For example, I just read the 2017 Google attention paper a few days ago, and with ChatGPTs help I was able to build a complete implementation using only numpy.

It took a full day to generate and organize the code and unit tests. Then two days of debugging and cross referencing.

But, this was impossible before. I barely knew anything about transformers or neural network implementations.

I can’t even imagine what truly motivated people are doing with it.

But code has a pretty strict check on correctness afterwards, thinking about it its pretty scary how chatgpt results can be used without proper validation. And they will, the step from "looks good to me" to "it is good" is a small one when you just want an answer.

If I were a student, yeah it could probably do my homework. As a professional, I find that it greatly helps me navigate through related ideas.

I've been using it learn Python and produced Conway's Game of Life with pygame and I had never touched Python before that!

Now I'm working on another python project to try and split the audio of songs into individual lines to learn the lyrics and one approach has been downloading lyrics videos of youtube and munging them with ffmpeg to detect frame changes which should give me the timestamps to then split the audio on etc. It gave me all sorts of wrong enough to be slightly annoying advice about ffmpeg, but in the end what it did give me was _ideas_ and a starting point! I've had to wind up hashing the images and comparing the hashes, but I've since learned that that produces false positives and it was able to give me advice on using ensemble methods to up the accuracy etc which have panned out and helped me solve the problem.

I think for me, this is stuff I could have accomplished using Google if I had been sufficiently motivated, but it's lowered the level of frustration quite significantly being able to ask it follow up questions in context.

In the end, I don't think I mind the random bullshit it makes up from time to time. If I try it and it doesn't work, then fine. It's the stuff I try and it does work that matters!

I taught myself how to architect a software DSP engine in about 30 minutes last night. "Now rewrite that method using SIMD methods where possible" was a common recurrence. It's incredible how much you can tweak things and stay on the rails if you are careful. Never before would I have attempted to screw with this sort of code of my own volition. Seeing the trivial nature by which I can request critical, essential information makes me reconsider everything. The moment I get frustrated or confused, I can pull cards like "please explain FIR filters to me like I am a child".

for high level api exploration of reasonably popular frameworks openai can offer something different, at times very valuable despite the very frequent hallucinations.

on the other hand sometimes I re-appreciate good old documentation; openai here has a psychological effect of removing 'api-fear'

I have also tested it retroactively on some tricky debugging sessions that I had previously spent a lot of time on. It really goes down the wrong path. Without asking leading questions and, well, proper prompting, you may end up wasting a lot of time. But that's the thing - when you're investigating something, you don't know the root cause ahead of time, you _can't_ ask questions that'll nudge it in the right direction. It ends up being a case of blind leading the blind.

I keep seeing people saying they are using it for technical work. It must be design-oriented and less about troubleshooting because most of my experience with the latter has been abysmal.

I love it when these bots apologize.

Yeah, yeah. Just like "your call is very important to us."