Submitted 5 months ago by SparroHawc@lemmy.zip to technology@lemmy.world
https://futurism.com/artificial-intelligence/new-findings-ai-coding-overhyped
“No Duh,” say senior developers everywhere.
The article explains that vibe code often is close, but not quite, functional, requiring developers to go in and find where the problems are - resulting in a net slowdown of development rather than productivity gains.
About that “net slowdown”. I think it’s true, but only in specific cases. I the user already knows well how to write code, an LLM might be only marginally useful or even useless.
However, there are ways to make it useful, but it requires specific circumstances. For example, you can’t be bothered to write a simple loop, you can use and LLM to do it. Give the boring routine to an LLM, and you can focus on naming the variables in a fitting way or adjusting the finer details to your liking.
Can’t be bothered to look up the exact syntax for a function you use only twice a year? Let and LLM handle that, and tweak the details. Now, you didn’t spend 15 minutes reading stack overflow posts that don’t answer the exact question you had in mind. Instead, you spent 5 minutes on the whole thing, and that includes the tweaking and troubleshooting parts.
If you have zero programming experience, you can use and LLM to write some code for you, but prepare to spend the whole day troubleshooting something that is essentially a black box to you. Alternatively, you could ask a human to write the same thing in 5-15 minutes depending on the method they choose.
This is a sane way to use LLM. Also, pick your poison, some bots are better than others for a specific task. It’s kinda fascinating to see how other people solve coding problems and that is essentially on tap with a bot, it will churn out as many examples as you want. It’s a really useful tool for learning syntax and libraries of unfamiliar languages.
On one extreme side of LLM there is this insane hype and at the other extreme a great pessimism but in the middle is a nice labour saving educational tool.
so is the profit it was foretold to generate, but it actually costs money than its actually generating.
You mean relying blindly on a statistical prediction engine to produce sophisticated software without any understanding of the underlying principles or concepts doesn’t magically replace years of actual study and real-world experience?
But trust me, bro, the singularity is imminent, LLMs are the future of human evolution, true AGI is nigh!
I can’t wait for this idiotic “AI” bubble to burst.
Its great for stupid boobs like me, but only to get you going. It regurgitates old code, it cannot come up with new stuff. Lately there have been less Python errors, but again the stuff you can do is limited. At least for the free stuff that you can get without signing up.
It regurgitates old code, it cannot come up with new stuff.
The trick is, most of what you write is basically old code in new wrapping. In most projects, I’d say the new and novel part is maybe 10% of the code. The rest is things like setting up db models, connecting them to base logic, set up views, api endpoints, decoding the message on the ui part, displaying it to user, handling input back, threading things so UI doesn’t hang, error handling, input data verification, basic unit tests, set up settings, support reading them from a file or env vars, making UI look not horrible, add translatable text, and so on and on and on. All that has been written in some variation a million times before. The actual new interesting part is gonna be a small small percentage of the total code.
I totally agree with this. However, you can’t get there without coding experience and knowledge of the problem as well as education in computer science or experience in the field. I’m a generalist, I’m loving what I can do at home. But I still get the run around using AI. I have to read and understand the code to try to nudge the AI in the right direction or I’ll end up going in circles if I don’t.
Yea, I use it for home assistant, it’s amazingly powerful… And so incredibly dumb
It will take my if and statements, and shrunk it to 1/3 the length, while being twice as to robust… While missing that one of the arguments is entirely in the wrong place.
These types of articles always fail to mention how well trained the developers were on techniques and tools. In my experience that makes a big difference.
My employer mandates we use AI and provides us with any model, IDE, service we ask for. But where it falls short is providing training or direction on ways to use it. Most developers seem to go for results prompting and get a terrible experience.
I on the other hand provide a lot of context through documents and various mcp tooling, I talk about the existing patterns in the codebase and provide sources to other repositories as examples, then we come up with an implementation plan and execute on it with a task log to stay on track. I spend very little time fixing bad code because I spent the setup time nailing down context.
So if a developer is just prompting “Do XYZ”. It’s no wonder they’re spending more time untangling a random mess.
Another aspect is that everyone seems to always be working under the gun and they just don’t have the time to figure out all the best practices and techniques on their own.
I think this should be considered when we hear things like this.
