Hacker News Reader

Comment by gr_norm | Hacker News Reader

If so, where are all the new features in the open-source projects I use? Why hasn't GIMP replicated Photoshop? Why hasn't CUDA been fully reverse-engineered as an open source toolchain? These are unreasonable expectations, but only in response to unreasonable claims of productivity. What before took ten years should now only take one, right?

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.

View on HN

> Why hasn't GIMP replicated Photoshop?

My guess is that open source folks overindex on being anti-AI.

They don't though, very few people were anti AI in the early days. Open source projects were largely initially very interested in LLM usage, and generally had a pro AI contribution policy. This changed when it did not help their lives

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

Scaling AI use in highly-collaborative environments, which open source projects are, is very challenging. I totally understand the apprehensiveness around it.

How is it challenging for open source projects? You just write a PR, and try to get it to meet the maintainer's quality bar. That's exactly how all code is written in any environment - you do code review and refine a PR until its good enough to be merged

With AI and how a lot of people use it, the quantity of written PRs goes up, but the quality goes down, and the maintainers of the project, who are often volunteers, now have to deal with this in addition to the standard work they've been doing before.

This doesn't seem to match the claims of 10x productivity that are being levied though. In fact it seems like the overall productivity is lower

Just my experience, but the relatively few fully AI-generated merge requests we've received that were "big features" were all really bad.

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. :)

Yeah, why hasn't AMD added every single CUDA feature lol

Why are Amazon and Walmarts mobile apps still janky crap that costs them money? Why does Meta still have four redundant, broken sign-up flows before you can give them money to advertise?

Wherever these “10000x” engineering gains are… I’m wondering why they’re only for shit nobody sees or touches.

Those websites are janky despite having all the (non AI) solutions available already. They suck for reasons independent of the quality of LLMs. I have not worked inside a megacorp but my understanding is they suck because of what are essentially political roadblocks within the organisation.

Ok, let’s assume that’s the case and we’d never see those improve under any circumstances.

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.

>Where are all the killer new things?

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.

Reply on news.ycombinator.com