where exactly do you see comments by proggeramlug?

On other sites, like github and reddit. This exchange was funny though. He eventually gets called out by the other commenter to stop responding with an LLM: https://github.com/PerryTS/perry/issues/139#issuecomment-429...

This exchange is actually funny.

Now, to tell you the truth, for a lot of tickets AI does generate legit the better answers than me. And my time is quite limited.

That said, I will always stand behind my posts, AI-assisted or not.

Now the thread you specifically point at here is ironic in many ways. The other guy says "and claude won't tell you that", while in fact Claude said (in my response) the exact thing he claimed Claude would not be able to do. Anyways, the technical merrits of the conversation are certainly worthwhile and important.

I seriously cannot fathom how a person chooses to use an LLM to write an Internet comment, so out of genuine curiosity, what drives a person to do that?

Do you somehow find yourself with hundreds of comments to write and nary a moment to spare? Do you genuinely think Claude can better answer questions in your domain of expertise than you can? Is English a second language and not one you're comfortable writing in?

> what drives a person to do that

There are courses on how to use an LLM to better market to people, to do PR, to communicate with other people. Grifters don't care about the human aspect or whatever, their goal is to grift. Comments and other text are just a means to an end (money).

This is how software development works now. We have to live with it.

The models are good enough that this works.

You can keep disagreeing for a while, but know that almost all the code in the industry is written like this now.

Trust of a project long term always was and continues to be of concern when choosing a critical dependency .

The concern basically boils down to how large and serious is the team and what if they abandon the project in few weeks or months .

These were always the risks, many here have been burned by betting years of their career building against promising but what turned out to be weak projects

OP is alluding to the fact that today commit frequency, lines of code or how active the contributors in the issue trackers are no longer good signals to use as proxy.

When the underlying project to yours is few million lines of code written by machines only it is not going to be feasible fork and maintain or in-house it if the maintainers abandon it

To be clear users of a library or a tool aren’t owed anything when it available gratis and fully open source .

However not everyone has access to unlimited tokens to disregard the quality (in terms of history and usage ) or size of the underlying project completely

I think the primary value of a project like this is the demonstration that this is possible and a proof that it does not incur some unknown tradeoff you'll discover after spending resources doing it.

IMO the maintenance story is more or less solved if you can keep AI agents refactoring and improving it in a loop.

> However not everyone has access to unlimited tokens

Apologies. I did not consider this when writing my comment, being spoilt by unlimited 'free' AI.

Free in quotes because, presumably, training agents on AI usage from developers is worth more than the cost of providing free AI.

> IMO the maintenance story is more or less solved if you can keep AI agents refactoring and improving it in a loop.

That’s a weak argument, though, if the future of AI is totally unreliable when it comes to cost and quality. Right now I definitely wouldn’t want to depend on being able to infinitely access AI tools for such an important part of the toolchain.

Aside from that it’s just not attractive to trust a project made by one person.

Not at all, I can assert that the Spring code on my current project is classical programming.

In many places AI tools aren't even allowed to touch customer repos.

I have used AI agents extensively for coding and my experience is that it's fine for prototypes, but in large projects like this there is risk that the codebase becomes unmaintainable.

This is a very popular opinion that is sort of obsolete now in my opinion.

It was a valid concern last year. We have seen tremendous progress on this in the last 4-6 months.

Even if your initial prototypes are unmaintainable slop, the state of the art models are fairly good at refactoring and fixing things.

In large projects there is always a risk, if not an inevitability, that a code base becomes unmaintanable by some definition. AI surfaces this faster, but also AI lowers the cost of testing and refactoring. AI gives a linear multiplier in producing solutions, but complexity gives a quadratic increase in problems. The art of producing software has always been in choosing what not to do.

Hey smasher164,

thanks for bringing up the concern. I am proggeramlug (or amlug, either way), Ralph, founder of perry.

Yes, Perry is heavily AI-assisted - similar to the Bun to Rust port is too. Your concern is real and I don't claim to have all the answers for that.

I do know that AI is much better at finding bugs when it comes to compilers than most humans will ever be. Decoding bytecode and knowing what it should be is nothing humans are good at - AI does it trivially.

So yes, lots of AI, but this is not a "vibe-coded" slop project either. I have decades of experience in programming (native) and so I'm quite confident that this will work out well - but I totally see your skepticism.

Why do you think ai is better at finding bugs?

Because it can debug the LL output code directly and much more efficiently than humans could.