I have 3 questions, and I’m coming from a heavily AI-skeptic position, but am open:
Do you believe that providing all that context, describing the existing patterns, creating an implementation plan, etc, allows the AI to both write better code and faster than if you just did it yourself? To me, this just seems like you have to re-write your technical documentation in prose each time you want to do something. You are saying this is better than ‘Do XYZ’, but how much twiddling of your existing codebase do you need to do before an AI can understand the business context of it? I don’t currently do development on an existing codebase, but every time I try to get these tools to do something fairly simple from scratch, they just flail. Maybe I’m just not spending the hours to build my AI-parsable functional spec. Every time I’ve tried this, asking something as simple as (and paraphrased for brevity) “write an Asteroids clone using JavaScript and HTML 5 Canvas” results in a full failure, even with multiple retries chasing errors. I wrote something like that a few years ago to learn Javascript and it took me a day-ish to get something that mostly worked.
Speaking of that context. Are you running your models locally, or do you have some cloud service? If you give your entire codebase to a 3rd party as context, how much of your company’s secret sauce have you disclosed? I’d imagine most sane companies are doing something to make their models local, but we see regular news articles about how ChatGPT is training on user input and leaking sensitive data if you ask it nicely and I can’t imagine all the pro-AI CEOs are aware of the risks here.
How much pen-testing time are you spending on this code, error handling, edge cases, race conditions, data sanitation? An experienced dev understands these things innately, having fixed these kinds of issues in the past and knows the anti-patterns and how to avoid them. In all seriousness, I think this is going to be the thing that actually kills AI vibe coding, but it won’t be fast enough. There will be tons of new exploits in what used to be solidly safe places. Your new web front-end? It has a really simple SQL injection attack. Your phone app? You can tell it your username is admin’joe@google.com and it’ll let you order stuff for free since you’re an admin.
I see a place for AI-generated code, for instant functions that do something blending simple and complex. “Hey claude, write a function to take a string and split it at the end of every sentence containing an uppercase A”. I had to write weird functions like that constantly as a sysadmin, and transforming data seems like a thing an AI could help me accelerate. I just don’t see that working on a larger scale, though, or trusting an AI enough to allow it to integrate a new function like that into an existing codebase.
Thank you for reading my comment. I’m on the train headed to work and I’ll try to answer completely. I love talking about this stuff.
Do you believe that providing all that context, describing the existing patterns, creating an implementation plan, etc, allows the AI to both write better code and faster than if you just did it yourself?
For my work, absolutely. My work is a lot of tickets that were setup from multiple stories and multiple epics. It would be like asking me if I am really framing a house faster with a nail gun and compressor. If I were just hanging up a picture or a few pictures in the hallway, it’s probably faster to use a hammer than to set up the compressor and nail gun, plus cleanup.
However, a lot of that documentation already exists by the time it gets to me. All of the Design Documents and Product Requirement Documents have already been formed, discussed, and approved by our architecture team and team leads. Imagine if you already had this documentation for the asteroid game; how much better do you think your LLM would do? Maybe this is the benefit of using LLMs for development at an established company. Btw, a lot of those Documents were also created with the assistance of AI by the Product Team, Architects, and Principle/Staff/Leads anyway.
how much twiddling of your existing codebase do you need to do before an AI can understand the business context of it?
With the help of our existing documents and codebase(s) I feel I dont have any issues with the model knowing what we’re doing. I do have to set up my own context for how I want it to be done. To me this is like explaining to a Junior Engineer what I need them to help me with. If you’re familiar with “Know when to Direct, when to Delegate, or when to Develop” I would say it lands in between Direct and Delegate. I have markdown files with my rules and guidelines and provide that as context. I use Augment Code which is pretty good with codebase context.
write an Asteroids clone using JavaScript and HTML 5 Canvas
I would try “Let’s plan out the steps needed to write an Asteroids game using JavaScript and HTML 5. Identify and explain each step of the development plan. The game must build with no errors, be playable, and pass all tests. Do not wrote code at this time until our plan is approved” Then once it comes back with the initial steps, I would guide it further if needed. Finally I would approve the plan and tell it to execute while tracking it’s steps (Augment Code uses a task log).
