There is huge gap between 3.5 and 4 especially in coding related questions. GPT3.5 does not have large enough token size to handle harder code related questions.
sure, I’m not saying GPT4 is perfect, just that it’s known to be a lot better than 3.5. Kinda why I would be interested to see how much better it actually is.
This is why I like Bing Chat for this kind of thing, it does a web search in the background and will often be working right from the API documentation.
Developing with ChatGPT feels bizzarely like when Tony Stark invented a new element with Jarvis’ assistance.
It’s a prolonged back and forth, and you need to point out the AIs mistakes and work through a ton of iterations to get something that is close enough that you can tweak it and use, but it’s SO much faster than trawling through Stack Overflow or hoping someone who knows more than you can answer a post for you.
Yeah if you treat it is a junior engineer, with the ability to instantly research a topic, and are prepared to engage in a conversation to work toward a working answer, then it can work extremely well.
Some of the best outcomes I’ve had have needed 20+ prompts, but I still arrived at a solution faster than any other method.
In the end, there is this great fear of “the AI is going to fully replace us developers” and the reality is that while that may be a possibility one day, it wont be any day soon.
You still need people with deep technical knowledge to pilot the AI and drive it to an implemented solution.
AI isnt the end of the industry, it has just greatly sped up the industry.
I use chatgpt semi-often… For generating stuff in a repeating pattern. Any time I have used it to make code, I don’t save any time because I have to debug most of the generated code anyway. My main use case lately is making python dicts with empty keys (e.g. key1, key2… becomes “key1”: “”, “key2”: “”,…) or making a gold/prod level SQL view by passing in the backend names and frontend names (e.g. value_1, value_2… Value 1, Value 2,… Becomes value_1 as Value 1,…).
I know this is gonna sound annoying but I just use vim for stuff like this. Even notepad++ has a macro thing too, right? My coworkers keep saying how much of a productivity boost it is but all I see it do is mess up stuff like this that only takes a few seconds in vim to setup and I know it’ll be correct every time
I use vim keybinds (via doom emacs) for this sort of stuff if I’m doing it for personal projects, my professional work is all done in an online platform (no way around it) so it’s just faster and easier to throw the pattern and columns at the integrated chatgpt terminal rather than hop to a local editor and back
Exactly, I also find that it tends to do a pretty good job pointing you in the right direction. It’s way faster than googling or going through sites like stackoverflow because the answers are contextual. You can ask about a specific thing you want to do, and and an answer that gives you a general idea of what to do. For example, I’ve found it to be great for crafting complex sql queries. I don’t really care if the answer is perfect, as long as it gives me an idea of what I need to do.
It’s pretty fun, interesting times ahead. I wonder what kind of bullshit will take place and can’t wait to see that lol. Between all the climate, ai, warmongering future won’t be boring guys that is certain. Unpack your popcorn
Now non-coders can finally wield the foot-gun once reserved only for coders! /s
Truth be told, computer engineering should really be something that one needs a licence to do commercially, just like regular engineering. In this modern era where software can be ruinous to someone’s life just like shoddy engineering, why is it not like this already.
Look, nothing will blow up if I mess up my proxy setup on my machine. I just won’t have internet until I revert my change. Why would that be different if I were getting paid for it?
Nothing happens if you fuck up your proxy, but if you develop an app that gets very popular and don’t care about safety, so hackers are able to take control over your whole Server they can do a lot of damage. If you develop software for critical infrastructure it can actually cost human lives if you fuck up your security systems.
I don’t see how this supports your point then. If “setting up proxy” means “packaging it to run on thousands user machines” then isn’t there obvious and huge potential for a disastrous fuckup?
No, because it either runs the program successfully, or it fails to launch. I don’t mess with the protocol. It runs as root because it needs to set the iptables when turned on to be a “global” proxy
You can also play with it to try and get closer to correct. I had problems with getting an Excel macro working and getting unattended-updates working on my pihole. GPT was wrong at first, but got me partly there and I could massage the question and Google and get closer to the right answer. Without it, I wouldn’t have been able to get any of it, especially with the macro.
I think where it shines is in helping you write code you’ve never written before. I never touched Swift before and I made a fully functional iOS app in a week. Also, even with stuff I have done before, I can say “write me a function that does x” and it will and it usually works.
Like just yesterday I asked it to write me a function that would generate and serve up an .ics file based on a selected date and extrapolate the date of a recurring monthly meeting based on the day of the week picked and its position (1st week, 2nd week, etc) within the month and then make the .ics file reflect all that. I could have generated that code myself by hand but it would have probably taken me an hour or two. It did it in about five seconds and it worked perfectly.
Yeah, you have to know what you’re doing in general and there’s a lot of babysitting involved, but anyone who thinks it’s just useless is plain wrong. It’s fucking amazing.
Edit: lol the article is referring to a study that was using GPT 3.5, which is all but useless for coding. 4.0 has been out for a year blowing everybody’s minds. Clickbait trash.
