81–90 of 184 posts
Earlier quoted context omitted.
If you can TELL someone used AI, its always, without fail, a bad use of AI.
I disagree with that. I can easily tell when my non-native English speaking coworkers use AI to help with their communications. Nine times out of ten, their communication has been improved through the use of AI.
I like the idea of donating money instead of tokens. I think django contributors are likely to know how to spend those tokens better than I might, as I am not a django core contributor. Some projects ( https://news.ycombinator.com/item?id=46730504 ) are setting a norm to disclose AI usage. Another project simply decided to pause contributions from external parties ( https://news.ycombinator.com/item?id=46642012 ). In…
Shameless plug: I wrote an essay a few weeks ago pushing this exact same thesis. https://essays.johnloeber.com/p/31-open-source-software-in-t... Instead of people buying the tokens themselves, they should just donate the money to the core contributors and let those people decide how to spend on tokens.
Beggars can't be choosers. I decide how and what I want to donate. If I see a cool project and I want to change something (in what I think) is an improvement, I'll clone it, have CC investigate the codebase and do the change I want, test it and if it works nicely I'll open a PR explaining why I think this is a good change. If the maintainers don't want to merge it for whatever reasons that's fine and nature of open s…
Think most people recognize though that AI can generate more than humans can reviewing so the model does need to change somehow. Either less AI on submitting side or more on reviewing side (if that’s even viable)
Suppose I encounter a bug in a FOSS library I am using. Suppose then that I fix the bug using Claude or something. Suppose I then thoroughly test it and everything works fine. Isn’t it kind of selfish to not try and upstream it?
It was so easy prior to AI.
Great message but I wonder if the people who do everything via LLM would even care to read such a message. And at what point is it hard/impossible to judge whether something is entirely LLM or not? I sometimes struggle a lot with this being OSS maintainer myself
"the people who do everything via LLM". That's a bit of a straw man characterization. I don't believe that there are many professional developers "do everything with an LLM'. I don't even know what that statement means.
Perhaps we should start making LLM- open source projects (clearly marked as such). Created by LLMs, open for LLM contributions, with some clearly defined protocols I'd be interesting where it would go. I imagine it could start as a project with a simple instruction file to include in your project to try to find abstractions which can be useful to others as a library and look for specific kind of libraries. Some peopl…
Please do, that would be amazing. You'd have to manage the contributions, or get your AI bots to manage them or something, but it would be great to have honeypots like this to attract all the low effort LLM slop.
Earlier quoted context omitted.
> Django in particular is optimized for LLMs Meanwhile, a different take: Now, what we’ve been told about models is that they’re only as good as their training data. And so languages with gargantuan amounts of training data ought to fare best, right? Turns out that models kind of universally suck at Python and Javascript (comparatively). The top performing languages (independent of model) are C#, Racket, Kotlin, and…
I am using Claude Code with Elm, a very obscure language, and I find that it's amazing at it.
By what metric is “the level of quality is much, much higher” in the Django codebase? ‘cause other than the damn thing actually working, the primary metric of a codebase being high quality is how easy it is to contribute to. And evidently, it’s not.
I think it's perfectly doable to use an LLM to write into the Django codebase, but you'll have to supervise and feedback it very carefully (which is the article's point).