My guess is that open source folks overindex on being anti-AI.
Eg if you check out Godot, initially they were cool with LLM code, and then had to clamp down on it because it was not leading to anything good
For instance, we had one person submit a merge request to convert the entire GIMP codebase into Python, and it was just tons of files with stubcode implementations. Another person submitted a fix for our OpenEXR plug-in to load YCbCr images, and when I reviewed it, they'd "imported" a non-existent version of the library that had an imaginary "OpenYCbCrOpenEXR ()" function. :)
Wherever these “10000x” engineering gains are… I’m wondering why they’re only for shit nobody sees or touches.
Where are all the killer new things? All we see are glorified hobby projects (guilty as charged) and an unimpressive pace of equally unimpressive feature additions to all the same old stuff.
Businesses with resources out there should tackling huge things we all want, that they never could have undertaken. But I’m not seeing it. Everything is just… more or less the same. And this tooling didn’t arrive last Wednesday.
Or did we get magical AI at a time when we’re also completely out of ideas? That seems unlikely.
This shit just isn’t adding up. We’re not seeing anything that isn’t more “here’s some more ai tools and services… they’re basically the same as the ones you saw last month.”
What for? Someone show me.
I agree they are over-hyped. Maybe the problem is that all the things LLMs are great for are essentially quality of life improvements. There are a dozen killer apps worth of functionality I get out of LLMs - near instant proofreader, one-shot code bugfixer, excellent cursory research tool, bibtext generator, etc. All of these things extend what I can do a bit but none of them instantly result in me being able to generate category killing apps. They still make stupid mistakes, they don't reduce my workload, but they reduce cognitive load on mundane tasks.
It seems likely that the gains from generating tons of code are being offset by the debt incurred to understanding what you're doing. We see lots of greenfield projects one-shotted with GPT or GLM or whatnot, but very little on the side of projects with long-term maintenance goals. This is telling, to me, that the _effective_ gains are much lower than perceived (it's lots of fun to see the thing crank out code at breakneck pace, probably contributing to this). Still quite nice, and very useful, but not a totally new paradigm.