Generally you want to the reference material used to improve that first version to be correct though. Otherwise it’s just swapping one problem for another.
I wouldn’t use a textbook that was 52% incorrect, the same should apply to a chatbot.
Yeah, but the non-tech savvy business leaders see they can generate code with AI and think ‘why do I need a developer if I have this AI?’ and have no idea whether the code it produces is right or not. This stat should be shared broadly so leaders don’t overestimate the capability and fire people they will desperately need.
Yeah management are all for this, the first few years here are rough with them immediately hitting the “fire the engineers we have ai now”. They won’t realize their fuckup until they’ve been promoted away from it
Programming jobs will be safe for a while. They’ve been trying to eliminate those positions since at least the 90s. Because coders are expensive and often lack social skills.
But I do think the clock is ticking. We will see more and more sophisticated AI tools that are relatively idiot-proof and can do things like modify Salesforce, or create complex new Tableau reports with a few mouse clicks, and stuff like that. Jobs will be chiseled away like our unfortunate friends in graphic design.
You, along with most people, are still looking at automation wrong. It’s never been about removing people entirely, even AI, it’s about doing the same work with less cost.
If you can eliminate one programmers from your four person team by giving the other three AI to produce the same amount of work, congrats you’ve just automated one programming job.
Programming jobs aren’t going anywhere, but either the amount of code produced is about to skyrocket, or the number of employed programmers is going to drop (or most likely both of those things).
Right on. AI feels like a looming paradigm shift in our field that we can either scoff at for its flaws or start learning how to exploit for our benefit. As long as it ends up boosting productivity it’s probably something we’re going to have to learn to work with for job security.
It’s already boosting productivity in many roles. That’s just going to accelerate as the models get better, the processing gets cheaper, and (as you said) people learn to use it better.
I wonder if this will also have a reverse tail end effect.
Company uses AI (with devs) to produce a large amount of code -> code is in prod for a few years with incremental changes -> dev roles rotate or get further reduced over time -> company now needs to modernize and change very large legacy codebase that nobody really understands well enough to even feed it Into the AI -> now hiring more devs than before to figure out how to manage a legacy codebase 5-10x the size of what the team could realistically handle.
Writing greenfield code is relatively easy, maintaining it over years and keeping it up to date and well understood while twisting it for all new requirements - now that’s hard.
AI will help with that too, it’s going to be able to process entire codebases at a time pretty shortly here.
Given the visual capabilities now emerging, it can likely also do human-equivalent testing.
One of the biggest AI tricks we haven’t started seeing much of yet in mainstream use is this kind of automated double-checking. Where it generates an answer, and then validates if the answer is valid before actually giving it to a human. Especially in coding bases, there really isn’t anything stopping it from coming up with an answer compiling, running into an error, re-generating, and repeating until the code passes all unit tests or even potentially visual inspection.
The big limit on this right now is sheer processing cost and context lengths for the models. However, costs for this are dropping faster than any new tech we’ve seen, and it will likely be trivial in just a few years.
There are some areas I’m hoping get addressed by the coming skyrocket in programmer productivity:
Several phone apps aren’t utter garbage anymore. I’m not holding my breath on this one.
Online grocery websites aren’t shit-full-of-timing errors. If I get this, I’ll also wish for $1 million and buy a lottery ticket.
Municipalities and their allies (townships, city services, various local unions) will have barely passable specialized software support that actually fits their size, location and maybe even culture.
I think that last one stands to be strongly enabled by AI code assist tools. It might not be the sexiest or highest paying job, but it’ll be work that matters that largely isn’t even being done today.
It won’t happen like that. Leadership will just under-hire and expect all their developers to be way more efficient. Working will be really stressful with increased deadlines and people questioning why you couldn’t meet them.
LLMs program at the level of a junior engineer or an intern. You already need code review and more senior engineers to fix that shit for them.
What they do is migrate that. Now that junior engineer has an intern they are trying to work with. Or… companies realize they don’t benefit from training up those newbie (or stupid) engineers when they are likely to leave in a year or two anyway.
Bad take. Is the first version of your code the one that you deliver or push upstream?
LLMs can give great starting points, I use multiple LLMs each for various reasons. Usually to clean up something I wrote (too lazy or too busy/stressed to do manually), find a problem with the logic, or maybe even brainstorm ideas.
I rarely ever use it to generate blocks of code like asking it to generate “a method that takes X inputs and does Y operations, and returns Z value”. I find that those kinds of results are often vastly wrong or just done in a way that doesn’t fit with other things I’m doing.
LLMs can give great starting points, I use multiple LLMs each for various reasons. Usually to clean up something I wrote (too lazy or too busy/stressed to do manually), find a problem with the logic, or maybe even brainstorm ideas.
Impressed some folks think LLMs are useless. Not sure if their lives/workflows/brains are that different from ours or they haven’t given at the college try.
