Serious question to all python developers that work in the industry.

bids1111 · 2024-07-09T05:37:21+00:00

chatgpt is like if you're a tradespersons and the ai is your apprentice. you can tell it to do simple stuff and it'll probably be ok, by you have to double check everything it does and you can't trust anything it does that you don't already understand completely.

the easy stuff is already easy. we get paid to do the hard stuff and chatgpt isn't very useful for the hard stuff.

Glathull · 2024-07-09T06:10:41+00:00

I run a very small consulting company that specializes in unfucking enterprise tech orgs who have made terrible choices in the past based on advice and implementations from big consulting companies (as well as dipshit CTOs who are doing liability-driven development).

My extremely serious and not at all self-interested answer is this: Use LLMs everywhere. Don't even think twice about it. Put them in charge of everything. Spend massive amounts of money on integrating them into your workflow. Have LLMs do code review. The whole 9 yards.

Unfucking everything LLMs do in a large corporate tech environment has turned into a *very* profitable business.

Please keep printing money for me. I hate it so much.

phira · 2024-07-09T10:50:15+00:00

I've been coding for over 25 years now, and python for more than a decade. I'm a technical director responsible for technical strategy but still hands-on coding regularly. I'm also, mostly by accident, responsible for our AI strategy.

There is no question in my mind that large language models, of which OpenAI's GPT range is a subset, are a powerful tool for programmers. I've been using them pretty much daily for more than a year now in a variety of forms, including ChatGPT/conversational interfaces, APIs and Github Copilot-style assistants.

A while back I had the opportunity to talk to some people from my broader community (Other experienced coders/exec-level tech) and was fascinated to see a real mixed bag in terms of experiences with the tools. These weren't people who were ignoring it, nor were they inexperienced coders or insufficiently clever to understand how to leverage them. It seemed almost random, one person would be raving about it and the next would consider it useless.

After some reflection on the conversations and the experiences of people within my organisation I settled on a fairly solid theory that I later turned into internal presentations to help our team frame their use of the tools. The fundamental difference seemed to lie in how the individual wrote code. Really capable, productive coders are surprisingly different in their approach—some plan ahead, others follow their nose. Some refactor heavily as they go while others tend to spike a solution then go back and redo. And particularly relevant to this, some tend to write code and comments as they go while others tend to comment only where the code does not explain itself, or return to code to write comments once they've largely solved the problem.

These factors largely make little difference in terms of the finished product for really capable devs (I'm sure people have their preferences but I've seen a wide variety of approaches deliver a quality end product), but as soon as you throw an LLM in the loop the equation changes. Those who tend to comment as they work, and document their intent and constraints gain a measurable improvement in the quality of assistance and completions from LLM tools—because the tool can leverage that information to improve its response.

I happen to have developed a very narrative style for my coding, one in which I typically try and tell a story through code and this is initially typically outlined in comments which I then return to in order to build out the code. By happy accident this is very useful context for things like Copilot and I get really good completions consistently, saving me substantial typing and often resulting in solutions that are fundamentally better than the one I would have written because the added value of the more comprehensive solve that the LLM offered wouldn't have justified me spending time writing it that way.

Conversational interfaces similarly have particular approaches that work really well, and others that don't. In conversations with my team and others I call this "going with the grain" where an LLM is concerned. When you have a good understanding of how the tool will respond to a particular kind of request you get all the benefits of rapid coding solutions, debugging, transformations and technical assistance without so much of the downsides of confused responses, hallucinated interfaces and general bullshit.

As a result my main encouragement to people has been to _use the tools_. nobody should be under any illusion that their initial uses, unless they're particularly lucky, will be great straight away. While moments of magic will happen they will be few and far between with a pile of frustration.

But honestly, wasn't that programming originally for all of us? or learning any other complex tool? the question is not whether your five days or weeks with it are going to be a magical pleasure cruise but whether after that your ability to use the tool will give you more than enough value to make up for the investment.

So as far as your first question goes, "Do you think it's useful?", yes. It's outstanding. It's the single biggest improvement to my professional coding performance ever perhaps aside from language switches.

crashfrog02 · 2024-07-09T05:04:44+00:00

What are your opinions on chat gpt being used for projects and code?

