My rules of thumb is much shorter: don't.
The open source world has already been ripped off by AI the last thing they need is for AI to pollute the pedigree of the codebase.
My rules of thumb is much shorter: don't.
The open source world has already been ripped off by AI the last thing they need is for AI to pollute the pedigree of the codebase.
Suppose almost all work in the future is done via LLMs, just like almost all transportation is done today via cars instead of horses.
Do you think your worldview is still a reasonable one under those conditions?
But all work isn't done by LLMs at the moment and we can't be sure that it will be so the question is ridiculous.
Maybe one day it will be.. And then people can reevaluate their stance then. Until that time, it's entirely reasonable to hold the position that you just don't
This is especially true with how LLM generated code may affect licensing and other things. There's a lot of unknowns there and it's entirely reasonable to not want to risk your projects license over some contributions.
I use them all the time at work because, rightly or wrongly, my company has decided that's the direction they want to go.
For open source, I'm not going to make that choice for them. If they explicitly allow for LLM generated code, then I'll use it, but if not I'm not going to assume that the project maintainers are willing to deal with the potential issues it creates.
For my own open source projects, I'm not interested in using LLM generated code. I mostly work on open source projects that I enjoy or in a specific area that I want to learn more about. The fact that it's functional software is great, but is only one of many goals of the project. AI generated code runs counter to all the other goals I have.
Basically all of my actual programming work has been done by LLMs since January. My team actually demoed a PoC last week to hook up Codex to our Slack channel to become our first level on-call, and in the case of a defect (e.g. a pagerduty alert, or a question that suggests something is broken), go debug, push a fix for review, and suggest any mitigations. Prior to that, I basically pushed for my team to do the same with copy/paste to a prompt so we could iterate on building its debugging skills.
People might still code by hand as a hobby, but I'd be surprised if nearly all professional coding isn't being done by LLMs within the next year or two. It's clear that doing it by hand would mostly be because you enjoy the process. I expect people that are more focused on the output will adopt LLMs for hobby work as well.
> just like almost all transportation is done today via cars instead of horses.
That sounds very Usanian. In the meantime transportation in around me is done on foot, bicycle, bus, tram, metro, train and cars. There are good use cases for each method including the car. If you really want to use an automotive analogy, then sure, LLMs can be like cars. I've seen cities made for cars instead of humans, and they are a horrible place to live.
Signed, a person who totally gets good results from coding with LLMs. Sometimes, maybe even often.
As someone who enjoys working with AI tools, I honestly think the best approach here might be bifurcation.
Start new projects using LLM tools, or maybe fork projects where that is acceptable. Don't force the volunteer maintainers of existing projects with existing workflows and cultures to review AI generated code. Create your own projects with workflows and cultures that are supportive of this, from the ground up.
I'm not suggesting this will come without downside, but it seems better to me than expecting maintainers to take on a new burden that they really didn't sign up for.
I say let people hold this stance. We, agentic coders, can easily enough fork their project and add whatever the features or refinements we wanted, and use that fork for ourselves, but also make it available for others in case other people want to use it for the extra features and polish as well. With AI, it's very easy to form a good architectural understanding of a large code base and figure out how to modify it in a sane, solid way that matches the existing patterns. And it's also very easy to resolve conflicts when you rebase your changes on top of whatever is new from upstream. So, maintaining a fork is really not that serious of and endeavor anymore. I'm actually maintaining a fork of Zed with several additional features (Claude Code style skills and slash commands, as well as a global agents.md file, instead of the annoying rules library system, which I removed, as well as the ability to choose models for sub-agents instead of always inheriting the model from the parent thread; and yes, master branch Zed has subagents! and another tool, jjdag)
That seems like a win-win in a sense: let the agentic coders do their thing, and the artisanal coders do their thing, and we'll see who wins in the long run.
Well at least you, agentic coders, already understand they need to fork off.
Saves the rest of us from having to tell you.
> We, agentic coders, can easily enough fork their project
And this is why eventually you are likely to run the artisanal coders who tend to do most of the true innovation out of the room.
Because by and large, agentic coders don't contribute, they make their own fork which nobody else is interested in because it is personalized to them and the code quality is questionable at best.
Eventually, I'm sure LLM code quality will catch up, but the ease with which an existing codebase can be forked and slightly tuned, instead of contributing to the original, is a double edged sword.
Maybe! Or maybe there is really a competitive advantage to "artisanal" coding.
Personally, I would not currently expect a fork of RedoxOS that is AI-implemented to become more popular than RedoxOS itself.
That would only be a world where the copyright and other IP uncertainties around the output (and training!) of LLMs were a solved and known question. So that's not the world we currently live in.