Can we also flag AI generated projects posted on Show HN?
I used to love reading HN for the handwritten articles and handmade projects, but in the LLM era the quality has deteriorated significantly. I find myself flocking to other message boards where LLM content is flagged, discouraged, or banned.
Not necessarily. In many (most?) areas of tech the rate of advancement follows a logarithmic curve. That is to say, the first 90% is achieved quickly but the last 10% takes significantly more time.
I blocked AI overview because it starves websites of their own traffic and revenue.
Websites accepted Google scraping their content because it gave them a prominent blue link plus excerpt to drive traffic. Now everyone’s content is blended together and maybe, if they’re lucky, their site is chosen amongst the blend to get a tiny citation link.
Not sure if you're aware, but defer is proposed for C2Y [1]. It's already available in Clang behind a compiler flag. It is interesting how the languages continue to diverge.
I think there is one major difference that separates the two eras: in ye olden days you bought software for a fixed price and while it's understood you might only receive updates for a limited time, you could continue using it so long as you had the ability to run it. For example, you didn't have to upgrade to Windows XP if you were satisfied with Windows 98. With subscriptions, it's a recurring fee to continue accessing the software at all.
Data is being licensed by AI companies, but negotiations are limited to those with the capital [1][2][3]. You write about "imbalance" but ignore that large firms can cut deals while small creators languish.
You seem to believe advancement only happens in the private sector while ignoring academic institutions and publicly funded research. You've dismissed the possibility of public models entirely.
You fail to consider that when you financially disincentivize individual creators from publicly distributing their work, you starve future models resulting in a world were data is licensed only to those who can afford it anyway.
Start by legally compelling companies that trained on unlicensed data to either (1) license the data, (2) publish their model, or (3) destroy their model.
If models are trained on the collective whole, they must be owned by the collective whole. If you believe funding creators for the training of private models is too slow, inconvenient, or creates a global disadvantage, then embrace collective ownership.
How about forcing businesses to be owned by their employees [1]? Instead of taxing the owning class and being paid UBI peanuts, you become the owning class and reap the rewards directly.
How about requiring AI companies to pay creators for training rights? Alternatively, models trained on the commons must be owned by the commons. Right now these AI companies are trying to have it both ways: it’s The People’s Data for training on comrade but ownership is privatized.
> In my opinion, for open-source projects, scoring the project's AI sloppiness based on the timeline of commits would be a good indicator.
You can’t necessarily judge by timeline. I’ve always developed my projects privately and then squashed to one initial public commit. I’ve got a private repo now with thousands of commits developed over years and I still intend to squash.
Why is slop assumed inevitable? These models are plagiarization and copyright laundering machines. We need a great AI model reset whereby all published works are assumed to opt-out of training and companies pay to train on your data. We've seen what AI can do, now fund the creators.
I'd rather we democratize ownership [1]. Instead of taxing the owning class and being paid UBI peanuts, how about becoming the owning class and reaping the rewards directly?
> reflects the software engineering philosophies of the 1980s.
It has a microkernel architecture. That's already an improvement over the "modern" monolithic kernels we are stuck with today. Given Big Tech's interest in hardening security and sandboxing you'd think this would get more attention.
I used to love reading HN for the handwritten articles and handmade projects, but in the LLM era the quality has deteriorated significantly. I find myself flocking to other message boards where LLM content is flagged, discouraged, or banned.