I almost always have to use my head before a language model’s output is useful for a given purpose. The tool almost always saves me time, improves the end result, or both. Usually both, I would say.
It’s a very dangerous technology that is known to output utter garbage and make enormous mistakes. Still, it routinely blows my mind.
What’s especially troubling is that many human programmers seem to prefer the ChatGPT answers. The Purdue researchers polled 12 programmers — admittedly a small sample size — and found they preferred ChatGPT at a rate of 35 percent and didn’t catch AI-generated mistakes at 39 percent.
Why is this happening? It might just be that ChatGPT is more polite than people online.
It’s probably more because you can ask it your exact question (not just search for something more or less similar) and it will at least give you a lead that you can use to discover the answer, even if it doesn’t give you a perfect answer.
Also, who does a survey of 12 people and publishes the results? Is that normal?
I’m a 10 year pro, and I’ve changed my workflows completely to include both chatgpt and copilot. I have found that for the mundane, simple, common patterns copilot’s accuracy is close to 9/10 correct, especially in my well maintained repos.
It seems like the accuracy of simple answers is directly proportional to the precision of my function and variable names.
I haven’t typed a full for loop in a year thanks to copilot, I treat it like an intent autocomplete.
Chatgpt on the other hand is remarkably useful for super well laid out questions, again with extreme precision in the terms you lay out. It has helped me in greenfield development with unique and insightful methodologies to accomplish tasks that would normally require extensive documentation searching.
Anyone who claims llms are a nothingburger is frankly wrong, with the right guidance my output has increased dramatically and my error rate has dropped slightly. I used to be able to put out about 1000 quality lines of change in a day (a poor metric, but a useful one) and my output has expanded to at least double that using the tools we have today.
Are LLMs miraculous? No, but they are incredibly powerful tools in the right hands.
I think AI is good with giving answers to well defined problems. The issue is that companies keep trying to throw it at poorly defined problems and the results are less useful. I work in the cybersecurity space and you can’t swing a dead cat without hitting a vendor talking about AI in their products. It’s the new, big marketing buzzword. The problem is that finding the bad stuff on a network is not a well defined problem. So instead, you get the unsupervised models faffing about, generating tons and tons of false positives. The only useful implementations of AI I’ve seen in these tools actually mirrors you own: they can be scary good at generating data queries from natural language prompts. Which is, once again, a well defined problem.
Overall, AI is a tool and used in the right way, it’s useful. It gets a bad rap because companies keep using it in bad ways and the end result can be worse than not having it at all.
If you’re careless with your prompting, sure. The “default style” of ChatGPT is widely known at this point. If you want it to sound different you’ll need to provide some context to tell it what you want it to sound like.
Or just use one of the many other LLMs out there to mix things up a bit. When I’m brainstorming I usually use Chatbot Arena to bounce ideas around, it’s a page where you can send a prompt to two randomly-selected LLMs and then by voting on which gave a better response you help rank them on a leaderboard. This way I get to run my prompts through a lot of variety.
You wish. The sheer idea of calling yourself a “pro” disqualifies you. People who actually code and know what they are doing wouldn’t dream of giving themselves a label beyond “coder” / “programmer” / “SW Dev”.
Because they don’t have to. You are a muppet.
Hey! So you may have noticed that you got downvoted into oblivion here. It is because of the unnecessary amount of negativity in your comment.
In communication, there are two parts - how it is delivered, and how it is received. In this interaction, you clearly stated your point: giving yourself the title of pro oftentimes means the person is not a pro.
What they received, however, is far different. They received: ugh this sweaty asshole is gatekeeping coding.
If your goal was to convince this person not to call themselves a pro going forward, this may have been a failed communication event.
while your measured response is appreciated, I hardly consider a few dozen downvotes relevant, nor do I care in this case. It’s telling that those who did respond to my comment seem to assume I would consider myself a “pro” when that’s 1) nothing I said and 2) it should be clear from my comment that I consider the expression cringy. Outside memeable content, only idiots call themselves a “pro”. If something is my profession, I could see someone calling themselves a “professional <whatever>” (not that I would use it), but professional has a profoundly distinct ring to it, because it also refers to a code of conduct / a way to conduct business.
“I’m a pro” and anything like it is just hot air coming from bullshitters who are mostly responsible for enshittification of any given technology.
Refreshing to see a reasonable response to coding with AI. Never used chatgpt for it but my copilot experience mirrors yours.
I find it shocking how many developers seem to think so many negative thoughts about it programming with AI. Some guy recently said “everyone in my shop finds it useless”. Hard for me to believe they actually tried copilot if they think that
Omg, I feel sorry for the people cleaning up after those codebases later. Maintaing that kind of careless “quality” lines of code is going to be a job for actual veterans.
And when we’re all retired or dead, the whole world will be a pile of alien artifacts from a time when people were still able to figure stuff out, and llms will still be ridiculously inefficient for precise tasks, just like today.
