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Comment by lucideer | Hacker News Reader

> I recently asked ChatGPT to produce similar CSS and it did so flawlessly.

I use ChatGPT every day for many tasks in my work and find it very helpful, but I simply do not believe this.

> The article points this out: middling generalists can now compete with specialists.

I'd say it might allow novices to compete with middling generalists, but even that is a stretch. On the contrary, ChatGPT is actually best suited to use by a specialist who has enough contextual knowledge to construct targeted prompts & can then verify & edit the responses into something optimal.

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That's about my experience.

The worst dev on my team uses ChatGPT a lot, and its facilitated him producing more bad code more quickly. I'm not sure it's a win for anyone, and he's still unlikely to be with the team in a year.

It allows a dev who doesn't care about their craft or improving to generate code without learning anything. The code they generate today or a year from today is the same quality.

Part of it is that it allows devs who lean into overcomplicating things to do so even more. The solutions are never a refinement of what already exists, but patch on top of patch on top of patch of complexity. ChatGPT is not going to tell you how to design a system, architect properly, automate, package, test, deploy, etc.

For the team it means there's a larger mess of a code base to rewrite.

> ChatGPT is not going to tell you how to design a system, architect properly, automate, package, test, deploy, etc.

If you ask the right questions it absolutely can.

I’ve found that most people thinking ChatGPT is a rube are expecting too much extrapolation from vague prompts. “Make me a RESTful service that provides music data.” ChatGPT will give you something that does that. And then you’ll proceed to come to hacker news and talk about all the dumb things it did.

But, if you have a conversation with it. Tell it more of the things you’re considering. Some of the trades off you’re making—how the schema might grow over time, it’s kind of remarkable.

You need to treat it like a real whiteboarding session.

I also find it incredibly useful for getting my code into more mainstream shape. I have my own quirks that I’ve developed over time learning a million different things in a dozen different programming languages. It’s nice to be able to hand your code to ChatGPT and simply ask “is this idiomatic for this language?”

I think the people most disappointed with ChatGPT are trying to treat it like a Unix CLI instead of another developer to whiteboard with.

This has been my experience as well.

Every person I've noticed who says that ChatGPT isn't good at what it does has the same thing in common - they're not great at talking to people, either.

Turns out when you train an AI on the corpus of human knowledge, you have to actually talk to it like a human. Which entirely too many people visiting this website don't do effectively.

ChatGPT has allowed me to develop comprehensive training programs for our internal personnel, because I already have some knowledge of training and standardization from my time in the military, but I also have in-depth domain knowledge so I can double-check what it's recommending, then course correct it if necessary.

> Every person I've noticed who says that ChatGPT isn't good at what it does has the same thing in common - they're not great at talking to people, either.

I think that the people who nowadays shit on ChatGPT's code generating abilities are the same blend of people who, a couple decades ago, wasted their time complaining that hand-rolled assembly would beat any compiled code in any way, shape, or form, provided that people knew what they were doing.

> But, if you have a conversation with it. Tell it more of the things you’re considering. Some of the trades off you’re making—how the schema might grow over time, it’s kind of remarkable.

You're not wrong, but I would caution that it can get really confused when the code it produces exceeds the context length. This is less of a problem than it used to be as the maximum context length is increasing quite quickly, but by way of example: I'm occasionally using it for side projects to see how to best use it, one of which is a game engine, and it (with a shorter context length than we have now) started by creating a perfectly adequate Vector2D class with `subtract(…)` and `multiply(…)` functions, but when it came to using that class it was calling `sub(…)` and `mul(…)` — not absolutely stupid, and a totally understandable failure mode given how it works, but still objectively incorrect.

I frequently run into this, and it’s quite maddening. When you’re working on a toy problem where generating functioning code is giving you a headache - either because it’s complex or because the programming language is foreign or crass - no problem. When you’re trying to extend an assemblage of 10 mixins in a highly declarative framework that many large-scale API contracts rely on to be correct, the problem is always going to boil down to how well the programmer understands the existing tools/context that they’re working with.

To me, a lot of this boils down to the old truism that “code is easier to write than maintain or extend”. Companies who dole out shiny star stickers for producing masses of untested, unmaintainable code will always reap their rewards, whether they’re relying on middling engineers and contractors alone, or with novices supercharged with ChatGPT.

> But, if you have a conversation with it

It can't tell you a straight answer or halucinates API. It can't tell you "no, this cannot be done", it tries to "help" you.

For me it's great for writing simple isolated functions, generating regexes, command line solutions, exploring new technologies, it's great.

