You should see 52% of the first version of my code.
It doesn’t have to be right to be useful.
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.
I say let it happen. If someone is dumb enough to fire all their workers… They deserve what will happen next
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).
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.
Yeah cause my favorite thing to do when programming is debugging someone else’s broken code.
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.
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.
It’s been a tremendous help to me as I relearn how to code on some personal projects. I have written 5 little apps that are very useful to me for my hobbies.
It’s also been helpful at work with some random database type stuff.
But it definitely gets stuff wrong. A lot of stuff.
The funny thing is, if you point out its mistakes, it often does better on subsequent attempts. It’s more like an iterative process of refinement than one prompt gives you the final answer.
The funny thing is, if you point out its mistakes, it often does better on subsequent attempts.
Or it get stuck in an endless loop of two different but wrong solutions.
Me: This is my system, version x. I want to achieve this.
ChatGpt: Here’s the solution.
Me: But this only works with Version y of given system, not x
ChatGpt: <Apology> Try this.
Me: This is using a method that never existed in the framework.
ChatGpt: <Apology> <Gives first solution again>
I used to have this issue more often as well. I’ve had good results recently by **not ** pointing out mistakes in replies, but by going back to the message before GPT’s response and saying “do not include y.”
Agreed, I send my first prompt, review the output, smack my head “obviously it couldn’t read my mind on that missing requirement”, and go back and edit the first prompt as if I really was a competent and clear communicator all along.
It’s actually not a bad strategy because it can make some adept assumptions that may have seemed pertinent to include, so instead of typing out every requirement you can think of, you speech-to-text* a half-assed prompt and then know exactly what to fix a few seconds later.
*[ad] free Ecco Dictate on iOS, TypingMind’s built-in dictation… anything using OpenAI Whisper, godly accuracy. btw TypingMind is great - stick in GPT-4o & Claude 3 Opus API keys and boom
It’s incredibly useful for learning. ChatGPT was what taught me to unlearn, essentially, writing C in every language, and how to write idiomatic Python and JavaScript.
It is very good for boilerplate code or fleshing out a big module without you having to do the typing. My experience was just like yours; once you’re past a certain (not real high) level of complexity you’re looking at multiple rounds of improvement or else just doing it yourself.
It is very good for boilerplate code
Personally I find all LLMs in general not that great at writing larger blocks of code. It’s fine for smaller stuff, but the more you expect out of it the more it’ll get wrong.
I find they work best with existing stuff that you provide. Like “make this block of code more efficient” or “rewrite this function to do X”.
I was recently asked to make a small Android app using flutter, which I had never touched before
I used chatgpt at first and it was so painful to get correct answers, but then made an agent or whatever it’s called where I gave it instructions saying it was a flutter Dev and gave it a bunch of specifics about what I was working on
Suddenly it became really useful…I could throw it chunks of code and it would just straight away tell me where the error was and what I needed to change
I could ask it to write me an example method for something that I could then easily adapt for my use
One thing I would do would be ask it to write a method to do X, while I was writing the part that would use that method.
This wasn’t a big project and the whole thing took less than 40 hours, but for me to pick up a new language, setup the development environment, and make a working app for a specific task in 40 hours was a huge deal to me… I think without chatgpt, just learning all the basics and debugging would have taken more than 40 hours alone
This is because all LLMs function primarily based on the token context you feed it.
The best way to use any LLM is to completely fill up it’s history with relevant context, then ask your question.
Sometimes ChatGPT/copilot’s code predictions are scary good. Sometimes they’re batshit crazy. If you have the experience to be able to tell the difference, it’s a great help.
Due to confusing business domain terms, we often name variables the form of XY and YX.
One time copilot autogenerated about two hundred lines of a class that was like. XY; YX; XXY; XYX; XYXY; … XXYYXYXYYYXYXYYXY;
It was pretty hilarious.
But that being said, it’s a great tool that has definitely proven to worth the cost…but like with a co-op, you have to check it’s work.
The amount of reference material it has is also a big influence. I’ve had to pick up PLC programming a while ago (codesys/structured text, which is kinda based on pascal). While chatgpt understands the syntax it has absolutely no clue about libraries and platform limitations so it keeps hallucinating those based on popular ones in other languages.
Still a great tool to have it fill out things like I/O mappings and the sorts. Just need to give it some examples to work with first.
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.
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.
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
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.
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.
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.
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https://piped.video/dDUC-LqVrPU
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I’m open-source; check me out at GitHub.
I’m a 10 year pro,
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.
Ask “are you sure?” and it will apologize right away.