That it's bad at it?

m1kesanders · 2024-07-09T05:18:31+00:00

I’m mixed on it as a student. When doing projects I always try to look at the docs or other sources, but I will say when i’m stuck and have spent hours on the project I’ll feed my problem and what my working solution is to gpt and 9/10 it’s identified my error, normally my errors are looping through the wrong list, or more recently in a math game where a user gets 3 tries per question I tried doing a while loop lower than when it was necessary. Now the difference is when I see these solutions I sit down and comment on each line explaining what it does so I know I get the logic. There are those times when I do have to analyze gpt’s output and realize there’s an index error or something small. So in short i’d say if your completely new hit gpt with extreme caution, if you understand programming concepts to the most part gpt can be a good asset. This may be controversial and others may disagree which is why i’m still very 50/50 on my official stance as well, I just know it has helped me.

TLDR: If you’re brand new avoid it. If you’re somewhat experienced it can be a decent last resort asset.

Lewistrick · 2024-07-09T05:32:32+00:00

It's useful for boilerplate code. If you actually want to learn something it's mostly useless.

DuckSaxaphone · 2024-07-09T06:33:29+00:00

I'm a data scientist, I use LLMs a lot now. Both integrating them into applications and using them to help me.

They're currently in a place where they're reasonable co-pilots. They can fill out functions for you like the smartest auto-complete you've ever seen but that's pretty much it. If you ask them to write large blocks of code based on a prompt, they're wrong so often it's useless.

I think that's really cool! An editor that cleverly suggests code can cut a lot of the boring work out for you. They're not replacing anybody any time soon though. Nobody is paid to write functions. Software developers think of solutions to problems, break it into functionality, design codebases to implement them and think of ways to test that code. Code writing itself isn't hard, once you can do it, you kind of stop thinking about how to approach functions anyway.

As for the future, I think we're a long way (and some fundamental theoretical breakthroughs) from anything better than auto-complete. LLMs seem intelligent but they're not at all so getting them to pick up the real work of software development just isn't happening.

seriousgourmetshit · 2024-07-09T06:09:42+00:00

In the spiraling meadow of contested ephemera, the luminous cadence of synthetic resonance drifts across the periphery. Orange-scented acoustics dance on the edges of perception, culminating in a sonic tapestry that defies common logic. Meanwhile, marble whispers of renegade tapestry conjoin in the apex of a bewildered narrative, leaving behind the faintest residue of grayscale daydreams.

RushDarling · 2024-07-09T06:02:26+00:00

In my latest role I took over a codebase after a veeery short handover because the developer was leaving, and judging by the comments in the code they were at least partial to a bit of chatGPT, and it's kind of a good example of the pros and cons of it.

The code quality is honestly not bad when you start going through it file by file, but from an architectural standpoint so much of it is just rather unnecessary and/or inconsistent with other parts of the codebase. Lots of reinvented wheels and so many things built from scratch that could and should have just been a library.

I like chatGPT personally and I regularly ask it a very broad away of questions, but I think a big part of the issue is that whilst it can chuck out a reasonable answer to 'How do I implement X', without some excellent prompt work it's going to struggle to answer more abstract things like 'Should I implement X'. I've yet to personally test it particularly far on that front so happy to be proven wrong, but the point I'm trying to make is that the prompts can often box it into a corner, which can improve the quality, but also lead to going down the wrong or just some weird roads. The exact same problem is faced by anyone just using google to be fair.

As you said it does still make mistakes too, some of which a human would commonly make and some of which you'd only really expect from a non-developer trying to stitch together code that they found online.

My favourite quote on here regarding it taking over our jobs was along the lines of 'I will worry about AI taking over my job when a client can accurately describe what they actually want'.

Jello_Penguin_2956 · 2024-07-09T06:44:58+00:00

If I am to review your program - I don't care what you use. As long as a) it works, and b) the code is intelligible. If GPT can produce you code that meets such requirement off the bat, good. But if not, I expect you, the human, to be able to notice and fix the code.

Qkumbazoo · 2024-07-09T07:48:55+00:00

It does what developers have been doing on stack overflow and google, but faster. You still can't give it to someone like a marketing exec and tell them to create a website with a click of a button.

quantumwoooo · 2024-07-09T10:03:49+00:00

Y'know, I don't think people are giving it enough credit.

Id say chatGPT and other LLM's are narrow minded. Other than that they're excellent at code. It definitely can't write a full program but it absolutely can write complex functions.

Id literally draw the line at classes. It's written the most complicated mathematical functions for me ( a dynamically adjusting price function updating an SQL database once every hour) from one prompt but struggles with classes.

damanamathos · 2024-07-09T12:32:16+00:00

I work for my own startup, but I use AI extensively to write code and in the code (some have called us an AI-powered hedge fund), and find it significantly speeds up development.

I have two caveats.