Are you running your models locally, or do you have some cloud service? If you give your entire codebase to a 3rd party as context, how much of your company’s secret sauce have you disclosed?
We are required to use the frontier models that my employer has contracts with and are forbidden from using local models. In our enterprise contracts we have negotiated for no training on our data. I imagine we pay for that. I’m not involved in that level of interaction on the accounts.
How much pen-testing time are you spending on this code, error handling, edge cases, race conditions, data sanitation? An experienced dev understands these things innately, having fixed these kinds of issues in the past and knows the anti-patterns and how to avoid them. In all seriousness, I think this is going to be the thing that actually kills AI vibe coding, but it won’t be fast enough. There will be tons of new exploits in what used to be solidly safe places.
We have other teams that handle a lot of these tasks. These teams are also using AI tools to get the job done. In addition, we have static testing tools on our repo like CodeRabbit and another one I can’t remember the name of that looks specifically for security concerns. It will comment on the PR directly and our merge would be blocked until handled. Code coverage for testing is at 85% or it blocks the merge and we have a full QA department of Analysts and SDETs to QA. In addition to that we still have human approvals required (2 devs + Sr+). All of these people involved are still using AI tools to help them in each step.
I hope that answers your questions and gives you some insight into how I’ve found success in my experience with it. I will say that on my personal projects I don’t go this far with process and I don’t experience the same AI output that I do at work.
I’m not a programmer in any sense. Recently, I made a project where I used python and raspberry pi and had to train some small models on a KITTI data set. I used AI to write the broad structure of the code, but in the end, it took me a lot of time going through python documentation as well as the documentation of the specific tools/modules I used to actually get the code working. Would an experienced programmer get the same work done in an afternoon? Probably. But the code AI output still had a lot of flaws. Someone who knows more than me would probably input better prompts and better follow up requirements and probably get a better structure from the AI, but I doubt they’ll get a complete code. In the end, even to use AI, you have to know what you’re doing to use AI efficiently and you still have to polish the code into something that actually works.
From my experience, AI just seems to be a lesson in overfitment. You can’t use it to do things Noone has done before. Furthermore, you only really get good responses from prompts related to Javascript
This article sums up a Stanford study of AI and developer productivity. TL;DR - net productivity boost is 15-20%, which tracks with my own experience as a dev.
Perhaps it should read “All AI is over hyped, over done and we should be over it”
I think maybe a good comparison is to written papers/ assignments. It can generate those just like it can generate code.
But it is not about the words themself, but about the content.
Senior Management in much of Corporate America is like a kind of modern Nobility in which looking and sounding the part is more important than strong competence in the field. It’s why buzzwords catch like wildfire.
Lmao calling nobility would imply we can’t vote our senior management and it often ends up being whoever “the king” wants or one of their children.
Wait…
Are you trying to tell me that the people want to sell me their universal panacea for all human endeavours were… lying…? Say it ain’t so.
I mean, originally they thought they had come upon a magic bullet. Turns out it wasn’t the case, and now they’re going to suffer for it.
You’re assuming honesty and they’ve earned the opposite.
Of course. Shareholders want results, and not just results for nVidia’s bottom line.
i use it for programming arduinos for my smarthome. its pretty nice but also aggravating.
I think right now companies are competing until they're only 1 or 2 that clearly own the majority of the market.
Afterwards they will devolve back into the same thing search engines are now. A cesspool of sponsored ads and links to useless SEO blogs.
They'll just become gate keepers of information again and the only ones that will be heard are the ones who pay a fee or game the system.
Maybe not though, I'm usually pretty cynical when it comes to what the incentives of businesses are.
This struck upon one of the greatest wishes of all corporations. A way to get work without having to pay people for it.
AI is a financial scam. Basically companies that are already mature promise great future profits thanks to this new technological miracle, which makes their stock more valuable than it otherwise would be. Cory Doctorow has written eloquently about this.
From what I’ve seen and heard, there are a few factors to this.
One is that the tech industry right now is built on venture capital. In order to survive, they need to act like they’re at the forefront of the Next Big Thing in order to keep bringing investment money in.
Another is that LLMs are uniquely suited to extending the honeymoon period.
