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I've actually had great success with the automation of boiler plate DevOps nonsense using GPT-4, including: GitHub actions, Caddy scripts, Docker files, etc. E

I've actually had great success with the automation of boiler plate DevOps nonsense using GPT-4, including: GitHub actions, Caddy scripts, Docker files, etc. Every second that I didn't have to: - go look up a similar problem on Google - open three or four stack overflow tabs - read through the stack overflow links - copy out the answer - change all the variables to match variable names that I want All of this represents a huge time saver for me. I'm honestly baffled that people lack the ability to use LLMs in a productive and optimal manner.

consp 3y ago
Did you learn anything in the process? You'd still have to over the output and understand it, otherwise nothing has changed except the method of producing something you don't understand.
- rolisz 3y ago
  If you're not DevOps, why bother learning whatever framework is used today, but which will be replaced by something else next year? I just want to get my stuff deployed and that's it.
  - ChatGTP 3y ago
    Yeah that’s true, why bother actually knowing anything about the systems they run your software /s
    - rolisz 3y ago
      I have a finite amount of time. I can choose to learn and know more about the software that I'm developing and make the software better, or I can spend some amount of time to learn the DevOps part, or some mix in between. In some cases, the ratio is 90:10 in favor of software development.
      - ChatGTP 3y ago
        Is nginx hard to understand or something ? in surprised how few services developers I’ve encountered who don’t know hot to configure a web sever or LB.
        burnished 3y ago
        Yeah honestly when I took a stab at it a couple years ago it was frustrating - all the material I ran into told you what to do but not why which left me high and dry when things didnt work as expected, or when I wanted to find more information about the meaning and organization of directories or other contextual background information.
    - falcor84 3y ago
      Here's a thought experiment - seeing how software problems are often people problems, how knowledgeable should we expect a software engineer to be about biochemistry (the systems that "run" people)?
      - ChatGTP 3y ago
        Nginx for a developer vs biochemistry though ? That’s a leap.
  - input_sh 3y ago
    Saying that DevOps tooling changes every year is absolute nonsense. The tooling is fairly standardised nowadays (Docker, Kubernetes, Ansible, Terraform, Prometheus, CI/CD tool of your choice) and every part of it is over half a decade old. ChatGPT will only help you with the simplest of tasks, and even then you're gonna fail if you don't know to correct it. If you go beyond the most basic of the tasks you can learn how to do in a few hours, good luck fitting them in a chat prompt.
    - rolisz 3y ago
      The comolexity of Docker I've needed over the last 5 years definitely could have been solved by ChatGPT. The amount of Kubernetes I needed was probably more than what ChatGPT can solve, but at the same time, I spent an awful amount of time looking at various templating solutions.
    - duckmysick 3y ago
      I'm not sure about standardized tooling. Docker is being replaced with Podman. Supposedly a drop-in replacement, but the tools built for Docker don't play nicely. You have Ansible, but there's also Puppet, Salt, Chef. There's Terraform and also CDKTF (from the same company too), Pulumi, plus tools from each cloud provider. There's Prometheus and Nagios, Datadog, Sentry, New Relic. The irony of CI/CD tool of your choice is not lost on me. I'll give you Kubernetes itself as the system. Yes, the concepts (containers, orchestration, reproducible deployment, infrastructure as code, unified monitoring, continuous integration) are well-established. How you get there? Not at all. Yes, the OP was hyperbolic when claimed the tools change every year. But the landscape of devop tools is changing. There are dozens of dev tool companies funded in the 2023 winter batch of Y Combinator alone. Someone sees potential to disrupt the established tools. And it will be disrupted. There was a world before Kubernetes and Terraform. It wasn't that long ago. https://www.ycombinator.com/companies?batch=W23&tags=Developer%20Tools https://www.ycombinator.com/companies?batch=W23&tags=Develop...