But after making it write a few methods, classes, it just gets extremelely tedious to make it add/change code, to the point I just write it myself.

Further, when operating at the edge of your knowledge, it also leads you on, whereas a human expert would just tell you "aaah, but that's just not possible/not a good idea".

I think that's a fair description. While I have not yet found ChatGPT useful in my "real" day job (its understanding of aerospace systems is more than I would have guessed, but yet not enough to be super helpful to me), I have found it generally useful in more commonplace scripting tasks and what-not.

With the caveat of, I still need to understand what it's talking about. Copy-pasting whatever it says may or may not work.

Which is why I remain dubious that we're on the road to LLMs replacing software engineers. Assisting? Sure, absolutely.

Will we get there? I don't know. I mean, like, fundamentally, I do not trust LLMs. I am not going to say "hey ChatGPT, write me a flight management system suitable for a Citation X" and then just go install that on the plane and fly off into the sunset. I'm sure things will improve, and maybe improve enough to replace human programmers in some contexts, but I don't think we're going to see LLMs replacing all software engineers across the board.

In a similar vein, ChatGPT can be an amazing rubber duck. If I have strange and obscure problems that stumps me, I kinda treat ChatGPT like I would treat a forum or an IRC channel 15 - 20 years back. I don't have "prompting experience or skills", but I can write up the situation, what we've tried, what's going on, and throw that at the thing.

And.. it can dredge up really weird possible reasons for system behaviors fairly reliably. Usually, for a question of "Why doesn't this work after all of that?", it drags up like 5-10 reasons for something misbehaving. We usually checked like 8 of those. But the last few can be really useful to start thinking outside of the normal box why things are borked.

And often enough, it can find at least the right idea to identify root causes of these weird behaviors. The actual "do this" tends to be some degree of bollocks, but enough of an idea to follow-up.

> The worst dev on my team uses ChatGPT a lot, and its facilitated him producing more bad code more quickly.

This is great. The exact same is true with writing, which I think it's trivial for anyone to see. Especially non-native speakers or otherwise bad writers can now write long-winded nonsense, which we're starting to see all over. It hasn't made anyone a good writer, it's just helped bad ones go faster.

> Especially non-native speakers or otherwise bad writers can now write long-winded nonsense

You have now described 95% of Quora's content.

Isn't it expected? Chatgpt was trained on such texts as well.

Bless this person your team, he is creating work out of thin air and will keep your team and possibly other teams employed for a really long time

Exactly - and people say AI will take away jobs!

This could even play out on a broader scale and even increase the demand for software engineers.

What is going to happen if more and more people are creating lots of software that delivers value but is difficult to maintain and extend?

First it's going to be more high-level stuff, then more plumbing, more debugging and stitching together half-baked solutions than ever before.

AI might make our jobs suck more, but it's not going to replace them.

https://en.wikipedia.org/wiki/Parable_of_the_broken_window

I have a hunch that using ChatGPT might be a skill in of itself and it doesn’t necessarily hurt or help any particular skill level of developers.

In previous replies in this thread the claim is it helps novices compete with associates or associates with seniors but in reality, it will probably help any tier of skill level. You just have to figure out how to prompt it

One hundred percent. Most people I’ve seen dismiss ChatGPT simply refuse to engage it appropriately. It’s not likely to solve your most complex problem with a single prompt.

Asking the right questions is such an important skill in and of itself. I think we’re seeing to some extent the old joke about engineers not knowing how to talk to people manifest itself a bit with a lot of engineers right now not knowing quite how to get good results from ChatGPT. Sort of looking around the room wondering what they’re missing since it seems quite dumb to them.

I had a friend jokingly poke fun at me for the way I was writing ChatGPT prompts. It seemed, to him, like I was going out of my way to be nice and helpful to an AI. It was a bit of an aha moment for him when I told him that helping the AI along gave much more useful answers, and he saw I was right.

They use GPT 3.5, prompt it with "Write a javascript login page" and then look at the code an go "Damn, this thing is stupid as fuck".

I use “chatGPT” (really bing chat which is openAI under the hood as I understand) more than anyone on my team but it is very rarely for code.

I most often use it for summarizing/searching through dense documentation, creating quick prototypes, “given X,Y,Z symptoms and this confusing error message, can you give me a list of possible causes?” (basically searches Stack Overflow far better than I can).

Anyway basically the same as I was using google when google was actually good. sometimes I will forget some obscure syntax and ask it how to do something, but not super often. I’m convinced using it solely to generate code is a mistake unless it’s tedious boilerplate stuff.

