's three ways to get stuff done: do it yourself, get someone else to do it, or get a machine to do it.
#2 doesn't scale, since someone still has to do it. If we want every person to not be required to do it (washing, growing food, etc), #3 is the only way forward. Automation and specialization have made the unthinkable possible for an average person. We've a long way to go, but I don't see automation as a fundamentally bad thing, as long as there's a simultaneous effort to help (especially those who are poor) transition to a new form of working.
> Those of us who write software professionally are literally in a field premised on automating other people's jobs away.
Depends what you write. What I work on isn't about eliminating jobs at all, if anything it creates them. And like, actual, good jobs that people would want, not, again, paying someone below the poverty line $5 to deliver an overpriced burrito across town.
> Automating jobs away is good for workers. Not bad.
Sure, if you completely disregard the past 200 years or so of history.
Some people love programming, for the sake of programming itself. They love the CS theory, they love the tooling, they love most everything about it.
Other people see all that as an means to an end - and find no joy from the technical aspect of creating something. They're more interested in the end result / product, rather than the process itself.
I think that if you're in group A, it can be difficult to understand group B. In vice versa.
I'm a musician, so I love everything about creating music. From the theory, to the mastery of the instrument, the tens of thousands of hours I've poured into it...finally being able to play something I never thought I'd be able to, just by sheer willpower and practice. Coming up with melodies that feel something to me, or I can relate to something.
On the other hand, I know people that want to jump straight to the end result. They have some melody or idea in their head, and they just want to generate some song that revolves around that idea.
I don't really look down on those people, even though the snobs might argue that they're not "real musicians". I don't understand them, but that's not really something I have to understand either.
So I think there are a lot of devs these days, that have been honing their skills and love for the craft for years, that don't understand why people just want things to be generated, with no effort.