    - sebastiennight 3y ago
      Well, you say > Saying that DevOps tooling changes every year is absolute nonsense. And then you list 5 technologies with an alleged lifespan of half a decade, which sort of hints at the idea that someone would need to learn/update a new tooling skillset once a year. In that light, GP's comment doesn't sound so wild.
- CGamesPlay 3y ago
  No, before I had “a problem I don’t know how to solve” and now I have “a solution I don’t know how to maintain”. Both may be suboptimal in some contexts, but one results in my application having a deploy pipeline.
LASR 3y ago
> I'm honestly baffled that people lack the ability to use LLMs in a productive and optimal manner. Huge +1. I’ve got it to do a large part of my work for me by chaining some simple API calls. The fundamental conceptual gap I see - people often ask it to do some “thinking”. Then are annoyed by the inaccurate output. A simple example is doing word count and it getting the wrong answer very confidently. Of course it sucks at that. It doesn’t have a counter internally. But if you ask it to number each word in the input and output a list, then ask for the word count, it gets it right every time. Almost like how a human might count words manually.
- therein 3y ago
  I agree. I often use it as if it is a code generator with a decent grasp of human language. Here is what I asked it to do the other day, and it just did it right away and got it entirely right. > Write async tokio Rust code that takes in a Vec<u8>, writes it to a temporary file, calls /usr/bin/svc infer pathToTemporaryFile, reads and deletes temporaryFile+".out" and returns Result<Vec<u8>> containing the contents of temporaryFile+".out" I knew what I wanted, I could have written it myself, there was no unknown but it took fewer keystrokes and honestly when asked to write small components like this with pedantic detail, it does an incredible job and the output is easy to validate quickly.
  - karmakaze 3y ago
    If this is a good example of using ChatGPT for writing Rust, I suspect that the problem may be with Rust async.
- seba_dos1 3y ago
  If "chaining some simple API calls" is "a large part of your work", I can see it being incredibly useful. I already used it in this way a few times and it saved me some time indeed. However, as a software developer I feel like it's actually a tiny minority of my work; most of it is "doing some thinking" and slowly unfolding the inner parts of systems in order to understand how they work and how to fix them or make them do what I want, for which GPT is at best useful as a rubber duck to speak to.
barrkel 3y ago
I've found that LLMs give good results for really basic stuff, and are increasingly fantastical as you head towards the tail of the distribution. If you just need the beginner's gist or are completely unfamiliar with something, they're not bad. Getting specific answers to corner cases is often a waste of time.
- penjelly 3y ago
  luckily, large complex things are often made up of small simple things
LeBit 3y ago
Sure, but I have never seen it provide best practices solutions. You always have to walk back and do more research to find the best practice. And it doesn't learn. I stopped counting the times I told it it was wrong only for it to acknowledge the error and provide the same answer as a fix. ChatGPT is nice to learn about new ways of doing things, new ways of composing things, but then a human must intervene to make sense of the mess that was generated.