The initial impression you get from an LLM chatbot is significant. This is a chatbot that actually talks like a person. A VC mogul sitting down to have a conversation with ChatGPT, when it was new, was a mind-blowing experience. This is a computer program that, at first blush, appears to be able to do most things humans can do, as long as those things primarily consist of reading things and typing things out - which a VC, and mid/upper management, does a lot of. This gives the impression that AI is capable of automating a lot of things that previously needed a live, thinking person - which means a lot of savings for companies who can shed expensive knowledge workers.
The problem is that the limits of LLMs are STILL poorly understood by most people. Despite constructing huge data centers and gobbling up vast amounts of electricity, LLMs still are bad at actually being reliable. This makes LLMs worse at practically any knowledge work than the lowest, greenest intern - because at least the intern can be taught to say they don’t know something instead of feeding you BS.
It was also assumed that bigger, hungrier LLMs would provide better results. Although they do, the gains are getting harder and harder to reach. There needs to be an efficiency breakthrough (and a training breakthrough) before the wonderful world of AI can actually come to pass because as it stands, prompts are still getting more expensive to run for higher-quality results. It took a while to make that discovery, so the hype train was able to continue to build steam for the last couple years.
Now, tech companies are doing their level best to hide these shortcomings from their customers (and possibly even themselves). The longer they keep the wool over everyone’s eyes, the more money continues to roll in. So, the bubble keeps building.
Writing apps with AI seems pretty cooked. But I’ve had some great successes using GitHub copilot for some annoying scripting work.
I think it’s useful for writing mundane snippets I’ve written a million times or helping me with languages I’m less familiar with, but anything more compex becomes pretty spaghetti pretty quick.
AI is works well for mindless tasks. Data formatting, rough drafts, etc.
Once a task requires context and abstract thinking, AI can’t handle it.
Trusting it to have not fucked with your data while formatting it is pretty bold.
Eh, I don’t know. As long as you can break it down into smaller sub-tasks, AI can do some complex stuff. Just have to figure out where the line is. I’ve nudged it along into reading multiple LENGTHY API documentation pages and written some fairly complex scripting logic.
I have not seen an AI generated code which is correct. Not once. I’ve certainly seen it broadly correct and used it for the gist of something. But normally it fucks something up - imports, dependencies, logic, API calls, or a combination of all them.
I sure as hell wouldn’t trust to use it without reviewing it thoroughly. And anyone stupid enough to use it blindly through “vibe” programming deserves everything they get. And most likely that will be a massive bill and code which is horribly broken in some serious and subtle way.
for me it typically don’t cause syntax errors, but the main thing it fucks up is what you specifically told them to do, where the output straight up does not perform the way your specification requires. If it’s just some syntax errors at least the compiler can catch them, this you won’t even know if you don’t bother testing the output.
I’ve used Claude code to fix some bugs and add some new features to some of my old, small programs and websites. Not things I can’t do myself, but things I can’t be arsed to sit down and actually do.
It’s actually gone really well, with clean and solid code. easily readable, correct, with error handling and even comments explaining things. It even took a gui stream processing program I had and wrote a server / webapp with the same functionality, and was able to extend it with a few new features I’ve been thinking to add.
These are not complex things, but a few of them were 20+ files big, and it manage to not only navigate the code, but understand it well enough to add features with the logic touching multiple files (model, logic, view layer for example, or refactor a too big class and update all references to use the new classes).
So it’s absolutely useful and capable of writing good code.
would you deploy this server?
This is the truth. It has tremendous value but it isn’t a solution – it’s a tool. And if you don’t know how to code or what good code looks like, then it is a tool you can’t use!
Eh I had it write a program that finds my PCs ip and sends it to the Unifi gateway to change a rule. Worked fine but I guess tech ically it is mostly using the go libraries written by someone else.
I’ve seen and used AI for snippets of code and it’s pretty decent at that.
With my colleagues I always compare it to a battery powered drill. It’s very powerful and can make shit a lot easier. But you’d not try to build furniture from scratch with only a battery powered drill.
What bothers me the most is the amount of tech debt it adds by using outdated approaches.
For example, recently I used AI to create some python scripts that use polars and altair to parse some data and draw charts. It kept insisting to bring in pandas so it could convert the polars dataframes to pandas dataframes just for passing them to altair. When I told if that altair can use polars dataframes directly, that helped, but two or three prompts later it would try to solve problems by adding the conversion again.