I’ve found that the better I’ve gotten at writing prompts and giving enough information for it to not hallucinate, the better answers I get. It has to be treated as what it is, a calculator that can talk, make sure it has all of the information and it will find the answer.
One thing I have found to be super helpful with GPT4o is the ability to give it full API pages so it can update and familiarise it’s self with what it’s working with.
Anyone who claims llms are a nothingburger is frankly wrong,
Exactly. When someone says that it either indicates to me that they ignorant (like they aren’t a programmer or haven’t used it) or they are a programmer who has used it, but are not good at all at integrating new tools into their development process.
Don’t throw out the baby with the bathwater.
Yup. The problem I see now is that every mistake an ai makes is parroted over and over here and held up as an example of why the tech is garbage. But it’s cherry picking. Yes, they make mistakes, I often scratch my head at the ai results from Google and know to double check it. But the number of times it has pointed me in the right direction way faster than search results has shown to me already how useful it is.
I’ve used chatgpt and gemini to build some simple powershell scripts for use in intune deployments. They’ve been fairly simple scripts. Very few have of them have been workable solutions out of the box, and they’ve often filled with hallucinated cmdlets that don’t exist or are part of a thirdparty module that it doesn’t tell me needs to be installed. It’s not useless tho, because I am a lousy programmer its been good to give me a skeleton for which I can build a working script off of and debug myself.
I reiterate that I am a lousy programmer, but it has sped up my deployments because I haven’t had to work from scratch. 5/10 its saved me a half hour here and there.
I’m a good programmer and I still find LLMs to be great for banging out python scripts to handle one-off tasks. I usually use Copilot, it seems best for that sort of thing. Often the first version of the script will have a bug or misunderstanding in it, but all you need to do is tell the LLM what it did wrong or paste the text of the exception into the chat and it’ll usually fix its own mistakes quite well.
I could write those scripts myself by hand if I wanted to, but they’d take a lot longer and I’d be spending my time on boring stuff. Why not let a machine do the boring stuff? That’s why we have technology.
In recent years, computer programmers have flocked to chatbots like OpenAI’s ChatGPT to help them code, dealing a blow to places like Stack Overflow, which had to lay off nearly 30 percent of its staff last year.
That’s a staggeringly large proportion for a program that people are relying on to be accurate and precise, underlining what other end users like writers and teachers are experiencing: AI platforms like ChatGPT often hallucinate totally incorrectly answers out of thin air.
For the study, the researchers looked over 517 questions in Stack Overflow and analyzed ChatGPT’s attempt to answer them.
The team also performed a linguistic analysis of 2,000 randomly selected ChatGPT answers and found they were “more formal and analytical” while portraying “less negative sentiment” — the sort of bland and cheery tone AI tends to produce.
The Purdue researchers polled 12 programmers — admittedly a small sample size — and found they preferred ChatGPT at a rate of 35 percent and didn’t catch AI-generated mistakes at 39 percent.
The study demonstrates that ChatGPT still has major flaws — but that’s cold comfort to people laid off from Stack Overflow or programmers who have to fix AI-generated mistakes in code.
The original article contains 340 words, the summary contains 199 words. Saved 41%. I’m a bot and I’m open source!
You are not logged in. However you can subscribe from another Fediverse account, for example Lemmy or Mastodon. To do this, paste the following into the search field of your instance: [email protected]
This is the official technology community of Lemmy.ml for all news related to creation and use of technology, and to facilitate civil, meaningful discussion around it.
Ask in DM before posting product reviews or ads. All such posts otherwise are subject to removal.
Rules:
1: All Lemmy rules apply
2: Do not post low effort posts
3: NEVER post naziped*gore stuff
4: Always post article URLs or their archived version URLs as sources, NOT screenshots. Help the blind users.
5: personal rants of Big Tech CEOs like Elon Musk are unwelcome (does not include posts about their companies affecting wide range of people)
6: no advertisement posts unless verified as legitimate and non-exploitative/non-consumerist
7: crypto related posts, unless essential, are disallowed
I don’t even bother trying with AI, it’s not been helpful to me a single time despite multiple attempts. That’s a 0% success rate for me.
For someone doing a study on LLM they don’t seem to know much about LLMs.
They don’t even mention which model was used…
Here’s the study used for this clickbait garbage :
https://dl.acm.org/doi/pdf/10.1145/3613904.3642596
Worth noting this study was done on gpt 3.5, 4 is leagues better than 3.5. I’d be interested to see how this number has changed
There is huge gap between 3.5 and 4 especially in coding related questions. GPT3.5 does not have large enough token size to handle harder code related questions.
4 made up functions that didn’t exist last time I asked in a programming question.
sure, I’m not saying GPT4 is perfect, just that it’s known to be a lot better than 3.5. Kinda why I would be interested to see how much better it actually is.