Yes, agreed. The best way of putting this is "using google when google was actually good."

Bing is far and away worse than gpt4 through ChatGPT or the API, just FYI. Don't even consider it comparable, even if they say it is the same model under the hood. Their "optimizations" have crippled it's capabilities if that is the case.

Well, it works pretty well for me, and cites its sources with links, and I have yet to catch it making up something.

We were talking about code generation though. It's horrible at it.

My parent commen says I rarely use it for code generation and I think if you're using these tools purely for that you're doing it wrong... that was like my entire point

Right, but your reference for the point is a weak model. Your view would likely change a bit if you used GPT-4. It is substantially more powerful and skilled.

Exactly what I was trying to say. Thank you.

Still works best if you know what you are doing and can give very detailed instructions, but GPT-4 is vastly more capable than Bing for these tasks.

But I don't need it to be. Like at all. I've yet to find a programming task that GPT-4 would have made faster, and yes I have used it.

On the flip side, one can use ChatGPT as only a starting point and to learn from there. One isn't stuck with actually using what it outputs verbtim, and really shouldn't until at least a hypothetical GPT-6 or 7... and to use it fully now, one has to know how to nudge it when it goes into a bad direction.

So overall it's more an amplifier than anything else.

I have a lot of juniors floating around my co-op and when I watch them use chatgpt it seems it becomes a dependency. In my opinion it's harming their ability to learn. Rather than thinking through problems they'll just toss every single roadblock they hit instantly into a chatgpt prompt.

To be fair I've been doing the same thing with simple mathematics into a calculator in my browser that at this point I'm pretty sure I'd fail at long division by hand.

Maybe it won't matter in a few years and their chatgpt skills will be well honed, but if it were me in their position I wouldn't gamble on it.

Yeah that's my larger point about the guy and the pattern. It doesn't lead to growth. I've seen zero growth whatsoever.

And he slacks coworkers like he is talking to ChatGPT too, slinging code blobs without context, example input data, or the actual error he received..

> Yeah that's my larger point about the guy and the pattern. It doesn't lead to growth. I've seen zero growth whatsoever.

If Chat GPT can solve a problem consistently well, I don't think it's worth the effort to master it.

My examples are regexes, command lines to manipulate files, kafka/zookeeper commands to explore a test environment.

For me it's a big win in that regard.

> So overall it's more an amplifier than anything else.

Overall it would be an amplifier if that were how the majority used it. Sadly I don't believe that to be the case.

That's been the case with every technology made by man since fire.

Yup. We should embrace it, but without be naïve about what great things it's bringing us :)

Ouch! Hot!

If the results were more akin to a google or stack overflow where there was a list of results with context.. sure.

But people are using the singular response as "the answer" and moving on..

> If the results were more akin to a google or stack overflow where there was a list of results with context.. sure.

I don't think the history of the usage of either shows that most people make any use of that context.

Especially these days you have to know how to use/read Google and SO results too.

(And I should have said ChatGPT4 earlier, if you're a bad to medicore developer taking ChatGPT3.5 literally you'll probably wind up in a Very Bad Place.)

Phind is a bit more like this

This has been my experience as well - For repetitive things, If what you're looking for is the shitty first draft, it's a way to get things started.

After that, you can shape the output - without GPT's help - into something that you can pull off the shelf again as needed and drop it into where you want it to go, because at that point in the process, you know it works.

> nudge it when it goes into a bad direction

It happenes a few times for me that Chat GPT gets stuck in a bullshit loop and I can't get it unstuck.

Sure I could summarise the previous session for Chat GPT and try again, but I'm too tired at that point.

I get that with humans sometimes too. Even here, even before LLMs became popular. Someone gets primed on some keyword, then goes off on a direction unrelated to whatever it was I had in mind and I can't get them to change focus — and at least one occasion (here) where I kept saying ~"that's not what I'm talking about" only to be eventually met (after three rounds of this) with the accusation that I was moving the goalposts :P

Yeah, regardless of hallucinations and repeating the same mistake even after you tell it to fix it, iterating with ChatGPT is so much less stressful than iterating with another engineer.

I almost ruined my relationship with a coworker because they submitted some code that took a dependency on something it shouldn't have, and I told them to remove the dependency. What I meant was "do the same thing you did, but instead of using this method, just do what this method does inside your own code." But they misinterpreted it to mean "Don't do what this method does, build a completely different solution." Repeated attempts to clarify what I meant only dug the hole deeper because to them I was just complaining that their solution was different from how I would've done it.