This makes sense too, because the training material, on average, is probably older than the change that enabled altair to use polars dataframes directly. And a lot of code out there just uses pandas in the first place.
The result is that in all these cases, someone who doesn’t know this would probably be impressed that the scripts worked, and just not notice the extra tech debt from that unnecessary dependency on pandas.
It sounds like it’s not a big deal, but these things add up and soon your AI enhanced code base is full of additional dependencies, deprecated APIs, unnecessarily verbose or complicated code, etc.
I feel like this is one aspect that gets overlooked a bit when we talk about productivity gains. We don’t necessarily immediately realize how much of that extra LoC/time goes into outdated code and old fashioned verbosity. But it will eventually come back to bite us.
To add on to what others have said, vibe coding is ushering in a new golden age for black hat hackers. If someone is rely entirely on AI to generate code they likely don’t understand what the code they have is actually doing. This tends to lead to an app that works correctly for what the prompted specified but behaves badly the instant it has to handle anything outside of the prompt, like a malformed request or data outside the prompted parameters. As a result these apps tend to be easy to exploit by malicious actors, often in ways the original prompter never thought of.
If the code doesn’t compile, or is badly mangled, or uses the wrong APIs / imports or forgets something really important then it’s broken. I can use AI to inform my opinion and sometimes makes use of what it outputs but critically I know how to program and I know how to spot good and bad code.
I can’t speak for how you use it, but if you don’t have any real programmers and you’re iterating until something works then you could be producing junk and not know it. Maybe it doesn’t matter in your case if its a bunch for throwaway scripts and helpers but if you have actual code in production where money, lives, reputation, safety or security are at risk then it absolutely does.
It’s not bad for your use case but going beyond that without issues and actual developpers to fix the vibe code is not yet possible for llms
The people talking about AI coding the most at my job are architects and it drives me insane.
Software architects that don’t write code are worse than useless
I am a software architect, an mainly usw it to refactor my own old code… But i am maybe not a typical architect…
I don’t really care if people use it, it’s more that it feels like a quarter of our architect meeting presentations are about something AI related. It’s just exhausting.
I’d much rather write my own bugs to have to waste hours fixing, thanks.
According to Deutsche Bank the AI bubble is a pillar of our economy now.
So when it pops. I guess that’s kinda apocalyptic.
Only for taxpayers ☝️
So when the AI bubble burst, will there be coding jobs available to clean up the mess?
There already are. People all over LinkedIn are changing their titles to “AI Code Cleanup Specialist”.
I mean largely for most of us I hope. But I feel like the tech sector was oversatured because of all the hype of it being an easy get rich quick job. Which for some people it was.
EVERYTHING about AI is overhyped. Its people trying to cash in on the latest buzz/trend.
www.youtube.com/watch?v=VsE0BwQ3l8U&t=1492s
And the band plays on
I code with LLMs every day as a senior developer but agents are mostly a big lie. LLMs are great for information index and rubber duck chats which already is incredible feaute of the century but agents are fundamentally bad. Even for Python they are intern-level bad. I was just trying the new Claude and instead of using Python’s pathlib.Path it reinvented its own file system path utils and pathlib is not even some new Python feature - it has been de facto way to manage paths for at least 3 years now.
That being said when prompted in great detail with exact instructions agents can be useful but thats not what being sold here.
I will concur with the whole ‘llm keeps suggesting to reinvent the wheel’
And poorly. Not only did it not use a pretty basic standard library to do something, it’s implementation is generally crap. For example it offered up a solution that was hard coded to IPv4, and the context was very ipv6 heavy
I have a theory that it’s partly because a bunch of older StackOverflow answers have more votes than newer ones using new features. More referring to not using relatively new features as much as it should.
If it wasn’t for all the AI hype that it’s going to do everyone’s job, LLMs would be widely considered an amazing advancement in computer-human interaction and human assistance. They are so much better than using a search engine to parse web forums and stack overflow, but that’s not going to pay for investing hundreds of billions into building them out. My experience is like yours - I use AI chat as a huge information index mainly, and helpful sounding board occasionally, but it isn’t much good beyond that.