This is why I like Bing Chat for this kind of thing, it does a web search in the background and will often be working right from the API documentation.
Developing with ChatGPT feels bizzarely like when Tony Stark invented a new element with Jarvis’ assistance.
It’s a prolonged back and forth, and you need to point out the AIs mistakes and work through a ton of iterations to get something that is close enough that you can tweak it and use, but it’s SO much faster than trawling through Stack Overflow or hoping someone who knows more than you can answer a post for you.
Yeah if you treat it is a junior engineer, with the ability to instantly research a topic, and are prepared to engage in a conversation to work toward a working answer, then it can work extremely well.
Some of the best outcomes I’ve had have needed 20+ prompts, but I still arrived at a solution faster than any other method.
In the end, there is this great fear of “the AI is going to fully replace us developers” and the reality is that while that may be a possibility one day, it wont be any day soon.
You still need people with deep technical knowledge to pilot the AI and drive it to an implemented solution.
AI isnt the end of the industry, it has just greatly sped up the industry.
I use chatgpt semi-often… For generating stuff in a repeating pattern. Any time I have used it to make code, I don’t save any time because I have to debug most of the generated code anyway. My main use case lately is making python dicts with empty keys (e.g. key1, key2… becomes “key1”: “”, “key2”: “”,…) or making a gold/prod level SQL view by passing in the backend names and frontend names (e.g. value_1, value_2…
Value 1,Value 2,… Becomes value_1 asValue 1,…).I know this is gonna sound annoying but I just use vim for stuff like this. Even notepad++ has a macro thing too, right? My coworkers keep saying how much of a productivity boost it is but all I see it do is mess up stuff like this that only takes a few seconds in vim to setup and I know it’ll be correct every time
I use vim keybinds (via doom emacs) for this sort of stuff if I’m doing it for personal projects, my professional work is all done in an online platform (no way around it) so it’s just faster and easier to throw the pattern and columns at the integrated chatgpt terminal rather than hop to a local editor and back
Sure, but by randomly guessing code you’d get 0%. Getting 48% right is actually very impressive for an LLM compared to just a few years ago.
Exactly, I also find that it tends to do a pretty good job pointing you in the right direction. It’s way faster than googling or going through sites like stackoverflow because the answers are contextual. You can ask about a specific thing you want to do, and and an answer that gives you a general idea of what to do. For example, I’ve found it to be great for crafting complex sql queries. I don’t really care if the answer is perfect, as long as it gives me an idea of what I need to do.
Just useful enough to become incredibly dangerous to anyone who doesn’t know what they’re doing. Isn’t it great?
It’s pretty fun, interesting times ahead. I wonder what kind of bullshit will take place and can’t wait to see that lol. Between all the climate, ai, warmongering future won’t be boring guys that is certain. Unpack your popcorn
Now non-coders can finally wield the foot-gun once reserved only for coders! /s
Truth be told, computer engineering should really be something that one needs a licence to do commercially, just like regular engineering. In this modern era where software can be ruinous to someone’s life just like shoddy engineering, why is it not like this already.
Look, nothing will blow up if I mess up my proxy setup on my machine. I just won’t have internet until I revert my change. Why would that be different if I were getting paid for it?
Nothing happens if you fuck up your proxy, but if you develop an app that gets very popular and don’t care about safety, so hackers are able to take control over your whole Server they can do a lot of damage. If you develop software for critical infrastructure it can actually cost human lives if you fuck up your security systems.
Yes, but people with master’s degrees also fuck this up, so it’s not like some accreditation system will solve the issue of people making mistakes
Yeah, but its probably more likely that the untaught might fuck up some stuff.
Is it, though? A lot of self-taught programmers do great work. I’m not sure this is true
Setting up proxy is not engineering.
I have to actually modify the code to properly package it for my distro, so it’s engineering because I have to make decisions for how things work
I don’t see how this supports your point then. If “setting up proxy” means “packaging it to run on thousands user machines” then isn’t there obvious and huge potential for a disastrous fuckup?
No, because it either runs the program successfully, or it fails to launch. I don’t mess with the protocol. It runs as root because it needs to set the iptables when turned on to be a “global” proxy
You can also play with it to try and get closer to correct. I had problems with getting an Excel macro working and getting unattended-updates working on my pihole. GPT was wrong at first, but got me partly there and I could massage the question and Google and get closer to the right answer. Without it, I wouldn’t have been able to get any of it, especially with the macro.
You should see 52% of the first version of my code.
It doesn’t have to be right to be useful.
Yeah cause my favorite thing to do when programming is debugging someone else’s broken code.
Yeah I’ve already got enough legacy code to deal with, I don’t need more of it faster.
Get it to debug itself then.
I think where it shines is in helping you write code you’ve never written before. I never touched Swift before and I made a fully functional iOS app in a week. Also, even with stuff I have done before, I can say “write me a function that does x” and it will and it usually works.
