[elliotto]

Are you using the paid gpt-4? It is a world of difference of improvement over the free tier.

Like the author I am now writing 80% + of my code in chatgpt. Every now and then something pops up that it doesn't quite understand and I have to pick up my shovel and head back into the mines, but mostly with good prompting in chat gpt and preceding everything I write in my ide with a comment explaining what I'm doing copilot can do the rest.

It's a great tool in the way that google search once was, and programming IDEs are. But it takes some time to feel it out and see where it's useful and where it isn't, similar to learning how to google search and feeling out the opaque functionality in an ide.

At an AWS event last week there was a quote 'jobs aren't going to be replaced by ai. But people doing jobs without ai will be replaced by people with ai'.

[self_awareness]

Are you at least testing the code? Or are you delegating testing to AI as well?

[elliotto]

I describe the test in natural language and AI writes the boilerplate. A typical workflow for adding a simple new backend endpoint to my company's web application will be:

1) Send a chat gpt request with the existing router code and describe in natural language the name of the new endpoint, what I need it to do, and any functions that I want it to use. 2) Read through it and check that the logic is ok. 3) Send a chat gpt request with the existing router tests and describe in natural language what endpoint I want to hit, and what I want the test to verify. 4) Check that the response makes sense and run the test. 5) Any errors either debug on my own, or iterate back and forth again with chat gpt.

We are still in the early stages of what it means to have an intelligent natural language system at our fingertips. For me, it means no longer really needing to bother with repetitive boilerplate code and test harnessing. This is a huge speedup for my professional workflow and a significantly more enjoyable coding experience.