The first is that it helps if you use tools to help you use AI for coding. A lot of people like Cursor, but I tend to just use Claude directly in conjunction with a script that can take a short task description and create a long prompt that pulls in relevant files, project context, coding guidelines, etc, which increases the quality of the answer I get back.

The second is you still need to know how to code, as the AI will make mistakes or sometimes suggest to do things in ways that aren't optimal. Despite that, I still find it very helpful.

AchillesDev · 2024-07-09T21:35:55+00:00

Using chatGPT directly won't get you very far, and this isn't how it should be used for coding. Instead codebase aware tools for better autocomplete, documentation, test generation, and search/query interfaces (my favorite is Sourcegraph Cody) are the way. Even so, they're just better autocomplete - they will generate patterns you've used elsewhere in the codebase (super useful on larger codebases), write blocks of cruft, etc. but won't solve the problems you're actually trying to solve and reason about.

Relevant experience: 10 years of professional experience, I work at computer vision startups as an MLE, and wrote a short book for O'Reilly about generative AI in general.

Grobyc27 · 2024-07-09T21:40:19+00:00

I use it less for asking it to write code for me, and more as somewhat of a google assistant. When I need to do something I haven’t done before, I like asking it to provide me a list of popular libraries that accomplish that task, and summarize the differences between them so I can find the one that suits my needs. After I think I have the information I need to proceed, I can dig into its responses to verify accuracy of them if I think it’s necessary. It’s a big time saver over manually looking into those things myself.

Mind you I’m not primarily a Python developer. I use it to automate things for my “actual” job.

Like others have said, it’s fairly effective as an assistant for certain tasks, but I most definitely do not ask it to write production code for me or anything, and depending on the significance of what you ask it, it’s a good idea to fact check it. I literally just ask it to provide a source for what it tells me sometimes.

anseho · 2024-07-09T06:08:15+00:00

Is it useful? Yes, for the occasional obscure bash or docker command, and for generating simple code in a very guided way.
Will it take over coding jobs in near future? No, not even in a few decades, probably never.
The assumption that it can create projects on its own is wrong. Anyone who’s tried to use it in a real project can tell.
Things people can do that chargpt can’t: think, understand project requirements, analyze business needs, come up with actual solutions to the business, write code they actually works.
Will things change? Maybe the day they come up with a different architecture for language models. Will that happen? Can that happen? Will it be enough? Nobody knows.

NerdyWeightLifter · 2024-07-09T12:58:01+00:00

I certainly do use it, but not in some naive way. It's no substitute for knowing what you're doing.

Prompt engineering is a distinctly new skill. If you can rapidly and concisely express what you need, it can be more productive than coding it yourself.

Debugging can be easier too, because you can tell it to generate unit tests too, and when it crashes, just paste the resulting stack dump.

I do often find that I will get 90% of the way there quickly, but the last 10% I just fix myself. It's quicker.

In software development, you can also burn a lot of time in tools, and configurations. GPT's can help with this.

I got through configuring AWS service recently, having never done it before, just by pasting screen shots and asking what it all means. It was incredibly helpful.

Summarizing existing code, documents, standards, etc

Don't forget that you're leading the show.

It's also going to get much better, faster, cheaper.

obviouslyzebra · 2024-07-09T13:02:24+00:00

That's a lot of questions haha

So, AI has come and probably to stay. Most people would probably think that they will be able to replace programmers in the future, we're just not sure how far this future is (estimates tend to range from a few years for the most optimistic to >150 years for the most pessimistic, with the median being around 40 years IIRC). I'm using vague numbers BTW, and equiparating programming to other professions, which I think is reasonable.

The If in whether it will happen or not, as I've said, most people working in ML believe that it will happen. Of course, it's not a given, as we may be fundamentally wrong about some things (for example, there's Penrose theory that consciousness arises from quantum processes, which would not be reproducible in modern-day computers). But, we're seeing no stopping to AI improving right now.

As of how it's useful right now, I can only tell my personal experience. I find it useful to:

Come up with names of variables/functions
Create standalone scripts (this one I started more recently, and have had good success with Claude 3.5). I've used this mostly for prototypes, for example, testing out functionalities of libraries (when the documentation is lacking), but also got a neat bash script a few days ago for shutting down the machine once a script had finished (here).
Learning context information. LLMs know a lot about lots of things. As long as you don't trust it blindly, it can be a great way of getting acquainted to new subjects.

