It hasn't. I've seen a lot of developers leaning on chat GPT as a crutch. Their first instinct as soon as they get stuck rather than figuring something out is to go on ChatGPT and have it answer the solution for them. The problem is a lot of the time the answers it gives are incorrect or error prone and the very same Devs using it as a crutch don't know how to tweak the answers to actually get a working and useable solution. I tried GitHub co-pilot but didn't end up paying after the trial as I found as a .Net developer the tooling and auto complete within Visual Studio and Visual Studio code made development a lot more efficient than using any AI linked tools. I'd agree with a lot of the sentiment on here that AI ik general is overhyped at the moment and not all that it's cracked up to be.

• ChatGPT: has saved me hours of googling and searching stack overflow. Initially I was very hesitant to use it. However, its ability to allow me to keep asking why and drilling further and further down on a problem is really revolutionary. I am 100% on board the AI coding hype train. Its like a more personal google search and its awesome

I'll start the conversation with my experience:

• AI autocomplete isn't that big a deal for me, but I could see that changing with iterative improvements to this type of tech.
• ChatGPT is most helpful in exploring a paradigm I'm not as familiar with and having a really fast "no stupid questions" loop. I can be a bit more adventurous than I had been.
• Latency is still more than I want. It's easy to genuinely be slowed down by this tech on that issue alone.
• I write a lot of boilerplate with GPT.

I feel GitHub Copilot accelerates me a lot and it does change how I go about coding. I will often pause and wait for it to auto-complete something, where I wouldn't have before. Sometimes it generates code that isn't quite what I would write, but as long as it's good code I will leave it alone. I never accept bad code from it, or code I don't understand.

I don't use ChatGPT very often, but it's in my toolbox now as a method of last resort. If I look at documentation and use my noggin and I still can't figure something out, I'll ask ChatGPT and sometimes as if by magic it understands my question and knows a great solution. So it's just one more resource for me, but it can be a pretty powerful one sometimes.

It's been 4 months since I started using github copilot. I use it mainly to develop a personal design system in sveltekit (html, css, typescript).

It helped me a lot when doing repetitive tasks. Like for example, I started writing css variables like :

:root{
    --primary: rgb(59 130 246);
    --primary-dark: rgb(37 99 235);
}

And the prompt automatically completed for me the secondary, success, danger, warning and info.

When I started doing a btn class :

.btn.btn-primary {
    background-color: var(--primary-bg-button, var(--primary));
    color: var(--primary-text-button, var(--white));
    border: 1px solid var(--primary-border-button, var(--primary));
}

It took the template and used it for all the other variables.

I also used it for a small symfony (PHP) project. I started writing the controllers. When I moved on to the tests, copilot wrote lots of basic tests (some with dataProvider). I only had to tweak them a bit or to add edge cases tests.

In the end, I find it very useful for redundant tasks. And as an autocomplete on steroids. I don't think I used it in other use cases.

I don't use chatGPT, and I use very lighlty Google Bard to sometime get a help writing documentation or when I don't want to Lorem Ipsum my interfaces.

AI has dramatically impacted the way I troubleshoot and transform API responses in the Integration space. Although it is far from being able to develop scalable and efficient technical solutions on it's own, it assists me in minor tasks such as script development and enhancements.

Have included it in my workflow for some time. Useful so long as you understand its limits and respect that it lies like a weasel when it reaches the limit of its abilities. Always double check generated code, and learn from it rather than cut and paste. May not be replacing developers yet, but is pretty impressive.

I've started out mostly using it for refactoring smaller chunks of code - first explaining what I want to do, then pasting my current code. From there, I test it and review it to see if it actually does what I want, and if it is, in fact, a better written/more condensed version of my original. I may ask them to rewrite it and see some additional options. I probably use their suggestions about 1/3rd of the time.

🤓 Personal side projects

I use chat-GPT on free tiers for dummy questions that help me save a lot of time

🤵‍♂️ Pro

We use GH Copilot, for now mostly for programmers and data-science & engineering.
As we use it, we tend to make better comments to get better results... and the question on maintaning documentation started to raise, especially this one :

"Why writing and maintaining documentation on a code that will probably change over time while we can get the doc just on time ?"

Also, reading this book brought me a lot of inspiration around this topic, which helped imagine the future of programmers work at the AGI era:

I primarily use it for

• doing mundane and repetitive coding tasks
• getting a quick overview of concepts I am not familiar with

It really helps me to wrap up work faster and close my laptop sooner