Open-source collaboration in the age of AI
I am not going to reconstruct the entire disagreement that prompted this post.
Some of the public exchanges have been deleted. Other parts happened privately or by email. I have no interest in tracing every sentence, assigning blame line by line, or producing a forensic account of who said what. That would only prolong a conflict that has already taken up too much space.
But people noticed that I removed my participation from the project, and I have started receiving emails from strangers asking what happened. So I want to explain the broader issue, without turning a personal disagreement into public theatre.
Open-source collaboration does not necessarily become easier because AI makes it easier to inspect code, identify bugs, produce patches, and write detailed issues. In some ways, it becomes considerably harder.
You can spend time formulating an issue carefully and professionally, only for it to be perceived by the repository owner as a list of orders.
That owner is, of course, entirely free to reject the contribution. They can close the issue, say that it is outside the project’s scope, explain that it is not on the roadmap, or simply decide that they do not want to pursue it. There are thousands of legitimate ways to govern an open-source project.
Governance by burnout is not one of them.
When you use an AI tool to help document a problem, the resulting issue may contain precise examples, references to specific lines, screenshots, reproduction steps, and concrete observations gathered by running the software outside the environment of its original author.
That can be useful. It can also be overwhelming.
The problem is that the intention behind such a contribution may not survive the way it is received. You may believe that you are documenting a bug thoroughly. The maintainer may see a wall of text, an unsolicited audit, or an attempt to dictate the project’s direction.
You can ask questions first. You can soften the language. You can repeatedly state that the maintainer is free to ignore the suggestion. None of that guarantees collaboration when the other side does not experience the contribution as collaborative.
Most of my experiences have been different.
I have submitted issues and pull requests to several projects, including repositories connected to my professional work. Some of those contributions were written with the help of AI—not because I could not be bothered to write them myself, but because the tool already had the context: the code, the logs, the behaviour I had observed, and the steps that exposed the bug.
When the issue was valid, it was investigated. When the patch fixed a real problem, it was reviewed. Sometimes it was merged directly. Sometimes the maintainer rewrote it to match the project’s architecture, conventions, or preferred way of working.
That is normal. A contribution is not an instruction. It is material offered to the project.
For Indiekit, for example, I submitted a skeleton pull request of roughly two thousand lines containing the foundations of a Microsub plugin. It was not something the lead developer could review immediately. It required time and several email exchanges. But the contribution was handled professionally.
That first pull request opened the way for further work, with the plugin I already use in production gradually being reviewed and reconstructed under the oversight of the person responsible for the project.
I have seen similar responses in projects such as Dolibarr, Odysseus, and ComfyUI. A suspected bug was investigated. Its existence was corroborated. A fix was discussed or implemented. Everyone using the project could then benefit.
That is one of the core ideas behind open source.
This is not an argument that AI is inherently good or bad. It is an argument that collaboration still depends on people who actually want to collaborate.
A project can publish its code under an open-source licence without being socially open to outside participation. That is entirely legitimate, but it should be understood honestly. Otherwise, openness risks becoming more of a posture than a practice.
The recent conflict also showed me how easily both sides can misread each other.
The maintainer indicated that they could not follow or process what I had written because it was too much. I interpreted this as a request for greater clarity and replied with another comment summarizing my previous points, together with screenshots from my implementation.
I believed I was reducing the burden.
In reality, the person was angry. They had perceived my earlier issues as orders and felt overwhelmed by my participation as a whole. My attempt to clarify the situation therefore became another contribution to the very problem they were describing.
I later tried to explain myself privately and apologized. I was also transparent that some of the issues and comments had been written partly, or sometimes almost entirely, with Claude’s assistance.
Again, this was not because I had randomly asked a model to invent criticisms of somebody else’s project. The tool was helping me work on the implementation. It already held the context of the bugs and limitations I had encountered, so using that context to draft an issue was the most direct workflow.
But the origin of the text does not erase its effect on the person receiving it.
Everyone is overwhelmed. Maintainers especially are often expected to write the software, review code, answer questions, manage releases, handle support, moderate discussions, and absorb the emotional reactions of users—all without compensation.
Adding more explanations, even explanations intended to correct a misunderstanding, can simply produce more pressure.
My response was therefore to withdraw. I removed my contributions and comments where I could, apologized for the parts for which I could take responsibility, and moved on.
That was not a protest against my work being rejected. Nobody is required to accept an issue, a patch, an idea, or a contribution. In a world where AI makes bootstrapping increasingly accessible, people can fork a project and take it in another direction. That freedom is essential, and it should not change.
The issue is not rejection.
The issue is the growing potential for misunderstanding, misperception, and misjudgment between people working at radically different speeds, with radically different expectations, using tools that can generate more material than any small project is equipped to absorb.
Over the coming years, situations like this will become common.
Contributors will have to learn that the ability to identify ten problems does not mean that a maintainer can process ten issues. The ability to generate a detailed analysis does not mean that detail is always helpful. A technically correct contribution can still arrive in a socially destructive form.
Maintainers, in turn, may need clearer ways to communicate what kind of participation they want, how much they can realistically review, and whether outside contributions are genuinely welcome.
We are entering largely uncharted territory. The tools are evolving faster than the social practices around them.
We will need to rediscover some old rules of collaboration and invent new ones: smaller contributions, clearer boundaries, explicit expectations, consent before large interventions, and a better awareness that attention—not code—is often the scarcest resource in an open-source project.
AI can help us produce more.
It cannot make us ready to receive more.
Whoever reads this, I came and go in Peace, I will keep doing my part the way I see fit, but I have definitely learned a lesson here and for that, I’m thankful.
en.andros.dev:quality(85)/https://andros.dev/media/thumbnails/jurian-kersten-nZI4q-nmzjU-unsplash.jpg)