Like just yesterday I asked it to write me a function that would generate and serve up an .ics file based on a selected date and extrapolate the date of a recurring monthly meeting based on the day of the week picked and its position (1st week, 2nd week, etc) within the month and then make the .ics file reflect all that. I could have generated that code myself by hand but it would have probably taken me an hour or two. It did it in about five seconds and it worked perfectly.
Yeah, you have to know what you’re doing in general and there’s a lot of babysitting involved, but anyone who thinks it’s just useless is plain wrong. It’s fucking amazing.
Edit: lol the article is referring to a study that was using GPT 3.5, which is all but useless for coding. 4.0 has been out for a year blowing everybody’s minds. Clickbait trash.
3.5 is still reasonably useful for the same reasons you described, imo… Just less so.
To be fair, I’m starting to fear that all the fun bits of human jobs are the ones that are most easy to automate.
I dread the day I’m stuck playing project manager to a bunch of chat bots.
Generally you want to the reference material used to improve that first version to be correct though. Otherwise it’s just swapping one problem for another.
I wouldn’t use a textbook that was 52% incorrect, the same should apply to a chatbot.
Yeah, but the non-tech savvy business leaders see they can generate code with AI and think ‘why do I need a developer if I have this AI?’ and have no idea whether the code it produces is right or not. This stat should be shared broadly so leaders don’t overestimate the capability and fire people they will desperately need.
And they’ll find out very soon that they need devs when they actually try to test something and nothing works.
Yeah management are all for this, the first few years here are rough with them immediately hitting the “fire the engineers we have ai now”. They won’t realize their fuckup until they’ve been promoted away from it
Programming jobs will be safe for a while. They’ve been trying to eliminate those positions since at least the 90s. Because coders are expensive and often lack social skills.
But I do think the clock is ticking. We will see more and more sophisticated AI tools that are relatively idiot-proof and can do things like modify Salesforce, or create complex new Tableau reports with a few mouse clicks, and stuff like that. Jobs will be chiseled away like our unfortunate friends in graphic design.
You, along with most people, are still looking at automation wrong. It’s never been about removing people entirely, even AI, it’s about doing the same work with less cost.
If you can eliminate one programmers from your four person team by giving the other three AI to produce the same amount of work, congrats you’ve just automated one programming job.
Programming jobs aren’t going anywhere, but either the amount of code produced is about to skyrocket, or the number of employed programmers is going to drop (or most likely both of those things).
Right on. AI feels like a looming paradigm shift in our field that we can either scoff at for its flaws or start learning how to exploit for our benefit. As long as it ends up boosting productivity it’s probably something we’re going to have to learn to work with for job security.
It’s already boosting productivity in many roles. That’s just going to accelerate as the models get better, the processing gets cheaper, and (as you said) people learn to use it better.
I wonder if this will also have a reverse tail end effect.
Company uses AI (with devs) to produce a large amount of code -> code is in prod for a few years with incremental changes -> dev roles rotate or get further reduced over time -> company now needs to modernize and change very large legacy codebase that nobody really understands well enough to even feed it Into the AI -> now hiring more devs than before to figure out how to manage a legacy codebase 5-10x the size of what the team could realistically handle.
Writing greenfield code is relatively easy, maintaining it over years and keeping it up to date and well understood while twisting it for all new requirements - now that’s hard.
AI will help with that too, it’s going to be able to process entire codebases at a time pretty shortly here.
Given the visual capabilities now emerging, it can likely also do human-equivalent testing.
One of the biggest AI tricks we haven’t started seeing much of yet in mainstream use is this kind of automated double-checking. Where it generates an answer, and then validates if the answer is valid before actually giving it to a human. Especially in coding bases, there really isn’t anything stopping it from coming up with an answer compiling, running into an error, re-generating, and repeating until the code passes all unit tests or even potentially visual inspection.
The big limit on this right now is sheer processing cost and context lengths for the models. However, costs for this are dropping faster than any new tech we’ve seen, and it will likely be trivial in just a few years.
There are some areas I’m hoping get addressed by the coming skyrocket in programmer productivity:
I think that last one stands to be strongly enabled by AI code assist tools. It might not be the sexiest or highest paying job, but it’ll be work that matters that largely isn’t even being done today.
I say let it happen. If someone is dumb enough to fire all their workers… They deserve what will happen next
Well the firing’s happening so, i guess let’s hope you’re right about the other part.
It won’t happen like that. Leadership will just under-hire and expect all their developers to be way more efficient. Working will be really stressful with increased deadlines and people questioning why you couldn’t meet them.
Mentioned it before but:
LLMs program at the level of a junior engineer or an intern. You already need code review and more senior engineers to fix that shit for them.
What they do is migrate that. Now that junior engineer has an intern they are trying to work with. Or… companies realize they don’t benefit from training up those newbie (or stupid) engineers when they are likely to leave in a year or two anyway.
Bad take. Is the first version of your code the one that you deliver or push upstream?
