One potential benefit should be that with the right tooling around it it should be able to translate your code base to a different language and/or framework more or less at the push of a button. So if a team is wondering if it would be worth it to switch a big chunk of the code base from python to elixir they don't have to wonder anymore.
I tried translating a python script to javascript the other day and it was flawless. I would expect it to scale with a bit of hand-railing.
It seems that this kind of application can really change how the tech industry can evolve down the line. Maybe we will more quickly converge on tech stacks if everyone can test new one's out "within a week".
ChatGPT is trained well enough on all things AWS that it can do a decent job translating Python based SDK code to Node and other languages, translate between CloudFormation/Terraform/CDK (in various languages).
It does a well at writing simple to medium complexity automation scripts around
AWS.
If it gets something wrong, I tell it to “verify your answer using the documentation available on the web”
>>ChatGPT is trained well enough on all things AWS
It was scary to me how to chatting with GPT or Claude would give me information which was a lot more clear than what I could deduce after hours of reading AWS documentation.
Perhaps, the true successor to Google search has arrived. One big drawback of Google was asking questions that can't be converted to a full long conversation.
To that end. LLM chat is the ultimate socratic learning method tool till date.
ChatGPT is phenomenal for trying new techniques/libraries/etc. It's very good at many things. In the past few weeks I've used it to build me a complex 3D model with lighting/etc with Three.JS, rewrote the whole thing into React Three Fiber (also with ChatGPT), for a side project. I've never used Three.JS before and my only knowledge of computer graphics is from a class I took 20 years ago. For work I've used it to write me a CFN template from scratch and help me edit it. I've also used it to try a technique with AST - I've never used ASTs before and the first thing ChatGPT generated was flawless. Actually, most of the stuff I have it generate is flawless or nearly flawless.
It's nothing short of incredible. Each of those tasks would normally have taken me hours and I have working code in actual seconds.
And we are still at the beginning of this. Some what like where Google search was in early 2000s.
As IDE integration grows and there are more and better models, that can do this better than ever. We will unlock all sort of productivity benefits.
There is still skepticism about making these work at scale, with regards to both electricity and compute requirement for the larger audience. But if they can get this to work, we might see a new era tech boom way bigger than we have seen anything before.
I see your point but that specific analogy makes me wince. Google search was way better in the 2000s. It has become consistently dumber since then. Usefulness doesn't necessarily increase in a straight line over time.
I tried translating a python script to javascript the other day and it was flawless. I would expect it to scale with a bit of hand-railing.