Eventually I just showed them in code what I was asking for (it was a very small change!) and they got mad at me for making such a big deal over 3 lines of code. Of course the whole point was that it was a small change that would avoid a big problem down the road...

So I'll take ChatGPT using library methods that don't exist, no matter how much you tell them to fix it, over that kind of stress any day.

Use the edit button

>One isn't stuck with actually using what it outputs verbtim, and really shouldn't until at least a hypothetical GPT-6 or 7... and to use it fully now, one has to know how to nudge it when it goes into a bad direction.

Exactly.

chatGPT is the single smartest person you can ask about anything, and has unlimited patience.

>ChatGPT is not going to tell you how to design a system, architect properly, automate, package, test, deploy, etc.

Really? Please explain how chatGPT can not, but you can? What magic is it that you know that it's incapable of explaining?

because it seems to have built in assumptions about the scale, scope and complexity of soluton you are trying to develop. Specifically, unless you tell it to think about automated testing, or architecting, or making code be loosely coupled, it will hack.

this is because a lot of the time what a beginner needs IS a hack. But if you always hack at your job, things stop working.

I want to reiterate. One of the reasons the LLM is so helpful is because it has been RLHF'ed into always treating you as a beginner. This is core to it's usefulness. But it also limits it from producing quality work unless you bombard it with an enormous prompt explaining all of the difference between a beginne's and an expert's code. Which is tedious and lengthy to do every time when you just want a short change that doesnt suck.

Humans are able to learn from all sorts of external cues what level they should pitch an idea at. Startups should focus on hacky code, for velocity. Large buisnesses should focus on robust processes.

I agree with this. There are cases where it produces good results, but there are also cases where it produces bs, and it's not always obvious. I find it to work fine for cases where I know what I want but could use a starting point, but it often invents or misunderstands all kinds of things.

The most frustrating situations are those where it invents a function that would miraculously do what's necessary, I tell it that function does not exist, it apologizes, shuffles the code around a bit and invents a different function, etc. It's the most annoying kind of debugging there is.

Another very obvious thing it does when it comes to code is take the most common misconceptions & anti-patterns used within the programming community & repeats them in an environment where there's no-one to comment. People have critiqued Stack Overflow for having so many "wrong" answers with green checkmarks, but at least those threads have surrounding context & discussion.

A case in point: I asked ChatGPT to give me some code for password complexity validation. It gave me perfectly working code that took a password and validated it against X metrics. Obviously the metrics are garbage, but the code works, and what inexperienced developer would be any the wiser. The only way to get ChatGPT to generate something "correct" there would be to tell it algorithmically what you want (e.g. "give me a function measuring information entropy of inputs", etc.) - you could ask it 50 times for a password validator: every one may execute successfully & produce a desired UI output for a web designer, but be effectively nonsense.

> there are also cases where it produces bs, and it's not always obvious

Particularly annoying because I wind up risking not actually saving time because it’s producing subtle bugs that I wouldn’t have written myself.

So, you save yourself the time of thought and research at the risk of going down new and mysterious rabbit holes

For me the trick to avoiding this trap is to limit usage to small areas of code, test frequently, and know its limits. I love using copilot/GPT for boilerplate stuff.

> There are cases where it produces good results, but there are also cases where it produces bs, and it's not always obvious.

Pessimistically, this is the medium term role I see for a lot of devs. Less actual development, more assembly of pieces and being good enough at cleaning up generated code.

If an LLM can get you even 25% there most of the time, that's a massive disruption of this industry.

I mean, especially in webdev we've been heading in that direction for a while now anyway. So much of the job is already just wiring up different npm packages and APIs that someone else has written. I've read substantially similar comments back in the mid 2010s about how people weren't learning the fundamentals and just pulling things like left pad off of a repo. That did cause a disruption in how people coded by abstraction away many of the problems and making the job more about integrating different things together.

> ChatGPT is actually best suited to use by a specialist who has enough contextual knowledge to construct targeted prompts & can then verify & edit the responses into something optimal.

I agree with this, but what that means is that specialists will be able to create next generation tools--across all professions including coding--that do supercharge novices and generalists to do more.

> ChatGPT is actually best suited to use by a specialist who has enough contextual knowledge to construct targeted prompts

This is my take also.

ChatGPT for novices is dangerous, its the equivalent of a calculator. If you don't know your expected output you're just wrong faster.

But if you know what to expect, whats your bounds and how to do it normally anyway, it can make you faster.

I wrote just the tool to optimize AI in the hand of a coding expert https://observablehq.com/@tomlarkworthy/robocoop

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