LLMs can give great starting points, I use multiple LLMs each for various reasons. Usually to clean up something I wrote (too lazy or too busy/stressed to do manually), find a problem with the logic, or maybe even brainstorm ideas.
I rarely ever use it to generate blocks of code like asking it to generate “a method that takes X inputs and does Y operations, and returns Z value”. I find that those kinds of results are often vastly wrong or just done in a way that doesn’t fit with other things I’m doing.
Impressed some folks think LLMs are useless. Not sure if their lives/workflows/brains are that different from ours or they haven’t given at the college try.
I almost always have to use my head before a language model’s output is useful for a given purpose. The tool almost always saves me time, improves the end result, or both. Usually both, I would say.
It’s a very dangerous technology that is known to output utter garbage and make enormous mistakes. Still, it routinely blows my mind.
For the upteenth time - an llm just puts words together, it isn’t a magic answer machine.
Yeah but it’s just going to get better at magicking. Soon all us wizards will be out of a job…
Just as soon as we no longer need to drive.
Self driving cars need to convince regulators that they’re safe enough, even if assuming they master the tech.
LLMs has already convinced our bosses that we are expendable, and can drastically reduce cost centres for their next earnings call.
A parrot blabbing the theory of relativity doesn’t make it Einstein.
It’s probably more because you can ask it your exact question (not just search for something more or less similar) and it will at least give you a lead that you can use to discover the answer, even if it doesn’t give you a perfect answer.
Also, who does a survey of 12 people and publishes the results? Is that normal?
I have 13 friends who are researchers and they publish surveys like that all the time.
(You can trust this comment because I peer reviewed it.)
Even this Lemmy thread has more participants than the survey
We need a comparison against an average coder. Some fucking baseline ffs.
I’m a 10 year pro, and I’ve changed my workflows completely to include both chatgpt and copilot. I have found that for the mundane, simple, common patterns copilot’s accuracy is close to 9/10 correct, especially in my well maintained repos.
It seems like the accuracy of simple answers is directly proportional to the precision of my function and variable names.
I haven’t typed a full for loop in a year thanks to copilot, I treat it like an intent autocomplete.
Chatgpt on the other hand is remarkably useful for super well laid out questions, again with extreme precision in the terms you lay out. It has helped me in greenfield development with unique and insightful methodologies to accomplish tasks that would normally require extensive documentation searching.
Anyone who claims llms are a nothingburger is frankly wrong, with the right guidance my output has increased dramatically and my error rate has dropped slightly. I used to be able to put out about 1000 quality lines of change in a day (a poor metric, but a useful one) and my output has expanded to at least double that using the tools we have today.
Are LLMs miraculous? No, but they are incredibly powerful tools in the right hands.
Don’t throw out the baby with the bathwater.
I think AI is good with giving answers to well defined problems. The issue is that companies keep trying to throw it at poorly defined problems and the results are less useful. I work in the cybersecurity space and you can’t swing a dead cat without hitting a vendor talking about AI in their products. It’s the new, big marketing buzzword. The problem is that finding the bad stuff on a network is not a well defined problem. So instead, you get the unsupervised models faffing about, generating tons and tons of false positives. The only useful implementations of AI I’ve seen in these tools actually mirrors you own: they can be scary good at generating data queries from natural language prompts. Which is, once again, a well defined problem.
Overall, AI is a tool and used in the right way, it’s useful. It gets a bad rap because companies keep using it in bad ways and the end result can be worse than not having it at all.
In fairness, it’s possible that if 100 companies try seemingly bad ideas, 1 of them will turn out to be extremely profitable.
On the other hand, using ChatGPT for your Lemmy comments sticks out like a sore thumb
If you’re careless with your prompting, sure. The “default style” of ChatGPT is widely known at this point. If you want it to sound different you’ll need to provide some context to tell it what you want it to sound like.
Or just use one of the many other LLMs out there to mix things up a bit. When I’m brainstorming I usually use Chatbot Arena to bounce ideas around, it’s a page where you can send a prompt to two randomly-selected LLMs and then by voting on which gave a better response you help rank them on a leaderboard. This way I get to run my prompts through a lot of variety.
You wish. The sheer idea of calling yourself a “pro” disqualifies you. People who actually code and know what they are doing wouldn’t dream of giving themselves a label beyond “coder” / “programmer” / “SW Dev”. Because they don’t have to. You are a muppet.
elon?
Hey! So you may have noticed that you got downvoted into oblivion here. It is because of the unnecessary amount of negativity in your comment.
In communication, there are two parts - how it is delivered, and how it is received. In this interaction, you clearly stated your point: giving yourself the title of pro oftentimes means the person is not a pro.
What they received, however, is far different. They received: ugh this sweaty asshole is gatekeeping coding.