consp 3y ago

Did you learn anything in the process? You'd still have to over the output and understand it, otherwise nothing has changed except the method of producing something you don't understand.

rolisz 3y ago
If you're not DevOps, why bother learning whatever framework is used today, but which will be replaced by something else next year? I just want to get my stuff deployed and that's it.
- ChatGTP 3y ago
  Yeah that’s true, why bother actually knowing anything about the systems they run your software /s
  - rolisz 3y ago
    I have a finite amount of time. I can choose to learn and know more about the software that I'm developing and make the software better, or I can spend some amount of time to learn the DevOps part, or some mix in between. In some cases, the ratio is 90:10 in favor of software development.
    - ChatGTP 3y ago
      Is nginx hard to understand or something ? in surprised how few services developers I’ve encountered who don’t know hot to configure a web sever or LB.
      - burnished 3y ago
        Yeah honestly when I took a stab at it a couple years ago it was frustrating - all the material I ran into told you what to do but not why which left me high and dry when things didnt work as expected, or when I wanted to find more information about the meaning and organization of directories or other contextual background information.
  - falcor84 3y ago
    Here's a thought experiment - seeing how software problems are often people problems, how knowledgeable should we expect a software engineer to be about biochemistry (the systems that "run" people)?
    - ChatGTP 3y ago
      Nginx for a developer vs biochemistry though ? That’s a leap.
- input_sh 3y ago
  Saying that DevOps tooling changes every year is absolute nonsense. The tooling is fairly standardised nowadays (Docker, Kubernetes, Ansible, Terraform, Prometheus, CI/CD tool of your choice) and every part of it is over half a decade old. ChatGPT will only help you with the simplest of tasks, and even then you're gonna fail if you don't know to correct it. If you go beyond the most basic of the tasks you can learn how to do in a few hours, good luck fitting them in a chat prompt.
  - rolisz 3y ago
    The comolexity of Docker I've needed over the last 5 years definitely could have been solved by ChatGPT. The amount of Kubernetes I needed was probably more than what ChatGPT can solve, but at the same time, I spent an awful amount of time looking at various templating solutions.
  - duckmysick 3y ago
    I'm not sure about standardized tooling. Docker is being replaced with Podman. Supposedly a drop-in replacement, but the tools built for Docker don't play nicely. You have Ansible, but there's also Puppet, Salt, Chef. There's Terraform and also CDKTF (from the same company too), Pulumi, plus tools from each cloud provider. There's Prometheus and Nagios, Datadog, Sentry, New Relic. The irony of CI/CD tool of your choice is not lost on me. I'll give you Kubernetes itself as the system. Yes, the concepts (containers, orchestration, reproducible deployment, infrastructure as code, unified monitoring, continuous integration) are well-established. How you get there? Not at all. Yes, the OP was hyperbolic when claimed the tools change every year. But the landscape of devop tools is changing. There are dozens of dev tool companies funded in the 2023 winter batch of Y Combinator alone. Someone sees potential to disrupt the established tools. And it will be disrupted. There was a world before Kubernetes and Terraform. It wasn't that long ago. https://www.ycombinator.com/companies?batch=W23&tags=Developer%20Tools https://www.ycombinator.com/companies?batch=W23&tags=Develop...
  - sebastiennight 3y ago
    Well, you say > Saying that DevOps tooling changes every year is absolute nonsense. And then you list 5 technologies with an alleged lifespan of half a decade, which sort of hints at the idea that someone would need to learn/update a new tooling skillset once a year. In that light, GP's comment doesn't sound so wild.
CGamesPlay 3y ago
No, before I had “a problem I don’t know how to solve” and now I have “a solution I don’t know how to maintain”. Both may be suboptimal in some contexts, but one results in my application having a deploy pipeline.

LASR 3y ago

> I'm honestly baffled that people lack the ability to use LLMs in a productive and optimal manner. Huge +1. I’ve got it to do a large part of my work for me by chaining some simple API calls. The fundamental conceptual gap I see - people often ask it to do some “thinking”. Then are annoyed by the inaccurate output. A simple example is doing word count and it getting the wrong answer very confidently. Of course it sucks at that. It doesn’t have a counter internally. But if you ask it to number each word in the input and output a list, then ask for the word count, it gets it right every time. Almost like how a human might count words manually.

therein 3y ago
I agree. I often use it as if it is a code generator with a decent grasp of human language. Here is what I asked it to do the other day, and it just did it right away and got it entirely right. > Write async tokio Rust code that takes in a Vec<u8>, writes it to a temporary file, calls /usr/bin/svc infer pathToTemporaryFile, reads and deletes temporaryFile+".out" and returns Result<Vec<u8>> containing the contents of temporaryFile+".out" I knew what I wanted, I could have written it myself, there was no unknown but it took fewer keystrokes and honestly when asked to write small components like this with pedantic detail, it does an incredible job and the output is easy to validate quickly.
- karmakaze 3y ago
  If this is a good example of using ChatGPT for writing Rust, I suspect that the problem may be with Rust async.
seba_dos1 3y ago
If "chaining some simple API calls" is "a large part of your work", I can see it being incredibly useful. I already used it in this way a few times and it saved me some time indeed. However, as a software developer I feel like it's actually a tiny minority of my work; most of it is "doing some thinking" and slowly unfolding the inner parts of systems in order to understand how they work and how to fix them or make them do what I want, for which GPT is at best useful as a rubber duck to speak to.

barrkel 3y ago

I've found that LLMs give good results for really basic stuff, and are increasingly fantastical as you head towards the tail of the distribution. If you just need the beginner's gist or are completely unfamiliar with something, they're not bad. Getting specific answers to corner cases is often a waste of time.

penjelly 3y ago
luckily, large complex things are often made up of small simple things

LeBit 3y ago

Sure, but I have never seen it provide best practices solutions. You always have to walk back and do more research to find the best practice. And it doesn't learn. I stopped counting the times I told it it was wrong only for it to acknowledge the error and provide the same answer as a fix. ChatGPT is nice to learn about new ways of doing things, new ways of composing things, but then a human must intervene to make sense of the mess that was generated.