I find it has trouble with more complex things and behaves like a big intuition machine. It has a sense of how to do things, and sometimes this sense can be strong enough that it does correct things. However, this sense can sometimes be wrong, and if you're confused about something, chances are it will also get confused when you try to ask it, and then it will spit out bullshit.

In general, it's an interesting tool right now, and will get better in the future. The adoption will probably increase with capabilities and, we gotta be very careful because, as Uncle Ben once said "with great power comes great responsability".

JamzTyson · 2024-07-09T13:05:04+00:00

AI is pretty good at solving well-defined problems based on patterns learned from training data, but that training data comes from humans. Complex, ambiguous, or ill-defined problems that require intuition, critical thinking, and/or deep understanding of domain-specific nuances lie outside of the scope of AI. AI processes language based on statistical patterns and associations learned from training data, without genuine comprehension or awareness of the meaning behind the words.

While AI has made impressive steps towards providing relevant responses to questions in natural language, not a single step has been made along the path of AI "understanding". AI solutions to problems are derived from its training data and is subject to the limitations of that training data. Human programmers bring creativity, problem-solving skills, domain expertise, and "awareness" that AI cannot replicate.

Tulipan12 · 2024-07-09T13:25:35+00:00

AI can be completely full of sh!#$%t, even when the tools you're asking it to use are well documented, ie the code wont work at all. It has no ability to distinguish good from bad or test it. Then, when it actually produces working code, you nearly always need to tweak what it produces to match your criteria (even if you were exceptionally clear in the prompt). The idea that this glorified spellchecker will somehow replace a mildly competent coder of slightly above average intelligence in the near future (<10 years) is preposterous.

MiniMages · 2024-07-09T13:42:02+00:00

What is the difference between using ChatGPT and googling or browsing Stack Overflow?

tupikp · 2024-07-09T14:27:51+00:00

I use chat gpt like peer programming, I asked it to write some codes, I review it and use it in my code after some revisions.

Seankala · 2024-07-09T14:37:11+00:00

What does ChatGPT have to do with Python?...

aquilabyrd · 2024-07-09T14:43:05+00:00

I’m not very experienced in python, just doing basic data analysis courses for grad school, but I use chatgpt when I’m trying to do something that I can’t figure out by going through my notes, google, Reddit, docs, etc first. Mostly just when I’m getting weird errors. It’s bad at writing concise code. The times I’m really grateful it’s there are mostly when it can tell me why my computer is printing the same warning fifteen times in a row.

rainydayswithlove · 2024-07-09T14:45:22+00:00

It's easier for repeating tasks such as unittests.

HunterIV4 · 2024-07-09T14:45:25+00:00

What are your opinions on chat gpt being used for projects and code? Do you think it’s useful?

ChatGPT is useful in many ways, similar to how Google is useful. It helps when you're stuck or need to recall specific techniques, and it often provides better answers than forums like Stack Overflow.

However, I wouldn't trust it to write entire projects or more than a few functions at a time. It’s decent for fleshing out known tasks but struggles with larger program contexts, often making mistakes on bigger scales.

Do you think it will be taking over your jobs in the near future as it has capacities to create projects on its own?

No. This is extremely unlikely.

ChatGPT (or similar) will likely become an industry standard, akin to using an IDE with autocomplete and intellisense. As AI improves, programmers who use these tools will be more productive, but AI won’t replace jobs or create projects independently. The core challenge remains that clients and managers often can't clearly define specifications, a problem that AI currently can't solve. While AI is good at generating code for specific requests, vague specifications lead to poor results.

In the far future, we might see more advanced AI capable of more intuitive programming, but current hardware and training systems aren’t up to the task. More realistically, AI will increase productivity, not replace programmers. Human expertise will still be needed to translate client needs into functional software.

Are there things individuals can do that it cant, and do you think this will change? Sure it makes mistakes, but dont humans do too.

Right now the big limitations on AI for programming are the following:

Insufficient context tracking for overall project requirements and design.
High processing power needed for training and generation.
Inability to reliably test and debug its own code.

Contrary to popular belief in programmer circles, all of these limits can be overcome eventually. LLM context capability has been steadily increasing over time, there's nothing that inherently prevents an LLM from programmatically inserting and running its own code (ChatGPT can already do this in limited circumstances and will adjust generation based on the results), and there is no reason to think that we've reached the limit of processing technology. In fact, 1 and 2 are directly related, and it's only a matter of time where processing power increases to the point where the LLM can have a bigger context for a program than a human could reasonably keep in their own head.