They are so much better than using a search engine to parse web forums and stack overflow,
The hallucinations (more accurately bullshitting) and the fact they have to get new training data but are discouraging people from engaging in the structures that do so make this highly debatable
Oh yes. The Great pathlib. The Blessed pathlib. Hallowed be it and all it does.
I’m a Ruby girl. A couple of years ago I was super worried about my decision to finally start learning Python seriously. But once I ran into pathlib, I knew for sure that everything will be fine. Take an everyday headache problem. Solve it forever. Boom. This is how standard libraries should be designed.
Pathlib is very nice indeed, but I can understand why a lot of languages don’t do similar things. There are major challenges implementing something like that. Cross-platform functionality is a big one, for example. File permissions between Unix systems and Windows do not map perfectly from one system to another which can be a maintenance burden.
But I do agree. As a user, it feels great to have. And yes, also in general, the things Python does with its standard library are definitely the way things should be done, from a user’s point of view at least.
I disagree. Take a routine problem and invent a new language for it. Then split it into various incompatible dialects, and make sure in all cases it requires computing power that no one really has.
Im not super surprised, but AI has been really useful when it comes to learning or giving me a direction to look into something more directly.
Im not really an advocate for AI, but there are some really nice things AI can do. And i like to test the code quality of the models i have access to.
I always ask for a ftp server and dns server, to check what it can do and they work surprisingly well most of the time.
I’ve found success using more powerful LLMs to help me create applications using the Rust programming language. If you use a weak LLM and ask it to do something very difficult you’ll get bad results. You still need to have a fundamental understanding of good coding practices. Using an LLM to code doesn’t replace the decision making.
Based on my experience with claude sonnet and gpt4/5… It’s a little useful but generally annoying and fails more often than works.
I do think moderate use still comes out ahead, as it saves a bunch of typing when it does work, but I still get annoyed at the blatantly stupid suggestions I keep having to decline.
I remember GPT 4 being useless and constantly giving wrong information. Now with newer models they’ve become significantly more useful, especially when prompted to be extremely careful and to always double check to ensure the best response.
LLMs work great to ask about tons of documentation and learn more about high-level concepts. It’s a good search engine.
The code they produce have basically always disappointed me.
On proprietary products, they are awful. So many hallucinations that waste hours. A manager used one on a code review of mine and only admitted it after I spent the afternoon chasing it.
Those happen so often. I’ve taken to stop calling them hallucinations anymore (that’s anthropomorphising and over-selling what LLMs do imho). They are statistical prediction machines, and either they hit their practical limits of predicting useful output, or we just call it broken.
I think the next 10 years are going to be all about learning what LLMs are actually good for, and what they are fundamentally limited at no matter how much GPU ram we throw at it.
Not even proprietary, just niche things. In other words anything that’s rarely used in open source code, because there’s nothing to train the models on.
I sometimes get up to five lines of viable code. Then upon occasion what should have been a one liner tries to vomit all over my codebase. The best feature about AI enabled IDE is the button to decline the mess that was just inflicted.
In the past week I had two cases I thought would be “vibe coding” fodder, blazingly obvious just tedious. One time it just totally screwed up and I had to scrap it all. The other one generated about 4 functions in one go and was salvageable, though still off in weird ways. One of those was functional, just nonsensical. It had a function to check whether a certain condition was present or not, but instead of returning a boolean, it passed a pointer to a string and set the string to “” to indicate false… So damn bizarre, hard to follow and needlessly more lines of code, which is another theme of LLM generated code.
But will something be done about it?
NOooOoOoOoOoo. As long as it is still the new shiny toy for techbros and executive-bros to tinker with, it'll continue.
I use AI as an entryway to learning or for finding the name or technique that I’m thinking of but can’t remember or know it’s name so then i can look elsewhere for proper documentation. I would never have it just blindly writing code.
Sadly search engines getting shitter has sort of made me have to use it to replace them.
Then it’s also good to quickly parse an error for anything obviously wrong.
Evotech@lemmy.world 5 months ago
For most large projects, writing the code is the easy part anyway.
Jankatarch@lemmy.world 5 months ago
Writing new code is easier than editing someone else’s code so the idea of a tool that essentially has you edit the entire code from start to the end is just funny.