Earlier quoted context omitted.
> try it in a language and environment you know nothing about and so aren't tempted to keep taking the wheel. That's a good insights. Its almost like to use AI tools effectively, one needs to stop caring about the little things you'd get caught up in if you were already familiar and proficient in a stack. Style guidelines, a certain idiomatic way to do things, naming conventions, etc. A lot like how I've stopped orga…
How do you know the code is correct, isn’t insecure, and performant, without caring about the code?
AI tooling must be disclosed for contributions
281–290 of 482 posts
Re: AI tooling must be disclosed for contributions
#282I like the pattern of including each prompt used to make a given PR, yes, I know that LLM's aren't deterministic, but it also gives context of the steps required to get to the end state.
Re: AI tooling must be disclosed for contributions
#283Earlier quoted context omitted.
I've been struggling to apply AI on any large scale at work. I was beginning to wonder if it was me. But then my wife sort of handed me a project that previously I would have just said no to, a particular Android app for the family. I have instances of all the various Android technologies under my belt, that is, I've used GUI toolkits, I've used general purpose programming languages, I've used databases, etc, but wit…
> try it in a language and environment you know nothing about and so aren't tempted to keep taking the wheel. That's a good insights. Its almost like to use AI tools effectively, one needs to stop caring about the little things you'd get caught up in if you were already familiar and proficient in a stack. Style guidelines, a certain idiomatic way to do things, naming conventions, etc. A lot like how I've stopped orga…
This makes me a little sad. Part of the joy of writing software is expressing yourself through caring about these little things. Stylistic coherence, adhering to consistent naming conventions, aligning code blocks, consistently applying patterns, respecting the way the language and platform's APIs work together rather than fighting it... heck, even tiny things like alphabetizing header declarations. None of these things make the finished product better/faster/more reliable, but all of these demonstrate something about the author: What he believes in. That he is willing to sand, polish and beautify the back of the cabinet that nobody is going to see. As Steve Jobs said:
"Even though it faces the wall and nobody will ever see it. You’ll know it’s there, so you’re going to use a beautiful piece of wood on the back. For you to sleep well at night, the aesthetic, the quality, has to be carried all the way through."
If you surrender to the AI, you're no longer carrying the aesthetic and quality all the way through. You're abandoning the artistry. You're living with the barf because it works, and because it's much harder to go back and beautify it than it is to build it beautifully from the start.Re: AI tooling must be disclosed for contributions
#284Earlier quoted context omitted.
If the AI slop was that valuable a project regular, who actually knows and understands the project, would be just as capable of asking the AI to produce it.
Not according to ghostty maintainer Hashimoto per above. It takes attempts, verifying the result behaves as desired, and iterative prompting to adjust. And it takes a lot of time to wait on agents in between those steps (this work isn’t a one shot response). You’re being reductive.
I have no clue in ghostty but I've seen plenty of stuff that doesn't compile much less pass tests. And I assert there is nothing but negative value in such "contributions".
If real effort went into it, then maybe there is value-- though it's not clear to me: When a project regular does the same work then at least they know the process. Like if there is some big PR moving things around at least the author knows that it's unlikely to slip in a backdoor. Once the change is reduced to some huge diff, it's much harder to gain this confidence.
In some projects direct PRs for programmatic mass renames and such have been prohibited in favor of requiring submission of the script that produces the change, because its easier to review the script carefully. The same may be necessary for AI.
Re: AI tooling must be disclosed for contributions
#285There is also IP taint when using "AI". We're just pretending that there's not. If someone came to you and said "good news: I memorized the code of all the open source projects in this space, and can regurgitate it on command", you would be smart to ban them from working on code at your company. But with "AI", we make up a bunch of rationalizations. ("I'm doing AI agentic generative AI workflow boilerplate 10x gettin…
Courts (at least in the US) have already ruled that use of ingested data for training is transformative. There’s lots of details to figure, but the genie is out of the bottle. Sure it’s a big hill to climb in rethinking IP laws to align with a societal desire that generating IP continue to be a viable economic work product, but that is what’s necessary.
It is strange that you think the law is settled when I don't think even this "societal desire" is completely settled just yet.
Re: AI tooling must be disclosed for contributions
#286We don't yet know how courts will rule on cases like Does v Github (https://githubcopilotlitigation.com/case-updates.html). LLM-based systems are not even capable of practicing clean-room design (https://en.wikipedia.org/wiki/Clean_room_design). For a maintainer to accept code generated by an LLM is to put the entire community at risk, as well as to endorse a power structure that mocks consent.
Re: AI tooling must be disclosed for contributions
#287Earlier quoted context omitted.
Steering via prompting isn't the same as fundamentally changing the LLM by teaching, as you can do with humans. I think OP understands this better than you.
Can't tell if you're responding in earnest or not here? LLMs are trained to be steerable at inference time via context/prompting. Fine tuning is also possible and often used. Both count as "feedback" in my book, and my point is that both can be effective at "changing the LLM" in terms of its behavior at inference time.
Re: AI tooling must be disclosed for contributions
#288Earlier quoted context omitted.
Not according to ghostty maintainer Hashimoto per above. It takes attempts, verifying the result behaves as desired, and iterative prompting to adjust. And it takes a lot of time to wait on agents in between those steps (this work isn’t a one shot response). You’re being reductive.
We may be talking cross purposes. I read the grandparent poster discussing provably untested patches. I have no clue in ghostty but I've seen plenty of stuff that doesn't compile much less pass tests. And I assert there is nothing but negative value in such "contributions". If real effort went into it, then maybe there is value-- though it's not clear to me: When a project regular does the same work then at least the…
Having the original prompts (in sequence and across potentially multiple models) can be valuable but is not necessarily useful in replicating the results because of the slot machine nature of it
Re: AI tooling must be disclosed for contributions
#289Provenance matters. An LLM cannot certify a Developer Certificate of Origin ( https://en.wikipedia.org/wiki/Developer_Certificate_of_Origi... ) and a developer of integrity cannot certify the DCO for code emitted by an LLM, certainly not an LLM trained on code of unknown provenance. It is well-known that LLMs sometimes produce verbatim or near-verbatim copies of their training data, most of which cannot be used witho…
Re: AI tooling must be disclosed for contributions
#290There is also IP taint when using "AI". We're just pretending that there's not. If someone came to you and said "good news: I memorized the code of all the open source projects in this space, and can regurgitate it on command", you would be smart to ban them from working on code at your company. But with "AI", we make up a bunch of rationalizations. ("I'm doing AI agentic generative AI workflow boilerplate 10x gettin…
Courts (at least in the US) have already ruled that use of ingested data for training is transformative. There’s lots of details to figure, but the genie is out of the bottle. Sure it’s a big hill to climb in rethinking IP laws to align with a societal desire that generating IP continue to be a viable economic work product, but that is what’s necessary.
Seems to me the training of AI is not radically different than compression algorithms building up a dictionary and compressing data.
Yet nobody calls JPEG compression “transformative”.
Could one do lossy compression over billions of copyrighted images to “train” a dictionary?