If your goal was to convince this person not to call themselves a pro going forward, this may have been a failed communication event.
while your measured response is appreciated, I hardly consider a few dozen downvotes relevant, nor do I care in this case. It’s telling that those who did respond to my comment seem to assume I would consider myself a “pro” when that’s 1) nothing I said and 2) it should be clear from my comment that I consider the expression cringy. Outside memeable content, only idiots call themselves a “pro”. If something is my profession, I could see someone calling themselves a “professional <whatever>” (not that I would use it), but professional has a profoundly distinct ring to it, because it also refers to a code of conduct / a way to conduct business.
“I’m a pro” and anything like it is just hot air coming from bullshitters who are mostly responsible for enshittification of any given technology.
A lot of rage for a small amount of confidence
Here we observe a pro gatekeeper in their natural habitat…
As a fellow pro, who has no issues calling myself a pro, because I am…
You’re spot on.
The stuff most people think AI is going to do - it’s not.
But as an insanely convenient auto-complete, modern LLMs absolutely shine!
Refreshing to see a reasonable response to coding with AI. Never used chatgpt for it but my copilot experience mirrors yours.
I find it shocking how many developers seem to think so many negative thoughts about it programming with AI. Some guy recently said “everyone in my shop finds it useless”. Hard for me to believe they actually tried copilot if they think that
Omg, I feel sorry for the people cleaning up after those codebases later. Maintaing that kind of careless “quality” lines of code is going to be a job for actual veterans.
And when we’re all retired or dead, the whole world will be a pile of alien artifacts from a time when people were still able to figure stuff out, and llms will still be ridiculously inefficient for precise tasks, just like today.
https://youtu.be/dDUC-LqVrPU
Here is an alternative Piped link(s):
https://piped.video/dDUC-LqVrPU
Piped is a privacy-respecting open-source alternative frontend to YouTube.
I’m open-source; check me out at GitHub.
I’ve found that the better I’ve gotten at writing prompts and giving enough information for it to not hallucinate, the better answers I get. It has to be treated as what it is, a calculator that can talk, make sure it has all of the information and it will find the answer.
One thing I have found to be super helpful with GPT4o is the ability to give it full API pages so it can update and familiarise it’s self with what it’s working with.
Exactly. When someone says that it either indicates to me that they ignorant (like they aren’t a programmer or haven’t used it) or they are a programmer who has used it, but are not good at all at integrating new tools into their development process.
Yup. The problem I see now is that every mistake an ai makes is parroted over and over here and held up as an example of why the tech is garbage. But it’s cherry picking. Yes, they make mistakes, I often scratch my head at the ai results from Google and know to double check it. But the number of times it has pointed me in the right direction way faster than search results has shown to me already how useful it is.
I’ve used chatgpt and gemini to build some simple powershell scripts for use in intune deployments. They’ve been fairly simple scripts. Very few have of them have been workable solutions out of the box, and they’ve often filled with hallucinated cmdlets that don’t exist or are part of a thirdparty module that it doesn’t tell me needs to be installed. It’s not useless tho, because I am a lousy programmer its been good to give me a skeleton for which I can build a working script off of and debug myself.
I reiterate that I am a lousy programmer, but it has sped up my deployments because I haven’t had to work from scratch. 5/10 its saved me a half hour here and there.
I’m a good programmer and I still find LLMs to be great for banging out python scripts to handle one-off tasks. I usually use Copilot, it seems best for that sort of thing. Often the first version of the script will have a bug or misunderstanding in it, but all you need to do is tell the LLM what it did wrong or paste the text of the exception into the chat and it’ll usually fix its own mistakes quite well.
I could write those scripts myself by hand if I wanted to, but they’d take a lot longer and I’d be spending my time on boring stuff. Why not let a machine do the boring stuff? That’s why we have technology.
It’s programming spell check
deleted by creator
This is the best summary I could come up with:
In recent years, computer programmers have flocked to chatbots like OpenAI’s ChatGPT to help them code, dealing a blow to places like Stack Overflow, which had to lay off nearly 30 percent of its staff last year.
That’s a staggeringly large proportion for a program that people are relying on to be accurate and precise, underlining what other end users like writers and teachers are experiencing: AI platforms like ChatGPT often hallucinate totally incorrectly answers out of thin air.
For the study, the researchers looked over 517 questions in Stack Overflow and analyzed ChatGPT’s attempt to answer them.
The team also performed a linguistic analysis of 2,000 randomly selected ChatGPT answers and found they were “more formal and analytical” while portraying “less negative sentiment” — the sort of bland and cheery tone AI tends to produce.
The Purdue researchers polled 12 programmers — admittedly a small sample size — and found they preferred ChatGPT at a rate of 35 percent and didn’t catch AI-generated mistakes at 39 percent.
The study demonstrates that ChatGPT still has major flaws — but that’s cold comfort to people laid off from Stack Overflow or programmers who have to fix AI-generated mistakes in code.
The original article contains 340 words, the summary contains 199 words. Saved 41%. I’m a bot and I’m open source!