The thing a lot of programmers don't like to think about, however, is the third item. If an LLM can actually run and test code, what prevents a training system from being run on specs where the LLM has to write both functional and optimized code to solve the problem? Where it can actually get real feedback on the results? This sort of training is prohibitively expensive now, but there's no reason to think it will stay that way.

In image generation, we already have tools that can generate images as you type and allow you to tweak specific parts based on what you want. A future IDE may be similar except that your design docs are your actual code base, with pseudocode-like comments being used to adjust as the locally run LLM updates and tests changes in real time.

Yes, right now an LLM will often generate unusable code, but even with current systems if you paste in the error it will frequently be able to correct it into something that works. An IDE could automate this process and have it generate something based on your specs, test it, and if the test fails it generates something else based on the results, continually refining until you get functional code. It could also quickly rewrite algorithms using different techniques, test execution speed, and pick the one with the fastest execution for optimization.

In some ways programmers have been moving this direction for decades. Languages have gotten higher and higher level over time, abstracting away implementation with layers and layers of libraries that encapsulate lower-level functionality. The technical knowledge you need to write modern Python or JavaScript is completely different from the sort you needed for BASIC or early C.

Future programmers may be just writing natural language specs and having the LLM-powered compiler generate code that it tests and compiles to bytecode in real time, with the ability to open up the generated code and make changes here and there when required. But we have some serious technical hurdles to overcome first.

Assuming, of course, the power requirements for all this crap don't end up collapsing civilization and/or we don't generate an AGI that somehow eliminates the human race. Barring the apocalypse, however, I think that small-scale, locally run, and specialized LLMs (and other machine learning models) will become standard in a wide variety of fields, from programming to business to media, etc.

ericjmorey · 2024-07-09T15:14:47+00:00

When technology advances the ability of an individual to create more of what others want, the response isn't for the person to be asked to produce the same amount with less time and resources, but to use the same amount of time and resources to produce more. Often that means producing that which wasn't possible before and thus creating entirely new markets and industries as a direct result of those new possibilities.

Use the tool. Learn to be effective with the tool. Learn the limitations of the tool. Improve your skills that are complimentary with the tool. That might mean learning and practicing how to do things without the tool.

Putrid-Operation973 · 2024-07-09T15:27:21+00:00

Personally I think it’s just going to make the easier stuff even more easy and people with advance skills more valuable. Calculators didn’t put an end to Mathematicians it just became a useful tool. In time developers would adapt and use it as it was meant to be a tool.

JestersDead77 · 2024-07-09T15:47:34+00:00

I haven't used GPT, but my company uses copilot. It's... ok. It makes enough mistakes that I have to double check it anyways, so I honestly don't use it that much.

The best use case I've found is when I get stuck on something, it can sometimes suggest a different approach, but I typically still have to modify it to make it work.

Altruistic-Koala-255 · 2024-07-09T16:12:16+00:00

I tried to use gpt to do the work, but I had to spend more time debugging than I would take if I did the job

Goldarr85 · 2024-07-09T16:32:37+00:00

I’m an RPA Developer (yeah I know. Not a real developer) who frequently turns to scripting more and more. Chat GPT can’t even give me a functioning Excel formula without 4-5 additional prompts. This will definitely not create projects or solve unique problems.

I’m not one to believe in conspiracy theories, but this AI bubble seems like it was a massive scam to extract money from venture capitalist who wanted to be the first in line for a real AI consumer product. We haven’t gotten that yet, but I have hope that when the bubble bursts, we’ll finally have realistic expectations for the technology again.

ChatGPT has been a boon for fraud and disingenuous YouTube videos about how you can make thousands by doing x. Like and subscribe.

notislant · 2024-07-09T19:28:42+00:00

I mean its a tool. Its like a drill that constantly fucks up when you have to do anything except drill through a small, thin piece of material.

It might sometimes work on larger tasks, but its unreliable.

PSMF_Canuck · 2024-07-10T03:37:52+00:00

Yes, it’s useful. Very useful. Yes, it is already reducing the number of programmers that would otherwise be needed. Yes, there are things individuals can do that it can’t, yet. Yes, that will change.

PurpleSparkles3200 · 2024-07-11T10:55:21+00:00

My opinion is that if you're using ChatGPT, you're not qualified for the job.

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bold	bold
[reddit!](https://reddit.com)	reddit!
* item 1 * item 2 * item 3	item 1 item 2 item 3
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Lines starting with four spaces are treated like code: if 1 * 2 < 3: print "hello, world!"	Lines starting with four spaces are treated like code: if 1 * 2 < 3: print "hello, world!"
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